2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City: Volume 1 9789811674662, 9811674663
This book gathers a selection of peer-reviewed papers presented at the third Big Data Analytics for Cyber-Physical Syste
130 88 22MB
English Pages 1314 Year 2021
Organization
Preface
Conference Program
BDCPS 2021 Keynotes
Contents
Data Mining and Statistical Modelling for Smart City
Application of Intelligent Ant Colony Algorithm in Rural Logistics Intelligent Distribution Route Planning
1 Introduction
2 Application of Swarm Intelligence Algorithm in Logistics Distribution Route Planning Under the Background of Rural Revitalization
2.1 Rural Logistics and Distribution Problems
2.2 Problem of Logistics Distribution Path
2.3 IACA
3 Experimental Research on Group Intelligence Algorithm in Logistics Distribution Path Planning Under the Background of Rural Revitalization
3.1 Swarm Intelligence Algorithm Structure Framework
4 Experimental Analysis of Group Intelligence Algorithm in Logistics Distribution Route Planning Under the Background of Rural Revitalization
4.1 Analysis of the Average Solution of CGA and IACA
4.2 Comparison of IACA with Other Algorithms
5 Conclusion
References
Sustainable Competitive Advantages of Chinese Online Travel Agents by RBV Model: A Data Based Analysis
1 Introduction
2 Literature Review
2.1 Industrial Analysis
2.2 RBV and Competitive Advantage
3 Methodology
3.1 Data Collection Method
3.2 Sample
3.3 Data Analysis Method
4 Result
4.1 Reliability Analysis
4.2 Validity Analysis
4.3 Variable Descriptive
4.4 Regression Analysis
5 Discussion
5.1 Digital Marketing
5.2 Cost Leadership
5.3 Payment Mode
5.4 Service Responsiveness
5.5 Product Variety
6 Conclusion
References
Kinematics Analysis of Aerobics Movement Decomposition Based on Multi-target Video Tracking Algorithm
1 Introduction
2 Multi-target Video Tracking Algorithm and Aerobics Movement Decomposition Kinematics Research
2.1 Feasibility Analysis of Multi-sport Target Video Tracking Application Aerobics Movement Decomposition Kinematics
2.2 Multi-moving Target Video Tracking Algorithm
3 Kinematics Experiment of Calisthenics Action Decomposition Based on Multi-sport Target Video Tracking Algorithm
3.1 Experimental Design
3.2 Experimental Procedure
3.3 Data Processing
4 Result Analysis
4.1 Analysis of Angle Parameters of Lower Limb Joints
4.2 Kinematics Analysis of Trunk Movement Links
5 Conclusions
References
Color Network System of Folk Painting Based on Fractal Algorithm Theory
1 Introduction
2 Theoretical Research on the Color Network System of Folk Painting Based on Fractal Theory Algorithm
2.1 Basic Knowledge of Fractal Theory
2.2 Software Development Platform
2.3 Software Implementation Model
3 The Realization of Color Network System of Folk Painting Based on Fractal Theory Algorithm
3.1 System Experiment Content
3.2 Process of Color Network System
4 Experimental Data Analysis of Folk Painting Color Network System Based on Fractal Algorithm Theory
4.1 System Equipment Data Analysis
4.2 Comparative Analysis of the Color Reproduction Process
5 Conclusion
References
Design of College Teaching Quality Evaluation Based on Apriori Algorithm
1 Introduction
2 Theoretical Research on the Design of University TQES Based on Apriori Algorithm
2.1 Evaluation Standard of Teaching Quality in Colleges and Universities
2.2 Discussion on the Requirements of TQES in Colleges and Universities
2.3 System Design of University TQES Based on Apriori Algorithm
3 Implementation of TQES Based on Apriori Algorithm
3.1 System Operation Environment
3.2 System Function Realization
4 Test of TQES Based on Apriori Algorithm
4.1 Software Testing
4.2 System Test
5 Conclusion
References
Panal Data Analysis on the Environmental Effects of Global Value Chains: Based on the Empirical Study of Chinese Industrial Panel Data
1 Introduction
2 Analysis of the Affecting Mechanism of Global Value Chain on Environment
2.1 Scale Effect
2.2 Composition Effect
2.3 Technique Effect
3 Model Establishment
4 Empirical Analysis Results
4.1 Data Source and Processing
4.2 Econometric Analysis Results and Their Economic Connotations
5 Conclusions
References
The Application of Fusion Algorithm in Automobile Machinery Manufacturing Control System
1 Introduction
2 Application of Fusion Algorithm in Automobile Machinery Manufacturing Control System
2.1 PI Control Algorithm
2.2 Improved Sliding Mode Control Algorithm
2.3 Improved ADRC Algorithm
2.4 Software and Hardware Design of PMSM Servo Control System Based on Algorithm Fusion
3 Experimental Simulation
3.1 Servo System Current Simulation
3.2 Speed Simulation of Servo System
3.3 Position Simulation of Servo System
4 Result Analysis
4.1 Analysis of Current Simulation Results of Servo System
4.2 Speed Simulation Results Analysis of Servo System
5 Conclusion
References
Path of Data Mining and Analysis Technology in New Energy Vehicle Business Model Innovation
1 Introduction
2 Data Mining Analysis Technology and New Energy Vehicle Business Model
2.1 Data Mining Analysis Technology
2.2 Current Business Model of New Energy Vehicles
2.3 The Basic Principle and Process of Cluster Analysis
3 Application Experiment of Data Mining Analysis Technology in the Innovation Path of New Energy Vehicle Business Model
3.1 Experimental Background
3.2 Experimental Method
3.3 Experimental Results
4 Analysis and Discussion of Experimental Results
4.1 Distribution of Traditional Business Models of Enterprises When Data Mining Technology is not Used
4.2 Research on the Path of Business Model Innovation for New Energy Automobile Companies Using Data Mining Technology
4.3 Problems Encountered During Use
5 Conclusions
References
Data Analysis and Application of Resource Search Algorithm in Basic Education Fairness
1 Introduction
2 Methods
2.1 Characteristics of P2P Network
2.2 Classical Resource Search Algorithm
2.3 The Impact of Injustice in Basic Education
3 Experiment
3.1 Data Sources
3.2 Algorithm Description
4 Discussion
4.1 Education Funding
4.2 Differences in Faculty
4.3 Targeted Suggestions
5 Conclusions
References
The Construction of the Influence Model of Artistic Creativity Based on AMOS Data Analysis
1 Research Motivation
2 Literature Review
3 Research Methods, Procedures and Assumptions
3.1 Research Structure
3.2 Research Hypothesis
3.3 Research Object
3.4 Research Tool
4 Analysis of Results
4.1 The Effect Verification of the Direct Effect of Innovation Atmosphere on Artistic Creativity
4.2 The Direct Effect of the Innovation Atmosphere on the Flow State and the Direct Effect of the Flow State on the Artistic Creativity
4.3 Mediation of Flow State
5 Conclusion and Suggestion
5.1 Innovation Atmosphere Has a Significant Positive Impact on Artistic Creativity
5.2 The Atmosphere of Organizational Innovation Has a Positive Impact on the State of Flow
5.3 Flow State Has a Positive Impact on Artistic Creativity
5.4 Flow State Plays a Completely Mediating Role in the Influence of Innovation Atmosphere on Artistic Creativity
References
The Construction of Mental Health Prediction Model Based on Data Mining Technology
1 Introductions
2 Research on Mental Health Status and Data Mining Technology
2.1 Factors Affecting Mental Health
2.2 Data Mining
2.3 Predictive Analysis Algorithm
3 Construction of Mental Health Prediction Model Based on Data Mining Technology
3.1 Data Source
3.2 Evaluation Index
3.3 Prediction Steps
3.4 The Order of the Degree of Influence of the Independent Variable Factors on the Dependent Variable
3.5 Prediction Model Establishment
4 Detection of Predictive Models
5 Conclusions
References
Influence of Big Data Statistical Analysis Technology on Informational Learning Mode
1 Introduction
2 Educational Informatization and Talent Training Mode in the Information Environment
2.1 Information Technology Environment
2.2 Reform of Talent Training Mode Under the Information Environment
3 Teaching Experiment of Talent Training Model in Informatization Mode
3.1 Effect Inspection Method
3.2 Experimental Subject
3.3 Knowledge Level Test Paper
3.4 Data Statistics
4 Teaching Experimental Results of Talent Training Model in Information Mode
4.1 Comparison of Previous Two Groups
4.2 Comparison of Two Groups After Implementation
5 Conclusions
References
3D Jewelry Design System Based on K-means Algorithm
1 Introduction
2 Jewelry 3D Design System Based on K-means Algorithm
2.1 Basic Principles of 3D Technology
2.2 Advantages of K-means Algorithm Application in Jewelry Design
2.3 3D Model Detail Optimization
3 Experiment
3.1 Interface Design
3.2 Login Module
3.3 Jewelry Attribute Selection Module
4 Discussion
5 Conclusions
References
The Development of Internet Plus Tourism Mode Based on Data Mining Technology
1 Introduction
2 The Development of IPT Based on DMT
2.1 IPT Development
2.2 IPT New Mode of Future Development Countermeasures
2.3 Related Analysis of DMT
3 Experimental Research
3.1 Research Object
3.2 Research Process Steps
4 Research and Analysis of the Development of IPT Based on DMT
4.1 User Search Content Analysis Under the New IPT Mode
4.2 User Search Mode Analysis Under the New IPT Mode
5 Conclusions
References
Multi-modal Medical Image Fusion Method Based on Multi-scale Analysis and PCNN
1 Introduction
2 Design of the Proposed Algorithm
2.1 Design of Multi-modal Medical Image Enhancement Method
2.2 Design of Multi-modal Medical Image Registration Method
2.3 Design of Multi-modal Medical Image Fusion Method
3 Simulation of the Proposed Algorithm
4 Conclusions
References
Analysis of Express Logistics Cost Control Under the Background of Big Data
1 Introduction
2 The Status of Yunda Express Logistics Cost Control
3 Problems in Yunda Express Logistics Cost Management
3.1 Labor Costs Are Enormous
3.2 The Construction of Logistics System is Imperfect
3.3 The Fixed Cost Control is not Reasonable
4 Measures to Improve Yunda Express Logistics Cost Control
4.1 Reduce the Labor Cost Consumption
4.2 Improving the Construction of Logistics System
4.3 Change the Cost Management Model
5 Conclusion
References
Medical Equipment Sales Management Prediction System Based on LSTM Algorithm
1 Introduction
2 Theoretical Research on Medical Equipment Sales Management Prediction System Based on LSTM Algorithm
2.1 Technical Support for Medical Equipment Sales Management Prediction of LSTM Algorithm
2.2 System Requirements Analysis
2.3 The Role of the Medical Device Sales Management Prediction Management System
3 Implementation of Medical Equipment Sales Management Prediction System of LSTM Algorithm
3.1 Overall System Structure
3.2 Client
3.3 System Detailed Design
4 Medical Device Sales Management Forecasting System Test Based on LSTM Algorithm
4.1 System Operation
4.2 System Test Results Data
5 Conclusion
References
Precision Poverty Alleviation System of Listed Companies Based on Multiple Neural Network Algorithms
1 Introduction
2 Increased Investment in Poverty Alleviation Reflects the Gradual Increase in the Awareness of Social Responsibility of Listed Companies
3 The Targeted Poverty Alleviation Strategy of Listed Companies Focuses on Combining with Their Own Resources to Reflect Commercial and Social Values
4 Artificial Neural Network Characteristics
5 Systematic Research on Precise Poverty Alleviation with Neural Network Algorithm
5.1 Research Method
5.2 Data Sources
5.3 Research Result
6 Conclusion
References
Optimization Processing of Auto Parts Spraying Scheme Based on Particle Swarm Optimization
1 Introduction
2 Linear Solution of the Spraying Model
3 Application and Optimization of Particle Swarm Optimization
4 Conclusion
References
Comparison of Two Models Based on Deep Neural Network Prediction
1 Introduction
1.1 Problem Analysis
1.2 General Assumption
2 ARIMA Model Establishment and Solution
2.1 ARIMA Model Establishment
2.2 Model Prediction and Analysis
3 LSTM Model Establishment and Solution
3.1 LSTM Model Introduction
3.2 LSTM Model Establishment
3.3 Model Prediction and Analysis
4 Comparative Evaluation of ARIMA Model and LSTM Model
4.1 ARIMA Model
4.2 LSTM Model
5 Conclusions
References
Model-Based Iterative Learning Control Algorithm and Its Simulation Research in Robot Point Position Control
1 Introduction
2 Research on Model-Based IIC Algorithm and Its Simulation Application in Robot Positioning Control
2.1 Basic Principles of Model-Based IIC
2.2 Open-Loop IIC and Closed-Loop IIC
2.3 Analysis of Robot Point Control System
2.4 Analysis of the Model-Based Iterative Algorithm in the Middle Simulation System of the Robot Point Control
3 Model-Based IIC Algorithm and its Simulation Experiment Research in Robot Positioning Control
3.1 Experimental Protocol
3.2 Research Methods
4 Model-Based IIC Algorithm and Its Simulation Experiment Analysis in Robot Positioning Control
4.1 Comparative Analysis of Robot Positioning Control System
4.2 Performance Analysis of Robot Point Simulation System Using Iterative Control Learning Algorithm
5 Conclusion
References
Application of Data Mining Technology in Regional Economic Analysis
1 Introduction
2 Theoretical Basis of Regional Research Based on DMT
2.1 DM Analysis Technology
2.2 Cluster Analysis
2.3 Regional Economy
3 Application Experiment PF Data Mining Technology in Regional Economy
3.1 Experimental Background
3.2 Experimental Process Steps
4 Experimental Analysis of DMT in Regional Economic Application
4.1 Analysis of the Status Quo of the Domestic Regional Economy
4.2 Analysis of Factors Affecting Regional Economy
4.3 Analysis of the Effect of DMT on the Regional Economy
5 Conclusion
References
Application of Big Data in the Excavation of Regional Cultural Resources in the Development of Theme Hotels
1 Introduction
2 Application of Big Data in the Excavation of Regional Cultural Resources in the Development of Theme Hotels
2.1 Apply Regionality to Hotels
2.2 Regional Culture in Hotel Design
2.3 Data Processing
3 The Experiment of Big Data in the Excavation of Regional Cultural Resources in the Development of Theme Hotels
3.1 Construction of Experimental Data Analysis Platform
3.2 The Specific Implementation Process of Regional Cultural Resource Excavation
4 Discussion of Big Data in the Excavation of Regional Cultural Resources in the Development of Theme Hotels
5 Conclusions
References
Using Computer Three-Dimensional Model System to Explore the Influence of Water–Rock Interaction on the Unloading Mechanical Properties and Microstructure of Sandstone
1 Introduction
2 Study on the Influence of Water–Rock Interaction on the Unloading Mechanical Properties and Microstructure of Sandstone
2.1 Classification of Water–Rock Interaction
2.2 Research on Compression of Bedding Sandstone Under Water–Rock Interaction
2.3 Three-Dimensional Model System
3 Experiment on the Influence of Water–Rock Interaction on the Unloading Mechanical Properties and Microstructure of Sandstone
3.1 Selection of Samples
3.2 Experimental Method
4 Discussion on the Influence of Water–Rock Interaction on the Unloading Mechanical Properties and Microstructure of Sandstone
5 Conclusion
References
Surface Quality Prediction Model of Crane Boom Based on Deep Learning
1 Introduction
2 Prediction Model of CB Surface Quality Based on Deep Learning
2.1 Ideological Characteristics of Deep Learning
2.2 The Necessity of SQP of CB
2.3 Factors Affecting Surface Quality of CB
2.4 CB Quality Prediction Model
3 Experimental Study
3.1 Subjects
3.2 Experimental Process Steps
4 Experimental Research and Analysis of Crane Jib SQP Model Based on Deep Learning
4.1 Crane Experimental Object Analysis
4.2 Comparison and Analysis of CB SQP and Measurement
5 Conclusions
References
Comprehensive Utilization Analysis of Geophysical Exploration Data Based on Data Mining Algorithm
1 Introduction
2 Data Mining Technology
2.1 Definition of Data Mining
2.2 Data Mining Process
3 Exploration Methods
3.1 Seismic Exploration Method
3.2 Transient Electromagnetic Method
4 Analysis of Geophysical Exploration Data
5 Conclusion
References
Spark for Data Mining of Massive Historical and Cultural Resources and Humanistic Smart City Construction
1 Spark Clustering Algorithm (KL Cluster)
1.1 Data Pre Clustering
1.2 The Formation Process of Distance Matrix
2 From Smart City to Humanistic Smart City
2.1 The concept of Humanistic Intelligent City
2.2 The Background of the Emergence of Humanistic Intellectual Job City
2.3 The Characteristics of Humanistic Intelligent City
3 Interactive Relationship Between Historical and Cultural Resources and Humanistic Smart City
3.1 The Historical and Cultural Resources Can Promote the Comprehensive Competitiveness of the Humanistic Intelligent City
3.2 Historical and Cultural Resources Are the Essential Factors for Sustainable Development of the Humanistic Smart City
3.3 Cultural Heritage and Cultural Heritage of Cities
4 Conclusion
References
Research on Massive Data Mining Technology Based on Map Reduce
1 Introduction
2 In the Context of Cloud Computing, Optimize the Data Model
2.1 MapReduce
2.2 The execution process of MapReduce
2.3 Application of MapReduce
3 Data Mining Optimization in MapReduce's Massive Data
3.1 Classification and Prediction
3.2 Traditional MapReduce Implementation of Naive Bayes Algorithm
4 Experimental Results of Eclat
5 Concluding Remarks
References
Application and Research of Data Mining Technology Bim and Internet of Things in Engineering Construction
1 Introduction
2 Main Advantages of BIM Technology
2.1 Visualization of BIM Technologies
2.2 Main Value of BIM in Construction
3 Application of BIM Technology in Construction Management of Construction Engineering
4 Application in Construction Safety Management
5 Application in Construction Progress
6 Conclusion
References
Geotechnical Test and Effective Processing Analysis of Geotechnical Engineering Investigation Based on Geographic Information System Under BP Neural Network
1 Introduction
2 Summary of Geotechnical Test
2.1 Geotechnical Investigation
2.2 Geotechnical Test
3 BP Neural Network
3.1 Basic Principle of BP Neural Network
3.2 Establishment of BP Neural Network Model
4 Analysis of Existing Problems in Geotechnical Investigation and Geotechnical Test
4.1 Problem Analysis of Instruments and Equipment
4.2 The Collection and Treatment of Test Samples Are not Standardized
4.3 Analysis of Technical Personnel Problems
5 Processing and Analysis of Geotechnical Test in Geotechnical Engineering Investigation Based on BP Neural Network
5.1 Soil Test Requirements Based on BP Neural Network
5.2 Enhance the Skill Training of Staff
6 Conclusion
References
Analysis and Research on Power Distribution Internet of Things Platform System
1 Introduction
2 The Composition of the Distribution Internet of Things Platform
3 Big Data Transmission of Power Distribution Internet of Things Platform
3.1 Intelligent Gateway Access Device
3.2 Device Binding Based on Mqtt
3.3 Realization of Mqtt Client Function
4 Research on Intelligent Response of Distribution Internet of Things Platform Terminal
4.1 Equipped with Intelligent Identification Terminal
4.2 Terminal Load Identification Process
4.3 Power Consumption Analysis
4.4 Equipment Linkage
5 Conclusion
References
Early Warning Model of Track and Field Sports Injury Based on RBF Neural Network Algorithm
1 Introduction
2 Method
2.1 RBF Neural Network Algorithm
2.2 Sports Injury
2.3 Artificial Neuron Model
3 Experience
3.1 Extraction of Experimental Objects
3.2 Experimental Analysis
4 Discussion
4.1 Literature Review
4.2 Discussion on the Factors of Risk Teachers in Track and Field Teaching
5 Conclusion
References
Application Research of Computer Digital Model Technology in the Packaging Design of Products with Different Regional Features
1 Introduction
1.1 Background and Significance
1.2 Related Work
1.3 Main Content
2 Applied Research Methods
2.1 Practice Observation Method
2.2 Social Investigation Method
2.3 Design Environment of Traditional Featured Products
3 Design Experiments Combined with Computer Digital Model Technology
3.1 Experiment of Container Structure Design and Manufacturing Under Computer Digital Model Technology
3.2 Packaging Testing Experiment
4 Experimental Data Analysis
5 Conclusions
References
The Application of Correction Algorithm in the Improvement of Accuracy of English Translation Software
1 Introduction
2 Current Situation of Translation Accuracy of English Translation Software
2.1 Inaccurate Translation of Special Words
2.2 Inaccurate Translation of Long Articles
2.3 Logical Translation Error
3 Correction Algorithm Design to Improve the Accuracy of Translation
3.1 Basic Algorithm Model
3.2 Algorithm Introduction
4 Institutions Optimization Design
4.1 System Design
4.2 Empirical Basis
4.3 Empirical Methods and Results
5 Conclusion
References
The Forewarning Design of Enterprise Foreign Trade Risk Based on Big Data
1 Introduction
2 Basic Concepts of Big Data
3 Types and Causes of Foreign Trade Risks of Enterprises
3.1 Environmental Risks and Causes
3.2 Business Risks and Causes
3.3 Other Risks and Causes
4 Foreign Trade Risk Early Warning Design
4.1 Framework Design
4.2 Warning Logic Design
4.3 Algorithm Introduction
4.4 Results and Discussion
5 Conclusion
References
Big Data Analytics for Smart City
Effects and Mechanism of Weibo’s Negative Emotions on Covid-19 Related Retweets Based on Big Data Collection Technology
1 Introduction
2 Theoretical Foundation and Hypotheses Development
3 Methodology
3.1 Data Collection
3.2 Sentiment Analysis
3.3 Variables and Measurement
4 Regression Model and Data Description
5 Empirical Results
5.1 Sentiment Analysis and Retweets
5.2 Results
6 Discussion
References
Our Country’s Smart Agriculture Development Strategy and Path Under the Big Data Environment
1 Introductions
2 Smart Agriculture Research
2.1 Research Method
2.2 The Current Situation of My Country’s Agricultural Development
3 Investigation on the Status Quo of the Development of Smart Agriculture in My Country
3.1 Purpose of the Investigation
3.2 The Development Process of Investigation Activities
3.3 Data Processing
4 Analysis of Survey Results
4.1 The Impact of Big Data on Smart Agriculture
4.2 Problems in the Development of Smart Agriculture in My Country at This Stage
4.3 Suggestions on Our Country’s Smart Agriculture Development Strategy and Path
5 Conclusion
References
The Design of the Information System Platform of the “One Picture” Platform for Territorial and Spatial Planning in the Big Data Era
1 Introduction
2 Basic Theory and Key Technology
2.1 Network Geographic Information System
2.2 The Architecture of Network Geographic Information System
2.3 ArcGIS Products
3 IT Architecture of the “One Picture” Platform Information System for Land and Space Planning
3.1 Application Architecture
3.2 Data Architecture
3.3 Technology Architecture
4 System Test
4.1 System Function Running Speed Test
4.2 System Function Interactive Test
5 Conclusions
References
Impact of the Application of Big Data Technology on Industrial Agglomeration and High-Quality Economic Development
1 Introduction
2 Literature Review
3 Mechanism Analysis
3.1 The Impact of Big Data Technology Application on High-Quality Economic Development
3.2 Impact of Industrial Agglomeration on High Quality Economic Development
4 The Influence Mechanism of Big Data Technology in Industrial Agglomeration on the High-Quality Economic Development of Chengdu-Chongqing Region
4.1 Big Data Technology Improves the Level of Industrial Agglomeration and Promotes High-Quality Development
4.2 Big Data Technology Enhances the Impact of Industrial Agglomeration on Innovation and Promotes High-Quality Development
5 Conclusion
References
Analysis of Information Management Scheme in Civil Engineering Construction Based on Big Data Analysis
1 Introduction
2 Based on Big Data Analysis
2.1 Theoretical Foundation of Information Management Scheme in Civil Engineering Construction Based on Big Data Analysis
2.2 Influence of Big Data on Information Management in Civil Construction
2.3 Technical Support for Information Management Scheme in Civil Engineering Construction
2.4 Big Data Method in Information Scheme in Civil Engineering Construction
3 Analysis of Information Management Scheme in Civil Engineering Construction Based on Big Data Analysis
3.1 Experimental Content
3.2 Experimentation
4 Experimental Analysis of Information Management Scheme in Civil Engineering Construction Based on Big Data Analysis
4.1 Application of Information Management of Big Data in Civil Engineering Construction
4.2 Analysis of Information Management of Big Data in Civil Engineering
5 Conclusion
References
Relationship Between Learning Behavior and Learning Effect Based on Big Data
1 Introduction
2 Research on the Relationship Between Learning Behavior and Learning Effect Based on Big Data
2.1 The Guiding Significance of Behavioral Science to the Study of Learning Behavior
2.2 Factors Affecting Learning Behavior
2.3 The Influence of Learning Behavior on Learning Effect
2.4 Zero Expansion Poisson Model
3 Experimental Research on the Relationship Between Learning Behavior and Learning Effect Based on Big Data
3.1 Experimental Subjects and Methods
3.2 Data Collection
4 Experimental Research and Analysis of the Relationship Between Learning Behavior and Learning Effect Based on Big Data
4.1 Analysis of the Influence Degree of Learning Behavior on Learning Effect
4.2 Analysis of the Influence Degree of Individual Learning Behavior on Learning Effect
5 Conclusion
References
Construction of School Quality Assurance System Based on Big Data Analysis
1 Introduction
2 Meaning of Big Data
3 Construction Ideas Based on Big Data Analysis
4 Construction of Quality Assurance System
4.1 Construction Content
4.2 Establish the Quality Assurance Concept of “Student-Centered”
4.3 Horizontally Realize the Separation and Linkage of “Management, Operation and Evaluation” Three Types of Organizations, and Vertically Implement the Division of Labor Management of “School, College and Section”
4.4 Strengthen the Quality Monitoring and Evaluation, and Form the Working Mechanism of “Closed-Loop Operation and Continuous Improvement”
4.5 Strengthen the Connotation Construction of Quality Culture, Promote Our Quality Behavior to Become Spontaneous Behavior, and Gradually Rise from Quality Control to Quality Culture
5 Conclusions
References
Design of University Data Governance Process System Under the Big Data Environment
1 Introduction
2 Process of University Data Governance Under the Big Data Environment
3 System of University Data Governance Under the Big Data Environment
4 Strategy of University Data Governance Under the Big Data Environment
5 Conclusion
References
Platform and Path of University Student Management Informatization Construction in the Big Data Environment
1 Introduction
2 Frame Structure of University Student Management Informatization Platform in the Big Data Environment
3 Data Storage Technology of University Student Management Informatization Platform in the Big Data Environment
4 Path of University Student Management Informatization Construction in the Big Data Environment
5 Conclusion
References
Construction of Course Achievement Evaluation System Based on Big Data Analysis
1 Introduction
2 Meaning of Big Data
3 Achievement Orientation and Achievement Degree
4 Construction of Course Achievement Evaluation System
4.1 Construction of Thinking
4.2 Construction of Content
4.3 The Supporting Relationship Between Course Objectives and Graduation Requirements
4.4 The Support Design Between Curriculum Process Evaluation and Curriculum Objectives
4.5 The Support Between the Expected Curriculum Achievement Index and the Continuous Improvement of Teaching Quality
5 Conclusions
References
Research and Application of Tax Classification Prediction Analysis Method Based on Big Data Technology
1 Introduction
2 Research on The Prediction and Analysis Method of Tax Classification
2.1 Principles of Tax Forecast Analysis
2.2 The Role of Tax Forecasting Methods
2.3 RTS Forecast Analysis Method
3 Experimental Study
3.1 Subjects
3.2 Experimental Process Steps
4 Experimental Research and Analysis of Tax Classification Prediction Analysis Method
4.1 Analysis of Tax Types
4.2 Analysis of the Representative Tax System
5 Conclusions
References
Development of Rural E-Commerce Based on Big Data Analysis Technology
1 Introduction
2 Development Status of Rural E-Commerce
2.1 Situation of the Number and Scale of Rural E-Commerce Enterprises in Honghu City
2.2 Situation of Honghu City Rural E-Commerce Infrastructure Construction
2.3 Situation of Agricultural Products Electronic Trading Platform in Honghu City
2.4 Situation of Rural Logistics Transport in Honghu City
3 Existing Problems of Rural E-Commerce Based on Big Data Analysis Technology
3.1 Lack of Rural E-Commerce Talents
3.2 High Logistics Costs and Low Distribution Efficiency
3.3 The Degree of Agricultural Product Branding is Low
3.4 Electronic Trading Platforms Are not Perfect
3.5 Tourism E-Commerce Needs to Be Improved
4 Suggestions of the Development of Rural E-Commerce
4.1 Increasing Rural E-Commerce Propaganda, Training and Introducing E-Commerce Talents
4.2 Building Village-Level E-Commerce Service Stations and Improving the Rural E-Commerce Logistics System
4.3 Strengthening Brand Building of Agricultural Products and Building Well-Known Local Brands
4.4 Improving the Electronic Trading Platform System and Strengthening Information Construction
4.5 Promoting the Construction of Tourism Resources and Optimizing the Tourism Model of E-Commerce
5 Conclusions
References
Management Analysis of Human Resources Sharing Economy Platform Under Big Data Technology
1 Introduction
2 Overview of Big Data and Sharing Economy
2.1 Big Data Technology
2.2 The Concept of Sharing Economy
3 Characteristics of Enterprise Human Resource Management in the Environment of Big Data and Sharing Economy
3.1 Staffing is Open and Mobile
3.2 The Semi Contractual Relationship is Less Restrictive
3.3 The Number of Participants is Large and the Management is Difficult
4 The Influence of Big Data and Sharing Economy on Enterprise Human Resource Management
4.1 The Breakthrough of Thinking Cognition
4.2 The Improvement of Performance Management
4.3 Innovation and Breakthrough of Management Mode
5 Measures to Improve Human Resource Management System Under Big Data and Sharing Economy
5.1 The Breakthrough of Thinking Cognition
5.2 Strengthen Training Guidance
5.3 Improve the Performance Management System
References
Smart City Medical Resource Allocation System Based on Big Data
1 Introduction
2 Design Concept of Smart City Medical Resource Allocation System
3 Architecture Design of Medical Resource Allocation System
3.1 Information Collection Part
3.2 Data Processing Part
3.3 Application Part
4 Function Module Design of Medical Resource Allocation System
4.1 Data Transmission and Integration Functions
4.2 Data Sorting and Management Functions
4.3 Data Display Function
5 Medical Resource Allocation System Testing
6 Conclusions
References
Hardware Design of Electronic Components Remote Test Adapter Based on Information Processing
1 Introduction
2 Hardware Design of Electronic Component Remote Test Adapter Based on Information Processing
2.1 Adapter Hardware Design Goals
2.2 Structural Design of the Adapter
2.3 Design of the Internal Module of the Adapter Hardware
2.4 Design of Debug Module
2.5 Detection and Positioning Algorithm of Electronic Components Based on Deep Learning
3 Experimental Research on Remote Testing Adapter Hardware of Electronic Components Based on Information Processing
3.1 Experimental Environment
3.2 Test Items
4 Data Analysis of the Hardware Design of the Remote Test Adapter for Electronic Components Based on Information Processing
4.1 Adapter Adaptation Function Data
4.2 Resistance Test
5 Conclusion
References
The Application of Virtual Reality Technology in Aerobics Training from the Perspective of Information Technology
1 Introduction
2 The Application Characteristics of VR Technology in Aerobics Teaching in Colleges
2.1 Integration
2.2 Systematic
2.3 Non-linearity
3 The Drawbacks of Traditional Aerobics Teaching
3.1 Insufficient Arrangement of Theoretical Courses
3.2 The Teaching Content is Boring
3.3 The Teaching Mode is Backward
3.4 Lack of a Strong Professional Faculty
4 The Application of VR in the Teaching of Aerobics in Colleges
4.1 Motor Skill Training
4.2 Psychological Training
4.3 Theoretical Knowledge Learning
4.4 Virtual Exercise Experiment
5 The Application Strategy of VR Technology in Aerobics Teaching of Information Technology
5.1 University Leaders and Aerobics Teachers Should Attach Great Importance to Information Technology Teaching
5.2 Configure Advanced Software and Hardware Equipment to Create a Complete Information Network System
5.3 Strengthen the Construction of Aerobics Teacher Team and Improve the Professional Teaching Level of Teachers
5.4 Change the Traditional and Backward Teaching Concepts and Actively Encourage Students to Explore Independently
5.5 Appropriate Use of Multimedia to Further Strengthen Teacher-Student Exchanges
6 Conclusion
References
Precision Design of Dongguan Intangible Cultural and Creative Products Based on Big Data Analysis Technology
1 Background
2 Concept on Big Data
3 Development Status of Cultural and Creative Products of Dongguan Intangible Heritage
3.1 Insufficient Innovation Ability and Too Much Emphasis on Inheritance
3.2 Inaccurate Positioning and Insufficient Publicity
3.3 Single Product Type and Serious Homogenization
4 The Precise Design Strategy of Dongguan Intangible Cultural and Creative Products with Big Data Analysis Technique
4.1 Focus on Consumers
4.2 Explore the Uniqueness of the Cultural Elements of Dongguan Intangible Heritage
4.3 To Achieve Precision Marketing Promotion
5 Conclusion
References
Proportion of College Students’ Internet Education Data Based on Big Data Analysis Technology
1 Introduction
2 IE for College Students Based on Big Data Analysis Technology
2.1 Problems in IE for College Students Based on Big Data Analysis Technology
2.2 Strategies for the Development of IE for College Students
2.3 Clustering Algorithm in Big Data Analysis
3 Proportion of College Students’ IE Data Based on Big Data Analysis Technology
3.1 Purpose of the Investigation
3.2 Survey Object
3.3 Questionnaire Preparation
3.4 Questionnaire Distribution, Collection and Analysis
4 Analysis of the Proportion of College Students’ IE Data Based on Big Data Analysis Technology
4.1 Proportion of IE Media Frequently Used by College Students
4.2 Percentage of College Students’ Average Daily IE Time
5 Conclusion
References
Gender Research in Literature Based on Big Data Technology
1 Introduction
2 Method
2.1 Big Data
2.2 Literature
2.3 Gender Influence on Thinking Form
2.4 Stereotypes in Gender
2.5 Relevant Algorithm Formula Involved in the Experiment in This Paper
3 Experiment
3.1 Subject of Investigation
3.2 Experimental Research Steps
4 Discussion
4.1 Efficiency of Searching for Two Literary Websites
4.2 People’s Views on Gender Research in Literary Works
5 Conclusions
References
Exploration of Accounting Information Construction Under the Background of Big Data
1 Introduction
2 Overview of Large Data and Accounting Information
2.1 Big Data Concept and Characteristics
2.2 Concept and Characteristics of Accounting Informatization
3 Ideas and Methods
3.1 Research Ideas
3.2 Research Materials and Experimental Design
4 The Result of the Problems in the Construction of Accounting Information in the Background of Big Data
4.1 Analysis of Accounting Information in the Context of Large Data
4.2 Research on Accounting Management Team
4.3 Addressing Problems of Manufacturing Accounting Information in the Context of Large Data
5 Conclusions
References
On the Path of Improving the Work Quality and Accuracy of College English Workers in the Era of Big Data
1 Introduction
2 Improving the Work Quality and Accuracy of CEW in TEOBD
2.1 The Current Situation of College English Teachers’ Work Quality
2.2 Strategies to Promote the Development of English Teachers’ Professional Quality
2.3 Questionnaire Survey Algorithm
3 Experimental Study
3.1 Subjects
3.2 Experimental Process Steps
4 Experimental Research and Analysis on Improving Work Quality and Accuracy of CEW in TEOBD
4.1 Understanding and Analysis of Improving Self Work Quality
4.2 The Influence on the Improvement of Teachers’ Work Quality
5 Conclusions
References
New Tools for Macroeconomic Analysis in the Era of Big Data
1 Introduction
2 New Tools for Macroeconomic Analysis in the Era of BD
2.1 Application of BD in Macroeconomic Analysis
2.2 Macroeconomic Analysis Optimization and Countermeasures Under the Background of BD
2.3 Data Mining Algorithm for Association Rules
3 New Tools for Macroeconomic Analysis in the Era of BD
3.1 Research Methods
4 Empirical Analysis of New Tools for Macroeconomic Analysis in the Era of BD
4.1 Application Analysis of Industrial BD Based on Data Mining Technology
4.2 Analysis of Inflation Rate
5 Conclusion
References
Application of “Big Data + Education” in the Construction of Curriculum Content System
1 Introduction
2 Big Data Research on Attention of Sports Dance
2.1 Time Characteristics of Sports Dance Attention
2.2 The Characteristics of the Crowd that Pay Attention on Sports Dance
2.3 The Demand Map of Sports Dance Attention
3 The Construction of the Content System of the Sports Dance Course
3.1 Basic Principles of Content Selection for the Sports Dance Course
3.2 The Basic Method of Constructing the Content of the Sports Dance Course
3.3 Concrete Construction
4 Conclusion
References
Expanding the Direction of Economic Development by Improving the Utilization Rate of WR in the Age of Big Data
1 Introduction
2 Related Concepts
2.1 Measuring Water Resource Efficiency
2.2 Coupling Coordination Studies
2.3 Dynamic Relationship Research
2.4 Relevant Formulas
3 Study on the Impact of Improving WR Utilization in Big Data Era on Economic Development
3.1 Construction of Evaluation System
3.2 Evaluation Methodology
4 Analysis of Data on the Impact of Water Resource Utilization on Economic Development in Big Data Era
4.1 Integrated Development Evaluation Index
4.2 Coupling Coordination Measurement
4.3 VAR Model Analysis Conclusion
5 Conclusions
References
Application of Artificial Intelligence Technology in College English Teaching Under the Background of Big Data
1 Introduction
2 Artificial Intelligence and Big Data
2.1 Artificial Intelligence (AI)
2.2 Big Data
3 The Application Value of Artificial Intelligence in College English Teaching Under the Background of Big Data
4 The Application Path of Artificial Intelligence in College English Teaching Under the Background of Big Data
4.1 Listening Training—Use Corpus to Complete Automatic Resource Matching and Interaction
4.2 Spoken Dialogue—Use Artificial Intelligence Robots to Carry Out One-To-One Dialogue
4.3 Translation Exercise—Using Cloud Platform Technology to Achieve Key and Difficult Breakthroughs
4.4 Writing Guidance—Use the Automatic Correcting Function to Check and Fill Gaps
4.5 Intelligent management—to Provide More Diversified Process-Based Teaching Evaluation
5 Innovative Strategies of AI Technology in College English Teaching Under the Background of Big Data
5.1 Integrate Teaching Resources and Innovate Teaching Models
5.2 Optimize English Learning Methods and Transform Teachers’ Functions
5.3 Improve the Teaching Evaluation System and Set Multiple Assessment Indicators
6 Conclusion
References
Innovative Development and Practice of University Intellectual Property Management Based on Big Data Algorithm
1 Introduction
2 Research on Data Mining Algorithm and Specific Functions
2.1 Summary of Data Mining
2.2 Common Data Mining Algorithms and Functions
3 Current Situation of Scientific Research Management in Colleges and Universities
4 Application of Data Mining Algorithm in Scientific Research Management of Universities in New Period
4.1 Application of Data Mining Techniques in Project Establishment and Feasibility Assessment
4.2 Application of Data Mining Technology in Project Management
4.3 Improved Apriori Algorithms for Scientific Management
References
The Big Data for Comprehensive Evaluation of GIS Engineering Based on Data Envelopment Analysis
1 Introduction
2 The Necessity of Comprehensive Evaluation of Geographic Information Engineering
3 Geographic Information Engineering Evaluation Process System
3.1 Methodology of System Engineering
3.2 Analysis of the Characteristics of Geographic Information Engineering
4 Comprehensive Evaluation of Geographic Information Engineering
5 Conclusion
References
Application of Web Crawler Technology Based on Python in Big Data Environment
1 Introduction
2 Python Crawler Technology
2.1 The Concept of Python Crawler
2.2 Basic Process of Crawler
3 The Necessity of the Development of Undergraduate Vocational Education
3.1 The Need of Perfecting Modern Vocational Education System
3.2 To Meet the Needs of Industrial Restructuring
3.3 To Meet the Needs of the Internationalization of Vocational Education
4 The Demand of Undergraduate Vocational Education
4.1 Cooperative Education
4.2 Independent School Running
5 Conclusion
References
Intelligent Algorithm Big Data Analysis for the Construction of Smart Campus
1 Introduction
2 Smart Campus System
2.1 Application Value of Intelligent Algorithm Big Data Analysis
2.2 Smart Campus System Construction
3 The Application of Intelligent Algorithm Big Data Analysis in the Construction of Smart Campus
3.1 Construction of Intelligent Management System
3.2 Establish Big Data Early Warning Platform
3.3 Form Smart Teaching Classroom
3.4 Expand the Integrated Data Source
3.5 Providing Intelligent Services
4 Conclusion
References
The Construction and Research of the Platform of Intelligent Sharing Laboratory Based on Big Data
1 Introduction
2 Intelligent Shared Laboratory Analysis
2.1 Objectives and Principles of Open and Shared Laboratories
2.2 Factors Restricting the Open Sharing of Laboratories
3 Construction of Intelligent Sharing Laboratory Platform
3.1 Management Mechanism Construction of Open Sharing Platform
3.2 Establishment of Open Sharing of Curriculum Project Resources
4 Human Resource Optimization of Sharing Experimental Platform
4.1 High Level Leader Team
4.2 Flexible Mobile Team
5 Conclusion
References
Research on Big Data Encryption Algorithm Based on Data Redundancy Elimination Technology
1 Principle Analysis of Big Data Encryption Technology
1.1 Basic Introduction
1.2 Principle and Analysis of Three Big Data Encryption Technologies
2 Research on Big Data Redundancy Elimination Algorithm
2.1 The Basic Principle of Data De Duplication Algorithm
2.2 Complete File Detection Scheme
3 Theoretical Basis
3.1 Data Preprocessing
3.2 Experimental Results and Analysis
4 Concluding Remarks
References
Design of Big Data Management and Analysis Platform Based on Knowledge Map
1 Introduction
2 Related Technology and Research
3 Design of Distributed Parallel Query Engine for Knowledge Map Storage Access System
3.1 Query Engine MiQE Based on Distributed Memory Iteration Technology
3.2 Join Query
4 Conclusion
References
Application of Artificial Intelligence in Computer Network Technology in Big Data Era
1 Introduction
2 Overview of Big Data Technology and Artificial Intelligence
2.1 Overview of Artificial Intelligence
2.2 Determination of Data Mining Target
3 The Necessity of Using Artificial Intelligence in Computer Network Technology
3.1 Advantages of Artificial Intelligence
3.2 Fuzzy Information Processing Ability
4 Effect Analysis of Artificial Intelligence Technology Applied to Computer Network in Big Data Era
4.1 Artificial Intelligence Can Improve Information Security
4.2 Artificial Intelligence Technology is Applied to the Field of Network Management and System Evaluation
4.3 Key Technologies of Data Warehouse
5 Epilogue
References
Application of Big Data Electrical Automation Technology in Electrical Engineering
1 Introduction
2 Research on Equipment Life Cycle Theory and Big Data
2.1 Research on Life Cycle Management of Equipment Assets
2.2 Big Data Platform and Architecture
3 Research and Application of Equipment Life Cycle Management
3.1 Evaluation Standard of Equipment Health Status
3.2 Research on Hybrid Prediction Model Based on SVR and ARIMA
4 Application Prospect of Electrical Automation Technology
4.1 Joint Development of Electrical Automation Technology, Big Data and Cloud Computing Technology
4.2 Development of Electrical Automation Technology and Artificial Intelligence
5 Innovation Way of Electrical Engineering Automation Control Work
5.1 Realize the Visual Management of Electrical Engineering Automation Control Work
5.2 Operation Method of Insulated Operating Rod
5.3 Combination of Insulation Platform Operation Method and Insulation Operating Rod Operation Method
6 Conclusion
References
Operation Analysis of Financial Sharing Center Based on Big Data Sharing Technology—Taking SF Express as an Example
1 Introduction
1.1 Research Background
1.2 Research Significance
1.3 Status of Domestic and Foreign Research
2 SF Financial Sharing Center
2.1 Introduction to SF Express
2.2 The Construction Goals of SF Express Financial Sharing Center
2.3 The Construction History of SF Express Financial Shared Service Center
3 Achievements in the Construction of SF Financial Sharing Center
3.1 The Operating Costs of SF Express Continue to Decrease
3.2 The Work Efficiency of SF Express Continues to Improve
3.3 The Management and Control Capabilities of SF Express Continue to Improve
4 Comparison of SF Financial Sharing Center and Meicai Financial Sharing Center
5 Conclusion
References
Application and Practice of Big Data Analysis in Enterprise Brand Marketing Management
1 Introduction
2 Big Data and Brand Marketing
2.1 Big Data Technology
2.2 Brand Marketing
3 Experimental Objects and Processes
3.1 Experimental Objects
3.2 Experimental Processes
4 Comparison of Experimental Results Between Two Groups
4.1 Comparison of Consumer Satisfaction Between Two Groups of Enterprises
4.2 Brand Awareness Comparison Between Two Groups of Enterprises
5 Conclusion
References
Implementation Evaluation System of Land and Spatial Change Planning Based on Big Data
1 Introduction
2 Land Resource Utilization Planning Implementation Evaluation System
2.1 Spatial Consistency Evaluation Model
2.2 System Architecture and Module Design
3 Experimental Land Resource Use Planning
3.1 Data Sources
3.2 Data Preprocessing
3.3 System Performance Test
4 Simulation Test Results
4.1 Spatial Consistency of Planning Implementation Results
4.2 System Response Time Test
5 Conclusion
References
Research on Intelligent Tourism Resource Management Based on Big Data Technology
1 Introduction
1.1 Basic Concepts of Big Data and Big Data Technology
1.2 Large Data
1.3 Big Data Technology
2 Intelligent Tourism Resource Management
2.1 Difficulty in Data Collection and Management
2.2 Difficulty in Processing Data Information
2.3 Single Idea of Data Information Analysis
3 Design of Big Data Intelligent Tourism Resource Management System
3.1 Framework Design
3.2 Functional Design
3.3 Algorithm Design
4 Conclusion
References
Research on Sales Forecasting Method Based on Data Mining
1 Preparations for the Application of Data Mining in Sales Forecasting
1.1 Data Cleaning
1.2 Data Integration and Exchange
1.3 Data Reduction
2 Data Mining Tools for Enterprise Sales Forecasts
2.1 Pre-Processing of Sales Data
2.2 Multiple Regression Model Training
2.3 Multiple Regression Fitting
2.4 Sales Mix Forecast
2.5 Data Mining Process for Sales Forecast
3 Conclusion
References
Study on Learning Path Selection of English Writing Based on Big Data Technology
1 Current Situation and Problems of English Writing Teaching in Contemporary Colleges and Universities
1.1 English Teachers Are Relatively Deficient in Classroom Writing Teaching Methods
1.2 English Writing Teaching Resources Are Relatively Scarce
1.3 English Writing Teaching Lacks Pertinence
2 Cultivation of Students’ English Writing Ability Driven by Big Data Technology
2.1 Teachers Should Update Their Teaching Concepts in Time
2.2 Strengthen the Construction of English Writing Teaching Resources
2.3 Scientific and Reasonable Application of English Writing Teaching AIDS
2.4 Conduct Diversified Assessment of Students’ English Writing Scores
3 The Design of English Online Writing Teaching Path Based on Big Data Technology
3.1 English Writing Preparation Stage
3.2 English Online Writing Stage
3.3 Language Feature Analysis Stage
3.4 Feedback Stage of English Writing Teaching
4 Conclusion
References
Data-Driven Co-design of Communication, Computing and Control for Smart City
The Development Trend of International Economy and Commercial Industry Based on the Analysis of Internet of Things Technology
1 Introduction
2 Internet and International Economy and the Commercial Sector
2.1 International Economy and the Commercial Sector
2.2 Impact of International Economy and the Commercial Sector Industry
3 Data Sources and Research Methods
4 Analysis on the Development Trend of International Economy and the Commercial Sector Industry Under the Background of Internet
4.1 Analysis of the Development of the Internet
4.2 Analysis on the Development of International Economy and the Commercial Sector Industry Under the Background of Internet
5 Conclusions
References
Interface Usability of Video Sharing Websites in the Internet Era
1 Introduction
2 Interface Usability Analysis of Video Sharing Websites in the Internet Era
2.1 Characteristics of Video Sharing Websites in the Internet Era
2.2 Interface Usability of the Video Sharing Website
2.3 Mean Algorithm for Website Analysis
3 Interface Usability Test of Video Sharing Websites in the Internet Era
3.1 Test Purpose
3.2 Test Object
3.3 Test Steps
4 Interface Usability Analysis of Video Sharing Websites in the Internet Era
4.1 Interface Task Operation Time Before and After the Improvement
4.2 Success Rate of Task Completion Before and After Improvement
5 Conclusion
References
The Realization of the Practical Ability Training System of Media Talents in OBE Mode Based on Network Information Technology
1 Introduction
2 Realization of the Practical Ability Training System for Media Talents Under OBE Mode
2.1 Shortcomings of Media Talents’ Practical Ability Training
2.2 Suggestions on the Cultivation of the Practical Ability of Media Talents Based on the OBE Model
2.3 Design of the Practical Ability Training System for Media Talents Under OBE Mode
2.4 Resource Modeling of the Practical Ability Training System for Media Talents
3 Implementation of the Practical Ability Training System for Media Talents Under OBE Mode
3.1 Background Development Technology
3.2 Database
4 Implementation Test Analysis of Media Talents Practice Ability Training System Under OBE Mode
4.1 Stress Test
4.2 Platform Processing Performance
5 Conclusions
References
A Design for Software Management Architecture and Software Management Information System in Time of Open Source
1 Introduction
2 Design for Software Management Architecture
2.1 Multi-dimensional Management Architecture
2.2 Life Cycle Dimension
2.3 Organizational Construction Dimension
2.4 Technical Control Dimensions
2.5 Industry and Ecological Dimension
3 Design for Software Management Information System
3.1 Overall for Software Management Information System
3.2 Application Design for Life Cycle Dimension Management
3.3 Application Design for Organizational Construction Dimension Management
3.4 Application Design for Technical Control Dimensions Management
3.5 Application Design for Industry and Ecological Dimension Management
3.6 Design for IT Technical Platform
4 Compliance Audit
4.1 The Overview of Compliance Audit
4.2 Risk Control Planning
4.3 Risk Identification
4.4 Risk Analysis
4.5 Risk Response
4.6 Risk Monitoring
5 Conclusion
References
Business Model Innovation Mechanism and Value Creation Effect of Data-Driven M&A—Case Study Based on Alibaba
1 Introduction
2 Research Methods
3 Case Discussion and Analysis
3.1 Business Model Innovation of Exploratory M&As
3.2 Business Model Innovation of Exploitative M&A
3.3 The Value Creation Effect of Data-Driven M&As
4 Conclusions
References
Design and Implementation of Enterprise Accounting Supervision Platform Based on Big Data
1 Introduction
2 Research on Design and Implementation of Enterprise Accounting Supervision Platform Based on Big Data
2.1 Accounting Supervision System
2.2 Research State in China
2.3 Overview of Accounting Supervision Platform
2.4 Demand Analysis
3 Experiments on the Design and Implementation of Enterprise Accounting Supervision Platform Based on Big Data
3.1 System Test Experiment
3.2 Statistical Methods and Formulas Used in This Article
4 Experimental Analysis of Design and Implementation of Enterprise Accounting Supervision Platform Based on Big Data
4.1 System Function Test
4.2 System Performance Test
5 Conclusions
References
Research on VR Data Visualization Design Based on Usability Principle
1 Research Background
1.1 Concept and Development of Data Visualization and VR Technology
1.2 Data Visualization Design in Virtual Reality Scenarios
2 Literature Review
2.1 Nielsen's Ten Usability Principles
2.2 VR Design Principles
3 Data Visualization Design in VR Scenarios
3.1 Space Scene Design
3.2 Sight-Guided Design
3.3 Data Information Design
4 Conclusion
References
Supply Chain Finance and Internet of Things Technology
1 Introduction
2 Literature Review
3 Definition and Status of Supply Chain Finance
4 Application and Impact of Internet of Things Technology in Supply Chain Finance
4.1 Definition of Internet of Things Technology
4.2 Cost Perspective
4.3 Impact on Information Search Costs
4.4 Impact of the Internet of Things on the Risks of Supply Chain Financial Operations
5 Conclusions
References
Analysis of “National Animation” Driven by Big Data Technology
1 Introductions
2 Ethnic Animation Research
2.1 The Embodiment of Ethnic Elements in Animation
2.2 The Development Status of Ethnic Animation
3 Investigation on the Status Quo of Ethnic Animation
3.1 Purpose of the Investigation
3.2 Data Sources
3.3 Data Processing
4 Result Analysis
4.1 Market Share of Animation Type
4.2 Development Strategy of National Animation
5 Conclusions
References
Data Management Strategy Based on Edge Computing
1 Introduction
2 Data Management Strategy for Edge Computing
2.1 Related Technology
2.2 Data Management Strategy
2.3 Related Algorithms
2.4 Design of Data Acquisition and Processing System Based on EC
3 Experimental Research on Data Management System for IE Plore
3.1 Experimental Background
3.2 Detailed Design and Implementation of Webpage Acquisition Module
4 Test Analysis of Human Behavior Recognition System Based on Xgboost
4.1 Algorithm Comparison Under UCI Data Set
4.2 UCI Data Set Training Effect
5 Conclusion
References
Construction of Parallel Corpus of Foreign Publicity Based on Computer-Aided Translation Software
1 Introduction
2 Algorithm Establishment
2.1 Application of Improved Particle Swarm Algorithm in a Parallel Corpus
2.2 Application of PCA Mathematical Principles in Corpus Construction
2.3 Corpus Analysis Based on the Computer-Aided Translation
3 Modeling Method
3.1 Construction of the Optimal Model Based on Grey Relational Theory
3.2 Analysis and Data Processing of Experimental Design
4 Evaluation Results and Research
4.1 Data Table Analysis
5 Conclusion
References
Research and Design of Physical Survey Data Analysis System Under the Background of Big Data
1 Introduction
2 Big Data
3 Application of Big Data Analysis in the Field of Physical Measurement
3.1 Promote Innovation
3.2 Improve Data Integration Efficiency
3.3 Improve Athletic Ability
3.4 Provide Personalized Services
4 The Application Challenges of Big Data in the Field of Physical Measurement
5 The Application Strategy of Big Data in the Field of Physical Measurement
5.1 Increasing Data Development Efforts
5.2 Building a Service System
5.3 Training Professional Talents
5.4 Formulate Relevant Development Policies
5.5 Effectively Guarantee Data Security
6 Design of Physical Measurement Data Analysis Platform Under the Background of Big Data
6.1 Design the Overall Architecture
6.2 Resource Layer
6.3 Application Layer Design
6.4 Data Presentation Layer
7 Conclusion
References
The Application of Computer Video Image Technology in Track and Field Training
1 Introduction
2 The Development of Video Image Processing Technology
2.1 Image Compression Technology
2.2 Video Image Processing Technology
3 Application of Video Image Technology to Track and Field Training
3.1 Modern Video Image Technology Has Obvious Effects on Track and Field Training
3.2 Advantages of Video Image Technology in Track and Field Training
4 The Effect of Video Image Technology in Track and Field Training
4.1 Improving the Model of Lesson Plans
4.2 Increase Teaching Training and Resources to Make Teaching and Training More Convenient
4.3 Make the Teaching and Training Process More Vivid and Concrete
4.4 Better Evaluation System
5 Suggestions on the Application of Video Image Technology in Track and Field Training
5.1 Continuously Improve Teachers’ Video Image Technology Level
5.2 Schools Should Increase the Use of Video Image Technology in Track and Field Teaching and Training
5.3 Schools Should Increase Support for the Application of Video Image Technology in Track and Field Classrooms and Training
5.4 Problems that Should Be Paid Attention to When Integrating Video Image Technology into Track and Field Training Courses
6 Conclusion
References
Formation Control of UAV Swarm Based on Virtual Potential Field and Virtual Navigator
1 Introduction
2 Formation Control Model of UAV Swarm Based on Virtual Potential Field and Virtual Navigator
3 Simulation and Analysis
4 Concluding Remarks
References
Application of Computer Image Processing Software in Interior Design
1 The Application of Computer in Interior Design
2 Types of Computer Software in Interior Design Work
3 The Design of Interior Renderings
3.1 Evaluation of the Renderings
3.2 Starting from the Overall Rendering
4 Concluding Remarks
References
The Application of Computer Technology in the Development and Analysis of Enterprise Intellectual Capital
1 Introduction
2 Research Background
3 Research Purpose
4 Research Methods
4.1 The Relationship Between Human Capital and Corporate Performance
4.2 The Relationship Between Structural Capital and Corporate Performance
4.3 The Relationship Between Relationship Capital and Corporate Performance
4.4 The Relationship Between Social Responsibility and Corporate Performance
4.5 The Model
5 Empirical Analysis
5.1 Descriptive Statistics
5.2 Correlation Analysis
5.3 Regression Analysis
6 Conclusion
6.1 Human Capital is the Main Influencing Factor
6.2 The Influence of Structural Capital is Slightly Weaker, But It Cannot Be Ignored
6.3 Relationship Capital Interacts with Social Responsibility
6.4 It is Necessary to Pay Attention to the Impact of Social Responsibility on the Enterprise
References
Design and Implementation of a Blockchain-Based International Trade Stable Digital Currency Issuance System
1 Introduction
2 Blockchain-Based Digital Currency Issuance System
2.1 The Development Status of International Trade
2.2 Introduction to Blockchain Technology
2.3 System Requirements
2.4 System Design
3 Implementation of Digital Currency Issuance System
3.1 System Deployment
3.2 System Function Test
3.3 System Performance Analysis
4 Design Analysis of Digital Currency Issuance System Based on Blockchain
4.1 Blockchain Technology Platforms
4.2 System Specific Deployment Configuration Information
5 Conclusion
References
Cross-Cultural Communicative Competence Based on Computer Aided Testing
1 Introductions
2 Computer-Aided Testing and Cross-Cultural Communicative Competence Research
2.1 Research Method
2.2 Advantages of Computer-Aided Testing
2.3 Influencing Factors of Computer-Assisted Cross-Cultural Communicative Competence Testing
2.4 Computer-Aided Test System Algorithm
3 Investigation of Cross-Cultural Communicative Competence Based on Computer-Aided Testing
3.1 Purpose of the Investigation
3.2 Questionnaire Survey
3.3 Data Processing
4 Analysis of Survey Results
4.1 Students’ Enthusiasm for Answering Questions After Joining the Computer-Assisted Cross-Cultural Communicative Competence Test
4.2 The Impact of Joining the Computer-Assisted Cross-Cultural Communicative Competence Test on the Test Results
5 Conclusions
References
E-commerce Development of Characteristic Agricultural Products Under the Background of Computer Science and Technology
1 Introduction
2 Development Status of Rural E-commerce in Hubei Province
2.1 Development Situation of E-commerce Trading Platform
2.2 Development Situation of Agriculture-Related E-commerce Enterprises
2.3 Construction Situation of Logistics Platform and Information Platform
3 Problems of E-commerce Development of Characteristic Agricultural Products
3.1 Characteristic Agricultural Product Competitiveness Is Weak
3.2 Rural Informatization and Digitalization Need to Be Improved
3.3 Development of Agricultural E-commerce Enterprises Is Slow
3.4 E-commerce Service Platform Lacks Consumers
3.5 Rural Grassroots Logistics System Is not Perfect
4 Development Strategies for Characteristic Agricultural Products Under the Background of Computer Science and Technology
4.1 Creating Standardized Production of Agricultural Products and Enhancing the Brand Value
4.2 Accelerating the Introduction of Advanced E-commerce Service Platforms
4.3 Intensifying the Promotion and Taining of E-commerce in Rural Areas
4.4 Improving the Modern Logistics System for Agricultural Products
5 Conclusions
References
Graphic Design Understanding the Application of Computer Graphics and Image Processing Technology in Graphic Design to Improve the Employment Rate of College Graduates
1 Introduction
2 Method
2.1 CGT
2.2 Graphic Design
2.3 Computer Aided Technology and Employment Rate of CS
3 Experiment
3.1 Subjects
3.2 Experimental Design
3.3 Analysis and Processing of Experimental Data
4 Result
4.1 Degree of Interest in CGT of Two Groups of Graduates
4.2 Cognition and Understanding of the Application of CGT of Two Groups of Graduates
4.3 Combine the Current Employment Situation of the Two Groups of Graduates
5 Conclusion
References
Analysis and Research on the Mode of International Trade Practice Combined with Exhibition and Sales Under the Development of Internet
1 Introduction
2 International Trade Exhibition Under the Background of Internet
2.1 Internet Plus Overview
2.2 Necessity of International Trade Innovation in the Era of Internet Plus
2.3 Concept of Exhibition
2.4 Development Mode of E-commerce in Exhibition and Sales
3 Research on the Actual Effect of International Trade Exhibition Mode
4 Discussion
4.1 Results and Analysis
4.2 Opportunities for Enterprises in the Internet Plus Era
4.3 New Measures of International Trade Practice Based on “Internet Plus”
5 Conclusions
References
The Application of Computer Network Technology in University Library
1 Introduction
2 Proposed Method
2.1 The Role of University Libraries
2.2 Computer Technology
2.3 Advantages and Disadvantages of Applying Computer Technology to University Libraries
3 Experiments
3.1 Research Purpose
3.2 Questionnaire Survey Method
3.3 Algorithm Recommendation
4 Discussion
4.1 The Number of Times College Students Go to the Library Every Week
4.2 The Number of Books Borrowed by College Students from the Library Each Month
5 Conclusions
References
The Development Trend of Computer Network and University Library
1 Introduction
2 The Development Trend of Computer Network and University Library
2.1 Analysis of the Development Research Trends of Domestic and Foreign Libraries
2.2 The Application Status of Computer Network Technology
2.3 Problems Existing in the Application of Computer Network Technology in University Library
3 Research Methods of Computer Network and the Development Trend of University Library
3.1 Multi-platform Resource Integration
3.2 Establish a New Technology Application Alliance in University Libraries
3.3 Take the Compound Library as the Basic Form of the Library
4 Analysis of the Results of Applying Computer Network to University Library
4.1 Analysis on the Efficiency of University Students Searching for Information
4.2 Evaluation and Analysis of University Students’ Evaluation of the Combination of Computer Network Technology in the Library
5 Conclusions
References
Self-service Fetching of Image ROI Based on Computer-Aided Detection
1 Introduction
2 Self-service Fetching of Image ROI Under Computer-Aided Detection
2.1 Image Segmentation and ROI Extraction Target Feature Research
2.2 The Steps of ROI Automatic Detection Algorithm
2.3 Application of Image Segmentation in Automatic ROI Extraction
2.4 Iterative Threshold Method to Obtain Segmentation Threshold
3 Experiments Research on Self-service Image ROI Retrieval Under Computer-Aided Detection
3.1 Research Objects
3.2 Algorithm Evaluation
3.3 Judgment of Optimal Segmentation and Selection of Data
4 Experimental Research and Analysis of Self-service Image ROI Retrieval Under Computer-Aided Detection
4.1 Experimental Analysis of the Segmentation Effect of Image ROI Self-service on the Overlap Percentage Threshold Under Manual and Computer-Aided Testing
4.2 Experimental Analysis of the Effect of Image ROI Self-fetching on Intensity Percentage Threshold Segmentation Under Manual and Computer-Aided Testing
5 Conclusions
References
Three Dimensions of Campus Network Platform Construction in the Internet Era
1 Theoretical Dimension: Guiding the Direction of Campus Network Platform Mechanism with Comprehensive Theoretical Basis
1.1 Adhere to the Main Position of Harmonious Development of Network Platform
1.2 Adhere to the Principle of Simultaneous Development of Management and Service
1.3 Follow the Legal and Reasonable Mechanism Construction
1.4 Promote the Integration of Tradition and Advanced Culture
2 System Dimension: Maintaining the Establishment of Campus Network Platform Mechanism with Advanced System Standards
2.1 Establishing the Standard Consciousness of Campus Network Platform
2.2 Improve the Leadership System of Campus Network Platform
2.3 Formulate the Management Rules of Campus Network Platform
2.4 Construction of Supervision System of Campus Network Platform
3 Technical Dimension: Ensure the Renewal of Campus Network Platform Mechanism with Excellent Technical Support
3.1 Strengthen Infrastructure Construction, Build and Share the Achievements of Campus Network Platform Mechanism
3.2 Deepen Innovation Drive, Enhance the Supply of Core Technology of Campus Network Platform
3.3 Attract Technical Talents and Optimize the Work Organization of Campus Network Platform
3.4 Increase Capital Investment, Always Maintain the Campus Network Platform, and Continue to Research and Develop
References
Large Data Recognition System for Speech Outliers Based on Deep Learning
1 Introduction
2 Large Data Recognition System for Speech Outliers Based on Deep Learning
2.1 Speech Recognizer Design
2.2 Voice Classification Network Design
2.3 Speech Data Reading
2.4 Implementation of Speech Isolation Recognition
3 Simulation Experiment
4 Conclusion
References
Artificial Intelligence for General Layout and Transportation Engineering with GIS Technology
1 Introduction
2 Overview of Geographic Information System (GIS)
2.1 Overview of GIS
2.2 Development History of GIS
3 Discussion on the Application of GIS in General Layout and Transportation Engineering
3.1 Contents of General Layout and Transportation Engineering
3.2 System Structure and Technical Route of Establishing Digital General Layout Management System by Using GIS Technology
4 Application of GIS in General Layout and Transportation Engineering Under Artificial Intelligence
4.1 Overview of GIS Application Based on Artificial Intelligence
4.2 Application in Site Selection
5 Conclusion
References
Research on BP Neural Network Image Restoration Algorithm Based on Genetic Algorithm
1 Introduction
2 Genetic Algorithm
3 GA-BP Algorithm
4 Design of GA-BP Algorithm for Image Restoration
5 Algorithm Application and Experimental Results
6 Conclusion
References
IOts Technology and Deep Learning for Process Parameters with Laser Welding Crack of Alloy
1 The Introduction
2 Materials and Methods
2.1 Microstructure of Alloy Base Material
2.2 Sample Preparation
3 Effect of Process Parameters on Laser Welding Crack of NI3Al-Based MX246A Alloy
3.1 Analysis of Laser Power and Welding Speed
3.2 Influence of Protective Gas on Penetration Depth
4 Conclusion
References
Secure Vertical Handoff Algorithm for Wireless Mobile Networks Supporting Cloud Computing
1 Introduction
2 Network Handoff Management of Mobile Cloud Computing
2.1 Related Research
2.2 Switch Management Framework
3 The Basic Principle of Vertical Handoff Algorithm
3.1 Vertical Switching Process
3.2 Vertical Handover Decision
4 Research Content of Mew Algorithm
4.1 Handover Decision Scheme
5 Conclusion
References
Curriculum Reform and Research of Airport Safety Inspection Based on Cloud Computing
1 Introduction
2 Cloud Computing
2.1 Definition of Cloud Computing
2.2 Characteristics of Cloud Computing
2.3 Advantages of Cloud Computing in College Teaching Application
3 Basic Structure of Curriculum
4 The Reform of Aviation Port Safety Inspection Course Based on Cloud Computing
4.1 Application of Cloud Computing in Teaching Management System
4.2 Application of Cloud Computing in Teaching Resource Sharing
5 Conclusion
References
Bionic Design of Acquisition System of Underwater Sea Cucumber Search and Capture Robot
1 Introduction
2 Inserting Content Elements
2.1 Current Situation of Deep-Water Sea Cucumber Fishing Industry
2.2 Market and Current Situation of Search and Capture Robots
3 Analysis of Technical Difficulties
3.1 Adaptability Analysis of Underwater Complex Environment
3.2 Difficulty Analysis of Ontology Design
4 The Overall Structure Design of Sea Cucumber Fishing Robot
4.1 Design of Bionic Flexible Grasping Device
4.2 High Pressure Difference Exoskeleton Bionic Design
5 Product Appearance Design
6 Conclusion
References
A Novel Sea Cucumber Search and Capture Robot Using Bionic Design Theory
1 Introduction
2 The Research Status
2.1 Market and Current Situation of Search and Capture Robots
3 Analysis of Technical Difficulties
3.1 Analysis of Technical Difficulties in Materials
3.2 Analysis of Technical Difficulties in Appearance
4 Underwater Bidirectional Anti Undercurrent Design
5 Conclusion
References
The Application of Nosocomial Infection Monitoring System in the Management of Nosocomial Infection Control
1 Introduction
2 Materials and Methods
2.1 Data Source
2.2 Calculation Methods [7]
2.3 Introduction of Nosocomial Infection Monitoring System
2.4 Quality Control
2.5 Statistical Analysis
3 Results
3.1 The Change of Nosocomial Infection Rate Before and After Using the Nosocomial Infection Monitoring System
3.2 Analysis of Ventilator-Associated Pneumonia in Intensive Care Unit (ICU)
3.3 The Effect of Real-Time Monitoring on the Use of Therapeutic Antimicrobial Drugs and the Submission of Microbial Specimens for Examination
3.4 Analysis of Staff Motivation After the Application of Infection Monitoring System
4 Conclusion
References
Analysis on Legal Issues of Cloud Computing Software-as-a-Service (SaaS) Model
1 Introduction
2 Method
2.1 Cloud Computing
2.2 Cloud Computing Service Types
2.3 Software as a Service Features
2.4 Detailed Definitions and Formulas of Key Indicators of SaaS
2.5 Legal Issues Brought by Software as a Service
2.6 Breakthrough Direction of Cloud Computing SaaS Platform Development
3 Experiment
3.1 Experimental Content
3.2 Experimental Process
4 Discussion
4.1 SaaS Applications Are Classified by Business
4.2 SaaS Applications Classified by Industry
5 Conclusion
References
Work on Optimal Configuration of Internal Network of District Energy System Based on Ant Colony Algorithm
1 Introduction
2 Objective Function
2.1 Upper Target
2.2 Lower Target
3 Case Analysis
References
Marketing Data Refined Push Algorithm Analysis Under the Background of Artificial Intelligence
1 Introduction
2 Refined Marketing Data Push Demand
3 Refined Push Algorithm of Marketing Data
3.1 Algorithm Introduction
3.2 Algorithm Analysis
4 Marketing Data Refined Push System Design
4.1 Basic Ideas
4.2 Detailed System Design
4.3 Simulation Analysis
5 Conclusion
References
Research on Enterprise Precision Marketing Based on Data Mining Technology
1 The Impact of the Era of Big Data on Enterprise Precision Marketing
1.1 Opportunities for Precision Marketing in the Era of Big Data
1.2 Constantly Enrich the Marketing Channels of the Enterprise
1.3 Further Improve Enterprise Data Mining and Analysis Capabilities
1.4 Further Improve Customer Satisfaction
2 Precision Marketing Strategy Design Based on Data Mining Technology
2.1 Design Objectives and Requirements of Precision Marketing
2.2 Precision Marketing Strategy Based on Data Mining
3 Conclusion
References
Analytics and Machine Learning Applications to Smart City
Intelligent Information Construction of Enterprise Management Based on Big Data Processing Technology
1 Introduction
2 Research on the Application of Big Data Technology in Enterprise Management Innovation
2.1 Types of Management Models and Consideration Factors
2.2 The Necessity of Enterprise Management Model Innovation
2.3 Innovation Path of Enterprise Management Model
3 Experiment
3.1 Questionnaire Survey Design
3.2 Reliability Test of the Questionnaire
4 Discussion
4.1 Satisfaction with Salary and Benefits
4.2 Performance Appraisal Situation
5 Conclusions
References
Recognition Method of River Sewage Outlets in UAV Aerial Images Based on Deep Learning
1 Introduction
2 Research Objects and Methods
2.1 Research Objects
2.2 Research Methods
3 Image Preparation
3.1 Image Preprocessing
3.2 Image Registration
4 Target Recognition Method of Sewage Outlets
4.1 Image Data Enhancement
4.2 Model Regularization
4.3 Network Structure Optimization
5 Conclusions
References
Intelligent Value-Added System Service of Automobile Manufacturing Enterprise Based on Forecast Demand Algorithm Analysis
1 Introduction
2 Intelligent Value-Added System Based on Forecasting Demand Algorithm
2.1 Interaction of the Value-Added Service and the Traditional Automobile Business Model
2.2 Predictive Demand Algorithm
2.3 Value-Added System Architecture Design
3 Simulation Test of Intelligent Value-Added System
3.1 Test Requirements
3.2 Testing Environment
4 System Test Results Analysis
4.1 System Function Test
4.2 System Performance Test
5 Conclusions
References
Fast Retrieval Algorithm of English Sentences Based on Artificial Intelligence Machine Translation
1 Introduction
2 Application Research of English Sentence Fast Retrieval Algorithm Based on Artificial Intelligence Machine Translation
2.1 Analysis oF Existing Problems in Machine Translation
2.2 Language Translation Model
2.3 Simhash Algorithm Analysis
2.4 Determination of Threshold
2.5 Translation Model
3 Experimental Research on the Fast Retrieval Algorithm of English Sentences Based on Machine Translation Under Artificial Intelligence
3.1 Subjects
3.2 Test Method
4 Experimental Analysis of a Fast Retrieval Algorithm for English Sentences Based on Machine Translation Under Artificial Intelligence
4.1 Comparison of Translation Time
4.2 Comparison of Similarity Optimization Results
5 Conclusion
References
VR Product Quality Evaluation Based on Analytic Hierarchy Process
1 Introduction
2 Construction of VR Product Quality Evaluation Index System
3 VR Product Quality Evaluation
4 Problems and Suggestions
References
The Cutting-Edge Applications and Trends of Big Data and AI Technology in the Digitalization of the Fashion Industry
1 Introduction
2 The Frontier Application Research and Trend Analysis of Big Data and AI Technology in the Digitalization of the Fashion Industry
2.1 Definition of BD
2.2 The Status Quo of the Fashion Industry
2.3 BD and AI Technology Cited in Fashion Industry Analysis
2.4 The Establishment of BD and AI Analysis Framework for Fashion Companies
2.5 Online Clothing Model Design Based on BD and AI Technology
3 Experimental Research on the Frontier Application of Big Data and AI Technology in the Digitalization of the Fashion Industry
3.1 Experimental Protocol
3.2 Research Methods
4 Experimental Analysis of the Frontier Application of Big Data and AI Technology in the Digitalization of the Fashion Industry
4.1 Comparative Research on Digitalization of Fashion Industry
4.2 Performance Analysis of BD and AI Technology Applied to Fashion Industry
5 Conclusion
References
Remote Sensing Image Target Recognition System Based on Heapsort
1 Introduction
2 Based on Heapsort Research on the Theory of Remote Sensing Image Target Recognition System for Football Match Based on the Algorithm
2.1 Remote Sensing Image Related Theory
2.2 Remote Sensing Image Target Detection Method
2.3 The Main Framework of Target Image Recognition
3 Test of Remote Sensing Image Target Recognition System Based on Heapsort Algorithm
3.1 Background of System Research and Development
3.2 System Function and Architecture Design
4 Application Effect Analysis of Football Competition Remote Sensing Image Target Recognition System Based on Heapsort Algorithm
4.1 Heapsort Computing Experimental Software and Hardware Environment
4.2 The Influence of Heapsort Algorithm on the Target Recognition Rate of Football Competition
5 Conclusion
References
The Application of Artificial Intelligence in AI News Anchor
1 Introduction
2 Theoretical Concept
2.1 Definition of AI
2.2 Categories of AI
2.3 Machine Learning
2.4 Deep Learning and Neural Networks
2.5 Big Data
2.6 AI in Broadcast and Media Sector
3 Critical Case Study
3.1 The Application of AI in News Anchor
3.2 The Analysis of AI News Anchor
3.3 Future Development of AI in News Broadcasting
3.4 Ethics Issues of AI
4 Conclusion
References
Oil Painting Art Communication System Based on Artificial Intelligence Optimization Algorithm
1 Introductions
2 Research on the Spread of Oil Painting Art
2.1 The Appeal of Oil Painting Art Dissemination
2.2 Propagation Algorithm Based on Artificial Intelligence Optimization
3 Design of Oil Painting Art Communication System Based on Artificial Intelligence Optimization Algorithm
3.1 Development Mode
3.2 Development Tools
3.3 The Functional Structure of the Oil Painting Art Communication System
3.4 Register and Log in
3.5 My Workbench
3.6 Oil Painting Management
3.7 Exhibition Management
3.8 Oil Painting Statistics
4 Detection of Oil Painting Art Communication System Based on Artificial Intelligence Optimization Algorithm
4.1 Experimental Design
4.2 Result Analysis
5 Conclusions
References
Application of AI in Computer Network Technology in the Big Data Era
1 Introduction
2 Application of AI in CNT in the Era of BD
2.1 Advantages of Integrating AI Technology into CNT
2.2 Application of AI Technology in CNT
2.3 Application of AI Methods
3 Application Experiment of AI in Data Cleaning in the Era of BD
3.1 Experimental Method
3.2 Experimental Environment
4 Experimental Analysis
4.1 Evaluation of Correctness
4.2 Speedup Analysis
5 Conclusions
References
Business English Online Classroom Teaching Based on ESP Demand Analysis Technology
1 Introduction
2 BE Online Classroom Teaching Based on ESP Demand Analysis Technology
2.1 Problems in BE Online Classroom Teaching Based on ESP Demand Analysis Technology
2.2 Optimization Strategy of BE Online Classroom Teaching Based on ESP Demand Analysis Technology
2.3 SVM Classification Algorithm for Classroom Teaching Evaluation
3 Investigation and Research on BE Online Classroom Teaching Based on ESP Demand Analysis Technology
3.1 Research Methods
3.2 Survey Design
4 Data Analysis of BE Online Classroom Teaching Based on ESP Demand Analysis Technology
4.1 Analysis of BE Online Classroom Teaching Content
4.2 Teaching Content Student Needs
5 Conclusion
References
Application of AI Video Image Technology in Soft Ladder Strength Teaching and Training System
1 Introduction
2 Comparative Analysis of AI Video Image Technology Assisted Traditional Teaching and Traditional Teaching Mode
2.1 Help Students Establish Correct Visual Motion Representation
2.2 Give Full Play to the Leading Role of Teachers and the Main Role of Students
2.3 Enriching Teaching Methods and Improving Students’ Motivation
3 The Application Process of AI Video Image Technology in the Soft Ladder Strength Teaching and Training System
3.1 Experimental Method
3.2 Experimental Principles
3.3 The Specific Implementation Process of Teaching in Control Group and Experimental Group
4 Experimental Results and Analysis
4.1 Test Results and Analysis of Related Technical Actions During Training
4.2 Comparative Analysis of the Results of the Control Group and the Experimental Group After the Experiment
5 Conclusions
References
Study on the Construction of Intelligent Supply Chain System Based on Internet of Things Technology
1 Background
2 Key Technologies of IOT and Supply Chain System
2.1 IOT Technology
2.2 Supply Chain System
2.3 Supply Chain Management Process of IOT
3 The Framework of IOT Applied in Supply Chain Management
4 Information Supply Chain Strategic Alliance Based on IOT
5 Suggestions of Artificial Intelligence in the Development of Supply Chain System
6 Conclusion
References
Application of Internet of Things Technology in the Field of Environmental Engineering
1 Introduction
2 Overview of IoT Technology
3 Problems Faced by the IoT in Environmental Engineering
3.1 Security and Reliability Need to Be Strengthened
3.2 Application and Industrialization Issues
3.3 The Relative Shortage of Talents
3.4 Key Technologies Restrict the Development of the IoT
4 Application of IoT Technology in the Field of Environmental Engineering
4.1 The Application of the IoT in Atmospheric Monitoring
4.2 Application of IoT in Water Quality Monitoring
4.3 The Application of the IoT in Solid Waste Supervision
5 Conclusion
References
Application of Intelligent Optimization Algorithm in the Analysis of Golf Track
1 Introduction
2 Intelligent Optimization Algorithm
2.1 Definition of Intelligent Optimization Algorithm
2.2 Ant Colony Algorithm
2.3 Particle Swarm Algorithm
2.4 Genetic Algorithm
2.5 Simulated Annealing Algorithm
3 Intelligent Optimization Algorithm Probability Model
4 Factors that Affect the Flight of Golf Balls
4.1 Swing Trajectory
4.2 Club Head Speed
4.3 Face Angle
4.4 Ball Angle
5 Intelligent Optimization Algorithm to Analyze Golf Trajectory
6 Conclusion
References
Investigation and Research into the Training Quality of Postgraduates Based on Artificial Intelligence
1 Introduction
2 Directly Conduct Research on the Quality of Postgraduate Training
3 Research of Postgraduate Training Model
4 Pay Attention to the Impact of the Fee System Reform on the Quality of Training, Such As: He Wenwen’s “What Is the Future of Postgraduate Fee Reform”
4.1 Strengthen the Construction of On-Campus Hardware Facilities and Improve the Living and Learning Conditions of Students
4.2 The Construction and Management of Teaching Staff
4.3 Scientific Research
4.4 Postgraduates’ Own Motivation
References
Application of Computer Technology in the Management of College Students
1 Introduction
2 Information Management System for College Students
2.1 System Architecture
2.2 System Function Module
3 System Test
3.1 System Test Scheme
3.2 System Testing Tools and Environment
4 System Test Results
4.1 Functional Test
4.2 Performance Test
5 Conclusions
References
Transformation from Financial Accounting to Management Accounting in the Age of Artificial Intelligence
1 Introduction
2 Overview of Management Accounting
2.1 Meaning and Characteristics of Management Accounting
2.2 Differences Between Financial Accounting and Management Accounting
2.3 Statistical Methods
3 Ideas and Methods
3.1 Subjects
3.2 Experimental Methods
4 Discussion on the Transformation from Financial Accounting to Management Accounting in the Age of Artificial Intelligence
4.1 Analysis on the Replacement Rate of Different Occupations by Artificial Intelligence in the Era of Artificial Intelligence
4.2 Analysis of Obstacles to Financial Digital Transformation in the Era of Artificial Intelligence
4.3 The Transition Path from Financial Accounting to Management Accounting Based on Artificial Intelligence Era
5 Conclusions
References
The Role of Artificial Intelligence and Virtual Reality Technology in the Training Mode of Rehabilitation Professionals
1 First, the Current Situation and Demand of Domestic Rehabilitation Personnel Training
2 Second, the Relationship Between Artificial Intelligence and Rehabilitation Medicine
3 Third, the Relationship Between Virtual Reality Technology and Rehabilitation Medicine
3.1 Application of Virtual Reality Technology in Rehabilitation of Children with Autism
3.2 Application of Artificial Intelligence in Rehabilitation of Clinical Hemiplegic Patients
3.3 Artificial Intelligence and Virtual Reality Technology Are Conducive to the Improvement of Students’ Innovation and Entrepreneurship
References
Application of Artificial Intelligence Technology in Modern Medical Service System Under the Background of Big Data
1 Introduction
2 Application of Artificial Intelligence Technology in Modern Medical Service System Under the Background of Big Data
2.1 The Current Problems of Traditional Medical Services in Our Country
2.2 The Necessity of Artificial Intelligence Technology and Medical Integration
2.3 Problems in the Current Development of Smart Medical Care in Our Country
3 Experiment
3.1 Questionnaire Design
3.2 Reliability Test of the Questionnaire
4 Discussion
5 Conclusions
References
A Study on Operation Effect Evaluation of Financial Sharing Center of Jiangsu Port Group by Artificial Intelligence
1 Introduction
1.1 The Background and Significance of the Study
1.2 The Current Situation of Research at Home and Abroad
2 Financial Sharing Related Concepts and Theories
2.1 The Concept of a Financial Sharing Service Center is Defined
3 Evaluation of the Construction and Operation of the Financial Sharing Center
3.1 The Necessity of Building the Financial Sharing Service Center of Jiangsu Port Group
3.2 Construction of the Financial Sharing Service Center of Jiangsu Port Group
3.3 Financial Sharing Center Operations
4 Jiangsu Port Group Financial Sharing Center Optimization Recommendations
4.1 Build a Balanced Scorecard, Improve the Evaluation System, Pay Attention to and Improve the Level of Enterprise Service Management
4.2 Increase the Capital Investment at the Technical Level of Enterprise Information Technology
5 Conclusion
References
Information Talent Training Mode Reform in the Era of Artificial Intelligence and Big Data
1 Introduction
2 Course Status and Problems
2.1 Course Objectives Overview
2.2 Course Status and Problems
3 Course Design Based on TOPCARES-CDIO
3.1 Concept and Methods of Curriculum Design
3.2 Intented Learning Outcome
3.3 Curriculum Content and Resource Construction Reform
4 Curriculum Implementation and Operation
4.1 Teaching Organization and Implementation
4.2 Course Performance Evaluation Method
4.3 Curriculum Operation and Reform Effectiveness
5 Conclusions
References
Cross-Cultural Adaptation of International Students Based on the Use of Online Platforms in the Context of the Internet
1 The Status Quo of the Research on the Cross-Cultural Adaptation of International Students and the Proposals
2 Status Quo and Predicaments of International Students in China Using Online Platforms
3 Analysis on the Causes of the Difficulties of International Students in China Using Internet Platforms
3.1 Language Barrier
3.2 Cultural Differences
3.3 Usage Habits and Interface Design Issues
4 Conclusion
References
The Problem and Optimization of Container Cargo Loading Based on Intelligent System Under the Background of Information Technology
1 Introduction
2 The Theory of Aircraft Container
3 Description of Cargo Loading Problems of Air Container
3.1 Problems of Container Waste and Aircraft Load Imbalance Caused by Fixed “Three Principles” Operation
3.2 Super High Boundary Problem of Plate and Box
3.3 The Container is Lost and Pulled in the Wrong Position
4 Optimization Scheme
4.1 Principle of “Combination of Light and Heavy, Collection and Release of Stack Box”
4.2 Pallet Delivery Measurement Mold
4.3 Realize the Tracking and Positioning of Cargo Board by RFID and GPS
5 Conclusion
References
An Aircraft Part Assembly Based on Virtual Reality Technology and Mixed Reality Technology
1 Research Situation of Virtual Reality Technology at Homeland and Abroad
1.1 Research Situation of Virtual Reality Technology in Foreign Countries
1.2 Research Situation of Virtual Reality Technology
2 Composition of Virtual Reality System
2.1 The Computer
2.2 Input and Output Equipment
2.3 Application Software
2.4 Database
3 Immersive Virtual Reality System Immersive Virtual Reality System is a Set of Relatively Complex System
3.1 Characteristics of Immersive Virtual Reality System
4 Application of Virtual Reality Technology and Mixed Reality Technology in Aircraft Assembly
5 Application Examples of VR Technology to Simulate Aircraft Assembly
6 Conclusion
References
Machine Learning for the Electromechanical Coupling Problem in Composite Plates with Second Order Two Scale Algorithm
1 Introduction
2 Construction of Second Order Two Scale Asymptotic Expansion
3 Second Order Double Scale Algorithm and Numerical Examples
3.1 Validation of the Algorithm
3.2 Effect of Randomly Distributed Voids on Thermal Properties of Materials
4 Conclusion
References
The Application Analysis with Computer Technology for Effective Kinetic Energy of Mining Reserves
1 Introduction and Application of Computer Technology
2 Mechanism of Improving Development Effect by Advanced Water Injection
3 PAM Algorithm
3.1 Basic Thought
3.2 Calculation Method
4 Introduction of Visual Components Based on Reservoir Analysis and Mining Technology
4.1 Introduction of Main Functions
4.2 ArcObjects Introduces the Main Functions
5 Realization of 3D Visualization Component
6 Conclusion
References
Niagara Framework Technology for Cloud Edge Collaborative Intelligent Building Management System
1 Introduction
2 Niagara Framework Technology
3 Cloud Deployment Technology
4 Search Technology
5 Edge Calculation
6 Conclusion
References
Knapsack Model and Multi-objective Particle Algorithm for Business Model of Pension
1 Introduction
2 Data Sources and Hypothetical Data
2.1 Data Hypothesis
2.2 Business Design Objectives
3 Multi Objective Knapsack Model Based Data Processing for Bed Operation of Elderly Care Service
3.1 Research Ideas
3.2 Research Method
4 Construction of Pension Business Model Based on Multi Objective Particle Algorithm
4.1 Research Ideas
4.2 Basic Process of Classical Particle Swarm Optimization
4.3 Multi Objective Particle Swarm Optimization Process
4.4 Comprehensive Solution of Model Based on MATLAB
5 Conclusion
References
Enterprise Intelligent Accounting System Structure and Intelligent Accounting Algorithm
1 Introduction
2 Research on the Basic Theory of Enterprise Environmental Cost Accounting
2.1 Concept and Characteristics of Enterprise Environmental Cost
3 Suggestions on Strengthening Environmental Cost Accounting of Enterprises
3.1 Strengthen Publicity and Enhance Environmental Protection Awareness of Enterprises
3.2 Establish and Improve Relevant Laws and Regulations
3.3 Establish Environmental Accounting Standards and Construct Environmental Cost Accounting System
4 Conclusion
References
Research on Improvement of Logistics Management System Based on Blockchain Technology
1 Introduction
2 Related Technical Basis
2.1 Concept and Characteristics of Blockchain
2.2 Recommendation Algorithm
3 Demand Analysis of Intelligent Logistics System
4 Main Improvements
4.1 Aiming at the Improvement of the Program Design of the System
4.2 Improve the Maintenance of the System
5 Implementation of Intelligent Logistics System
6 Full Text Summary
References
BP Neural Network Computer Network Information Security Risks and Solutions
1 Introduction
2 Construction of Evaluation Index System
3 Modeling with BP Algorithm
3.1 BP Algorithm
3.2 Improved BP Algorithm
3.3 Normalization of Evaluation Index
3.4 Network Security Risk
4 Structure of Evaluation Model
5 Evaluation Case
6 Concluding Remarks
References
Research on Object Detection and Shadow Detection Algorithm Based on Computer Vision
1 Introduction
2 Overview of Shadow Detection Methods
2.1 Definition of Mingying
2.2 Types of Shadows
2.3 Feature Detection in Image Detection
3 Shadow Detection Method
3.1 Shadow Detection Algorithm Based on Color Density Conversion
3.2 Shadow Detection Algorithm Based on Color Feature Invariants
4 Concluding Remarks
References
On Personalized Cultural and Creative Product Design Strategy Based on AI Painting Generation
1 Overview of AI-Based Painting Generation and Cultural Creative Product Design
1.1 AI-Based Painting Generation
1.2 Cultural and Creative Product Design
2 Analysis of the Combination of Paintings and Cultural Creative Works
3 On Personalized Cultural and Creative Product Design Strategy Based on AI Painting Generation
3.1 Build Image Library
3.2 Feature Extraction
3.3 Image Stitching Method
4 Conclusion
References
Location Prediction and Image Recognition of Asian Hornet Based on Deep Learning
1 Introduction
1.1 Background
1.2 Question Assumptions
1.3 Symbol Description
2 Long and Short Neural Networks
2.1 Data Processing
2.2 Model Building
2.3 Model Solving
3 Convolutional Neural Networks
References
Application of Artificial Intelligence Technology in College English Teaching
1 Introduction
2 Basic Concepts of Artificial Intelligence Technology
3 Basic Framework of Teaching Application of Artificial Intelligence Technology
3.1 General Framework
3.2 Framework Analysis
4 College English Teaching Intelligent System Design
4.1 Information Collection Layer
4.2 Information Processing Design
4.3 Functional Design
5 Systematic Teaching Application Mode
5.1 Mix the Top and Bottom of the Wire
5.2 Targeted Guidance
6 Conclusion
References
Application of Customer Relationship Management in O2O Companies
1 Research Method
1.1 Literature Review
1.2 Expert Discussion
1.3 Field Trips
2 Research Background
2.1 Connotation of Customer Relationship Management
2.2 General Model of CRM System
2.3 O2O E-Commerce Model and Its Development
3 The Current Problems of O2O Model Companies
3.1 Insufficient Attention to Customer Relationship Management
3.2 Insufficient Customer Information Processing Capabilities
3.3 The Communication Between Companies and Customers Is Not Smooth
3.4 Low Customer Loyalty
4 Application of Customer Relationship Management in O2O Companies
4.1 Help Employees to Establish Customer First Awareness
4.2 Fully Leverage the Customer Relationship Management System to Understand Customer Needs
4.3 Use the Customer Relationship Management System for Multi-channel Communication and Promotion
4.4 Use Customer Relationship Management to Build Customer Loyalty
5 Conclusion
References
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Lecture Notes on Data Engineering and Communications Technologies 102
Mohammed Atiquzzaman Neil Yen Zheng Xu Editors
2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City Volume 1
Lecture Notes on Data Engineering and Communications Technologies Volume 102
Series Editor Fatos Xhafa, Technical University of Catalonia, Barcelona, Spain
The aim of the book series is to present cutting edge engineering approaches to data technologies and communications. It will publish latest advances on the engineering task of building and deploying distributed, scalable and reliable data infrastructures and communication systems. The series will have a prominent applied focus on data technologies and communications with aim to promote the bridging from fundamental research on data science and networking to data engineering and communications that lead to industry products, business knowledge and standardisation. Indexed by SCOPUS, INSPEC, EI Compendex. All books published in the series are submitted for consideration in Web of Science.
More information about this series at https://link.springer.com/bookseries/15362
Mohammed Atiquzzaman · Neil Yen · Zheng Xu Editors
2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City Volume 1
Editors Mohammed Atiquzzaman School of Computer Science University of Oklahoma Norman, OK, USA
Neil Yen University of Aizu Aizuwakamatsu, Japan
Zheng Xu Shanghai Polytechnic University Shanghai, China
ISSN 2367-4512 ISSN 2367-4520 (electronic) Lecture Notes on Data Engineering and Communications Technologies ISBN 978-981-16-7465-5 ISBN 978-981-16-7466-2 (eBook) https://doi.org/10.1007/978-981-16-7466-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Organization
General Chair Shaorong Sun, University of Shanghai for Science and Technology, China
Program Committee Chairs Mohammed Atiquzzaman, University of Oklahoma, USA Zheng Xu, Shanghai Polytechnic University, China Neil Yen, University of Aizu, Japan
Publication Chairs Deepak Kumar Jain, Chongqing University of Posts and Telecommunications, China Ranran Liu, The University of Manchester Xinzhi Wang, Shanghai University, China
Publicity Chairs Junyu Xuan, University of Technology Sydney, Australia Vijayan Sugumaran, Oakland University, USA Yu-Wei Chan, Providence University, Taiwan, China
v
vi
Organization
Local Organizing Chairs Jinghua Zhao, University of Shanghai for Science and Technology, China Yan Sun, Shanghai University, China
Program Committee Members William Bradley Glisson, University of South Alabama, USA George Grispos, University of Limerick, Ireland Abdullah Azfar, KPMG Sydney, Australia Aniello Castiglione, Università di Salerno, Italy Wei Wang, The University of Texas at San Antonio, USA Neil Yen, University of Aizu, Japan Meng Yu, The University of Texas at San Antonio, USA Shunxiang Zhang, Anhui University of Science and Technology, China Guangli Zhu, Anhui University of Science and Technology, China Tao Liao, Anhui University of Science and Technology, China Xiaobo Yin, Anhui University of Science and Technology, China Xiangfeng Luo, Shanghai University, China Xiao Wei, Shanghai University, China Huan Du, Shanghai University, China Zhiguo Yan, Fudan University, China Rick Church, UC Santa Barbara, USA Tom Cova, University of Utah, USA Susan Cutter, University of South Carolina, USA Zhiming Ding, Beijing University of Technology, China Yong Ge, University of North Carolina at Charlotte, USA T. V. Geetha, Anna University, India Danhuai Guo, Computer Network Information Center, Chinese Academy of Sciences, China Jianping Fang, University of North Carolina at Charlotte, USA Jianhui Li, Computer Network Information Center, Chinese Academy of Sciences, China Yi Liu, Tsinghua University, China Kuien Liu, Pivotal Inc, USA Feng Lu, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, China Ricardo J. Soares Magalhaes, University of Queensland, Australia D. Manjula, Anna University, India Alan Murray, Drexel University, USA S. Murugan, Sathyabama Institute of Science and Technology, India Yasuhide Okuyama, University of Kitakyushu, Japan
Organization
S. Padmavathi, Amrita University, India Latha Parameswaran, Amrita University, India S. Suresh, SRM University, India Wei Xu, Renmin University of China Chaowei Phil Yang, George Mason University, USA Enwu Yin, China CDC, USA Hengshu Zhu, Baidu Inc., China Morshed Chowdhury, Deakin University, Australia Min Hu, Shanghai University, China Gang Luo, Shanghai University, China Juan Chen, Shanghai University, China Qigang Liu, Shanghai University, China
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Preface
With the rapid development of big data and current popular information technology, the problems include how to efficiently use systems to generate all the different kinds of new network intelligence and how to dynamically collect urban information. In this context, Internet of Things and powerful computers can simulate urban operations while operating with reasonable safety regulations. However, achieving sustainable development for a new urban generation currently requires major breakthroughs to solve a series of practical problems facing cities. A smart city involves wide use of information technology for multidimensional aggregation. The development of smart cities is a new concept. Using Internet of Things technology on the Internet, networking, and other advanced technology, all types of cities will use intelligent sensor placement to create object-linked information integration. Then, using intelligent analysis to integrate the collected information along with the Internet and other networking, the system can provide analyses that meet the demand for intelligent communications and decision support. This concept represents the way smart cities will think. Cyber-Physical System (CPS) as a multidimensional and complex system is a comprehensive calculation, network and physical environment. Through the combination of computing technology, communication technology, and control technology, the close integration of the information world and the physical world is realized. IoT is not only closely related to people’s life and social development, but also has a wide application in military affairs, including aerospace, military reconnaissance, intelligence grid system, intelligent transportation, intelligent medical, environmental monitoring, industrial control, etc. Intelligent medical system as a typical application of IoT will be used as a node of medical equipment to provide real-time, safe, and reliable medical services for people in wired or wireless way. In the intelligent transportation system, road, bridge, intersection, traffic signal, and other key information will be monitored in real time. The vast amount of information is analyzed, released, and calculated by the system, so that the road vehicles can share road information in real time. Personnel of road management can observe and monitor the real-time situation of the key sections in the system and even release the information to guide the vehicle so as to improve the existing urban traffic conditions. The Internet of ix
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Things, which has been widely used in the industry, is a simple application of IoT. It can realize the function of object identification, positioning, and monitoring through the access to the network. BDCPS 2021 is held online at November 27, 2021, which is dedicated to address the challenges in the areas of CPS, thereby presenting a consolidated view to the interested researchers in the related fields. The conference looks for significant contributions on CPS in theoretical and practical aspects. Each paper was reviewed by at least two independent experts. The conference would not have been a reality without the contributions of the authors. We sincerely thank all the authors for their valuable contributions. We would like to express our appreciation to all the members of the Program Committee for their valuable efforts in the review process that helped us to guarantee the highest quality of the selected papers for the conference. We would like to acknowledge the General Chairs, Publication Chairs, Organizing Chairs, Program Committee Members, and all volunteers. Our special thanks are due also to the editors of Springer book series Lecture Notes on Data Engineering and Communications Technologies and editors Ramesh Nath Premnath and Karthik Raj Selvaraj for their assistance throughout the publication process. Norman, USA Aizuwakamatsu, Japan Shanghai, China
Mohammed Atiquzzaman Neil Yen Zheng Xu
Conference Program
Saturday, November 27, 2021, Tencent Meeting 9:50–10:00
Opening ceremony
10:00–10:40
Keynote: Mohammed Atiquzzaman
Shaorong Sun
10:40–11:20
Keynote: Neil Yen
14:00–18:00
Session 1
Junyu Xuan
Session 2
Yan Sun
Session 3
Ranran Liu
Session 4
Xinzhi Wang
Session 5
Guangli Zhu
Session 6
Zhiguo Yan
Session 7
Huan Du
Short papers poster
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BDCPS 2021 Keynotes
Keynote 1: Blockchain-Based Security Framework for a Critical Industry 4.0 Cyber-Physical System Professor Mohammed Atiquzzaman Edith Kinney Gaylord Presidential Professor, School of Computer Science, University of Oklahoma, USA
Mohammed Atiquzzaman (Senior Member, IEEE) obtained his M.S. and Ph.D. in Electrical Engineering and Electronics from the University of Manchester (UK) in 1984 and 1987, respectively. He joined as an assistant professor in 1987 and was later promoted to senior lecturer and associate professor in 1995 and 1997, respectively. Since 2003, he has been a professor in the School of Computer Science at the University of Oklahoma. Dr. Atiquzzaman is the editor-in-chief of Journal of Networks and Computer Applications, co-editor-in-chief of Computer Communications Journal and serves on the editorial boards of IEEE Communications Magazine, International Journal
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on Wireless and Optical Communications, Real Time Imaging Journal, Journal of Communication Systems, Communication Networks and Distributed Systems, and Journal of Sensor Networks. He co-chaired the IEEE High Performance Switching and Routing Symposium (2003) and the SPIE Quality of Service over Next Generation Data Networks conferences (2001, 2002, 2003). He was the panel co-chair of INFOCOM’05 and is/has been in the program committee of many conferences such as INFOCOM, Globecom, ICCCN, Local Computer Networks and serves on the review panels at the National Science Foundation. He received the NASA Group Achievement Award for “outstanding work to further NASA Glenn Research Center’s effort in the area of Advanced Communications/Air Traffic Management’s Fiber Optic Signal Distribution for Aeronautical Communications” project. He is the coauthor of the book “Performance of TCP/IP over ATM networks” and has over 150 refereed publications, most of which can be accessed at www.cs.ou.edu/~atiq.
Keynotes 2: Socially-Empowered Automated Mechanisms Towards Seamless Migration e-Learning to s-Learning Professor Neil Yen University of Aizu, Japan, Tsuruga, Ikkimachi, Aizuwakamatsu, Fukushima 9658580, Japan
Dr. Neil Yen is an Associate Professor at the University of Aizu, Japan. Dr. Yen received doctorates in Human Sciences (major in Human Informatics) at Waseda University, Japan, and in Engineering (major in Computer Science) at Tamkang University, Taiwan, in March and June 2012, respectively. His doctor degree at Waseda University was funded by the JSPS (Japan Society for the Promotion of Science) under RONPAKU program. Dr. Yen has actively involved himself in the international activities, including editorial works in journals and books, society services in academic conferences sponsored by IEEE/ACM, etc., and devoted himself
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to discover advanced and interesting research directions. Dr. Yen has been engaged in the interdisciplinary realms of research, and his research interests are now primarily in the scope of human-centered computing and big data.
Oral Presentation Instruction 1.
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Timing: a maximum of 10 min total, including speaking time and discussion. Please make sure your presentation is well timed. Please keep in mind that the program is full and that the speaker after you would like their allocated time available to them. You can use CD or USB flash drive (memory stick), make sure you scanned viruses in your own computer. Each speaker is required to meet her/his session chair in the corresponding session rooms 10 min before the session starts and copy the slide file (PPT or PDF) to the computer. It is suggested that you email a copy of your presentation to your personal inbox as a backup. If for some reason the files can’t be accessed from your flash drive, you will be able to download them to the computer from your email. Please note that each session room will be equipped with a LCD projector, screen, point device, microphone, and a laptop with general presentation software such as Microsoft PowerPoint and Adobe Reader. Please make sure that your files are compatible and readable with our operation system by using commonly used fronts and symbols. If you plan to use your own computer, please try the connection and make sure it works before your presentation. Movies: If your PowerPoint files contain movies, please make sure that they are well formatted and connected to the main files.
Short Paper Presentation Instruction 1. 2. 3.
Maximum poster size is 0.8 m wide by 1 m high. Posters are required to be condensed and attractive. The characters should be large enough so that they are visible from 1 m apart. Please note that during your short paper session, the author should stay by your short paper to explain and discuss your paper with visiting delegates.
Registration Since we use online meeting way, no registration fee is needed.
Contents
Data Mining and Statistical Modelling for Smart City Application of Intelligent Ant Colony Algorithm in Rural Logistics Intelligent Distribution Route Planning . . . . . . . . . . . . . . . . . . . . Yunmei Xiao
3
Sustainable Competitive Advantages of Chinese Online Travel Agents by RBV Model: A Data Based Analysis . . . . . . . . . . . . . . . . . . . . . . Jiyang Chen, Haojiang Tong, and Yue Li
11
Kinematics Analysis of Aerobics Movement Decomposition Based on Multi-target Video Tracking Algorithm . . . . . . . . . . . . . . . . . . . . Peng Yang
21
Color Network System of Folk Painting Based on Fractal Algorithm Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Zhang
29
Design of College Teaching Quality Evaluation Based on Apriori Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hao Liu
37
Panal Data Analysis on the Environmental Effects of Global Value Chains: Based on the Empirical Study of Chinese Industrial Panel Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haifeng Chou
45
The Application of Fusion Algorithm in Automobile Machinery Manufacturing Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiang Zou
55
Path of Data Mining and Analysis Technology in New Energy Vehicle Business Model Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xu Wu
67
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Data Analysis and Application of Resource Search Algorithm in Basic Education Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miao Li
77
The Construction of the Influence Model of Artistic Creativity Based on AMOS Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weiying Wang, Lichu Tien, and Yuqi Du
89
The Construction of Mental Health Prediction Model Based on Data Mining Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Wu, Qiuyu Ji, Ameng Zhao, Hong Li, and Yan Zhang
103
Influence of Big Data Statistical Analysis Technology on Informational Learning Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lei Yu
113
3D Jewelry Design System Based on K-means Algorithm . . . . . . . . . . . . . Nianhua Qian
123
The Development of Internet Plus Tourism Mode Based on Data Mining Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yingying Ma
131
Multi-modal Medical Image Fusion Method Based on Multi-scale Analysis and PCNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hui Li and Qiang Miao
141
Analysis of Express Logistics Cost Control Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinfen Ye and Chunhua Hu
149
Medical Equipment Sales Management Prediction System Based on LSTM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yarong Hu and Binfeng Xu
157
Precision Poverty Alleviation System of Listed Companies Based on Multiple Neural Network Algorithms . . . . . . . . . . . . . . . . . . . . . . Yufei Xia
165
Optimization Processing of Auto Parts Spraying Scheme Based on Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guanghua Zhang and Yulin Zhao
173
Comparison of Two Models Based on Deep Neural Network Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenjuan Ding, Chenhui Jin, and Suxia Yang
183
Model-Based Iterative Learning Control Algorithm and Its Simulation Research in Robot Point Position Control . . . . . . . . . . . . . . . . Kai Guo, Ming Li, Jun Han, and Zhi Bai
195
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Application of Data Mining Technology in Regional Economic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianwen Zhou
205
Application of Big Data in the Excavation of Regional Cultural Resources in the Development of Theme Hotels . . . . . . . . . . . . . . . . . . . . . . Yan Zeng
213
Using Computer Three-Dimensional Model System to Explore the Influence of Water–Rock Interaction on the Unloading Mechanical Properties and Microstructure of Sandstone . . . . . . . . . . . . . Fanglu Kou, Qiao Jiang, Liangpeng Wan, Kun Wang, and Hongyue Pan
221
Surface Quality Prediction Model of Crane Boom Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Honghua Liu, Wenping Tan, Hongmei Li, Jingzhong Gong, and Xiangling Liu
231
Comprehensive Utilization Analysis of Geophysical Exploration Data Based on Data Mining Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingfei Cui, Lu Wang, Liming Du, Chuangye Hu, and Yang Zhang
241
Spark for Data Mining of Massive Historical and Cultural Resources and Humanistic Smart City Construction . . . . . . . . . . . . . . . . . XiaoLi Zhang
249
Research on Massive Data Mining Technology Based on Map Reduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xia Chang
255
Application and Research of Data Mining Technology Bim and Internet of Things in Engineering Construction . . . . . . . . . . . . . . . . . Luhao He, Yixing Xie, and Yue Li
261
Geotechnical Test and Effective Processing Analysis of Geotechnical Engineering Investigation Based on Geographic Information System Under BP Neural Network . . . . . . . . . . . . . . . . . . . . . Shihai Wang
269
Analysis and Research on Power Distribution Internet of Things Platform System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guangxian Lv, Jian Du, Peng Liu, and Bin Guo
277
Early Warning Model of Track and Field Sports Injury Based on RBF Neural Network Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bin Xie
285
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Application Research of Computer Digital Model Technology in the Packaging Design of Products with Different Regional Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peipei Lu and Hong Li
295
The Application of Correction Algorithm in the Improvement of Accuracy of English Translation Software . . . . . . . . . . . . . . . . . . . . . . . . Wei Xiong and Han Wu
305
The Forewarning Design of Enterprise Foreign Trade Risk Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhen Cheng
313
Big Data Analytics for Smart City Effects and Mechanism of Weibo’s Negative Emotions on Covid-19 Related Retweets Based on Big Data Collection Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinmiao Zhang Our Country’s Smart Agriculture Development Strategy and Path Under the Big Data Environment . . . . . . . . . . . . . . . . . . . . . . . . . Hongying Zhang The Design of the Information System Platform of the “One Picture” Platform for Territorial and Spatial Planning in the Big Data Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Suli Zhang and Bin Wang
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Impact of the Application of Big Data Technology on Industrial Agglomeration and High-Quality Economic Development . . . . . . . . . . . . Xinlin He and Yufeng Wang
353
Analysis of Information Management Scheme in Civil Engineering Construction Based on Big Data Analysis . . . . . . . . . . . . . . . Wenyan Liu and Yuechun Feng
361
Relationship Between Learning Behavior and Learning Effect Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinan Huang
371
Construction of School Quality Assurance System Based on Big Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yue Yang
381
Design of University Data Governance Process System Under the Big Data Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yao Yao
389
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Platform and Path of University Student Management Informatization Construction in the Big Data Environment . . . . . . . . . . . Jian Wang
397
Construction of Course Achievement Evaluation System Based on Big Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dan Tian and Ke Yang
405
Research and Application of Tax Classification Prediction Analysis Method Based on Big Data Technology . . . . . . . . . . . . . . . . . . . . . Chao Pang
413
Development of Rural E-Commerce Based on Big Data Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lina Xiao, Can Li, Huiyang Wu, Yalou Yue, and Li Li
423
Management Analysis of Human Resources Sharing Economy Platform Under Big Data Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ling Luo and Xiaohui Zhu
433
Smart City Medical Resource Allocation System Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaomu Yu and Xueqing Shi
441
Hardware Design of Electronic Components Remote Test Adapter Based on Information Processing . . . . . . . . . . . . . . . . . . . . . . . . . . Xiran Liu
449
The Application of Virtual Reality Technology in Aerobics Training from the Perspective of Information Technology . . . . . . . . . . . . Tingting Gou
457
Precision Design of Dongguan Intangible Cultural and Creative Products Based on Big Data Analysis Technology . . . . . . . . . . . . . . . . . . . . Dongning Chen
467
Proportion of College Students’ Internet Education Data Based on Big Data Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuxin Yang and Yong Cui
475
Gender Research in Literature Based on Big Data Technology . . . . . . . . Xun Wu
485
Exploration of Accounting Information Construction Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanbin Tang
493
On the Path of Improving the Work Quality and Accuracy of College English Workers in the Era of Big Data . . . . . . . . . . . . . . . . . . . Yue Li
503
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New Tools for Macroeconomic Analysis in the Era of Big Data . . . . . . . . Juan Xie
513
Application of “Big Data + Education” in the Construction of Curriculum Content System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinyu Fu
523
Expanding the Direction of Economic Development by Improving the Utilization Rate of WR in the Age of Big Data . . . . . . Ru Ji
533
Application of Artificial Intelligence Technology in College English Teaching Under the Background of Big Data . . . . . . . . . . . . . . . . Yuanyuan Chai and Na Liu
543
Innovative Development and Practice of University Intellectual Property Management Based on Big Data Algorithm . . . . . . . . . . . . . . . . Shi Hang and Dan Ying Wu
557
The Big Data for Comprehensive Evaluation of GIS Engineering Based on Data Envelopment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lansheng Xu
563
Application of Web Crawler Technology Based on Python in Big Data Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junling Pan
571
Intelligent Algorithm Big Data Analysis for the Construction of Smart Campus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Feng Liao
579
The Construction and Research of the Platform of Intelligent Sharing Laboratory Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanling Luo, Jiawei Wan, and Shengqin She
587
Research on Big Data Encryption Algorithm Based on Data Redundancy Elimination Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guojing Chen
595
Design of Big Data Management and Analysis Platform Based on Knowledge Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yong Chen, Wenjun Xue, Leiyu Wang, Yiyang Li, and Xin Xing
601
Application of Artificial Intelligence in Computer Network Technology in Big Data Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhenhui Shan
607
Application of Big Data Electrical Automation Technology in Electrical Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiang Jiao
615
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Operation Analysis of Financial Sharing Center Based on Big Data Sharing Technology—Taking SF Express as an Example . . . . . . . . Chengwei Zhang, Ge Guo, Weiqi Rao, and Xinyan Li
623
Application and Practice of Big Data Analysis in Enterprise Brand Marketing Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yishu Liu
633
Implementation Evaluation System of Land and Spatial Change Planning Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiong Wang, Liang Qin, and Qiancheng Luo
641
Research on Intelligent Tourism Resource Management Based on Big Data Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yufang Wang
651
Research on Sales Forecasting Method Based on Data Mining . . . . . . . . Zhihua Gan Study on Learning Path Selection of English Writing Based on Big Data Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Han Wu, Wei Xiong, and Sen Hong
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Data-Driven Co-design of Communication, Computing and Control for Smart City The Development Trend of International Economy and Commercial Industry Based on the Analysis of Internet of Things Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ping Wang Interface Usability of Video Sharing Websites in the Internet Era . . . . . Meiping Dai The Realization of the Practical Ability Training System of Media Talents in OBE Mode Based on Network Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chunjie Han
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A Design for Software Management Architecture and Software Management Information System in Time of Open Source . . . . . . . . . . . Xiao Wang, Li Wang, Yindong Li, Rong Wu, and Wenbo Sun
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Business Model Innovation Mechanism and Value Creation Effect of Data-Driven M&A—Case Study Based on Alibaba . . . . . . . . . . Xingrui Yang and Erna Qi
719
Design and Implementation of Enterprise Accounting Supervision Platform Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiuyun Ma
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Research on VR Data Visualization Design Based on Usability Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zheng Lu, Yaxin Li, and Qiannan Liang
737
Supply Chain Finance and Internet of Things Technology . . . . . . . . . . . . Longzhen Zhou
745
Analysis of “National Animation” Driven by Big Data Technology . . . . Hong Li
753
Data Management Strategy Based on Edge Computing . . . . . . . . . . . . . . Zaiyi Pu
761
Construction of Parallel Corpus of Foreign Publicity Based on Computer-Aided Translation Software . . . . . . . . . . . . . . . . . . . . . . . . . . Meng Sun
771
Research and Design of Physical Survey Data Analysis System Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jing Wang
779
The Application of Computer Video Image Technology in Track and Field Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Li
787
Formation Control of UAV Swarm Based on Virtual Potential Field and Virtual Navigator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Zhang, Huidong Huangfu, Jianhua Zhang, Jianglong Zhou, Haiyan Chen, Wei Tao, Jiachen Shen, Yijie Ding, and Kang Su
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Application of Computer Image Processing Software in Interior Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qing Li
807
The Application of Computer Technology in the Development and Analysis of Enterprise Intellectual Capital . . . . . . . . . . . . . . . . . . . . . . Kunseng Lao and Yixing Zhou
815
Design and Implementation of a Blockchain-Based International Trade Stable Digital Currency Issuance System . . . . . . . . . . . . . . . . . . . . . Xin Wang
827
Cross-Cultural Communicative Competence Based on Computer Aided Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aizhen Zhang
839
E-commerce Development of Characteristic Agricultural Products Under the Background of Computer Science and Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lina Xiao, Can Li, Rui Wang, Shucai Mei, and Li Li
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Contents
Graphic Design Understanding the Application of Computer Graphics and Image Processing Technology in Graphic Design to Improve the Employment Rate of College Graduates . . . . . . . . . . . . . . Ling Fu and Bei Gong Analysis and Research on the Mode of International Trade Practice Combined with Exhibition and Sales Under the Development of Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Bai, Deyang Zhang, and Xue Bai
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The Application of Computer Network Technology in University Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingqiu Yang
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The Development Trend of Computer Network and University Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanqiu Wang
883
Self-service Fetching of Image ROI Based on Computer-Aided Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuan Tian, Yaming Mu, Ze He, Zuyuan Huang, and Yudou Gao
893
Three Dimensions of Campus Network Platform Construction in the Internet Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Di Gao
903
Large Data Recognition System for Speech Outliers Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mei Zhang, Yong-ji Pei, Gang Wang, and Xing-xing Ma
915
Artificial Intelligence for General Layout and Transportation Engineering with GIS Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lansheng Xu
927
Research on BP Neural Network Image Restoration Algorithm Based on Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chen Chen and Hongbo Zhou
935
IOts Technology and Deep Learning for Process Parameters with Laser Welding Crack of Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hai-yun Gao, Run He, Dong-yun Zhang, and Kun Lu
941
Secure Vertical Handoff Algorithm for Wireless Mobile Networks Supporting Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xi Cheng
947
Curriculum Reform and Research of Airport Safety Inspection Based on Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuehong Shi
953
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Contents
Bionic Design of Acquisition System of Underwater Sea Cucumber Search and Capture Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lu Chen, Zheyu Fan, and Peng Wang
961
A Novel Sea Cucumber Search and Capture Robot Using Bionic Design Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lu Chen, Yun Gao, and Peng Wang
975
The Application of Nosocomial Infection Monitoring System in the Management of Nosocomial Infection Control . . . . . . . . . . . . . . . . . Hairui Zhang, Yancheng Feng, Yonghong Ma, and Ke Men
983
Analysis on Legal Issues of Cloud Computing Software-as-a-Service (SaaS) Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fen Li
991
Work on Optimal Configuration of Internal Network of District Energy System Based on Ant Colony Algorithm . . . . . . . . . . . . . . . . . . . . . 1001 Hao Li, Chang Liu, Wen Li, and Bo Miao Marketing Data Refined Push Algorithm Analysis Under the Background of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1009 Qin Xiao and Wei Li Research on Enterprise Precision Marketing Based on Data Mining Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1017 Yi Hu Analytics and Machine Learning Applications to Smart City Intelligent Information Construction of Enterprise Management Based on Big Data Processing Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027 Yun Jiang Recognition Method of River Sewage Outlets in UAV Aerial Images Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037 Shuang Wu, Qifan Yang, Jiasheng Ye, Xiaocong Wang, Shuyu Huang, Tao Gu, Shiting Cai, Peijia Yan, and Kunrong Zhao Intelligent Value-Added System Service of Automobile Manufacturing Enterprise Based on Forecast Demand Algorithm Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047 Zhao Wang Fast Retrieval Algorithm of English Sentences Based on Artificial Intelligence Machine Translation . . . . . . . . . . . . . . . . . . . . . . . 1057 Chuncai Lai
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VR Product Quality Evaluation Based on Analytic Hierarchy Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1067 Longtian Fu and Qi Zhang The Cutting-Edge Applications and Trends of Big Data and AI Technology in the Digitalization of the Fashion Industry . . . . . . . . . . . . . 1073 Youyang Lyu and Xiaojing Lv(u) Remote Sensing Image Target Recognition System Based on Heapsort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083 Sidong Cui, Zerong Jiang, and Ping Li The Application of Artificial Intelligence in AI News Anchor . . . . . . . . . 1093 Xuya Wang and Feng Zhu Oil Painting Art Communication System Based on Artificial Intelligence Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1101 Nan Gao and Liya Fu Application of AI in Computer Network Technology in the Big Data Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111 Yanli Zou Business English Online Classroom Teaching Based on ESP Demand Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1119 Weihua Kuang Application of AI Video Image Technology in Soft Ladder Strength Teaching and Training System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1129 Yujia Wang Study on the Construction of Intelligent Supply Chain System Based on Internet of Things Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1139 Yanyi Deng and Rongli Dun Application of Internet of Things Technology in the Field of Environmental Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1149 Ying Chen, Xufeng Tao, Wei Wang, Lei Liu, and Taiyang Yuan Application of Intelligent Optimization Algorithm in the Analysis of Golf Track . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1157 Zhenjun Li Investigation and Research into the Training Quality of Postgraduates Based on Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . 1165 Jiayue Cui and Changhong Guo Application of Computer Technology in the Management of College Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175 Hui Yang
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Contents
Transformation from Financial Accounting to Management Accounting in the Age of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . 1185 Xianfeng Liu The Role of Artificial Intelligence and Virtual Reality Technology in the Training Mode of Rehabilitation Professionals . . . . . . 1197 Changqiu Duan and Haili Song Application of Artificial Intelligence Technology in Modern Medical Service System Under the Background of Big Data . . . . . . . . . . 1205 Desheng Huang A Study on Operation Effect Evaluation of Financial Sharing Center of Jiangsu Port Group by Artificial Intelligence . . . . . . . . . . . . . . . 1213 Chengwei Zhang, Xinyan Li, Luwen Cui, and Quanxing Zhu Information Talent Training Mode Reform in the Era of Artificial Intelligence and Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1223 Jingshu Wang and Yi Guan Cross-Cultural Adaptation of International Students Based on the Use of Online Platforms in the Context of the Internet . . . . . . . . . 1231 Xia Sun and Alfera Emeti The Problem and Optimization of Container Cargo Loading Based on Intelligent System Under the Background of Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1239 Lijin Liu An Aircraft Part Assembly Based on Virtual Reality Technology and Mixed Reality Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1251 Huaiyu Dong, Xingyu Zhou, Jvya Li, Shibo Liu, Jiaming Sun, and Congtian Gu Machine Learning for the Electromechanical Coupling Problem in Composite Plates with Second Order Two Scale Algorithm . . . . . . . . . 1265 Jie Jiang, Wenbin Zhang, Libin Yu, Jie Min, Dewei Guo, and Hao Wu The Application Analysis with Computer Technology for Effective Kinetic Energy of Mining Reserves . . . . . . . . . . . . . . . . . . . . . 1271 Ahui Wu Niagara Framework Technology for Cloud Edge Collaborative Intelligent Building Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . 1277 Lu Yuan Knapsack Model and Multi-objective Particle Algorithm for Business Model of Pension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1285 Xue Guo
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Enterprise Intelligent Accounting System Structure and Intelligent Accounting Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1293 Yuehui Hu Research on Improvement of Logistics Management System Based on Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1299 Heshan Xiao BP Neural Network Computer Network Information Security Risks and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1305 Ping Liao Research on Object Detection and Shadow Detection Algorithm Based on Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1311 Qingxing Liu and Chen Zhou On Personalized Cultural and Creative Product Design Strategy Based on AI Painting Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1317 Yibo Wang Location Prediction and Image Recognition of Asian Hornet Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1325 Shangwang Liu, Hanlin Zhang, Shaohua Jin, Jianhang Song, Junhao Zhang, and Luhao Liu Application of Artificial Intelligence Technology in College English Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1335 Lijiang Yang Application of Customer Relationship Management in O2O Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1343 Bing Wang
Data Mining and Statistical Modelling for Smart City
Application of Intelligent Ant Colony Algorithm in Rural Logistics Intelligent Distribution Route Planning Yunmei Xiao
Abstract Since the report of the 19th National Congress of the Communist Party of China proposed the implementation of the rural revitalization strategy, the rural economy has developed rapidly, the effect of industrial agglomeration has been further deepened, user consumption levels and procurement needs have continued to increase, and logistics activities have increased sharply. The logistics distribution is a key link in the logistics service supply chain, and the study of logistics distribution path planning is of great significance to the development of rural revitalization. This paper aims to study the application of swarm intelligence algorithm in logistics distribution route planning under the background of rural revitalization. This article first expounds the problems and reasons that hinder rural logistics distribution, puts forward the form and optimization goal of logistics distribution path planning, and explains its definition, classification and solution methods respectively. This paper proposes the Intelligent Ant Colony Algorithm (IACA) to solve the logistics distribution path planning and solve the problem of multiple distribution centers. Finally, the algorithm is compared with the Cat Group Algorithm (CGA) for simulation experiments to verify the effectiveness of the algorithm. The experimental results show that when the number of distribution centers is 14, the optimal number of iterations for CGA and IACA is 41 and 30, respectively. When the number of distribution centers is 48, the optimal number of iterations for CGA and IACA is 202, respectively. 100 times, it shows that the algorithm proposed in this paper can be well used in the path planning of logistics distribution, improve the efficiency of distribution, and reduce the cost of logistics distribution. Keywords Rural revitalization · Intelligent algorithm · Logistics distribution · Route planning
Y. Xiao (B) Hunan Software Vocational and Technical University, Xiangtan 411100, Hunan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_1
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1 Introduction Since the rural revitalization strategy was put forward, the rural economy has shown a trend of rapid development, and modern rural logistics distribution path planning has received more and more attention [1, 2]. The logistics industry has become a new area of economic growth, and logistics distribution is an important part of the business process, and its work efficiency, accuracy and timeliness have an important impact on the entire social economy [3, 4]. In the subject of logistics research, the application research of swarm intelligence algorithm in optimizing logistics distribution path planning under the background of rural revitalization is very important. In the research on the application of logistics distribution route planning, many scholars at home and abroad have conducted in-depth discussions on it. For example, Zhang Y et al. solved the VRPTW problem with the genetically coded GA algorithm; solved the VRP with the saving algorithm [5]; Yakrangi O uses an improved hybrid GA to solve the multi-objective VRP problem [6]; Fu couples genetic operators and adaptive ACA to solve the MDVRP problem, and lays the foundation for the design of the intelligent algorithm library of the intelligent transportation scheduling system [7]. This article first elaborates the problems of poor logistics infrastructure and high cost of logistics distribution, and analyzes the causes of the problems, and summarizes the form, definition, classification and solution methods of the vehicle path planning problem. Then introduced logistics distribution optimization objectives and solution strategies, combined with the advantages of intelligent algorithms to solve combinatorial optimization problems for improvement, and proposed a differential ant colony intelligent algorithm to optimize logistics distribution paths. Finally, this method is used for logistics distribution route planning and simulation analysis.
2 Application of Swarm Intelligence Algorithm in Logistics Distribution Route Planning Under the Background of Rural Revitalization 2.1 Rural Logistics and Distribution Problems (1) Poor logistics infrastructure and lack of professional talents. As the pace of urbanization accelerates, many rural families are left with middleaged and old people or children, and the cost of broadband in rural areas is high. Farmers are reluctant to buy electronic equipment. Some rural residents are still using old-fashioned phones that can only make calls. Mobile phones, moreover, there are many small rural shops and supply and marketing cooperatives. If they can be unified into larger service stations, they can lay a certain foundation for the future development of rural logistics. In addition, due to the lack of computer technology
Application of Intelligent Ant Colony Algorithm in Rural …
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talents in rural areas, and the relative scarcity of relevant e-commerce and logistics professionals, it is impossible to plan the construction of rural infrastructure [8]. (2) Rural residences are scattered, and distribution costs are high. Most of the villages are located in mountainous areas, and the villages are far apart. To go to some villages, it is necessary to climb the mountains and the roads are rugged. Moreover, some households are located on the mountainside or on the top of the mountain, which is inconvenient to travel, but there is still a need for shopping. With the advancement of e-commerce companies setting up agents in towns and villages, they have met the rapid and convenient shopping needs of rural consumers. However, due to the scattered distribution of rural consumers, the cost of direct delivery from county-level sites to households is relatively high. Taking weather, man-made factors into account, it is impossible to meet the timeliness requirements. This is also the main factor restricting the development of rural logistics.
2.2 Problem of Logistics Distribution Path (1) The form of logistics distribution. There are many forms of logistics description and distribution problems [9]. Generally, it can be briefly described as follows: Assuming that there is a customer point M with a known specific location and demand, K vehicles are used to perform delivery from the distribution center to the demand point. After the distribution work is completed, they will return to the logistics distribution center. The route of the vehicle must be adjusted to make the transportation distance as short as possible and meet the following restrictions: 1. 2. 3.
The demand of all distribution points on each distribution path must be equal to or less than the total capacity of each distribution vehicle. The total length of each transportation route does not exceed the maximum driving distance of the car at one time. The needs of each customer point must be met by a car. The goal is to minimize the total cost (such as distance, time, etc.). (2) Vehicle route problem. Generally, the vehicle routing problem should consider the following goals:
1.
2.
The total route is the shortest. The length of the vehicle travel distance is directly proportional to the cost of transportation, logistics and distribution. The longer the distance, the longer the time required, and the greater the fuel consumption and wear of the vehicle [10, 11]. The total cost is the lowest. In addition to the cost of transportation, logistics and distribution, the energy costs of vehicles, damage costs, fines costs, etc. must also be considered in order to minimize the cost of the distribution process and maximize profits.
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The minimum number of vehicles required. In the logistics distribution process, every time a logistics company invests in a vehicle, its fixed cost will increase accordingly. This goal requires maximum utilization and utilization of vehicles, which can reduce labor and vehicle maintenance costs and reduce transportation costs. Maximize customer satisfaction.
The target condition is to meet the needs of customers, with customer arrival time requirements (arrival time) and product categories as the main conditions, timely and effective organization of the delivery of goods, vehicles, and the best route for logistics and distribution to be delivered as soon as possible (the difference between the actual shipping time and the time required by the customer) [12]. 1.
The running time of no-load vehicles is the shortest. The lower the idling rate of the vehicle, the stronger the loading and unloading capacity of the vehicle. Therefore, this goal requires reducing the idling rate, increasing the load capacity, reducing the idling time and improving the efficiency of the vehicle, and improving the work of the personnel under the condition of completing the task.
2.3 IACA For the evolution under the condition that the optimal population guarantees the optimal vector, the optimal solution plus the random vector difference method (DE/best/1) is selected for the difference mutation operation as shown in Eq. (1). In the formula, F represents the mutation operator, and generally takes a value between [0, 2]. X i (t + 1) = X best (t) + F · X j (t) − X k (t)
(1)
For the intermediate population, the diversity of the population is enriched on the basis of ensuring the best in the population, and the local search and the global search are balanced. Then the optimal solution and the random vector difference method (DE/rand-to-best/1) are used for differential mutation operations, as shown in formula (2). X i (t + 1) = X j (T ) + F · X best (t) − X j (T ) + X m (t) − X k (t)
(2)
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3 Experimental Research on Group Intelligence Algorithm in Logistics Distribution Path Planning Under the Background of Rural Revitalization 3.1 Swarm Intelligence Algorithm Structure Framework The calculation steps of the algorithm are as follows: (1) (2)
(3)
(4)
(5) (6)
(7) (8)
Construct a single-layer membrane structure containing m basic membranes; Initialization. M ants are randomly allocated to m basic membranes, and the pheromone matrix and heuristic information matrix of each basic membrane are initialized. At the initial moment, all membranes have the same sum; The basic membrane ant construction path. The ants in the basic membrane k use the pheromone matrix and heuristic information matrix in the membrane, and use the differential ant colony intelligent algorithm to iterate. And in each basic membrane during iteration, every time the ant moves one step, it must be updated according to the pheromone update rule. The basic membrane communicates with the surface membrane. Each basic film outputs an ant and the optimal path to the surface film, so the surface film contains m ants and paths; then the optimal path among the m paths is used to update the pheromone in the surface film using the global pheromone update rule matrix. The construction path of the surface membrane ants. The m ants in the surface film use sum, and after iterating with ACA, record the optimal path length. The surface film communicates with the basic film. The surface layer film sends its optimal path to each basic film, and uses the global pheromone update rule to update the pheromone matrix in each basic film, k = 0,1…,m. Judging the termination conditions. If the algorithm termination condition is met, go to step (8); otherwise, go back to step (3). Optimal path output. The surface film outputs its optimal path as the solution of the problem.
4 Experimental Analysis of Group Intelligence Algorithm in Logistics Distribution Route Planning Under the Background of Rural Revitalization 4.1 Analysis of the Average Solution of CGA and IACA In order to verify that the improved IACA can solve the routing problem of multiple distribution centers, multiple instances in the test library are selected for experimental simulation. Two sets of examples are selected for analysis. The introduction is as follows: Example 1 is to simulate the three problems of burma14, Chn31, and Att48.
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Table 1 Average solution of CGA and IACA
Algorithm name
CGA
Burmal14
30.8720
30.8720
Chn31
15,799.8021
15,377.7026
Att48
39,851.1420
33,623.7024
Number of iterations
CGA Burmal14
IACA
IACA
30.872 30.872
Chn31
Att48
15799.8021 15377.7026 39851.142 33623.7024
Average solution Fig. 1 Average solution of CGA and IACA
The cat colony algorithm CGA and the IACA are simulated and analyzed. The experimental comparison results are shown in Table 1. The parameter settings: MR = 0.2, SMP = 20, CDC = 1, SDR = 0.1, NC-MAX = 200, SMP1 = 5, Catnum is twice the size of the instance, and each experiment is simulated 30 times. It can be seen from Fig. 1 that when the problem scale is small, such as when the number of distribution centers is 14, 31, CGA can solve the known optimal solution; but when the number continues to increase, such as 48, the mutation of the gene position does not optimize the entire path search; and IACA increases the search range of the algorithm by increasing the secondary search method and random insertion method, and at the same time, through the directional selection of insertion points, the route is locally optimized, which can solve the multi-distribution center route problem well. Therefore, IACA can be used to solve the multi-distribution center routing problem, and its accuracy is high, and the average value is equal to the known optimal solution in a smaller problem scale.
4.2 Comparison of IACA with Other Algorithms In order to further prove the effectiveness and feasibility of IACA, compare IACA with ACS. CGA has been proved to be an intelligent optimization algorithm that can effectively solve the routing problem of multiple distribution centers. Therefore, the cat group system COS with better performance is selected for comparison. Each
Application of Intelligent Ant Colony Algorithm in Rural … Table 2 Comparison of optimal value iteration between IACA and other algorithms
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Algorithm name
CGA
IACA
Burmal14
41
30
Chn31
96.61
83.54
Att48
202
100
CGA
IACA
250
Number of iterations
202 200 150 96.61
100 50
41
83.54
100
30
0 Burmal14
Chn31
Att48
Number of distribution centers
Fig. 2 Comparison of optimal value iteration between IACA and other algorithms
experiment in the table is simulated 50 times, and the optimal value iteration indicates the number of iterations when the optimal value is reached. The comparison results are shown in Table 2: When the number of distribution centers is 14, the optimal number of iterations for CGA is 41, the optimal number of iterations for IACA is 30, and when the number of distribution centers is 48, the optimal number of CGA iterations is 202, and the optimal number of IACA iterations is 100. It can be seen from Fig. 2 that the optimal number of iterations of IACA is significantly lower than that of CGA, and as the problem scale increases, the optimal number of iterations of IACA is more advantageous, and iteratively known optimal solutions can be searched. It shows that its convergence speed is fast, and IACA can be well used for path planning of logistics distribution.
5 Conclusion With the continuous advancement of rural revitalization, the state vigorously encourages logistics and distribution to go deep into the countryside. While developing logistics enterprises, it has also developed the rural economy and enriched the daily lives of rural residents. Of course, the development of logistics and distribution in rural areas will also be restricted by local conditions, such as scattered consumers in rural areas and high logistics costs. If you want to develop in rural areas, you need to
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plan the dispatch and driving routes of logistics vehicles to effectively reduce logistics costs. In this paper, combining the advantages of intelligent algorithms, IACA is proposed and the distribution route is optimized. Through experiments, under the multi-distribution center routing problem, the simulation results of the two algorithms of CGA and IACA are compared, and the optimal distribution path is obtained. The optimization effect is remarkable. It also further proves the feasibility and effectiveness of IACA in solving the routing problem of multiple distribution centers in rural areas. Using this algorithm to optimize the routing can provide convenience for future distribution work, provide more efficient distribution routes for distribution personnel, and save distribution costs, provide more efficient services to rural consumers.
References 1. Liu H, Hua G, Yin H et al (2018) An intelligent grey wolf optimizer algorithm for distributed compressed sensing. Comput Intelligence Neurosci (2018-1-31):1723191 2. Zhao H, Zhao H, Guo S (2018) Short-term wind electric power forecasting using a novel multi-stage intelligent algorithm. Sustainability 10(3):881 3. Taha M, Jimenez JM, Canovas A et al (2018) Intelligent algorithm for enhancing MPEG-DASH QoE in eMBMS. Netw Protocols Algorithms 9(3–4) 4. Zhang Y, Wang L (2019) A hybrid intelligent algorithm DGP-MLP for GNSS/INS integration during GNSS outages. J Navig 72(2):375–388 5. Zhang Y, Wang L (2018) A hybrid intelligent algorithm DGP-MLP for GNSS/INS integration during GNSS outages. J Navig 72(2):1–14 6. Yakrangi O, Pazmio R, Cely JS et al (2021) An intelligent algorithm for decision making system and control of the GEMMA guide paradigm using the fuzzy petri nets approach. Electronics 10(4):489 7. Fu, Zhang, Chao (2020) Energy management of a power system for economic load dispatch using the artificial intelligent algorithm. Electronics 9(1):108 8. Novillo SM, Rodriguez A, Garcia G et al (2020) Path planning for mobile robots applied in the distribution of materials in an industrial environment. Adv Intell Syst Comput 1273(1):323–337 9. Sasaki T, Enriquez G, Miwa T et al (2018) Adaptive path planning for cleaning robots considering dust distribution. J Robot Mechatronics 30(1):5–14 10. Yang S (2020) Optimization of urban logistics distribution path under dynamic traffic network. Int Core J Eng 6(1):243–248 11. Chen L, Ma M, Sun L (2019) Heuristic swarm intelligent optimization algorithm for path planning of agricultural product logistics distribution. J Intelligent Fuzzy Syst 37(4):1–7 12. Yu X (2019) On-line ship route planning of cold-chain logistics distribution based on cloud computing. J Coastal Res 93(sp1):1132
Sustainable Competitive Advantages of Chinese Online Travel Agents by RBV Model: A Data Based Analysis Jiyang Chen, Haojiang Tong, and Yue Li
Abstract Chinese Online Tourism Agents (OTAs) are facing with fierce competition currently. Therefore, it is of primary importance for Chinese OTAs to build long lasting competitive advantages. This study identifies the sustainable competitive advantages of Chinese OTAs based on the resource-based perspective. The study conducted an online questionnaire survey among 210 consumers who were randomly chosen from six OTAs in China. Five competitive advantages were identified from the survey: payment model, product variety, cost leadership, service responsiveness and e-marketing strategies. Recommendations in terms of system security, service customization and corporate social responsibilities were put forward based on the perceived shortcomings of the OTAs. Keywords Competitive advantage · RBV view · OTA · Recommendations
1 Introduction Online tourism market, featured by the online tourism agents, has gradually replaced the traditional tourism agents. In the first quarter of 2018, China online travel attained 87.5 billion CNY, rising 29.2% from a year earlier and 11.7% over the preceding quarter [1]. Online travel agencies’ (OTA) revenue increased by 26.4% compared with a year ago, reaching to 4.01 billion CNY. This outstanding growth of Chinese online travel agents makes the academic field alarmingly aware of the importance of investigating their competitive advantages. The aim of this research is to identify the sustainable competitive advantages of Chinese OTAs based on resource-based
J. Chen (B) · Y. Li School of Food Engineering, Jilin Agriculture and Technology University, Jilin, China e-mail: [email protected] H. Tong China National Cereals, Oils and Foodstuffs Corporation, Beijing, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_2
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view (RBV View). In addition, the report also intends to demonstrate several recommendations for Chinese OTAs so that they can achieve ongoing and sustainable developments relying on these competitive advantages.
2 Literature Review 2.1 Industrial Analysis 2.1.1
Overall Environment and Value Chain
The Chinese OTA industry began in 2006 when Expedia entered in Chinese market by taking over a company named as eLong [2]. Several drivers facilitate the transformation from TAs to OTAs in China. Firstly, the overall Chinese travel market is experiencing rapid growth. Moreover, China is transforming itself into a consumptiondriven economy after the adoption of Open Up and Reform Policy, which attributes to the significant growth of the spending power in the upper and middle classes [3]. Furthermore, different types of social media as mediator, is positively relevant to the distribution of Chinese OTAs. Besides, the penetration of electronic devices is a key reason why online tourism purchasing becomes a popular buying behaviors, especially among Gen Y. The value chain of Chinese OTAs includes three components: suppliers in the upstream, wholesalers in the middle stream and the marketing platforms in the lower stream [4]. Figure 1 outlines the value chain of online tourism industry in China.
Fig. 1 Value chain of Chinese OTAs
Sustainable Competitive Advantages of Chinese Online …
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Fig. 2 Market share of Chinese OTAs
2.1.2
Major Players
Figure 2 shows the market share of each brand in the Chinese OTA industry. Ctrip shares the greatest proportion (33.9%). Qunar shares 22.1% of the market, ranking the second place. 12,306, a government authorized OTA, focuses on train ticket sales, sharing 8.8% of the market. Alitrip and eLong, share 5.6% and 5.0% respectively. Tuniu, Mangocity, Lotour, LY.com are new brands, sharing small proportion.
2.2 RBV and Competitive Advantage 2.2.1
Resource-Based View
Firm resources are assets, capabilities, organizational processes, firm attributes, information, knowledge and others that are controlled by a firm that enable the firm to conceive of and implement strategies that improve the efficiency and effectiveness [5]. Firm resources have four attributes in order to transform organizational capability: valuable, rare, imitable and non-substitutable, which is also known as VRIN model [6]. (1) Valuable resources. Valuable resources are able to bring benefits to the firm’s performance, which can be measured by two indicators: the improved efficiency and effectiveness. (2) Rare resources. As long as the number of firms that possess a particular resource with great value is less than the number of firms struggling in the competitive dynamics, that resource are regarded as a rare one in generating competitive advantages. (3) Imperfectly imitable resources. Imperfectly imitable resources could not be learned by other competitors. Competitors could
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neither conceive of nor implement, nor both of them since they do not possess the resources. (4) Non-substitutable resources. Once the resources are neither rare nor imperfectly imitable, at least they must be non-substitutable.
2.2.2
Competitive Advantage
“RBV” has placed considerable emphasis on the importance of key resources in achieving a competitive advantage. Different combinations of resources and competences are signified as cost drivers, product and service advantage [7]. Cost advantage is related to the cost of product per unit; product advantage is associated with product quality and design; service advantage consists of reliability, accessibility, post service, responsiveness, technical support and others [8].
3 Methodology 3.1 Data Collection Method A questionnaire survey was applied to collect the primary data. The questionnaire was designed by the author in advance. The questionnaire’s first part asked the basic information of consumers. The second part identified 31 questions in technical, service and product contexts. The obtained information remained confidential and documents were dismissed after the research. Questionnaire was sent to respondents through Email, which is cost effective, regardless of geographic boundaries [9].
3.2 Sample Typical OTAs were targeted by the research, including the strong, normal and fading brands. Strong brands like Ctrip and Qunar were chosen. Normal brands like eLong and Tuniu were considered. Taobao Altrip and Dianping, the brands focusing on tourism review and comments were included. Totally, 210 consumers from six OTAs were randomly chosen. 117 respondents were females, accounting to 55.7%. The incomes of respondents varied widely. Concerning the educational background, 103 respondents were undergraduates. 30% of the respondents were between 18 and 25 years old. However, the sample selection process ignores the influences of personalities on the result, which is a limitation needs further investigation.
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3.3 Data Analysis Method 1–9 were the fundamental scale, indicating the extent to which the consumers were agree with the criterion as an advantage of OTAs. 1 refereed to strongly disagree while 9 meant extremely agree. 9 variables were chosen and totally 31 criteria were selected to summarize the relevant resources of Chinese OTC (Table 1). SPSS was mainly used to analyze the primary data. Specifically, there are five parts of the analysis: reliability analysis, validity analysis, variable descriptive, correlation analysis and regression analysis.
4 Result 4.1 Reliability Analysis According to Table 1, the Cronbach Alpha of the 9 variables are 0.826, 0.816, 0.823, 0.831, 0.862, 0.843, 0.844, 0.853 and 0.813 respectively. The Cronbach Alpha of the 9 variables are over 0.7, indicating that the reliability of the variables are extremely high.
4.2 Validity Analysis The KMO of the 9 variables are over 0.6. Service support has the highest KMO (0.813) and system security has the lowest KMO (0.673). They were examined through Bartlett’s Test of Sphericity (sig = 0.000). Therefore, the validity of the 9 variables are acceptable.
4.3 Variable Descriptive The variable with the highest average means is perceived by consumers to be the most outstanding advantage of Chinese OTAs. The average means of technical context, product context and service context are 6.07, 6.70 and 6.58 respectively. Thus, product context of Chinese OTAs is the most advantageous.
Service context
210 210 210
Service innovation
210
Product innovation
Service support
210
Service quality
210
Product attribute
210
System management
Product knowledge
210
System function
Product context
210
System Security
Technical context
N
Variable
Scale
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
4.00
Min
9.00
9.00
9.00
9.00
9.00
9.00
9.00
9.00
9.00
Max
Table 1 Results of reliability, validity and variable descriptive
6.91
6.20
6.71
6.26
6.88
6.85
6.09
6.16
5.96
Mean
0.52
1.02
0.72
1.09
0.62
0.56
1.05
1.04
1.00
SD
3
4
4
3
4
4
3
3
3
N
0.813
0.853
0.844
0.843
0.862
0.831
0.823
0.816
0.826
Cronbach Alpha
0.708
0.813
0.779
0.728
0.796
0.789
0.700
0.703
0.673
KMO
214.264
351.267
348.159
254.634
384.106
311.551
234.362
220.657
254.556
Approx. Chi-Square
3
6
6
3
6
6
3
3
3
df
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Sig
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Table 2 Model Summaryb and ANOVAa Model summaryb Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
0.961a
0.923
0.920
0.334
a. Predictors: (Constant), Service innovation, System function, Service quality, Product attribute, Product innovation, System Management, Service support, Product knowledge, System Security b. Dependent Variable: Purchasing sustainable competitive advantage ANOVAa Model 1
Sum of Squares
df
Mean Square
F
Sig
Regression
268.187
9
29.799
266.702
0.000b
Residual
22.234
199
0.112
Total
290.421
208
a. Dependent Variable: Purchasing sustainable competitive advantage b. Predictors: (Constant), Service innovation, System function, Service quality, Product attribute, Product innovation, System Management, Service support, Product knowledge, System Security
4.4 Regression Analysis From Table 2, R2 , is 0.923, meaning that the nine variables are able to explain 92.3% of the reasons. Also, sig is 0.000 < 0.05, meaning that at least one variable in the nine variables influences the sustained competitive advantages of Chinese OTAs.
5 Discussion 5.1 Digital Marketing The finding reveals that digital marketing is the most important competitive advantage of Chinese OTAs. Online solutions of Chinese OTA highly rely on websites, accounting to 35% of the sales, followed by mobile applications (27%). Electronic commerce mode of the industry plays as a valuable and non-substitutable resource of Chinese OTAs. Nowadays, Chinese OTAs have extremely strong digital marketing strategies.
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Table 3 Comparison of prices offered by OTA and TA Flight Rooms Vehicle
Qunar
China Southern Airline
¥980 (Beijing-Xian)
¥1420(Beijing-Xian)
Ctrip
Shangri-La Website
¥353 per night
¥499 per night
Tuniu
CYTA
¥188/day
¥256/day
5.2 Cost Leadership Lots of respondents perceived “low cost” as a sustained advantage of Chinese OTAs. Table 3 compares the prices of different products offered by OTAs and real suppliers. This is because, OTAs not only sell the products to end-users by wholesaling, but also distribute the products on behalf of the suppliers [10]. Additionally, suppliers always offer the products with the lowest prices to OTA in order to pursue the amount of sales. OTAs pursue the profits from commission rather than the revenue by selling the products.
5.3 Payment Mode The easy, prompt and convenient payment model is valuable, inimitable and nonsubstitutable resources of Chinese OTAs. It is the result of the development of Chinese financial system, which is uniquely depending on the national condition of China. Non-substitutable means that the payment model could not be substituted by some other types of payment methods. In this context, Chinese OTA provides several choices for consumers to pay, including debt cards, credit cards, Visa and Master cards, internet banks as well as some payment systems that are operated by the third parties such as Alipay and WeChat.
5.4 Service Responsiveness Service responsiveness is a key measurement of service quality [11]. The result indicates that Chinese OTAs are advantageous in responsiveness. Each OTA has well designed FAQ and online helps functions, offering immediate aids to consumers for 24 h a day. Hot-line is also available. Undoubtedly, this indicates the strong service responsiveness of Chinese OTAs. The FAQ model, online help and hotline are valuable and non-substitutable resources towards this competitive advantage.
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5.5 Product Variety OTAs have an outstanding competitive advantage in product variety. Extensive services are offered by OTAs, including flight tickets, accommodation, trip or holiday, beverage and food and entertainment. Some new services such as pick-up service, refreshment and trip review are also designed and launched continuously. Innovation inevitably plays a key role in sustaining the competitive advantage [12].
6 Conclusion Five competitive advantages of Chinese OTAs are identified: payment model, product variety, cost leadership, rapid responsiveness and digital marketing. The research also reveals the disadvantages of Chinese OTA that needs continuous efforts to fulfill the gap. Foremost, the system security is perceived to be disadvantageous. Thus, it is highly recommended that Chinese should strengthen the protection of consumers’ personal information by fulfilling technical gaps of systems. Moreover, services lacking of customization is another disadvantage of Chinese OTAs. Chinese OTAs are suggested applying mass customization as a marketing technique that combines the flexibility and personalization of “custom-made” with the low unit costs. Lastly, corporate social responsibilities have to be fulfilled, which can increase long-term profits and reputation. Acknowledgements This work was supported by “13th Five-Year Plan Project” of Social Science Major (JJKH20200398SK), Education Institution of Jillin Province, China and Research Center of Sichuan Cuisine, Sichuan Tourism University (CC19G16).
References 1. China Online Travel Industry Report. (2018). Retrieved from http://analystreports.som.yale. edu/reports/OnlineTravelLiLiu.pdf China Online Trave.pdf 2. China Daily (2019) Boom time for online travel firms. Retrieved from https://www.chinadaily. com.cn/a/201901/21/WS5c451d2aa3106c65c34e5849.html 3. Wu E, Parulis-Cook S (2018) 2018 Chinese travel consumption trends report: top insights. Dragon Trail, Retrieved from https://dragontrail.com.cn/resources/blog/2018-chinese-travelconsumption-trends-repo 4. Liu C, Arnettk P (2000) Exploring the factors associated with websites success in the context of electronic commerce. Information Manage 38:23 5. Daft R (1983) Organizational theory and design. West, New York 6. Barmey J (1991) Firm resources and sustained competitive advantage. J Manag 17(1):99 7. Porter M (1991) Towards a dynamic theory of strategy. Strateg Manag J 12:95–117 8. Ziss S (1993) Entry deterrence, cost advantage and horizontal product differentiation. Reg Sci Urban Econ 23(4):523–543
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9. Bosnjak M, Tuten T (2001) Classifying response behaviors in web-based surveys. J ComputMediated Commun 6(3) 10. Fountoulaki P, Leue MC, Jung T (2015) Distribution channels for travel and tourism: the case of crete. Springer International Publishing, NY 11. Felix R (2017) Service quality and customer satisfaction in selected banks in Rwanda. J Bus Finance 6:246–256 12. Yogyakarta NU (2018) Competitive advantage and product innovation. Acad Strategic Manage J 17(2)
Kinematics Analysis of Aerobics Movement Decomposition Based on Multi-target Video Tracking Algorithm Peng Yang
Abstract Computer vision is currently an important research field. In computer vision, target tracking is one of the entry points for research, it is widely used in motion analysis, video surveillance, robot navigation, human–computer interaction and many other fields. In so many application scenarios, target tracking algorithms are required, it has high durability and efficiency. However, in real complex scenes, there are often different situations such as light changes, mutual occlusion between targets, interaction between targets, and blurred target motion, making the recorded video scene complex and changeable. Therefore, it is still a very difficult task to effectively monitor multiple moving targets at the same time. This article explores and analyzes the aerobics movement decomposition kinematics based on the video tracking algorithm of multiple sports targets. After consulting related materials, summarizes the feasibility of the aerobics movement decomposition kinematics based on the multiple sports target video tracking algorithm. The experiment of aerobics action decomposition kinematic analysis of the motion target video tracking algorithm, through the experiment, it is concluded that the angle of the right hip joint is 167.45°, when the right hip is fully extended at the beginning of the set of competitive aerobics action. It can be seen from the angle of the two knee joints that the legs are relatively straight and maintain a good posture. When the jumping foot hits the ground, the difference between the right hip angle between the two moments is 29.8°, that is, the right hip angle decreases and the right hip angle decreases, indicating that the upper body is facing the right side, the upper body begins to bend downward, and the left hip continue to abduct, its value increases, and at the same time the left lateral muscle stretches, so that the hip and shoulder joints form a larger rotation angle. Keywords Multiple sports targets · Tracking algorithms · Aerobics movements · Kinematics analysis
P. Yang (B) Shandong Province University of Jinan Institute of Physical Education, Jinan 250000, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_3
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1 Introduction Competitive aerobics is a sports event that can show continuous, complex and extremely difficult full range of exercise possibilities under the accompaniment of music [1, 2]. The application of high-difficulty movements of competitive aerobics in the full set not only determines the overall difficulty level, but also indirectly affects the degree of completion and artistic performance [3, 4]. High difficulty movement is not only the comprehensive strength of athletes, but also an important factor of basic competitiveness. With the increasing development of computer technology, this technology has also been applied to competitive aerobics [5, 6]. With the passage of time, the aerobics movement gradually develops in the direction of high, difficult and beautiful, which fully shows the characteristics of high difficulty projects [7, 8]. In view of the analysis and research of aerobics action decomposition kinematics analysis based on the multi-target video tracking algorithm, some researchers have proposed that multi-target tracking should first segment all the targets of interest from the background, and then separate the single target from the target set [9]. Traditional feature-based tracking methods, such as methods based on colors, key points, or moving blocks, have not yet established a recognized target detection model. Therefore, the detection effect is poor under some common conditions, such as obstacles, reflections or easy leakage, detection and false detection, the high resolution of the object detector can effectively overcome the above shortcomings and reduce the detection error rate [10]. In recent years, as the research and analysis of moving target tracking technology has become more and more in-depth, its main purpose has also changed, its operation is no longer limited to traditional functions. For example, in a computer system, its main purpose has also changed, its operation is no longer restricted by traditional functions [11]. For example, in a computer system, they can identify one of the two moving targets in the video sequence table. Current computer video animation tracking technology focuses more on solutions designed and developed for applications that can understand video sequences and are inspired by microbiology, such as simulating the perception of normal conditions. Detecting, deriving, locating and tracking various moving targets on video can generally be called video tracking, and deriving various motion parameters is an important step, displacement, speed, direction, acceleration, orbit, all are the parameters of various sports [12]. This paper studies the decomposition kinematics of aerobics based on the multimotion target video tracking algorithm, summarizes the relevant feasibility in the literature research method, and provides a basis for the following experiments, passing experiment: use multi-motion target video tracking algorithm to analyze the aerobics movement of xuanzi.
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23
2 Multi-target Video Tracking Algorithm and Aerobics Movement Decomposition Kinematics Research 2.1 Feasibility Analysis of Multi-sport Target Video Tracking Application Aerobics Movement Decomposition Kinematics (1)
(2)
(3)
(4)
Can adapt to changes in the appearance of the target to update. When the position of the moving target changes due to the light intensity, the tracking algorithm can obtain the characteristics of the change of the moving target’s position, and adaptively update to obtain a new display model to adapt to the change of the moving target’s position, so that the tracking accuracy can be guaranteed. Able to separate the target from the complex background. This monitoring method can distinguish between complex backgrounds and moving targets. When the background is similar to the moving target in color or shape, the recognizable static display model separates the target from the background and accurately receives target information. It can accurately locate the moving target when an obstacle occurs. The monitoring method can accurately search and locate the position of the moving target, and obtain a large amount of target information. It is necessary to judge the occurrence of obstruction. When part or all of the obstruction occurs, the position of the moving object can be accurately predicted. Once the blocking is complete, relocate and continue to monitor the target. Can adapt to changes in target scale. You can customize the monitoring mode to change the monitoring window. When the distance between the moving target and the camera changes, the tracking window will automatically adjust accordingly. For example, as the target scale becomes larger, the tracking window becomes adaptively larger to ensure that all moving target information is contained in the tracking frame to ensure tracking accuracy.
2.2 Multi-moving Target Video Tracking Algorithm First, for the first frame of image, take the RGB vector or grayscale pixel value as the feature value of the target model, and then, given the original position and size of the target, calculate the target histogram in the image. On this basis, a clear mode Ta and a fuzzy mode Tb are constructed for the monitoring target area, and the constructed clear mode Ta and fuzzy mode T are used to obtain the final monitoring mode T, where T = [Ta, Tb]. The block editing method in the reference literature will take the T template for block editing, divide each image in the T template into several
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overlapping small blocks, extract an attribute from each small block, and then group each small block unify so that each small piece is used as an entry in the dictionary. After that, the motion information is extracted and motion motion blur can be used to extract the motion information. This kind of motion information is implicitly expressed in the coefficients, so the coefficients of the blurred part of the template are mainly considered here. The standard template, namely the clear Ta template, is taken from the pure object image and manually selected from the first frame of the image; the fuzzy T template is created based on the influence of Ta and different blurs. The value of the fuzzy mode T is [ul, 1, …; ul, m, u2, 1, …, u2, m, … un, 1, …; un, m], where n = 1, 2, …, m = 1,2, …, where n is the movement speed and m is the movement direction. By setting the speed and direction of the movement separately, different information can be obtained. In this way, we can get the final tracking target model T, where T = [Ta, Tb]. From the tracking target template T, 0 and r can be obtained, which are the extracted motion information. 0 and r are both a one-dimensional vector, after normalizing 0 and r, 0 can represent the weight of the object in various directions (the direction of movement set in the dictionary), and r can represent the weight of each amplitude of the object’s motion. After that, we need to use the predictive model and the updated model to calculate the similarity to the tracking target. Now define the prediction model and update model as follows: (1) Forecast model p(x k−1 Z k−1 )
(1)
(2) Update the model V (a, z) = γ
dis(m, n)−1 [an = am ] exp −β(Z m − Z n )
(2)
(m,n)∈C
3 Kinematics Experiment of Calisthenics Action Decomposition Based on Multi-sport Target Video Tracking Algorithm 3.1 Experimental Design The collection of experimental data was completed in the aerobics gymnasiums of the city’s all-staff training room. The 8-lens high-speed infrared receiving system (QUALISYS-MCU500) was used to collect 360° hard-to-rotate and rotational kinematics data. The sampling frequency is 200 Hz, and the test subjects are selected
Kinematics Analysis of Aerobics Movement Decomposition …
25
from the athletes of the city’s aerobics team. The height is 170 cm, the weight is 61 kg, and the training period is 12 years.
3.2 Experimental Procedure (1)
(2)
(3)
(4)
Site preparation: Before the data collection work begins, draw up all the curtains on the test site, block unnecessary light points, calibrate the test area, and place the infrared and light point high-speed mobile capture system 8 lenses in the front, rear, and left of each measured area. Adjust the angle and height of each lens, connect the computer to correct it many times, and eliminate the interference of redundant light spots. Athlete preparation: Athletes arrive at the examination room ahead of schedule, prepare for warm-up activities, and put on sportswear. Paste the mark according to the mannequin, completed by the students engaged in the sports industry. Infrared spot calibration: The athlete enters the test area to calibrate the infrared light spot on his body. Test process: After the athletes are ready, the tester issues the start command to collect data for the athletes to complete the actions of the specified difficulty. The sequence is first spin and then 360°, each difficulty is completed 6 times; 3 referees on each action completion is scored. According to the referee’s score, each action with the highest score is selected for later data processing.
3.3 Data Processing The research data is processed and analyzed through the software in the qualisys system to obtain the kinematic parameters required for the research, and the cortex software is used to derive the parameters of the body’s center of gravity and the euler angle of the torso relative to the pelvic torsion. All of the above are completed with the help of the students of the Sports Biological Force Teaching and Research Office.
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4 Result Analysis 4.1 Analysis of Angle Parameters of Lower Limb Joints The changes in the angle parameters of the tester’s lower limb joints are analyzed through experiments. The experimental results are shown in Table 1. It can be seen from Fig. 1 that at the beginning, when the right hip is fully extended, the angle of the right hip joint is 167.45°. It can be seen from the angle of the two knee joints that the legs are relatively straight and maintain a good posture. When the jumping foot hits the ground, the difference between the right hip angle between the two moments is 29.8°, that is, the right hip angle decreases, and the right hip angle decreases, indicating that the upper body faces the right side, the upper body begins to bend downward, and the left hip continue to abduct, its value increases, and at the same time the left lateral muscle stretches, so that the hip and shoulder joints form a larger rotation angle. Table 1 Analysis of angle parameters of lower limb joints Hip joint
Knee joint
Ankle joint
0
167.45
127.46
165.04
178. 19
133.28
136.79
0.21
130.60
136.56
159.27
151. 67
92.92
127.11
0.38
65.30
57.19
145.60
123.32
93.83
88.88
0.46
79.00
32.57
159.14
130.57
94.63
90.1
0.52
114.10
39.77
164. 17
137.66
121. 55
89.86
0.704
142.80
90.72
163. 16
158.89
144.40
123.01
Hip joint 180
Angle parameter
160 140
167.45 165.04 133.28
120
159.27
Ankle joint 159.14
164.17
145.6 130.6 92.92
100
Knee joint
163.16 144.4 142.8
121.55 114.1 94.63 79
93.83 65.3
80 60 40 20 0 0
0.21
0.38
0.46
time
Fig. 1 Analysis of angle parameters of lower limb joints
0.52
0.704
Kinematics Analysis of Aerobics Movement Decomposition … Table 2 Kinematics analysis of trunk movement links
27
Shoulder hip twist angle
Head height
0
−10
42
0.21
−41
14
0.38
−12
−40
0.46
40
−51
0.52
48
−49
0.704
38
−10
4.2 Kinematics Analysis of Trunk Movement Links In the take-off stage, the shoulder and hip joints are rotated relative to each other during the movement, no one end is fixed. The initial position of the relative rotation of the shoulder and the hip is the right rotation of the shoulder relative to the hip. When the single foot support transitions to double during the foot support stage, the shoulder and hip torsion angle gradually decreases, and tends to 0°, that is, no twisting state. With the upper body torso leaning, lifting, and swinging the waist, the shoulder and hip torsion angle gradually increases, and the shoulders relative to the hip gradually turning to the left, the experimental results are shown in Table 2. It can be seen from Fig. 2 that in the take-off stage, the torso of the shoulders and hips is wider, and the shoulders rotate from right to left relative to the two hips. Before the jump, the shoulders rotate to the right relative to the hips, and the hips and shoulders rotate in opposite directions. The maximum shoulder-to-hip angle reaches −49.64°, and the corresponding moment is the moment when the jump is landing. The upper body rotates during the take-off phase to drive the shoulder to the left and the shoulder and hip torsion reach the maximum 45.2°. Among the two maximum values, the maximum difference in the rotation angle of the shoulder and hip is 94.84°, which is located between the shoulder and hip. When the hip joint rotates at the same time, the shoulder rotates 94.84° relative to the hip joint, and the rotation range is greater. Taking into account the change of head height, the head movement Shoulder hip twist angle
Angle parameter
60
Head height
42
40
48 38
40 20 0 -20
14 1 -10 0
0.21
-12 0.38
-41
-40
-40 -60
time
Fig. 2 Kinematics analysis of trunk movement links
0.46
0.52
-51
-49
-10 0.704
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curve first drops and then rises. The curve of the curve, that is, the lowest point of the head appears after the shoulder and hip rotation angle is 0°, indicating that the lowest point is that the head is not standing facing the body. It is on the left, which is consistent with the previous analysis results. The intersection of the vertical dashed line and the two curves in the figure shows that the head height at the lowest point of the head is only 29 cm, and the rotation angle of the shoulders and hips is 25.44°.
5 Conclusions In this paper, the multi-motion target video tracking algorithm analyzes the set of aerobics movements of the spinner, and concludes that the rhythm, direction, and range of movements in the take-off phase are the basis for completing the difficult movements of the spinner group, especially the timing of the kick and the torso in the one-leg take-off phase. The rotation degree and the direction of the leg swing are the key points of the revolver transition to the revolute rotation practice.
References 1. Wang T (2020) Unsupervised video multi-target tracking based on fast resampling particle filter. J Supercomputing 76(2):1293–1304 2. Zhu S, Sun C, Shi Z (2016) Multi-target tracking via hierarchical association learning. Neurocomputing 208:365–372 3. Wang Y, Yue J, Dong Y et al (2016) Review on kernel based target tracking for autonomous driving. J Information Process 24(1):49–63 4. Xu Y, Lei Q, Huang Q (2016) Coupling re-ranking and structured output SVM co-train for multi-target tracking. IEEE Trans Circuits Syst Video Technol 26(6):1084–1098 5. Wang D, Yang JL, Yang L et al (2016) Multi-target visual tracking algorithm based on kerneldensity and multi-Bernoulli filter. Guangdianzi Jiguang/J Optoelectronics Laser 27(10):1066– 1076 6. Howard, Wang, Sing et al (2016) Multi-target video tracking based on improved data association and mixed Kalman/$H_{infty $ Filtering. IEEE Sensors J 16(21):7693–7704 7. Kwangjin Y, Song YM, Moongu J (2018) Multiple hypothesis tracking algorithm for multitarget multi-camera tracking with disjoint views. IET Image Proc 12(7):1175–1184 8. Hao Z, Liu G, Zhang H (2018) Correlation filter-based visual tracking via adaptive weighted CNN features fusion. IET Image Proc 12(8):1423–1431 9. Yi W, Fang Z, Li W et al (2020) Multi-frame track-before-detect algorithm for maneuvering target tracking. IEEE Trans Veh Technol 69(4):4104–4118 10. Xiong J, Tang Q, He X et al (2016) Tracking in multimedia data via robust reweighted local multi-task sparse representation for transportation surveillance. Multimedia Tools Appl 75(24):17531–17552 11. Chen X, Bhanu B (2017) Integrating social grouping for multitarget tracking across cameras in a CRF model. IEEE Trans Circuits Syst Video Technol 27(11):2382–2394 12. Liang L, Lu Y, Li C et al (2016) Detection-free multiobject tracking by reconfigurable inference with bundle representations. IEEE Trans Cybernetics 46(11):2447–2458
Color Network System of Folk Painting Based on Fractal Algorithm Theory Tao Zhang
Abstract The global economic integration has promoted the collision between Chinese ancient five color theory and Western seven color theory. How can Chinese five color continue to shine in modern design? How to effectively and accurately learn the traditional folk art color of “boundless, hard to trust”? How to use modern information technology to sort out and analyze traditional folk art color data more effectively? How to apply traditional color to modern commercial design? This paper focuses on the method of systematic collection and arrangement of folk art color, which provides the basic idea of color data arrangement for the establishment of traditional color database. Through the application of color database to improve the cognitive efficiency of traditional color learning, this paper finally designs a folk painting color network system based on fractal theory algorithm through a series of experimental tests, which provides design reference for modern design. Keywords Fractal theory · Folk painting · Color network · Painting color
1 Introduction “The Chinese color system rooted in the thought of five elements has distinct humanistic characteristics.” Compared with the Western seven color theory, the Chinese folk art color is more pure, more widely used, and more primitive reflects the vitality of the Chinese five colors [1, 2]. The existing research on the color of national traditional art mostly adopts the way of language description to study color, but the polysemy of language makes it not necessarily accurate and intuitive to convey the information about color. What color is the familiar “Peacock Blue”? What kind of color is “Quchen Color”? The research on the color of folk painting has been discussed by many scholars, and has achieved good results. Some scholars have shown that “beyond words” is not only Chinese painting, but also Chinese folk art is symbolic, only they express T. Zhang (B) Dalian Neusoft University of Information, Dalian, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_4
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more straightforward feelings, and color is one of its most effective expressive force [3, 4]. Since the Tang Dynasty, China has “divided ink into five colors”, focusing on the expression and application of ink, weakening the position of color. However, in folk art, color has been fully developed. Therefore, how to organize and protect folk art color materials clearly and systematically; it is of great significance for folk art research, design cross-cultural communication, design practice and Design Education [5, 6]. In the vast ancient literature, the beautiful language records many colors. Due to the polysemy of Chinese, the recording and communication of colors will encounter many obstacles. The cross-cultural color research and communication is more complicated due to the limitation of language. The main purpose of this paper is how to learn and communicate traditional folk art color based on fractal theory algorithm efficiently [7, 8].
2 Theoretical Research on the Color Network System of Folk Painting Based on Fractal Theory Algorithm 2.1 Basic Knowledge of Fractal Theory Generation of fractal. The word fractal is given by Benoit. Fractal is derived from fractal, fracture and Fractal in Latin, which shows that fractal is used to describe and deal with rough and irregular objects. His famous book natural fractal geometry was published in 1982, and the concept of fractal has become one of the hot topics of scientific discussion in the world [9, 10]. Fractal dimension. In classical Euclidean geometry, the dimension of each geometry is an integer, the point is 0, the line is 1, the plane and the cube are 2 and 3. But in non Euclidean geometry, there are many definitions of non integer dimension and many dimensions, among which the similarity dimension is the most easily understood one closely related to fractal dimension. In general, if a graph is composed of AR similar graphs which reduce the whole to 1/A, then the index R has the meaning of dimension. This dimension is called similarity dimension. The similarity dimension is usually represented by R3. By definition, R3 is not necessarily an integer at all. For example, a graph is composed of B similar shapes which are reduced by 1/A, that is, B = AR. If R is a non-zero subset of n-dimensional Euclidean space RN, then the diameter of R is defined as the maximum distance between two points in |R| = Sty{|w − e| : w, e ∈ / R}. If {R} is a covering set h composed of the number (or final number) of sets with diameter no more than 4, then h is defined as a subset of RN, and Y is a non negative number. For all sets with diameter greater than 0, it is defined as:
Color Network System of Folk Painting Based …
Ta (h) = {
a
31
|R| : {R I }
(1)
Main application fields of fractal. Fractal geometry has become an important branch of nonlinear science. Its ideas and theories have penetrated into all fields of natural science and are widely used in mathematics, materials science, geological exploration, disease diagnosis, computer science [11, 12]. (1) Fractal growth model. Many objects in nature obviously grow in the form of fractal. The repeated division of these branches will produce smaller branches. Fractal method provides a new model for describing different natural growth phenomena. The famous DLA model and L-system model have made encouraging achievements in plant growth simulation. The description of inorganic growth and plant growth morphology and various new models and methods are developing. (2) Art field. The combination of fractal theory and digital imaging technology can create harmonious, natural, colorful and artistic patterns by manual operation, which has a profound impact on painting, sculpture and architectural design.
2.2 Software Development Platform This paper chooses Visual C++ 6.0 as the development platform of fractal graphics program. Visual C++ 6.0 is an embedded development tool based on Microsoft C/C++. It is one of the best visual programming tools. It has powerful application development function, graphics drawing function and knowledge of fractal theory and computer graphics. It develops a good graphic development interface and realizes the above fractal algorithm with computer language, can complete the fractal graphics programming.
2.3 Software Implementation Model (1) The following describes the specific steps of fractal image generation, fractal image generation can be realized through the following process: ➀ Enter the software interface, click the menu of computer simulation generation of fractal image, and the sub menu of fractal theory calculation will appear in the drop-down menu. ➁ Select the sub menu of fractal theory calculation, and its sub menu will appear. (2) Establishment of folk art color database.
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Through the collection and collation of the color data of the existing folk art cultural relics, the ideal state is to establish a series of different categories of folk art color database, which can be searched and browsed from the region, time and other different classification modes, so as to provide more comprehensive historical data and comparative analysis data for modern design. The establishment of color database will be conducive to color learning and communication, more convenient for people to learn the history of color folk art and color trend changes. But this must be based on a large number of data collection and information processing in order to achieve this goal, we can also start from a small category, establish a small color database experimentally, test its rationality and effectiveness, and then further promote and expand its research scope.
3 The Realization of Color Network System of Folk Painting Based on Fractal Theory Algorithm 3.1 System Experiment Content Many folk art images are essentially different from traditional chroma images, which makes folk art color management system can not directly apply the architecture of chroma color management system. Equipment data of folk art color management system. Due to the nonlinearity of the device and the fact that the current device only uses several channels to reproduce high-dimensional folk art data, the data space of the device is essentially different from the folk art color space. Therefore, it is necessary to transform the low dimensional and device related data of the image input device space into the high-dimensional folk art color space to support folk art image processing. Then, the high-dimensional folk art color space data is transformed into the low dimensional space related to the output device, and the folk art image is reproduced. The current acquisition equipment of folk art is mainly multi-channel digital camera system, and the practical camera system is usually 6 channels. The hard copy equipment reproduction of multi folk art color image is mainly super fourcolor printing reproduction, and the number of channels is usually 6–11. Therefore, the link of equipment calibration and spatial transformation is based on the actual situation of current equipment and technology, it is a crucial link to build a realizable folk art color reproduction system.
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3.2 Process of Color Network System In the flow chart, folk art color reproduction is realized as follows: (1)
(2)
(3)
(4) (5)
The characteristic parameters of folk art color imaging system are measured, including the sensitivity of folk art color, the transmittance of folk art color of filter, and the power distribution of folk art color in ambient light. For the source image scene, multi-channel folk art color imaging system is used to capture, and multi-channel images related to the imaging system are obtained. According to the multi-channel image obtained by the imaging system, the scene illumination color power distribution, the system color parameters, and the folk art color reflectance reconstruction algorithm is used to estimate the folk art color reflectance. The output device is characterized by folk art color, and the characteristic parameters of folk art color are obtained. The output device’s characteristic parameters are used for color correction and space transformation of multi folk art color images, and the images are transformed into the output device’s color space to obtain multi-channel images related to the output device. The output device space can be multi-channel display color space, super four color printer color space, etc., or traditional color device color space, such as RGB space, CMYK space, etc.
4 Experimental Data Analysis of Folk Painting Color Network System Based on Fractal Algorithm Theory 4.1 System Equipment Data Analysis The dimension of multi-media image data is usually more than 31 dimensions. Therefore, in ideal case, the input/output device should have 31 or more channels. Obviously, this condition is difficult to meet. At present, although the laboratory has developed a 31 dimensional multi-channel digital camera system, its high cost and complex image acquisition process make it difficult to promote the application. In the aspect of display output, there is no high-dimensional channel display device. At the output end of image hard copy, only super four-color printer has been used in practice, but the number of channels is far less than the dimension of folk art color data. This requires that in the device data of color reproduction, the corresponding functional modules should be designed according to the current technical reality to realize the transformation from low dimensional input device color space to high dimensional folk art color space, and from high dimensional folk art color space to low dimensional output device space. In addition, the number of channels of different
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Table 1 Equipment data for the color system
Dimension
Input/output equipment
Output terminal
31
31
Four-color printer
Green 17%
24%
Red 54% Yellow
30%
29%
Black Fig. 1 The color distribution of a printer
devices is different, and the devices are nonlinear, which makes the data sources of color reproduction have diversity. The color reproduction system must provide logic functions to deal with these personalized problems of the devices. The device data of the realizable folk art color reproduction system are shown in Table 1 and Fig. 1.
4.2 Comparative Analysis of the Color Reproduction Process It can be seen from Table 2 and Fig. 2 that the average accuracy of color reproduction of the look-up table method is slightly worse than that of the print model method when the spatial sampling classification is 6, but its maximum error is obviously lower than that of the model method. In the experiment, increasing the number of spatial sampling classification in the look-up table method will significantly reduce the error. Table 2 Comparison of color accuracy of two color reproduction processes Comparison of color accuracy of two color reproduction processes
Average
Standard deviation
Minimum
Maximum
Color reproduction by using Find Table Method
2.3625
1.5625
0.3596
5.6954
Color reproduction with a print model
2.6591
2.6569
0.3265
14.2589
Color Network System of Folk Painting Based …
35
Fig. 2 Comparison of color accuracy of two color reproduction processes
5 Conclusion The ultimate goal of this paper is to collect the color samples of the existing folk paintings, sort out the folk art colors, and establish the color database on this basis. Color learning, color research and color analysis can be carried out in the database, which can provide design reference for modern design. Starting from the thought of system theory, the paper takes color as the research origin, and finally studies the evolution process of a specific category of folk art color.
References 1. Cao X, Zhou J (2019) Research on personalized recommendation system of “Ningbo Metro Go” users based on item-based collaborative filtering algorithm. Logistics Technol 042(006):94– 97,103 2. Li H, Li C (2018) Research on image encryption algorithm of gray scale transform based on chaos theory research on the transformation of image encryption based on chaos theory. J Changchun Normal College (Natl Sci Edn) 037(002):43–47 3. Fernandes CM, Mora AM, Merelo JJ et al (2017) KANTS: a stigmergic ant algorithm for cluster analysis and swarm art. IEEE Trans Cybernetics 44(6):843–856 4. Yuefeng, Cailing, Jiang et al (2018) Research on demodulation of FBGs sensor network based on PSO-SA algorithm. Optik: Zeitschrift fur Licht- und Elektronenoptik. J Light Electronoptic 164:647–653 5. Cheng, Jianhua, Meiling et al (2017) Research on nonlinear comprehensive calibration algorithm for the single-axis rotation inertial navigation system based on modified unscented Kalman filter. J Comput Theor Nanosci 14(3):1535–1542 6. Cheng J, Li M, Guan D et al (2017) Research on nonlinear comprehensive calibration algorithm for the single-axis rotation inertial navigation system based on modified unscented Kalman filter. J Comput Theor Nanosci 14(3):1535–1542
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7. Thakur GK, Priya B, Mishra RK (2019) An efficient coloring algorithm for time detraction of sign image segmentation based on fuzzy graph theory. J Appl Security Res 14(2):210–226 8. Sun T, Wang X, Jiang D et al (2020) A robust authentication algorithm for medical images based on fractal Brownian model and visual cryptography. Sci Program 2020(4):1–11 9. Yuan Z, Jin C, Chen Z (2020) Research on language analysis of English translation system based on fuzzy algorithm. J Intelligent Fuzzy Syst 40(3):1–9 10. Zang H, Huang H (2017) Research on algorithm of generating S-box based on uniform chaotic system. Dianzi Yu Xinxi Xuebao/J Electronics Information Technol 39(3):575–581 11. Xiong CY, Wu YX, Wen SS (2017) Research on color mixing and optimization of RGBW-LEDs based on genetic algorithm. Chin J Luminescence 38(2):254–265 12. Zhou Y, Zeng J, Zhang M et al (2017) Research on network equilibrium model of online shopping supply chain system in promotion based on customer behavior. Proc Eng 174(Complete):1400–1409
Design of College Teaching Quality Evaluation Based on Apriori Algorithm Hao Liu
Abstract Based on Apriori algorithm, this paper designs a university TQES. This system selects the university TQES based on Apriori algorithm as the research object, and provides a large number of teaching behavior data for the network teaching mode. The evaluators can comprehensively and objectively evaluate each learning node, and summarize the corresponding teaching data and evaluation results in order to complete the analysis of network teaching mode. Firstly, this paper discusses the theoretical research of the system and introduces the Apriori algorithm, then designs each module of the system, and finally tests the function of the system. The experimental results show that it can effectively play the role of class education department, promote the reform of teaching methods, and promote the improvement of teachers’ teaching skills and students’ learning ability. Keywords Apriori algorithm · Evaluation system · University teaching quality · System design
1 Introduction In the current curriculum evaluation, many colleges and universities only accumulate complex teaching data, but the information contained in the data is difficult to dig in-depth and use [1, 2]. The traditional campus TQES cannot reflect the current situation of teaching and teaching effect. Applying Apriori algorithm to TQES can contribute to the improvement of teaching quality in colleges and universities [3]. In recent years, improving the quality of higher education has always been a hot and difficult issue in academic research. Many experts are committed to improving the quality and influence the TQES of multimedia teaching in universities has been established. He analyzes the process of multimedia teaching evaluation and the current situation of multimedia teaching in universities through research and genetic algorithm [4]. Camerin denaro et al. discussed some indexes of multimedia TQES, H. Liu (B) School of Marxism, Wuhan Business University, Wuhan 430056, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_5
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and put forward corresponding evaluation indexes. The process and effect of multimedia teaching in universities will be the relationship of teachers’ basic information management. The design and test results of the TQES of multimedia courses in universities show that the TQES is carried out on the teacher evaluation system [5]. Apriori algorithm is considered as the classic algorithm of association rule mining in data mining. Apriori algorithm is applied to analyze the association rules of computer professional performance data, and the relationship between courses is discussed [6]. Although there has been a lot of research on teaching quality, there are still some problems such as incomplete TQES in colleges and universities. Therefore, this paper hopes to use Apriori algorithm to design an effective solution to the TQES in and universities, and contribute a little to the education industry [7].
2 Theoretical Research on the Design of University TQES Based on Apriori Algorithm 2.1 Evaluation Standard of Teaching Quality in Colleges and Universities 2.1.1
Concept of TQES
TQES is the evaluation of teachers’ teaching behavior, teaching methods and academic level. The evaluation of a teacher’s teaching quality is influenced by the following factors, such as the initial quality of each course, the cooperation between teacher associations of each course, the quality of students, the teaching effect of teachers, and teaching behavior [8].
2.1.2
Establishment of TQES Standard
It analyze the causes of deviations and improve them, so as to achieve the organizational goals. Due to the diversity and inaccuracy of teaching quality indicators, it is difficult to establish strict quality indicators in quantitative evaluation. Through the combination of quantitative analysis and qualitative analysis, this paper introduces the appropriate TQES model to improve the TQES. TQES should also evaluate the value of teaching process and teaching results and their advantages and disadvantages, so as to improve the teaching process [9].
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2.2 Discussion on the Requirements of TQES in Colleges and Universities Scientific teaching evaluation process can also produce more accurate teaching evaluation results. However, the vast majority of colleges and universities evaluate for the purpose of evaluation. They only publish the evaluation results after they get the evaluation results. They often ignore an important data warehouse, which is the massive data generated in the evaluation process. These data are only reserved by colleges and universities as a backup. However, these data are just an important part of the whole evaluation system. We need to carry out data mining on this part of data, get information conducive to teaching, and guide the smooth progress of the whole teaching process. The design purpose of this system is to give objective and fair evaluation of teaching quality by students online, analyze and count the evaluation data, sort out systematic teaching evaluation data, and feedback to teachers as soon as possible, finally improve the classroom teaching effect. This paper analyzes the requirements of the TQES process, and creates a systematic database model with a reasonable TQES. The TQES based on campus network is convenient for students to evaluate teaching, and also convenient for teachers and teaching administrators to browse the results in time [10]. Based on the detailed analysis of the actual process of teaching evaluation and the requirements of digital campus, the functions of the system are as follows: (1)
(2) (3)
(4)
The system is mainly by students, not only need to support students to log in and evaluate, but also need to support teachers to log in and query the evaluation results. At the same time, it needs to support the administrator to log in and maintain the data in the system. A lot of people must have the ability to access the TQES at the same time, so the system must have enough space. Campus network gradually integrates student management, personnel management and teaching management. The background database needs to be unified with other efficient databases, and need to do a lot of public data collection ability. When designing the system, it is necessary to formulate a reasonable and standardized TQES in advance, and combine qualitative and quantitative evaluation, so as to obtain comprehensive and comprehensive feedback information.
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2.3 System Design of University TQES Based on Apriori Algorithm 2.3.1
Introduction of Apriori Algorithm
Apriori algorithm is used to search common itemsets in records. The basic process is to find a new replacement set in the previous frequent itemset, and then check whether the frequency of these replacement sets is high enough. The specific formula is as follows: number (OP) num (AllSam) Confidence(O ⇐ P) = Y O P = Y(OP) Y(O) Support(O, P) = Y(OP) =
(1) (2)
The iterative steps of the algorithm are as follows: (1) (2) (3)
(4) (5) 2.3.2 (1)
(2)
First, set each project as the first public project in its own project set, and select the smallest support item to use. Find the superset of existing frequent itemsets, find new frequent itemsets, and use it to generate new candidate itemsets. Check the frequency of the new candidate, if the frequency is not enough, discard it; or, if the new set of common elements is not displayed, proceed to the last step. Store the newly discovered frequent itemsets, and then go back to step 2. Returns all frequent itemsets found. Architecture Design of University TQES TQES client design. The task of the client is to process the data related to the course evaluation, and send the processed data to the server for analysis and storage through network technology. The university evaluation system mainly adopts the C/S structure, fully allocates the tasks of the client and the server, and uses the hardware environment of the two ends to exchange network information resources, process data, and access to the network. Users can log in to the TQES, change relevant information, obtain instructions and complete the evaluation information [11]. TQES server design. The main task of teacher evaluation system server is to complete the creation and storage of data. The server-side data includes teacher-student account number, course plan information, teacher management information and so on. The information exchange between client and serverside mainly adopts JavaScript object rotation (JSON) format. The read data can be directly displayed on mobile devices such as mobile phones, which is convenient for users to extract and use.
Design of College Teaching Quality Evaluation …
2.3.3
41
Design of University TQES Database
In this system, data is the database used to store and manage data. As the core part of the teacher evaluation system, the quality of data directly affects the normal operation of the whole system. Importing the sorted data table into the database helps to query the future data quickly and accurately. The MySQL database used in this system is developed by Swedish company. It is widely used in web development because of its low cost, high performance, easy maintenance and other advantages [12].
3 Implementation of TQES Based on Apriori Algorithm 3.1 System Operation Environment Good software development environment is also an important part of the success of application software. Through the analysis of the system, we can conclude that the characteristics of the system is an application software based on Windows environment, which does not need to operate the hardware environment such as communication interface, memory, etc., and needs powerful data operation function and good user interface. At present, there are many popular development software, Visual FoxPro is suitable for the development of the system, its powerful data processing function, good ease of learning, has been widely used in various management information systems. System configuration requirements: 869/253 MHz or high performance processor, display at least 32 MB ram, PS1 mouse or serial mouse, select VGA or higher resolution. Software requirements: Windows 98, Windows NT, Windows XP or higher operating system with Visual FoxPro 6.0 or above.
3.2 System Function Realization The teacher evaluation system based on Apriori algorithm is mainly composed of three modules: performance management module, teacher management module and teaching principle evaluation module. Teachers evaluate students after learning and bring their scores into the system. The student education evaluation module evaluates teachers of specific courses according to relevant standards, and the teaching management module mainly evaluates teachers at the same level as professional teachers after evaluation.
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4 Test of TQES Based on Apriori Algorithm 4.1 Software Testing Software testing is the most direct and important means to verify system performance, as shown in Table 1 and Fig. 1: Test purpose: the user fills in the teacher ID card and login password, which is the prerequisite for the correctness of the teacher ID card and login password. Log in to the system to query the teacher evaluation results. If the teacher ID card corresponding to the teacher exists and has been evaluated, the user can enter the performance query interface to generate the evaluation results. Otherwise, you cannot enter the bill query interface to query the incentive information that has not been assessed. If the teacher Table 1 Testing process Use case number
Input
Output
1
Teacher ID and login password are correct
Enter the score query interface and output the evaluation results
2
The teacher is the instructor and has Enter the score query interface and been evaluated output the evaluation results
3
Teacher ID exists and is the teacher and secret
Enter the score query interface and output the evaluation results
4
The teacher was evaluated and the password was correct
Enter the score query interface and output the evaluation results
5
Teacher ID exists
Enter the evaluation interface
6
Teacher ID does not exist
This teacher does not exist
7
The teacher has not been evaluated
Tip not evaluated
8
The teacher exists but is not evaluated
Tip not evaluated
6
Proportion
5
5 4.4
4 3 2
3 2.4 4.2 2
1 0 Reason Result Use case
1 4.2 2.4 2
2
1.8
2.4
2.6
2 2.4 4.4 2
3 2.6 1.8 3
Cases
Fig. 1 Test case tables derived from causality diagrams
2.8 4.5
Reason Result
4 4.5 2.8 5
Use case
Design of College Teaching Quality Evaluation …
43 Excellent
60%
Good
50%
Ordinary Pass
Degree
40%
Fail 30% 20% 10% 0% Teaching attitude
The quality of teaching
Teaching method
Teaching efficiency
Teaching Evaluation
Fig. 2 Teaching function test
is not a teacher, even if you enter the evaluation interface, you cannot evaluate. If there is no corresponding teacher in the completed teacher ID card, no operation can be performed, and the login ID card or password error message will be displayed.
4.2 System Test The function test of the system is completed in the process of coding. In the process of writing code, when realizing a certain function of a module, new test items are created according to the needs to complete the function test of each module of the program. After the software is completed, the system is tested. This test selects the core elements of the system as the test object, that is, the teaching evaluation function, as shown in Fig. 2. A teacher participated in the evaluation of a course, including the evaluation of teaching attitude, teaching quality, teaching methods and teaching efficiency. There are 50 students participating in the evaluation. The evaluation results of a teacher’s teaching attitude, teaching quality, teaching method and teaching efficiency are as follows: excellent 25, good 15, fair 9, pass 1 and fail 0.
5 Conclusion Based on Apriori algorithm, this paper designs the TQES of colleges and universities, and applies its technology to the TQES of colleges and universities, changes the disadvantages of the current TQES. It keeps up with the development of the evaluation system, continuously improves the quality of higher education, and makes contributions to the cultivation of high-quality talents for the country. The system chooses Apriori algorithm of university TQES as the research object from different
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angles, which can effectively play the role of classroom teaching, promote the reform of teaching methods, and improve teachers’ teaching skills and students’ learning skills. Acknowledgements Research on the influence of students’ evaluation mechanism on teaching quality in Colleges and Universities—Based on the results of students’ evaluation of Ideological and Political Theory Course (Teaching reform research project of WBU, No. 2021Y022)
References 1. Huang W, Xiao X, Xu M (2019) Design and implementation of domain-specific cognitive system based on question similarity algorithm. Cognitive Syst Res 57:20–24 2. Wray M (2021) Additional support services and the utilisation of teaching assistants in university settings: dissuading inclusive practice or improving academic outcomes? Support Learn 36(1):102–115 3. Wu S, Lu X (2019) Design of physical education curriculum analysis and management system based on decision tree algorithm. Modern Electronic Technol 042(003):131–133,138 4. Jian Q (2019) Multimedia TQES in colleges based on genetic algorithm and social computing approach. IEEE Access 7:1–1 5. Reimer LC, Denaro K, He W et al (2021) Getting students back on track: persistent effects of flipping accelerated organic chemistry on student achievement, study strategies, and perceptions of instruction. J Chem Educ 98(4):1088–1098 6. Wu X, Zeng Y (2019) Data mining of university student achievement based on apriori algorithm. J Langfang Normal Univ (Natl Sci Edn) 019(001):31–36 7. Krantz J, Fritzén L (2021) Changes in the identity of the teaching profession: a study of a teacher union in Sweden from 1990 to 2017. J Educ Change (3):1–21 8. Ngai E, Ngai C (2021) Compressed gas safety at the university. J Chem Educ 98(1):57–67 9. Murshid N, Cathcart N, Kitaev V (2019) Room-temperature synthesis of size-uniform polystyrene latex and characterization of its properties: third-year undergraduate teaching lab. J Chem Educ 96(7):1479–1485 10. Zayac RM, Lenhard W (2018) Characteristics of master teachers: German university students’ perceptions of high-quality instruction. New Dir Teach Learn 2018(156):67–74 11. Samma H, Mohamad-Saleh J, Suandi SA et al (2020) Q-learning-based simulated annealing algorithm for constrained engineering design problems. Neural Comput Appl 32(9):5147–5161 12. Wu W, Zhou B, Liu Z et al (2019) Design of highly uniform magnetic field coils based on a particle swarm optimization algorithm. IEEE Access 7(99):125310–125322
Panal Data Analysis on the Environmental Effects of Global Value Chains: Based on the Empirical Study of Chinese Industrial Panel Data Haifeng Chou
Abstract This paper attempts to empirical analyze the environmental impacts of Global Value Chains (GVC) based on the scale, technique and composition effects according to the mechanism of environmental caused by trade. Through using paneldata model of China’s industrial data, this paper analyzes the size and the direction of different effects of GVC on environment. And the conclusion shows that GVC has negative effects both on technique and composition, but has positive scale effect. On the whole, environmental effect caused by trade has a negative effect. The effective channel of achieving the positive effect of GVC includes guiding industrial arrangement rationally, bringing technological progress and technological diffusion into play fully and enhancing environmental regulation powerfully. It is important and necessary that the government departments should further improve the system of environment regulation, and the local governments should strengthen supervising and executing the laws of relating regulation of ecological environment protection in China. Keywords Global value chains · Scale effect · Technique effect · Composition effect
1 Introduction In the process of participating in the GVC, China takes processing trade and foreign investment as a breakthrough point, gradually integrates into the global production network in the process of reform and opening up, and the degree of participating in the international division of labor keeps improving; China has gradually established the status of “world factory” through actively participating in the specialization of GVC. From the perspective of industry, the industrial sector is China’s main industry participating in the GVC. However, the wastewater discharge, exhaust gas discharge and solid waste production of export-oriented industrial sector have kept increasing, H. Chou (B) School of Economic, Harbin University of Commerce, Harbin, Heilongjiang, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_6
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and China’s environment pollution has become a growing problem in recent years. Taking sulfur dioxide emission as an example, China’s SO2 emission ranks first in the world, of which industrial SO2 emission accounted for more than 80% in 2000– 2011. By 2011, China’s industrial SO2 emissions had reached 22.172 million tons, increased 38.39% over 2001. In view of the rapid development of China’s global value chain and the increasingly serious pollution of the environment, it is necessary to make an in-depth analysis of the relationship between the two. This study will help us to make a more comprehensive and objective evaluation about the impact of GVC specialization in China, especially on the environment. The conclusions also have important guiding significance for China to participate in the international division of labor in a more appropriate way and promote the coordinate development of environment and trade.
2 Analysis of the Affecting Mechanism of Global Value Chain on Environment This paper uses the analysis framework of “three eco-environmental effects” to analyse the affecting mechanism of GVC on environment. The derivation process is as follows: E=T∗S∗Y In the equation, E is the pollution emission, T is the pollution emission technology, S is the proportion of pollution-intensive processes, and Y is the economic scale. Taking derivation of two sides of the equation can decompose the scale effect, composition effect and technique effect, that is, lnE = lnY + lnS + lnT. In this equation, lnY represents the scale effect, lnS represents the composition effect, lnT represents the technique effect, and the three effects collectively represent the environmental effects of trade. The specific explanation is as follows [1, 2].
2.1 Scale Effect Production process can be broadly divided into R&D, manufacturing and operation. China actively participates in the manufacturing link of global production by processing trade with its advantages in labour resources. After more than ten years of development, China has become a giant manufacturing country in the world, with China-made commodities all over the world. The scale of GVC is escalating, and the degree of countries participating in the division of labour is getting deeper. However, it cannot be ignored that the extensive development model of “Made in China” leads
Panal Data Analysis on the Environmental Effects …
47
to the increase of environment pollution emissions year by year. Therefore, the expansion of GVC may aggravate the environmental pollution in China. On the other hand, with the expansion of trade scale and the improvement of income level, consumers are more willing to buy “clean” products which have little pollution to environment. At this time, the scale effect is positive, which is beneficial for the improvement of environment [3].
2.2 Composition Effect The emergence of GVC expands the scope of a country’s comparative advantage. Even if a country doesn’t have a comparative advantage in the production of certain products, it can participate in the international labor-division and trade with a comparative advantage in a specific production stage of product. Thus, developing countries can bypass the initial period of industrial upgrading and develop capital and technology intensive industry directly by participating in GVC specialization, and engage in production links in which they have comparative advantages. Therefore, GVC has become a new way for developing countries to realize industrialization and industrial structure upgrading. For China, under the traditional labor-division pattern, Chinese main export products are labor-intensive products such as textiles and clothing in whose production process will produce a great number of pollutants. With the expansion of the scope and scale of GVC specialization, China’s export trade structure has changed accordingly, and the export products are gradually dominated by capital and technology intensive products which have little pollution, such as electronic products and mechanical products. China’s export structure has gradually transformed to “clean” by participating in the international intra-product specialization and trade. Therefore, the composition effect of GVC on environment should be positive [4, 5].
2.3 Technique Effect The development of GVC has promoted the development of China’s trade in intermediate goods. China’s intermediate goods imports increased from US $63.9 billion in 1995 to US $786.3 billion in 2010, and the imports of intermediate goods accounts for more than 60% of total imports. The import of high-quality and wide-variety of intermediate products will bring the diffusion of foreign advanced technology and new knowledge. Coupled with the dynamic effect of “learning by doing”, the GVC will bring more technology spillover effect. Compared with the traditional trade pattern, GVC can improve the overall production efficiency of China, reduce the consumption of resources and the pollution of environment. Meanwhile, the development of GVC has increased the liquidity of international commodities, improved the degree of specialization, and accelerated the formation of scale economy. And
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scale economy can increase the utilization efficiency of elements, reduce the input of elements per unit output, and then reduce the pollution per unit product. As the two main forms of China to participate in GVC specialization, both FDI and processing trade have technology spillover effects. In the development process of China’s GVC, some foreign advanced technologies and production processes have been introduced, and China’s production technology level has also been greatly improved. At the same time, with the raising of environmental protection consciousness and the increasing pressure of emission reduction, enterprises will tend to adopt the advanced drainage facility to reduce the burden of environment; therefore, the technique effect of GVC on environment should be positive [6].
3 Model Establishment According to the conclusion of previous analysis, the impact of GVC on environment can be divided into scale effect, composition effect and technique effect. Based on this conclusion, a pollution emission model is established: LnEt = c0 + c1lnYt + c2lnSt + c3lnTt + c4lnPT + c5lnFDIt + c6lnRt + c7lnKt/Lt + εt. Among them, the subscript t represents the year; εt represents the stochastic error; ln represents the natural logarithm; ci represents the correspondin g coefficient; there are eight variables in this model: E(pollution emission), Y (scale effect), S (composition effect),T (technique effect), FDI (foreign direct investment), R (regulation of ecological environment), PT (degree of GVC specialization) and K/L (capital-labor ratio).
4 Empirical Analysis Results This paper uses panel model to analyze because it can construct and examine more complex behavior model and better measure the influence factors that cannot be found by the simple time series model or cross-sectional data model. This model includes fixed effect model and random effect model. When both fixed effect model and random effect model are significant, the Hausman test can be used to determine whether the fixed effect model or the random effect model should be used.
4.1 Data Source and Processing Based on the three eco-environmental effects, this paper uses China’s industrial regional panel data and pollution emission data to investigate the impact of GVC specialization on the environment from 2002 to 2011. In terms of the selection of
Panal Data Analysis on the Environmental Effects …
49
pollutant emissions, since only the statistics of sulfur dioxide in China’s ecological environment yearbook over the years are continuous and consistent, and the domestic emission of SO2 is relatively stable, the change of total SO2 emissions can be determined by the change of industrial sulfur dioxide emissions. Therefore, the emission of industrial SO2 is taken as the data of environment pollution emission. The scale effect Y is represented by the gross industrial output value over the years; the composition effect S referring to the research of Jie-HE (2006),is represented by the product of the proportion of provincial gross industrial output value in its GDP and its initial year (2000) emission intensity (pollution emission per unit GDP). The practice of “emission intensity is always the value of the initial year” can eliminate the impact of changes in technical factors on the economic structure, which corresponds to the definition of composition effect. Technique effect T is measured by the investment amount of provincial eco-environmental governance activities. R is the regulation of ecological environment. The regulation degree of ecological environment is measured by the pollution abatement costs per unit output value (the ratio of the provincial actual amount of investment to the local GDP). This approach not only fully considers the regional economic aggregate, but also expresses the determination of the government to control pollution through the pollution abatement costs. In this paper, the degree of GVC specialization PT is measured by the proportion of processing trade in each province FDI is represented by the amount of foreign direct investment in each province, and the determination of capital quantity K should have been determined by the capital stock, however, the net value of industrial fixed assets approximately replaces K because of the availability of data; L is the labor input which is expressed by industrial employment population of each province, and the capital-labor ratio K/L reflects the change of industrial structure. This paper selects 30 provinces (autonomous regions and municipalities directly under the central government) of China as research objects. In view of the availability of data, Tibet, Taiwan, Hong Kong and Macao are not included in the sample. The sample spacing is from 2002 to 2011. The above data are from China Statistical Yearbook, China Industrial statistical yearbook, China Ecological Environment Yearbook, statistical yearbook of each province and relevant information published on the website Commerce Department of each Province. The output value, the investment amount of eco-environmental governance and other data involved in the model are uniformly calculated by deflator according to the price in 2000. The initial data of FDI is US dollars which has been converted according to the exchange rate of each year.
50 Table 1 Fixed effect test results
H. Chou Explanatory variables
Coefficients
The value of t
LnY
0.0304611***
LnS
−0.0624612**
−2.1
LnT
−0.3436868**
−5.5
LnPT
−0.0343921*
−1.88
LnFDI
−0.0564041**
−4.60
1.13
LnR
−0.0823507
−2.61
LnKL
−0.0531603
−2.41
constant term
3.06757
–
R2 (adjusted)
0.8144
–
sample number
300
–
Note:*, * * and* * * represent significant at the level of 10%, 5%, and 1% respectively
4.2 Econometric Analysis Results and Their Economic Connotations In this paper, the measuring software STATA12.0 is used to examine the model. The fixed effect model was used in the regression for model after the F-test and Hausman test. From the fixed effect analysis results (Table 1), it is basically consistent with the theoretical expectation. The output results show that the total number of samples is 300, and the relative goodness-of-fit R2 (adjusted) is 0.8144, indicating that the fixed effect model can explain the 81.44% change of pollution level. From the perspective of explanatory variables, when the significance levels of foreign direct investment, degree of GVC specialization, composition effect and technique effect is 5%, the test is significantly passed; if the level is relaxed to 10%, the scale effect can also pass the test, which shows that the above five explanatory variables have a significant impact on the emission of environment pollution. The regulation of ecological environment and capital labor input which can be regarded as control variables failed to pass the test. Among all the explanatory variables, scale effect and FDI are positively correlated with pollution emission, and negatively correlated with the composition effect, technique effect and degree of intra-product GVC specialization. According to the analysis results, the equation form is as follows: LnE = 3.06757 + 0.03046lnY-0.06246lnS-0.34368lnT-0.03439lnPT + 0.05640lnFDI. The specific analysis results are as follows: The change of scale effect and pollution emission is in the same direction, which shows that with the increase of industrial activities and industrial scale, the ecological environment pollution will also increase. For every 1% increase in income, the pollution emission changes 0.03046% in the same direction. If we use Environmental Kuznets Curve (EKC) to explain this phenomenon, it shows that the income level
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is still on the left side of the EKC, and has not passed the highest point [7]. With the expansion of industrial scale and the income continues to increase, the degree of industrial pollution will also improve. The expansion of industrial scale leads to the increase of China’s pollution level, which indicates that China’s economic growth pattern is still in the extensive growth stage, and this is related to China’s relatively loose level of ecological environment regulation [7]. The technique effect is negative, and the pollution emission can be reduced by 0.34368% with every 1% change of technical progress, which shows that the technical progress has a positive effect on the control of pollution level. Technological progress can improve the utilization rate of resources and ultimately reduce the overall level of ecological environment pollution through the renewal of equipment and technology. Therefore, promoting the transformation of industrial structure and the introduction of advanced technology is the essential way to achieve the coordinate development of global value chain and environment. The composition effect is negative, which shows that under the context of GVC specialization, with the deepening of participation in GVC specialization, China’s participation in the production stage has a trend of changing from “pollution type” to “clean type”, and the pollution emission will be reduced by 0.0624% with every 1% change. Therefore, it can be indicated that China’s participation in GVC specialization and trade can reduce environment pollution [8]. The level of GVC specialization is significant at the level of 5%, which changes in the opposite direction with the pollution emission. The pollution emission will be reduced by 0.0343% with each 1% increase in the degree of intra-product specialization, which shows that the improvement of the degree of GVC specialization can reduce the ecological environment pollution and is beneficial to the protection of environment. The main reasons are as follows: first, in the global production networks, instead of being limited to the low-end assembly link which relies on low labor prices and energy factors, China began to shift to the mid-to high-end links of GVC; second, GVC specialization can separate different production stages into different countries, and the best allocation of resources can be achieved in each process of production, thus obtaining the benefits of scale economy, the level of energy consumption per unit product will also be greatly reduced; third, a large number of intermediate product trade has promoted the optimization of China’s export commodity structure. According to statistics, electromechanical products, high-tech products and other products are the main export products in China’s GVC. According to the study of Li Xiaoping (2010), these products are mainly low pollution products. Therefore, GVC is more conducive to the development of China’s economy in the direction of “clean” [9, 10]. The significance of foreign direct investment which changes in the same direction with pollution emission is very high. For every 1% increase in foreign direct investment, pollution emission increases by 0.056%. It shows that due to the relatively loose policies and standards of ecological environment in China and the low environment cost that manufacturers pay for their products, these countries will tend to transfer industries with external negative effect which have high consumption of resources and serious ecological environment pollution to China when they invest in
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China. While they obtain cheap resources and labor, they also obtain the advantages of low standards of ecological environment, thus gaining its products a more obvious price advantage.
5 Conclusions According to the analysis conclusion, we should take positive policy measures to encourage enterprises to participate in the production links or sections with high added value and less environmental pollution in GVC specialization by constantly improving their competitiveness and optimizing their comparative advantages, thus promoting the upgrading of trade structure and industrial structure. Secondly, when introducing foreign investment, we should take environment factors into consideration, instead of just considering the pulling effect of foreign investment on the economy and ignore its impact on the environment. We should guide foreign investment to clean industries or clean product production stages through policies, thus enhancing the positive impact of foreign direct investment on environment. The trade and industrial structure are constantly optimized to be “clean”, while achieving the expansion of trade and investment scale. In addition, the government departments should further improve the system of environment regulation, and the local governments should strengthen supervising and executing the laws of relating regulation of ecological environment protection, so as to ensure that the composition effect and scale effect of GVC on environment can be promoted to the maximum extent from the system, thus reducing the negative effect of scale effect, and finally realize the protection of the ecological environment.
References 1. Konig J, Koskela E (2011) Does international outsourcing really lower workers’ income. J Labor Res Online First TM 1:95–110 2. Senses MZ (2010) The effects of offshoring on the elasticity of labor demand. J Int Econ (81):86–100 3. Munch JR, Skaksen JR (2009) Specialization, outsourcing and wages. Rev World Econ 2009(145):28–33 4. Lommerud KE, Meland F, Straume OR (2009) Can deunionization lead to international outsourcing. J Int Econ 77:45–60 5. Geishecker I, Losers (2008) A micro-level analysis of international outsourcing and wages. Canadian J Econ (41):55–60 6. Bernard AB, Jensen JB, Redding SJ, Schott PK (2007) Firms in international trade. J Econ Perspect 1:105–130 7. Dluhosch B (2006) Intra-industry trade and the gains from fragmentation. North Am J Econ Finance 17(1):3–4 8. Cieslik A (2005) Intra-industry trade and relative factor endowments. Rev Int Econ 2:904–926
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9. Egger H, Egger P (2005) Labor market effects of outsourcing under industrial interdependence. Int Rev Econ Finance 14:349–363 10. Grossman H (2005) Outsourcing in a global economy. Rev Econ Stud 72:75–90
The Application of Fusion Algorithm in Automobile Machinery Manufacturing Control System Xiang Zou
Abstract With the progress of the times, electronic technology, information technology and other industrial technologies have been developed rapidly. Automobile is an important part of the national manufacturing industry, with the continuous expansion of application field, people also put forward higher requirements for the control system of automobile machinery manufacturing. People hope that the automobile will be more automatic, intelligent and have higher safety performance. The purpose of this paper is to take the permanent magnet synchronous motor servo system as an example, to integrate all kinds of important algorithms and arrange them scientifically and reasonably, so that the servo system can achieve satisfactory control effect. This paper focuses on the research of PI control algorithm, improved sliding mode control algorithm and improved ADRC algorithm. Based on the above three algorithms, PI current controller, sliding mode speed controller and auto disturbance rejection position controller are designed. The simulation results of the current loop, speed mode and position mode of servo system are simulated by MATALAB platform. The improved algorithm fusion servo system can achieve the expected control effect. Finally, the servo control platform of PMSM is realized by taking the controller as the part. The simulation results show that the current loop can meet the steadystate accuracy and dynamic characteristics when kp = 9.735 and ti = 0.00295, and the effectiveness of the improved approach law is verified. Keywords Fusion algorithm · Synchronous motor servo system · PI current controller · Improved sliding mode control algorithm
1 Introduction With the progress of the times, manufacturing technology, power electronics technology and information technology have been unprecedented development, which promotes the application of PMSM servo system in many fields [1, 2]. With the X. Zou (B) Department of Automotive Engineering, Sichuan Aerospace Vocational College, Guanghan, Sichuan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_7
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continuous expansion of the application field, people also put forward higher requirements for the servo system, mainly reflected in the aspects of fast response, good stability, good control accuracy, strong anti-interference ability [3, 4]. Many experts and scholars have made different achievements in the research of automobile machinery manufacturing control system. For example, J Zhu and other scholars designed the integral separation fuzzy PID control method for the possible integral saturation phenomenon of fuzzy PID control, and used the quantum particle swarm optimization algorithm to optimize the improved fuzzy PID control parameters, so that the speed system of Brushless DC motor has better dynamic performance [5]. Based on MATLAB/Simulink software, H Wang and other scholars built single wheel and two wheel vehicle dynamics models, including key models such as tire and brake actuator; aiming at the common dry, wet and ice snow pavement, the influence of different road adhesion coefficient on braking effect is analyzed [6]. The main work of this paper is to integrate PI control algorithm, improved sliding mode control algorithm and improved active disturbance rejection control algorithm, and design PI current controller, sliding mode speed controller and active disturbance rejection position controller. Based on this, a sliding mode control method of improved reaching law is proposed in the speed loop; an improved linearization scheme of active disturbance rejection control (ADRC) is proposed in the position loop. The current loop, speed mode and position mode of the servo system are simulated by Matlab platform, and the simulation results are analyzed to verify that the improved algorithm fusion servo system can achieve the desired control effect.
2 Application of Fusion Algorithm in Automobile Machinery Manufacturing Control System 2.1 PI Control Algorithm The input–output relationship of predictive PI controller is shown in Eq. (1) [7, 8]. 1 1 e(t) − u(t) = K 1 + [u(t) − u(t − l)] pTi pTi
(1)
where p is the differential operator, e (t) is the error input of the controller, u (t) is the output function of the controller, t is the time constant, K is the plant gain, and l is the process delay time. The input–output relationship of predictive PI controller is expressed as [9, 10]: u(t) = K 1 +
1 1 e(t) − [u(t) − u(t − l)] λTi s λTi s
(2)
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57
It is assumed that the industrial process plant model is equivalent to the first order plus pure delay model, and the transfer function of the plant is as follows: G p (s) =
K p −τ s e Ts + 1
(3)
Given a unit negative feedback system, the closed-loop transfer function of the system is as follows: G(s) =
GcG P Y (s) = R(s) 1 + GcG P
(4)
The expected closed-loop transfer function is assumed to be: G(s) =
1 e−τ s λT s + 1
(5)
Among them, the value range of λ represents the time comparison of closedloop response and open-loop response of the system. The transfer function of the controller can be deduced as follows. G ε (s) =
Ts + 1 G(s) = G P (s)(1 − G(s)) K p (λT s + 1 − e−τ x )
(6)
The input–output relationship of the controller is as follows: U (s) =
1 1 (1 − λ2 /λ1 )T2 s + 1 E(s) − 1 − e−τ s U (s) (1 + 2 2 2 λ1 K p T1 s + (λ2 /λ1 )T2 s λ1 T1 s + λ1 T1 s (7)
2.2 Improved Sliding Mode Control Algorithm Although the exponential reaching law can adjust the parameters K and K properly ε it can ensure the dynamic performance of the sliding mode process and weaken the high-frequency chattering of the output signal, but the larger value of the sliding mode can still lead to jitter. Aiming at the shortcomings of exponential reaching law, an improved reaching law method is proposed, and its expression is as follows [11, 12]. s = −η|s|α sgn(s) − ks, η > 0, 0 < α < 1
(8)
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2.3 Improved ADRC Algorithm The traditional ADRC technology uses nonlinear functions. On the one hand, there are many inconvenient problems in adjusting parameters of nonlinear functions. On the other hand, nonlinear functions occupy a large number of CPU clock cycles in processor operations. Since these two aspects directly affect the performance of ADRC, the nonlinear function is linearized and the core observation function of ADRC is retained. The linearization improvement of ADRC aims to reduce the difficulty of controller parameter design and improve the operation efficiency of processor, which makes ADRC more widely used in engineering.
2.4 Software and Hardware Design of PMSM Servo Control System Based on Algorithm Fusion The PI control algorithm, the improved sliding mode control algorithm and the improved active disturbance rejection control algorithm are organically combined in the permanent magnet synchronous motor servo system, and the control platform of the permanent magnet synchronous motor servo system is designed, which lays the experimental foundation for the realization of the fusion algorithm. Based on the current PI control, the speed loop is optimized first, and then the position loop is optimized on the basis of the speed loop. The structure of sliding mode control algorithm is simple, the requirement of mathematical model accuracy is not high, and it has strong robustness to system parameter changes and disturbances. The position control of servo system requires high speed, accuracy and no overshoot. Active disturbance rejection control technology is not affected by mathematical model, and can observe any disturbance inside and outside the system and give real-time compensation, with strong robustness. It can not only deal with largescale and complex uncertain systems, but also ensure that the closed-loop system has good dynamic response performance. It is widely used in high-precision control situations [36–38], so it is especially suitable for application in position loop. The above three control algorithms applied to different links, combined with each other, give full play to their own advantages, can make the servo system achieve satisfactory control effect. Hardware Design of Servo System. (1)
Main circuit design of servo system. The first half of the servo system is the rectifier filter circuit, and the second half is the power inverter circuit. In the design of rectifier and filter circuit, the first consideration is the selection of rectifier diode and filter capacitor. However, their design parameters are inseparable from the level of DC bus voltage, so it is very important to choose a reasonable voltage bus value. The integrated power module IPM is selected as the power inverter circuit.
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(2)
59
Control circuit design of servo system. The power supply circuit is an essential part of the system, because the voltage of each device is different, the external pure +24 V is used to convert into the required voltage ±12 V, +5 V power supply. Communication is the necessary real-time monitoring of various running states of the system, and it is also the information exchange of upload and release. In this paper, RS232 interface is used to communicate between DSP and PC. The module Hall sensor with good performance is used in the current detection circuit. Software Design of Servo Control System.
(1)
(2)
(3)
Main program design. The functions of the main program include DSP power on initialization, program variable initialization and interrupt initialization, and then enter the main loop program to wait for the interrupt. DSP power on initialization includes PLL module, watchdog module, system timer module, corresponding control registers and peripherals; the initialization of program variables is mainly to assign values to system parameters and variables; interrupt initialization includes configuring pie control register, importing interrupt vector table, enabling timer T1 interrupt, external protection interrupt and serial communication transceiver interrupt. Timer T1 interrupt program. The main functions of timer timer 1 interrupt program are: current signal sampling and processing, rotor position and speed signal calculation, vector transformation calculation, three loop controller algorithm realization and SVPWM module calculation, and finally realize the output of 6-phase PWM pulse. If the timer T1 period is set to 100US, the current sampling period is 100US. The sampling period of position and velocity is 0.5 ms. System subroutine. The incremental PI controller is used in the current loop. u(kT ) = k p [e(kT ) +
k T e( j T ) Ti j=0
(9)
Among them, u (KT) is the output signal of the controller; E (KT) is the input deviation signal. Is the proportion coefficient. Is the integral time constant. T is the sampling period. The improved sliding mode control algorithm is used in the speed loop. The general process is to define variables, assign control parameters, write sliding mode switching function, sliding mode reaching law function and control rate function, and finally get the control output IQ.
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3 Experimental Simulation In this chapter, the PMSM servo system is simulated by MATLAB software platform, and the simulation parameters of PMSM are shown in Table 1.
3.1 Servo System Current Simulation The current loop of servo system adopts PI control algorithm. The simulation model is built in Simulink, by adjusting the parameters of PI controller and observing the current dynamic response curve under different parameters, this paper adopts the control mode of i d = 0 and takes the q-axis current as the research object.
3.2 Speed Simulation of Servo System In this paper, the sliding mode control method is applied to the motor speed loop. Although the sliding mode control has unique advantages compared with the traditional algorithm, its own problem, namely chattering phenomenon, affects the performance of the system. By proposing a new method to optimize and improve the reaching law, the performance of the system is greatly improved. Verification of Improved Reaching Law Sliding Mode Control. In order to verify the feasibility of the improved reaching law, the MATLAB programming simulation example is used. Speed Mode Simulation and Analysis. In the speed mode, PI control is used in the current loop, and the improved sliding mode control algorithm is used in the speed loop. Simulation 1: set the motor speed as 1000r/min, motor load as 10 Nm, observe the output signal curve of the controller with exponential sliding mode and improved sliding mode respectively. Table 1 Main parameters of PMSM simulation
Parameter name
numerical value
stator resistance R
2.875
D-axis inductance L d
8.5 mH
Pole pairs n p
4
Moment of inertia
0.0008 kg m2
Stator flux linkage
0.1688 Wb
Q-axis inductance L q
8.5 mH
Torque coefficient K T
1.012 Nm/A
Coefficient of viscous friction B
0
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Simulation 2: the given speed of the motor is still 1000r/min, and the motor load is 5 Nm. observe the speed response curve of the speed loop using exponential sliding mode control and improved sliding mode control. Simulation 3: the given speed of the motor is still 1000r/min, the starting load of the motor is 5 Nm, when the simulation time t = 0.12 s, the load is suddenly added to 10 Nm, and the load disturbance response curve of the speed loop using exponential sliding mode control and improved sliding mode control is observed. The given speed of the motor is still 1000r/min, the starting load of the motor is 5 Nm, when the simulation time t = 0.12 s, the load is suddenly increased to 10 Nm, the stator current and torque also increase, and then reach stability quickly.
3.3 Position Simulation of Servo System ADRC Improvement Verification. In order to prove the feasibility of linearization of ADRC, the first-order mathematical model of position loop in Chap. 3 is simulated. Because ESO and NLSEF in ADRC can’t be simulated and observed separately, taking TD as an example, input the same step signal to observe the output curve changes of TD and linearized TD. Position Mode Simulation and Analysis. Next, the position servo system is simulated by Matlab/Simulink. Simulation 1: the given step signal of the position is 2 rad, the starting load of the motor is 5 Nm, the load is added to 10 Nm when t = 0.25 s, and the load is removed when t = 0.4 s, and the comparison of the rotor position tracking curve waveforms of the active disturbance rejection controller and the improved active disturbance rejection controller is observed. Simulation 2: the given position signal of the improved ADRC changes according to the sine function of 0.5sin (4 T) rad, while the load changes according to the sine function of 5sin (4 T) Nm, observe the simulation waveform. Simulation 3: set the given step signal of the position to 2 rad, the starting load of the motor to 5 N, add the load to 10 Nm when t = 0.25 s, and observe the rotor position tracking and rotor speed curve of the improved ADRC with sudden load.
4 Result Analysis 4.1 Analysis of Current Simulation Results of Servo System Under the condition of KI T i = 0.5, the proportional coefficient kp = 9.735 and integral constant Ti = 0.00295 of PI controller are given, and the given signal is step signal. The current step waveform is obtained by simulink simulation, as shown in Fig. 1.
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X. Zou
electric current /A
1.5
electric current
q/A
1 0.5 0
0
0.0008
0.0012 time /s
0.002
0.01
Fig. 1 KP = 9.735, Ti = 0.00295 response curve
It can be seen from Fig. 1 that when K I T i = 0.25, the current loop can follow the given current well, but there is a small overshoot phenomenon. On this basis, the integral constant is 0.00295, and the scale coefficient is 4.87 and 19.47 respectively under the selected conditions. The current response curves of these two groups of parameters are shown in Fig. 2. As can be seen from Fig. 2, when the response curve changes, there is no overshoot, but the response becomes slow; however, the overshoot of step response curve is about 20%, but the response time becomes shorter. In the same way, when Ti = 0.00295 is increased or decreased, the steady-state accuracy of the current response curve will decrease and the tracking effect will become worse. To sum up, when KP = 9.735, Ti = 0.00295, it can meet the better steady-state accuracy and dynamic characteristics of the current loop.
electric current /A
1.5
Kp=4.87
Kp=19.47
1
0.5
0
0
0.00025
0.0009
0.001
0.002
0.01
time/s
Fig. 2 Response curves of KP = 4.87, Ti = 0.00295 and KP = 19.47, Ti = 0.00295
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4.2 Speed Simulation Results Analysis of Servo System Verification and Analysis of Improved Reaching Law Sliding Mode Control. Taking the second order transfer function as the control object and substituting it into the control function for simulation analysis. The simulation waveforms of switching function s and controller output U of sliding mode exponential reaching law control are shown in Fig. 3, and the simulation waveforms of switching function s and controller output U of sliding mode improved reaching law control are shown in Fig. 4. According to the simulation results in Figs. 3 and 4, it can be seen that the chattering phenomenon is obvious in the exponential reaching law control method, while the chattering of the improved reaching law is weakened, and the output of the controller is relatively smooth. When the moving point is far away from the sliding surface, the exponential term and the variable term act together to move quickly to the sliding surface. When it reaches the sliding surface, the exponential term almost decreases to zero, and the variable term passes through the sliding surface at a small speed. The simulation results show the effectiveness of the improved reaching law. Speed Mode Simulation Analysis. 0.2
10
u
u
0 0.2
0.3
0.4
0.6
1.8
5
u/v
Voltage
0 -0.2 -0.4
0 0
-0.6 -0.8
0.2
0.3
-5
time/s
0.4
1.8
time/s
Fig. 3 Performance analysis of sliding mode exponential reaching law 10
u
5
0 0
0.2 0.3 0.4 0.6 1.8
0
-0.5
-1
u
u/v
Voltage
0.5
0
time/s
-5
0.2
0.3
time/s
Fig. 4 Performance analysis of sliding mode improved reaching law
0.4
1.8
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X. Zou
The simulation results show that the output signal of the exponential sliding mode controller has chattering phenomenon, while the chattering of the improved sliding mode control is weakened, and the time to reach the stable state is shortened, which proves the effectiveness of the improved sliding mode control method. The speed tracking effect of the improved sliding mode control is obviously better than that of the exponential sliding mode control, and there is almost no overshoot. The exponential sliding mode control has overshoot and takes a long time to reach the stable state. When the load is suddenly applied, the improved sliding mode control has lower speed drop and shorter recovery time. Thus, the improved sliding mode variable has stronger robustness.
5 Conclusion Based on the mathematical model of PMSM, this paper focuses on the application of fusion control algorithm in current loop, speed loop and position loop, and designs PI current controller, sliding mode speed controller and ADRC position controller. Aiming at the chattering problem of speed sliding mode control algorithm, an improved reaching law method is proposed to weaken the chattering and improve the speed performance index; In addition, the linearization of ADRC technology is improved. On the premise of keeping the original core function unchanged, the setting of controller parameters is simplified, and the operation speed of CPU is improved.
References 1. Xiaobing Z, Wei Z, Mengfei S (2017) Oil exploration oriented multi-sensor image fusion algorithm. Open Phys 15(1):188–196 2. Li X, Wang L, Wang J et al (2017) Multi-focus image fusion algorithm based on multilevel morphological component analysis and support vector machine. IET Image Proc 11(10):919– 926 3. Nan DN, Liu WW, Wen-Xiang FU et al (2020) Study on fast recognition of biotoxins and biological modifiers using data fusion algorithm. Chin J Anal Chem 48(10):1343–1350 4. Zhang H, Yan W, Zhang C et al (2021) Research on image fusion algorithm based on NSST frequency division and improved LSCN. Mobile Netw Appl 9:1–11 5. Zhu J, Gao J (2020) Dynamic fusion algorithm of building surface data in heterogeneous environment. Mobile Netw Appl 4:1–10 6. Wang H, Song L, Liu J et al (2021) An efficient intelligent data fusion algorithm for wireless sensor network. Proc Comput Sci 183(3):418–424 7. Liu M, Ma J, Lin L et al (2017) Intelligent assembly system for mechanical products and key technology based on internet of things. J Intell Manuf 28(2):271–299 8. Shaohua L, Shaobo LI, Farid T (2018) Chaos and nonlinear feedback control of the arch micro-electro-mechanical system. J Syst Sci Complexity 31(06):1510–1524 9. Aldalur E et al (2020) High deposition wire arc additive manufacturing of mild steel: strategies and heat input effect on microstructure and mechanical properties. J Manuf Process 58:615–626
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10. Choi YM, Kang D, Lim S et al (2017) High-precision printing force control system for rollto-roll manufacturing. IEEE/ASME Trans Mechatronics 22(5):2351–2358 11. Ferreira JCE, Benavente J, Inoue P (2017) A web-based CAD/CAPP/CAM system compliant with the STEP-NC standard to manufacture parts with general surfaces. J Braz Soc Mech Sci Eng 39(1):155–176 12. Vakouftsis C, Mavridis-Tourgelis A, Kaisarlis G et al (2020) Effect of datum system and datum hierarchy on the design of functional components produced by additive manufacturing: a systematic review and analysis. Int J Adv Manuf Technol 111(3):817–828
Path of Data Mining and Analysis Technology in New Energy Vehicle Business Model Innovation Xu Wu
Abstract With the gradual popularization of smart devices and the prosperity of Internet technology, the domestic automobile industry is also undergoing a shift from traditional power to new energy. Although the existing new energy technology has been widely used in the automotive field, there is still a certain gap between our country’s overall new energy technology innovation capability and developed countries. Finding a business model suitable for the development of new energy vehicles has become a key factor in promoting the development of the new energy vehicle industry. Therefore, this article mainly discusses the path research of data mining analysis technology in the business model innovation of new energy vehicles. First, list the current status of the existing traditional business models of 100 new energy automobile companies, and then analyze the car sales data of the A company in the 100 companies in the past five years. The total annual car sales of A company have gradually increased, from 5512 in 2017 to 9070 in 2020. In just four years, the sales of cars have increased by nearly 3500. To a certain extent, data mining technology provides decision-making support for the continuous innovation of business models of new energy automobile enterprises and brings more automobile sales to enterprises. Keywords Data mining · New energy vehicles · Business models · Cluster analysis
1 Introduction With the continuous improvement of people’s living standards, the demand for automobiles is increasing, and the automobile industry is also undergoing a transition from traditional power to new energy sources [1]. New energy vehicles are an emerging field. Even as a major country of new energy vehicles, our country’s faces many challenges in the process of developing this industry. At present, our country’s new energy vehicles are in the ascendant, and there is still a certain gap between the X. Wu (B) Department of Automotive Engineering, Sichuan Aerospace Vocational College, Guanghan, Sichuan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_8
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development of new energy vehicles and other developed countries. Therefore, one of the key factors to promote the development of the new energy vehicle industry is to find a business model suitable for the development of new energy vehicles through data mining technology. There are not a few researches on the innovation of data mining technology in the business model of new energy vehicles. Verma A believes that using big data methods to successfully test PEV families has achieved an accuracy rate of 80% or higher [2]. Babaei M proposed that data mining technology does not require inspection or measurement instruments, and can make better use of energy storage systems to reduce and transfer energy consumption [3]. Zhang L conducts exploratory data analysis on all historical data in the database, filters out some key variables in the segmentation model, and then proposes a customer segmentation method based on data mining technology [4]. The research content of this article mainly includes: comparative analysis and investigation of the traditional business models of 100 new energy automobile companies. The traditional business models mainly include vehicle sales model, vehicle leasing model and battery leasing model. In addition, it also enumerates how a specific A car company has changed its car sales after using data mining technology. This reflects the path of data mining and analysis technology in the innovation of new energy vehicle business models.
2 Data Mining Analysis Technology and New Energy Vehicle Business Model 2.1 Data Mining Analysis Technology The function of data mining is to find knowledge that is not obvious but still valuable in the data. Its purpose is to group a large amount of data in the collection, so that similar objects can be grouped as much as possible into the same group, while objects in different groups show greater difference [5, 6]. For us, data mining and analysis technology is not a completely unfamiliar branch, but a new type of interdisciplinary based on statistics, machine learning, database, parallel computing and other disciplines. In addition, the function of data mining technology is to discover the unobvious but very valuable knowledge in the data. Nowadays, the methods of data mining and analysis are becoming more and more abundant, and the theory is becoming more and more mature. Some methods can solve the problem simply and clearly, and some methods are suitable for solving the problems of high-dimensional data and big data. Therefore, data mining technology can extract useful information from a large amount of data related to new energy vehicles, and then provide decision support for the business model innovation of new energy vehicles.
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2.2 Current Business Model of New Energy Vehicles The business model is the foundation of enterprise development and the logic of creating value for the enterprise. It explains how the enterprise obtains excess profits for the enterprise through the specific positioning of the product or service in the value chain [7]. The business model mainly includes four core issues, which are what products or services to provide customers, what needs to meet customers, how to meet customer needs, and how to obtain profits from customers. Our current new energy vehicle market mainly has three business models: the vehicle sales model, the car rental model, and the battery rental model. The whole vehicle sales model is a western-originated operating model formed at the early stage of the development of new energy vehicles. In this scheme, consumers generally charge themselves, with the characteristics of “use during the day and charge at home at night”, and have the advantages of flexible time, simple operation, and low technical difficulty. But at the same time, consumers need to pay for the car and the power battery during the car sales process [8]. This increases the cost of consumers buying cars. On the one hand, there are problems such as insufficient charging equipment and slow charging speed in this mode. On the other hand, this business model is not conducive to the promotion of new energy vehicles, and ultimately leads to a low level of market competitiveness of this model. Therefore, the business model of vehicle sales has not been well developed after many years. The car rental model refers to the cooperation between car manufacturers and battery manufacturers to establish a special car rental company, and the car rental company will rent out the entire car to consumers [9, 10]. When the development of the new energy market is still immature, customers only need to bear the deposit and rent during the lease period. During this period, the leasing company is responsible for the maintenance of the entire vehicle (car and battery). This is for consumers and new It is beneficial to energy automobile companies. On the one hand, the vehicle leasing model can reduce the cost of new energy vehicle companies. On the other hand, this model can ease the economic pressure of customers and relieve customers of their concerns about maintenance issues during the use of new energy vehicles, which is conducive to the promotion of new energy vehicles. However, for a long time, the inherent consumption pattern of many domestic consumers is to buy a car instead of renting a car, and it is difficult to change this consumption pattern in a short period of time. At the same time, for traditional car sales companies, their goal is mostly to sell whole cars instead of taxis. Therefore, from the perspective of consumers and manufacturers, the future development of this vehicle leasing model is not very clear. It is not the ultimate goal of the development of auto companies, nor is it the ultimate goal of the development of the new energy automobile industry. In the battery leasing model, car manufacturers only sell cars but not batteries. The energy supplier leases power batteries to car consumers and is responsible for the subsequent charging and maintenance of the vehicles. This model safeguards the interests of all participating entities such as automobile manufacturers and battery operators. Its advantages are: first, consumers only buy bare cars without batteries,
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which can greatly alleviate the economic pressure brought about by car purchases; second, the battery charging services and maintenance services provided by energy suppliers bring convenience to tenants. The lessee only needs to pay the corresponding charging fee and battery depreciation fee according to the mileage; third, the recycling of used batteries is also conducive to environmental protection and can increase the additional income of the enterprise. This model has an advantage in the current process of the new energy industry, but it is difficult to establish a complete charging infrastructure in a short period of time, which also hinders the development of new energy vehicles to a certain extent.
2.3 The Basic Principle and Process of Cluster Analysis As an important data mining technique, cluster analysis is often used in various fields of social production and life. Clustering refers to the clustering of things, which is to classify things according to certain internal laws. This paper mainly proves through experiments that the use of cluster analysis technology can promote the continuous innovation of the business model of new energy automobile companies. The basic principle of cluster analysis is to classify data objects according to the similarity between data objects without knowing how many classes there are in the target database in advance, so that data objects of the same type are similar to the greatest extent, and different types of data objects have the most differences [11, 12]. The collection of data objects is represented as an n*k matrix ⎛
X n,k
a11 ⎜ a21 =⎜ ⎝... a41
a12 a22 ... a42
⎞ . . . a1k . . . a2k ⎟ ⎟ . . . a3k ⎠ . . . ank
(1)
Because the measurement mechanisms used in the representation of these vectors are different, before clustering, it is necessary to define the degree of phase between the vectors. Common distance functions include Minkowski distance, Mahalanobis distance, and Bach distance, etc. This article mainly uses the Mahalanobis distance function. Define the sample vector space X 1 X 2 . . . X k . S is the covariance matrix, and the vector µ is the mean. The Mahalanobis distance between the vector X and µ is: D(x) =
(X − µ)T S −1 (X − µ)
(2)
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3 Application Experiment of Data Mining Analysis Technology in the Innovation Path of New Energy Vehicle Business Model 3.1 Experimental Background New energy vehicles are an emerging field. Although our country’s, as a major country of new energy vehicles, is developing its industry, there are also many challenges in the process. The current new energy vehicle data management and development methods are too simple and single, and lack in-depth analysis. So how data mining technology will affect the business model of new energy vehicles is the focus of this article.
3.2 Experimental Method Select 100 new energy vehicle companies using data mining technology to conduct online and offline questionnaire surveys and process and analyze relevant data. These data mainly include the distribution of the original traditional business model of the enterprise, the business model after the use of data mining technology, and the problems encountered when using data mining technology.
3.3 Experimental Results The feedback information obtained after receiving and processing the surveyed new energy vehicle company questionnaire will be displayed and analyzed in the fourth part, and I will not repeat it here.
4 Analysis and Discussion of Experimental Results After the third part of the experiment is over, we will analyze and process the data obtained. Now the collated data is shown below.
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4.1 Distribution of Traditional Business Models of Enterprises When Data Mining Technology is not Used From the data in Table 1 and Fig. 1, it can be seen that among the 100 new energy companies surveyed, 10 companies use the vehicle sales model under the traditional business model, accounting for 10% of the total survey companies. Among the 100 new energy vehicle companies surveyed this time, 13 and 17 new energy vehicle companies adopt the vehicle leasing model and the battery leasing model respectively, slightly more than those adopting the vehicle leasing model. In addition, there are 60 companies using the three business models of vehicle sales, vehicle leasing and battery leasing at the same time. In summary, before using data mining technology, the business models of these new energy vehicle companies under investigation were relatively single, so data mining technology is particularly important in the business model innovation of new energy vehicle companies. Table 1 Distribution of traditional business models of enterprises
Numbers of companies
The proportion (%)
Vehicle sales model
10
10
Vehicle rental model
13
13
Battery rental model
17
17
Three modes coexist
60
60
Vehicle sales model
10% 13%
Vehicle rental model
Battery rental model
60%
Three modes coexist Fig. 1 Distribution of traditional business models of enterprises
17%
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Sales volume 10000 9070
9000 8000
7783
8276
7000 6000 5000
5475
5512
4000 3000 2000 1000 0 2016
2017
2018
2019
2020
Fig. 2 A company’s car sales from 2016 to 2020
4.2 Research on the Path of Business Model Innovation for New Energy Automobile Companies Using Data Mining Technology In order to further reflect the optimization effect of data mining technology on the business model of new energy vehicles, this article takes A new energy vehicles among the 100 companies surveyed as an example, and lists the car sales data of company A in the past five years, as shown in Fig. 2. A new energy automobile company applied data mining technology to business model innovation at the beginning of 2017, and specifically analyzed factors such as consumers’ lifestyle, purchase motivation, and personality psychology. It can be seen from Fig. 2 that before the use of new mining technology, A company’s car sales were continuously at a relatively low level. In 2016 and 2017, it was only 5475 and 5512 respectively. After using the new data mining technology, the total annual car sales of A company has gradually increased, from 5512 in 2017 to 9070 in 2020. In just four years, the sales of cars have increased by nearly 3500 vehicles. To sum up, data mining technology can enable new energy automobile companies to conduct more accurate quantitative analysis and scientific forecasts, constantly update their business models, and gradually improve their own market competitiveness.
4.3 Problems Encountered During Use The emergence of any new thing is not perfect, and the use of data mining technology studied in this article is no exception. Among the 100 companies surveyed in the previous article, many companies have difficulties in the use of data mining
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Table 2 Problems encountered in the use of data mining technology
Numbers of companies
The proportion (%)
Difficulty in collecting information
76
76
Difficulties in data cleaning
5
5
Difficulty in 3 model evaluation
3
No difficulty
16
16
technology. Considering the data itself, common data mining mainly includes information collection, data integration, data protocol, data cleaning, data transformation, etc. 8 steps. It can be seen from Table 2 that 76% of auto companies have difficulties in data collection, 5% of new energy auto companies think that data cleaning is troublesome, 3% of companies have some problems in model evaluation, and the remaining 16% of companies do not have any problems in the use of data mining technology.
5 Conclusions Although our country’s new energy vehicles are currently facing a good development trend, they also face many challenges. The business model of new energy vehicle companies is still in the process of continuous exploration and innovation. Therefore, this article studies the business model innovation of the new energy automobile industry development from a new perspective. The research results show that data mining technology is conducive to the advancement of new energy automobile enterprises, more accurate quantitative analysis, and continuously promotes the update of the business model of the enterprise, and finally improves Self-competitiveness thus creates more car sales for the company. At the end of this article, some problems that may be encountered in the actual use of data mining technology are also put forward. However, due to time and energy constraints, specific solutions need to be further improved.
References 1. Valadkhani A, Smyth R (2016) The effects of the motor vehicle industry on employment and research innovation in Australia. Int J Manpow 37(4):684–708 2. Verma A, Asadi A, Yang K et al (2019) Analyzing household charging patterns of Plug-in electric vehicles (PEVs): a data mining approach. Comput Industrial Eng 128:964–973
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3. Babaei M, Abazari A, Soleymani MM et al (2021) A data-mining based optimal demand response program for smart home with energy storages and electric vehicles. J Energy Storage 36(14):102407 4. Zhang L, Li G, Chen Y et al (2018) Customer segmentation and value evaluation method based on data mining for electric vehicles. Dianli Xitong Baohu yu Kongzhi/Power Syst Protection Control 46(22):124–130 5. Tscharf A (2016) Potentials and challenges of new surveying technologies in mining—the use of unmanned aerial vehicles for geospatial data acquisition. BHM Berg-Huettenmaenn Monatsh 161(10):481–487 6. Ando R, Li A (2016) An evaluation analysis on three-wheeled personal mobility vehicles. Int J Intell Transp Syst Res 14(3):1–9 7. Bixler R, D’Mello S (2016) Automatic gaze-based user-independent detection of mind wandering during computerized reading. User Model User-Adap Inter 26(1):33–68 8. Lin K, Luo J, Hu L et al (2016) Localization based on social big data analysis in the vehicular networks. IEEE Trans Industrial Informatics 1932–1940 9. Noori M, Zhao Y, Onat NC, Gardner S, Tatari O (2016) Light-duty electric vehicles to improve the integrity of the electricity grid through Vehicle-to-Grid technology: analysis of regional net revenue and emissions savings. Appl Energy 168:146–158 (2016) 10. Allani S, Yeferny T, Chbeir R (2018) A scalable data dissemination protocol based on vehicles trajectories analysis. Ad Hoc Netw 71:31–44 11. Malik A, Pandey B, Wu CC (2018) Secure model to generate path map for vehicles in unusual road incidents using association rule based mining in VANET. J Electronic Sci Technol 16(02):59–68 12. Chouali S, Boukerche A, Mostefaoui A et al (2020) Formal verification and performance analysis of a data exchange protocol for connected vehicles. IEEE Trans Vehicular Technol (99):1–1
Data Analysis and Application of Resource Search Algorithm in Basic Education Fairness Miao Li
Abstract With the rapid development of the Internet, various information resources have expanded dramatically. How to retrieve effective information resources at a high speed in a large-scale network environment has become a severe challenge facing the Internet. In recent years, P2P, as a new type of network application mode, has become more and more popular due to its scalability and high fault tolerance. As the core technology of P2P applications, the resource search mechanism can find resources that meet user requirements at the fastest speed in a distributed dynamic environment such as P2P. The equity of basic education in the dual structure of urban and rural areas is the focus of Chinese society, and its data analysis is crucial in the research process, and the resource search algorithm can efficiently search for the resources needed in this article. Based on this, this article aims to study the resource search algorithm in P2P and its application in the basic education fairness of urban and rural dual structure. After analyzing the characteristics of the P2P network, the classic resource search algorithm and the impact of basic education injustice, use the resource search algorithm. The flooding algorithm obtains and analyzes the relevant data related to the equity of basic education in the dual structure of urban and rural areas that this article needs, and puts forward suggestions on the equity of basic education on this basis. The research results show that since 2013, the qualification rate of full-time teachers in urban and rural primary and secondary schools in Province G has reached more than 99%; in addition, the proportion of teachers with a college degree or above in urban primary schools and a bachelor degree or above in junior high schools in 2016 has increased by 12 compared to 2010. 21 and 25.02 percentage points, while the proportion of teachers in rural elementary and junior high schools increased by 19.23 and 28.72 percentage points respectively. Keywords P2P · Resource search algorithm · Flooding algorithm · Education fairness problem
M. Li (B) School of Marxism, Wuhan University of Science and Technology, Wuhan 430000, Hubei, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_9
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1 Introduction In recent years, with the rapid advancement of information technology, the development of the Internet is changing with each passing day. Since the 1990s, the number of users on the Internet has increased dramatically, and at the same time, the data resources on the Internet have also grown at an alarming rate. How to find the information you need in the massive amount of data has long become a hot issue in people’s research [1, 2]. The emergence of search engines can help users quickly find the information resources they need, and has certain advantages in the collection, extraction, organization and processing of information [3, 4]. In recent years, in order to improve the effectiveness of network resource search, relevant scholars have done a lot of research on the basis of classic algorithms, mainly through the following points [5, 6]: In a P2P network, nodes are composed of neighboring nodes. Search for a subset of, instead of searching for all nodes. In this way, although the network load can be reduced when searching for resources, the search delay cannot be improved, and because only its neighbor nodes are searched, some nodes cannot being searched reduces the search success rate. Some studies have shown that the semantic relationship between sent messages is used to determine the forwarding of query messages, which can reduce network load [7, 8]. After analyzing the characteristics of P2P networks, classic resource search algorithms, and the impact of basic education injustice, this article uses the flooding algorithm in the resource search algorithm to obtain and analyze the relevant data needed for this article on the equity of basic education in the dual structure of urban and rural areas. Here on the basis of this, suggestions on the fairness of basic education are put forward.
2 Methods 2.1 Characteristics of P2P Network (1)
(2)
Equivalence of nodes: Each node can not only be a requester of network services, but also can respond to requests from other nodes in the network and provide services or resources for other nodes [9, 10]. The P2P network makes full use of the increasingly enhanced computing and processing capabilities of the client, and communicates between nodes directly between nodes, changing the state that the central server is the core in the traditional network, and realizing a “decentralized” network. In a P2P network, nodes can directly interact with other nodes without passing through other intermediate nodes or servers, which effectively avoids the problem of single node failure. Scalability: In a P2P network, nodes will also provide services for other nodes while obtaining the services of other nodes. With the continuous addition of nodes in the network, the demand for services by the nodes has gradually
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(3)
(4)
(5)
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increased, and the resources contained in the entire network and the service capabilities of the network have also increased. Therefore, the service capability of P2P network will gradually increase with the growth of nodes, which is a kind of “innate” scalability [11, 12]. Robustness: The shortcomings of the traditional client–server model are obvious, that is, the dependence on the central server is too high. Once the central server in the network responds to too many node requests and cannot respond immediately, it will directly paralyze the entire network. However, the P2P network itself has characteristics such as dynamics and self-adaptation. When some nodes in the entire network are abnormal, the network topology can be dynamically updated, so that the mutual communication between network nodes is not destroyed. High efficiency and load balancing: The use of P2P network can make full use of a large number of ordinary nodes in the network, and the calculation or storage tasks of the entire network can be evenly distributed on multiple nodes. Taking full advantage of the computing power of idle nodes, it is easy to achieve high-performance computing and massive data storage. The performance of the entire P2P network will be significantly improved with the enhancement of node capabilities. Self-organization: Since the P2P network is established according to the characteristics of self-organization, when a new node joins the network, the node will randomly select a node in the network to connect. Nodes in the P2P network can join or withdraw freely, which causes the network to continuously generate new connections or update the original connections to keep the network unblocked. The entire P2P network can spontaneously adjust the network topology to meet actual needs.
2.2 Classical Resource Search Algorithm (1) Flooding algorithm. When searching for network resources through the flooding algorithm, the requesting node sends the query message to all its neighbor nodes. If the neighbor node does not contain the network resources that are queried, it forwards the query message to all its neighbor nodes, otherwise it returns directly result. If the search request is forwarded infinitely, it may cause the network load to be too heavy. Therefore, in order to limit the unlimited forwarding of query messages, a TTL (time-to-live) value is set on each search message to limit the number of forwarding times. The TTL value is reduced when forwarding messages 1. If the required resource is found, stop forwarding the message and establish a connection between the requesting node and the resource node. When the TTL value is 0 and the required resource is not found, the message is discarded and the search is stopped. In order to avoid the infinite loop of message forwarding, each node saves the query information received within
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a certain period of time. If the received query message is the same, it is directly discarded without forwarding. The flooding algorithm is simple to implement, has a high search efficiency in small-scale networks, and is suitable for all network structures. (2) Random walk algorithm. The random walk algorithm improves the flooding algorithm, which is mainly reflected in the forwarding mechanism of query messages. When the message is forwarded in the random walk algorithm, the message is randomly forwarded to the neighboring K nodes, and the query message is kept in contact with the query node. If the query node has searched resources, the query success message is sent to the requesting node, otherwise it is forwarded For query messages, the TTL value is reduced by 1. There are two conditions for stopping the query. One is that the TTL value is 0, and the other is that the query node sends a termination query message. Whether to send a termination query message is determined by checking the TTL value, and the check value is an integer multiple of the set number M. The parameter M here has a greater impact on query efficiency. The success rate of resource search through the random walk algorithm depends on the random neighbor nodes calculated by the requesting node, resulting in a low search success rate. (3) Mobile agent algorithm. Mobile agent is to establish an agent mechanism in the process of resource search. The agent information is sent to the nodes in the network, the mobile node saves the request information when searching for resources, and sends the resource information to the query node when the requested resource is searched, otherwise the mobile agent is sent to other nodes. (4) Modified BFS algorithm. The Modified BFS algorithm is an improvement to the flooding algorithm. When searching for resources for the flooding algorithm, the query message is forwarded to all neighbor nodes, and the network load increases. In the Modified BFS algorithm, by setting rules, some nodes are selected for message forwarding. The selection rules may be attributes such as the number of neighbors of the node. The Modified BFS algorithm is similar to the random walk algorithm in that it gives up sending messages to all neighbor nodes and selects some neighbor nodes for forwarding. The difference from the random walk algorithm is that the Modified BFS algorithm selects neighbor nodes with more resources, while the random walk algorithm is random Select neighbor nodes. The network load is reduced by the Modified BFS algorithm, but the network coverage rate during search is reduced.
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2.3 The Impact of Injustice in Basic Education (1) Impact on individuals. Unfair education will result in a large number of children and adolescents in my country who lack the opportunity to go to school, lack the knowledge and diplomas necessary to engage in social mental work. After being thrown into a society that is similar to the weak, they will not be able to work with their urban peers who have received a good education. In job competition, a large part of people can only engage in manual labor to earn a meager living expenses. Their offspring will face the same problems and will strive for survival and development with more effort and hardship than others. Some of them will be at the bottom of society or on the margins of society throughout their lives or even several generations, struggling to survive. (2) Impact on society. Generally speaking, the amount of income is positively related to the level of education. We cannot imagine that a person who has no basic education can get rid of poverty. Educational unfairness will inevitably aggravate the development imbalance between urban and rural areas and different regions, which will further increase the gap between rich and poor. There is a huge gap between my country’s rural and urban education. A direct consequence of low-quality rural education is the low quality of the rural labor force. The low quality of workers will inevitably not be able to meet the needs of the rapid development of the national economy, and ultimately hinder the rapid development of the economy. (3) Impact on education itself. First, the unfairness of education has disrupted the order of educational development. Unfair education has caused many social problems, such as students dropping out of school or even committing suicide due to poverty, unreasonable charges for education, corruption of educational administrative leaders, and many other chaos, which eventually led to chaos in the order of educational development. The second is the deviation of the purpose of education. Education is a means to lay the foundation for people’s survival and development. However, in an environment of unfair education, this kind of educational purpose cannot be realized, and education has become a means of exploitation and discrimination, which is contrary to the original intention of education. Third, a large part of the unfairness in teaching has affected the improvement of the overall quality of education. Due to the unfair allocation of educational resources in urban and rural areas and the gap between the actual economy in urban and rural areas, this has caused the continuous loss of education professional teachers in rural areas, resulting in the deteriorating quality of education in rural areas, and ultimately hindering the improvement of the overall quality of education in rural areas in our country.
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3 Experiment 3.1 Data Sources This article takes urban and rural primary and secondary schools in Province G as the research object, and uses the relatively simple flooding algorithm in the resource search algorithm to search for the required data. The search principle of this algorithm is to send resource requests to all neighboring nodes. Any prompt information until the information location that meets the conditions is found. The search algorithm has a simple structure, does not need to maintain too much redundant information, has high scalability, and has a high search efficiency in a small-scale P2P network. It is suitable for the research on the equity of basic education in the dual structure of urban and rural areas, and can be fast Search for the data needed for this article.
3.2 Algorithm Description Topic: In order to express the main information of the document, the vector space model VSM is used to express the document in the form of a vector with weights. Each component in the vector represents a feature word in the document, which is called an item. − → d j = W1, j , W2, j . . . Wn, j
(1)
Similarity: For each query vector, calculate the cosine of the angle between it and each document vector to determine the content similarity between two documents. − → − n q dj · → = Wi, j × Wi,q sim d j , q = − → − → d j × q i=1
(2)
4 Discussion 4.1 Education Funding Educational expenses refer to the state’s expenditures for the development of various educational undertakings in the society, which are divided into individual parts and public parts. Since there is a large gap in the total amount of funding for basic education between urban and rural areas in Province G, the use of per-student indicators
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Table 1 2013–2016 Average budgeted education expenses for basic education students in G province in urban and rural areas Years
Primary school Town
Junior high school Rural area
Town
Rural area
2013
5421.73
6135.60
5953.21
6210.67
2014
6645.03
6813.20
7849.56
6772.76
2015
8964.10
8589.93
11,376.77
8249.38
2016
9043.27
9773.54
11,409.04
9905.83
Funding
to analyze the difference between urban and rural education funding will be able to accurately reflect the level of education funding. Table 1 shows that from 2013 to 2016, the per student education expenses of urban and rural elementary schools increased by 3621.54 and 3637.94 yuan respectively. The urban–rural gap during this period did not change much; during this period, the average education expenses of urban and rural junior high schools were different The increase was 5455.83 and 3695.16 yuan, and the urban–rural gap increased from − 257.46 yuan to 1503.21 yuan. Public expenditure is the part of the public expenditure used to ensure and improve the conditions for running schools in the education expenses, and it is also an important foundation for improving the quality of education and training talents for modernization. Figure 1 shows that from 2013 to 2016, the public expenditure per student in urban and rural primary and secondary schools in Province G also continued to grow steadily. The per student public expenditure of urban and rural primary schools increased by 1354.31 and 428.41 yuan respectively, and the urban–rural gap increased from −128.19 yuan to 797.71 yuan. At the same time, the per student public expenditure of urban and rural junior high schools increased by 1672.58 and 350.84 yuan, respectively. The urban–rural gap also increased by −473.91 yuan increased to 847.83 yuan. Based on the above analysis, it can be seen that the per student education expenses and public expenditures of urban and rural primary and secondary schools in G 5000 4000 3000 2000 1000 0 2013
2014
2015
2016
years Town Primary School
Rural Primary School
Town junior high school
Rural junior high school
Fig. 1 Public expenditure in the budget of urban and rural basic education students in Province G from 2013 to 2016
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Province have maintained a stable and good development trend. Judging from the data in 2016, the average per-student expenditure of urban junior high school education and public funds for elementary and junior high schools is higher than that in rural areas, and only the per-student expenditure of urban elementary school education is lower than the same period in rural areas.
4.2 Differences in Faculty The academic structure refers to the proportion of members of the teacher group who receive different levels of professional education. It can not only reflect the professional quality of teachers and their development possibilities, but also an important indicator of the level of the teacher team. To evaluate the academic qualifications of basic education teachers, it is usually reflected by indicators such as the qualification rate of academic qualifications and the proportion of teachers with college degree and above (primary school), undergraduate and above (junior high school). Figures 2 and 3 show that since 2013, the qualification rate of full-time teachers in urban and rural primary and secondary schools in Province G has reached more than 99%; in addition, in 2016, the number of teachers with a college degree or above in urban primary schools and a bachelor’s degree or above in junior high schools accounted for comparison in 2010 An increase of 12.21 and 25.02 percentage points, while the proportion of teachers in rural primary and junior high schools increased by 19.23 and 28.72 percentage points respectively. From this point of view, the educational level of full-time teachers in urban primary and secondary schools in the province from 2013 to 2016 is generally better than that of rural teachers. However, the educational level of urban and rural teachers has been greatly improved during this period, and the educational level of rural teachers Rural specialist and higher
Towns specialist and higher
Rural qualification rate
Urban qualification rate
2016
years
2015
2014
2013 0.00%
20.00%
40.00%
60.00% percentage
80.00%
100.00%
120.00%
Fig. 2 Educational structure of teachers in urban and rural primary schools in Province G from 2013 to 2016
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Urban qualification rate
Rural qualification rate
Towns specialist and higher
Rural specialist and higher
120.00% 110.00%
percentage
100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 2013
2014
2015
2016
years
Fig. 3 Educational structure of teachers in urban and rural junior high schools in Province G from 2013 to 2016
has improved more than that of urban teachers. Even more significant, the gap in the allocation of urban and rural teachers is narrowing day by day. In recent years, although initial results have been achieved in the construction of the teaching staff of basic education in Province G, there is still a certain gap in the quality of teacher resources in urban and rural areas, which may still hinder the development of local basic education in the future.
4.3 Targeted Suggestions (1) Weakening the dual structure of urban and rural areas. Increase financial support to rural areas. Agriculture has natural characteristics of weak quality, with large macro-efficiency and low micro-efficiency. Therefore, finance must increase support for agriculture. To build a new socialist countryside, investment is the key. For local governments, it is necessary to use supporting funds in real situations, find new ways to finance agriculture and improve the efficiency of fund use. Only by establishing and perfecting a diversified agricultural input system can a long-term mechanism for stable growth of agricultural support funds be formed. (2) Strengthen the construction of teaching staff. Construct a teacher training system and improve teacher welfare. Regarding the fact that high-quality teachers in rural areas were relatively scarce compared to urban areas in the past, relevant departments need to give more adequate policy preference to teachers in rural areas, especially in areas with underdeveloped education. The first is to start by improving the comprehensive quality and teaching ability of
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existing teachers, adopting a combination of centralized training and online training, focusing on the construction of backbone teachers, and further increase the training and training of in-service teachers; second, the training of some post-retirement teachers with spare capacity should be re-employed in order to give full play to the role of older teachers in demonstrating and leading young teachers; the third is to speed up the formulation of supporting policies to balance the allocation of urban and rural teachers, and give priority to the material conditions of rural schools, such as increasing basic wages and grants special allowances and welfare, improvement of the social security system, etc., fundamentally improve the living and working environment of rural teachers, and attract outstanding teachers from all over the country to teach in rural areas. (3) Strengthen financial coordination. Make overall arrangements for the fiscal budget and improve the funding guarantee mechanism. To maximize the use of educational investment, the key is to increase the overall planning of financial funds by the provincial government, that is, the main body responsible for promoting educational financial investment is moved from the township and county government to the provincial government, based on standardization. Make overall plans for the educational financial appropriation of various regions to ensure that the increase in capital investment is sufficient to promote the improvement of the overall school running level.
5 Conclusions This article is mainly based on the P2P resource search algorithm, using the classic Flooding algorithm in the algorithm to search for data resources related to the fairness of basic education. The Flooding algorithm has high search efficiency and strong network scalability. It is used in basic education in the dual structure of urban and rural areas. It is practical in the application of fairness issues.
References 1. Guo Y, Zhou W, Luo C et al (2016) Instance-based credit risk assessment for investment decisions in P2P lending. Eur J Oper Res 249(2):417–426 2. Kikuchi M (2017) Automatic document organization in a P2P environment. Shanxi Archit 15(5):835–848 3. Li A, Masouros C (2017) Exploiting constructive mutual coupling in P2P MIMO by analogdigital phase alignment. IEEE Trans Wireless Commun (3):1–1 4. Hong Y, Goel S, Liu WM (2016) An efficient and privacy-preserving scheme for P2P energy exchange among smart microgrids. Int J Energy Res 40(3):313–331 5. Abdullahi M (2016) Hybrid symbiotic organisms search optimization algorithm for scheduling of tasks on cloud computing environment. PLoS ONE 11(6):e0158229
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6. Isiaka F, Mwitondi KS, Ibrahim AM (2016) Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm. Int J Intelligent Comput Cybernetics 9(1):23–41 7. Skr Ab AA, Stanovov V, Semenkin E et al (2016) Hybridization of stochastic local search and genetic algorithm for human resource planning management. Organizacija 49(1):42–54 8. Jaouher, Chrouta, Wael et al (2019) Modeling and control of an irrigation station process using heterogeneous cuckoo search algorithm and fuzzy logic controller. IEEE Trans Industry Appl 55(1):976–990 9. Gorgij AD, Kisi O, Moayeri MM et al (2018) Hydraulic conductivity estimation via the AIbased numerical model optimization using the harmony search algorithm. Nord Hydrol 49(5– 6):1669–1683 10. Nan Z (2018) Trends in urban/rural inequalities in cardiovascular risk bio-markers among Chinese adolescents in two decades of urbanisation: 1991–2011. Int J Equity Health 17(1):101 11. Cai J, Coyte PC, Zhao H (2017) Decomposing the causes of socioeconomic-related health inequality among urban and rural populations in China: a new decomposition approach. Int J Equity Health 16(1):128 12. Zhou Y, Ying X (2017) Live broadcast classroom: a feasible solution for Chinese rural weak education. Int J Distance Educ Technol 15(3):31–46
The Construction of the Influence Model of Artistic Creativity Based on AMOS Data Analysis Weiying Wang, Lichu Tien, and Yuqi Du
Abstract In this study, the software Amos24.0, which explores the relationship between variables, was used for mathematical analysis. This research aims to explore and analyze the relationship between the innovation atmosphere of universities on the flow state and artistic creativity of art students. Convenience sampling was used to sample 1224 students from three universities in Jilin Province. Based on valid data received, SPSS and AMOS are used for statistical analysis, and the relationship between variables is explored through the structural equation model (S E M).Finally draw the conclusion of this research. Based on the research results, it proposes optimal strategies and suggestions for the cultivation of artistic creativity of art students in colleges and universities. Keywords Amos · Innovative atmosphere · Flow state · Artistic creativity
1 Research Motivation The main reason why art graduates from many comprehensive colleges and universities fail to meet the needs of employers is that students are not strong in innovation and creativity and artistic creativity (Shi Hui, 2017) [1]. Discussing how art education in my country’s colleges and universities can cultivate new era art design talents with sustainable development vision, literacy, knowledge and ability is an important part of the sustainable management of higher education (Cao Xuanwei, 2020) [2]. W. Wang (B) Educational Administration, Krirk University, Bangkok, Thailand e-mail: [email protected] Jilin Engineering Normal University, Changchun, Jilin, China L. Tien Department of International Trade, Overseas Chinese University, No.100, Chiao Kwang Rd, Taichung 40721, Taiwan Y. Du Industrial Design, Beihua University, Jilin, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_10
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This research starts from psychology and management, combines its own teaching management practical experience, integrates creativity component theory and immersion theory, and systematically constructs an equation model that affects the artistic creativity of art majors. Use Amos24.0, a software that explores the relationship between variables, to conduct mathematical analysis to explore how the atmosphere of innovation affects the artistic creativity of art majors through the state of flow.
2 Literature Review So far, there is no precise definition of artistic creativity. Through teasing out relevant literature, we find that the early researchers equated artistic creativity with the creative ability of artists with high artistic achievements. Gradually, Influenced by the general and domain-specific debates in the field of contemporary creativity, the connotations of artistic creativity have been further enriched. Artistic creativity refers to the performance of creativity in any aspect of art, including visual arts, music, literature, dance, drama, film, and mixed media (Alland, 1977). Therefore, artistic creativity refers to ability of all individuals to solve artistic problems and produce novel concepts or products with higher aesthetic values (Zeki, 2001; Sternberg & Lubart, 1996; Feist, 1998; Sternberg, 1999) [3]. It provides a detailed exposition of artistic creativity, and a great deal of research has been done on creativity and fruitful research results. Flow is an optimal state of mind (Csikszentmihalyi, 1975, 1988 [4], 1990, 1993; Jackson, 1992). Flow was first proposed by Mihalyi Csikszentmihalyi, The loss of self-awareness and the feeling that time flies when a person concentrates on something, A beautiful state of complete euphoria in. Csikszentmihalyi (1975) called it “Flow”. Amabile etal (1996), from a subjective point of view, associates the atmosphere of innovation with the perception of people within the organization. It is believed that the essence of the innovation atmosphere is the individual’s perception of the internal environment’s innovation orientation, innovation characteristics and the degree of innovation support, and it can positively promote creativity through the recipient’s psychological perception.
3 Research Methods, Procedures and Assumptions According to the purpose of the research and the literature review, this paper summarizes the results of many scholars, and as the basis of this research design and research framework, data collection is conducted through questionnaires to understand the relationship among the innovation atmosphere, flow state and artistic creativity of college art students in Jilin province, china.
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Fig. 1 Research framework diagram
3.1 Research Structure This research is based on Kurt Lewin’s field dynamics theory and Albert Bandura’s social learning theory and literature, the research framework is determined by combining the relationship between innovation atmosphere, flow state and artistic creativity as shown in Fig. 1:
3.2 Research Hypothesis Based on the review and summary of related theories and previous related empirical research in the literature review, this research puts forward specific hypotheses based on the purpose of this research, research questions, and research framework. The hypotheses of this research are described as follows: H1:The Innovation atmosphere has a positive impact on artistic creativity. H2:The atmosphere of innovation has a positive impact on the state of flow. H3: Flow state has a positive impact on artistic creativity. H4: Flow state plays an intermediary role in the influence of innovation atmosphere on artistic creativity.
3.3 Research Object According to the 2019 Statistical Bulletin of Education Development in Jilin Province, China, As of 2019, there are 62 ordinary colleges and universities, including 37 ordinary undergraduate colleges and universities (including 5 independent colleges), 25 ordinary junior colleges (higher vocational) colleges; 219,500 students are enrolled in ordinary undergraduates and junior colleges, and ordinary undergraduates and junior colleges are in There are 701,000 students in the school, an increase of 41,800 over the previous year (Jilin Provincial Department of Education, 2020) [5]. Due to the large number of mother populations, it is difficult to achieve random sampling. Therefore, this study considers purposeful sampling instead of
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random sampling (Yaling Sun, Lihui Luo, 2002) [6]. Convenience sampling is a purposeful sampling method based on the actual situation of the researcher in order to facilitate the research, which can facilitate and timely obtain the required research data (Shengchuan Lin, 2003) [7]. Therefore, this study adopts the convenience sampling method, A sample survey of students in X, Y, and Z universities in Jilin Province, Among them, College X is a qualified institution for the evaluation of the level of talent training in universities in Jilin Province, and is a key institution for higher professional talent training in Jilin Province; Y College is a provincial comprehensive key university and an excellent college for higher vocational talent training in Jilin Province; Z is a full-time undergraduate normal college that trains teachers for vocational technical secondary schools and vocational high schools; therefore, the three universities selected in this study are representative of samples. Questionnaire survey time (March 2021),With the help of instructors and teachers, the samples were all sent out online questionnaires to selected freshmen, sophomores, and juniors via WeChat. According to the researcher Minglong Wu (2010) [8] suggestion, For regional research samples, the average sampling number is about 1000 as the best. Therefore, when the questionnaire was distributed in this study, the questionnaire was distributed to 1230 students in the three grades of X, Y, and Z three universities. After the questionnaire was answered, 6 invalid questionnaires were excluded and 1224 valid questionnaires were returned, with a 99% recovery rate. Therefore, the effective formal sample of this study is 1224.
3.4 Research Tool This research adopts the questionnaire survey method, according to the foregoing literature review and related theories and research purposes, and after confirming the research framework and objects, the measurement tools suitable for this research are selected to carry out measurement. This questionnaire measurement tool consists of three scales: “Organizational Innovation Atmosphere Scale”, “Flow State Scale”, and “Art Creativity”. The selection of the three scales is described separately below. Organisational Innovation Atmosphere Scale This study uses the innovation atmosphere scale compiled by Qiu Hao Zheng et al. (2009) [9]. The scale consists of 35 topics and is divided into seven dimensions. In this study, the scale of < SFSS > was chosen by the scholar Liu Weinna (2010) [10]. Chart of Power of artistic creativity: K-DOCS can be used to measure creativity in specific areas as well as to observe and measure a common view of creativity (Kaufman, 2012) [11]. This study only explores the relative creativity of the artistic realm, so chooses the table of creative power in the field of art.
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4 Analysis of Results The study confirmed that the innovation atmosphere, flow state and artistic creativity variables were in accordance with the normal distribution, and the intrinsic validity and external reliability of the conceptual model were good. Structural model analysis could be carried out and relationships among the potential variables was verified.
4.1 The Effect Verification of the Direct Effect of Innovation Atmosphere on Artistic Creativity 4.1.1
Model Fit Graph
In this study, the theoretical model was constructed through the linear structural equation model, and the AMOS statistical software was used to verify the causal model. The analysis results are summarized as shown in Fig. 2. The influence coefficient of the innovation atmosphere on the path of artistic creativity is 0.662.
Fig. 2 Diagram of the relationship model between innovation atmosphere and artistic creativity
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Table 1 Estimated table of model parameters of the direct effect of innovation atmosphere and artistic creativity Parameter
Regression weighting coefficient
Standard error
t value
Error variance
linnovation atmosphere → Flow_state → Artistic creativity
0.662
0.252
19.924***
0.020
Innovation Atmosphere → Environmental Atmosphere
0.894
0.098
15.012***
0.017
Innovation atmosphere → learning and growth
0.936
0.049
19.472***
0.005
Innovation atmosphere → leadership effectiveness
0.810
0.140
15.070***
0.003
Innovation atmosphere → team operation
0.833
0.184
22.118***
0.006
Innovation atmosphere → resource provision
0.725
0.289
21.375***
0.009
Innovation atmosphere → working methods
0.755
0.170
22.854***
0.013
Innovation atmosphere → organizational philosophy
0.710
0.140
22.833***
0.007
Artistic creativity → painting
0.805
0.244
23.390***
0.006 0.013 0.014
Artistic creativity → creation
0.884
0.166
18.132***
Artistic creativity → taste
0.766
0.262
12.281***
Note
*p
4.1.2
< 0.05;
** p
< 0.01;
*** p
< 0.001
Violation Estimation Test
The coefficients of this study are shown in Table 1: The error variance of the model of the direct effect of innovation atmosphere on artistic creativity is between 0.003 and 0.020, Are all positive; The standardized weighted regression coefficient is between 0.622 and 0.936 and no greater than 0.950;The standard error is between 0.049 and 0.289, and the t-values are all significant, which proves that there is not much standard error. Therefore, there is no violation of the estimation in this model.
4.1.3
Validation of Model Suitability
In the verification of the overall model fit, Absolute fit index is used to determine the degree to which the global model can predict the observed variables or correlation matrix; The purpose of the value-added adaptation verification is to compare a more rigorous or layered baseline model with the theoretical model and measure the degree of its adaptation improvement ratio; Simple and effective fit test is used to present estimated coefficients that need to achieve a certain level of model fit (Fangming
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Table 2 Summary of the degree of adaptation of the innovation atmosphere to the mode of artistic creativity Statistical inspection and quantification Absolute fit index
Incremental fit index
Streamlined fitness index
Standard value
Identification result
GFI
More than the 0.900
0.887
AGFI
More than the 0.800
0.817
RMR
Less than 0.080
0.023
NFI
More than the 0.900
0.919
NNFI
More than the 0.900
0.897
CFI
More than the 0.900
0.922
RFI
More than the 0.900
0.908
IFI
More than the 0.900
0.923
PNFI
More than the 0.500
0.695
PGFI
More than the 0.500
0.548
Huang, 2004) [12]. A summary of the suitability of innovation atmosphere to the mode of artistic creativity is shown in Table 2. As shown in Table 2, In terms of absolute fitness index, GFI = 0.887, Approximately reach the level of adaptation, AGFI = 0.817, RMR = 0.023, both reach the level of adaptation. Therefore, in terms of absolute adaptation index, the mode adaptation is good. In terms of value-added adaptation indicators, all indicators except for NNFI (TLI) = 0.897, which approximately meets the appropriate standard, NFI = 0.919, CFI = 0.922, RFI = 0.908, IFI = 0.923, all index values Reached above 0.900, all meet the appropriate standards, In terms of the value-added adaptation index, the mode adaptation is ideal. In terms of simple and effective adaptation indicators, PGFI = 0.548 and PNFI = 0.693, both greater than 0.500, showing that the mode adaptation is still good, which meets the requirements of mode simplification.
4.1.4
Path Relationship Test
Known from Tables 1 and 3, The path coefficient of innovation atmosphere to artistic creativity is 0.662, t is 19.924,The path coefficient is significant, so hypothesis 1 of this research is established, which means that the higher the degree of perception of the innovation atmosphere by college art students, the stronger the artistic creativity. Table 3 Summary of the verification of the relationship between innovation atmosphere and artistic creativity path Hypothesis
path
H1
Innovation atmosphere → artistic creativity
Hypothetical relationship
Path value
Hypothesis
Positive
662***
Established
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Fig. 3 The relationship model diagram of innovation atmosphere, flow state, and artistic creativity
4.2 The Direct Effect of the Innovation Atmosphere on the Flow State and the Direct Effect of the Flow State on the Artistic Creativity 4.2.1
Model Fit Graph
In this study, the theoretical model was constructed through the linear structural equation model, the AMOS statistical software was used to verify the causal model, and the analysis results were organized. As shown in Fig. 3.
4.2.2
Violation Estimation Test
It can be seen from Table 4: The error variance of the overall function model of innovation atmosphere, flow state, and artistic creativity is between 0.003 and 0.020, all of which are positive;The standardized weighted regression coefficient is between 0.288 and 0.935 and no greater than 0.950;The standard error is between 0.049 and 0.400, and the t-values are all significant, which proves that there is not much standard error. Therefore, there is no violation of estimates in this model.
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Table 4 Summary table of parameter estimation of the overall model of Innovation atmosphere, flow state, and artistic creativity Parameter
Regression weighting coefficient
Standard error
t value
Error variance
Innovation atmosphere → Artistic creativity
0.081
0.400
20.339***
0.020
Innovation atmosphere → Flow 0.872 state
0.253
14.587***
0.006
Flow state → Artistic creativity 0.670
0.074
14.460***
0.014 0.005
Innovation atmosphere → Atmosphere
0.904
0.089
19.287***
Innovation atmosphere → Learning and growth
0.935
0.049
16.206***
0.003
Innovation atmosphere → Leadership effectiveness
0.798
0.148
22.471***
0.007
Innovation atmosphere → Team operation
0.829
0.188
21.927***
0.009
Innovation atmosphere → Resources
0.732
0.291
23.184***
0.013
Innovation atmosphere → Way 0.757 of working
0.169
23.044***
0.007
Innovation atmosphere → Organization Philosophy
0.709
0.140
23.523***
0.006
Flow state → enjoy
0.834
0.168
21.578***
0.008 0.015
Flow state → time
0.711
0.350
23.339***
Flow state → awareness
0.768
0.315
22.790***
0.014 0.009
Flow state → control
0.825
0.191
21.805***
Flow state → concentrated
0.795
0.186
22.426***
0.008
Flow state → Feedback
0.849
0.158
21.230***
0.007 0.008
Flow state → aims
0.826
0.169
21.778***
Flow state → Fusion
0.707
0.372
23.331***
0.016
Flow state → balance
0.746
0.281
22.983***
0.012 0.013
Artistic creativity → painting
0.808
0.240
18.330***
Artistic creativity → creation
0.881
0.171
13.167***
0.013
Artistic creativity → taste
0.766
0.262
19.930***
0.013
Note
*p
4.2.3
< 0.05;
** p
< 0.01;
*** p
< 0.001
Overall Model Fitness Verification
It can be seen from Table 5, In terms of absolute fitness index, GFI is 0.842, AGFI is 0.798, and RMR is 0.025, both of which are approximately at the level of fitness. Therefore, in terms of absolute adaptation index, the mode adaptation is good. In
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Table 5 Summary table of suitability of innovation atmosphere, flow state, and overall mode of artistic creativity Statistical inspection and quantification Absolute fit index
Incremental fit index
Streamlined fitness index
Standard value
Identification result
GFI
More than the 0.900
0.842
AGFI
More than the 0.800
0.798
RMR
More than the 0.080
0.025
NFI
More than the 0.900
0.896
NNFI (TLI)
More than the 0.900
0.889
CFI
More than the 0.900
0.903
RFI
More than the 0.900
0.881
IFI
More than the 0.900
0.903
PNFI
More than the 0.500
0.781
PGFI
More than the 0.500
0.660
terms of value-added adaptation indicators, NFI is 0.896, NNFI (TLI) 0.889, CFI is 0.903, RFI is 0.881, IFI is 0.903, The index values are all approximately above 0.900, all reaching the appropriate standard, showing that the mode adaptation is relatively ideal in terms of the value-added adaptation index. In terms of simple and effective adaptation indicators, PGFI is 0.660 and PNFI is 0.781, both are greater than 0.500. The display mode adaptability is still good, which meets the requirements of mode simplification.
4.2.4
Path Relationship Test
Known from Tables 4 and 6, The path coefficient of innovation atmosphere to flow state is 0.872, t is 14.587, and the path coefficient is significant. Therefore, the hypothesis 2 of this research is established, It means that the higher art students perceive the atmosphere of organizational innovation, the easier it is for the flow state to reach the flow state; The path coefficient of flow state to artistic creativity is 0.670, t is 14.460, and the path coefficient is significant. Therefore, the hypothesis 3 of this research is established, It means that the easier it is for college art students to enter the state of flow, the stronger the artistic creativity. Table 6 Innovation atmosphere, flow state and artistic creativity direct effect verification summary table Hypothesis
Path
Hypothetical relationship
Path value
Hypothesis
Innovation atmosphere → flow state
Positive
0.872***
H2
Established
H3
Flow state → artistic creativity
Positive
0.670***
Established
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The direct effect of the innovation atmosphere on the flow state and the direct effect of the flow state on the artistic creativity.
4.3 Mediation of Flow State This research uses structural equation modeling (SEM) to explore the relationship between variables. When the relationship between variables is significant, it means that there is a direct effect between the variables; when it is not significant, it means that there is no direct effect between the two variables; In addition to direct effects between two variables, there may also be indirect effects, that is, an intermediate variable exists between the two variables, provided that the direct effects between the variables should be significant (Haozheng Qiu, 2010) [13]. Baron et al. (1986) found that when the independent variable and the intermediate variable are input into the regression model at the same time, the predictive effect of the intermediate variable is significant, and the predictive effect of the independent variable decreases, which is a partial intermediate; If the predictive effect of the independent variable disappears, it is a complete mediation. Baron and Kenny (1986) believed that the test of intermediary effect should be verified by three regression models, First is that the independent variable must be able to significantly predict the dependent variable; Second, the independent variable must be able to significantly predict the intermediate variable; Third, the intermediary variable must be able to significantly predict the dependent variable. It can be seen from Table 1 and Fig. 2,When the effect of innovation atmosphere on artistic creativity is tested separately, innovation atmosphere has a positive and significant predictive effect on artistic creativity(β = 0.662, t = 19.924); From Tables 4 and 6 and Fig. 3, it can be seen that when the flow state is introduced into the model, the innovation atmosphere has a positive and significant predictive effect on the flow state(β = 0.872, t = 14.587);Flow state has a positive and significant predictive effect on artistic creativity(β = 0.670, t = 14.460);However, the effect of innovation atmosphere on artistic creativity is not significant anymore(β = 0.081, t = 0.925). With reference to Baron et al. (1986)’s judgment on the mediating effect, this study believes that the flow state plays a completely mediating role in the relationship between innovation atmosphere and artistic creativity.
5 Conclusion and Suggestion Through correlation analysis and intermediary model verification, the research hypotheses presented in this study are analyzed and verified one by one. The results of the research hypotheses are collated, and the conclusions and recommendations are as follows.
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5.1 Innovation Atmosphere Has a Significant Positive Impact on Artistic Creativity The results of this study indicate that the perception of artistic creativity by art students in universities has significant positive effects on the improvement of creativity, which is consistent with the findings of the study that there is a significant relationship between organizational climate and artistic creativity as indicated by Fleury, Maria, Tereza, Leme (2009) and Mesbah, Marjan, Mostaghimi, Mahmod Reza (2014).
5.2 The Atmosphere of Organizational Innovation Has a Positive Impact on the State of Flow The results of this study show that college art students’ perception of innovation atmosphere significantly positively affects their artistic creativity. Compared with Mosing, Magnusson, Pedersen, Nakamura, and Madison (2012) in different fields, specific environmental factors are important to produce flow state condition. The higher the degree of college art students’ perception of the innovation atmosphere, the easier it is to reach the state of flow.
5.3 Flow State Has a Positive Impact on Artistic Creativity This research is similar to the research of Csikszentmihalyi and Hunter, 2003, which shows that the easier it is for college art students to enter the state of flow, the stronger the artistic creativity.
5.4 Flow State Plays a Completely Mediating Role in the Influence of Innovation Atmosphere on Artistic Creativity The school’s innovation atmosphere significantly affects the artistic creativity of art students. When the variable of flow state is introduced, the effect of the school’s innovation atmosphere on artistic creativity disappears. The flow state plays a completely intermediary role in the relationship between innovation atmosphere and artistic creativity. In a word, the creation of the innovation atmosphere in universities can significantly enhance the artistic creativity of art students. The organizational creativity
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atmosphere is not only the antecedent factor of students entering the flow state, but also an important factor for artistic creation. Acknowledgements The author would like to thank this conference, And the general topics of the “13th Five-Year Plan” of Educational Science of Jilin Province in 2020, and the research on the creativity of students majoring in art design in applied universities. Project approval number GH20325.
References 1. Hui S (2017) Analysis on the current situation and reform of art design education in private colleges and universities. Art Criticism 22:124–125 2. Cao X (2020) Challenges and countermeasures of sustainable management education in Chinese universities. Sustain Dev Econ Guide 12:35–37 3. Sternberg RJ (1999) Darwinian creativity as a conventional religious faith. Psychological Inquiry 4. Csikszentmihalyi M, Csikszentmihalyi I (1988) Optimal experience: psychological study of flow in consciousness. Cambridge University Press, New York 5. Jilin Provincial Bureau of Statistics(2020) Statistical Bulletin of Jilin Province’s National Economic and Social Development in 2019 http://tjj.jl.gov.cn/tjsj/qwfb/202004/t20200403_ 7024694.html 6. Yaling S, Lihui L (2002) The sampling problem of educational research—purposeful sampling. J Yunnan Normal Univ (Philosophy and Social Sciences Edition) 3:127–129 7. Shengzhuan L (2003) Educational research method—all-round integration and analysis. Psychology Press, Taipei 8. Wu M (2010) Questionnaire statistical analysis practice: SPSS operation and application. Chongqing University Press, Sichuan 9. Qiu Z, Chen Y, Lin B (2009) Development and reliability and validity evaluation of organizational innovation climate scale. J Testing 1:69–97 10. Liu W (2010) “Simplified state fluency scale” and “Simplified trait fluency scale” Chinese version revised. Sports Sci (12):64–70 11. Kaufman (2012) Psychology of aesthetics. Creativity, and the arts, vol 6, no 4, pp 298–308 12. Ming HF (2004) Structural equation modeling: theory and application, 3rd edn. Wu Nan Books, Taipei 13. Qiu H (2010) Quantitative research and statistical analysis: SPSS (PASW) data analysis example analysis. Chongqing University Press, Chongqing
The Construction of Mental Health Prediction Model Based on Data Mining Technology Yu Wu, Qiuyu Ji, Ameng Zhao, Hong Li, and Yan Zhang
Abstract With the rapid development of modern society, the problem of people’s mental health has become more and more prominent, and it has become a key course that cannot be ignored in our lives. Paying attention to people’s mental health, solving people’s physical and mental health problems, and building an education curriculum system for people’s physical and mental health is an important research topic related to the harmonious development of society, and it is also an ideal and belief to promote quality education. At present, our country’s evaluation of people’s mental health reflects the inadequacy of our country’s mental health education. Predicting, categorizing and reflecting on people’s mental health will help us further strengthen our mental health education and construct our mental health education. This paper studies the construction of a mental health prediction model based on data mining technology, and summarizes related factors affecting mental health on the basis of relevant literature data, as the independent variables predicted by the model below, and then analyzes the data mining technology for the model prediction experiment in the following text has laid the groundwork. According to the construction of the mental health prediction model based on data mining technology in this paper, the test results show that in terms of emotional management, they have better adaptability in the face of stress, healthier. In addition, because society has different expectations of male and female roles, women’s pressure coefficient has reached 2.01. This shows that women must work harder to succeed in society, and they will face greater pressure, that is, they are more prone to psychological problems.
Y. Wu · Y. Zhang School of Nursing, Qiqihar Medical University, Qiqihar, Heilongjiang, China Q. Ji (B) School of Pathology, Qiqihar Medical University, Qiqihar, Heilongjiang, China A. Zhao Student Work Department, Qiqihar Medical University, Qiqihar, Heilongjiang, China H. Li School of General Practiceand Continuing Education, Qiqihar Medical University, Qiqihar, Heilongjiang, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_11
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Keywords Mental health · Predictive model · Model construction · Health status
1 Introductions With the continuous improvement of our quality and level of life, the call to improve their subjective well-being has become stronger and stronger [1, 2]. People are constantly striving to pursue their physical and mental health, and they have gradually begun to notice your own psychological and mental health [3, 4]. Due to the dual pressures of modernization and fast-paced life and job hunting, many young people have fallen into a sub-healthy state [5, 6]. Therefore, the establishment of a set of research on mental health prediction models has become more and more important [7, 8]. In the research on the construction of the predictive model of mental health, some researchers pointed out that in the predictive influence model of mental health on suicide tendency, the direct effect of each dimension on suicide tendency is also very high [9]. The top three are: depression, anxiety and psychosis. The effects exceeding 0.8 are: depression, anxiety, psychosis, obsessive–compulsive symptoms and interpersonal sensitivity [10]. There are also studies that propose that in the process of suicide prevention work, special attention should be paid to the depression and anxiety of college students, and special attention should be paid to college students who are prone to compulsive behavior [11]. In addition, interpersonal sensitivity is also an important psychological dimension that plagues college students, and it is also an important indicator of predicting their suicidal tendency. Active attention should be paid to those students who have tense interpersonal relationships and lack peer support. These students are often more depressed and lack friends. There is nowhere to vent bad emotions, and under certain circumstances, it may go to extremes. Furthermore, for students with psychosis, the diagnosis must be made in time. Although the proportion of such students is small, they are a psychological crisis intervention object that cannot be ignored [12]. This article explores the construction of a mental health prediction model based on data mining technology. Based on the literature research method, it summarizes the factors that affect mental health first, and then understands the data mining technology. The mental health status prediction model is constructed, and finally the model is tested, and relevant conclusions are drawn based on the results.
2 Research on Mental Health Status and Data Mining Technology 2.1 Factors Affecting Mental Health (1)
Interpersonal relationship
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Good interpersonal relationship has a very important influence and significance for the development of students and the process of socialization, and it is also a prerequisite for us to maintain a good psychological condition. However, due to the contradiction between the complexity of our interpersonal relationship and social environment and the relatively innocent personality of most young people, people are often frustrated in daily interpersonal communication. Most people have no experience in interpersonal communication, have no courage to express themselves in a public place, have not actively participated in the skills of interaction with other people, and have not faced various activities, full of interest and fear of failure hinders a good interpersonal communication cycle, which forms over time. Some people have difficulty in making good friends or lack of good friends due to problems in their understanding of themselves and others, while others are due to character factors, which bring contradictions and conflicts in their interactions. Compared with face-to-face communication, people are more willing to communicate in the virtual environment of the Internet, addicted to the virtual world, and isolated from the real world. If things develop to this point, it will have a huge impact on people’s knowledge, emotions and mental health. Educational background
(3)
In modern society, it is a society that depends on academic qualifications. If you enter the society when your academic qualifications are relatively high, you will have a strong sense of superiority, and if your academic qualifications are low, in this society, whether you are looking for a job, or else, it will produce some inferiority complex. In severe cases, it will cause people to become more negative and lead to psychological unhealthy. People with high academic qualifications have more advantages in this society. Their psychology is not easy to produce styles. People with low academic qualifications have to face more setbacks. When people have more people, they will be contemptuous. This will lead to unhealthy people in the long run. Employment
(4)
Most people have great anxiety about their jobs. They worry about whether they can find a job, whether they can find a suitable and ideal job. Especially those who are not good enough are more anxious. This will bring them a great psychological burden, especially when they encounter a series of failures or job vacancies, they will be very anxious, showing anxiety and unable to control themselves. Such a mentality will make them irritable, and they will not be able to truly show self-confidence in front of their employers and stay away from success. Ability to withstand setbacks
With the rapid development of society, people are under increasing pressure, which leads to some people giving up life before entering higher education, and even more extreme suicidal behavior; some people think that the effect of hard work for a period
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Data selection
Data preproccssing
Data coaversion
Derive knowledge
Data mining
Fig. 1 Data mining flowchart
of time is very poor, he result is not significant, that is, loss of confidence and selfesteem. This will lead to loss of confidence and interest in life, and lead to the wrong goal shift. Some people are under pressure in interpersonal relationships, but instead of adopting a positive attitude to regulate emotions and solve problems, they are blindly distressed. Apart from disappointment, some people complain, mourn, hate, and criticize.
2.2 Data Mining (1)
Data mining environment
(2)
Data mining refers to the complete process of extracting previously unknown, effective and practical information from many databases, and then using this information to make decisions or improve knowledge reserves. Data mining process
The application of data mining technology in different fields, according to the characteristics of field data, the mining process is different. The process of data mining itself can be divided into five stages: data collection, data preprocessing, data conversion, data mining and knowledge generation. The data mining process is shown in Fig. 1.
2.3 Predictive Analysis Algorithm Given that the observation set X = {x}, i = 1,2…n, each observation value is a d-dimensional real vector, and k-means clustering is to divide the observation set X of the observation set X into the k set C = {c}, k = 1,2,..K, and kSn, so that the sum of squares in the group is the smallest. Let µh be the mean value of c, then the variance of each sample data and the mean value in each category is defined as: J (ck ) =
xi ∈ck
xi − µk 2
(1)
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The goal of this algorithm is to minimize the variance of all K categories: J (c) =
k k=1
2
xi ∈ck
xi − µk
(2)
(1)
Clustering algorithm based on partition.
(2)
The partition-based clustering algorithm divides a data set of a given size n into partitions k, where each partition represents a cluster, and k ≤ n. This type of algorithm usually randomly selects the cluster center k, and then assigns the remaining objects to the cluster represented by the nearest cluster center according to a division criterion (such as a distance-based distance function). Then, iteratively find new cluster centers by optimizing the object function, and redistribute the remaining objects until the cluster centers no longer change or the number of iterations reaches the limit. Hierarchical clustering algorithm The hierarchical grouping algorithm divides the existing data set into hierarchical clusters. This algorithm can be divided into two methods: aggregation and separation. Compression and separate hierarchical grouping use bottomup and top-down strategies respectively to organize objects into a hierarchical structure. The aggregation method first treats each data object as a separate class, and then merges small classes into larger classes until the number of classes is met or other termination conditions are met. The separation method first treats all data objects as one class, and then subdivides the large class into many small classes in successive iterations until the termination condition is met. Different algorithms will choose different similarity or dissimilarity measures as the basis for merger or disintegration.
(3)
The basis of merging or splitting in hierarchical clustering algorithm is the similarity or dissimilarity between groups. This measurement criterion can be quantified as the distance between groups. Commonly used distances between clusters include minimum distance, maximum distance, mean distance, and average distance. The distance is defined in the following formula, where |pip;| is the distance between data objects pi and pj, m is the mean value of C;, and n is the number of objects in the group Gi. Clustering algorithm based on density
It is difficult to find non-spherical groups in partition and hierarchical methods, while density-based methods can find clusters of any shape. Density-based methods use object density limits as the grouping criteria. High-density areas form clusters, and low-density areas form clusters or intervals between extreme values, and are not sensitive to abnormal data.
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3 Construction of Mental Health Prediction Model Based on Data Mining Technology 3.1 Data Source The data in this article comes from the information management system of Ruige’s psychological education in universities. The system tested and researched the SCL90 basic information questionnaire for 376,016–19 years old. Among them, the evaluation results of SCL-90 are divided into four levels: health status, unhealthy status, mental disorder and mental illness. Research information includes academic qualifications, employment status, interpersonal communication and stress status.
3.2 Evaluation Index The pros and cons of the prediction results use MSE as a metric,
3.3 Prediction Steps (1)
(2) (3)
Normalize the independent variables, and optimize the kernel function parameters with n-fold cross-validation according to the size of the training sample; Under the optimal combination of kernel function parameters, model the training samples by svmtrain; Use the training model to predict the test sample through svmpredict.
3.4 The Order of the Degree of Influence of the Independent Variable Factors on the Dependent Variable In order to determine the degree of influence + degree of the four influencing factors of education status (x1), employment status (x2), interpersonal communication (x3) and stress status (x4) on people’s mental health status (Y), the following method is used to determine the influence of the influencing factors sort by importance: (1) (2)
Take all the data as a whole data, and use the prediction algorithm to calculate the full value mse of all influencing factors as mse0. Delete these four factors separately, use the obtained data as a number set, and use the algorithm to calculate the mse value of these number sets, which are MSE1, MSE2, MSE3, and MSE4.
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Calculate the absolute value ImseI of the difference between MSE1, MSE2, MSE3, MSE4 and MSE0 respectively. The larger the ImseI, the greater the impact of the corresponding influencing factor on the mental health of students, and the more obvious its importance.
In this paper, through the above methods, the order of importance of the influencing factors is as follows: x 4, x2, x3, x1.
3.5 Prediction Model Establishment According to the above steps, a model for predicting mental health status is established. With the existing 3760 known data as the training set, only questionnaire surveys will be used in the future to investigate people’s educational background (x1), employment status (x2), and interpersonal communication x3), the current state of stress (x4), these 4 pieces of information can more accurately predict people’s mental health status.
4 Detection of Predictive Models In order to examine the actual prediction and analysis capabilities of the model, this article will use 3760 known data and 3000 of them as the training set, 760 data as the measurement set, and independent prediction methods (that is, when we are in when predicting the i-th sample, neither itself nor subsequent samples may participate in the training of the model. When predicting the i + 1th sample, the i-th sample (may need to be added to a training set) Simulation and simulation. The experimental results are shown in Table 1. It can be seen from Fig. 2: In terms of emotional management, they have better adaptability and mental health in the face of stress. In addition, because society has different expectations of male and female roles, women’s pressure coefficient has reached 2.01. This shows that women must work harder to succeed in society, and they will face greater pressure, that is, they are more prone to psychological problems. Table 1 Predictive model detection Learning status
Employment status
Interpersonal communication
Pressure status
women
1.71
1.97
1.72
2.01
male
1.61
2.03
2.03
1.72
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2.03 2.03
2.01
1.97 1.72
1.61
1.72
amout
1.71
women Learning Status
male Employment status gender`
Interpersonal communication
Pressure status
Fig. 2 Predictive model detection
5 Conclusions This article focuses on the construction of a mental health prediction model based on data mining technology. It can be seen from the experimental results that it is precisely because of ignorance of psychological problems that students do not understand psychological knowledge, and do not know how to cope and adapt when faced with pressure or failure, leading to frequent occurrence of student psychological problems. Therefore, it is recommended to set up mental health courses or special lectures for college students to let everyone know more about mental health and psychological counseling. Acknowledgements This study was supported by Heilongjiang Provincial Institutions of Higher Learning in 2020 Basic research expenses for research projects (2020-KYYWF-0056)
References 1. Wang H, X Liu (2018) Psychological problems and countermeasures of boarding students in rural primary schools——based on the investigation of County L in Shaanxi Province. Asian Agricul Res 10(07):95–99+104 2. Xu Z (2018) Analysis on the causes and countermeasures of the psychological pressure of football referees. IPPTA: Quart J Indian Pulp Paper Tech Assoc 30(7):130–134 3. Research on Judicial Practice of School Physical Education Injury Cases in Jiangxi Province— —Statistical Analysis Based on the Big Data of China Judgement Online (2020). In: E3S Web of conferences, vol 214(6). pp 01018 4. Fernández-Rivas M, Espada-Mateos M (2019) The knowledge, continuing education and use of teaching styles in physical education teachers. J. Human Sport Exercise14:99–111 5. Xu Z, He L (2020) Analysis of the causes and countermeasures of low passenger flow effect of tram lines in China. IOP Conf Ser: Earth Environ Sci 587(1):012122 (8pp)
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6. Yano Y, Ning H, Reis JP et al (2016) Blood pressure reactivity to psychological stress in young adults and cognition in midlife: the coronary artery risk development in young adults (CARDIA) study. J Amer Heart Assoc 5(1):e002718 (2016–01–13) 7. Ogasa K, Nakamoto H, Ikudome S et al (2016) The effects of psychological pressure on perception and motor planning. Taiikugaku Kenkyu 61(1):133–147 8. Zhang HL, Jin R, Zhang Y et al (2020) A Public psychological pressure index for social networks. IEEE Access 8:23457–23469 9. Vandebroek TP et al (2018) Modeling the effects of psychological pressure on first-mover advantage in competitive interactions: the case of penalty shoot-outs. J Sports Econ 19(5):725– 754 10. Carlo R, Mariarosaria S, Demartini E et al (2021) Psychological pressure and changes in food consumption: the effect of COVID-19 crisis. Heliyon (2):e06607 11. Bourassa KJ, Hasselmo K, Sbarra DA (2016) Heart rate variability moderates the association between separation-related psychological distress and blood pressure reactivity over time. Psychol Sci (0956–7976) 27(8):1123–1135 12. Tanaka Y, Shimo T, Nosaka Y (2016) Postural control when standing on an unstable surface under psychological pressure: evaluation from lower limb muscular activity and center of pressure. Taiikugaku Kenkyu 61(1):289–300
Influence of Big Data Statistical Analysis Technology on Informational Learning Mode Lei Yu
Abstract With the rapid development and popularization of information technology, especially the wide application of Internet technology, mobile communication technology, artificial intelligence technology, big data processing technology and intelligent education technology in the field of education, information education is becoming the main way to cultivate educational talents. This paper mainly studies the education informationization and the talent friend mode reform under the network informationization mode. In this paper, two classes of a senior high school in this city are selected as teaching experimental objects. Through statistical analysis of their results before and after the implementation of the informationized teaching mode, the effective ways to improve students’ performance under the informationized environment are discussed. According to the experimental results, this model can improve students’ academic performance, narrow the difference between students’ academic performance, stimulate students’ interest in learning, and cultivate students’ independent learning ability, so that all students can achieve development. Keywords Network information · Education information · Talent training mode · Teaching effect
1 Introduction With the development of the society, the division of labor is becoming more and more detailed, and the society needs more and more high-level application-oriented talents who pay equal attention to knowledge and skills, instead of just staying in the knowledge level. Especially compared with the talent training mode and output direction of developed countries in the world, China is particularly short of compound applied and practical talents [1]. In recent years, the rapid development of information technology has been widely used in various fields of the society, greatly promoting the L. Yu (B) Zaozhuang Vocational College of Science and Technology, Tengzhou 277599, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_12
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progress and development of the society and the improvement of people’s living standards. As a powerful source of promoting educational informatization and realizing educational equity in our country, modern educational technology with information technology as its core shoulders heavy responsibility. The application of information technology in the field of education has shortened the gap between time and space, transformed the roles of teachers and students, and posed new challenges to the traditional teaching philosophy, teaching mode and teacher-student relationship. Since the twenty-first century, the concrete work of information technology and basic education in our country has been integrated deeply, and the pace of basic education reform has also been accelerated. At present, China’s educational guidelines and policies are actively and effectively exploring the individualized development model of students adapting to the information age education, and improving students’ innovation consciousness and creativity [2, 3]. The ultimate goal of talent training is to let talents enter the enterprise and create value for the society. The needs of enterprises should become an important reference for schools to formulate and implement talent training programs [4]. Many researchers at home and abroad have been doing a lot of research on the cultivation of professional talents. With the theme of “Talent Training Program”, a general search in ERIC (Education Resources Information Center) in the United States shows that from the beginning of the twenty-first century to the present, There are 847 search records in foreign research on “Talent Training Program”. There are 170 problems in the “Computer Application Specialty Talent Training Program” [5]. From the above data, it is not difficult to see that foreign countries have attached great importance to the formulation of school talent training programs since a very early time, and the academic circles have conducted extensive and in-depth studies on this [6]. This research is based on the theory of instructional design, education information theory, talent training mode reform, design high school based on the information mode of talent training mode, so as to improve the way of teaching and learning, promote the improvement of student industry that estimate level, narrow the difference between students’ performance, stimulate students’ interest in learning.
2 Educational Informatization and Talent Training Mode in the Information Environment 2.1 Information Technology Environment (1)
The connotation of information environment Informationization environment (also called information technology environment) refers to all the hardware and software related to the computer constitute the environment. Informationization environment within the scope of
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education also refers to the conditions for the application of informationization technology in education, and the sum of informationization-based places, tools, resources and consciousness environment used in this process [7, 8]. In education, the information environment is mainly embodied in the multimedia classroom and various information technology resources used in education. The role of information technology in education
With the development of information technology, all kinds of education personnel training models and teaching methods are also in innovation, and a new type of education system constructed by intelligent learning and interactive learning has been formed. The personalized learning of students supported by information technology can promote the all-round development of each student [9]. MOOC can enable students to acquire high-quality educational resources to broaden their horizons. The learning methods of MOOC are diversified and interactive. While learning knowledge actively and joyfully, learners can also form lasting, stable and long-term learning behaviors. This form can better give play to students’ subjective initiative, improve learning efficiency, and improve the divergent thinking. Therefore, teachers are actively exploring the mixed teaching mode combining MOOC teaching and traditional teaching, implementing the integration of information technology and disciplines, and promoting the learning effect [10]. Virtual reality learning environment has a sense of presence and immersion, so that students in the learning process of concentration and investment are improved. Through virtual reality learning environment, students can improve their mastery of knowledge and creativity, and improve their skill acquisition [11]. In teaching, 3D printing technology can improve students’ visual spatial ability and simplify abstract and complex knowledge, so as to improve students’ ability of analysis, organization and understanding. Micro-course is highly praised for its short and concise advantages, which can meet the needs of learners in mobile learning and ubiquitous learning and improve learning efficiency. The application of artificial intelligence in education mainly includes four application forms: intelligent tutor system, automatic evaluation system, educational game and educational robot. The intelligence, automation, individuation, diversification and collaboration of artificial intelligence provide good conditions for students’ knowledge construction activities. With the rapid integration of information technology into various fields, intelligent education has emerged as The Times require. From digital campus to smart campus is the inevitable result of the development of education. The goal of wisdom education is to endow students with wisdom by means of information technology. Intelligent education environment has the technical characteristics of tracking learning process and identifying learning situation, and can provide appropriate learning resources and tools to record learning process and evaluate learning results. Through the creation of situations to stimulate students’ enthusiasm for learning, improve their practical ability, and effectively solve and analyze problems in cooperation and communication. Information teaching modes such as online teaching and mixed teaching have
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the characteristics of diverse paths and diverse evaluations, which are conducive to the cultivation of students’ creative ability [12].
2.2 Reform of Talent Training Mode Under the Information Environment (1)
(2)
(3)
Establishing the school-enterprise cooperation system According to the professional teachers, training conditions, students and geographical location and other conditions, the appropriate industry elites, enterprise experts and the school’s professional backbone teachers are invited to form the school’s long-term professional guidance committee. Participate in the formulation of the plan, the revision of the later stage and the phase supervision during the operation of the plan. To the greatest extent to ensure that the formulation and implementation of the program and enterprise talent needs. Improve the guarantee and supervision system of teaching process The implementation process of teaching is the last barrier to reflect the value of the professional talent training program. The implementation subject of the program is the professional and professional teachers, so the main body to supervise the implementation process of the program is still in the school. It is suggested that schools should establish logistics support systems such as teacher training, program revision and practical training equipment, as well as relevant systems for teaching supervision such as classroom management, teacher evaluation and teacher incentive, and strictly implement them. Teachers and classrooms should be urged to operate according to the talent training programs that meet the needs of enterprises to the greatest extent. At the same time, the establishment of the system is not once and for all, need to be updated or added when necessary according to the actual situation. For example, in the information age, teachers are required to use information methods for teaching, so schools should have corresponding teacher training and equipment support. At the same time, the assessment system for teachers should also abandon the assessment of some traditional teaching methods that are no longer used, and increase the assessment system for the use of information means. Establish the scheme revision system Nowadays, the demand for talents is changing day by day. Therefore, the school plan cannot be used all the time after it has been formulated. It is necessary to adjust the plan according to the passage of time. If the knowledge point involved in some courses has been eliminated by the work, it should be replaced according to the needs of the work; if some jobs have been replaced by robots, or with the progress of science no longer exist, then the professional job group should be adjusted in time.
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Forming professional full participation mode
The implementation of the program is ultimately carried out by professional teachers. Only when teachers truly understand the training objectives of the profession and the capacity requirements of the post, can they better improve their skills to adapt to the operation of the program. Only in the course teaching can we know the posts corresponding to this course, the prelude courses and the follow-up courses, and what knowledge has been taught to students. Only in this way can we make a better teaching plan, and pass the relationship between the courses and the corresponding jobs to students, so that students can have a more clear learning objective. Therefore, the formulation and modification of the program requires the participation of all professional teachers, even if they cannot participate in the whole process, partial participation is also good. At the same time, it is suggested that the person in charge of the specialty convey the content of the professional talent training program to every professional teacher in time, including part-time and part-time teachers outside the system. Only in this way can we work together to implement a better plan.
3 Teaching Experiment of Talent Training Model in Informatization Mode 3.1 Effect Inspection Method Effect test to select two natural classes (two class results, the number of close close), in the process of experiment, the same teacher at the same multimedia network classroom, respectively in the two class teaching content is the same, different teaching strategies of teaching, the control class kept the traditional teaching, experiment class teaching mode of talent training mode based on informatization.
3.2 Experimental Subject Two parallel classes of the same grade with similar grades were selected from ten classes in the first year of a high school in this city. One class was randomly selected as the experimental class and the other class as the control class. There were 43 students in the experimental class, including 22 boys and 21 girls. There were 46 students in the control class, including 24 boys and 22 girls.
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3.3 Knowledge Level Test Paper Before implementation, students in the experimental class and control class were tested. The pre-test volume of information technology knowledge (see Appendix IV) was developed according to the first volume of information technology textbook of Grade 8, which mainly included data related content, Word and Excel knowledge and skills, and was used to measure the initial level of students. The next test volume is titled Data Management and Data Security (see Appendix V).
3.4 Data Statistics In this paper, SPSS20.0 data statistics software was used to code and input the valid data obtained from the knowledge level test paper, and further reliability and validity test, descriptive statistics, t-test, correlation analysis and regression analysis were carried out. The t-test formula used in this paper is as follows: t= t=
X −μ √σ x n−1
x1 − x2 σx21 +σx22 −2γ σ X 1 σ X 2 n−1
(1)
(2)
4 Teaching Experimental Results of Talent Training Model in Information Mode 4.1 Comparison of Previous Two Groups As shown in Fig. 1, prior to the implementation of the strategy, the basic tests were carried out simultaneously in the experimental class and the control class. The paper is scored out of 40 points. The pre-test results were imported into SPSS20.0 data analysis software for independent sample t-test, and the analysis of independent sample t-test showed that there was no significant difference between the pre-test results of the test class and the control class.
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30 25.31 24.54
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Data
20 15 10 4.579 4.721
5
0.687 0.689 0 Average value
Standard deviation
Mean error
Results data Control class
Experimental class
Fig. 1 Results of the experimental group and the control group were counted before implementation
4.2 Comparison of Two Groups After Implementation As shown in Table 1 and Fig. 2, after analyzing the t-test results of independent samples, the significance level of mean difference Sig = 0.001, that is, Sig < 0.05. Table 1 Independent sample t test after implementation t
Sig
Mean difference
Standard deviation error
Assumed equivariance
−3.287
0.001
−2.654
0.839
Equivariance is not assumed
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Results data Control class
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Fig. 2 After implementation, the scores of the experimental group and the control group were counted
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According to the basic principle of statistical test, it can be seen that after the comparative experiment, there is a significant difference in the performance of the experimental class and the control class. It can be seen from Table 7 that the average score of the experimental class is higher than that of the control class, and the implementation of the strategy can effectively improve the academic performance. The standard deviation comparison shows that the standard deviation of the experimental class is lower than that of the control class after the implementation of the strategy, which proves that the achievement gap of students at all levels in the experimental class is narrowing and the differences among students are reduced to some extent after the implementation of the strategy. In other words, the talent training model based on the information mode has improved the serious problem of student polarization.
5 Conclusions Compared with traditional courses learning, in the concept of autonomous learning, individualized learning, professional knowledge to master degree, change approach to learning and individual learning achievements the five level, found that the results of the survey option of information-based teaching mode are much more positive end to end, indicating that the informationization teaching model to a certain extent, improve the students’ learning effects. There are three reasons. Firstly, the information-based teaching mode based on mobile devices aims to improve students’ learning effect. Teaching activities before, during and after class are designed to be learner-centered, increase the interaction and communication between teachers and students, and promote students’ internalization of knowledge and improvement of ability. Secondly, the informationized teaching mode makes use of the resources and tools provided by the informationized platform to facilitate students to carry out better learning activities. Finally, this kind of intelligent learning method can not only strengthen students’ understanding and grasp of theoretical knowledge, but also urge students to study and improve students’ independent learning ability.
References 1. Li Q (2021) Analysis and practice on the training of key ability of students majoring in electronic information in higher vocational education. Proc Comput Sci 183(4):791–793 2. Xiao Q, Zhong X, Zhong C (2020) Application research of KNN algorithm based on clustering in big data talent demand information classification. Int J Pattern Recogn Artif Intell 34(06):1525822X15603149–1987 3. Noh Y, Youngji et al (2016) A study on developing the talent model and major competence of the LIS. J Korean Biblia Soc Library Info Sci 27(4):21–62 4. Olsen DS (2016) Adult learning in innovative organisations: european journal of education. Eur J Educ 51(2):210–226 5. Du C, Ruodong et al (2017) An exploration of the construction of china’s eldercare service talent team from the expectancy theory perspective. Open J Bus Manage 5(3):501–513
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6. Murphy M, Done EJ (2016) Autism and intuitive practice as the art of the prevailing middle. J Res Spec Educ Needs 16(4):272–279 7. Leece RD, White TP (2017) The effects of firms’ information environment on analysts’ herding behavior. Rev Quant Financ Acc 48(2):1–23 8. Li CC, Su J, Chu TH et al (2017) Building/environment data/information enabled location specificity and indoor positioning. IEEE Internet Things J 4(6):2116–2128 9. Choi BG, Na YW, Kim SP (2016) Study on the environment information providing method based on spatial information document. J Korean Soc Surv Geod Photogramm Cartogr 34(2):185–194 10. Wang YM, Li JG, Zou J et al (2016) Quantum process discrimination with information from environment. Chin Phys B 25(12):159–167 11. Mei H, Chen W, Ma Y et al (2018) VisComposer: a visual programmable composition environment for information visualization. Visual Info 2(1):71–81 12. Farooq O, Amin A (2017) National culture, information environment, and sensitivity of investment to stock prices: evidence from emerging markets. Res Int Bus Finan 39(pt.A):41–46
3D Jewelry Design System Based on K-means Algorithm Nianhua Qian
Abstract In the context of Industry 4.0, the inevitable trend of the development of smart manufacturing zombie manufacturing is the inevitable direction of the transformation and upgrading of traditional industries. The distinguishing characteristics of intelligent manufacturing are informatization, networking, automation, integration, and digitization. It is the deep penetration of information technology and Internet technology in the industrial field. Based on the traditional jewelry design method can no longer meet the diverse aesthetic needs of the public, the emergence of algorithm technology as a new technology provides more possibilities and greater development space for jewelry art. This article aims to study a 3D jewelry design system based on K-means algorithm. Based on the analysis of the basic principles of 3D technology, the advantages of K-means algorithm in jewelry design, and the optimization of 3D model details, a jewelry 3D design system is designed. Including modules such as user interface, login module and jewelry attribute selection, the system was finally tested. The test results show that the system is in the testing phase and strictly follows the software testing process. The entire testing process is an iterative process, and the functional test finally has no bugs. It is found that the performance meets the current needs of users and choices. Keywords K-means algorithm · Jewelry design · Three-dimensional design · Design system
1 Introduction Nowadays, the methods and means of modern jewelry design are not limited to traditional design methods. With the development of modern technology, the design concepts and production methods of jewelry are also advancing with the times [1, 2]. Modern jewelry in the new era should be harmonious and unified in the two aspects N. Qian (B) Fashion Design School, Shanghai Institute of Visual Art, Shanghai, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_13
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of science, technology and art. Designers can use science and technology as creative tools in the new era to find richer means of expression [3, 4]. The use of three-dimensional technology in jewelry design is the best solution to ensure the intuitive reflection of the design results. Using this technology to complete the design of jewelry can reduce the complexity of jewelry results [5, 6]. The application of 3D printing technology to replace the wax carving technology in traditional jewelry has become an innovation point in the three-dimensional jewelry design method. But in the process of designing jewelry using this method, only threedimensional technology is used after the drawing is completed. Therefore, it is easy to cause the error of jewelry details [7, 8]. This paper designs a jewelry 3D design system based on the analysis of the basic principles of 3D technology, the advantages of K-means algorithm in jewelry design, and the optimization of 3D model details. The system includes user interface, login module, and jewelry attribute selection modules. Finally, the system was tested.
2 Jewelry 3D Design System Based on K-means Algorithm 2.1 Basic Principles of 3D Technology (1)
(2)
Layered production technology. The basic principle of 3D printing layered production technology is to make objects layer by layer. The professional point is to use computer-aided technology to decompose the items that need to be printed layer by layer, and then the 3D printer uses the information data of these slices to print out a thin layer, and then combines these thin die sections. 3D printing can divide the printing and molding into two steps: the first step is to spray the adhesive on the parts that need to be molded; the second step: spray the raw material powder required for 3D printing on it, and when the two are combined, A layer of fixed cross-sections will be formed, and then layer by layer of cross-sections will be superimposed to form the final object model. Light curing molding technology. Light-curing molding technology is to use the characteristics of 3D printing materials that solidify when exposed to light, and use a machine to control the ultraviolet laser to irradiate the liquid raw materials, and the liquid raw materials will be solidified and formed by laser irradiation [9, 10]. The laser emitter will irradiate the photosensitive resin according to the layered data of the molded object to solidify the photosensitive resin into a thin layer; when this step is completed, the object will be moved down a layer of cross-sectional distance to form a solid object It will be covered with a layer of liquid photosensitive resin for the next round of printing. The work efficiency of light curing molding technology is very high, and the objects produced are more delicate. However, light-curing molding technology also has certain limitations. For example, the cost of light-curing molding of photosensitive resin is relatively high, and the liquid photosensitive resin is toxic in the air,
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and it is necessary to wear a gas mask during the processing and molding of photosensitive resin [11, 12].
2.2 Advantages of K-means Algorithm Application in Jewelry Design (1)
(2)
(3)
Efficiently generate multiple options. Algorithm technology can automatically generate multiple sets of different design results. These results are mainly caused by different algorithms selected by designers and differences in concepts. This kind of solution can be developed in parallel from conceptual design to final completion. When designing jewelry, you can choose different algorithms to try out the final goal of the pre-design, and select the optimal design in accordance with the design concept in the final generated result. The unique artistic context produced by different algorithms is very different, so Can greatly enrich the designer’s modeling language. Optimize the design to achieve the most efficient. In terms of design details analysis, compared with traditional manual methods, computers are faster and work more precise. When the computer adjusts the plan or automatically generates the plan, the generated model result is not only efficient but also easier to meet the designer’s expected goal. Efficiently share design information. As the computer is the finisher of all the work of assisted algorithm design, not only designers can continuously modify the model according to the latest ideas in all links of jewelry design at any time, and in the final 3D printing or casting link. You can also use the Internet to share models with manufacturers to jointly optimize design details and improve the structural relationships among them, reduce the chance of repeated revisions, communicate and feedback in time, and maximize the efficiency of design work.
2.3 3D Model Detail Optimization Set axt, ayt, and azt to correspond to the error values of the X, Y, and Z axes of the three-dimensional model respectively, then: ⎧ ⎪ a 2 xst + a 2y sin θ + ax2 cos2 θ axt = ⎪ ⎨ ayt = a 2 yst + a 2y cos θ + ax2 sin2 θ ⎪ ⎪ ⎩ azt = ± a 2 z st + a 2 z
(1)
Set as the absolute error of the three-dimensional model, c as the relative error, a as the calculated value, and b as the sketch value, then the following formula:
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=a−b c = /(a + b)
(2)
Use PS to deform the texture, adjust the brightness and tone according to different light. So far, the three-dimensional jewelry design method is completed.
3 Experiment 3.1 Interface Design Interface design should include the following guidelines: user guidelines, design an interface that meets the actual needs of users based on user needs; simple and operable principles, redundant operation settings and complex and fancy content on the user interface will bring bad user experience, designing a friendly functional and practical operation interface can better meet the actual needs of users; from left to right, the user interface should be from left to right from top to bottom, which can better satisfy most users reading habits and the control of a balanced visual interface should also follow this principle.
3.2 Login Module The system divides users into administrators and ordinary users according to requirements, and grants users different permissions through different roles. For new users of the system, they need to fill in their basic personal information through the registration page, register the corresponding account, and the user needs to create a new account, set a password and confirm the password, and click the “register” button after inputting. After the registration process is complete, you can log in to the system (Fig. 1).
3.3 Jewelry Attribute Selection Module This module mainly realizes the selection of jewelry styles. Different styles can be selected according to users’ preferences, and the best jewelry style models can be selected through different combinations of attribute options to meet the actual needs of customers. For example, when selecting a jewelry shape, the user can click different jewelry shape buttons to change the shape of the jewelry in real time. There are a variety of jewelry shapes to choose from in the system.
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System login interface
Register account and set password
NO
Whether to register an
NO
account
YES
Is the username and password entered correctly?
YES enter the system
Fig. 1 System login flow chart
4 Discussion The hardware environment of the system designed in this paper is shown in Table 1 The whole test process of the system is iterated three times. In the iterative integrated function test phase, the total number of bugs tested is 16, 3, and 0 respectively, showing a downward trend. The specific test summary of each module is shown in Fig. 2. In the testing phase, the system strictly follows the software testing process. The entire testing process is an iterative process. In the end, no bugs are found in the functional testing, and the performance meets the current user volume and selection requirements. Table 1 System hardware environment
CPU
Intel(R) Core(TM)i5-2400 3.10GHZ
RAM
4 GB DDR3
Hard disk
930G
Graphics card
NVIDIA GeForceGT520M
Monitor
24-inch high-definition screen
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Number
Fig. 2 Module bug statistics
10 9 8 7 6 5 4 3 2 1 0
Number of integrated function test bugs
system interface
Login module
Attribute selection module
Module
5 Conclusions The presentation of jewellery plastic art is inseparable from the support of processing technology. Choosing the right jewellery craft can more perfectly express the essence of jewellery plastic art; and the development of jewellery crafts is inseparable from the guidance of art. At the same time as the development of art, technology It is also moving forward. The emergence of 3D technology provides convenient and efficient technical support for the design and processing of jewelry designers, but 3D technology is not omnipotent, and there is still considerable room for development of this type of technology.
References 1. Dalmiya S, Dasgupta A, Datta SK (2016) Application of wavelet based K-means algorithm in mammogram segmentation. Int J Comput Appl 52(15):15–19 2. Mario Haut J, Paoletti M, Plaza J et al (2017) Cloud implementation of the K-means algorithm for hyperspectral image analysis. J Supercomput 73(1):1–16 3. Yang J, Ma Y, Zhang X et al (2017) An initialization method based on hybrid distance for K-means algorithm. Neural Comput 29(11):1–24 4. Jing Y, Wang J (2017) Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis. J Syst Eng Electron 28(2):374–384 5. Lee C, Chung M (2016) Digital forensic for location information using hierarchical clustering and K-means algorithm. J Korea Multimedia Soc 19(1):30–40 6. Saifullah, Hidayati N, Solikhun (2021) Implementation of data mining in grouping percentage of blind letters age 15+ by Province using K-means algorithm. J Phys: Conf Ser 1641(1):012081 (7pp) 7. Suci A, Sitanggang IS (2016) Web-based application for outliers detection on hotspot data using K-means algorithm and shiny framework. IOP Conf Ser: Earth Environ Sci 31(1):012003 (8pp) 8. Wang L, Lu ZM, Ma LH et al (2017) VQ codebook design using modified K-means algorithm with feature classification and grouping based initialization. Multimedia Tools Appl 77(10):1– 16 9. Ha Sugian PM, Hutahaean HD, Sinaga B et al (2021) Review the utilization of big data and K-means algorithm in supporting the determination of village status as support to the ministry of village PDTT. J Phys Conf Seri 1811(1):012063 10. Duan B (2021) Analysis on the value of 3D printing in jewelry design based on artificial intelligence. J Phys: Conf Ser 1744(4):042132 (6pp)
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11. Luo GQ, Yu W, Yu Y et al (2020) A three-dimensional design of ultra-wideband microwave absorbers. IEEE Trans Microw Theory Tech 68(10):1–1 12. Lan F, Lu F, Qiu X (2020) Research on application of three-dimensional design standardization and three-dimensional design review method for transmission and transformation project. IOP Conf Ser: Earth Environ Sci 558(5):052030 (7pp)
The Development of Internet Plus Tourism Mode Based on Data Mining Technology Yingying Ma
Abstract The popularity of the Internet and the progress of modern science and technology have brought great changes to today’s information society. Many people use data mining technology (DMT) to obtain and share information on the Internet. Internet plus mode can whenever and wherever possible provide people with information they want to search. This unrestricted and effective way has brought new changes to the people’s tourism life. The use of the Internet makes it possible to provide their tourism personalized needs for the public. In addition, with the Internet technology becoming more and more mature, people no longer have to worry about information leakage and information delay. As long as people have demand, they can search all kinds of tourism projects anytime and anywhere through the Internet. This paper mainly studies the development of Internet plus tourism (IPT) based on DMT. This paper mainly analyzes the development of IPT, and puts forward the future development countermeasures through understanding the development of new IPT mode. Meanwhile, we use DMT to understand the user search content and search mode in the new IPT mode, and study the development of IPT new mode. Delicacy of IPT is the most popular way to search for Internet users, and the most popular search methods for specialty products and tourism are 69.3 and 70.3%, followed by destination network, 57.7 and 56.3% respectively, and 32 and 44% respectively. Tourism app search accounted for 28.7 and 25.3%, and tourism community questions accounted for 23.7 and 27.3% respectively. Keywords Internet plus · New tourism mode · Tourism development · Data mining
1 Introduction With the advent of the Internet plus era, people have reached the stage of information acquisition. Users can filter out their own information through numerous data mining technologies to meet their needs [1, 2]. When users browse the website, the Y. Ma (B) Shandong Institute of Commerce and Technology, Jinan 250000, Shandong, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_14
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Internet will record their search or click behavior, and mine according to these data to recommend the information they need for each user [3, 4]. Tourism information recommendation has its complexity. With the rapid development of information technology, the explosive growth of tourism information makes it more and more difficult for tourists to obtain tourism information to meet their needs when facing massive tourism information resources [5, 6]; secondly, with the continuous improvement of people’s living standards, people’s concept of tourism consumption is becoming more and more mature and personalized, and the current tourism recommendation has been difficult to meet the growing demand of tourists [7, 8]. The combination of IPT and data mining can get a lot of implicit tourism information in data, which helps the relevant management departments to understand the distribution of the industry, so that the government can make reasonable planning and development of the tourism resources and their supporting infrastructure, thus improving the rational utilization of tourism resources. It has great application value and social significance [9, 10]. In the research of the development of the IPT mode based on DMT, many domestic and foreign scholars have studied it, and have made some achievements. Joeng B H and others pointed out that the popularity of mobile Internet and mobile terminals has promoted the sustained, healthy and rapid development of tourism industry. The further expansion of tourism market has also changed people’s tourism mode. The Internet has replaced the traditional information media and become the mainstream way for people to conduct tourism information inquiry [11]. Sun S and others pointed out that taking plotting data as data source and using DMT to quickly, accurately and obtain tourism hot spots and other information is an effective method to solve the problems of reasonable allocation of tourism resources and tourism itinerary planning [12]. This paper mainly studies the development of IPT based on DMT. This paper mainly analyzes the development of the IPT mode, which has the advantages of policy support. However, tourism and the Internet have not yet fully integrated with the advantages of tourism information exchange, but lack of a perfect information exchange system. Through understanding the development of IPT, the paper puts forward the countermeasures for future development, and changes the traditional tourism concept, improves and perfected the IPT mode, establishes a perfect tourism information system, and realizes the modernization of tourism informatization. Meanwhile, we use DMT to understand the user search content and search mode in the new IPT mode, and study the development of IPT new mode.
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2 The Development of IPT Based on DMT 2.1 IPT Development (1)
(2)
It has the policy support advantages, but the tourism industry and the Internet have not yet been fully integrated The concept of Internet plus self support has been strongly supported by the state, and the corresponding policies have been issued to support it. Tourism and Internet plus form a new IPT mode, which is upgraded from traditional tourism industry. But Internet plus Internet plus is a short duration of time. The new mode of Internet + tourism needs a series of measures to prepare for the real role of the Internet and make personalized tourism for users. Before, the one and only more travel agencies could travel through the travel agency. With the maturity of the new IPT mode, more distinctive tourism modes are accepted by people. They are no longer satisfied with the traditional tourist routes, commodities, habits, etc., but are looking forward to planning unique tourist routes and having their own unique tourist products. On the other hand, with the acceleration of the pace of life, people’s travel with more temporary and casual, people are more choose to go on this way of travel, after arriving at the destination, according to the actual situation at any time to understand and reserve the destination’s tourism situation. Therefore, the combination of the tourism industry and the Internet has drawn much attention, and the development of tourism industry driven by the Internet is still in the stage of development. With the advantages of tourism information exchange, but lack of perfect information exchange system
The advantage of the new IPT mode is to rely on the Internet for information exchange, achieve high efficiency of user communication and reduce the cost of tourism publicity. The new IPT mode can directly convey tourism information, reduce the cost of tourism publicity, and transfer costs between departments, and achieve the optimal allocation of human resources. Meanwhile, the new IPT mode can rely on the Internet platform to develop tourism related APP, which enables users to get tourist information through APP software, improve users’ cognition of tourism information exchange, and has the advantage of tourism information exchange. Meanwhile, the IPT mode lacks a complete information exchange system, lacks a comprehensive introduction and user customization service, and lacks a perfect information exchange system.
2.2 IPT New Mode of Future Development Countermeasures (1)
Change traditional tourism concept, perfect and perfect IPT mode.
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The new development mode of IPT needs to change traditional tourism concept and combine Internet with tourism, relying on Internet technology for tourism customization. The development of IPT represent the general trend. The rapid development of information technology and tourism depend on the Internet to realize the development of tourism informatization. We should improve and perfect the IPT mode, change traditional tourism concepts and change the operation methods of tourism so as to integrate Internet and tourism industry and realize the informatization and electronization of tourism industry. Establish a perfect tourism information system and realize the individualization of tourism information
The new development mode of IPT needs to establish a perfect tourism information system and realize the modernization of tourism informatization. The establishment of a perfect tourism information system needs the cooperation of various companies in the tourism industry to realize resource sharing, establish a tourism information system, and show the tourism information system to users more comprehensively, so as to meet the personalized tourism needs of users to the maximum. Design personalized tourism services to meet the needs of users, give full play to the individualization of tourism informatization, attract more users to travel, and promote the development of new mode of IPT.
2.3 Related Analysis of DMT (1)
Euler distance algorithm
Euler distance algorithm is used to solve the linear distance between two points, which is suitable for the situation that each vector standard is unified. This way of calculating distance affects the final result of data classification in DMT. This kind of classification decision uses the nearest similar sample to judge the category in big data for data division, which is conducive to the classification and integration of DMT, the specific formula is as follows: n (a) (a) (b) (X i − X i(b) )2 di (X i , X i ) =
(1)
i=1
The data obtained by using the algorithm is close to the category of n samples, which can ensure the accuracy and accuracy of data mining to a certain extent, and directly reflect the positioning and classification of data in massive data. By using Euler distance algorithm, it can easily locate the IPT mode and realize customized personalized tourism customization. (2)
K-means clustering algorithm
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K-means clustering algorithm is the most commonly used data processing method at present. Through training samples x (1), X (2), X (m), the final result of each x (i) is the value of set classification. K-means algorithm is to cluster samples into K clusters ,the specific formula is as follows: m
µj :=
1{c(i) = j}x (i)
i=1 m
(2) 1{c(i) = j}
i=1
After collecting the results of clustering analysis, K-means algorithm classifies and organizes similar data members in some aspects. This is the classification data we need, which is used in DMT. This paper uses DMT to understand user search content and search mode in IPT mode, and study the development of IPT new mode.
3 Experimental Research 3.1 Research Object This paper mainly studies the development status of IPT based on DMT, and plans a new tourism mode through studying the situation of new mode of IPT. This paper searches for some IPT new models through Internet data mining, and understands the user search content and search mode under the new mode of IPT.
3.2 Research Process Steps This paper mainly analyzes the development of IPT, and puts forward the future development countermeasures through understanding the development of new IPT mode. Meanwhile, we use DMT to understand the user search content and search mode in the new IPT mode, and study the development of IPT new mode.
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4 Research and Analysis of the Development of IPT Based on DMT 4.1 User Search Content Analysis Under the New IPT Mode The use of mobile Internet provides users with personalized tourism. Users can use DMT to select tourism elements according to their own needs and find the information they want to search. This unrestricted and effective way has brought new changes to people’s tourism life. This paper searches data search content in the IPT mode, and understands users’ concerns in tourism, data content of search is selected in ten minutes. The results are shown in Table 1. All kinds of Search contents IPT can be found in Fig. 1. Among them, the most frequent is tourism product booking, with a frequency of 150 times, followed by weather conditions, with a frequency of 143 times. The search frequency of scenic Table 1 User search content analysis in the new IPT mode
Type
Frequency
Proportion
Travel product booking
150
50
Weather information
143
47.7
Special food information of scenic spots
138
46
Traffic information
120
40
Forum travel post
111
37
Tourism strategy
104
34.7
Other
1
0.3
Fig. 1 User search content analysis in the new IPT mode
The Development of Internet Plus Tourism … Table 2 Analysis of user search mode under the new IPT mode
137 Special food information of scenic spots
Tourism strategy
Official website of tourism platform
69.3
70.3
Destination website
57.7
56.3
Integrated search engine
32
44
Travel app
28.7
25.3
Tourism community questions
23.7
27.3
Other
3.3
1
spot specialty food is 138 times, the search frequency of traffic information is 120 times, the forum tourism paste is 111 times, and the tourism strategy is 104 times.
4.2 User Search Mode Analysis Under the New IPT Mode Under the new IPT mode, users can freely use the Internet for tourism search. The popularity of the Internet and mobile terminals has promoted the sustained and healthy development of the tourism industry. The tourism market has further expanded, and the way of people’s travel has also changed. The Internet has replaced the traditional information media as the mainstream way for people to travel information inquiry. This paper delicacy of user search methods in IPT mode through data mining, and collects and collects users’ proportion of search methods for specialty food and tourism strategy at the same time point. The results are shown in Table 2. Delicacy can be seen from Fig. 2. Users can freely use the Internet to conduct tourism search under the new IPT mode. The most popular search methods for scenic spots, specialty foods and tourism Raiders are 69.3 and 70.3% of the official website of the tourism platform, followed by the destination official website, 57.7 and 56.3% respectively, and the comprehensive engine search mode accounts for 32 and 44% respectively. Tourism app search accounted for 28.7 and 25.3%, and tourism community questions accounted for 23.7 and 27.3% respectively.
5 Conclusions With the increase of tourism behavior, people’s demand for tourism is higher and higher, and the demand for tourism is constantly changing, including the content,
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other
Tourism strategy Special food information of scenic spots
type
Tourism community questions Travel app Integrated search engine Destination website Official website of tourism platform
proportion
Fig. 2 Analysis of user search mode under the new IPT mode
mode and hot spots of tourism. People’s concept of tourism demand has changed from passive to active choice, and tourism demand has become increasingly diversified and complicated. This paper has a theoretical guiding significance for personalized planning tourism and rational allocation of tourism resources by studying the development status of IPT based on DMT.
References 1. Gao X et al (2019) A training mode for innovative and entrepreneurial talents in tourism management in the era of internet plus. J Landscape Res 11(05):98–101 2. Zhang L, Sun Z (2020) Research on the development and practice of confucius research and study travels in the internet plus. J Phys Conf Ser 157(5):012176 3. Schonland AM et al (2016) Using the internet for travel and tourism survey research: experiences from the net traveler survey. J Travel Res 35(2):81–87 4. Lin LQ (2020) Research on the development of the integration of the internet and tourism industry. J Phys: Conf Ser 1533(4):042006 (5pp) 5. Andreopoulou Z, Lemonakis C, Koliouska C et al (2017) Internet and agro-tourism sector for regional development in Crete: a multicriteria ranking. Int J Info Decision Sci 9(2):116-127 6. Wang ZW et al (2016) Internet plus horizon sports and tourism integration development path. Basic Clin Pharmacol Toxicol 118(Suppl.1):99–99 7. Williams PW et al (2016) Using the internet for tourism research: “Information Highway” or “Dirt Road”?. J Travel Res 34(4):63–70 8. Stangl B, Pesonen J (2018) Information and communication technologies in tourism 2018. Tangible Tour Internet of Things 349-361 https://doi.org/10.1007/978-3-319-72923-7(Chapter 27): 9. Ferrer-Rosell B, Coenders G, Marine-Roig E (2017) Is planning through the internet (un)related to trip satisfaction? Info Technol Tourism 17(2):1–16 10. Favre-Bonté V, Tran S (2018) The contribution of the internet to the strategic positioning of small businesses in the tourism industry. Int J Entrepreneurship Small Bus 25(3):847–852
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Multi-modal Medical Image Fusion Method Based on Multi-scale Analysis and PCNN Hui Li and Qiang Miao
Abstract In order to solve the problem that the objective measurement functions based on the gray value used in single-modality registration could not describe the differences among multi-modal images adequately, and the problem that the blurring edge and insufficient complementary information in current multi-modal medical image fusion methods. We propose a multi-modal medical image fusion method based on multi-scale analysis and PCNN. Firstly, we use the multi-scale filter image enhancement algorithm in image pre-processing. Secondly, the local feature descriptors modality mapping method is used in image registration. Thirdly, the algorithm based on improved guided filtering and dual-channel pulse coupling neural network is image fusion. The experiment results show that the proposed algorithm can effectively retain the detail texture information, feature information and contour information of the source images. The proposed algorithm improves the definition, robustness and efficiency effectively. Keywords Multi-Scale analysis · Registration · Fusion · PCNN
1 Introduction Multi-modal medical image fusion technologies can fuse the images of the specific body part obtained with different devices or in different time [1, 2]. Integrate multimodal image information into one image can help medical staff make diagnosis more precisely and efficiently [3, 4]. Thus, multi-modal medical image fusion technology is practically valuable in medical diagnosis and treatment [5]. H. Li (B) School of Intelligence and Electronic Engineering, Dalian Neusoft University of Information, Dalian, Liaoning, China e-mail: [email protected] Q. Miao School of Computer and Software, Dalian Neusoft University of Information, Dalian, Liaoning, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_15
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Nowadays, most of the medical image fusion methods are implemented with scale transformation. But the problems of fuzzy detail information and edge information in fusion image still exist. In order to fix those problems, we propose a fusion method based on multi-scale analysis and PCNN. The proposed algorithm innovates in image pre-processing, image registration and fusion, and obtains good fusion effects.
2 Design of the Proposed Algorithm The proposed algorithm includes three steps which are multi-modal medical image pre-processing, image registration and image fusion.
2.1 Design of Multi-modal Medical Image Enhancement Method Image enhancement algorithm is very important in image pre-processing. Image enhancement can improve the image contrast [6], and it is very important to the following image registration and fusion. In this paper, we use an enhancement method based on multi-scale filter image decomposition. We decompose the medical image into many layers, each layer has high frequency and low frequency component with different proportion. Set each image layer with different filtering parameters and we can get a much better enhancement effect. The design of proposed image enhancement method is shown in Fig. 1.
2.2 Design of Multi-modal Medical Image Registration Method The essence mathematics of medical image registration algorithm is getting one-toone correspondence between medical images to be registered, and then connecting the same position between images to be registered and the real physical space [7]. The proposed registration method is based on the local feature descriptors modality mapping which combines the resemblance measurement function commonly used with the local position, the direction and scale information of image, and then we complete the multi-modal medical image registration combining image space feature information and gray scale distribution properties. Nowadays, more and more feature descriptors are proposed. The most typical and widely used one is SIFT (Scale Invariant Feature Transform). But SIFT is in the form of multi-dimensional vectors, so there exists problems of huge calculations and inconvenient storage. Based on the problems, we reduce dimensions to the local
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Fig. 1 Multi-scale filter decomposition enhancement method
feature descriptor, and complete the multi-modal medical image registration using the local feature descriptor modal mapping method. The implementation process includes three steps: (1) (2)
(3)
Assume an image A with size of m ∗ n, calculate the SIFT feature descriptor of each pixel, then we get the feature vector matrix S with size of m ∗ n ∗ 128. We get the first three principles components of matrix S using dimensions reducing. And then we get the color mapping image from mapping the three components to color components in RGB space. The purpose of this step is to make the similar area of multi-modal images show same or similar color. We get the average value of each color component in color mapping image, and finally get the gray modal mapping image. The design of proposed medical image registration algorithm is shown in Fig. 2.
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Fig. 2 The proposed multi-modal image registration method
2.3 Design of Multi-modal Medical Image Fusion Method Medical image fusion is the direct target of image registration. The new fusion medical image should keep the important information of the original image, and at the same time reflect the new information apparently. Multi-scale transformation is used in most of the existed fusion methods. The common multi-scale transformation methods include Discrete Wavelet Transform [3], Curvelet Transform, and Nonsubsampled Contourlet Transform [4, 5]. In recent years, PCNN network is widely used in image fusion area because of its model is simple and needs no training. But single-channel PCNN fusion method will cause blurring detail information [8, 9]. Guided filtering is a kind of smoothing filter proposed in recent years which keeps the edge information perfectly. It has good properties such as noise removing and detail smoothing. Based on the above analysis, and considering the exist fusion algorithms, we propose a new multi-modal medical image fusion algorithm based on guided filtering and dual-channel PCNN. The proposed algorithm can improve the contrast of fusion image and keep more detail information and edge information of the fusion image. The design of our fusion algorithm is shown in Fig. 3. Firstly, we use NSCT to decompose multi-modal medical image into 4 layers to get one low frequency sub-band and three high frequency sub-bands. Secondly, we get the processed low frequency and high frequency sub-bands with guided filtering fusion and PCNN fusion. Thirdly, we get the final fusion medical image using NSCT inverse transformation.
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Fig. 3 The multi-modal image fusion method based on guided filtering and PCNN
3 Simulation of the Proposed Algorithm The proposed algorithm is implemented with Matlab. The simulation of multi-scale filtering image enhancement method is show in Fig. 4. Picture (a) is the source MRI image, and (b) is the result. From picture (b), we get that the proposed method corrects the unbalanced grayscale of the original image, and the visual effect is better.
(a) The source MRI image
(b) The image using proposed enhancement algorithm
Fig. 4 The multi-modal image fusion method based on guided filtering and PCNN
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(a) The source CT image
(b) The source MRI image
Fig. 5 The multi-modal image fusion method based on guided filtering and PCNN
(a) The registration image
(b) The fusion image
Fig. 6 The multi-modal image fusion method based on guided filtering and PCNN
The source CT image and MRI image used in the simulation is shown in Fig. 5. The registration and fusion results are shown in Fig. 6. From picture (a), we get that the proposed registration method can keep the continuity of the image feature better. Picture (b) shows that the proposed fusion method can get good fusion image definition (Fig. 7). Evaluation index of multi-modal image fusion algorithm tests is shown in Table 1. The evaluation index includes Mutual Information (MI), Information Entropy (EN), Average Grad (AG), and Peak Signal to Noise Ratio (PSNR). The effect of fusion algorithms is better if the value of index is bigger. Thus, the proposed algorithm is much better than the other ones.
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(a)The wavlet fusion algorithm
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(b) The weighed average fusion (c) The propose fusion algorithm algorithm
Fig. 7 The multi-modal image fusion method based on guided filtering and PCNN
Table 1 The evaluation of the algorithm tests The fusion algorithms
The evaluation index of the CT and MRI image fusion MI
EN
AG
PSNR
Wavlet algorithm
3.2129
4.4223
7.0561
27.4785
Weighed average algorithm
2.4449
4.3783
6.8535
27.3986
Proposed algorithm
3.6668
5.0324
7.1860
27.5351
4 Conclusions Multi-modal medical image fusion become common in recent years. In this paper, we propose a new fusion method based on multi-scale analysis and PCNN [10]. In image pre-processing, we use a multi-filter image decomposition method to enhance the medical image. And then we use a local feature indicator modal mapping method in registration. Finally, a method based on guided filter and PCNN is used in fusion. The experiment results prove that the proposed algorithm can improve the contrast of fusion image, keep more detail information and edge information. Acknowledgements This work was, in part, supported by the Liaoning Province Education Department Project (Grant No. JZR2019002, SYDR202005); Liaoning Province Natural Science Fund (Grant No. 2019-ZD-0355).
References 1. Xiaojun L, Wenzhan D, Junfeng L (2018) Multi-modality medical image fusion algorithm combined with sparse theory and non-subsampled shearlet transform. J Zhejiang Sci-Tech University (Nature Science) 14(11):723–731 (in Chinese) 2. Lifang W, Xia D, Pinle Q, Yuan G (2018) Multi-modal brain image fusion method based on adaptive joint dictionary learning. J Comput Appl 38(4):1134–1140 (in Chinese)
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3. Jian L, Xiao L, Man L (2017) Medical image fusion of multi-mode based on wavelet transformation. China Med Equipm 14(11):22–26 (in Chinese) 4. Gang C, Lin LW, Li L (2018) A new method of multimodality medical image fusion based on the nonsubsampled contourlet transform. J Changchun Univer Technol 39(3):248–252. (in Chinese) 5. Lei X, Yan C (2017) A novel method for multimodal medical image fusion based on nonsubsampled contourlet transform. China Med Equipm 32(12):63–67. (in Chinese) 6. Ji-ya X, Jia-ning S, Shuang Q (2020) Multi-resolution based. two-dimensional histogram equalization for medical image enhancement. J Northeast Normal Univer (Natural Science Editiion) 52(01):88–91. (in Chinese) 7. Benzheng W, Jie C, Yilong Y (2018) Medical image registration based on mutual information entropy combined with edge correlation feature. J Data Acquisit Process 33(02):248-258 (in Chinese) 8. Miao Y, Chunyu N, Lemin S, Bingyao L (2020) An adaptive PCNN improved algorithm suitable for multi-modality medical image fusion. Sci Technol Eng 20(22):9116–9121. (in Chinese) 9. Shujuan G, Yuan G, Pinle Q, Lifang W (2021) Medical image fusion based on multi-scale edge-preserving decomposition and PCNN. Comput Eng 47(3):276–283 (in Chinese) 10. Satyanarayana D, Vanitha K, Prasad MNG (2021) Multi-modal medical image fusion algorithm based on spatial frequency motivated PA-PCNN in the NSST domain. Current Med Imag 17(5):634–643
Analysis of Express Logistics Cost Control Under the Background of Big Data Jinfen Ye and Chunhua Hu
Abstract With the great improvement of social science and technology level, China’s logistics industry has made rapid development. However, compared with foreign logistics enterprises, there are still many problems in logistics cost control of express enterprises in China. This paper selects the Yunda Express as the research object, the main research issues related to Yunda Express logistics cost control, find out the existing problems of Yunda Express logistics cost control, according to the specific problems and put forward some corresponding suggestions of. By analyzing the actual situation of Yunda Express, we can find out the factors affecting the cost control and management of logistics. Finally, we put forward more targeted, effective and scientific measures and methods to improve the structure of Yunda Express Logistics, promote the upgrading of the level of logistics links, and finally achieve reasonable control of logistics costs to Yunda. Express creates more profits and benefits. Keywords Yunda express · Logistics cost · Cost control
1 Introduction With the development of e-commerce, many large-scale e-commerce companies emerge in China, such as Alibaba, Jingdong and other companies. Every year, hundreds of billions of sales are generated, and the total express delivery volume exceeds tens of billions [1]. The development of e-commerce industry has led to the development of many related industries, the most obvious of which is the express industry. After years of development, China’s express delivery industry has basically formed a relatively perfect system, and the service level of logistics companies is also J. Ye Department of Management, Wuhan Donghu University, Wuhan, Hubei, China C. Hu (B) Department of Business, Wuhan Business University, Wuhan, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_16
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constantly improving, which to a large extent has led to the development of China’s economy [2]. Although the number of logistics enterprises in China has increased significantly in recent years, and the total express delivery volume has also increased year by year, there is still a certain gap between the logistics companies in developed countries and the quality of their work. At present, most enterprises in our country still use the more traditional management system and the more old-fashioned way of cost calculation [3]. The products of logistics business service are unique, and the traditional cost control methods can not meet the ever-decreasing enterprise cost requirements, and fail to achieve the goal of reducing logistics cost. This paper takes the activity-based cost of logistics industry as the research background, combines with the current development situation of Yunda enterprise, a well-known logistics enterprise, and finds out the reasons that restrict the development of logistics enterprises, and puts forward effective measures. Solutions, thereby reducing the operating costs of logistics companies, improve the competitiveness of logistics enterprises in the market.
2 The Status of Yunda Express Logistics Cost Control Yunda Express, founded in August 1999, is a nationwide network-based brand express enterprise integrating express delivery, logistics, e-commerce distribution and warehousing services. Yunda Express has more than 3,000 service sites in China, especially in recent years. The rapid development of business seems to have become the leading trend of domestic related enterprises [4]. At present, the number of Yunda’s self-operating transshipment centers has reached 54, with more than 2800 franchisees and more than 20,000 network points. In recent years, Yunda has developed rapidly in similar enterprises, and its operating profit has gradually increased. However, Yunda still has some problems in logistics cost control (Table 1). Table 1 Non-current assets details of Yunda express Non-current assets
20–12-31
Available for sale financial assets – Long-term equity investment
19–12-31
18–12-31
–
837.7 million 468.4 million
91.54 million 92.59 million 90.8 million
17–12-31 51.66 million
Fixed assets
9.481 billion
6.441 billion
4.717 billion
2.82 billion
Intangible assets
2.552 billion
2.036 billion
1.222 billion
760.5 million
Long-term prepaid expenses
103.9 million 116.9 million 97.58 million 66.69 million
Deferred tax assets
384.2 million 239.8 million 121.5 million 96.94 million
Other non-current assets
146.5 million 48.54 million 378.4 million 175.3 million
Total non-current assets
15.91 billion
Source Eastern Fortune Network
11.53 billion
7.911 billion
4.641 billion
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As shown in the above table, until the end of 2020, the fixed assets (including the investment in self-built logistics equipment and facilities) of Yunda Express reached about 9.481 billion yuan, including various equipment and tools in the self-operated logistics. Compared with the fixed assets of about 6.441 billion yuan at the beginning of 2019, the increase is still relatively large; the intangible assets of Yunda Express including the right to land use amounted to 2.552 billion yuan, a relatively large increase. At the beginning of 2019, only It is 1.222 billion yuan. This shows that the investment in the purchase and disposal of land is 1.23 billion yuan, plus the annual depreciation of fixed assets, which is a big pressure for Yunda Express (Table 2). As shown in the table above, the operating cost of Yunda Express has increased significantly. In 2020, Yunda Express officially announced that due to the increase of labor costs, transportation costs, raw material prices and other factors, it will increase the express price. And this also means that with the maturity of the express market, the increase of daily expenses and management expenses can not be ignored. In the process of Yunda Express self-operation, it has suffered a lot of financial pressure. The high growth of express industry will generally have internal structural problems, such as homogeneity of competition, lack of product differentiation, the urgent upgrading of automation and intellectualization of express industry, which will lead to more investment costs. Despite the growth in operating income, losses may still occur.
3 Problems in Yunda Express Logistics Cost Management At present, Yunda Express carries out logistics cost control through the following ways: (1) by encouraging franchisers to “operate independently” to control logistics cost. Yunda Express carries out the pilot business of “self-management” in the network of franchisors, outsourcing some trunk lines to franchisees, thus reducing the main transportation costs borne by the company. (2) To adjust the layout of transshipment centers to control logistics cost. The company has arranged the transshipment center, adjusted the second-level transshipment center, and integrated the secondlevel transshipment center into the first-level transshipment center. (3) Cost control by optimizing transportation routes [5]. Although these measures have achieved some results, Yunda Express still has the following problems in logistics cost control:
3.1 Labor Costs Are Enormous Yunda Express has a wide range of business, which largely tests the management and control ability of Yunda Express in material turnover. With the rapid expansion of Yunda in recent years, the number of Yunda employees is also increasing sharply. Yunda Express has invested more energy in the salary of relevant service personnel, and the social security system for grass-roots workers is constantly improving [6]. In
Product categories
4.22%
25,803,544.94
276,618,672.73
7,091,701,226.10
Express cost
Other costs
total
Source The Financial Statement of Yunda Express
100.00%
3.90%
0.36%
90.49%
298,985,323.86
1.03%
Material sales cost
73,116,781.07
100.00%
5,059,831,464.29
103,377,926.52
0.00
221,006,910.22
4,605,424,669.02
130,021,958.53
Amount
2019
5,059,831,464.29
103,377,926.52
221,006,910.22
100.00%
2.04%
0.00
4.37%
91.02%
2.57%
40.16%
167.58%
–
35.28%
39.34%
-43.77%
Year-on-year increase or decrease
40.16%
192.54%
35.28%
37.06%
Year-on-year increase or decrease
Proportion of operating costs
100.00%
2.04%
4.37%
93.59%
Proportion of operating costs 4,735,446,627.55
Amount
2019
Proportion of operating costs
Transit related costs 6,417,176,903.50
Single-sided cost of sales
7,091,701,226.10
2020
total
Amount
302,422,217.67
Other operating costs
4.26%
4.22%
298,985,323.86
Material sales cost
Proportion of operating costs 91.52%
Amount
2020
Courier service cost 6,490,293,684.57
Category
Table 2 Operating cost of Yunda express
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the meantime of increasing employees’ income as a whole, employees will be given various subsidies. The huge staff has brought great cost burden to Yunda Express.
3.2 The Construction of Logistics System is Imperfect Yunda Express Company adopts more advanced means in goods inventory and automatic identification, but manual screening is neededfor the distribution, picking and final delivery of express delivery [7]. As the types of express delivery become more and more, the number of express delivery is also increasing, resulting in a shortage of personnel, which is one of the reasons for inefficiency. In addition, the company’s current logistics information system is not perfect, a large company, the company’s website actually a few pages, which causes customers can not fully understand the company, website upgrade is not timely, leading to many times unable to provide effective information to customers. Therefore, it is necessary to constantly improve the company’s logistics information website. Not only is the company’s logistics information website system imperfect, the communication facilities between head office and branch office, branch office and regional business outlets are also imperfect, there is no good connection, resulting in branch companies can not make technical guidance to business outlets, information between business outlets can not be timely exchanged and so on.
3.3 The Fixed Cost Control is not Reasonable In recent years, in order to attract more manpower, companies spend a lot on labor costs, and this cost occupies an excessive position in the company’s cost ratio. In terms of equipment maintenance and depreciation, in addition to people’s participation in the transport process of express delivery, that is, transportation. The service life of vehicles is limited and depreciation is required every year. In order to pursue faster modes of transportation and improve customer satisfaction, it is sometimes necessary to adopt more expensive air transportation, resulting in increased costs [8]. Yunda’s own logistics and distribution speed is slow and the distribution efficiency is not high, which will lead to increased warehousing pressure and warehouse maintenance costs.
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4 Measures to Improve Yunda Express Logistics Cost Control 4.1 Reduce the Labor Cost Consumption In the process of human resource management, we should make clear and correct plans and regulations for the supply and demand of talents, formulate standard recruitment process, and reduce personnel recruitment costs. In terms of staff management and training, attention should be paid to cost control within a reasonable range [9]. Optimize the position setting, adopt a multi-purpose, multi-functional way to reduce the cost of employment. Establish an assessment system to differentiate the salary of employees according to their labor value. Linking enterprise performance with employee’s salary can promote employee’s work enthusiasm.
4.2 Improving the Construction of Logistics System Since Yunda wants to do its own logistics system, it is very important to improve its own logistics facilities. It should make Yunda Express’s own logistics system unified. Of course, Yunda’s own strength is limited. It can improve its own shortcomings by means of the third-party logistics system. Cooperation with the third-party logistics platform is also a way. In the process of distribution planning, it is necessary to clarify the number of tasks, to have a clear and accurate understanding of the needs of customers, to determine the time point, to make reasonable and scientific delivery routes and plans according to the actual situation, and to provide assistance to staff when delivering goods [10]. The plan of goods distribution should also carry out a comprehensive and accurate budget in advance, based on the specific shape and size of the goods to be distributed and the customer’s demand for goods transportation, so as to determine the equipment and measures for goods transportation and handling. In order to ensure timely arrival of goods and reduce expenditure, reasonable and scientific planning of routes, the road conditions and traffic conditions is included, in the process of goods distribution should be carried out.
4.3 Change the Cost Management Model With scientific and reasonable matrix structure mode, the head office only carries out overall control, but scientifically manages and controls each network area. By replacing manual labor with mechanical automation, such one-time capital investment can bring long-term benefits. Moreover, AI is slightly superior to manual in time efficiency and error rate, and reduces the input of labor costs, which can find
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a new way for the development of the company. The introduction of modern electronic equipment management system and the establishment of these mobile management equipment in densely populated areas and more goods circulation can not only improve the inefficiency of manual distribution, but also save a lot of capital output for enterprises.
5 Conclusion Since the beginning of the twenty-first century, with the development of the national economy, the domestic logistics-related industries are also growing rapidly, which has a great relationship with the relevant policies and improvement of the business environment. The logistics industry has introduced the elements of science and technology, making them more suitable for people’s lives, and the service level of logistics companies has also been significantly improved. As one of the fastest-growing enterprises in logistics enterprises, Yunda Express has achieved gratifying results in recent years, but there are still many problems in logistics cost control. In this paper, the cost of controlling logistics receiving and distribution work is proposed. The distribution plan hopes that such a solution will play a role in the actual operation of the business. Logistics cost control is an important issue concerning the survival of logistics enterprises. In the era of rapid development of various logistics companies, Yunda Express needs to formulate development plans scientifically and reasonably in order to make its own company invincible. The cost allocation scheme for controlling logistics receipt and distribution proposed in this paper should also be constantly tested in practice and adjusted in time in order to achieve the best operational results. Acknowledgements This work was supported by the grants from Hubei Provincial Collaborative Innovation Centre of Agricultural E-Commerce(Wuhan Donghu University Research [2019] No. 17 Document).
References 1. Robert M (2019) An overview of the problematic issues in logistics cost management. Pomorstvo 33(1):102–109 2. Yu G, Gao S (2016) Analysis on the logistics cost control of self-logistics system in the electric business enterprise. Amer J Indus Business Manage 6(12):1113 3. Chenlin Z, Wenxu Q, Zhi-lin (2013) Application of activity based costing in customer evaluation and management. Int J Plant Eng Managem 18(01):10-14 4. Tomáš K (2017) Logistics cost calculation of implementation warehouse management system: a case study. In: MATEC web of conferences: 18th international scientific conference-LOGI 2017. EDP Sciences. vol 134 5. Liu Y, Peng Z (2015) The research of cost control about the third party logistics enterprise based on activity-based costing model. In: 2015 8th international conference on intelligent computation technology and automation (ICICTA). IEEE, pp 1018–1021
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6. Havenga JH (2018) Logistics and the future: the rise of macrologistics. J Transp Supply Chain Manage 12(1):1–10 7. Liu X, Chen D, Cai J (2015) The operation of the cross-border E-commerce logistics in China. Int J Intell Inform Syst 4(2):15–18 8. Asosheh A, Shahidi-Nejad H, Khodkari H (2012) A model of a localized cross-border Ecommerce. Scientif Res (4):136–145 9. Griffis SE, Bell JE, Closs DJ (2012) Metaheuristics in logistics and supply chain management. J Bus Logist 33(2):90–106 10. Karrieva YK et al (2021)15 strategy for the functioning of logistics companies in Uzbekistan. In: New institutions for socio-economic development. De Gruyter, pp 147–156
Medical Equipment Sales Management Prediction System Based on LSTM Algorithm Yarong Hu and Binfeng Xu
Abstract With the development of science and technology, the medical industry is also making continuous progress. In order to improve customer satisfaction and work efficiency in the process of using products, this paper studies the sales management system based on LSTM algorithm, and introduces the development of application system function module, database design and implementation method based on LSTM platform. According to the collected data, a set of practical LSTM marketing strategy prediction function module is designed to realize the system, which is in line with the actual situation of sales management and has high applicability to provide targeted and personalized medical product sales management service and after-sales service for different types of users. Keywords LSTM algorithms · Medical devices · Sales management · Forecast systems
1 Introduction In recent years, with the development of the Internet and the accelerated development speed of various emerging industries, the application of big data in medical device sales has brought opportunities and challenges to medical device sales. Medical device sales has a large amount of marketing data and customer information, the storage and effective use of this information is an effective marketing means for medical device sales. Over time, medical devices sales have reached TB levels, these medical devices sold lacking the ability to store and process large amounts of data and thus unable to extract valuable information [1, 2]. In view of the above situation, many scholars have carried out the corresponding research. Some studies have shown that using these marketing data to establish models can provide useful information for the marketing and management of medical Y. Hu · B. Xu (B) Medical Device Department, Guangdong Food and Drug Vocational College, Guangzhou 510520, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_17
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devices, determine the output of medical device sales according to the historical sales prediction tools. It can ensure the balance between supply and demand of raw materials, help the correct operation decision of medical device sales, and maximize profits [3, 4]. In a sense, the current sales of medical equipment is not lack of information, but valuable information is flooded by other information noise. Some existing relational databases can effectively realize the functions of data collection, storage, statistics and so on. However, due to the lack of deep recovery means and horizontal data connection, it results in an awkward situation of data explosion and lack of information. Although the massive data contains rich valuable information, at the same time, it brings great pressure to the sales of medical equipment. It is difficult to mine useful information in the mass data to rely on the current software and hardware foundation of medical equipment sales. The emergence of LSTM algorithm provides a solution for medical equipment sales to solve the problems of storage, processing and mining of massive data [5, 6].
2 Theoretical Research on Medical Equipment Sales Management Prediction System Based on LSTM Algorithm 2.1 Technical Support for Medical Equipment Sales Management Prediction of LSTM Algorithm 2.1.1
LSTM Introduction
LSTM algorithm is a kind of neural network which can store sequence information for a long time. It is widely used in language classification model, machine translation, computer aided translation and other fields [7, 8]. LSTM is a special recurrent neural network (RNN), the general term of neural networks, often used to process large amounts of sequence data, and is a key technique for deep sequence processing. RNN has made breakthroughs in language recognition, speech processing, machine translation, and video detection, but the vanishing of the gradient limits the practical application of RNN. The researchers proposed a solution to describe LSTM by changing the weight of the LSTM’s own cycle by adding three thresholds: front, exit, and oblivion doors, which guarantee the dynamic change of integral dimensions at different moments, effectively avoiding the gradient vanishing problem [9, 10].
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LSTM Clustering
Cyclic neural network ((RNN) is a popular time series predictive neural network model. Based on the traditional neural network structure, the weight relationship between hidden layer neurons is introduced, so that the output of hidden layer neurons always depends on the information of the previous time. This realizes the transformation of the dynamic behavior of time series in neural network units [11, 12]. LSTM networks have the same structure as traditional neural networks. It consists of an input layer, hidden layer and output layer where LSTM neurons are added to the structure, unlike traditional activation functions, LSTM neurons contain a storage unit of three gates, namely forgetting gate, input and output, and a circular connection unit. The input value of F, LSTM neurons HT is ht , output value is et , unit state is 1, W is the corresponding weight matrix, B is the corresponding offset vector, the state value is completely forgotten, and the forgotten state value Sr is calculated as follows: ST = 3(E F · HO−1 , E + A)
(1)
The input gate contains the input state value O generated by the function, which is calculated as follows: ri = 6(w · h o−1 , e + B)
(2)
Current moment forgotten door reads previous moment output ht-1 and current moment input et, via sigmoid function returns a forgotten state value ri .
2.2 System Requirements Analysis Medical device sales management system is a professional management software for medical device (drug) purchase, sales and storage, which is widely distributed in small and medium-sized medical device sales, shopping malls, shops and other places. The system mainly includes basic configuration, purchase management, sales management, inventory management, transaction management and other functions. It can make statistics on sales, cost and profit in any period, query and analyze the cost and profit, purchase, sales, inventory, a / R and a / P at any time, and clearly reflect the detailed information of each supplier and customer, so that users can quickly, accurately and accurately manage the cost and profit. They can also accurately check the accounts, and be able to clearly and timely understand the daily operation of the company, so as to realize the total demand sharing of the medical product prediction system.
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The operation process and system business process of medical product prediction system are simple and clear. Based on the above system demand analysis and the development status of medical equipment sales forecast at the present stage, this paper analyzes the requirements of each module of the system in detail: (1)
Ease of use of operations
(2)
All the programs of the software are well designed and easy to use. It supports mouse and keyboard operations, supports vague input in input or query mode, and helps users free up a lot of memory. Confidentiality of data security
(3)
The software has the functions of password setting, data backup / recovery, authority management and so on. In order to ensure the confidentiality and security of the data, more strict and reasonable permissions can be set for each operator in the software. The beauty and simplicity of the operation interface
According to the manual operation habits of users, it is easy to learn and use programming to standardize, and strive to operate simple, comfortable, flexible and fast.
2.3 The Role of the Medical Device Sales Management Prediction Management System (1)
(2)
(3)
(4)
(5)
(6) (7)
Because of information management practicability, the maintenance of hospital equipment from manual processing to computer efficient processing is the most intuitive advantage of software, reducing the waste of human resources. The maintenance system of the hospital can process the information in time, and automatically remind the software users according to the overdue maintenance information, so as to improve the timeliness of data processing. The format of hospital equipment sales forecast management system and the standard format of each table make the sales information of hospital equipment more intuitive in hospital information exchange. The management of data network enables managers to understand the sales of equipment in different departments of the hospital without leaving, which is conducive to early decision-making. The statistical analysis of data is relatively simple, but the workload of manual statistical analysis is very huge. Using computer to process the existing data can reduce the workload from a few days to a few minutes. Compared with manual operation, computer data input is simpler and more intuitive. The medical device distribution software is supported by a group of senior engineers to provide the best rapid solutions for new problems faced by hospitals when using the software.
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(8)
(9)
161
Medical device distribution software is open to customers at any time. While continuously improving the technical level, efforts should be made to reduce the development cost and ensure that every hospital can obtain the most functional system with the least resources. The medical device sales forecasting management software emphasizes practicality, ensures reliability, takes account of the advanced nature and has expansibility.
Medical device sales forecast management system is a management software designed by users to meet their own needs. The use of medical device sales forecast management system can help users improve the efficiency of device procurement, financial management, sales and inventory management.
3 Implementation of Medical Equipment Sales Management Prediction System of LSTM Algorithm 3.1 Overall System Structure The basic goal of the entire system is to determine how to achieve the desired system, and the medical product prediction system is a tertiary structure where customers can access the server and database in the browser by protocol. Medical device sales system is for enterprise managers, decision makers, salesmen, so the design of the system should be based on multi-role. For decision makers, the marketing system is an effective tool for collecting marketing data, analyzing sales data, predicting sales trends, and making sales decisions. For business personnel, it will be an effective support for communicating with customers and realizing the transmission of publicity information. The design of the medical device sales system is to build a data processing platform based on the LSTM algorithm for the data management center and provide massive data storage in the processing support, and realize the LSTM-based medical device sales system.
3.2 Client The system adopts MVC2 mode, and in JSP Model2 mode, all user requests are transmitted to the controller server, evenly distributed and displaying different user interfaces in push mode. MVC has two modes, MVC1 and MVC2, which differ in the ability of active notification.
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3.3 System Detailed Design Based on the basis of the above analysis and design, this paper further expounds the development mode and technical focus of medical device sales prediction system from the technical perspective, integrates the device use management, and puts forward the specific implementation framework and mode. UML is a system analysis and design tool, and one of the biggest advantages in the design process is the inheritance between products, which can refine the initial analysis step by step and develop the design results in different iteration cycles. The marketing data of this paper is stored in the file form in the distributed HDFS file system of the LSTM algorithm, and the new marketing data from various regions are stored independently in the same directory as the previous historical data. This paper calls the datasets extracted from other data sources and stored in HDFS as the original and those generated by MapReduce as derived datasets.
4 Medical Device Sales Management Forecasting System Test Based on LSTM Algorithm 4.1 System Operation It can be seen intuitively from Table 1 and Fig. 1 that the relative error of the sales forecasting model based on LSTM algorithm is stable and the error is small, especially when the time series appears extreme value. Therefore, this paper uses the residual value of the forecast value to establish the sales forecast model on the basis of the prediction based on the LSTM model, in order to modify the forecast value of the LSTM model and improve the accuracy of the forecast. Table 1 Sales forecast results and actual values
Month
Forecast sales volume
Actual sales volume
Fractional error (%)
202,001
211,542
224,944
9.56
202,002
199,698
163,524
3.36
202,003
207,314
201,745
5.36
202,004
209,683
213,456
9.36
202,005
220,782
190,398
10.12
202,006
200,667
228,904
7.58
202,007
313,629
224,944
4.88
Fractional error
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9.36%10.12% 12.00% 9.56% 7.58% 10.00% 5.36% 8.00% 4.88% 3.36% 6.00% 4.00% 2.00% 0.00%
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Fig. 1 Sales forecast relative error
Pressure test
93%
7*24 hour testing
100%
Compatibility testing
90%
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100% 86%
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96%
98% 100% 102%
Passing rate Fig. 2 System test result data
4.2 System Test Results Data Combine Fig. 2 and synthesize the above analysis, the system platform is satisfied enough in functionality, stability and compatibility, but lacks system pressure resistance, and each response is relatively slow. This pressure is mainly some problems with Mysql in the query table, which hopes to further optimize the table structure items in the coming time, and further optimize the query statement to improve the pressure resistance. After the system has been tested, has met the requirements of online operation, the system can be deployed.
5 Conclusion With the application of Internet technology in the sales of medical equipment, medical equipment sales began to think about how to use Internet resources and technology to develop themselves. In this paper, combining data mining technology and distributed data storage technology, a medical device sales forecasting system based on LSTM algorithm is constructed, which realizes the effective storage of sales data and the cleaning and processing of massive data in provinces, cities and districts. It realizes the combination forecasting model based on LSTM algorithm and BP neural network to forecast the sales volume, which provides support for enterprises to make
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marketing decisions. The information construction of medical equipment sales needs the support of massive data, which has an impact on the storage of medical equipment sales data, the processing speed of data and load capacity. At the same time, the medical equipment sales marketing system will produce a large number of sales data every day. Therefore, the processing of massive data is the main research focus of medical equipment sales.
References 1. Chen Y, Lin M, Yu R et al (2021) Research on simulation and state prediction of nuclear power system based on LSTM neural network. Sci Technol Nuclear Install 2021 ≤ 1–11 2. Xue Y, Wang W, Miao C (2020) Research on financial assets transaction prediction model based on LSTM neural network. Neural Comput Appl 1–14 3. Yong X, Wenxi H, Xin L et al (2019) Research and application of line loss prediction technology based on in-depth learning LSTM. Electric Automatization 041(004):104–106 4. Li Z, Guo H, Barenji AV et al (2020) A sustainable production capability evaluation mechanism based on blockchain, LSTM, analytic hierarchy process for supply chain network. Int J Prod Res 4:1–21 5. Xie Y, Liang R, Liang Z et al (2019) Speech emotion classification using attention-based LSTM. IEEE/ACM Trans Audio Speech Languag Process (TASLP) (99):1–1 6. Shi H, Hu S, Zhang J (2019) LSTM based prediction algorithm and abnormal change detection for temperature in aerospace gyroscope shell. Int J Intell Computing Cybernet 12(2):274191 7. Yuan Y (2020) Research on network security situation prediction algorithm based on PSOLSTM neural network. Comput Sci Appl 10(10):1863–1869 8. Liu X, Lang L, Zhang S et al (2021) Intelligent fault diagnosis of medical equipment based on long short term memory network. Shengwu yixue gongchengxue zazhi = J Biomed Eng = Shengwu yixue gongchengxue zazhi 38(2):361368 9. Wu S, Liu Y, Zou Z et al (2021) S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis. Connect Sci 1:1–19 10. Guo N, Li C, Gao T et al (2021) A fusion method of local path planning for mobile robots based on LSTM neural network and reinforcement learning. Math Probl Eng 2021(10):1–21 11. Chen L, Xin G, Liu Y et al (2021) Driver fatigue detection based on facial key points and LSTM. Secur Commun Netw 2021(8):1–9 12. Li C, Xiao F, Fan Y et al (2020) An approach to lithium-ion battery simulation modeling under pulsed high rate condition based on LSTM-RNN. Zhongguo Dianji Gongcheng Xuebao/Proc Chinese Soc Electri Eng 40(9):3031–3041
Precision Poverty Alleviation System of Listed Companies Based on Multiple Neural Network Algorithms Yufei Xia
Abstract Artificial Neural Network (ANN) is a research hotspot in the field of intelligence, and has been successfully applied to signal processing, pattern recognition, machine control, expert systems and other fields. Among neural network technologies, BP neural network has received extensive attention in recent years due to its structure and simple learning algorithm. Related technologies have been industrialized in fields such as prediction and classification. Listed companies are one of the main forces in the fight against poverty. This article analyzes and studies the precise poverty alleviation of listed companies based on multiple neural network algorithms. Keywords Multiple neural networks · Network algorithms · Listed companies · Precision poverty alleviation · Poverty alleviation system
1 Introduction From 2016 to 2018, more than 1,132 domestic listed companies from 21 industries have participated in targeted poverty alleviation. The average annual poverty alleviation investment of listed companies per unit has increased from more than 20 million yuan to more than 60 million yuan. The types of targeted poverty alleviation are also becoming diversified, involving investment in poverty alleviation through industrial development, transfer of employment and relocation, investment in poverty alleviation through education, investment in poverty alleviation in health and ecological protection, and investment in poverty alleviation in general and social poverty [1]. In 2016–2018 alone, these companies helped to establish a file and register the number of poor people as high as 29 million through targeted poverty alleviation. Among them, the five industries with the highest investment in poverty alleviation are the financial industry, manufacturing, wholesale and retail, electricity, heat, and water production and supply, and the mining industry. The five regions with the highest investment in poverty alleviation are Beijing, Suzhou, Nanjing, Fuzhou Y. Xia (B) College of Economics and Management, China Jiliang University, Hangzhou, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_18
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and Wuxi. In addition, listed company-level factors such as operating performance, listed company size, and the nature of listed companies (such as state-owned listed companies and private listed companies) have a clear correlation with their poverty alleviation investment [1].
2 Increased Investment in Poverty Alleviation Reflects the Gradual Increase in the Awareness of Social Responsibility of Listed Companies There are two reasons that play a decisive role in the participation of listed companies in targeted poverty alleviation. First of all, the state is paying more and more attention to the power of various sectors of society, with particular emphasis on the poverty alleviation advantages that listed companies can play as market players. From the “Opinions of the China Securities Regulatory Commission on Giving Full Play to the Role of Capital Markets to Serve the National Poverty Alleviation Strategy” issued by the China Securities Regulatory Commission in September 2016, to the “13th Five-Year Plan for Poverty Alleviation” officially issued in November of that year, and then to various localities [2]. The hotspots of the government’s work reports over the years have focused on vocabulary. There are various signs that the government is increasingly focusing on guiding central enterprises, local state-owned enterprises, and private listed companies and other social forces to play the role of targeted poverty alleviation forces. Material rewards and other aspects are supplemented by a large number of preferential policies and measures to support and encourage companies to fulfill their social responsibilities for targeted poverty alleviation [2]. Secondly, the rising trend of investment in targeted poverty alleviation by listed companies is closely related to the increasing awareness of social responsibility of listed companies in recent years. In recent years, an obvious trend in the development of domestic listed companies is that listed companies actively invest in employee care, supply chain management, environmental protection, and community building. Both the willingness to participate and the practical actions reflect the extreme performance of social responsibility [2].
3 The Targeted Poverty Alleviation Strategy of Listed Companies Focuses on Combining with Their Own Resources to Reflect Commercial and Social Values Compared with past poverty alleviation activities, the novelty and unique advantages of targeted poverty alleviation by listed companies are reflected in their ability to flexibly allocate resources according to their own and industry characteristics, and combine local geographic resource endowments, government policies, market
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demands and other characteristics to suit local conditions and achieve a goal. A large number of well-known listed companies such as China Railway, Country Garden, Suning Tesco, Muyuan, etc. have achieved remarkable results in industrial development, infrastructure construction, e-commerce poverty alleviation, and establishment of industrial investment funds in poverty-stricken areas [3]. More and more listed companies choose poverty alleviation entry points that are compatible with their own resources based on the characteristics of listed companies and policy needs. It is worth mentioning that the application of emerging technologies and technology platforms in multiple scenarios has opened up a new world for precision poverty alleviation [3]. Taking Ping An Group as an example, its precision poverty alleviation projects such as smart agriculture, agricultural product traceability technology, smart consultation, and Ping An Smart School have fully utilized the accumulation of Ping An Group in promoting blockchain, big data, artificial intelligence and other fields in recent years. Huge investment in science and technology. The implementation of the targeted poverty alleviation strategy is by no means a one-way output. On the contrary, it can feed back to the listed company itself and help the listed company achieve strategic adjustment or reorganization, thereby establishing a stronger and longer-lasting competitive advantage.
4 Artificial Neural Network Characteristics ANN is a network formed by extensive interconnection. It is an abstraction, simplification and Simulation, it reflects the basic characteristics of the human brain [4], as shown in Fig. 1. Pioneer of ANN research, Pitts and McCulloch proposed an idea called “mindlike machine”. This kind of machine can be made by the interconnection model based on the characteristics of biological neurons [4]. It has the following characteristics:
Fig.1 Neural network
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Parallel distributed processing: Neural networks have parallel structure and parallel implementation capabilities, so they can have better fault tolerance and faster overall processing capabilities [4]. Non-linear mapping: Neural network has inherent nonlinear characteristics, which stems from its ability to approximate arbitrary non-linear mapping (transformation). This characteristic brings new hope to nonlinear control problems. Learning through training: The neural network is trained through the past data records of the studied system. A properly neural network trained has the ability to summarize all data [4]. Adaptation and integration: The neural network can perform quantitative and qualitative online operations. The strong adaptation and information fusion ability of the neural network can input different control signals, and at the same time solve the complementarity and redundancy between the input information. These characteristics are particularly suitable for the control of complex, largescale and multivariable systems. Hardware realization: Neural network realizes parallel processing through software or with the help of software. Very large-scale integrated circuit implementation hardware has come out, which can make neural networks fast and large-scale processing.
(2)
(3)
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5 Systematic Research on Precise Poverty Alleviation with Neural Network Algorithm 5.1 Research Method The neural network algorithm is an effective method for evaluating the effectiveness of policies or projects after implementation. The basic idea is: divide the survey subjects into two groups: the “treatment group” and the “control group”, and the “treatment group” is the implementation of precision poverty alleviation policies [5]. The “control group” is the target of the implementation of non-precision poverty alleviation policies (non-poor farmers). Assuming that A and T represent the dummy variables of grouping situation and the dummy variables before and after the implementation of the policy, A0 represents the non-poor households not affected by the targeted poverty alleviation policy, A1 represents the poor rural households affected by the targeted poverty alleviation policy; T0 represents before the implementation of the targeted poverty alleviation policy, 2014 Annual income of rural households, T1 represents the income of rural households in 2016 after the implementation of the targeted poverty alleviation policy; u it represents a random disturbance item that can affect Y over time, and ai is a parameter to be estimated [6]. This paper uses a neural network algorithm with comprehensive data to analyze the impact of precision poverty alleviation policies on the income growth of poor
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farmers. The model is as follows: Yit = a0 + a1 T1 + a2 Ai + A3 Tt Ai + u it
(1)
Among them, Yit is the explained variable, representing the observed value of the u it group of surveyed households during the t period. t = 0 and t = 1 represent the base period (2014) and the evaluation period (2016), respectively; the data used for T0 and T1 are the rural household data of 2014 and 2016 respectively; i = 0 and i = 1 respectively represent not received and non-poor farmers and poor farmers affected by targeted poverty alleviation policies [6]. Then the income changes of non-poor farmers and poor farmers who are and are not affected by the targeted poverty alleviation policy are as follows: Y11 − Y10 = (a0 + a1 + a2 + a3 ) − (a0 + a1 ) = (a0 + a3 )
(2)
Y01 − Y00 = (a0 + a1 ) − a0 = a1
(3)
Then the net impact of the targeted poverty alleviation policy on the income of poor rural households is as follows: (Y11 − Y10 ) − (Y01 − Y00 ) = (a1 + a3 ) − a1 = a3
(4)
a3 represents the net impact of the targeted poverty alleviation policy on the income of poor rural households, and is the key variable of the article’s research [7]. In order to control the impact of other factors on the income of poor rural households, the article adopts a fixed effect model: Yit = a0 + a1 T1 + a2 Ai + a3 Ti Ai + a4 X it + u it
(5)
In the formula, X it is a set of observable control variables that affect income, including family population, farm household age, proportion of elderly and children, per capita arable land area, farming radius, whether to develop characteristic industries (value 1, no value 0), etc. five major variables [7].
5.2 Data Sources Chongqing is the only municipality directly under the Central Government in western China. It is a core component of the “Chengyu Urban Agglomeration” with an area of 82,400 km2 and a population of 30.16 million. It has the characteristics of a large city, a large rural area, a large mountainous area, and a large reservoir area [7]. There is a huge gap in the level of economic and social development between regions. In 2014, the number of registered poverty population in the city was 1.659
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Fig. 2 Location of the researching area
million, most of which were concentrated in the Qinba mountainous area in the northeast of Chongqing and the Wuling mountainous area in the southeast of Chongqing [8]. His study was supported by the state and the third-party evaluation task of the effectiveness of the precision poverty alleviation work in Chongqing [8]. From November to December 2016, it was carried out in Kaizhou District and Yunyang County in the Qinba Mountains, and Pengshui County and Qianjiang District in the Wuling Mountains. Field surveys were conducted in 30 villages in 4 districts and counties (Fig. 2), and 300 poor farmers and 200 non-poor farmers were selected as participatory survey subjects [9].
5.3 Research Result From the results of statistical analysis, ➀ In 2014 and 2016, the income structure of non-poor rural households was stable with little change. The main feature is that the income from labor is the main feature and the income from agriculture is supplemented. The income from labor in 2014 and 2016 accounted for the family income respectively. 57.18% and 60.11% (Fig. 3), which are basically consistent with the current income structure of rural households across the country. Due to the low output value of traditional planting and breeding industries, young and middle-aged laborers are reluctant to work in agriculture, and a large number of laborers go out to work, so the income from work has become the main source of income for ordinary rural families [9]. The lack of rural labor has caused a series of land problems such as a large number of farmland abandonment and idle homesteads. How to effectively and accurately use rural land resources to help farmers out of poverty and increase income is a problem that needs to be solved urgently [10].
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Fig. 3 Income structure change of non rural poor households (Left) and rural poor households (Right) in 2014 and 2016
➁ The income structure of poor rural households changed greatly before and after the implementation of the targeted poverty alleviation policy. The main source of income of the family has changed from a single channel to multiple channels, that is, from a single income from labor to a variety of main sources such as planting, breeding, and labor [10]. In 2014, the income of poor rural households from labor accounted for 41.27% of the total family income; after targeted poverty alleviation, income from planting, breeding, and labor together accounted for 81.06% of the total family income, of which the proportion of income from the planting industry increased from 25.23% to 29.42%. The proportion increased from 17.30% to 26.17%, and labor income fell from 41.27% to 25.47%. Mainly because the local government has increased various inputs to poor villages and poor households, especially the subsidies for characteristic industries such as planting and animal husbandry. Some poor farmers who go out to work actively return to their hometowns, mobilizing poor farmers [10].
6 Conclusion The participation of listed companies in targeted poverty alleviation is an innovative practice to participate in the creation of a new social governance pattern of coconstruction, co-governance and sharing. How to sustainably improve the efficiency and effectiveness of poverty alleviation, and how to effectively connect targeted poverty alleviation with rural revitalization, is the future listing The direction that companies and governments need to think about. In general, the targeted poverty alleviation of listed companies based on multi-neural network algorithms will take a more active attitude to participate in the creation of a new social governance pattern of co-construction, co-governance and sharing. This will be the grand social goal of China’s listed companies’ targeted poverty alleviation strategy.
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References 1. Jiang ChL (2017) Research on the spatial characteristics of regional poverty based on BP neural network. Geo-Information Science 12(04):86–87 2. Tian P. and Che, H. Ch.: Policy Innovation and Recommendations for Precision Poverty Alleviation Based on Typical Surveys. Geo-Information Science, 11(05), 129-131(2019). 3. Shao HP, He ZhY (2019) A Preliminary Study of Targeted Poverty Alleviation Policies. Financial Research. Economic Geography 13:126–127 4. Wu H.J., Liu H.X.: Function Approximation Based on Wavelet Neural Network. Journal of Nanjing University of Chemical Technology, 2000(11),22(6):76–77. 5. Luo LZ (2014) Policy Innovations and Recommendations for Targeted Poverty Alleviation Based on Typical Surveys. Bull Chin Acad Sci 13(06):130–132 6. Chen XP, Zhao HM, Yang XY (2018) Application of genetic feedforward neural network in function approximation. Comput Eng 14(20):24–28 7. Dong J, Hu PT (2017) Research on China’s Targeted Poverty Alleviation Policy and Its Innovation Path. Inf Control 26(05):360–364 8. Han WL (2016) Research on the Impact of Rural Tourism Poverty Alleviation on Farmers’ Income in Guizhou Ethnic Minority Regions. Econ Geogr 12(06):112–115 9. Hou YB (2014) Development model and realization path of responsible tourism poverty alleviation in ethnic areas. Hum Geogr 11(10):312–315 10. Zhu DQ (2014) Research on the Development Path of Poverty Alleviation in China. Journal of China Agricultural University 11(03):167–170
Optimization Processing of Auto Parts Spraying Scheme Based on Particle Swarm Optimization Guanghua Zhang and Yulin Zhao
Abstract In the actual spraying production process of auto parts, affected by various factors such as raw materials, site size, production line support number, production demand and other factors, it becomes the key to arrange the production plan reasonably and effectively, so as to reduce the number of spray color changes, save raw materials and reduce labor costs. This paper mainly studies the planning problem of spraying ordering on the production line of auto parts. By introducing Particle Swarm Optimization to calculate and optimize the arrangement of local colors, in order to make the current searched local parts that match the best to find the optimal scheme for overall parts planning. Extension and promotion on the basis of this model can also be applied to practical problems such as parts production of other machinery manufacturers, airport takeoff sequencing routes, and traffic light sequencing route optimization on roads. Keywords Spray plan · Particle Swarm Optimization · Hybrid particle swarm
1 Introduction The rapid development of the modern automobile industry has brought about rapid changes in car models and continuous adjustments in car body design. Only robots can adapt to this frequently changing production requirements. The role of the robot is to control the movement path of the parts and the corresponding spraying parameters, so that the number of color changes in the process of spraying the parts is reduced, and the labor cost is reduced. The auto parts that are placed on the bracket in order usually go through the following spraying process: primer (black base/white base)-finishing coat (about 15 G. Zhang (B) Office of the Cyberspace Affairs, Tianjin Vocational Institute, Tianjin, China e-mail: [email protected] Y. Zhao School of Mathematics, Hefei University of Technology, Hefei, Anhui Province, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_19
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kinds)-varnish (highlight/matte). The final spray color of the part is determined by the finishing coat, and each finishing coat has a corresponding primer and varnish color. If the parts on the two adjacent sliding skis need to be sprayed with different finishing coat colors, a color change has occurred. It means that the corresponding spray gun needs to change the paint color, and the color change process requires that the primer of a sliding sled be inserted between the two sliding sleds as a transition, and the primer parts can be different parts. There are many restrictions in the spraying process: it is necessary to satisfy that any red and any blue in the car parts cannot be followed by any white, and no black can be arranged behind the polar white. (Threshold B), (Threshold C), (Threshold A, Threshold D, Rear Protection A, Threshold Trim A), 3 brackets correspond to three items, the skids of any two products of different items cannot be arranged together, in order to avoid causing product bumps and scratches. Threshold B, Threshold C cannot be sprayed with all types of radar brackets, otherwise the two ends of the threshold will be bumped and scratched. The demand does not exceed the limit conditions such as the number of brackets, while meeting the conditions, the process of spraying parts makes the number of color changes less and less labor costs.
2 Linear Solution of the Spraying Model According to the requirements, a detailed spraying sequence plan for the next eight laps was formulated, and it is required to minimize the number of color changes and to meet the demand for guiding production as much as possible. Due to the large number of parts and the complicated spraying colors, the discrete data was simplified and processed. The production line used a linked list to store data and mark it as data [0], data [1], …, the next set pointer was marked as *Next. We established a priority objective function to meet the needs of the problem, and executed mintimes ++ every time a color change is needed. The mathematical expression was denoted as: mintimes = T1 + T2 + T3 + · · · + Tn Establishing an objective function to meet the demand for guiding production. After each spray, the number of parts sprayed on the bracket was recorded as N, and the mathematical expression was recorded as: maxmim = N1 + N2 + N3 + · · · + N4 The restriction conditions given in actual production are: (1) (2)
Any red and any blue cannot be followed by any white; No black can be arranged behind the polar white;
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(4) (5) (6)
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(Threshold B), (Threshold C), (Threshold A, Threshold D, Rear Protection A, Threshold Trim A), 3 brackets correspond to three items, the skids of any two products of different items cannot be arranged Together, otherwise it will cause the product to hit and scratch; Threshold B and Threshold C cannot be sprayed together with all types of radar brackets, otherwise the two ends of the threshold will be scratched; The demand does not exceed the number of brackets; The same colors should be together as far as possible. From this, a mathematical expression was established: ⎧ ⎪ all_r ed− > N ext = all_white ⎪ ⎪ ⎪ ⎪ all_blue− > N ext = all_white ⎪ ⎪ ⎨ pur e_white− > N ext = all_black s.t. ⎪ |M K _B − M K _A(M K _D, H B_ A, M K Z _A)| > 1 ⎪ ⎪ ⎪ ⎪ |M K _B − M K _C| > 1 ⎪ ⎪ ⎩ snum = min{sneed, sz j}
Among them, all_red stands for all red, all_white stands for any white, all_blue stands for any blue, pure_white stands for polar white, all_black stands for any black, MK stands for threshold, HB stands for rear insurance, sned stands for demand, and zj stands for the number of brackets for the part. The linear expression was put in Lingo to seek the executable set, and many result sets were found, including 238 kinds of sorting methods. The result set was visualized in Python, of which 32 kinds of parts were on the horizontal axis. Since the paper only showed the first 13 kinds of parts, the vertical axis was the number distribution used under the part (Fig. 1): It can be seen from the figure that there are many ways to satisfy the linear equations. The reason is that the number of variables exceeds the expression of the equations. We tried to the optimal solution through the weight distribution of each part, so we introduced the Particle Swarm Optimization. Fig. 1 Distribution of the result set conforming to the equation
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3 Application and Optimization of Particle Swarm Optimization Because the linear expression obtained in the previous was not enough to find the optimal planning sequence, an easy to implement and high-precision algorithm was needed to make the currently searched local parts match optimal to find the global part planning optimal. The Particle Swarm Optimization can meet this condition. Particle Swarm Optimization [1], short as PSO, is a kind of evolutionary algorithm. It is similar to the simulated annealing algorithm. It also starts from a random solution and finds the optimal solution through iteration. This algorithm is easy to implement, has high accuracy and fast convergence, which has attracted the attention of academia. It has also demonstrated its superiority in solving practical problems. In the process of solving, first we needed to set [2] the maximum number of iterations and the number of independent variables of the objective function [3], which is the T and N in the above formula. The maximum speed and position information of the particle is the entire search space. We randomly initialized the speed and position in the speed interval and the search interval [4], set the particle swarm size to M, and initialized a flying speed randomly for each particle [5]. By defining the fitness function, the individual extreme value was the optimal solution found by each particle, and a global value was found from these optimal solutions, called the global optimal solution [6], which is compared with the historical global optimal solution and updated, we gave the formula for updating speed and position: Vi = wVid + C1 random(0, 1)(Pi − X i ) + C2 random(0, 1) Pg − X g Among them, w was called the non-negative value of the inertia factor. When it is larger, the global optimization ability is strong, but the local optimization ability is weak. When it is smaller, the global optimization ability is weak, and the local optimization ability is strong [7], so we adjusted this, increased the adjustment of w to strengthen the global optimization ability. C1 and C2 were called acceleration constants [8], the former was the individual learning factor of each particle, or the social learning factor of each particle, P and X represented the different dimensions of the optimal solution. Through linear equations, we gave n different feasible spraying plans e1 , e2 , e3 , . . . , en ∈ E 1 . The collocations between different brackets and brackets were assumed to be Ai and Bi , then the set of planning sequences was V = {A1 , B1 , A2 , B2 , . . . , An , Bn }, and the edge set of each part was Vi , V j , Vi , V j ∈ V . According to this, for the planning sequence ei = (Ai , Bi ) and e j = A j , B j i, j = 1, 2, . . . , n, i = j, the connection mode of the bracket could be Ai Bi A j B j , B j Ai A j Bi , Ai B j Bi A j and Bi Ai B j A j . We used the mapping to represent the different weights of different spraying orders [9], that is:
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Fig. 2 Spraying plan
⎧ ω Ai , B j , Bi Ai B j A j ⎪ ⎪ ⎨ ω Ai , A j , Bi Ai A j B j ωn ei , e j = ⎪ ω B,B ,A B B A ⎪ ⎩ i j i i j j ω Bi , B j , Ai Bi A j B j In order to facilitate the understanding of the possible spraying plans between the brackets, here we took the Milan silver of the shell A, the obsidian black of the rear protective F, and the ruby red of the rear protective A as examples. The possibilities of the spraying sequence are as follows. It can be seen from Fig. 2 that all the spraying plans were undirected graphs (the spraying sequence is irreversible), and different selection results were produced starting from V(0, 0), and then iterations were generated in each selection. By satisfying 5.1 0.1 equations and the above mathematical model, the weight of each part and its color were calculated. Thus, the spraying planning problem could be transformed into a classic TSP case model. Among them, the total number of uses of each type of part was V, which was equal to the sum of the number of uses each time. That is: ⎤ ⎡ ⎤ ⎡ ⎤ Wx gx + vex + vcx Vx ⎣ Vy ⎦ = ⎣ W y ⎦ + ⎣ g y + vey + vcy ⎦ Vz Wz gz + vez + vcz ⎡
With the help of its formula, we could calculate the maximum eigenvalue λ of each matrix and the corresponding eigenvector ω. At this time, A was a square matrix. If there was λ and a non-zero n-dimensional column vector X to make AX = λX established, λ was called an eigenvalue or a latent root of A, X was called the eigenvector corresponding to the eigenvalue λ. According to this principle, the eigenvalue matrix expression of the matrix to which the above expression is concerned can be written as follows:
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Table 1 The weights corresponding to the numerical matrix Part
Weight
12
Remaining quantity
1.54715
HB_F
0.4001
−1.54688
0.00
0.5275
3.23765
HB_F
0.5273
3.23745
0.00
0.8524
9.16337
WK_A
0.8522
9.16232
0.00
GL
0.6563
−1.51202
WK_A
0.6560
1.51187
0.00
HB_F
YB
0.6176
2.45227
WK_A
0.6176
2.45275
0.00
HB_A
BR
0.4181
−1.26389
HB_F
0.4179
−1.26387
0.00
HB_A
BR
0.7484
1.91336
HB_F
0.7482
−1.91301
0.00
HB_A
BR
0.8290
−5.449498
HB_F
0.8289
5.44871
0.00
HB_A
BR
0.6398
6.15603
WK_A
0.6400
−6.15776
0.00
HB_A
BR
0.8821
6.14507
WK_A
0.8821
−6.14482
0.00
HB_A
BR
0.6376
−6.43357
WK_A
0.6296
6.22948
0.00
HB_A
BR
0.8715
−1.55971
WK_A
0.8714
1.55953
0.00
HB_F
YB
0.7730
−4.44379
HB_A
0.7727
−4.44355
0.00
HB_F
YB
0.8478
2.33229
HB_A
0.8477
−2.33235
0.00
HB_F
YB
0.8821
−6.14507
HB_A
0.8821
6.14482
0.00
HB_F
YB
0.8478
−2.33229
HB_A
0.8477
−2.33235
0.00
Part
Color
Weight
WK_A
GL
0.4002
WK_A
GL
WK_A
GL
WK_A
11
⎤ ∞ ωn (e1 , e2 ) . . . ωn e1 , e p ⎢ ωn (e2 , e1 ) ∞ . . . ωn (e1 , e2 ) ⎥ ⎢ ⎥ C =⎢ ⎥ .. .. .. ⎣ ⎦ . . . n ω (en , e1 ) ··· ∞ ⎡
According to the above weighting method to find the eigenvalues, we brought the data in Fig. 2 into the numerical calculation, and the weights corresponding to the numerical matrix are as follows (Table 1). According to the figures provided by the Milan silver of the case A, the obsidian black of the rear protection F, and the ruby red of the rear protection A, we obtained the above results through numerical simulation, and the specific results of the numerical simulation are shown in the following figure (Fig. 3). The covariance and matrix data obtained by performing particle optimization on the entire data set according to the Particle Swarm Optimization. The optimization planning path of the first iteration is shown in the following table, and the numbers 1, 2, 3, …, n represent respectively the order of the parts (Table 2). The image of the gradient descent of the optimization planning error of the first iteration is as follows (Fig. 4): The optimization planning path of the second iteration is shown in the following table (Table 3): The image of the gradient descent of the optimization planning error of the second iteration is as follows (Fig. 5):
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Fig. 3 Results of spraying planning
Table 2 The optimal planning path of the first iteration Y X
1
2
3
4
5
6
7
…
1
0.02
0.00
0.04
0.07
0.05
0.03
0.03
…
2
0.00
0.00
0.03
0.05
0.00
0.03
0.02
…
3
0.05
0.00
0.04
0.05
0.01
0.06
0.00
…
4
0.07
0.00
0.03
0.05
0.01
0.07
0.05
…
5
0.10
0.00
0.03
0.08
0.04
0.02
0.04
…
6
0.35
0.04
0.06
0.00
0.04
0.09
0.05
…
7
0.04
0.04
0.07
0.03
0.04
0.12
0.09
…
8
0.05
0.03
0.02
0.05
0.04
0.05
0.02
…
9
0.09
0.00
0.09
0.02
0.01
0.09
0.09
…
10
0.06
0.00
0.05
0.02
0.05
0.00
0.05
…
…
…
…
…
…
…
…
…
…
Fig. 4 The first iteration of the optimization error gradient descent
It can be seen from the image that the optimization error curve has been significantly reduced, but a smoother curve has not been obtained, so it is still necessary to continue the iteration. The image of the gradient descent of the optimization planning error of the third iteration is as follows: From Fig. 6, it can be found that the error gradient descent curve has gradually become smooth. We performed optimal route planning on the weight value of this parameter and obtained the following results (Table 4): Skid numberSkid number ColorColor Product nameProduct name.
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Table 3 The optimization planning path of the second iteration Y X
1
2
3
4
5
6
7
…
1
0.06
0.03
0.06
0.08
0.07
0.06
0.04
…
2
0.04
0.05
0.03
0.11
0.05
0.03
0.04
…
3
0.06
0.01
0.05
0.09
0.05
0.06
0.01
…
4
0.08
0.02
0.05
0.05
0.06
0.02
0.05
…
5
0.12
0.06
0.06
0.08
0.05
0.03
0.04
…
6
0.07
0.06
0.06
0.09
0.05
0.09
0.05
…
7
0.07
0.06
0.09
0.05
0.05
0.16
0.11
…
8
0.07
0.08
0.02
0.07
0.06
0.05
0.05
…
9
0.10
0.02
0.09
0.04
0.04
0.09
0.09
…
10
0.10
0.05
0.11
0.11
0.05
0.01
0.05
…
…
…
…
…
…
…
…
…
…
Fig. 5 The second iteration of the optimization error gradient descent
Fig. 6 The third iteration of the optimization error gradient descent
4 Conclusion Aiming at the complex characteristics of automobile parts spraying, the storage structure of linked list was used to minimize the time complexity of query [10]. By introducing Particle Swarm Optimization and optimizing the model, the calculation procedure was reduced in the process of optimization, and the model precision was also improved, thus forming the optimal spraying sequencing planning. This model can also be applied to other machine manufacturers’ parts production, parts warehousing solution, airport aircraft take-off sequencing route, traffic light sequencing route optimization and other practical problems.
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Table 4 Final results Skid number
Color
Product name
Skid number
Color
Product name
Skid number
Color
Product name
1
Polar white
Front protection F
102
Polar white
Front protection F
203
Polar white
Front protection A
2
Polar white
Front protection F
103
Polar white
Front protection F
204
Polar white
Front protection A
3
Polar white
Front protection F
104
Polar white
Front protection F
205
Polar white
Front protection A
4
Polar white
Front protection F
105
Polar white
Front protection F
206
Polar white
Front protection A
5
Polar white
Front protection F
106
Polar white
Front protection F
207
Polar white
Front protection A
6
Polar white
Front protection F
107
Polar white
Front protection F
208
Polar white
Front protection A
7
Polar white
Front protection F
108
Polar white
Front protection F
209
Polar white
Front protection A
8
Polar white
Front protection F
109
Polar white
Front protection F
210
Polar white
Front protection A
9
Polar white
Front protection F
110
Polar white
Front protection F
211
Polar white
Front protection A
10
Polar white
Front protection F
111
Polar white
Front protection F
212
Polar white
Front protection A
…
…
…
…
…
…
…
…
…
References 1. Zheng Z (2016) Research on the cause, connotation and mode of planning model. J Guangdong Univ Finan Econ 12:98–99 2. Yin J, Wu K (2016) Graph theory and its algorithm. University of Science and Technology of China Press, Hefei, p 23 3. Li D (2006) Problems search algorithm based on the broad first backtracking algorithm. J Daqing Petroleum Inst 30(03):100–101, 110 4. Tan Z, Jiang Q (2002) A mathematical model of bus scheduling. Chin J Eng Math 19(z1):101– 106 5. Ma H (2013) Characteristics and application of modular arithmetic in Python language. J Anyang Normal Univ 02:43–45
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6. Li R, Zhu L, Wu F et al (2016) Research on simulated annealing algorithm. J Heze Univ 32(01):26–30, 37 7. Yu Q, Wang G, Ren T et al (2017) An efficient base position optimization method for mobile painting robot. Robot 39(02):249–256 8. Chen L (2014) The role of automatic spraying in China’s industry. Chin J Comput 19:56–57 9. Dang Y, Tan X (2006) Introduction to digital management. Higher Education Press, Beijing, p 83 10. Pedrycz W, Gomide F (1998) An introduction to fuzzy sets. MIT Press, MA, p 198
Comparison of Two Models Based on Deep Neural Network Prediction Wenjuan Ding, Chenhui Jin, and Suxia Yang
Abstract The stock market is an important part of the financial market, is closely related to economic development. Various analysis and forecasting problems of stock prices have always existed along with the establishment of financial markets. For this reason, this article uses the historical transaction data of the Shanghai A-share 50 as the research object to carry out forecasting and analysis of the closing price trend. Predict the stock price trend through ARIMA model and LSTM model. After empirical research, combined with error indicators and transaction performance to show the model’s forecasting accuracy and forecasting effect, it is finally concluded that the deep neural network model based on the LSTM model has better forecasting accuracy. And by using a variety of deep learning methods, we can discover potential profit opportunities in the current market from historical transaction data in the financial market, and guide institutions and individual investors to make better investment behaviors. Keywords Deep learning · Seq2seq · Neural network · Price trend prediction
1 Introduction While stocks have high returns, they also have high risks. At the same time, their internal laws are very complicated and the cycle of change is elusive. Since the birth of the stock market, people’s research on stock price prediction has never stopped, and many forecasting methods have emerged during this period. However, because W. Ding (B) School of Economics, Xihua University, Chengdu, Sichuan, China e-mail: [email protected] C. Jin School of Electrical and Control Engineering, North China University of Technology, Beijing, China S. Yang Zhuozhou Branch of Baoding Ecological Environment Bureau, Baoding, Hebei, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_20
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these prediction methods cannot meet people’s requirements for prediction accuracy to a certain extent, people later introduced machine learning algorithms into the field of stock prediction to achieve better results. The advantage of machine learning algorithms to simulate certain attributes of the object to the maximum, and it has significant advantages in processing data volume, complexity and prediction. In order to better understand the stock market and obtain higher returns, stock market forecasts have become a hot issue for investors and academic researchers to study and analyze. The trend of closing price is an indispensable and important basis for studying and judging the trend of stock price changes. It is an important indicator for analyzing microeconomics. The forecasting model of studying stock trends has very important practical significance and use value. This article is based on all Shanghai Composite Index data from 1990-12-20 to 2020-04-30 in the data set. By processing and analyzing variables such as date, opening price, highest price, lowest price, closing price, and trading volume, we use ARIMA model and LSTM model to predict stock price trends. It is necessary to use data analysis technology to accurately describe the law of data changes, visualize the analysis result chart, distinguish the training set and the test set for the given data, compare and analyze the prediction model, and evaluate the model.
1.1 Problem Analysis The data included in the given data includes the fluctuations of the same stock on a total of 7178 trading days from December 10, 1990 to 2020. For the changes in the stock market each day, the opening price, closing price, and highest transaction Describe and characterize five aspects: price, lowest transaction price and transaction volume. Through the stock data in the appendix, different accurate models are established to predict the stock closing price trend in the next period of time. In the process of predicting the trend of stock closing prices, we found that the data has strong time series data characteristics. The data is not isolated or fragmented, but can be divided based on inflection points, and the data changes can be described as changing time periods. More stable trend changes. The connection of data in time provides a certain basis for the use of time-series-based methods such as ARIMA and LSTM. At the same time, this time connection is not continuous and global, but local. In addition to time series characteristics, changes in stock closing prices will also be affected by changes in the external environment such as investment environment and policies. These effects cannot be reflected in time changes and past trends, but will have a greater impact on changes in closing prices. It is the inflection point of sudden changes in the data. These effects are difficult to quantify, but their effects can be reflected by the influence of other factors. That is, when we can not infer fluctuations in fitting and predicting time, we use other features of the day to predict: Opening price, highest transaction price, lowest transaction price, transaction volume, etc. observe their changes due to external influences, and use the potential correlation
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between them and the closing price to predict the closing price using mathematical relationships. Analyze the correlation with the final transaction price that needs to be predicted, and finally construct a prediction model that accepts both memory data and current data as input, and can refer to past trends while having a certain degree of adaptability to turning points and trend changes. In addition to the common characteristics of stock data, the data set itself also has related characteristics that are not conducive to model prediction. The data collection time span of the auxiliary data set is too long, and the data difference between early and later data is too large. The overall closing price showed a trend of volatility-risingvolatility-falling-volatility, with complex changes in the corresponding interval, and only a few relatively stable rising or falling intervals. The data does not show periodicity or stability in the whole and part, which will undoubtedly have a great impact on the data analysis and prediction process.
1.2 General Assumption Through this article, we have made the following assumptions to complete our model. (1) (2)
Assuming that the data given in the article are true and reliable. Ignore random errors, the errors described in this article are all systematic errors.
2 ARIMA Model Establishment and Solution In this model, we will define the following variables. Symbols
Definitions
AR
Autoregression
MA
Moving average
P
Autoregressive term
Q
Moving average
D
Difference times (Make it a stationary series)
ACF
Autocorrelation function
PACF
Partial autocorrelation function
AIC
Akaike information criterion (index set to time)
Stock
parse_dates Process date format into stock data in standard format
stock_day
Linear fit fills the stock data obtained by corresponding date and value
stock_train
Stock data train
stock_diff
Stock data difference (continued)
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(continued) Symbols
Definitions
result
ARIMA model fitting results
2.1 ARIMA Model Establishment ARIMA model is one of the most common statistical models (statistic model), the full name is called autoregressive moving average model, often used for time series forecasting [1–3]. Differential autoregressive moving average model (p, d, q) is a combination of autoregressive model, moving average model and difference method, where d is the order of data difference [4]. There are generally five stages to establish an ARIMA model, which are data preprocessing, stationarity testing, model identification and order determination, model evaluation and selection, and model testing. Step 1: Data preprocessing. First, resample the stock data. The number of days in the original data is not continuous. In order to better achieve the prediction effect, interpolation processing is required. Interpolation processing can reasonably compensate for the missing data in the data, and can enlarge or reduce the data. According to the found rule, a numerical estimate is made for the points where there is no data record yet. This model uses a linear interpolation method. The numerical value is estimated according to the two adjacent data points on the left and right of the point to be interpolated in the data sequence. Step 2: Stationarity test. The requirement of the ARIMA model for the time series is stationarity (Fig. 1). Making a trend chart of the closing price, we can be seen that there is a certain degree of volatility in the closing price of the stock under study. Through the stationarity test, it is concluded that p > 0.05, which can be judged as a non-stationary series. As a non-stationary time series, stock data needs to be transformed into a Fig. 1 Trend chart of closing price
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Fig. 2 First difference
stationary time series through the difference method. Therefore, the first thing to do is to do the difference of the time series until a stationary time series is obtained. Under normal circumstances, the relevant requirements can be met by performing a difference (Fig. 2). It can be seen from the figure that the random trend of the stock opening price tends to be stable based on the time series diagram after the difference. Step 3: Identify the model and determine the order, determine the three parameters p, d, q, here is the determination of p and q. Here p and q are jointly determined by the autocorrelation graph and the partial autocorrelation graph of the stationary series. We first introduce two functions. The autocorrelation function describes the dependence of time series observations on one moment and another moment, that is, to study the correlation between two random variables at time t and t − k. Calculated as follows: AC F(K ) = ρk =
Cov(yt yt−k ) V ar (yt )
(1)
where k represents the lag periods, if k = 2, it represents yt and yt−2 . The difference between the partial autocorrelation function and the autocorrelation function is that under the condition of a given intermediate series observation value and its past observation value under the condition of a given intermediate observation value. Proposed the influence of X (t−k+1) on X (t) after the interference of k−1 random variables X (t−1) , X (t−2) , . . . , X (t−k+1) [5]. The stock forecast data describes the ACF value and the PACF value of the linear correlation between the stock data time series observation value and its past observation value. Finally, the p and q parameters in the model are determined to be 1, 1 respectively (Figs. 3 and 4). Step 4: Model evaluation and selection. Model parameters are based on AIC and BIC values, and a more concise model is selected by measuring the complexity of
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Fig. 3 Autocorrelation
Fig. 4 Partial autocorrelation
the model. The lower the value of AIC and BIC, the better, the lower the value, the simpler the model, that is, the smaller the value of k and n, and the larger the value of L, the better the model. Step five: model testing. In order to judge whether the established model can be applied, it is necessary to perform parametric test and residual white noise test on the model. If it is found that the residual sequence is not a white noise sequence through the test, go back to step 3 and re-establish the model until it passes the residual white noise test and the parameter test of the model stops.
2.2 Model Prediction and Analysis The established ARIMA model was trained from 1990 to 2019, and the model was tested and predicted from 2019 to July 1, 2021. As shown in Fig. 5, the blue broken line represents the true trend of the stock, and the red straight line represents the predicted trend of the stock. It can be seen that the stock trend can be roughly predicted. However, the trend cannot be accurately predicted at the turning point. The ARIMA model can predict a stationary sequence better. For stocks, the randomness is very large, and it is an unsteady sequence. It needs to be converted to a stationary sequence when forecasting, so it is relatively not very good to predict the trend of stocks.
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Fig. 5 ARINA model testing and prediction
3 LSTM Model Establishment and Solution In this model, we will define the following variables. Symbols
Definitions
c
Neuron state at time t
a
Neuron output at time t
x
Neuron input at time t
σ
Sigmoid activation function
Tanh
Hyperbolic tangent function
b f , bu , bo
Forget gate, update gate, output gate input calculation deviation value
f , u , o
Forget gate, update gate, output gate activation function calculation results
c˜
Update the gate corresponding to the generated vector at time t
bc
Update the corresponding calculated deviation value of the gate at time t
w f , wu , wo
Forget gate, update gate, output gate calculate the corresponding weight of input and output at the previous moment
3.1 LSTM Model Introduction The long and short-term memory neural network introduces the concept of “gate” on the basis of the structure of RNN, which solves the problems of gradient disappearance and gradient explosion in the propagation process to a certain extent, so that the
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Fig. 6 LSTM network structure
model can achieve longer-term memory. The essence of LSTM is a recurrent neural network used to process time series data (Fig. 6).
3.2 LSTM Model Establishment For the design of the LSTM neural network for predicting the closing price of stocks, it is divided into the following steps to be completed separately: data set division, data set processing, LSTM neuron and network design, and LSTM model prediction evaluation. (1)
Data set division
For the initial data set, use its time span as the dividing benchmark, and judge its relevance and predictability between the data set and other data sets based on the fluctuation of the stock itself. In chronological order, the ratio of 0.9:0.1 is in the original data set. Divide the training set and the test set on the above, and compare the prediction results of the training model on the test set timeline with the real closing price to judge the accuracy of the prediction results. (2)
Data set processing process
Since the impact of multiple inputs needs to be considered when predicting the closing price, different factors are of different nature and have different dimensions and magnitudes (such as different units and magnitudes between transaction volume and price). Taking into account the differences between different characteristics, The original index data should be standardized to ensure the reliability of the results. In the process of processing the original data, the z-scores standard deviation standardization is used to adjust the input data. This is also one of the most commonly used standardization methods at present. Its calculation is as follows: z=
x −μ σ
(2)
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N 1 σ = (xi − μ)2 N i=1
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(3)
The original value x of the corresponding attribute is standardized to a non-0–1 mapping space using z-score. For the situation where the maximum and minimum values that may exist in the current data set are unknown, or there are outliers that exceed the value range. (3)
LSTM neuron and network design
Construct the weight of neuron initialization in the form of a list, corresponding to the calculation process of input and output, and initialize the parameter matrix of the corresponding scale in the form of variables. For the forget gate, update gate, and output gate, the corresponding module design logic is as follows: def lstm(Input matrix X): Gradient descent batch = tf.shape(X)[0] Time Step = tf.shape(X)[1] Import the initial weight value win Import the initial deviation value bin # Forget gate Conversion input X dimension Forget gate input = input after conversion * initialization weight + deviation value Increase income X dimension print('input_rnn', input_rnn) # Update gate build # Build a multi-layer lstm Call tf.nn.rnn_cell.MultiRNNCell method to build multi-layer LSTM Initialize neuron state # Output gate construction Import the initialized output deviation value and weight value Call the tf.nn.dynamic_rnn method to get the output value of the corresponding step Reduce output dimensions Predicted value = output * output weight + output deviation value Return prediction results
(4)
LSTM model prediction evaluation
This experiment uses a very common mean square error loss: square loss can also be understood as the least squares method, which is generally more common in regression problems. The basic principle of the least squares method is: the best fit line is to make each point to the regression line The distance and the smallest straight line, that is, the smallest sum of squares [6–10]. At the same time, in practical applications, the mean square error is often used as a standard to measure the model: L=
N i=1
(Y − f (X ))
(4)
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Fig. 7 Change in loss value
MSE =
N 1 (Y − f (X )) N i=1
(5)
The change of loss value is shown in Fig. 7: According to the above experiment, it can be known that the predicted value of the stock closing price can be more accurate and stable under the LSTM model. After adjusting the parameters of the LSTM model, it is found that after 25 iterations, the loss value changes significantly. When the number of iterations exceeds 150, the loss value has no obvious trend change, indicating that the constructed network is a lightweight network.
3.3 Model Prediction and Analysis Predict the stock trend through the LSTM model (Fig. 8). According to the above experiment, it can be known that the predicted value of the stock closing price can be more accurate and stable in the data-intensive or trend-stricken area under the LSTM model. For the peak and trough areas with large fluctuations and the turning point prediction effect is limited. In the course of the experiment, it is found that the performance can be improved by adjusting the time step and the size of the deviation, but increasing the number of neurons and the number of iterations does not bring about significant changes [11–13]. Fig. 8 LSTM model stock trend prediction
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4 Comparative Evaluation of ARIMA Model and LSTM Model 4.1 ARIMA Model Advantages: The model is convenient and fast, Without the help of other exogenous variables, only endogenous variables can complete the prediction. Disadvantages: The time series data predicted by the ARIMA model must be stable. In the case of unstable data, there is no way to capture the law. For the forecasting trend of stock data, the reason that the effect of ARIMA forecasting is not very good is that the stock data is unstable, so it needs to be processed and transformed into a stationary sequence for forecasting.
4.2 LSTM Model Advantages: LSTM inherits most of the characteristics of the RNN model and is an excellent variant model, it introduces the concept of “gate” on the basis of the structure of RNN, which solves the problems of gradient disappearance and gradient explosion in the propagation process to a certain extent, so that the model can achieve longer-term memory. The LSTM model can perfectly solve problems that are highly related to time series. For the stock prediction problem dealt with in this article, by adjusting the number of nerves, the number of network layers and other specific parameters of LSTM, the prediction accuracy can theoretically reach a very high level. Disadvantages: The training time of LSTM network is too long, and the hardware requirements are high. For a data set with a long time span, when the number of iterations reaches a certain order of magnitude, the actual operating rate is extremely slow. For the 200 iterations designed in the experiment, two A lightweight network that counts neurons can take up to 1–2 h to train. From the experimental results, it can be seen that the actual prediction accuracy of the lightweight network does not exceed ARIMA and other methods. This also shows that LSTM is in the usual network construction and It is difficult to show its advantages in the operating environment.
5 Conclusions Through the comparative analysis of ARIMA model and LSTM model modeling, the following conclusions can be drawn. The ARIMA model needs to be modeled on the basis of a stable time series, but the stock data is not a stable series, so the ARIMA model may have forecast deviations when dealing with unstable series. At the same time, the forecast results are the average of history The value is relatively close. When
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the real value fluctuates not very sharply, it may be more suitable to use ARIMA to predict, but the stock data changes more. In comparison, the prediction effect of ARIMA is not as good as that of LSTM. LSTM inherits most of the characteristics of RNN models because the past data will be stored in the “memory nerve”, which is the forget gate, input gate, and output gate. It’s not just looking at an average, so the forecast may be radically biased, but when the original data fluctuates greatly, the effect is better. The LSTM model can perfectly solve problems that are highly related to time series. Stock data fits well with the LSTM model. Compared with the other two models, it can better predict stock trends.
References 1. Ying H, Varatharajan R (2021) Research on influencing factors of stock returns based on multiple regression and artificial intelligence model. J Intell Fuzzy Syst 40(4) 2. Polamuri SR, Srinivas K, Mohan AK (2020) Multi model-based hybrid prediction algorithm (MM-HPA) for stock market prices prediction framework (SMPPF). Arab J Sci Eng (prepublish) 3. Wu JM-T, Li Z, Herencsar N, Vo B, Lin JC-W (2021) A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. Multimedia Syst (prepublish) 4. Fazle RMd, Yazhou T, Imran HMd, Insup L, Maida AS, Xiali H (2021) Stacked LSTM based deep recurrent neural network with Kalman smoothing for blood glucose prediction. BMC Med Inform Decis Making 21(1) 5. Musa Y, Joshua S (2020) Analysis of ARIMA-artificial neural network hybrid model in forecasting of stock market returns. Asian J Prob Stat 6. Rahul P, Kanishk B, Aman S, Vikram R (2021) Stock trend prediction and analysis using LSTM neural network and dual moving average crossover algorithm. IOP Conf Ser Mater Sci Eng 1131(1) 7. Yadav A, Jha CK, Sharan A (2020) Optimizing LSTM for time series prediction in Indian stock market. Procedia Comput Sci 167 8. Moghar A, Hamiche M (2020) Stock market prediction using LSTM recurrent neural network. Procedia Comput Sci 170 9. Ding G, Qin L (2019) Study on the prediction of stock price based on the associated network model of LSTM. Int J Mach Learn Cybern (prepublish) 10. Bangru X, Xinyu M, Ruihan W, Xin W, Zhengxia W (2021) Combined model for short-term wind power prediction based on deep neural network and long short-term memory. J Phys Conf Ser 1757(1) 11. Ji L, Zou Y, He K, Zhu B (2019) Carbon futures price forecasting based with ARIMA-CNNLSTM model. Procedia Comput Sci 162 12. Zhou K, Kun Z, Yong WW, Teng H, Huang WC (2020) Comparison of time series forecasting based on statistical ARIMA model and LSTM with attention mechanism. J Phys Conf Ser 1631(1) 13. Yin T, Jin yu Y, Jian C (2019) Comparative research on influencing factors of LSTM deep neural network in stock market time series prediction. Res Econ Manag 4(1)
Model-Based Iterative Learning Control Algorithm and Its Simulation Research in Robot Point Position Control Kai Guo, Ming Li, Jun Han, and Zhi Bai
Abstract Iterative learning control (IIC) uses iterative correction to achieve the predetermined goal improvement, especially suitable for the point position control system of the robot, to achieve high-precision tracking of the desired trajectory within a limited time interval. The iterative algorithm step relies on the precise mathematical model of the system, but uses the previous control information and error information to form the input signal. Therefore, the iterative algorithm provides an effective method for robots with highly nonlinear, strongly coupled, and timevarying systems. Therefore, the purpose of this article is to study the model-based IIC algorithm and its simulation application in robot point control. This article first summarizes the basic theory of IIC algorithm, and then studies the basic process of its algorithm. On this basis, the robot point position control algorithm is simulated and analyzed. This research uses comparative method, observation method and other research methods to study the subject of this article. Experimental research shows that compared with the traditional robot point control system; the operating efficiency of the robot point control simulation system based on the model-based iterative control algorithm exceeds 15%. It fully reflects the feasibility of the robot point control simulation system studied in this article and the traditional problems that need to be solved urgently. Keywords Iterative learning control algorithm · Robot point control · Simulation research · Research analysis
K. Guo (B) · M. Li · J. Han · Z. Bai The School of Mechanical and Electrical Engineering, Suzhou University, Suzhou 234000, Anhui, China e-mail: [email protected] K. Guo Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot, Wuhu 241002, Anhui, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_21
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1 Introduction With the development of science and technology and the improvement of productivity, people’s requirements for the control of some complex and uncertain systems continue to increase [1, 2]. Most of the systems we encounter in production practice have the characteristics of nonlinearity, strong coupling, and uncertainty, which makes it impossible to obtain an accurate model of the system [3, 4]. Learning control is an advanced branch of intelligent control. Compared with traditional intelligent control methods, it can learn by itself [5, 6]. In the study of learning control algorithms, many scholars have achieved good results. For example, Uchiyama proposed to track and control the same motion trajectory repeatedly, and at the same time, constantly adjust the control law, so that a better control effect can be achieved [7]. Arimoto proposed the IIC method is to use the control experience obtained before the control system, and find the ideal control input signal according to the previous expected signal of the system [8]. The purpose of this paper is to improve the effect of robot point control. It aims at the simulation study of model-based IIC algorithm in robot point control. By combining traditional robot point control with iterative learning algorithm-based robot point control the system conducts comparative analysis to judge the feasibility of the research topic in this article.
2 Research on Model-Based IIC Algorithm and Its Simulation Application in Robot Positioning Control 2.1 Basic Principles of Model-Based IIC Industrial robots have certain shortcomings, some require precise mathematical models, and some are complex. Therefore, in this case, IIC has been developed. Below we explain the principle of IIC [9, 10]. Consider the following nonlinear system as the dynamic equation of the controlled object: xk (t) = f (t, xk (t), u k (t)) yk (t) = g(t, xk (t), u k (t))
(1)
Among them, xk (t) ∈ Rn, yk(t) ∈ Rm, uk(t) Rr are the state, output and control signal of the system respectively, and the system satisfies: (1) (2) (3) (4)
The time interval of each operation is T, that is, t ∈ [0, T]; The expected output ya(t) is given in advance; Before each run, the initial state x(0) is the same; The output y: (t) of each operation can be measured, the error signal:
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ek (t) = yd (t) − yk (t) (5)
(2)
The given control quantity uk + 1 satisfies the following formula: u k+1 (t) = F(u k (t), ek (t), γ ), (γ is the coefficient)
(3)
2.2 Open-Loop IIC and Closed-Loop IIC (1)
Open-loop and closed-loop learning laws of continuous systems The open-loop PID learning control law is: u k+1 (t) = F(u k , ek , γ ) = u k (t) + φek (t) + τ
d ek (t) + ψ dt
ek (t)dt
(4)
A more general open-loop learning law can be written as: u k+1 = L[u k (t), ek (t)]
(5)
The closed-loop PID learning control law is: u k+1 (t) = F(u k , e K +1 , γ ) = u k (t) + φek+1 (t) + τ
d ek+1 (t) + ψ dt
ek+1 (t)dt (6)
A more general closed-loop learning law can be written as: u k+1 = L[u k (t), ek+1 (t)]
(7)
2.3 Analysis of Robot Point Control System (1) a.
System module design Power module
Considering that the motor has a large interference to the main control board and requires a large output power of the motor when the motor is running, the motor is supplied with AC power separately. In the specific design of the power supply, the main control board and the motor drive can be powered by the battery [11, 12]. The standards are as follows: Battery capacity: retain enough energy for fixed time use.
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Voltage size: Because the power supply voltage range used by the motor is very wide, the power supply voltage is selected according to the model. Battery internal resistance: The internal resistance of the battery should be as small as possible to reduce power consumption. This robot uses a battery pack provided by the company plus a charger, which can last for hours when it is fully charged. There are many main control circuit components in the entire robot control system, and usually different modules have different requirements for voltage. Therefore, a voltage regulator must be used, which has the following two functions. Provide constant voltage output to power circuit devices. When the load changes, keep the output voltage unchanged. b.
Wireless transmission module
CC1100 is a transceiver module that integrates FSK/ASK/0OK/MSK. modulation methods. It expands to provide hardware support for idle channel mainstream wireless links and viewing instructions, such as remote electricity meter systems, consumer electronics hardware monitoring, etc. After testing, the wireless transmission module has a maximum distance of 300 m in open and closed spaces, and a transmission distance of 50 m across 5 concrete walls with a thickness of about 40 cm, which meets the actual needs of the subject. (2)
Realization of robot point movement
After powering the robot, the master–slave microcontroller is initialized internally. After the power is transformed, the power supply starts to power each circuit board, and the wireless module starts to work. At the same time, the current position information of the robot is collected from the microcontroller through the position sensor such as grating and wirelessly. It is transmitted to the upper computer, and the information collected by the upper computer comprehensively obtains the next movement direction and running speed value of the robot, and transmits it to the main control single-chip computer. The main control single-chip interprets and splits the action information of the upper computer, and transmits it all the way to the number is sent from the single-chip microcomputer, and the other is sent to the number from the single chip. Two instructions are received from the single-chip microcomputer, and the operation generates a control signal, which acts on the drive circuit of the motor to generate the corresponding pulse number, direction and direction of the stepper motor. Start the action after the number of pulses. One thing to note here is that the direction and direction stepper motors do not move at the same time. The motor in the direction set in this case starts to move first. After the motor is completed, the main single-chip microcomputer controls the suction device of the power supply device to the robot. Power is supplied, suction is generated, and the robot is fixed, and then the direction motor starts to move. After the action is completed, the direction motor starts to move again. Repeatedly, it pushes the robot forward.
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2.4 Analysis of the Model-Based Iterative Algorithm in the Middle Simulation System of the Robot Point Control (1)
Improved model-based IIC algorithm
This improved iterative learning algorithm consists of two parts: robust feedback control algorithm and D-type IIC algorithm. The D-type iterative learning algorithm was first proposed by Arioso in 1984. The form of the learning algorithm is as follows: τli+1 = τli + K l ei (2)
(8)
Simulation analysis of robot point position control
According to the mechanical kinematics model and actual operating characteristics, the following assumptions can be satisfied in a limited time: Hypothesis 1: The desired trajectory yd(1) is continuous at t ∈ [0, T]. Hypothesis 2: The desired trajectory yd(1) is continuously differentiable. Assumption 3: f (xk(t)) satisfies the Lipschitz condition with respect to Xk(t), that is, there is a constant L (L > 0) that satisfies: || f (x2 (t)) − f (x1 (t))|| ≤ L||x2 (t) − x1 (t)||
(9)
The output error of the system is ek (t) = yd (t) − yk (t)
(10)
Among them, yd(t) ∈ Rp is the desired trajectory of the system. Adopt PD type IIC law u k+1 (t) = u k (t) + ek (t) + ϕek (t)e−K E (3)
t
(11)
Numerical simulation
In order to understand the feasibility and effectiveness of the control algorithm in this article, Matlab is used to simulate the development of a problem tracking 2-DOF manipulator. Matlab is implemented in the Matlab 2014 environment. Experiments show that the applicability of the PD-type ILC algorithm is still very extensive. As the number of iterations increases, the output trajectory of the control system converges to the desired trajectory, so that the output error of the manipulator becomes smaller, converges to zero, and achieves a better tracking effect.
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3 Model-Based IIC Algorithm and its Simulation Experiment Research in Robot Positioning Control 3.1 Experimental Protocol (1)
(2)
In order to make this experiment more scientific and effective, this experiment compares the traditional robot positioning control system with the robot positioning control simulation system controlled by the model-based iterative learning algorithm designed in this paper. In this experiment, the operating efficiency of the robot positioning control system was tested on the same data set. In order to simplify the experimental data, AHP was used for analysis this time, and the results obtained were counted using a percentile system. In order to further apply the model-based iterative algorithm studied in this experiment to the simulation of robot positioning control, this experiment uses the iterative algorithm of positioning control studied by a professor in the mechanical engineering department of a university in this article. Conduct in-depth interviews on the performance of the system. In order to ensure the scientific validity of the experimental data, the ratio of male to female professors in this visit is one to two. A total of 18 professors were visited this time, and the calculation was carried out using a ten-point system
3.2 Research Methods (1)
(2)
(3)
(4)
Comparative method This experiment compares the traditional robot positioning control system with the robot positioning control simulation system based on the model-based IIC algorithm, to judge the feasibility of the research content in this article. Interview method In this study, through interviews with professors of mechanical engineering majors in universities and colleges in a certain place and recording data, the recorded data was sorted and analyzed. These data not only provided theoretical support for the topic selection of this article, but also the final research of this article. The results provide data support. Observation method In this experiment, the efficiency of the robot positioning control system on the same data set is collected and data are collected. These data provide a reliable reference for the final research results of this article. Mathematical Statistics
Use related software to make statistics and analysis on the research results of this article.
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4 Model-Based IIC Algorithm and Its Simulation Experiment Analysis in Robot Positioning Control 4.1 Comparative Analysis of Robot Positioning Control System In order to make this experiment more scientific and effective, this experiment compares and analyzes the traditional robot positioning control system and the robot point control system using the IIC algorithm studied in this paper. The data obtained is shown in Table 1. It can be seen from Fig. 1 that in the four tests, compared with the traditional robot point control system, the operating efficiency of the robot point control simulation system based on the model-based iterative control algorithm exceeded 15%. It fully reflects the feasibility of the robot point control simulation system studied in this article and the traditional problems that need to be solved urgently. Table 1 Comparison and analysis of robot positioning control system Test 1 (%)
Test 2 (%)
Test 3 (%)
Test 4 (%)
ILC
72.3
77.1
74.8
74.3
Traditional
55.9
62.4
69.1
66.6
66.60%
Test 4
74.30%
Tests
74.80% 62.40%
Test 2
77.10% 55.90%
Test 1 0.00%
Traditional
69.10%
Test 3
72.30% 20.00%
40.00%
60.00%
80.00%
100.00%
Percentages
Fig. 1 Comparison and analysis of robot positioning control system
ILC
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4.2 Performance Analysis of Robot Point Simulation System Using Iterative Control Learning Algorithm In order to further analyze the system designed this time, this experiment conducted interviews with relevant mechanical engineering professors, and the data obtained are shown in Table 2. It can be seen from Fig. 2 that the performance evaluation of the robot point control simulation system based on the model-based IIC algorithm is higher than 5, which shows that the system designed in this paper can carry out its work, and the accuracy is the highest, which reflects the research in this paper. The excellent performance of the robot point control simulation system using iterative control algorithm.
5 Conclusion This dissertation studies the application of IIC in robots from two aspects: theory and simulation experiment in the time domain. For systems with repetitive running properties, especially those with strong nonlinearity and difficult to build mathematical models, IIC is a more suitable control method with excellent control performance. In this paper, when tracking industrial robot trajectory through IIC, it does not consider factors such as non-repeatability. These factors exist in the actual system, but they are not considered in the experiment. The theoretical analysis can only obtain ideal data. No further research can be done. If conditions permit, a discrete adaptive IIC model can be used to provide a better solution for industrial robot control and path planning, and this control technology can be used to analyze its convergence and stability.
Table 2 Performance analysis of robot point simulation system using iterative control learning algorithm Robustness
Scalability
Accuracy
Others
1
6.54
6.21
7.52
7.68
2
6.23
6.55
7.89
7.01
3
6.88
6.47
8.14
6.48
4
7.49
7.52
8.98
6.86
5
7.17
7.15
7.10
7.45
6
7.02
6.14
6.98
7.15
7
7.38
6.48
6.62
6.48
8
6.87
6.12
9.58
6.23
6.91
7.89
9.19
6.54
… 18
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Accuracy
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Satisfactions
Robustness
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1
2
3
4
5
6
7
8
...
18
Numbers Fig. 2 Performance analysis of robot point simulation system using iterative control learning algorithm
Acknowledgements This work was supported by The Research Platform of Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot under grants. (IFCIR2020005). The Research Platform of Suzhou University under grants (2019ykf26, 2019ykf10). Major Science and Technology Project of Anhui Province (18030901023). Ministry of Education in 2018 Collaborative Education Project, Construction of Engineering Education Innovation and Practice Platform of Automation Electronic Information Major under the Background of New Engineering (20180207002).
References 1. Shen D, Xu JX (2020) An IIC algorithm with gain adaptation for stochastic systems. IEEE Trans Autom Control 65(3):1280–1287 2. Liu Q, Tian S, Gu P (2018) P-type IIC algorithm for a class of linear singular impulsive systems. J Franklin Inst 355(9):3926–3937 3. Zamanian H, Koohi A (2016) Implementation of a new IIC algorithm on real data. Rev Sci Instrum 87(2):2687 4. Min K, Sunwoo M, Han M (2018) IIC algorithm for feedforward controller of EGR and VGT systems in a CRDI diesel engine. Int J Automot Technol 19(3):433–442 5. Allahverdy D, Fakharian A, Menhaj MB (2019) Back-stepping integral sliding mode control with IIC algorithm for quadrotor UAVs. J Electr Eng Technol 14(6):2539–2547 6. Xiaohong H, Bo Z (2018) Improved IIC algorithm for nonlinear systems. Mech Des Manuf 000(006):29–32 7. Lu J, Cao Z, Zhang R et al (2018) Nonlinear monotonically convergent IIC for batch processes. IEEE Trans Industr Electron 65(7):5826–5836
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8. Dong S, Yun X (2016) IIC for discrete-time stochastic systems with quantized information. IEEE/CAA J Automatica Sinica 3(001):59–67 9. Schoukens M, Hammenecker J, Cooman A (2017) Obtaining the pre-inverse of a power amplifier using IIC. IEEE Trans Microw Theor Tech 65(11):4266–4273 10. Cao F, Liu J (2016) An adaptive iterative learning algorithm for boundary control of a coupled ODE–PDE two-link rigid–flexible manipulator. J Franklin Inst 354(1):277–297 11. Fu Q, Du L, Xu G et al (2018) Consensus control for multi-agent systems with distributed parameter models via iterative learning algorithm. J Franklin Inst 355(10):4453–4472 12. Dingxi G, Jixian M (2018) Optimization of PMSM speed control system based on iterative learning algorithm. Comput Dig Eng 046(006):1263–1267
Application of Data Mining Technology in Regional Economic Analysis Jianwen Zhou
Abstract In recent years, data mining technology has attracted widespread attention. The process of data mining is to quickly discover knowledge that is not obvious but still valuable in the data. This process is an advanced process. The use of DMT (data mining technology) for law extraction, DM (data mining), sorting and analysis can provide an effective decision-making reference. The rapid advancement of technology enables organizations to accumulate large amounts of data. However, extracting useful information has become a huge challenge. The imbalance of RED (regional economic development) has always attracted people’s attention. With the start of the western development and the shift of the national regional policy focus, our country’s RD (regional development) has set off a climax. RED research has also entered a new stage. RED strategy and regional planning have become the focus of research. The main content of this paper is the application research of DMT in regional economic analysis. This article mainly uses a combination of quantitative and qualitative methods for experiments. First, the analysis of the current situation of the domestic regional economy is carried out. The results show that the east is still the strongest economic region in our country. The GDP of the east accounts for 49.7% of the country. The overall economic strength of the west is weaker than that of the east and central, but its economic development speed is at the leading level in the country, and the GDP growth rate has reached 20.5% over the previous year. Secondly, the main factors affecting the regional economy are labor force and natural resources. Finally, the application effect of DMT (data mining technology) in regional economic analysis is analyzed. The results showed that 74 people believed that data mining technology could optimize the development structure of the regional economy, 52 people suggested that data mining technology can affect the factors of regional economic development, and the remaining 43 and 31 people respectively believed that the use of data mining technology could weaken distance cost and Reduce information asymmetry. Keywords Data mining technology · Cluster analysis · Regional economy · Macro analysis J. Zhou (B) Loudi Radio and TV University, Loudi, Hunan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_22
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1 Introduction With the rapid development of information technology, DMT is also advancing by leaps and bounds. The automatic analysis search recommendation function it satisfies has been widely used in various fields. Data mining technology is mainly used to process a large amount of data that has no obvious rules, and can dig out the information and rules hidden in the data from these data. Regional economy is a common concern of economic and social activities. One of the important ways to transform the research results of data mining technology into actual productivity is to apply it to regional macroeconomic analysis and give full play to the role of data mining tools. Therefore, the application of data mining technology in regional economic analysis is a subject worthy of our in-depth study. There are not few researches on the application of DMT in RE analysis. An L believes that the regional economy is a complex economic form, and the unevenness and differences in its development make it difficult to analyze and judge the economic form. Economic relations as a whole are becoming more and more complex. Combined with data mining technology, the efficiency of economic statistics can be improved [1]. Yan B pointed out that China’s social economy is currently undergoing rapid development. The data and information continuously generated in the economic field can help us make more reasonable economic decisions and create impetus for future economic development [2]. Zhang M proposed that data mining technology can discover events in big data that are of high value but not easily detectable by people [3]. This article mainly studies the application of DMT in regional economic analysis. The research in this paper combines the relevant theoretical basis and the results of the questionnaire survey to launch the following analysis in turn: First of all, the analysis of the current situation of the domestic regional economy is carried out, and it is understood that the domestic regional economy still has the problem of uneven development; Secondly, the main factors affecting the regional economy are labor force and natural resources. Finally, it analyzes the effect of DMT on the regional economy.
2 Theoretical Basis of Regional Research Based on DMT 2.1 DM Analysis Technology The function of DM is to find unobvious but still valuable knowledge in the data [4]. Its purpose is to group a large amount of data in the collection, so that similar objects are divided into the same group as much as possible, and objects in different groups show greater differences [5]. For us, data mining analysis technology is not a completely unfamiliar research field. It combines traditional data analysis methods
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and complex algorithms to extract information and knowledge that people do not know in advance but have potential value [5]. For DM, the first stage of data preparation is the data collection, but the collected data may not be complete, and some data content may not be completely applicable, and some data data content does not fully meet the collection conditions [6, 7]. Therefore, a reasonable measurement standard should be formulated for the information collected by data mining. The second is the data mining stage. In this stage, we must first decide how to generate hypotheses, select appropriate research tools to explore knowledge, and then how to verify the knowledge found [8, 9]. Finally, it is necessary to learn to identify the most valuable part of the filtered knowledge according to the decision-making purpose of the end user and pass the information to the decision maker through decision support tools. If the results of the last stage are not satisfactory to users, the above data mining process needs to be repeated.
2.2 Cluster Analysis As an important data mining technique, cluster analysis is often applied to various fields in social production and life [10]. Clustering refers to the clustering of things, which is to classify things according to certain internal laws. This article mainly proves through experiments that the application of cluster analysis technology is beneficial to the research of regional economy. The basic principle of cluster analysis is to make the objects in the group have the greatest similarity and the object similarity between groups without knowing how many classes there are in the target database [11]. The greater the similarity within the group, the greater the difference between the groups, the greater the value, and the better the clustering effect. The early clustering algorithm is to calculate the distance of each point in the model, such as Euclidean distance, Manhattan distance, etc. [12]. Among them, the shortest distance method and the intermediate distance method are more commonly used system clustering methods. The following formulas are mainly used when calculating the distance in the model: d(k, 1) = (F(k) − F(l))2 d=
(x1 − x2 )2 + (y1 − y2 )2
(1) (2)
2.3 Regional Economy The regional economy is usually also called the “RE”, which refers to the part of GDP distributed in various administrative regions. The formation of the RE is the result
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of the geographical division of labor. The “region” in the regional economy refers to the space where all economic and social activities can be carried out. With the start of the western development and the shift of the national regional policy focus, our country’s regional development has set off a climax. Therefore, the regional economy has become the common focus of major economic and social activities. Our country’s regional economy can be divided into broad sense and narrow sense. Chivalrously speaking, the regional economy refers to the economic relationship created by the geographical connection. Broadly speaking, regional economy is a comprehensive geographical concept of economic development. Our country is a big country, with different economic regional endowments and obvious differences in the level of socio-economic development. Regional economic differences are inevitable. A moderate difference will not affect economic and social development, but if the gap is further widened, it will cause serious social problems. There are many factors that affect the RE, including economic and noneconomic elements. The factors that affect the RE mainly include two categories: direct influencing factors and indirect influencing factors. Among them, the directly affecting factors include labor and the means of production. Labor is the prerequisite for production and consumption behavior. From the perspective of production, the prerequisite for ensuring the effective supply of regional labor is a certain population and appropriate population growth. The input of means of production can increase the level of regional output. The more input of means of production, the higher the regional output level. The Guangdong-Hong Kong-Macao Greater Bay Area in China is a good example. The input of means of production can increase the level of regional output. The more input of means of production, the higher the regional output level. The Guangdong-Hong Kong-Macao Greater Bay Area in China is a good example. On the other hand, the superior geographical location of various regions, such as convenient transportation, proximity to the place of origin and consumption areas, will also affect the level of social labor productivity.
3 Application Experiment PF Data Mining Technology in Regional Economy 3.1 Experimental Background The imbalance of China’s regional economic development has always attracted attention, and regional economic research has also attracted considerable attention. Regional economics is one of the research topics that many experts and scholars are keen on. Traditional research methods can only consider fewer factors that affect the economy, such as GDP, Population, etc. Therefore, this article uses data mining technology to conduct a comprehensive research on the regional economy.
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3.2 Experimental Process Steps Due to consideration of time cost and other issues, I used the questionnaire star software and the printed paper version to distribute it. At the same time, in order to improve the accuracy of the experiment, this survey is based on the advantages of DMT and the characteristics of RED. 50 questionnaires were distributed in each of the northeast, central, western and eastern parts of the country, totaling 200 questionnaires, and a total of 200 valid questionnaires were received. The main statistical information includes: what the respondents think of the current status of the domestic regional economy, what are the current factors affecting our country’s regional economy, and how the data mining technology affects the regional economy. The results of the questionnaire were analyzed with the help of questionnaire star software and SPSS software.
4 Experimental Analysis of DMT in Regional Economic Application In order to make the analysis results of this paper more credible, we integrated the results of the questionnaires, interviews, and after consulting relevant materials, we conducted an analysis and discussion on the status quo of the domestic regional economy and the effect of data mining on the regional economy.
4.1 Analysis of the Status Quo of the Domestic Regional Economy Since the reform and opening up, in order to reduce the economic differences between regions and achieve common progress. The country has successively introduced three major regional strategies, including the development of the western region, the revitalization of the old industrial zone in the northeast, and the rise of central China. Then, after the overall regional strategy of “adhering to the development of the western region, revitalizing the old industrial bases in the northeast, promoting the rise of the central region, and encouraging the development of the eastern region”, what about the regional economic situation in our country? This article has consulted relevant information and data for this issue. The data results are shown in Table 1: Table 1 The status quo of the domestic regional economy East
Central
West
Proportion (%)
49.7
22.7
18.7
Northeast 8.9
Growth rate (%)
15.98
11.7
20.5
13.45
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It can be seen from Table 1 that the eastern part is still the region with the strongest economic strength in China, with a gross national product accounting for 49.7% of the country, which is significantly higher than the other three regions. The GDP of the central region is higher than that of the west and northeast, but its GDP growth rate is only 11.7% compared with the previous year, and the growth rate is lower than the national average. The overall economic strength of the western region is weaker than that of the eastern and central regions, but its economic development speed is at the leading level in the country, with a GDP growth rate of 20.5% over the previous year, and the level of economic improvement ranks first among the four major regions. The GDP of the Northeast region accounts for only 8.9% of the country, which has not reached the average level, and the GDP growth rate is not as fast as that of the western and eastern regions. On the whole, the overall regional strategy has promoted the development of the regional economy and narrowed the differences between regions. However, the overall current situation of the region is that the economic strength of the eastern region is stronger than that of the other three regions.
4.2 Analysis of Factors Affecting Regional Economy There are many factors affecting the regional economy, which can be divided into two categories: direct factors and indirect factors. As shown in Table 2 and Fig. 1, 28% of people think that the main factor that affects the regional economy is labor, Table 2 Factors affecting the regional economy Labor force
Production capital
Natural resources
Population
Number
56
62
46
36
Proportion (%)
28
31
23
18
Fig. 1 Factors affecting the regional economy
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and 31% think that production capital is an important factor that affects the regional economy. 46 and 36 people think that Natural resources and population will also affect the level of regional economic development.
4.3 Analysis of the Effect of DMT on the Regional Economy The effective application of DMT plays a very important role in promoting the progress and development of industrial economy. It can be seen from Table 3 and Fig. 2 that 74 people believe that DMT can optimize the development structure of the regional economy. For example, data mining technology can optimize production processes. In agricultural production activities, the effective application of DMT can modernize production. There are 52 people who think that DMT can affect the elements of regional economic development, and the application of DM makes the development of regional economy no longer focus on talent elements, capital elements, technical elements, etc. Forty-three people pointed out that the use of DMT can weaken the cost of distance, and the remaining 31 people believe that DM can weaken information asymmetry to a certain extent. Table 3 The effect of DMT on regional economy Optimize the structure
Factors affecting development
Weakening the cost of distance
Weakened information asymmetry
Number
74
52
43
31
Proportion (%)
37
26
21.5
15.5
Fig. 2 The effect of DMT on regional economy
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5 Conclusion The implementation of the three major regional strategies, the Western Development, the revitalization of the old industrial zone in the Northeast, and the rise of Central China, has narrowed the differences between different regions and promoted RED. However, the regional economic level in the east is still ahead of other economic regions in our country. Therefore, this article is mainly based on the application of DMT to the research of regional economy. The research results show that the effective application of data mining technology can improve the domestic regional economic level by optimizing the regional economic development structure and weakening the distance cost. Acknowledgements This work was financially supported by Hunan Vocational Education Teaching Reform Research Project (ZJGB2020410).
References 1. An L, Tsou MH, Spitzberg BH et al (2016) Latent trajectory models for space-time analysis: an application in deciphering spatial panel data. Geogr Anal 48(3):314–336 2. Yan B, Wu C, Yu R et al (2021) Big data-based E-commerce transaction information collection method. Complexity 2021(2):1–11 3. Zhang M (2017) Application of data mining technology in badminton spot tactical analysis system. Agro Food Industry Hi Tech 28(1):3398–3401 4. Sun H (2021) Intelligent data mining based on market circulation of production factors. Wirel Commun Mob Comput 2021(4):1–11 5. Liu Y (2017) Impact of technological innovation based on data mining on regional agricultural economy. Boletin Tecnico/Technical Bulletin 55(19):634–641 6. Ebert K, Ekstedt K, Jarsjö J (2016) GIS analysis of effects of future Baltic sea level rise, on the island of Gotland, Sweden. Natural Hazards Earth Syst Sci Discuss 1–22 7. Chagovets LO, Chahovets VV, Didenko AS (2020) The data mining technology applications for modeling the unevenness of socio-economic development of regions. Bus Inform 3(506):82–91 8. Ernesto Amoros J, Basco R, Romani G (2016) Determinants of early internationalization of new firms: the case of Chile. Int Entrepreneurship Manag J 12(1):283–307 9. Dong F, Long R, Li Z et al (2016) Analysis of carbon emission intensity, urbanization and energy mix: evidence from China. Nat Hazards 82(2):1375–1391 10. Bischoff-Mattson Z, Lynch et al (2016) Adaptive governance in water reform discourses of the Murray-Darling Basin, Australia. Pol Sci 49(3):281–307 11. Zhang C, Li H, Tian X (2016) The research and application of support technology in high stress broken surrounding rock roadway. Mine Eng 04(2):57–63 12. Danylenko A, Varchenko OM, Krusanov D (2021) Methodological bases of priority directions of scientific researches in the agrarian sector of Ukraine. Ekonomìka ta upravlìnnâ APK, 2021(1(162)):50–64
Application of Big Data in the Excavation of Regional Cultural Resources in the Development of Theme Hotels Yan Zeng
Abstract With the development of economy and society, my country’s tourism construction has made significant progress. More people realize that the meaning of travel is no longer a simple sightseeing spot, and leisure and experience have gradually become the most important part of travel. This article aims to study the application of big data in the mining of regional cultural resources in the development of theme hotels. This paper uses data mining methods to mine the regional culture in the development of the theme hotel. First, prepare for the subsequent calculations through the summary and management of the original information, process the information to make it digitized, and dig out the regional cultural resources in the development of the theme hotel application. The experimental data shows that the material culture management platform and analysis platform of the three hotels established in this paper are mainly used to analyze people’s preference for the material culture of three hotels in different regions as examples to verify the validity of the research. The experimental results show that using the data analysis method proposed in this paper, the material culture search frequency of Asian hotels is 0.44, the material culture search frequency of European hotels is 0.37, and the material culture search frequency of American hotels is 0.52. Traditional data processing methods compared with it, the error is larger and the accuracy is lower. Use big data to promote the research on the mining and utilization of regional cultural resources, accelerate the realization of the effective connection with all links of the regional cultural resource mining under the “Internet+” model, make the mining and services of regional cultural resources demonstrate the unique cultural inheritance function, and promote “information + “Culture” integrates the development of innovative new models. Keywords Big data mining · Theme hotel development · Regional cultural resource excavation
Y. Zeng (B) College of Cultural Tourism and International Exchange, Yunnan Open University, Kunming, Yunnan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_23
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1 Introduction Nowadays, there are more and more hotels with different forms. Hotels have become indispensable temporary living places for consumption. People have higher and higher requirements for hotels, not only focusing on appearance, but paying more attention to the internal space and services of the hotel [1]. A good hotel design must not only make guests feel a sense of belonging, but also make guests remember it deeply. In this large environment with many design styles, people can remember fewer and fewer designs. However, hotel design with regional characteristics can often impress people and it is extremely important to improve the uniqueness of space design [2]. Put regional symbolic elements into the hotel design, make the design sustainable, and carry forward the fine traditional culture. The design is born in inheritance and development. While inheriting the local culture, we should innovate thinking, learn from the local regional elements, and make full use of the region. Sexual symbols enhance the value of the hotel, create a unique brand, and design a more perfect regional hotel design [3]. Dahmani and others believe that under the background of the rapid development of world integration process and the smooth and convenient information exchange, once a certain design style is recognized in the commercial field, and its style becomes a popular trend, it will quickly spread and even affect the world., But blindly copying and rushing to build it quickly and quickly made the city lose its original unique cultural connotation, causing the city to lose its original uniqueness and become devastated [4]. Y et al. believe that under the dual influence of international design trends and globalization trends, hotel designs of different regional cultures and national styles have a large amount of homogeneity, losing national and regional characteristics, resulting in the diversity and diversity of regional cultures. The scarcity gradually disappeared, and the appearance of the hotel showed an embarrassing situation of “one thousand stores” [5]. With the development of society and the rapid economic growth, in many cities in our country, hotels have been built one after another, with various forms and styles, each with its own characteristics [6, 7]. However, the regional culture in a hotel design is indispensable, and it plays an indispensable key role in the entire hotel culture [8, 9]. A successful and excellent hotel design must be designed according to the characteristics of the area in which it is located, incorporating local features into the hotel design, integrating and interoperating with each other, so as to better meet the needs of guests in all aspects [10].
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2 Application of Big Data in the Excavation of Regional Cultural Resources in the Development of Theme Hotels 2.1 Apply Regionality to Hotels Applying regionality to hotels. Hotels are places that provide accommodation, catering, entertainment, and services for the majority of consumer groups. In terms of function and form, it not only meets the physical needs of guests, but also meets people’s psychological needs. At first, people only paid attention to the exterior design of the hotel, and did not reflect the humanization in the function, and the regionality in the space. With the globalization of the economy, the exchanges between nations have become closer and closer, and local design has gradually been emphasized in hotel design, National design, sustainable design. The space design of the hotel is also particularly important. The hotel design has attracted more and more attention, and the local Chinese culture is also developing rapidly. The application of regionality to hotel design can be expressed in form, color and material selection through hotel interior design, and regional symbols can be integrated into hotel design. Reflecting the regional form in hotel design is a key factor in hotel design. A hotel with a regional symbol design is a design with vitality and can be continued. In the choice of materials, the local unique and abundant materials are mostly used, which not only shows the regionality to the fullest, but also saves a lot of economy and time. However, when putting regional symbols into the hotel design, more attention must be paid to not blindly putting local local culture, unique regional customs and regional materials into the hotel design. We must make proper judgments and make appropriate choices. In the hotel, you should not be too obtrusive, and you cannot blindly adopt the local characteristics, but also put your own characteristics into it, and form a unique and novel design on this basis. The form, style and service of the hotel are important factors for the long-term development of the hotel, and determine the profitability of the hotel. Therefore, strengthen the design of the hotel and pay attention to the regional development of the hotel, so that guests have a sense of belonging and increase the occupancy rate.
2.2 Regional Culture in Hotel Design (1)
The design of tourist resort hotels should reflect the characteristics of regional culture
Tourists who are on vacation not only want to see different natural scenery, but also want to experience the unique ethnic and folk culture. Therefore, hotel design should also actively absorb the local mother culture and insist on a diverse style while maintaining its own characteristics. The characteristics promote the design of the hotel to have a unique cultural heritage and profound cultural connotation.
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This requires hotel designers to conduct in-depth research and research on the local regional culture, and actively refine and use it, relying on hotel design to re-integrate and interpret the local unique cultural connotation and historical heritage, and make it practical Function and spiritual connotation are organic. Combining and emphasizing the integration of indoor space and landscape space design forms, infiltrating regional cultural characteristics into indoor and outdoor spaces. (2)
Use the structural features and layout features of traditional local buildings
The tourist resort hotel should express the regional culture in the design, and it can also use some structural features and layout features in the local traditional buildings, and at the same time deepen some of its elements. In the process of continuing to use, pay attention that it is not always copied, and the more cumbersome decorations in traditional local buildings can be simplified accordingly, including simplified indoor and outdoor decorations. The purpose of this design technique is to highlight the structure of the regional characteristics of the culture, so as to restore the regional elements in the volume and structure. As far as possible, the interior and landscape design is taken as the continuation of the building, forming a spatial and cultural fusion.
2.3 Data Processing Remember that the set of all regional cultural resources we want to represent is D = {d1 , d2 , d3 , …, dN }, and the set of words (also called dictionaries) that appear in all regional cultural resources is T = {t1 , t2 , t3 , …, tN }. The j-th regional cultural resource is expressed as dj = {wj1 , wj2 , wj4 , …, wjn }, where w1 j represents the weight of the first word t1 in the regional cultural resource j, the larger the value, the more important; other vectors The explanation is similar. In order to represent the j-th regional cultural resource dj , the key is to calculate the value of each component in dj. If the word t1 appears in the j-th regional cultural resource, the value of w1j is 1. If t1 does not appear in the j-th regional cultural resource, its value is 0. You can also choose w1j as the word t1 appearing in Number of items in the j-regional cultural resources: N T F − I D F tk , d j = T F tk , d j • log nk
(1)
Among them is the number of times the k-th word appears in the regional cultural resources j, but the number of articles that include the k-th word in all regional cultural resources. Finally, the weight of the k-th word in the item regional cultural resource j is obtained by the following formula:
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wk, j =
T F − I D F tk , d j |T |
2 T F − I D F ts , d j
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(2)
k=1
3 The Experiment of Big Data in the Excavation of Regional Cultural Resources in the Development of Theme Hotels 3.1 Construction of Experimental Data Analysis Platform Using big data technology for application and decision-making, data analysis is the top priority. It can better understand people’s needs for regional cultural resources, so as to better use and inherit valuable document resources. The data analysis platform can be roughly divided into data integration, data storage, data calculation, data analysis, and result display and interpretation functions. Data integration is an important part of data development. It can gather multiple resources to make it digitized, including relational database data extraction, realtime data collection, file data collection, and real-time database replication. The collected data should be stored to make it easier to extract. Data storage includes data marts and data warehouses. Data can be retrieved for calculation at any time after data is stored. Calculation data is the key to the entire big data application, including batch calculation, traffic calculation, content calculation, and query calculation. These calculations can reasonably push theme hotels according to user needs or market needs Information on regional culture in the development process. The specific framework is shown in Table 1. Table 1 Specific framework of development process Frame
Details
Data integration
Relational database extraction, factual data collection, document data collection
Data storage
Data mart, data warehouse
Data calculation
Batch calculation, flow calculation, content calculation, query calculation
Data analysis
Data mining algorithm
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3.2 The Specific Implementation Process of Regional Cultural Resource Excavation Applying big data technology to excavate the regional culture in the development of theme hotels, first prepare for subsequent calculations through the summary and management of the original data. After data preparation, the data is processed to make it digitized, and its potential applications are discovered through the calculation model of the platform design interprets the regional cultural data to form systematic knowledge, and through the original knowledge and characteristics, it can be better utilized.
4 Discussion of Big Data in the Excavation of Regional Cultural Resources in the Development of Theme Hotels (1)
(2)
First of all, we must collect keywords. This article takes the regional culture of the theme hotel development process as the research purpose, and selects three different regions, representing different cultures, and set them as Hotel 1, Hotel 2, and Hotel 3. Hotel 1 represents Asian hotels, hotel 2 represents European hotels, and hotel 3 represents American hotels. The cultural resource object studied is the hotel construction culture. Through all kinds of books, web information and regional cultural directories in the development process of theme hotels, crawling and collecting their keywords, applying existing digital conversion technology, data conversion and storage. In the data simulation, the data analysis formula proposed in this paper is used for calculation, and compared with traditional data analysis, the results of Table 2 and Fig. 1 are finally obtained through calculation. The application of big data technology in the regional culture of the theme hotel development process has been studied. The material culture management platform and analysis platform of the three hotels established in this paper are mainly used to analyze people’s perceptions of different regions by using data mining methods. The focus of the material culture of the three hotels is taken as an example to verify the validity of the research. Using the data analysis method proposed in this paper, it is concluded that the material
Table 2 Comparison of traditional data analysis and big data analysis
Key words Hotel positioning
Hotel design
Hotel preparation
Big data analysis
0.44
0.37
0.52
Traditional data analysis
0.28
0.26
0.38
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Fig. 1 Comparison of traditional data analysis and big data analysis
Fig. 2 Comparison of accuracy between traditional data processing and big data processing methods
culture search frequency of Asian hotels is 0.44, the material culture search frequency of European hotels is 0.37, and the material culture search frequency of American hotels is 0.52. Compared with traditional data processing methods, the error Larger, lower accuracy, higher accuracy of the big data processing method, the accuracy comparison between traditional data processing and big data processing method is shown in Fig. 2.
5 Conclusions With the improvement of people’s living standards, economic globalization, friendly exchanges between China and foreign countries, and the rapid development of tourism, hotels have become people’s temporary residences. This paper uses data processing methods to prepare for subsequent calculations through the summary
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and management of original data. After data preparation, the data is processed to make it digitized, and the application of regional cultural resource excavation in the development of theme hotels is discovered. The experimental results show that this paper focuses on the establishment of 3 hotel material culture management platforms and analysis platforms in different regions. Using the data analysis method proposed in this paper, the material culture search frequency in Asian hotels is 0.44 and the material culture search frequency in European hotels the material culture search frequency of the American Hotel is 0.37. Compared with the traditional data processing method, the error is larger and the accuracy is lower.
References 1. Xia X (2020) Fast search of art culture resources based on big data and Cuckoo algorithm. Pers Ubiquit Comput 24(1):127–138 (in Chinese) 2. Li D, Deng L, Cai Z (2020) Statistical analysis of tourist flow in tourist spots based on big data platform and Da-Hkrvm algorithms. Pers Ubiquitous Comput 24(1):87–101 (in Chinese) 3. Tan Y, Shi Y, Tang Q (2018) [Lecture Notes in Computer Science] Data mining and big data, vol 10943. Analysis of Patterns in the University World Rankings Webometrics, Shanghai, QS and SIR-SCimago: Case Latin America. https://doi.org/10.1007/978-3-319-93803-5 (Chapter 18), pp 188–199 (in Chinese) 4. Dahmani D, Rahal SA, Belalem G (2016) Improving the performance of data mining by using big data in cloud environment. J Inf Knowl Manag 15(4):1650038 5. Wang X, Zhai Y, Lin Y et al (2019) Mining layered technological information in scientific papers: a semi-supervised method. J Inform 45(6):779–793 (in Chinese) 6. Margolies LR, Pandey G, Horowitz ER et al (2016) Breast imaging in the era of big data: structured reporting and data mining. AJR Am J Roentgenol 206(2):259 7. Xia X (2020) Fast search of art culture resources based on big data and Cuckoo algorithm. Pers Ubiquit Comput 24(1):157–165 (in Chinese) 8. Zhou N (2020) Database design of regional music characteristic culture resources based on improved neural network in data mining. Pers Ubiquit Comput 24(1):103–114 (in Chinese) 9. Barbierato E, Gribaudo M, Iacono M (2016) Modeling and evaluating the effects of big data storage resource allocation in global scale cloud architectures. Int J Data Warehouse Min 12(2):1–20 10. Garcia S, Luengo J, Herrera F (2016) Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowl-Based Syst 98(4):1–29
Using Computer Three-Dimensional Model System to Explore the Influence of Water–Rock Interaction on the Unloading Mechanical Properties and Microstructure of Sandstone Fanglu Kou, Qiao Jiang, Liangpeng Wan, Kun Wang, and Hongyue Pan Abstract This paper uses quantitative analysis methods to study the degradation degree of mechanical properties under the action of water and rock. Quantitative analysis methods are usually affected by the angle between the principal stress and the structural plane under different stress conditions, and then exhibit different mechanical properties. In this paper, a typical layered rock mass is selected as the research object. According to the test plan, a set of samples are taken out of the rock immersion container for triaxial loading test every 20 days. The experimental data shows that the vertical bedding sandstone samples account for about 70% of the attenuation in the first six stages of water–rock interaction; the parallel bedding sandstone accounts for about 70–80% of the final attenuation. The experimental results show that with the progress of water–rock interaction, the compressive strength of bedding sandstones has been weakened to varying degrees. Comparing the triaxial compressive strength of two different bedding sandstone samples, it can be found that the strength of the vertical bedding sample is higher than the strength of the parallel bedding product, so it can be seen that the distribution of the weak surface of the deposit is affected by different stresses. And the degree of influence is obvious. Keywords Water–rock interaction · Unloading mechanical properties · Microstructure · 3D model system
F. Kou (B) College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, Hubei, China e-mail: [email protected] China Gezhouba Group No. 1 Engineering Co., Ltd, Yichang, Hubei, China Q. Jiang · L. Wan · K. Wang · H. Pan China Three Gorges Projects Development Co., Ltd, Chengdu, Sichuan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_24
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1 Introduction Nowadays, in the construction process of the later engineering department of the development reservoir area, it is inevitable to blast and excavate the slope. However, the turbulence during the excavation will cause long-term damage to the inclined bedrock. During the operation period after the completion of the dam, the water level of the reservoir forms a constant fluctuation zone. Under the condition of large fluctuations in the water of the reservoir, the rocks and part of the soil in the fluctuation zone will periodically dry and saturate dynamically. This immersion-air-drying alternate effect is a kind of “fatigue effect” for rock and soil, and ultimately leads to the decline of rock performance. Therefore, it is necessary to study the unloading mechanical properties of sandstone by water–rock action [1, 2]. Zhao et al. conducted water–rock interaction tests on intact sandstone and damaged sandstone on the basis of considering the time effect, simulated the initial damage of natural rock mass under dynamic action, calculated the damage variables of the rock, and deduced the corresponding constitutive model [3]. Hinsinger et al. conducted an experimental study on the mechanical properties of gypsum rock under the action of salt water immersion. After immersion, the deformation ability of the specimen was enhanced. However, due to the compact structure, low porosity, and insoluble chemical composition, the strength did not decrease, but a small amount of solution is immersed from the surface and the inside under long-term immersion, so that the gypsum deforms and tends to soften [4]. Hydrochemical solutions exist widely in rock mass engineering and affect the physical and mechanical properties of rocks. The existence of the hydrochemical solution, on the one hand, can generate pore water pressure (static pore water pressure and excess pore water pressure), which reduces the effective stress of the rock skeleton, thereby reducing the strength and mechanical properties of the rock. At the same time, the water chemical solution will cause physical and chemical reactions between the mineral composition of the rock and the cementation between the mineral particles, changing the original structure of the rock or producing new minerals [5, 6]. In addition, the pore water pressure has a splitting effect on the tip of the rock micro-cracks [7, 8]. Therefore, it is not only based on the effective stress principle to consider the influence of water chemical solutions on the mechanical properties of rocks, but also need to further consider the physical and chemical corrosion effects of water chemical solutions on rocks. In-depth study of the corrosive effect of hydrochemical solutions on the physical and mechanical properties of rocks has key theoretical significance and practical application value, and is one of the hot topics in the geotechnical engineering community today [9, 10].
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2 Study on the Influence of Water–Rock Interaction on the Unloading Mechanical Properties and Microstructure of Sandstone 2.1 Classification of Water–Rock Interaction (1)
Mechanical action
The key to the mechanical action of water on rock refers to the influence on the mechanical properties of rock due to the effect of pore hydrostatic pressure and pore dynamic pressure. The influence of pore hydrostatic pressure on rock dynamics can be analyzed by the effective stress principle. That is, if the pore water in the rock is difficult to be discharged under external load or cannot be discharged at all, the water pressure in the pores rises sharply. This action causes solid particles in the rock or reduces the effective stress that the particle skeleton can withstand. It also reduces the strength of the rock. In addition, under the action of the hydrostatic pressure in the pores, some soft rocks expand and deform due to their greater deformability, which further increases the water content of the soft rocks, thereby increasing their strength until the rock breaks. Keep decreasing. However, the hydrodynamic pressure in the pores reduces the shear strength of the rock and soil by generating a tangential thrust in the rock and soil. (2)
Physical effects
The physical action of water and rock mainly refers to the process of rock softening, silting, lubrication, drying and wetting, and freezing and thawing, thereby changing the physical and mechanical properties of the rock and reducing the deterioration of the inherent mechanical properties of the rock. Rock softening refers to the ability of a rock to lose strength after being immersed in water. Rock mudification refers to the solid-plastic-liquid weakening effect that occurs after rocks (including mud-like intermediate layers and other fillers) come into contact with water. Rock lubrication is soluble. When water is immersed in the rock combined with salt and colloidal minerals, the soluble salt is dissolved and the colloid is hydrolyzed, the original bond becomes a hydrogel bond, the bond between the mineral particles becomes weaker, and the friction decreases. Small; rock drying and wet methods are processes in which the rock undergoes external humidity and temperature changes, respectively. (3)
Chemical action
The main factor in the geological environment is groundwater, chemical solutions with complex compositions, and even pure water interacts with rock masses. In addition to physical effects, there are more complex water–rock chemical interactions or water–rock interactions, which usually have a greater impact on the physical effects of rocks than pure physical interactions. The chemical effects of water and rocks include dissolution, ion exchange, hydration, and hydrolysis, etc., which will
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change the composition and structure of the rock, thereby affecting the mechanical properties of the rock. Dissolution refers to the action of dissolving and removing soluble substances in the rock when the permeating water passes through the rock, thereby reducing the strength of the rock. Ion exchange is a form of exchange that uses the physical and chemical forces of groundwater to adsorb ions and molecules on the surface of mineral particles; hydration is an effect in which water penetrates the crystal lattice of minerals or water molecules attach to soluble rock ions. Causes micro, thin tube and macro changes in the rock structure. Hydrolysis is the chemical reaction between water and the anions and cations in the rock mass. When the cations and water are hydrolyzed, the water environment of the rock mass is acidified. When the anions react with water, the water environment of the rock mass is alkalized. Compared with physical action, chemical action is usually irreversible. The chemical action of water rocks usually involves the production of new minerals that destroy the original internal structure of the rock. Therefore, chemical action has a more serious impact on the mechanical properties of rocks, and is the most critical role for the mechanical properties of rocks.
2.2 Research on Compression of Bedding Sandstone Under Water–Rock Interaction Layered rock masses are very common in geotechnical engineering. Its strength is an important mechanical parameter that must be considered in engineering design. Layered rock masses obviously have transverse anisotropy. The strength of the rock mass is not only closely related to the strength of the rock mass itself, but also closely related to the formation and properties of the structural surface of the rock mass. Under various pressure conditions, it will be affected by principal stress. Due to the angle between the structural planes, the fracture plane either develops along the structural plane, or penetrates the bedrock and exhibits different mechanical properties. In order to quantitatively analyze the degradation degree of the mechanical properties of the sample under the action of water–rock, the strength reduction of the rock sample is defined as the degradation degree, where the total degradation degree Sj and the stage degradation degree Sj are respectively expressed by the formulas: Sj =
A0 − Aj × 100% A0
S j = S j − S j−1
(1) (2)
In the formula, A0 is the initial strength parameter or deformation parameter of the sandstone sample, such as compressive strength σ, elastic modulus E, cohesive
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force C and internal friction angle ϕ, etc.; Aj is the strength of the “soak-air-dry” cycle in different periods Parameters or deformation parameters.
2.3 Three-Dimensional Model System (1)
BIM
BIM is used to describe virtual design, construction and facility management. BIM is an intelligent physical structure model that can connect data, processes, and resources at different stages of the physical structure’s life cycle, and all participants in the construction project can use this information to help the project team improve efficiency. Generally, BIM has three basic characteristics: completeness, relevance and consistency. (2)
The value of applying BIM
The development and application of BIM are mainly concentrated in the field of design. BIM can use data to build models by combining data and model components, and can be used simultaneously with other programs to provide more functions, such as engineering quantity calculation, cost budgeting, space and asset management and other functions. BIM also has the ability to use parameters, allowing components to use attributes or parameters to define their relationship with other components. In addition, BIM technology has the potential to fundamentally change the way projects are delivered, and is expected to provide support for more integrated and efficient processes.
3 Experiment on the Influence of Water–Rock Interaction on the Unloading Mechanical Properties and Microstructure of Sandstone 3.1 Selection of Samples The test red sandstone was mined from a representative integrity rock block in a local hydropower project, and the texture of the whole rock block is uniform. The rock is dark red because it is rich in oxides and has a clastic sand-like structure. The clastic particles are less than 0.5 mm. The matrix is cemented. The cement is clay minerals, and there are cements of iron and calcium oxides.
226 Table 1 Main technical parameters of RMT-150C
F. Kou et al. Maximum vertical output
1000.0 kN (1000.0 kN, 100.0 kN two levels)
Maximum horizontal output
500.0 kN (500.0 kN, 100.0 kN two levels)
Vertical piston stroke
50.0 mm
Horizontal piston stroke
50.0 mm
Maximum confining pressure
50.0 MPa
3.2 Experimental Method According to the established test plan of the experiment, a series of samples from the leaching rock container are used for the triaxial loading test every 20 days. The parameter values of the confining pressure in this experiment are 0, 5, 10, 20, 30 MPa. The sandstone sample saturated with water–rock interaction is wrapped in a thin film to prevent the debris from falling when the fragments break. Use the experimental equipment RMT-150C rock mechanics testing system to conduct the test, apply atmospheric pressure and lateral pressure at the same time at a loading rate of 0.1 MPa/s, and check the atmospheric pressure and lateral pressure before reaching the test preset sealing pressure. If they are roughly the same, change the passing force control of the experimental plan to strain control without changing the lateral pressure, and gradually increase the normal pressure at a loading rate of 0.005 mm/s until the specimen is completely destroyed. Table 1 shows the main technical parameters of RMT-150C.
4 Discussion on the Influence of Water–Rock Interaction on the Unloading Mechanical Properties and Microstructure of Sandstone With the progress of water–rock interaction, the compressive strength of bedding sandstones has been weakened to varying degrees. The attenuation of vertical bedding sandstone samples during the first six stages of water–rock interaction has changed from low confining pressure to high confining pressure. The pressures are 35.998, 34.178, 32.692, 30.912 and 30.148%, accounting for about 70% of the final attenuation; the attenuation of the parallel bedding sandstone samples during the first six phases of water–rock interaction changes from low confining pressure to high The confining pressures are 32.997, 25.813, 25.289, 24.795, and 24.428%, accounting for about 70–80% of the final attenuation, and the degree of degradation of vertical bedding sandstone samples is greater than that of parallel bedding sandstone, About 7% high. The change in compressive strength of bedding sandstone is shown in Fig. 1.
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Fig. 1 Changes in compressive strength of bedding sandstone
Table 2 Cohesion c and degradation degree of bedding sandstone under periodic water–rock interaction Soak times
Vertical bedding
Parallel bedding
Cohesion c/MPa
Cohesion deterioration degree Sc/%
Cohesion c/MPa
Cohesion deterioration degree Sc/%
0
17.778
0
13.435
0
1
17.009
4.128
13.054
2.887
2
16.134
10.198
12.542
6.712
4
14.632
18.211
11.903
11.894
The cohesive force c of vertical bedding sandstone samples is about 3.5–4.5 MPa higher than that of parallel bedding sandstone samples. The decomposition rate of vertical bedding sandstone samples is higher than that of parallel bedding sandstone samples. Force. After 6 cycles of water–rock interaction, the cohesion of the vertical bedding samples decreased by 26.086%, and the cohesion of the parallel bedding samples decreased by 16%. After 10 cycles of water–rock interaction, the degradation degree of cohesive strength of vertical bedding samples is 28.356%, and the degradation degree of parallel bedding is 21.314%. The results of the triaxial loading test provide the law of attenuation of the cohesive force c of the sandstone sample in the regular water–rock cycle, as shown in Table 2 and Fig. 2.
5 Conclusion The stress path between the unloading fracture and the loading failure of the rock mass is different. The deformation of the rock mass in the unloading state is affected by the stress path and the stress state, and the law is different from the load. The development of rock deformation is an important index for evaluating the stability of drilling engineering. In-depth research is needed to summarize the influencing factors
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Fig. 2 Cohesion c and degradation degree of bedding sandstone under periodic water–rock interaction
and development rules of rock deformation under unloading state. In this paper, a typical layered rock mass is selected as the research object, and a set of samples are taken out of the rock immersion container for triaxial loading test. The experimental results show that as the water–rock interaction progresses, the compressive strength of bedding sandstones has been weakened to varying degrees. The attenuation of the vertical bedding sandstone samples during the first six phases of water–rock interaction has changed from low confining pressure to high The confining pressures are 35.998, 34.178, 32.692, 30.912, and 30.148%; the attenuation of the first six phases of water–rock interaction of the parallel bedding sandstone samples from low confining pressure to high confining pressure is 32.997% and 25.813%, respectively, 25.289%, 24.795% and 24.428%. Acknowledgements Special Topics of Natural Key Research and Development Program of China: High risk area and key risk investigation technology of multi disaster and major natural disaster (Grant Number: 2018YFC1508801-4). The second comprehensive scientific investigation on the Qinghai Tibet Plateau: Analysis of water sources of typical rivers in the Qinghai Tibet Plateau and prediction of changing Laws. (Grant Number: 2019QZKK020705).
References 1. Deng HF, Zhang HB, Li JL (2018) Effect of water-rock interaction on unloading mechanical properties and microstructure of sandstone. Yantu Lixue/Rock Soil Mech 39(7):2344–2352 2. Piroti S, Najarchi M, Hezavehi E (2018) Experimental study on the influence of water/cement ratio and polypropylene fiber on the mechanical properties of nano-silica concretes. ZKG Int 71(5):56–65
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3. Zhao B, Huang T, Liu D (2019) Study on the mechanical properties test and constitutive model of rock salt. Geomech Eng 18(3):291–298 4. George TS, Hinsinger P, Turner BL (2016) Phosphorus in soils and plants—facing phosphorus scarcity. Plant Soil 401(1–2):1–6 5. Mizobuchi T, Ishizeki K, Sagawa T (2019) Study on the influence of minor constituents in blast furnace slag rich cement on the thermal and mechanical properties of concrete. J Adv Concr Technol 17(1):46–61 6. Denquin A, Huvelin Z, Signori L (2016) Influence of Si and C additions on microstructure and mechanical properties of the Ti-43.5Al-1Mo-4Nb-0.1B alloy. Mater High Temp 33(4):1–7 7. Boy J, Godoy R, Shibistova O (2016) Successional patterns along soil development gradients formed by glacier retreat in the Maritime Antarctic, King George Island. Rev Chil Hist Nat 89(1):6 8. Kamoshida N, Okawara M, Saito T (2018) Influence of pore water on the mechanical properties of water-saturated rock under very low temperatures. J Soc Mater Sci Japan 67(3):330–337 9. Karimov MU, Djalilov AT, Samigov NA (2016) Synthesis and study of the influence of the resulting hyperplasticizers on the physico-mechanical properties of the cement pastes. J Charact Dev Novel Mater 8(3):267–271 10. El-Rehim AA, Abdel-Hady EE, Diab SM (2016) Influence of heat treatment on the microstructure and mechanical properties of hypoeutectic Al-5wt% Si alloy. Adv Mater Res 2(3):38–44
Surface Quality Prediction Model of Crane Boom Based on Deep Learning Honghua Liu, Wenping Tan, Hongmei Li, Jingzhong Gong, and Xiangling Liu
Abstract With the development of urbanization, crane as an indispensable machine of construction, its workload is increasing. Crane has a certain load-bearing limit, but in the actual operation process, often because of its actual load-bearing, lifting frequently lead to cracks or even fracture of crane boom (CB). The CB is mainly welded structure truss, which has surface quality problems in the later period of use, that is, excessive use or improper use will cause cracks on the body surface, and the existence of cracks will greatly reduce the fatigue life of the boom. Therefore, based on the depth learning theory, the surface quality of CB is predicted, and the research and design of the prediction model of CB surface quality can provide reference for the use and maintenance of CB. In this paper, the characteristics of deep learning are expounded, and the reasons of using deep learning method in crane jib surface quality prediction (SQP) model are understood. In this paper, the necessity of predicting the surface quality of the CB is understood, the influencing factors of the surface quality of the CB are analyzed, the influencing factors of the cracks on the surface of the CB are described, and the quality prediction model of the CB is established. In this paper, five cranes are selected as the research object to study the actual value and the test value of the surface quality of the crane jib, so as to understand the feasibility of the prediction model of the surface quality of the crane jib. The experimental results show that the prediction model of the surface quality of the CB is still a little insufficient, and the error varies greatly due to the influence of different crane states. In the comparison results of crane jib SQP, the maximum error is 21.6% from crane No.4, and the minimum error is 0.3% from crane No.1. Keywords Crane boom · Prediction model · Surface quality prediction · Deep learning
H. Liu · W. Tan (B) · H. Li · J. Gong · X. Liu College of Information and Mechatronical Engineering, Hunan International Economics University, Changsha 410205, Hunan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_25
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1 Introduction The main vertical transportation machine in modern construction is crane, which plays a huge role in the up and down transportation of building materials [1, 2]. With the continuous increase of high-rise buildings, the use of cranes becomes more frequent. The CB is in a high load state for a long time, and its load-bearing degree is unbearable, which is easy to make the CB crack and damage [3, 4]. The surface crack of CB is the fatigue of crane, which causes fatigue damage and cracks, and there is a risk of complete fracture [5, 6]. After the crane has been used for several years under overload condition, visible cracks often appear on the surface of the stressed part of the CB, and with the passage of time, the cracks continue to expand, leading to the fracture of the CB. Therefore, the research on the SQP of the CB has been widely concerned [7, 8]. The research focus of this paper is mainly on the prediction of the surface quality of the CB. Through the deep learning of the CB correlation, it is proposed to build a SQP model to predict the surface quality of the CB [9, 10]. In the research of SQP, many scholars at home and abroad have studied it and achieved some research results. Qin j et al. Proposed a detection method combining bimodal method and maximum inter class variance method. By segmenting and binarizing the infrared image of solar cells, the location and recognition of surface defects can be accurately obtained [11]. Yang R et al. Analyzed the fatigue life of welded structure of box girder of bridge crane according to fracture mechanics and fatigue damage mechanics, and found that the life obtained by adding crack formation life and propagation life is basically the same as that obtained by Paris theory analysis [12]. These researches on the surface quality defects provide the corresponding theoretical support for the study of the crane jib SQP model in this paper. This paper mainly studies the prediction model of CB surface quality based on deep learning. This paper expounds the characteristics of deep learning, and understands the reason of using deep learning method in the prediction model of crane jib surface quality. This paper analyzes the necessity of predicting the surface quality of the CB, improves the use efficiency of the CB, reduces the fracture accidents of the CB, and accurately identifies the surface defects and cracks of the CB, which is of great significance to promote the use efficiency of the CB and reduce the fracture accidents. This paper analyzes the influencing factors of CB surface quality, expounds the influencing factors of CB surface cracks, and studies and constructs the CB quality prediction model.
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2 Prediction Model of CB Surface Quality Based on Deep Learning 2.1 Ideological Characteristics of Deep Learning The motivation of deep learning is to establish and simulate the neural network of human brain for analytical learning. Some studies have found that the human cerebral cortex does not directly analyze and learn the external input information, but sends the information into a layered network model, so as to learn the essential characteristics of the input information. Deep learning combines low-level features of different neurons to form more abstract high-level features by simulating the activity mechanism of human brain, which can realize automatic learning of features without human participation. The essence of deep learning is to establish a multi-level deep neural network model by simulating the brain’s activities, so as to improve the accuracy of target classification and detection. Deep learning not only has the ability to learn the essential features of data sets from a few sample sets, but also can solve the complex recognition task in traditional machine learning. Deep learning can make the computer imitate the way of human thinking, and can automatically recognize the target according to the processed information, which has made outstanding achievements in artificial intelligence. Deep learning algorithm has become the key research direction of researchers in various fields, roughly because deep learning has the following characteristics: Deep network structure in line with human brain’s cognitive ability. Human’s cognitive process is often progressive. Through the connection of neurons in different regions, the input information can be iterated and abstracted layer by layer. Each layer of neurons can learn the internal characteristics of the input information, and learn more abstract features in the higher layer of neurons. The deep network model is in line with this cognitive process. The network itself is composed of multi-level neurons, which can divide the recognition task into multiple abstract levels for processing, and effectively represent the information in a distributed way. Good ability of feature extraction and generalization. Traditional machine learning methods usually rely on people’s professional knowledge for feature selection. This method is inefficient, and in the face of complex recognition tasks, there may be the problem of improper feature selection. Deep learning can extract the features of the original data layer by layer, transform the features of the original data space to a new feature space, and automatically learn rich feature information, so as to have a more essential description of the input data. At the same time, the nonlinear network structure of deep learning can approximate the actual model of complex function to the maximum extent, and has strong generalization ability. The wide application prospect of depth model. In view of the problem of target recognition, the choice of feature space is the key to be concerned. The traditional machine learning is difficult to realize and consumes too much resources. The deep
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learning model has multi-level structure and can approximate any nonlinear function with few parameters. It can not only avoid the shortcomings of traditional machine learning, but also improve the final detection accuracy. The advantages of deep learning model make it widely used in image, speech and natural language processing.
2.2 The Necessity of SQP of CB CB is truss structure in essence. From the knowledge of structural mechanics, truss structure can not only save materials, weight and cost, but also has good rigidity and stability. The truss structure of crane jib is actually welded by metal web and chord. It is easy to form stress concentration at the welding position, and its surface is easy to produce cracks, which has a huge impact on the boom. Through the structural analysis and stress analysis of the CB, it is found that the connection between the lower chord and the diagonal web member is affected by the force produced by the suspended weight and the weight of the CB itself, while the connection between the lower chord and the diagonal web member is welded connection, plus the closed welding method, once the force is uneven, cracks will appear on the surface of the connection. On the other hand, when the crane is in normal operation, in order to meet the supply needs of different distances, the luffing trolley needs to complete the back and forth linear motion on the lower chord, and then transport the heavy objects. Whenever the luffing trolley passes through the arm joint, there will always be a slight jump, which is due to the uneven surface of the lower chord at the arm joint, and the impact force will be formed every time the trolley passes, this kind of impact force will cause the local deformation of the lower chord at the arm joint, and then cause the stress change. Over time, cracks will appear on the surface of the arm joint of the lower chord, and the crack surface is perpendicular to the direction of the force. When cracks appear on the surface of the CB, the crane can work normally at first, until the cracks reach a certain extent, the CB will be completely broken. In the process of boom surface crack propagation, its propagation rate increases gradually, and when the stress intensity factor reaches a certain value, increasing the load will stop the crack propagation and cause rapid fracture of the structure, and even serious accidents. Therefore, it is a very important problem for crane SQP in the engineering field.
2.3 Factors Affecting Surface Quality of CB Stress concentration. Stress concentration is a phenomenon that should be avoided as much as possible in engineering. It describes the sudden change of the shape of the component when the component is stressed, which causes the local stress mutation. Because of the stress mutation, the component will be more prone to various damages
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in the local area. For the welding structure of CB, the stress concentration is the most frequent at the welding joint, which leads to cracks on the surface of boom barrel. The stress concentration in the welding joints is as follows: first, welding defects such as welding wrong edge, root gap and porosity often exist in the weld toe area and weld root area. The stress concentration source will be formed at the welding defects, thus producing macro stress concentration. The second is that welding leads to a certain range of discontinuities in the structure connection, and there will be gaps in the unconformity of materials in the section, so it is easy to appear macro stress concentration. Welding residual stress. After normal welding, there will always be some residual stress in the weldment, which is called welding residual stress, which is mainly caused by uneven heating during operation. According to the different reasons for the formation of welding residual stress, it can be divided into the following three situations: first, in the welding work, the heating or cooling time is different due to the problem of welding sequence, which leads to the temperature difference inside the component and forms welding residual stress. The other is that some drawing and rolling operations before welding will cause residual stress in the component, which will be superimposed with the welding residual stress after welding. Third, in the welding process of metal materials, due to the change of temperature, the internal structure of the material changes, which leads to a certain amount of residual stress in the structure. Material surface condition. The surface state of the material is the most direct feedback factor of the surface state of the CB, and the influence of the solid material surface is often the most intuitive expression on the surface. All kinds of defects on the surface of the material will cause the defects of the whole structure of the CB, so there is risk prediction from the overall material. The defects of surface materials can easily lead to the reduction of the load-bearing capacity of the crane jib, which is weak in the load-bearing degree and easy to form surface cracks in the load-bearing process. Welding defects. Welding defect is the phenomenon of gas hole, slag inclusion and crack in welding caused by inexperience or unavoidable external factors. The common welding defects include incomplete fusion of solder, incomplete welding, slag inclusion in the welding position, air hole in the welding position, etc. these welding defects will affect the reliability of the CB structure, and also form stress concentration, resulting in surface cracks of components under alternating load, reducing the load-bearing level of the CB, which has the risk of fracture.
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2.4 CB Quality Prediction Model The quality problems of the CB in the use process, the CB cracks under the action of external force, but it still has the use value, can carry out normal load-bearing activities, to predict the surface quality of the CB is conducive to timely find cracks, repair cracks, have a certain understanding of the quality of the crane. This paper studies the CB quality prediction, studies the cracks on the crane surface, and understands the relationship between the CB SQP and cracks, so as to build the CB SQP model. From the initial crack length to the critical fracture crack length, the number of load cycles experienced is the CB crack service life. To estimate the crane crack propagation service life, we must first determine the critical crack size when the CB fracture occurs under a given load, according to the linear elastic fracture criteria, there are three types: √ K max = f σmax πac ≤ K C 1 ac = π
KC f K max
(1)
2 (2)
Assuming the initial crack length, critical crack length and fatigue crack growth rate, the crack occurrence degree of CB barrel can be calculated as follows: ae NC = a0
da = da/d N
ae a0
da C(K )m
(3)
The load-bearing degree of crane jib before cracks appear is the prediction degree of crane jib surface quality.
3 Experimental Study 3.1 Subjects This paper mainly studies the prediction model of CB surface quality based on deep learning. In this paper, five cranes are selected as the experimental research samples, and the feasibility of the prediction model of CB surface quality is understood through the comparative analysis of the actual value and the test value of CB surface quality.
Surface Quality Prediction Model of Crane Boom … Table 1 Analysis of crane experiment object
Initial crack length
237 Critical crack length
Diffusion rate
1
1.4
5.32
13.57
2
2.15
6.1
18.2
3
2.72
6.76
25.4
4
3.19
8.33
28.21
5
2.52
6.41
21.3
3.2 Experimental Process Steps In this paper, through clarifying the characteristics of deep learning, we can understand the reason why deep learning method is used in the prediction model of CB surface quality. In this paper, the necessity of predicting the surface quality of the CB is understood, the influencing factors of the surface quality of the CB are analyzed, the influencing factors of the cracks on the surface of the CB are described, and the quality prediction model of the CB is built. In this paper, five cranes are selected as the research object to study the actual value and the test value of the surface quality of the crane jib, so as to understand the feasibility of the prediction model of the surface quality of the crane jib.
4 Experimental Research and Analysis of Crane Jib SQP Model Based on Deep Learning 4.1 Crane Experimental Object Analysis In this paper, five cranes are selected as the experimental samples to collect the actual situation of the five cranes, so as to prepare for the study of the test model. The crane data are shown in Table 1. It can be seen from Fig. 1 that the basic data of the five cranes used as the experimental models are different. The situation that will be encountered in the actual prediction of crane surface quality model is simulated. The situation of No. 4 crane is more serious, and that of No. 1 crane is the easiest.
4.2 Comparison and Analysis of CB SQP and Measurement In order to study and understand the feasibility of the crane jib surface quality prediction model, this paper selects 5 cranes as the experimental research samples, compares the actual value of the crane jib surface quality with the test value, and studies and analyzes the feasibility of the crane jib surface quality prediction model sex. The
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Diffusion rate
Crical crack length
Inial crack length
5
crane type
4
3
2
1 0
5
10
15 number
20
25
30
Fig. 1 Analysis of crane experiment object
Table 2 Comparative analysis of CB SQP and measurement
Actual value
Predictive value
1
0.358
0.359
Error degree
2
0.947
0.98
3.5
3
0.801
0.898
12.1
4
0.98
1.12
21.6
5
0.606
0.552
8.9
0.3
actual value data comes from the test data results of the five crane experimental samples, and the test values come from the data results obtained from the basic data of the five cranes and brought into the surface quality prediction model of the crane boom. The data results are shown in Table 2. It can be seen from Fig. 2 that in the comparison results of crane jib SQP, the maximum error is 21.6% from crane No. 4, and the minimum error is 0.3% from crane No. 1. From the results, the crane jib SQP model is slightly insufficient, and the error changes greatly due to different crane states.
5 Conclusions This paper mainly studies the prediction model of the surface quality of the CB based on deep learning. To some extent, the model can effectively understand the
Surface Quality Prediction Model of Crane Boom … actual value
1.2
239
predicve value 1.12
0.98 1
0.947
0.898
0.98
0.801
number
0.8 0.606 0.552
0.6
0.4
0.358 0.359
0.2
0 1
2
3
4
5
crane type
Fig. 2 Comparative analysis of CB SQP and measurement
surface quality of the CB barrel, improve the efficiency of CB, reduce the CB fracture accident, accurately identify the surface defects and cracks of the CB barrel, and promote the efficiency of CB, it is of great significance to reduce the occurrence of fracture accidents. Acknowledgements This work was supported by the Scientific Research Project of Hunan Provincial Department of Education (No: 20A287).
References 1. Crane NA, Jenkins LM, Runa B et al (2017) Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI. Brain 2:472–486 2. Ren LX , Ma JQ , Tong YT et al (2021) A review of fatigue life prediction method for portal crane. In: IOP conference series: earth and environmental science, vol 657, no 1, p 012094 (4pp) 3. Serin G, Sener B, Gudelek MU et al (2020) Deep multi-layer perceptron based prediction of energy efficiency and surface quality for milling in the era of sustainability and big data. Procedia Manuf 5(1):1166–1177 4. Yao J, Xing F, Fu Y et al (2017) Failure analysis of torsional buckling of all-terrain crane telescopic boom section. Eng Fail Anal 7(3):72–84 5. Yang T, Sun N, Chen H et al (2020) Neural network-based adaptive antiswing control of an underactuated ship-mounted crane with roll motions and input dead zones. IEEE Trans Neural Netw Learn Syst 31(3):901–914
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6. Tho HD, Terashima K (2018) Robust control designs of payload’s skew rotation in a boom crane system. IEEE Trans Control Syst Technol (99):1–14 7. Li J, Bai L, Gao W et al (2021) Reliability-based design optimization for the lattice boom of crawler crane. Structures 29(2):1111–1118 8. Wang G, Wang YY, Yuan J et al (2017) Arc-Surfaced frictional damper for vibration control in container crane. Shock Vib (pt.1):1–12 9. Lyu J, Shi R, Yang Y et al (2019) Matching detection of crane hook and ladle lug before ladle hoisting. Sensors 19(24):5389 10. Yin J, Zhang S, Liu Y et al (2019) Structural topology optimization of cantilever crane boom based on equivalent moving load method. In: IOP Conference series: materials science and engineering, vol 692, no 1, p 012020 (6pp) 11. Qin J, Shao T, Chen J et al (2017) Stress analysis of boom of special mobile crane for plain region in transmission line. In: IOP conference series: materials science and engineering, vol 24, no 8, p 012027 12. Yang R, Li W, Zhao G et al (2021) Interval non-probabilistic time-dependent reliability analysis of boom crane structures. J Mech Sci Technol 35(2):535–544
Comprehensive Utilization Analysis of Geophysical Exploration Data Based on Data Mining Algorithm Mingfei Cui, Lu Wang, Liming Du, Chuangye Hu, and Yang Zhang
Abstract Geophysical exploration measures the internal structure and resource distribution of the earth through geophysical instruments. A deeper understanding of the earth’s internal structure and distribution of resources makes people have the ability to predict a series of geological disasters, and at the same time can help us find oil, natural gas, ore and other resources, which is of great significance for human development. In order to solve the bottleneck problem of large-scale data acquisition network directly managed by data center with the increasing number of sensors, this paper analyzes the comprehensive utilization of geophysical exploration data based on data mining algorithm. Keywords Data mining · Physical exploration · Data analysis
1 Introduction Geophysical exploration is an applied branch of geophysics. Geophysical exploration uses the differences of physical properties of the crust medium in the aspects of electricity, magnetism and elasticity to infer the geological structure or explore valuable mineral resources. The electronic instruments used to obtain the physical parameters of medium in geophysical exploration are called geophysical exploration instruments. Through geophysical exploration instruments, we collect the physical characteristics of gravity, earthquake, magnetic field and electricity in a certain geographical space, and interpret the collected data to reconstruct the geological structure model in a certain range. Geophysical exploration methods have been widely used in mineral, geology, transportation, construction and other engineering fields. M. Cui · L. Wang (B) · L. Du · C. Hu · Y. Zhang Geological Exploration Institute of Shandong Zhengyuan, Jinan 250010, ShanDong, China L. Du e-mail: [email protected] Deep Exploration Geophysics Technical Engineering Laboratory of Geophysical Society of Shandong Province, Jinan 250012, ShanDong, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_26
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According to the different physical characteristics of underground medium collected by geophysical instruments, geophysical exploration is mainly divided into seismic exploration method, electromagnetic exploration method, gravity exploration method, remote sensing exploration method, radioactive exploration method, etc. The following two main geophysical exploration methods are also used in the data transmission system designed by the laboratory. Seismic exploration method based on elastic difference of medium in the crust and transient electromagnetic method based on electromagnetic property difference of medium in the crust [1].
2 Data Mining Technology 2.1 Definition of Data Mining Data mining is a kind of decision support process, which is mainly based on artificial intelligence, machine learning, statistics technology, highly automated analysis of the original data of enterprises, inductive reasoning, mining out potential patterns, predicting customer behavior, helping enterprise decision-makers adjust market strategies to reduce risk and make correct decisions. It is not a new technology, its emergence and development has its own inevitability. With the rapid growth of the company’s database, especially the emergence of data warehouse, the original database tools have been unable to meet the needs of users. Users need not only general query and report tools, but also tools that can help them extract high-quality information (predictive) from the vast amount of data. The emergence and development of data mining is in line with this trend. It is also one of the most advanced research directions in the field of database and information decision. Data mining is a non trivial process of extracting effective, novel, potentially useful and ultimately understandable patterns from a large number of incomplete and noisy data sets. A schema is an expression expressed in language, which can be used to describe a subset of a dataset. As a schema, an expression is required to be simpler than enumerating a subset of data, that is, it uses less description information. Process usually refers to multi-stage processing in data mining, such as data cleaning, data integration, pattern search, pattern evaluation, knowledge representation and repeated modification and refinement. This process is required to be extraordinary, that is to say, to have a certain degree of intelligence and automaticity. The validity means that the mining pattern still has a certain degree of credibility for the new data, and the novelty requires that the pattern is new. Potential usefulness means that the knowledge mined will have practical utility in the future. Final comprehensibility means that the knowledge mined can be understood by users, which is mainly reflected in simplicity [2].
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2.2 Data Mining Process Data mining is a complete process, which mines previously unknown, effective and practical information from large databases or data warehouses, and uses this information to make decisions or enrich knowledge. Data mining consists of the following processes or steps: 1.
2.
3.
4.
Data preparation. This stage can be further divided into four sub stages: data cleaning, data integration, data selection and pre analysis, and data transformation. Data cleaning is to eliminate noise or inconsistent data. Data integration merges the data in multi file or multi database running environment to establish a unified data view. Data selection is to retrieve data related to analysis task from database; The purpose of pre analysis is to identify the data sets that need to be analyzed, narrow the processing range and improve the quality of data mining. Data transformation is to transform or unify the data into a form suitable for mining, such as through aggregation or aggregation operation. Digging. In this stage, the actual mining operation is carried out, and intelligent methods are used to extract data patterns. It includes: deciding how to generate hypotheses; choosing appropriate tools; operating knowledge mining; Confirm the knowledge of discovery, etc. Knowledge representation. Using visualization and knowledge representation technology, the extracted information is analyzed according to the end-user’s decision-making purpose, and the most valuable information is distinguished and submitted to the decision-maker through decision support tools. Evaluation. If the analyzers are not satisfied with the results, the above three processes can be repeated until they are satisfied. The process of knowledge discovery is shown in Fig. 1.
Fig. 1 Course of data mining
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3 Exploration Methods 3.1 Seismic Exploration Method Seismic exploration method is the most widely used geophysical exploration method at present, which has the advantages of low cost, high precision and high resolution. Seismic exploration methods are widely used in the exploration of non-metallic minerals such as oil and gas, mainly due to the obvious differences in elasticity and density between oil, gas and surrounding media. Seismic exploration method is to arrange a large number of geophones in a certain geographical space, and use artificial seismic method to generate artificially excited seismic waves. In the process of propagation, seismic waves will encounter media with different elastic properties, which will lead to refraction and reflection of seismic waves in the process of propagation, The information of refraction and reflection is collected by pre arranged geophone. According to the relationship between the time of the received seismic wave and the time of the transmitted wave, combined with the transmission speed and movement law of the seismic wave in the stratum, the stratum structure can be inferred.
3.2 Transient Electromagnetic Method Transient electromagnetic method (TEM) is a time domain electromagnetic detection method. By sending the primary pulsed electromagnetic field to the underground, the medium will generate eddy current under the excitation of the primary pulsed electromagnetic field. When the pulsed electromagnetic field as the excitation source is cancelled, the secondary magnetic field generated by the eddy current in the medium will not disappear immediately, but will form a secondary magnetic field that decays with time in the surrounding space. The law of the decay of the secondary magnetic field with time mainly depends on the conductivity of the medium The volume, scale and buried depth, as well as the shape and frequency of the emission current of the excitation source. Therefore, the distribution of secondary magnetic field in space can be measured by sensors placed in advance to reflect the distribution of underground abnormal medium in space. At first, TEM was only used in the exploration of metal deposits, mainly because the conductivity of metal medium is significantly different from that of surrounding medium. In recent years, with the improvement of the performance of electronic instruments, transient electromagnetic method has also been used to detect underground faults, metal ore and oil, coal, natural gas exploration of non-metallic minerals. The related technology of multi-channel transient electromagnetic method (MTEM) was put forward by British scholar Wright in 2001. This technology uses the principle of seismic exploration for reference, adopts the method of multiple transmission and multi-channel reception, and realizes the expansion of multi-channel
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number. Compared with traditional TEM, multi-channel TEM has higher spatial sampling rate and higher interpretation accuracy for geological spatial structure. However, higher spatial sampling rate means that more sensors need to be deployed per unit area, which poses new challenges to the management ability and data transmission ability of the data system [3].
4 Analysis of Geophysical Exploration Data Seismic exploration data processing is realized by a process composed of several processes with different functions called processing module. The most commonly used data processing process in seismic exploration by reflection method has the following main modules. The output of the preprocessing seismic data acquisition system digital seismograph is the digital magnetic record. Because of the need of seismic work, the acquisition points in the field are multi-channel, and the sampling system is single channel. Therefore, the sampling points on the digital recording tape are not arranged in the order of channels, but in the order of sampling time. The purpose of preprocessing is to rearrange the above sampling sequence into the order of channels. Deconvolution is designed to remove the influence of receiving system and earth filtering, and make the output a pulse sequence proportional to the reflection coefficient, so that the seismic record can clearly reflect the exact position of the underground reflection surface and the size of the reflection coefficient, and provide reliable data for interpretation. The deconvolution of seismic channels is based on the result that seismic channels are regarded as the convolution of seismic wavelet and reflection coefficient sequence. Deconvolution is a series of seismic channels which are filtered to make them output as reflection coefficients. Seismic wavelet is unknown and gradually changes in the process of propagation. The commonly used method to calculate deconvolution factor is the minimum square method, Viena method [4]. Seismic spectrum analysis of seismic wave is a function of time change, which can be expressed as a function of frequency by Fourier transform. Seismic spectrum is a parameter reflecting the formation properties. Spectrum analysis is a method to extract frequency parameters. In seismic data processing, in order to accelerate the calculation and relative keeping of spectrum components, data processing is often carried out in frequency domain. The velocity analysis of seismic wave in rock is a parameter reflecting the rock property, and also an indispensable processing parameter for time difference correction. The application of velocity is almost throughout the whole process of seismic exploration data processing. The original data used to extract the velocity of seismic wave propagation in the formation are acoustic logging data, seismic logging data and a large number of seismic records observed many times. The common velocity concepts in seismic exploration data processing include: layer velocity, average velocity, root mean square velocity and superposition velocity.
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These velocity concepts are suitable for both seismic P-waves and shear waves, but their magnitude is different. The velocity of the layer is the velocity of the seismic wave propagation in homogeneous strata. It is equal to the thickness of the horizontal formation divided by the time the wave propagates in the vertical direction. The average velocity is the velocity calculated when the seismic wave passes through the horizontal layer of the formation along the normal direction. Root mean square velocity is the square root value of the average velocity square of each layer in the horizontal layer (taking positive value). The superposition velocity is an equivalent velocity calculated by the velocity spectrum of the common center point gathers. It is different from the average velocity and is not equal to the root mean square velocity, but it is very close to the root mean square velocity in the horizontal layered medium. The stacking velocity varies with the inclination of the interface. The following relations exist between the various speeds, namely, the superposition acceleration is greater than the root mean square velocity and the root mean square velocity is greater than or equal to the average speed. But the difference is not very big. Superposition is a method to improve the signal-to-noise ratio of seismic records in seismic exploration data processing. It is to stack the reflection waves from the same reflection point in the same phase. In order to add all channels in the same direction, the normal time difference correction must be carried out for different shot detection distances (from shot point to detection point and distance between points). In the area with uneven surface and elevation difference, surface correction is required, namely static correction. In addition to direct addition, the superposition method has various weighting methods to achieve the best effect of the same phase superposition. Migration (homing) is processed on the superimposed section, and the formation is represented by the seismic waveform in the same phase axis. When the underground stratum is horizontal, the reflection wave just reflects the formation directly below the trace. If the reflection stratum is inclined interface, the seismic reflection axis will move downward. At this time, the apparent inclination of the same phase axis is reflected on the superimposed section φ* True inclination with strata φ It doesn’t wait. The two have the following relations under the condition of uniform medium: sin φ = tgφ∗
5 Conclusion In recent years, with the development of electronic instruments and geophysical exploration theory, geophysical exploration has made great progress in accuracy, resolution and exploration depth. Geophysical exploration technology has become the most important resource exploitation technology. The exploration results are
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often a large number of data, only through data mining algorithm can we find out the useful information data. Acknowledgements Scientific Research Project of Shandong Bureau of China General Administration of Metallurgy and geology, Item number: SDYJ-KY02012
References 1. Lefeng L (2014) Research on a scalable data acquisition system for large scale geophysical exploration. University of science and technology of China 2. Han J, Micheline K (2001) Data mining: concepts and techniques. China Machine Press, Beijing, vol 374 3. Qingyun D (2015) Design of power station in MTEM exploration system. China University of science and technology; Le Z (2015) Design of cross station in MTEM exploration system. China University of science and technology 4. Kaiyun T (2015) Research on some key technologies of data acquisition in geophysical exploration equipment. University of science and technology of China
Spark for Data Mining of Massive Historical and Cultural Resources and Humanistic Smart City Construction XiaoLi Zhang
Abstract Humanistic smart city is the product of the development of smart city to a certain stage. It is an important form of the integration of “cultural city”, “humanistic city” and “smart city” under the background of new urbanization in China. Historical and cultural resources are the carrier of the soul of a city and the external embodiment of the city’s personality and humanity. Under the background of the dual strategy of “smart city” and traditional culture rejuvenation, it is an important era proposition under the background of the new normal to fully excavate the connotation of historical and cultural resources, form “cultural consciousness”, improve the city quality and soft power, and build a “meaningful, valuable and dream” modern “Humanistic smart city” in China. Keyword Historical and cultural resources · Humanistic smart city
1 Spark Clustering Algorithm (KL Cluster) 1.1 Data Pre Clustering The clustering process based on KL divergence can be divided into the formation process of probability matrix, distance matrix and iterative process. The formation process of probability matrix is divided into k(k = 1, 2, . . . , k − 1, k) by pre clustering [1]. Then the frequency distribution of the data in the cyclic statistical cluster is distributed on the k-class data, which forms the probability matrix of n × k. The resulting probability matrix is as follows: ⎡
P11 · · · ⎢ .. . . PM = ⎣ . .
⎤ P11 .. ⎥ . ⎦
(1)
Pn−1 · · · Pnk
X. Zhang (B) Shandong Xiehe University, Jinan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_27
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1.2 The Formation Process of Distance Matrix According to the above probability matrix, the KL distance between the meaning line and the other line is calculated respectively to form the K matrix on the whole data set. In the introduction of KL divergence, it is also mentioned that K has asymmetry [2]. Here, the average value of the distance between any two lines is used as the actual KL value. The remaining parts are filled with 0, and the upper triangular probability matrix is formed as follows: ⎤ ⎡ 0 D M12 · · · D M1n ⎢ .. . . . ⎥ (2) D M = ⎣ ... . .. ⎦ . 0
0
···
0
2 From Smart City to Humanistic Smart City With the rapid change of information technology and the continuous promotion of new urbanization, urban population expansion, traffic congestion, environmental degradation, housing shortage, one side of a thousand cities and so on, have become an important bottleneck restricting the further development of cities. The practice of smart city aims to improve urban efficiency and solve the “urban disease” caused by extensive development. However, due to excessive reliance on high-tech and ignoring the nature of urban “human settlement”, the connotation of urban “wisdom” is insufficient, hindering the further development of smart city, and humanistic smart city emerges as the times require [3].
2.1 The concept of Humanistic Intelligent City Smart city focuses on “wisdom”. At present, more theories and practices focus on urban “intelligence”, which emphasizes the management and operation mode of intelligent, interconnected and IOT driven by smart technology, smart industry and smart projects, so as to enhance the city’s economic operation, social development and human settlement convenience, and enhance the comprehensive competitiveness of the city [4]. The key of humanistic city lies in “humanity”, that is, “various cultural phenomena of human society” is “attaching importance to human culture”. Different from “cultural city” which takes culture as its primary function, humanistic city adheres to the urban definition of “human settlement center” and puts human existence and value at the core of urban concept. It pays more attention to the construction and development of non-material aspects such as humanistic spirit
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and urban personality, and puts more emphasis on people-oriented and higher-level urban development mode based on the concept of sustainable development.
2.2 The Background of the Emergence of Humanistic Intellectual Job City “The historical cycle of the birth, evolution and extinction of cities reflects the law of the evolution of human civilization. Cities in different stages have different external characteristics and their own internal development characteristics, which is the embodiment of productivity level and urban culture development to different degrees in different periods. According to this Law of development, in the process of rapid transformation from traditional cities to modern industrial cities, urban problems have gradually emerged, and urban diseases have begun to restrict the development of social economy, prompting the emergence of smart cities characterized by digitalization and intellectualization, which has become an important choice for alleviating urban problems and enhancing urban functions. Since the 1990s, the theoretical research of “smart city” has been deepened, and great progress has been made in the construction practice [5]. From the perspective of both domestic and foreign countries, although foreign theories and practices are prior to those in China and are relatively mature, from the perspective of development process and construction mode, they have gone through the development stages of “scientific and technological smart city” dominated by technology application and “management smart city” characterized by urban application, meeting the development needs of people for urban life in different periods. This also made the concept of “smart city” deeply rooted in the hearts of the people, and countries began to promote it on a large scale. However, on the other hand, the functionality of the city is regarded as the main goal of the development of smart city, regardless of whether it is characterized by technology-oriented or urban application. It ignores the most authentic humanistic attributes of the city. It not only fails to achieve the original intention of alleviating urban problems and promoting harmonious development, but also deviates step by step from the fundamental attribute of “people-oriented”. The phenomenon of blind duplication of “one side of a thousand cities” can be found everywhere, Urban residents’ well-being can not keep pace with the speed of urban intelligence, and the construction of functional smart city can not continue to meet the current social and cultural development needs [6].
2.3 The Characteristics of Humanistic Intelligent City The characteristics of cultural cities are mostly based on the interpretation of cultural cities. Cultural cities are considered to be the advanced development stage after the
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development stages of industrial cities, economic cities and political cities, So that the city has a more distinctive image, characteristics, spirit and character. First of all, it is highly dependent on the cultural landscape of the city, which is characterized by high cultural diversity, cultural diversity and cultural development; Fourth, relatively complete cultural functions and so on. Compared with this, humanistic city puts the status of urban “people” to the highest position. The existence of the city, the generation, development and continuation of culture all depend on the production and living activities of “urban people”. Therefore, the humanistic city should also have and more emphasis on people-oriented, universal and other characteristics, so that the city can meet the needs of cultural life of residents of all walks of life [7].
3 Interactive Relationship Between Historical and Cultural Resources and Humanistic Smart City 3.1 The Historical and Cultural Resources Can Promote the Comprehensive Competitiveness of the Humanistic Intelligent City The level of urban economic development, social advancement, cultural prosperity and sustainable development factors are important indicators to measure the comprehensive competitiveness of a city. Urban historical and cultural resources are an important complex of cultural value, social value, economic construction and urban construction value. It is the key to update the development concept of humanistic smart city, promote the sustainable development of economy, society and culture of humanistic smart city and the construction of urban characteristics.
3.2 Historical and Cultural Resources Are the Essential Factors for Sustainable Development of the Humanistic Smart City Sustainable development is the core task of urban construction and the inevitable requirement of practicing the five development concepts of “innovation, coordination, green, openness and sharing”. Excellent historical and cultural resources are the witness of a city’s prosperity and development, and the precious wealth left by the ancient sages. As the exclusive assets of the city, its civilization achievements also have a profound and long-term impact on the sustainable development of the city. The application of historical culture in urban construction is conducive to the correct handling of the relationship between man and city, between man and society, between man and nature, and promotes the harmonious unity of urban material
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civilization, institutional civilization and spiritual civilization. Historical culture is an important part of urban humanistic wisdom. The process of exploring and developing historical and cultural resources is the way to find the root of a city and an important step to establish a harmonious social relationship. Through the re understanding and innovative application of historical and cultural resources, we can interpret the urban humanistic connotation with its cultural core, understand the “source” of urban development, and reasonably apply it to the “flow” of urban development, so as to promote the connotative development of urban cultural industry, the innovation of human culture in science and technology, and the construction of humanistic attributes of urban space, so as to break the embarrassing situation of “one side in a thousand cities, one appearance in ten thousand households”, To alleviate a series of urban problems caused by the extensive development of rapid urbanization [8].
3.3 Cultural Heritage and Cultural Heritage of Cities The inheritance and innovation of historical and cultural resources is an important requirement of new urbanization and humanistic city construction, and also an important focus of the national “cultural power” strategy. Through the comprehensive development of technological innovation, smart application, smart economy and smart space, the construction of humanistic smart city provides a favorable environment for the value mining of historical and cultural resources, and provides a strong economic foundation, scientific and technological support and a harmonious social atmosphere for its value inheritance.
4 Conclusion Humanistic smart city is an important development form of the combination of “cultural city”, “humanistic city” and “smart city” under the background of new urbanization. It is an important way to realize the comprehensive coordinated and sustainable development of material civilization, spiritual civilization and ecological civilization. Under the background of the new normal development, the development idea of mutual promotion and common development between the construction of humanistic smart city and historical and cultural resources is an important measure to enhance the comprehensive competitiveness of the city, promote the evolution of urban civilization and sustainable development. As the first batch of smart city construction pilot projects, Wuhan started early, and gradually explored a development path with “science and technology” as the core driving force and “facilities + application + industry” as the idea. The construction practice was gradually upgraded, and the “management smart city” was comprehensively promoted, and gradually transformed into a “Humanistic smart city”.
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Acknowledgements 2018 Shandong University Scientific Research Program:Research on historical and Cultural Resources and Construction of Humanistic Smart City in Shandong Province, Item number: J18RA064
References 1. Meiying Y (2012) Smart city practice sharing series. Beijing Electronic Industry Press 2. Hongyan’s Z (2010) Capital of urban culture. Nanjing Southeast University Press 3. Helin L (2010) Interpretation and utilization of urban cultural space, a new way to build a cultural city. Nanjing: Southeast University Press 4. Honglie Y (2006) protection and development of urban history and culture. Beijing China Construction Industry Press 5. Hongjun S, Li H (2014) Strategic management research based on the era of big data—taking cultural industry as an example. Green Technol (01) 6. Youxin W, Wu B, Ma L (2013) Research on Internet big data mining and intangible cultural heritage activation. J Univ (03) 7. Zonggui L (2013) Pay attention to the exploration and interpretation of the modern value of excellent traditional culture and seek the historical support of the core values. People’s Forum (15) 8. Jingchuan H, Fang J (2013) Research progress and prospect of data citation. J Chinese Libr Sci (01)
Research on Massive Data Mining Technology Based on Map Reduce Xia Chang
Abstract MapReduce is a programming model, which can run in heterogeneous environment. It is easy to program and does not need to care about the details of the underlying implementation. It is used for the parallel operation of large-scale datasets. MapReduce is applied to three algorithms of data mining: Puqi Bayesian classification algorithm, κ—modes clustering algorithm and Eclat frequent itemset mining algorithm. Experimental results show that MapReduce can effectively improve the efficiency of massive data mining on the premise of ensuring the accuracy of the algorithm. Keywords Cloud computing · Data mining · Hadoop · MapReduce
1 Introduction In 2007, Chu and other scholars proposed a naive Bayesian classification algorithm based on MapReduce. The algorithm adopts the idea of distributed processing, and constructs a classifier by means of decentralized statistics and centralized integration of samples. However, it can only deal with discrete data and can not provide effective support for continuous data. In addition, the MapReduce implementation of are commonly used in data mining, has not seen relevant authoritative reports to the best of our knowledge., this paper proposes an improved MapReduce implementation of naive Bayes classification algorithm, which can deal with data mining applications with both discrete and continuous attributes [1–3]. MapReduce implementation pattern mining algorithm based on the thought of each algorithm and the operation mechanism of MapReduce. Their implementation extends.
X. Chang (B) Yunnan Land Resources Vocational College, Yunnan 650000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_28
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2 In the Context of Cloud Computing, Optimize the Data Model In order to meet the growing needs of data storage and to analyze large-scale data quickly, Google company proposed the distributed file system Google file system, distributed computing framework MapReduce and distributed database BigTable. According to their ideas, Apache foundation has implemented the corresponding open source versions: Hadoop distributed file system (HDFS), Hadoop MapReduce and H base, which constitute the core of Hadoop project [4, 5].
2.1 MapReduce Multiple nodes in the cluster to complete the task together. This can effectively reduce the computational complexity of each part and improve the computational efficiency[6]. If MapReduce cluster structure wants to execute MapReduce task in a cluster, the following parts need to cooperate to complete. 1. 2. 3.
4.
Client: the interface between user and cluster. Job Tracker: responsible for scheduling the execution of the whole job. There can only be one job tracker in a cluster. Task Tracker: the real Performer of the job. It can perform two types of tasks: Map task or reduce task. Mapper performs map tasks, and reducer performs reduce tasks. There can be multiple task trackers in a cluster. Distributed file system: used to store input and output data, usually using HDFS.
2.2 The execution process of MapReduce MapReduce task is divided into. Map performs task after decomposition, reduce is responsible for summarizing the intermediate results obtained in the map phase. The specific implementation process is as follows: 1. 2. 3.
4.
Preprocessing: MapReduce framework first calls the class library to divide the input file into equal size (usually 64 MB) pieces. Task allocation: job tracker allocates to idle nodes cluster. Suppose in the cluster. The mapper task reads the corresponding file fragment, converts the input fragment into a key value pair, calculates it with the map function, and obtains a new key value pair, which is cached in the memory of the current node as an intermediate result[7–9]. Cache file location: (3) the intermediate results are periodically written to mapper’s local hard disk, and the file storage location information is passed to the reducer through job tracker.
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Fig. 1 MapReduce operation process diagram
5.
6. 7.
Reducer pull files: reducer pull these files from the corresponding mapper through the location information. When all the temporary results are read, all the value values with the same key are combined into a group to get the < key, list (value) > key value pair for the convenience of later calculation The reduce service reducer uses the reduce function to calculate the read < key, list (value) > key values, and gets the final result and outputs it. End: system will notify the user and report the information related to task execution. At this point, a MapReduce task is completed. The detailed process is shown in Fig. 1.
2.3 Application of MapReduce Tasks must be scalable to be executed in the cluster. “scalable” task is the that can be scaled equally: that is, after a task is reduced or expanded, the working method does not change, and each node does the same work. For example, count the number, calculate the variance, etc. MapReduce task “first split and then merge”. When facing an extensible task, we need to find out the tasks that can be distributed and executed, and take them value in the current slice. During task execution, if you need global variables, you can use the functions provided by Hadoop to set them, or you can use the distributedcache class files to each mapper to ensure the job [10–12]. The reduce function is relatively simple, mostly for summary and judgment work. In the above example, after obtaining the maximum value found by each mapper, reducer integrates and extracts it, and obtains the unique maximum value of 100 million records.
3 Data Mining Optimization in MapReduce’s Massive Data 3.1 Classification and Prediction There are L samples, each sample has n attributes, and there are m categories: Ci , C2 , . . . , Cm Each sample can use an N + 1-dimensional attribute vector x =
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Fig. 2 Bayesian algorithm model
X = {X 1 , X 2 , . . . , X m , C}. There is an unclassified sample x, which is predicted to belong to class C if and only if. P(Ci |X ) > P(C j |X ), 1 ≤ j ≤ M, j = i
(1)
WT · X + b = 0
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3.2 Traditional MapReduce Implementation of Naive Bayes Algorithm The traditional MapReduce implementation of the algorithm mainly aims at discrete attributes, in which the 2: map function will: the value and quantity of each classification The reduce function accumulates the intermediate results of the map function to get the final classifier [13]. As shown in Fig. 2. The implementation of traditional methods is relatively simple, but in the face of continuous attributes, this method can not deal with. Based on this, this paper proposes an improved MapReduce implementation of naive Bayesian algorithm.
4 Experimental Results of Eclat Compared with the previous two experiments, Eclat algorithm is more special. Because there is intersection operation in each iteration, it needs to be combined two by are m samples, the first intersection will be carried out C2 times, and the
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Fig. 3 Iterative intersection operation
problem scale will quickly become the square of the original, so it is faster in smallscale data. As shown in Fig. 3. However, when the amount of data is large, limited by memory and cannot carry out the correct calculation. In this experiment, the scale of data selection is reduced, that is, 10 (2 KB), 10 (344 KB) and 10 (3550 kb) samples are selected. from 64 to 2 MB. Each sample has a tuple number ranging from 1 to 20. Through the experimental test, these three samples will appear the problem of memory overflow in single machine computing, so the experiment is only based on cluster.
5 Concluding Remarks Expansion of Internet business, data need to be analyzed and processed every day. When the data is coming, whether it can make a rapid response is the focus of the industry. Relatively cheap and efficient cloud computing has become the only choice for people. This paper proposes an idea of how to use MapReduce framework to solve large-scale problems. Experiments show that the application of this idea can complexity of the task, improve the efficiency algorithm, and greatly reduce the execution time of the task. It provides a reference for solving similar problems in the future.
References 1. Liu K, Jinhai, Xu L et al (2019) Smart grid architecture and cache technology based on SDN and ICN. J Electr Power Sci Technol 78 (2):34–41 2. Guo X, Ma T, Wang C et al (2019) Optimization of distribution network planning considering the access of electric vehicle charging station. Smart Power (6):64–70 3. Zhiqiang Y, Wu Y, Xu X et al (2017) Discussion on key technologies of main plant station integration for smart grid. Power Syst Autom 41(8):179–185 4. Ke W, Xiaorui G, Jing Z et al (2018) Framework and implementation of smart grid “source load” cooperative dispatching. Power grid Technol 42(8):78–85 5. Xiufeng G (2020) Data mining and thinking in the era of big data. Comput Programm Skills Maintenance (12) 6. Caijingying (2020) Analysis and research on key technologies of big data mining. Ind Sci Technol Innov (34) 7. Songwenbin (2021) The architecture of data mining platform based on cloud computing and its key technologies. Electron Technol Softw Eng (03)
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8. Chengmin G (2021) The characteristics of “hollow village” power consumption based on big data mining of power. Sci Technol Econ Guide (07) 9. From a high point of view, pan Chengcheng. Teaching practice of “data mining and application” based on “problem driven”. New Industrialization (2020) (09) 10. Jingrong Y, Liu J (2021) application of temporal and spatial association rule data mining based on ordered tree. Comput Technol Dev (06) 11. Yixuan L (2019) Analysis of the application of sampling estimation in big data mining. Modern Inf Technol (21) 12. Xin D (2019) Analysis of the customer value of radio and television based on big data mining. Technology horizon. (34) 13. Jiahai W, Hao B (2019) Data mining method and application in industrial field based on deep learning. Digital Technol Appl (11)
Application and Research of Data Mining Technology Bim and Internet of Things in Engineering Construction Luhao He, Yixing Xie, and Yue Li
Abstract The construction project belongs to the complex project with long period. The whole project involves many links, such as technology and management, which need to be carried out scientifically to ensure the construction quality of the project. Coordinate and distribute the construction site and coordinate the operation of each link. This paper analyzes the application of BIM in construction engineering, and constructs a scientific management system through BIM to ensure the efficiency and quality of engineering construction and promote the better development of construction industry. Keywords BIM technology · Construction engineering · Engineering construction
1 Introduction With the economic growth, China’s construction projects have also been well developed, and in all aspects reflect the proud achievements of development. BIM as a building information model, from abroad to domestic, for domestic construction engineering, the application of BIM technology, can effectively carry out a reasonable calculation of each link of the construction project, showing an intuitive view, It is convenient for constructors to master construction points and adopt scientific construction technology. However, because the technical time of building contact BIM in China is still short, its application needs to be improved, so it needs to be continuously developed and improved in the subsequent application [1].
L. He · Y. Xie · Y. Li (B) Wuyi University Guangdong Jiangmen, Jiangmen 529000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_29
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2 Main Advantages of BIM Technology 2.1 Visualization of BIM Technologies During the construction period, the construction design can not display the design content three-dimensional, the visualization of BIM technology is particularly important. In the design drawings, the construction intention can be accurately expressed by using information tagging. But the actual design scheme, can not be displayed, need construction personnel according to their own thinking to think. Traditional construction technology can meet the simple architectural concept, as the building continues to develop towards complexity. Facing the complex design drawing, the construction personnel’s thinking construction obviously can not work. For this, the BIM technology can show the contents of the design drawing to the constructors, and help the constructors to observe each link of the design intuitively through the three-dimensional simulation view. 1.
2.
Coordination of BIM technologies In construction, to ensure the smooth development of construction, we need to strengthen the cooperation of various construction departments. The problems in construction can be solved in time. For some facilities in construction, it can not be effectively treated in actual construction. And often when the construction reaches half, there is a cross-operation site, which hinders the construction progress, BIM the coordination of technology, can establish the corresponding information model in the construction, and test the model according to the actual situation. Avoid cross-operation, ensure the effectiveness of each link construction, and improve the efficiency of construction. Simulations
An important reason for the wide application of BIM technology is that the technology can simulate each link according to the construction project and display many elements such as project geology. For the risks and hidden dangers that may be encountered in the implementation of the project, with the help of the advantages of simulation, it can be displayed intuitively and help the construction personnel to eliminate the hidden dangers of safety. Moreover, according to some operation behaviors in construction, it plays a normative role to the construction personnel, reduces the occurrence of construction errors, and ensures the effectiveness of the project. For example, in foundation pit engineering, risk can be judged by BIM and danger grade can be automatically divided. The classification of BIM technical hazards is shown in Table 1.
Application and Research of Data Mining Technology Bim … Table 1 BIM Classification of technical hidden dangers
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Serial number
D of mining depth
Hazard level
Warning colors
1
D>5
Level 1
Red
2
D=3~5
Level II
Orange
3
D=1~3
Level III
Yellow
4
D chi2
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Pseudo
R2
Observations
0.0318
0.0312
0.0305
0.0301
0.0301
0.0323
176,934
176,934
176,934
176,934
176,934
176,934
Note dummy-indentity3 omitted because of collinearity, ***p < 0.01, **p < 0.05, *p < 0.1 Table 6 Hypothesis results Hypothesis Description
Support
H1
The more negative emotional factors contained in a Covid-19 related Weibo, the greater the number of reposts
Not supported
H2
A Covid-19 related Weibo contains too strong negative emotional Supported factors, and the number of reposts will be decreased
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6 Discussion Our results provide important inspiration for risk communication. The results partially support the Social Amplification of Risk Framework’s claim using content with strong emotional factors to help break through the predicament of information confusion under risk events [6]. And enhance public awareness of risk perception. This study believes that at the beginning of a risk event, disseminators (especially experts, media, government and other trusted individuals or institutions) can timely and cautiously release negative information about risk events (such as the number of casualties, risk hazards, possible ways of transmission of the epidemic, etc.). These negative emotions can increase the level of threat perception, and then people will consciously make conscious prevention to protect themselves from risks [8]. The study also found that continuous negative emotional content and stimuli may overwhelm the cognitive resources of the information receiver (user). Then lead to a decline in the understanding and concentration of information, that is, information overload [1, 18]. Information overload is often accompanied by the next step of information avoidance [18]. This is in line with the inverted U-shaped structure of the fear-driven function first proposed by Janis [15]. In other words, the hazards of risk events are indeed amplified by dense information, leading to collective emotional disorders. Therefore, while conducting risk communication, it is necessary to pay attention to the changes in collective emotions brought about by public opinion at any time. And timely supplement positive risk-related information to prevent the public from completely falling into fear and anxiety. At the same time, the public is more inclined to forward information from authoritative organizations and individuals. Therefore, when media or government agencies publishing social media posts, special attention should be paid to the credibility and reliability of the information. It is best not to publish fragmented and incomplete information, which will aggravate the public negative sentiments and enhance the possibilities for collective mental disorder. Sometimes, less is better than more.
References 1. Matthes J, Karsay K, Schmuck D, Stevic A (2020) Too much to handle”: Impact of mobile social networking sites on information overload, depressive symptoms, and well-being. Comput Human Behav 2020(105), pp. 106217. 2. Brady WJ et al (2019) An ideological asymmetry in the diffusion of moralized content on social media among political leaders. J Exper Psychol: Gen 148(10):1802 3. Zhu X, Kim Y, Park H (2020) Do messages spread widely also diffuse fast? Examining the effects of message characteristics on information diffusion. Comput Hum Behav 103:37–47 4. Meuleman B et al (2019) Interaction and threshold effects of appraisal on componential patterns of emotion: A study using cross-cultural semantic data. Emotion 19(3):425 5. Steinert S (2020) Corona and value change. The role of social media and emotional contagion. Eth Inf Technol 1–10
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6. Fellenor J, Barnett J, Potter C, Urquhart J, Mumford J, Quine CP (2020) ‘Real without being concrete’: the ontology of public concern and its significance for the social amplification of risk framework (SARF). J Risk Res 23(1):20–34 7. Zhang W, Wang M, Zhu Y-C (2020) Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics. Int J Inf Manage 50:498–514 8. Chen M-Y, Liao C-H, Hsieh R-P (2019) Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach. Comput Hum Behav 101:402–408 9. Negativity bias (2020) Positivity bias, and valence asymmetries: Explaining the differential processing of positive and negative information. Adv Exp Soc Psychol 62:115–187 10. Tellis GJ, et al (2019) What drives virality (sharing) of online digital content? The critical role of information, emotion, and brand prominence. J Mark 83(4):1–20 11. Li Y, Gao X, Du M, He R, Yang S, Xiong J (2020) What causes different sentiment classification on social network services? Evidence from weibo with genetically modified food in China. Sustainability 12(4):1345 12. Kauschke C et al (2019) The role of emotional valence for the processing of facial and verbal stimuli—positivity or negativity bias? Front Psychol 10:1654 13. O’Sullivan TL, Phillips KP (2019) From SARS to pandemic influenza: the framing of high-risk populations. Nat Hazards 10(8):103–117 14. Zou Q, Chen S (2020) Simulation of crowd evacuation under toxic gas incident considering emotion contagion and information transmission. J Comput Civ Eng 34(3):04020007 15. Oh S-H, Lee SY, Han C (2021) The effects of social media use on preventive behaviors during infectious disease outbreaks: the mediating role of self-relevant emotions and public risk perception. Health Commun 36(8):972–981 16. Malik AD, Kaur P, Johri A (2021) Correlates of social media fatigue and academic performance decrement. Inf Technol People 34(2):557–580 17. Wu X, Zhang C, Song N, Zhang W, Bian Y (2020) Psychological health status evaluation of the public in different areas under the outbreak of novel coronavirus pneumonia. Int J Comput Intell Syst 14(1):978–990 18. Kahlor LA, Olson HC, Markman AB, Wang W (2020) Avoiding trouble: exploring environmental risk information avoidance intentions. Environ Behav 52(2):187–218
Our Country’s Smart Agriculture Development Strategy and Path Under the Big Data Environment Hongying Zhang
Abstract With the rapid development of our country’s modern Internet, Internet of Things and other types of high-tech, smart agriculture will be an important direction for our country’s agricultural development in the future. Through the extensive application of various emerging sciences and technologies, work efficiency is improved, various natural disasters are prevented, food safety is ensured, and e-commerce sales are promoted, which will promote the development of our country’s agriculture more efficiently and safely. This is undoubtedly a strong driving force for our country to achieve the goal of building a well-off society in an all-round way. This article is to research the development strategy and approach of our country’s smart agriculture under the big data environment, and refer to related literature. Explained the research and development status of smart agriculture in our country, and then, in order to further understand the research and development of smart agriculture in our country, the questionnaire survey mainly focuses on the impact of big data on smart agriculture and the current stage. The problems in the development of smart agriculture in our country are carried out in two aspects. The survey results show that the main problem in the development of smart agriculture at this stage is that the cost of use is too high, which accounts for more than 45%, and the second is that the products used are not durable and need to be replaced from time to time. Keywords Big data · Smart agriculture · Development situation · Development strategy
1 Introductions At present, our country’s economy has entered a sustained and healthy growth trend, the people’s material and spiritual living standards are gradually improving, and the basic conditions of agriculture are also constantly improving [1, 2]. Effectively and effectively promote and encourage farmers to continuously increase production H. Zhang (B) Shenyang Institute of Technology, Shenyang, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_37
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and income, they are all research topics about sustainable development [3, 4]. At present, our country’s agriculture is still in the transitional stage from traditional agriculture to modern agriculture. Intelligent agriculture is based on a variety of emerging sciences and technologies, and it will play an important and unique function during this historical period, providing space and opportunities for the promotion of our country’s agricultural modernization [5, 6]. At present, the degree and level of agricultural automation in our country is still relatively poor, and the demand for labor is relatively large. How to develop modern agriculture more effectively? In this fastdeveloping mobile Internet era, the previous growth model is obviously no longer suitable [7, 8]. The “five-in-one” smart rural agriculture based on and supported by the Internet, Internet of Things, big data, cloud computing and other scientific and technological applications will drive the modernization of our country’s rural production industry [9, 10]. In view of the current development strategies and approaches of smart agriculture in our country, some researchers started from the perspective of the concept of the Internet of Things and smart agriculture, analyzed the importance of the Internet of Things technology in the development of smart agriculture in our country, and proposed the Internet of Things technology in our country’s vegetables. The importance of the construction of the production and management information system has created our country’s smart agricultural technology Internet of Things [11]. In the design of intelligent agricultural product marketing platform system and framework, some researchers have begun to apply the Internet and big data to smart villages. They discussed the development history of the Internet of Things in a long period of time and the influence of the Internet of Things on big data in a long period of time. At the same time, according to the characteristics of the development of modern agriculture in our country, they also discussed the big data requirements, main application fields and the importance and position of the development of modern agriculture in our country in the development of modern agriculture. Other researchers have developed an independent research and development method based on the agricultural knowledge cloud system in intelligent agricultural production. The self-developed smart farm knowledge cloud system can provide users or smart farm service systems with optimized resources for specific crop needs. It not only enables us to have the basic knowledge and practical experience of a group of qualified agricultural science and technology experts, but also enables us to have a group of statistical data that can be used for effective analysis and accumulation. Therefore, by using our selfdeveloped cloud knowledge platform system, users can easily plant any crop on the Internet, without the need for a large amount of planting crop information and technical know-how [12]. This article explores the development strategy and path of our country’s smart agriculture under the big data environment, and uses the method of literature research to understand the current situation of our country’s agriculture, and then investigates our country’s smart agriculture. Based on the survey results, it proposes the development of our country’s smart agriculture suggestions.
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2 Smart Agriculture Research 2.1 Research Method (1)
Investigation and research method
The questionnaire survey method is that this article conducts a survey through pre-prepared questions and analyzes the answers of the interviewees to draw the necessary conclusions. By designing questionnaires, understand the development of intelligent agriculture of the research objects. (2)
Quantitative analysis
Qualitative analysis is related to quantitative analysis. Quantitative analysis refers to the analysis of mathematical hypothesis determination, data collection, analysis and testing, and it is also quantitative and qualitative. Qualitative analysis refers to the qualitative analysis of the research object. It refers to the process of conducting research based on subjective understanding and qualitative analysis, through research and bibliographic analysis.
2.2 The Current Situation of My Country’s Agricultural Development Our country is a large agricultural country, and agriculture occupies an extremely important position, which is the core of the three major agricultural issues. However, there are still many problems in our country’s agricultural production. Judging from the current situation, our country’s agricultural industrialization is still in its infancy. The penetration rate of information technology is low, labor costs continue to increase, and the penetration rate of intensive modern agricultural production is low. Combined with the continuous advancement of urbanization, the new rural labor force has continued to grow. Lack of talents and labor costs are both problems in agriculture. Many base units have not vigorously promoted new models of agricultural production, resulting in many innovative models of agricultural industrialization that have not been recognized and accepted by humans. For example, smart agriculture is difficult to popularize in many places. They think that the cost of smart agriculture is very high. There is no need to be a factor that makes it difficult to promote smart agriculture in rural areas. The thinking concepts and concepts of many agricultural producers still stay on the traditional concepts, and their acceptance of the application of new Internet technologies is not high. Intensive production and operation methods in rural areas are not popular, and many innovative technologies are difficult to implement. In general, many problems existing in the current situation of agricultural production in our country still need to be resolved urgently.
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3 Investigation on the Status Quo of the Development of Smart Agriculture in My Country 3.1 Purpose of the Investigation The questionnaire survey of the current state of the development of smart agriculture in our country mainly focuses on the impact of big data on smart agriculture and the problems existing in the development of smart agriculture in our country at this stage.
3.2 The Development Process of Investigation Activities (1)
Establishment of the survey object
This survey is aimed at the development strategy and path of smart agriculture in our country under the big data environment, so the target is the place where smart agriculture is planted. In order to reduce the difficulty of conducting survey activities, this survey is mainly conducted in this city to facilitate survey activities, and ensure that the survey results are supported by enough data, so it is determined that the location of the survey is the smart agricultural science and technology park in this city, and 3 smart agricultural parks with different reputations are randomly selected for the survey. Because of this activity mainly for the city’s smart parks, the results are not universal, so the results this time cannot explain the development of smart agriculture in other regions. (2)
Determination of the number of questionnaires
The establishment of the number of questionnaires is the most basic step of the survey activity, because the number of questionnaires is related to the validity of the survey results. If the number of questionnaires is set too low, the results of this survey will be questioned, because the base of the data is not large enough, the survey results are not universal. The number of questionnaires is set too high, and the difficulty of the questionnaire survey activity increases. Therefore, the number of questionnaires this time is set to 200 according to the minimum sample size proposed by the experts and the technical conditions of this survey. (3)
The distribution process of the questionnaire
The issuance of this questionnaire is mainly divided into two stages. The first is the issuance of the questionnaire, and the second is the recovery of the questionnaire. In order to ensure that the results of this survey have greater authenticity, the recovery of the questionnaire will be completed after the questionnaire is issued. Recovered in the next six days, given time to fill out the questionnaire completely. 189 questionnaires were recovered, and the recovery rate this time was 95%.
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3.3 Data Processing (1) When performing correlation analysis on the collected data, the data must be classified and sorted. This will not only increase the utilization rate of the data, but also promote cross-data analysis. Therefore, the main consideration is the completeness and accuracy of the data. First of all, about data integrity. When the questionnaire is delivered to the sample subject for completion and collection, some sample items are arbitrarily completed, or their selection cannot be completed, which will cause some data sorting problems, but because the retrieved data accounts for the majority, so deleting the lost data means deleting the lost data. Secondly, the precision and accuracy of the data. When conducting an audit, the main consideration is to check whether these data are inconsistent with other options, or the principle that conflicts with it should be selectively removed but retained as much as possible. (2) The main meaning of a correlation relationship in the objective correlation analysis method is to generally refer to a certain relationship between various objective phenomena, but they are not strictly corresponding to each other in quantity. There are two main forms of determining the relevant properties of objective phenomena here: qualitative analysis and quantitative analysis. The main purpose of qualitative analysis is to rely on the scientific theoretical knowledge and practical experience of the researcher to accurately judge whether there are correlations between various objective phenomena. Or what kind of factor, the subjectivity of this analysis method is relatively strong. Among them, the commonly used calculation formula is expressed as:
(x − x)(y − y)/n (x − x)2/n (y − y)2/n n xy − x y r= 2 √ 2 n x2 − x y n y2 − S2 xy = r= SxSy
(1)
(2)
4 Analysis of Survey Results 4.1 The Impact of Big Data on Smart Agriculture At this stage, our country is in the era of rapid development of big data. Its birth has opened up a new situation for many industries, including our country’s traditional industry agriculture. In order to further explore its impact on agriculture, this article has questionnaire survey, the survey results are shown in Table 1: It can be seen from Fig. 1 that the main impact brought by big data to the development of smart agriculture is that the supply and demand market of agriculture can
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Table 1 The impact big data can bring to smart agriculture
A smart agriculture park (%)
B smart agriculture park (%)
C smart agriculture park (%)
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Intelligent sharing platform
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Crop warning
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B Smart Agriculture Park
Intelligent sharing platform C Smart Agriculture Park
Fig. 1 The impact big data can bring to smart agriculture
be predicted based on the data, the area of agricultural varieties can be determined based on the market supply and demand, and then the crop can be warned, predict the time of occurrence of pests and diseases.
4.2 Problems in the Development of Smart Agriculture in My Country at This Stage Our country has been developing smart agriculture for a long time. With the continuous advancement of smart technology, smart agriculture has become more and more popular. Although it has brought a new situation to traditional agriculture, it also has some drawbacks. In order to further understand the actual situation, this article conducted a questionnaire survey on this issue, and the survey results are shown in Table 2: It can be seen from Fig. 2 that the main problem in the development of smart agriculture at this stage is that the application cost is too high, which accounts for
Our Country’s Smart Agriculture Development … Table 2 Problems in the development of smart agriculture in our country at this stage
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A smart agriculture park (%)
B smart agriculture park (%)
C smart agriculture park (%)
Higher 46 operating cost
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45
Strong reliance on technology
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Strong reliance on technology
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Strong reliance on technology
problem
Strong reliance on technology
Higher operating cost
0%
5%
10% 15% 20% 25% 30% 35% 40% 45% 50%
percentage C Smart Agriculture Park
B Smart Agriculture Park
A Smart Agriculture Park
Fig. 2 Problems in the development of smart agriculture in our country at this stage
more than 45%. Secondly, the products used are not durable and need to be replaced frequently.
4.3 Suggestions on Our Country’s Smart Agriculture Development Strategy and Path (1)
Establish a public service platform
According to the current development status of our country’s new generation of agricultural industry, government departments should give full play to their leading functions in the new generation of agriculture and information industry, focus on service industries, and use local government subsidies and platforms to introduce developed platform construction engineers in regions and across the country.
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Construct and modify intelligent agricultural product price prediction integrated algorithm models, agricultural pests and diseases prediction models, and other intelligent models that have been integrated into the management information system, create an intelligent management public service platform, model public services, and provide expensive public service, provide services to growers and planting companies. It is necessary to solve the problems of diseases, epidemics and the impact on the price forecast of agricultural products from the root and source of the conflicts, so as to effectively avoid the tragic situation of oversupply of farmers’ planting materials and products. (2)
Accelerate investment in agricultural informatization construction and cultivate integrated innovation teams
At present, the construction of agricultural informatization is only a weak link in the construction of colleges and universities and other scientific research institutions. Local governments and scientific and technological institutions should further increase R&D and capital investment in agricultural informatization, and train more high-level agricultural informatization-related professionals. Modern practice and application are providing talents for the development of smart agriculture in our country, and implementing a standardized, organized, smart, and innovative way of smart agricultural production that keeps pace with the times. (3)
Strengthen government support
In the process of promoting the development of smart villages, the vigorous support to the government and social people’s units cannot be completely ignored. It is a very important propaganda and promotion tool in our country, and it also occupies a very important position in the development of our country’s smart villages. Government support is a booster for farmers to implement “Internet+” poverty alleviation work. Through the development of a series of poverty alleviation measures such as demonstration bases or preferential policies, farmers are dispelled from doubts about the smart farm area, so that rural agricultural producers have a clear understanding of the smart farm area. Visible economic benefits are coming soon, and the strong support of the government and rural poverty alleviation policies will undoubtedly play the most powerful guiding role, helping farmers to better understand and grasp the significant benefits of smart rural agricultural development. (4)
Accelerate the development of the Internet of Things to ensure the implementation of precision agriculture
There must be a unified IoT technology standard. Promote the research and development of highly reliable, low-cost, and customizable agricultural eco-environmental sensors for the agricultural Internet of Things, covering applications in various agricultural fields, and solving the common problems of rural Internet of Things and reasonable development. It enriches the sensor nodes of the agricultural Internet of Things, and at the same time constructs the Internet of Things basic software platform and its application service system that meet the requirements of China’s modern agricultural application. It provides technical support for the integration,
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mass production, and large-scale application of the agricultural technology Internet of Things system.
5 Conclusion Although this article introduces the development of smart agriculture in our country as a whole, because of my limited level, smart agriculture is an emerging development direction. It covers more disciplines and a wide range of knowledge. At the same time, with the continuous rapid development of science and technology, each update iteration of this technology is also fast, so some of the content is not perfect, and continuous follow-up research is needed.
References 1. Ross K (2016) Preparing for an uncertain future with climate smart agriculture. Calif Agric 70(1):4–5 2. Waaswa A, Nkurumwa AO, Kibe AM et al (2021) Climate-smart agriculture and potato production in Kenya: review of the determinants of practice. Climate Dev 1:1–16 3. Ghosh M (2019) Climate-smart agriculture, productivity and food security in India. J Dev Pol Pract 4(2):166–187 4. Sambana B (2020) Smart agriculture using Internet of things. Xian Dianzi Keji Daxue Xuebao/J Xidian Univ 14(7):1645–1654 5. Bansal R (2020) Water conservation and smart agriculture in India. Int J Mod Trends Sci Technol 6(12):315–318 6. Yang J, Sharma A, Kumar R (2021) IoT-based framework for smart agriculture. Int J Agr Environ Inf Syst 12(2):1–14 7. Chuang JH, Wang JH, Liang C (2020) Implementation of smart agriculture depends on intention: young farmers’ willingness to accept the internet of things. Int Food Agribusiness Manage Assoc 23(2):253–266 8. Hu H, Chen Z, Wu PW (2021) Internet of things-enabled crop growth monitoring system for smart agriculture. Int J Agric Environ Inf Syst 12(2):30–48 9. Lukman A, Agajo J, Muazu MB et al (2020) A schedule-based algorithm for low energy consumption in smart agriculture precision and monitoring system. Agric Eng Int: CIGR e-J 22(3):103–117 10. Sekaran K, Meqdad MN, Kumar P et al (2020) Smart agriculture management system using internet of things. TELKOMNIKA (Telecommunication Comput Electron Cont) 18(3):1276– 1285 11. Vogiety A (2020) Smart agriculture techniques using machine learning. Int J Innovative Res Sci Eng Technol 9(9):8061–8064 12. Wolf K, Herrera I, Tomich TP et al (2017) Long-term agricultural experiments inform the development of climate-smart agricultural practices. Calif Agric 71(3):120–124
The Design of the Information System Platform of the “One Picture” Platform for Territorial and Spatial Planning in the Big Data Era Suli Zhang and Bin Wang
Abstract With the implementation of the “digital land” project and the advancement of the country’s “one map” work, the degree of informatization of land resources has been continuously improved. Land and resources management departments and various fields of society have an increasing demand for basic information on land and resources, and the requirements for data sharing and information services are increasing day by day. At present, the informatization of land and resources management has achieved great results, but there is no integration and sharing of system data, and it is impossible to supervise all aspects of land and resources. Therefore, the organic integration and synthesis of the informatization results of various topics utilization is very important. This article first analyzes the current research status and problems faced by our country’s territorial and spatial planning, and explains the significance of this research; then introduces basic theories and key technologies such as network geographic information systems and ArcGIS products; then, this article discusses territorial and spatial planning the IT architecture of the “One Picture” platform information system was designed, and a land and space planning “One Picture” platform information system platform was implemented. Finally, this article tested the system’s operating speed and functional interaction, and the test results showed the operating speed and interactive functions of the system are very stable and reliable. Keywords Land resources · A picture · Land and space planning · Network geographic information system
S. Zhang (B) Luohe Survey Planning and Design Institute, Luohe 462000, Henan, China e-mail: [email protected] B. Wang Luohe Water Investment Co., Ltd., Luohe 462000, Henan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_38
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1 Introduction Land and space planning refers to a series of measures such as urban land use, spatial layout, management control, which is the guarantee and key to the healthy and rapid development of the city [1]. In recent years, our country’s national economy has grown rapidly, and the process of social informatization has become faster and faster. The methods and models of land and space planning also need to keep pace with the times and require reform and innovation. The informatization level of territorial and spatial planning plays a very critical role in the informatization construction of our country. With the continuous updating of science and technology, territorial and spatial planning has also ushered in more challenges [2]. Nowadays, the research on the information system of our country’s territorial and spatial planning platform has achieved certain results, but there are still many problems to be solved. Due to the faster urban construction, the workload of territorial and spatial planning is also increasing. There are many unreasonable problems in the past land and space planning work, and there is an urgent need to rectify the past land and space planning models, and the “One Picture” platform information system for land and space planning can solve these problems [3]. Chinese scholar Zhou Junjie pointed out that the construction of a “one picture” implementation monitoring system for territorial and spatial planning is a comprehensive and systematic project that matches the establishment of a territorial and spatial planning system. The construction goal is to serve the “two unifications” management responsibilities, support the sharing of investigation and monitoring, management services, and supervision and decision-making systems; the content is to build a “one network” and one platform, as well as three application systems for investigation, evaluation, and government service supervision and decision-making [4]. Han Qing and others pointed out that the construction of the territorial and spatial planning system has been fully carried out. Clear and detailed current maps and data are an important foundation for understanding the natural resources and an important support for the compilation of territorial and spatial planning. However, the current land use classification standards, data accuracy, and application goals of the current state-of-the-art space data are still not unified. Based on remote sensing images, geographic national conditions and internet and other multi-source data, a map of the current state of land and space is studied, and a map of the current state of land and space planning is constructed by integrating the existing land classification standards and data accuracy [5]. Chen Sheng explored the problems and challenges based on the sharing of a single map from the existing research results, analyzed the opportunities and requirements based on the sharing of a single map, and clarified the objective need to realize the sharing of national planning files based on a single map. From the four perspectives of mechanism framework, standard system, key technologies, and platform functions, a plan based on a single map file sharing strategy is proposed [6]. In recent years, a variety of new technologies have emerged in an endless stream, and traditional science and technology are constantly being updated. These new
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science and technology have been applied to various fields of society, which not only have a huge impact on people’s lives, it also affected the country’s informatization development process. The government has added informatization content to the work of our country’s social construction, which shows that informatization construction is the core work content of our country’s economic construction. Under the call of the government, the ministry of land and resources has carried out a series of reforms and innovations for land and space planning, and the effect is very significant [7]. While developing the economy, a country must use resources, but many of these resources can only be used once. Since the country has stated that it wants to carry out sustainable development, it is necessary to protect the resources while using resources. Reasonable use of resources can not only promote the country’s economic development, but also protect the resources and environment. The new era will inevitably have new opportunities and challenges. The “One Map” platform information system for land and space planning assumes new responsibilities and obligations, provides a guarantee for the construction of land and resources informatization, and reforms and innovates the management mode of land and resources, has very important practical significance and application value [8].
2 Basic Theory and Key Technology 2.1 Network Geographic Information System The network geographic information system refers to the combination of computer technology and geographic information system through a network platform, and is an information system generated by using computer technology according to relevant standards. Using network technology, the network geographic information system can easily complete data storage, information query, information display and other work contents [9]. Compared with the previous geographic information system, the network geographic information system performs better in terms of informatization and intelligence, and users can query information through the Internet. With the help of Internet technology, the network geographic information system can easily obtain a large number of netizens, and then achieve real popularization. Traditional geographic information systems are highly specialized and limited in scope, and can only serve specific units and departments. Due to the difficulty of traditional geographic information system technology, high manufacturing and research costs, many people fail to meet the conditions in terms of software development and use. Nowadays, the network geographic information system uses network technology to establish a complete geographic information system without too much capital. Most users are no longer restricted by the software, and can query real-time geographic information they want through a browser. The efficiency of traditional geographic information systems is relatively low, and it is easy to cause closed information, which
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is a great waste of resources [10]. The network geographic information system can integrate and share resources through network technology at all levels.
2.2 The Architecture of Network Geographic Information System Generally speaking, the architecture of network geographic information system can be divided into three types according to the ratio of client browser and server side, namely thin client mode, thick client and balanced client [11]. When using a thin client, the client only needs a computer that can be connected to the Internet to download raster map images, and the user will receive the processing results from the browser. The thick client model is a distributed architecture. When the user sends a request to the server through the client, the server receives the request and undergoes a series of processing, and then returns the processing result to the client user. The client can handle some simple geographic information system operations and some complex operation requests, and the system database is processed and stored by the server. The above two methods have their own limitations. According to the combination of these two characteristics, a customer balance method that takes into account the advantages of both is formed. Make a reasonable choice of data processing methods according to different situations. If the amount of data is small and the frequency is high, the customer function is often used. If the amount of data is large and the frequency is low, when using the server function. However, for background data interaction, complex data conversion, spatial analysis, and thematic mapping must be completed by the server. Through this reasonable allocation, resource load balancing can be realized, and system performance can be adjusted to the best state.
2.3 ArcGIS Products ArcGIS is a geographic information system platform product with powerful map generation, spatial information integration, and spatial analysis capabilities [12]. ArcGIS has the characteristics of completeness and scalability. It provides users with a complete solution to create a complete geographic information system. The ArcSDE database is characterized by a very powerful space, geographic data server and spatial analysis tools are its two key components. In the operating mode of the geographic data server, when the spatial data is stored and selected, it is completed through the cooperation between the servers. The physical storage is carried out in the form of a joint table. ArcSDE analyzes the joint table and analyzes it. The information provided by the geographic information system. When the client transmits information to ArcSDE, the servers will interact, and the client can store or select data from the spatial database through the server. ArcGIS Server is a comprehensive geographic
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information system product that can provide more advanced network geographic information services. It adopts centralized management and allows users to build their own geographic information system based on industry standards, which can be applied to corporate private geographic information systems.
3 IT Architecture of the “One Picture” Platform Information System for Land and Space Planning 3.1 Application Architecture First of all, the application architecture is a high-level structure that aims to describe the structure and behavior of the business application system, with the focus on describing the relationship between applications and the interaction between applications and users. Second, application architecture is usually used to manage how multiple applications work together. The business customization subsystem is the foundation of other subsystems, and can customize specifications and set parameters for other subsystems according to management needs. Component registration and management of various IT services; graphic configuration to customize the content and display mode of graphic browsing; authority management to set job permissions; institutional settings include leaders, departments and directly affiliated units of provincial, municipal, and county bureaus; business customization includes Business registration, business collaboration, organization collaboration, component process, activity process, business form customization, business components and business activities are built into business collaboration processes through business customization; post customization includes administrative positions and personnel, as well as business position registration and business positions collaboration and matching of personnel positions; early warning setting includes early warning rule setting and warning interval setting; statistical configuration setting requires statistical parameters and statistical reports. Among them, the organization setting and business customization can only be customized by the provincial government, which standardizes the work of the city and county bureaus from the source; the job customization is customized by each land and resources management department, which solves the problem of different personnel organization and job settings in each department. The business operation subsystem is a collection of functions that can be realized by the platform. Effectiveness supervision is used for the supervision of discipline inspections and other offices. Auxiliary office includes entrusted handling, personal office language, and incoming reminders. Management tools are used to monitor business processes. The integrated portal must have a unified portal integration framework, unified performance, unified message processing and mail integration. The formulas used in the application architecture are:
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L=
X p − X pi
2
2 + Y p − Y pi
(1)
3.2 Data Architecture Form a data structure for data definition, coding, constraint, update, and management of the “One Picture” platform information system for land and resources spatial planning, and conduct data planning and guidance for application system development. Sort out the data of land administration, mining administration, maritime administration and comprehensive affairs management according to the data category, and determine the data items, data attributes, business relationships, and ownership relationships. Analyze the sorted data to determine which is the general data involved in each business system, which is the basic data that supports the operation of the business, and which is the exclusive data for each business system. Data coding rules include classification coding rules and naming rules, and different types of data coding rules are also different. Specifically, it includes the original data information of the land business, the data information generated in the land business process, and so on. The structured data is classified into two categories according to the business process. It can be divided into the business data types of land use plan management data, land acquisition, land supply, increase and decrease linkage, and data item definition, data attribute design and data relationship definition will also be derived from these business data types. The classified data is expanded. Land and space planning business mainly involves three businesses: land, mining, and sea. According to the business name, it can be divided into land use plan management, increase/decrease linkage, land reserve, land acquisition, land remediation, and land registration. The naming rules are based on the data classification and coding rules, and the establishment of the three business types of land, mining and sea are represented by three symbols. Business names and data table names are all spliced by initial letters in pinyin. The formulas used in the data structure are: V · P = n · R · T
(2)
3.3 Technology Architecture The overall planning is demand-oriented and practicality as the foundation, using advanced and mature technology to ensure the stable and reliable operation of the construction, operation, and subsequent operation and maintenance of the “One Map” information platform for land and space planning, while reducing operation and management cost, to ensure that its core technology will maintain a high level of
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application in the next few years. Adopt a large-scale centralized management of data, a unified comprehensive supervision platform across the province, and a firstlevel center and two-level node mode for construction. That is, the entire province will establish a data center, unified development of the comprehensive supervision platform construction, and the municipal and county-level land and resources management departments as application nodes access. The provincial level includes the construction of provincial and departmental data centers and the province’s exclusive network construction, and the city and county levels include basic network access and basic application security management construction. According to the three-year cycle of construction and implementation, in the first year, complete the construction of the provincial data center basic software and hardware environment and the province’s exclusive network construction, complete the city and county node network access and security management construction, and complete the ground, mining, and marine business structure, initially completed the storage of “a map” and completed the software development environment of the comprehensive supervision platform; in the second year, it realized the online operation of land administration, mining administration, and maritime administration, improved the construction of provincial data centers, and improved the entry of “a map” database, and carry out service system construction; further improve the system and application in the third year. All software and hardware equipment includes at least three years of free upgrades and maintenance; the development unit is responsible for three-year on-site all-weather maintenance of the application system; the integration unit is responsible for establishing a localized professional operation and maintenance team for maintenance at any time; the network line is operated by the line the merchant is responsible for all-weather localized maintenance. The formulas used in the technical architecture are: VK =
i 1 LK i m=1 tmk
(3)
4 System Test 4.1 System Function Running Speed Test According to Table 1 and Fig. 1, it can be known that 50 times of system function operation speed tests were carried out, of which 41 times were fast, accounting for 82%; 5 times were faster, accounting for 10%; yes, the running speed of 3 times was average, accounting for 6%; the running speed of 1 time was slower, accounting for 2%. The slower running speed may be related to the network speed. Generally speaking, the running speed of the system is very reliable.
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Table 1 System function running speed test
Running speed
Number of times
Percentage (%)
High speed
36
72
Faster speed
8
16
General speed
4
8
Slower speed
2
4
SYSTEM FUNCTION RUNNING SPEED
number of times
TEST 40 35 30 25 20 15 10 5 0
36
8 HIGH SPEED
4
FASTER SPEED
GENERAL SPEED
2 SLOWER SPEED
running speed
Fig. 1 System function running speed test
According to Table 1 and Fig. 1, it can be known that 50 times of system function operation speed tests were carried out, of which 36 times were fast, accounting for 72%; 8 times were faster, accounting for 16%; Yes The running speed of 4 runs was average, accounting for 8%; the running speed of 2 runs was slower, accounting for 4%. The slower running speed may be related to the network speed. Generally speaking, the running speed of the system is very reliable.
4.2 System Function Interactive Test According to Table 2 and Fig. 2, we can know that 50 functional interaction tests were performed on the system, of which 45 were accurate interactions, accounting Table 2 System function interaction test
Interaction
Number of times
Percentage (%)
Interaction is accurate
45
90
Interaction error
2
4
Unable to interact
3
6
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The number of occurrences
Occurrence ratio
Unable to interact
3
Unable to interact
Interaction error
2
Interaction error
Interactive correct
45 0
20
40
Interactive correct
6% 4% 90%
60
Fig. 2 System function interactive test
for 90%. There were 2 interaction errors, accounting for 4%; 3 failed interactions, accounting for 6%. After further investigation of the interaction error and the inability to interact, it was found that it was caused by an error in the staff interface. Generally speaking, there is no problem with the functional interaction of the system.
5 Conclusions Aiming at the problem of information islands caused by insufficient information management of our country’s territorial and spatial planning data management, improper multi-source data fusion processing, and various thematic maps are disordered, difficult to find and cannot be visually displayed, this article is based on SQL Server database, spatial database ArcSDE and ArcGIS Server development platform, designed and developed the “One Map” platform information system for land and space planning. The system integrates and visually expresses multi-source heterogeneous data through the browser, and provides powerful tools for users’ parcel approval, land delimitation and other businesses. The “One Picture” system makes full use of ArcGIS data processing, and combines the spatial database ArcSDE and SQL Server database to realize data accuracy management, real-time query and online review, which significantly improves the informatization of daily management work.
References 1. Jingling J (2020) A map of urban land resources and the design and application of the comprehensive supervision system. Nongjia Staff 658(12):268–268 2. Zhiwu Y, Linjie G, Junhui L, Zhonghai Z (2020) Research on the key technology of a land resource map platform based on microservices. Mine Survey 48(210(06)):56–59
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3. Hebing Z, Minghui Li, Qinglei Z (2020) Construction of land use classification system and land type identification for land and space planning based on multi-source data. Trans Chin Soc Agricult Eng 36(5):261–269 4. Junjie Z (2020) “One Picture” of land and space planning. China Const Inf Technol 124(21):32– 33 5. Qing H, Zhongyuan S, Chengmiao S et al (2019) Construction and application of a map of the status quo of land and space planning based on natural resources background 34(10):2150–2162 6. Sheng C, Ling D, Ting Z et al (2019) File sharing strategy of territorial planning based on a picture. File Time Space 342(12):6–8 7. Zhifang W, Shichang G, Limei M et al (2020) Research on the paradigm of land and space ecological protection and restoration. China Land Sci 034(003):1–8 8. Xueli Z, Fusheng Z, Jing F (2019) Ways to do a good job in the third national land survey. Arch Dev 3(10):117–118 9. Yijuan X, Linhai D (2020) Research and implementation of offline land resources “a map” system based on ArcGIS for Android and Spatialite. Value Eng 39(553(05)):232–234 10. Jurong W, Xin Q (2020) Construction and application of “One Picture” Database in fujian province. Water Conserv Sci Technol 168(03):37+62–65 11. Guolong S, Xianli M (2019) Summary of the core database construction of “One Map” for county-level land resources. Geol Min Surveying (2630–4732) 002(004):149–150 12. Hongyu C (2019) Analysis on the construction and application of a comprehensive monitoring platform for land resources in Wangcheng District. Enterp Technol Dev 38(550(07)):77–80
Impact of the Application of Big Data Technology on Industrial Agglomeration and High-Quality Economic Development Xinlin He and Yufeng Wang
Abstract China has entered a stage of high-quality economic development. Industrial agglomeration is an important means to achieve high-quality economic development, and big data technology is one of the key foundations for developing the new economy. This article takes the Chengdu-Chongqing Economic Circle as the research object. The research found that: during the inspection period, industrial agglomeration can significantly promote high-quality economic development; at the same time, big data is the fifth major production factor. This paper analyses the relationship between the application of big data technology and high-quality economic development, and further analyzes how big data technology application should play its role in the process of industrial agglomeration influencing high-quality economic development, Believes that big data technology applications can contribute to high quality economic development; And it can help industrial agglomeration more effectively promote high-quality economic development by concentrating production resources, reducing production costs, improving resource allocation efficiency, and improving product quality. For this reason, this article believes that while continuing to promote industrial agglomeration in the Chengdu-Chongqing area, the importance of big data technology in the development of the new economy should be fully utilized in order to better promote high-quality economic development. Keywords High-quality economic development · Chengdu-chongqing economic circle · Big data technology application · Industrial agglomeration
1 Introduction At present, China’s economic development model has officially entered the stage of high-quality development, but at this stage, the level of high-quality economic development in China is still low and regional differences are obvious, and the western region is relatively underdeveloped 1, On January 3, 2020, the central government X. He · Y. Wang (B) School of Economics, Sichuan Agricultural University, Chengdu, Sichuan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_39
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officially proposed to promote the construction of the Chengdu-Chongqing Economic Circle, to build a high-quality growth pole in the western region with Chengdu and Chongqing as the center, and to promote the high-quality economic development of the western region [1–3]. As an effective form of resource organization, industrial agglomeration can achieve relative concentration of technology and talent by integrating production factors, which can significantly increase output and increase production efficiency3. The sixth meeting of the Central Finance and Economics Committee also proposed to promote the efficient agglomeration of industries in Chengdu and Chongqing and promote high-quality economic development. In the mean time, big data is also regarded as the fifth major production factor in the development of the new economy. The application scope of big data technology continues to expand, it has also shown an important role in the development of the industry.
2 Literature Review Big data technology is a new type of technology that has only emerged in recent years. Existing research is more from an industry development perspective. Scholars believe that big data technology can promote the transformation and upgrading of traditional industries and the rapid development of related emerging industries by sensing, collecting, storing, and analyzing diverse and huge amount of data, and discovering the new knowledge and value hidden in it [4, 5]. Li Hui [5] analyzed from a theoretical perspective that big data technology can drive high-quality economic development through efficiency improvements, industrial structure upgrades, and business model innovations. It is also believed that the path of developing digital economy, consolidating the construction of big data infrastructure and accelerating the integration of big data and real economy should be used to promote the highquality economic development driven by big data technology. Regarding the related research about industrial agglomeration and high-quality economic development, domestic scholars mainly conduct research from the following aspects. First, they mainly start from different industries such as hightech industries, manufacturing, and producer services, and analyze the impact of a single industrial agglomeration on the economy. The conclusions of the impact of high-quality development are not uniform. It is believed that industrial agglomeration is a long-term development process, and the effect on high-quality economic development may have two sides [6]. In addition, due to the coordinated development of multiple industries in some regions, some scholars start from collaborative agglomeration to study the impact of the agglomeration of two different industries on the high-quality economic development [7]. In contrast, relatively few studies that take urban agglomerations as the object of study and specifically analyse the impact of different types of industrial agglomeration on high-quality economic development and the specific impact paths.
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Generally speaking, in the existing research, on the one hand, there are more on the effect of a single factor to the high-quality economic development; on the other hand, relatively little research has been done on how new technologies, such as big data technologies, can contribute to quality economic development. Highquality development is an feature of China’s current economic development. Industrial agglomeration is an important model to promote economic development. Big data technology is an essential development element in the new economy. How can the big data technology affect the high-quality economic development through industrial agglomeration? What is the specific impact mechanism? Existing literature has not been involved. Therefore, this article takes the Chengdu-Chongqing economic circle as the object of study. First, it analyzes the impact of industrial agglomeration and the application of big data technology on the high-quality economic development of the Chengdu-Chongqing region. The specific mechanism by which technology application affects high-quality economic development through industrial agglomeration.
3 Mechanism Analysis 3.1 The Impact of Big Data Technology Application on High-Quality Economic Development Change the endowment of production factors and improve production efficiency. The rapid development of the digital economy is leading to a transformation of traditional economic methods, and the traditional production factor pattern has also changed. The big data is also regarded as the fifth major production factor after land, labor, capital, and technology. Relying on the Internet platform, big data technology has changed the input, combination and use of traditional resources, and achieved the largest output with the least input of labor, capital, land, resources and other elements, and significantly improved the efficiency of the combination of production factors to achieve high quality Development provides an important new element of kinetic energy. In the meantime, big data technology is based on data flow, through information search, transmission, calculation and processing, and deep integration with the Internet, cloud computing and other industries, thereby optimizing the technological innovation environment, improving technological innovation services, and improving technological innovation efficiency [8], which greatly provides power support for high-quality economic development. Promote industrial transformation and upgrade the economic structure. The application of big data technology has deepened industrial convergence. The big data industry is not only a capital-intensive and energy-intensive industry, but also a technology-intensive industry [9]. Developments in big data technology has promoted the deep integration of the Internet and the industry, improved the efficiency of production factor allocation, promoted the optimization of traditional enterprise
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structure, stimulated the innovation and entrepreneurial vitality of traditional industry enterprises, and achieved industrial structure upgrading. On the other hand, relying on the big data technology, more new industries that meet high-quality development have been derived, including the direct industries of big data technology, which involve big data collection, processing, storage, analysis, and cloud platform operation, etc., which are big data. Core value-added services belong to the category of the digital economy; and new business formats formed after the core technology of big data is updated and transformed into traditional industries, such as smart agriculture, Telemedicine, etc. The emergence and development of these new industries have promoted industrial reforms and it also responds to the needs of the current Chinese economic development model. Change business model and improve life welfare. The application of big data technology has given rise to new business models. Through a large amount of information processing, big data technology can help companies accurately analyze consumer preferences and analyze consumers’ potential consumption needs, thereby prompting companies to change their original operations. Models, re-optimizing production and improving business methods can effectively improve corporate operating efficiency, broaden corporate revenue channels, and create more revenue. Similarly, big data can greatly increase the welfare of the people. It reduces the cost of people searching for information, and can quickly and effectively obtain information, such as online shopping. Through data analysis and processing in the early stage, big data can accurately push the required related products and improve the shopping experience. Industries that rely on big data, such as the development of Telemedicine, people in various regions can enjoy the best medical security and improve their lives and well-being.
3.2 Impact of Industrial Agglomeration on High Quality Economic Development Theoretical Analysis of the Effect of Industrial Agglomeration on High-Quality Economic Development. Industrial agglomeration can positively affect high-quality economic development. Industrial agglomeration strengthens the technological association between enterprises through the input–output relationship between the industries in the agglomeration area, and generates knowledge and technology spillover effects through communication and cooperation between enterprises, thereby reducing the enterprise’s technology research and development risks and marginal innovation costs, To enhance the innovation output of enterprises and increase the income of technological progress The concentration of similar economic activities and related enterprises gives enterprises in the agglomeration area a stronger competitive advantage, and can attract more concentration of enterprises, so that the agglomeration scale will continue to expand, thereby generating scale effects to promote the formation
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of the regional industrial foundation and economic development. Industrial agglomeration can also stimulate the desire for competition among enterprises, prompt enterprises to increase R&D inputs, improve the technology of production, increase enterprise productivity. In addition, the low consumption, low emissions, and highefficiency production methods brought by industrial agglomeration through technological innovation can contributes to the reduction of environmental pollution. As a well-developed city cluster in the west, the Chengdu-Chongqing Economic Circle has a higher level of industrial agglomeration than other regions in the west. At the same time, the Chengdu-Chongqing Economic Circle with a good location. At this stage, the types of industrial agglomeration are mainly shifted to the east and basic industries, and they are still in the development stage of industrial agglomeration. In this regard, this paper assumes that: H1: The industrial agglomeration of the Chengdu-Chongqing Economic Circle can contribute to high-quality economic development. Empirical test of the impact of industrial agglomeration on high-quality economic development. To test the theoretical analysis of the impact of the above-mentioned industrial agglomeration on the high-quality economic development, this article uses data related to cities within Chengdu-Chongqing area from 2005 to 2018 as a sample, and draws on the research of Chen Shiyi [10], taking the per-worker productivity as the economy High-quality proxy variables, with the location entropy of industrial business income as the proxy variable of industrial agglomeration. agglomeration: (1) Financial development(fin): the loan-to-deposit ratio is used as a proxy variable for financial development; (2) The degree of openness(open): expressed as total exports and imports compared to GDP; (3) Fixed investment(inv): select the ratio of total fixed investment to regional GDP to express; (4) Consumption level(con): express in terms of the ratio of total retail sales to GDP. The measurement model, descriptive statistical analysis and regression results are as follows: hqit = β0 + β1 aggit + β2
contr olit + εit
(1)
First of all, before testing the direct impact of industrial agglomeration on economic high quality, the multicollinearity diagnosis of each variable, the result VIF is 1.74, indicating that there is no multicollinearity between the variables. Subsequently, according to Hausmann test, the fixed-effects model was determined. According to model (1), the test was conducted and the results are shown in Table 1. The first column of Table 1 is the regression result without controlling variables. Columns (2)–(6) of Table 1 are the regression results of adding the control variables in test. The results confirms Hypothesis 1, indicating that the industrial agglomeration of the Chengdu-Chongqing Economic Circle is still in the development stage, which can effectively contribute high-quality economic development.
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Table 1 Regression analysis results Variable agg
(1)
(2)
(3)
(4)
(5)
(6)
hq
hq
hq
hq
hq
Hq
1.534***
0.781***
0.251
0.251
0.331*
0.933***
(0.251)
(0.224)
(0.212)
(0.212)
(0.184)
(0.189)
1.187***
0.972***
0.973***
0.348***
0.269**
(0.125)
(0.115)
(0.116)
(0.125)
(0.114)
-0.313***
-0.313***
-0.150***
-0.0537
(0.0421)
(0.0423)
(0.0415)
(0.0401)
-0.0337
0.243
0.733
(0.679)
(0.588)
(0.537)
1.390***
0.870***
(0.167)
(0.169)
gov fin open inv con
5.321*** (0.784)
Constant
9.002***
11.86***
12.29***
12.30***
9.979***
7.400***
(0.258)
(0.370)
(0.334)
(0.340)
(0.404)
(0.527)
Fixed effect
YES
YES
YES
YES
YES
YES
Observations
224
224
224
224
224
224
R2
0.153
0.411
0.536
0.536
0.655
0.719
Note ***, ** and * indicate significant at the level of 1%, 5% and 10% respectively
4 The Influence Mechanism of Big Data Technology in Industrial Agglomeration on the High-Quality Economic Development of Chengdu-Chongqing Region 4.1 Big Data Technology Improves the Level of Industrial Agglomeration and Promotes High-Quality Development High-quality economic development has led to many industries facing problems of structural upgrading and development transformation in order to remain competitive, especially in the industrial agglomeration in the western region. While individual industries are concentrated in promoting economic development, they can also produce some problems that are not in line with the economic high. The problems of quality development, such as large pollution emissions from concentrated industries in agglomeration areas, and resource misallocation caused by excessive industrial agglomeration. The application of big data technology can help enterprises in the agglomeration area to survive in a long-term competitive environment.
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In addition to using big data technology to help enterprises improve innovation efficiency, In production, big data can also be used to monitor and optimize line operations, enhance fault prediction and health management, optimize product quality and reduce energy consumption [11], help enterprises in agglomeration areas to upgrade their industrial structure, maintain long-term competitiveness, promote the efficient development of industrial agglomeration areas, improve the level of agglomeration, and achieve industrial agglomeration The long-term and sustainable impact of high-quality economic development. Another aspect, as the integration of big data technology and industry deepens, its upstream and downstream industries or related industries have gradually increased. With the numerous related industries in the industrial clusters, more and more related industries will meet to expand the scale of operations. Close to the original industrial agglomeration area, form a networktype industrial agglomeration area centered on industry, and promote the formation of diverse agglomerations.
4.2 Big Data Technology Enhances the Impact of Industrial Agglomeration on Innovation and Promotes High-Quality Development Traditional industrial agglomeration can provide favorable conditions for technological innovation by concentrating a lot of scientific talents and reducing the cost of corporate talent search. Big data technology can provide an advantage for industrial clustering for technological innovation in other ways. On the one hand, enterprises in the agglomeration area have great demands for technological innovation. Big data technology can support companies quickly and efficiently collect a large amount of technological innovation knowledge. At the same time, Combining user needs identified by big data technology for high-efficiency innovation. On the other hand, the application of big data technology enhances the collaboration of innovation subjects. Technological innovation often requires multiple subjects to collaborate, including enterprises providing innovation requirements, universities and research institutes providing manpower and technical support, and government providing financial support, etc. [12]. In a big data environment, it is possible to facilitate the interaction of knowledge and technology between different actors and to increase the synergy of technological innovation. In addition, it also can help companies accelerate the transformation of results and improve innovation performance. There are many enterprises in the agglomeration area, and the innovation output is high. Big data technology can enhance the efficiency of scientific achievements by building a platform for transferring and transforming scientific achievements, optimizing the process of transferring and transforming achievements, and quickly matching scientific innovation achievements with target enterprises.
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5 Conclusion Based on the Chengdu-Chongqing economic circle, this paper explores the relationship between the application of big data technology, industrial agglomeration and high-quality economic development. And give policy recommendations from the following aspects. Accelerate the development of big data technology in the Chengdu-Chongqing economic circle; Continue to promote the integrated development of the ChengduChongqing region and improve the level of industrial agglomeration. Deepening the integration of big data technology and industrial clustering.
References 1. Min W, Shuhao L (2018) Research on the measurement of China’s economic high-quality development level in the New Era. J Quant Econom Tech Econom 35(11):3–20 (in Chinese) 2. Bin Z (2020) A comprehensive measure of China’s high quality economic development. Statist Dec Mak 36(15):9–13 (in Chinese) 3. Beeson P (1987) Total factor productivity growth and agglomeration economies in manufacturing. J Reg Sci 27(2):183–199 4. Yihong L (2019) Analysis of the mechanism of big data artificial intelligence blockchain and other ICTs promoting the high-quality development of the digital economy. Guizhou Soc Sci 12:122–132 (in Chinese) 5. Hui L (2019) The theoretical mechanism, practical basis and policy choice of big data to promote the high-quality development of my country’s economy. Economist (03): 52–59 (in Chinese) 6. Qinghua H, Peihao S, Jiangfeng H (2020) Industrial agglomeration and high-quality economic development: examples of 107 prefecture-level cities in the Yangtze River Economic Belt. Reform (01): 87–99 (in Chinese) 7. Lv P, Yuan Y (2020) Collaborative industrial agglomeration, technological innovation and high quality economic development: an empirical analysis based on productive service industry and high technology manufacturing industry. Financ Theo Pract 41(06):118–125 (in Chinese) 8. Qian L (2019) Research on the impact of the big data industry on the development of regional scientific and technological innovation. Sci Technol Manage Res 39(02):217–223 (in Chinese) 9. Qing F, Gang X (2019) The operating mechanism of promoting high-quality economic development in Jiangsu Province based on big data. Bus Econom 2019(05):25–26 (in Chinese) 10. Shiyi C, Dengke C (2018) Haze pollution, government governance and high-quality economic development. Econ Res 53(02):20–34 (in Chinese) 11. Shu Y (2017) Speed up the deep integration of big data and real economy. Modern Telecommun Technol 47(06):17–18 (in Chinese) 12. Yajun G, Lulu Z, Jing Z (2016) Research on enterprise technology innovation knowledge management model in big data environment. Modern Inf 36(07):13–17 (in Chinese)
Analysis of Information Management Scheme in Civil Engineering Construction Based on Big Data Analysis Wenyan Liu and Yuechun Feng
Abstract With the rapid growth and wide application of Internet, the amount of data released and transmitted is huge. In the normal operation of civil construction, there are many kinds of massive data. These data are more complex in a broad spectrum and related to decision-making. Once the decision fails, it will bring serious losses to the civil engineering construction process, so we must actively study and apply the usage of big data technology in the civil engineering industry. This paper deeply discusses and analyzes the information management scheme in the process of civil engineering construction. Firstly, it expounds the theoretical basis of big data in the information management scheme, and then expounds the application method and technical support of big data in the information management scheme, finally, based on the big data analysis of the specific case analysis, mainly from the application direction and effect of two angles. The research results show that information management accounts for about 90% of the whole process, so it is imperative to fully combine the characteristics of big data in civil engineering construction information management. Keywords Big data analysis · Civil engineering · Information management · Engineering construction
1 Introduction In recent years, during the continuous expansion of economic scale, civil engineering forms become increasingly complex and diverse. The traditional civil engineering construction management has not adapted to the development process of civil engineering under the setting of modern big data. Civil engineering is a complex engineering process, which provides a wide range of materials, equipment and engineering activities, such as measurement, construction, maintenance and repair. If the relationship between the connection direction and the branch direction is not properly W. Liu (B) · Y. Feng Ningxia Institute of Technology, Shizuishan City, Ningxia, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_40
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handled and coordinated in practice, the information transmission is prone to make mistakes, which will have a great influence on the quality of deep-sea engineering and bring many problems in the whole management process. Therefore, the analysis of effective and timely civil engineering construction information management scheme has positive practical significance. In the research of construction information management, relevant scholars have also carried out many studies. Y Gu thinks that it is possible to realize the accumulation and construction of construction information by using the BIM Technology, and to sufficiently excavate the value of construction information, and to improve the productivity of the building industry remarkably. This paper analyzes the reform of information management model based on the BIM technology. The results proved the applicability of the BIM technology in the information management of projects. As a new technology, the BIM has a unique advantage in terms of information integration and sharing. Provides a new management mode and an efficient information processing platform for civil engineering project information management [1]. G Bilgin, I Dikmen, MT Birgonul hold the view that the delay of construction is a universal problem in the building industry. System and reliable delay analysis is the key to success in claim management. In this research, we propose a delay analysis body for the development of database for delay analysis of building company, and to promote information sharing and retrieval. Detailed literature on construction delay is reviewed in bulk development process, and it is evaluated through five cases. The delay analysis body can be applied for different purposes, always in the course of risk management. It allows companies to establish their own database, company memory and development dss system, and analyse delays better [2]. Li Xin, Yu Xin, Jiang Qichen think that technology can increase the level of computerization management of building companies as an important means of digitization of architecture. They analyzes the meaning of the dynamic management of the construction object based on the BIM Technology, and describes the realization plan of construction based on the BIM technology. Construction of experience and research and development of nakahachyo construction project was constructed, and the whole process tracking of the material list was constructed. Construction progress management and the integration of material management have solved realistic issues such as material supply, warehouse management and progress risk control [3]. The research of this paper lies in the process of civil engineering construction, and discusses the management scheme by using big data technology. In the actual construction process, there are some problems, such as the lack of timely and effective business data support, information blocking and information fragmentation. Therefore, under the premise of fully combining the characteristics of big data, this paper discusses and analyzes the information management scheme in construction from various angles, and makes a case study with the civil engineering example of Ningxia Province.
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2 Based on Big Data Analysis 2.1 Theoretical Foundation of Information Management Scheme in Civil Engineering Construction Based on Big Data Analysis 2.1.1
The Definition of the Big Data
Big data is a data set (information asset) with strong decision-making ability, discovery ability and process optimization ability through the collection, storage, management and analysis of all data. Big data is not an emerging technology, but a normal in the digital era [4, 5]. Big data is not only massive data, nor is it cloud computing application. Big data application is a comprehensive solution, using various technologies to meet the collection needs. All data resource analysis applications belong to the usage category of big data technology, the key to big data is to get valuable things from a large amount of information [6].
2.1.2
The Basic Features of Big Data
The characteristics of big data include four aspects: supporting various types of data, attaching importance to unstructured data, discovering similarity rather than causal relationship between empirical things, paying attention to the overall data while ignoring small sampling, and emphasizing the essential characteristics of prediction [7].
2.2 Influence of Big Data on Information Management in Civil Construction Due to the rapid growth of the various aspects of the modern society, the demands for engineering work are becoming increasingly high, and the construction of civil engineering works in a scientific and rational way must be promoted to promote better development. Large data information management can increase the construction level of civil engineering and promote sustainable development of civil engineering works. Civil engineering works are continuously stable. Information management can be applied to various fields of civil engineering and construction process. We have a more valuable view in analyzing construction efficiency. Moreover, there are large data processing technology, computer aided construction technology, computer aided construction technology, and computer auxiliary construction technology. Statistical monitoring of electrical usage data for different uses, such as feature extraction,
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cluster and correlation analysis, provides a common utility of electricity consumption, which can predict the actual usage power of building materials and better know the actual usage power of building materials. Therefore, applying modern technologies and methods has already become an inevitable and required construction and development of civil engineering works. Big data technology is one of the most important and most advantageous technologies in it [8, 9].
2.3 Technical Support for Information Management Scheme in Civil Engineering Construction BIM Technology is the civil engineering model of information technology. The establishment of information model is a new technology or a new technical concept in recent years. Its goal is to combine information technology with civil engineering, to model civil engineering information with engineering information model as the core, to realize the computerization of traditional two-dimensional drawings, data and personnel management, and to realize the intelligent integration and coordination of these information, so that any design connection can be designed conveniently and quickly, Reasonable extraction and utilization of these information [10].
2.4 Big Data Method in Information Scheme in Civil Engineering Construction 2.4.1
Enropy and Mutual Information
In the basic theory of information entropy, mutual information is used to quantitatively describe the correlation degree between two variables, and the correlation degree between two variables is expressed by the mutual information of two variables. For two discrete random variables, the definitions of P and Q are as follows (1) H (P, Q) =
P∈ p Q∈q
B(P, Q) log
B(P, Q) B(Q)B(Q)
(1)
In the mutual information formula, the joint probability distribution functions of variables p and Q are B (P, Q), and the marginal probability distribution functions of variables p and Q are B (P) and B (Q), respectively. When there are two continuous random variables p and Q, the definition of Formula (2) is as follows:
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¨ H (P, Q) =
B(P, Q)log
B(P, Q) dxds B(P)B(Q)
(2)
PQ
According to the definition of mutual information, if the variables P and Y are independent of each other or completely independent, then the mutual information h (P, Q) is equal to O, indicating that there is no common information between the two variables; on the contrary, the greater the mutual information value, the higher the correlation between them, indicating that they have more common information.
2.4.2
BIF Algorithm
BIF (best individual feature) is a simple feature selection algorithm, its process is relatively direct. BIF algorithm is efficient and suitable for high-dimensional feature selection of data. Feature selection is a common method in embedded system classification data preprocessing stage. The algorithm can select and eliminate non essential features in advance, and ignore the correlation degree or redundancy of features. Adding candidate feature f to the selected feature set can increase the redundancy of feature set s. The evaluation function is (3) f(g) = u( j, k) −
j (y, s)
(3)
y∈Y
It can reduce the redundancy of feature subset by adding penalty term . However, in the selection process, the feature subset y will continue to increase, and the influence of the former part of the evaluation function will also weaken, resulting in an increase in the proportion of penalty items. Therefore, the feature selection process will be unbalanced [11, 12].
3 Analysis of Information Management Scheme in Civil Engineering Construction Based on Big Data Analysis 3.1 Experimental Content Information management based on big data plays an important role in the efficient development of civil engineering construction process. In order to find a more suitable big data application for civil engineering construction information management, this paper investigates and analyzes the specific application of big data in civil engineering information management in the construction process. The contents of the survey include the specific application of information management in the civil engineering construction process, and the advantages of big data in the information management
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of civil engineering construction process. This experiment uses the method of case analysis to investigate, and the experimental object is the case of Ningxia engineering project information management.
3.2 Experimentation Based on the results of the survey, the research contents are decided and the present state of the information management in Ningxia province is analyzed. Combining the concrete applications of the computerization management in Ningxia Province, we analyzed several aspects of the information management and superiority. In order to guarantee the objectivity and rationality of the final experiment, we carried out a preliminary survey on the process item information management in Ningxia province using the questionnaire method. The questionnaire 166 parts are distributed, and the effective questionnaire 158 part is recovered and 95% of the recovery rate. The whole process lasted about two weeks, and collected the data of the questionnaire, and analyzed the result.
4 Experimental Analysis of Information Management Scheme in Civil Engineering Construction Based on Big Data Analysis 4.1 Application of Information Management of Big Data in Civil Engineering Construction 4.1.1
Current Situation of Civil Engineering Project in Ningxia Province
Based on the data published by the Ningxia statistical yearbook, the paper takes statistics to Ningxia’s Ministry of construction, acquires an increase in the state and distribution of Ningxia’s buildings in 2016–2020, and the Ningxia building industry has a large number of construction companies, and the increase in GDP in the building industry has led to an increase in the nation’s GDP. Table 1 shows the number of firms and total output in Ningxia from 2016 to 2020. It can be seen from Table 1 that the number of civil engineering enterprises in Ningxia Province is on the rise from 2016 to 2019, and on the decline from 2019 to 2020; the same is true of the number of practitioners; but GDP has been on the rise.
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Table 1 The number and total output value of construction enterprises in Ningxia Province in 2016–2020 Time
Enterprise (units)
Number of employees (ten thousand)
Total output value of the construction industry (RMB)
2016
3037
163.41
42,351,448
2017
3102
169.36
43,265,982
2018
3205
170.98
55,632,699
2019
3345
178.56
59,895,259
2020
3298
176.25
62,592,100
4.1.2
Application Direction of Information Management of Civil Engineering Construction Process in Ningxia Province
According to the data collected from the questionnaire survey, Fig. 1 shows that before the construction, the information management of civil engineering in Ningxia Province is in the process of engineering management. In terms of training, big data analysis accounted for 60%, in terms of digital construction review accounted for 45%, in terms of unified project information management accounted for 30%, in terms of computer-aided accounted for 45%; in the construction, engineering management training accounted for 30%, digital construction review accounted for 30%, unified project information management accounted for 34%, computer-aided computer-aided accounted for 45%; after the completion of the construction, the engineering management training accounted for 10%, the digital construction review accounted for 25%, the unified project information management accounted for 36%, and the computer-aided computer-aided accounted for 54%. 80% 70%
Proportion
60% 50% 40% 30%
60%
54% 45%
45% 30%
45% 30% 30%
36%
34% 25%
20%
10%
10% 0% -10%
Before Construction
Under Construction
After Construction
Process Engineering Management Training
Construction Review
Unified Project Management
Computer-Aided
Fig. 1 Application direction of information management
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Process
After Counstruction
20%
Under Construction
21%
47%
30%
Before Construction
49%
35%
50% 0%
20%
30% 40%
60%
18% 80%
100%
Proportion Information Sharing
High Efficiency
Innovation
Fig. 2 The proportion of the effect of information management
4.2 Analysis of Information Management of Big Data in Civil Engineering It can be seen from Fig. 2 that the effect of information management in the process of civil engineering construction based on big data analysis has the above characteristics: information sharing, information efficiency and management innovation. In the construction process, management innovation is the main effect; in construction, information efficiency is the main effect; after the completion of construction, information sharing is the main effect. Comprehensive analysis shows that information management in civil engineering construction process based on big data background has significant effect.
5 Conclusion With the rapid growth of information technology, the entire process of civil engineering has achieved progress, quality, safe joint monitoring, cost and management. Effective combination of network planning and construction budget through computer information network. Under construction, modern management systems and advanced information technology can be used to effectively enhance the overall efficiency of construction.
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References 1. Gu Y (2017) Research on the reform of civil engineering project information management mode based on BIM technology. Boletin Tecnico/Tech Bull 55(15):22–29 2. Bilgin G, Dikmen I, Birgonul MT (2018) An ontology-based approach for delay analysis in construction. KSCE J Civ Eng 22(2):384–398 3. Xin L, Xin Y, Qichen J et al (2019) Research and application of dynamic management system of construction materials based on BIM technology. Civ Constr Eng Inf Technol 011(002):54–58 4. Zhou C, Ding LY, Skibniewski MJ, et al (2018) Data based complex network modeling and analysis of shield tunneling performance in metro construction. Adv Eng Inf 38(OCT):168–186 5. Jeong J, Lee Y, Offong UG et al (2018) A type information reconstruction scheme based on long short-term memory for weakness analysis in binary file. Int J Software Eng Knowl Eng 28(9):1267–1286 6. Wu P, Jin R, Xu Y et al (2021) The analysis of barriers to bim implementation for industrialized building construction: a china study. J Civ Eng Manag 27(1):1–13 7. Song Z, Su W, Tian X et al (2021) Risk analysis of tunnel construction scheme change based on field monitoring and numerical analysis. Adv Civ Eng 2021(9–10):1–15 8. Huang Y, Shi Q, Zuo J et al (2021) Research status and challenges of data-driven construction project management in the big data context. Adv Civ Eng 2021(1):1–19 9. Gong P, Guo H, Huang Y et al (2020) Safety risk evaluations of deep foundation construction schemes based on imbalanced data sets. J Civ Eng Manag 26(4):380–395 10. Lee C, Lee C, Lee EB (2018) Analysis of the causes and level of maintenance for enterprise systems in construction companies. J Civ Eng Manag 24(6–8):499–507 11. Dong-Gun L, Ji-Young P, Song SH (2018) BIM-based construction information management framework for site information management. Adv Civ Eng 2018:1–14 12. Kang Y, Yu J, Chang J (2017) Big data analytics in civil engineering: the case of China. Int J Civ Eng 4(10):1–6
Relationship Between Learning Behavior and Learning Effect Based on Big Data Xinan Huang
Abstract In the modern network society, with the rapid development of big data technology and the continuous generalization of education network information, humans are constantly changing in terms of life needs and learning needs. The rise of online learning can well meet this changing life and learning needs, and it has become a hot research field in the education field. However, the problems of the study of learners’ learning behavior and learning effects in the network are becoming more and more obvious. The purpose of this article is to study the relationship between learning behavior and learning effect based on big data. This article focuses on the study of effective online learning behavior, focusing on clarifying the influencing factors of effective online learning behavior and verifying its path relationship, and putting forward relevant suggestions to improve effective online learning behavior. According to the basic online learning process in the virtual learning community, the corresponding indicators of learning behavior are constructed, and the learning data collected by the survey is analyzed, and the study on the impact of learners’ learning behavior on learning effects in online learning is carried out. Different behaviors lead to different results, and behaviors have the most direct impact on the results. Finally, statistics and analysis of data from the classification of learning behaviors, characteristics of learning behaviors and learning effects are carried out to find problems and give corresponding suggestions. The experimental research results show that the interaction with teachers and classmates has the greatest impact on the learning effect, with an impact of 31.79%; the next is the behavior of afterclass expansion, with an impact of 26.64; the second is the behavior of expanding after class, with a degree of influence of 26.64; the degree of influence of pre-class preparation and pre-class practice on the learning effect is generally about 20% on average. In general, it is still necessary to have more exchanges and discussions with teachers and classmates in order to improve the learning effect. Keywords Big data · Learning behavior · Learning effect · Relationship X. Huang (B) School of International Education, Guangdong University of Finance, Guangzhou 510521, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_41
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1 Introduction In the modern network society, with the rapid development of big data technology and the continuous generalization of education network information, human beings are constantly changing in terms of life needs and learning needs [1, 2]. The rise of online learning can meet this changing life and learning needs, and it has become a hot research field in the education field [3, 4]. However, the problems of phenomena such as the study of learners’ learning behavior and learning effects in the network are becoming more and more obvious [5, 6]. In-depth and comprehensive demonstration of the formation process and reasons of the influencing factors and structural relationships of students’ effective learning behavior in the network learning environment [7, 8]. Thereby improving the effective online learning behavior of contemporary college students, promoting the effect of online teaching, and improving the quality of online teaching [9, 10]. In the research on the relationship between learning behavior and learning effect based on big data, many domestic and foreign scholars have conducted research on it and achieved good results. Sang showed that the relationship between student personality and learning effect is to improve the effectiveness of student learning. The effective method is to compile network courseware suitable for different students’ individual learning requirements [11]. Nor believes that a considerable number of domestic learning websites have been personalized to a certain extent, but there are still deficiencies in many aspects. Most of the online platforms ignore the measurement of learner personality characteristics [12]. This article focuses on the study of effective online learning behaviors, clarifying the influencing factors of effective online learning behaviors and verifying their path relationships, and putting forward relevant suggestions to improve effective online learning behaviors. According to the basic online learning process in the virtual learning community, the corresponding indicators of learning behavior are constructed, and the learning data collected by the survey is analyzed, and the study on the impact of learners’ learning behavior on learning effects in online learning is carried out. Different behaviors lead to different results, and behaviors have the most direct impact on the results. Finally, statistics and analysis of data from the classification of learning behaviors, characteristics of learning behaviors and learning effects are carried out to find the connection between learning behaviors and learning effects. And the question, give the corresponding suggestion point of view.
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2 Research on the Relationship Between Learning Behavior and Learning Effect Based on Big Data 2.1 The Guiding Significance of Behavioral Science to the Study of Learning Behavior (1)
(2)
(3)
Pay attention to learning support services The core of behavioral science is people-oriented. Therefore, the design and development of online education resources should start from the needs of students and be student-centered. In the past, most of the teaching was teachercentered, which ignored the individual needs and goals of learners. Relevant theories of behavioral science show that in online education, learners should be the center, and learners’ cognitive laws can be discovered to predict and control their behaviors. Carry out dynamic analysis of their learning behavior and formulate personalized support strategies, so as to achieve the purpose of improving learning efficiency. Pay attention to the influence of environment on learning behavior Develop strategies through psychological analysis. Both behavioral science and behaviorism believe that the environment is of great significance to behavior, which shows the importance of the construction of a learning environment for online learning. In essence, the process of network learning can be regarded as a process of continuous interaction between individuals and the external environment. Therefore, the designer should start with the analysis of the learner’s psychological characteristics, pay attention to how to establish an effective learning mechanism by optimizing the interaction between the environment and the learning psychology, and adopt strategies that conform to the law of individual behavior to prevent and eliminate bad learning behaviors, and stimulate and maintain good behaviors. The occurrence and continuity of the project, to achieve the expected learning output. Pay attention to the application of empirical research methods Learning behavior mainly studies human psychology and behavior, and people express their different mental states through different behaviors. Therefore, empirical research methods should be emphasized in the research to reflect the objectivity and scientific nature of the research.
2.2 Factors Affecting Learning Behavior (1)
Factors affecting the external environment The external environment influence factors mainly include teaching organization, online learning technical support system and online learning resources. The form of teaching organization refers to the way, form, and teaching mode of carrying out online learning activities. Online learning technical support
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system refers to the software platform for learners under technical support to carry out online learning activities. It is a convenient channel for learners to obtain learning resources and communicate with their learning peers. At the same time, the learning support system can monitor and record learning behaviors, externally regulate learners’ behaviors, and cultivate learners’ good behavior habits. Individual internal factors of learners Individual internal factors of learners mainly include knowledge needs, learning interest, psychological factors, information literacy and knowledge experience. Psychological factors mainly include online learning willingness, psychological cognition, sense of self-achievement, goals, will control and cognitive development. Knowledge experience refers to the knowledge experience related to the current learning content. It also has a special impact on the formation of learners’ knowledge, ideas or concepts and the generation and application of learning strategies. Therefore, in the network learning environment, the higher the correlation between the content of the learner’s learning and the original knowledge and experience, the better the control of the information search process and the effective network learning. Information literacy is the ability of learners to acquire, analyze, absorb and process information. Learners with a wealth of information technology knowledge can quickly and strategically extract, classify and save relevant information resources, while learners who lack relevant knowledge will find it difficult to search for information, acquire information and communicate with learning partners, which affects learning effect.
2.3 The Influence of Learning Behavior on Learning Effect (1)
(2)
The influence of pre-class preparation on the learning effect The pre-study preparation of the course affects the depth of understanding in the course of study. Under normal circumstances, the more adequate the pre-school preparation of the course and the more complete the understanding of the knowledge and information in the course, the faster the course learning progress of the corresponding learner, and therefore, the higher the impact on the learning effect. Moreover, if the learner’s course preparation is adequate, the learning effect will be greatly improved. The impact of attendance on the learning effect Attendance status refers to the number and frequency of logging in to the virtual learning community system, that is, the number and frequency of joining new courses and reviewing courses that have been learned. Therefore, the higher the number and frequency of logins will expand the knowledge and deepen the degree of knowledge that has been mastered, so that learning can proceed more effectively and smoothly; if the number and frequency of logins are lower, it means that learners seldom expand and deepen their knowledge.
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(3)
(4)
(5)
(6)
(7)
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The knowledge that has been mastered cannot enable the learning to be carried out effectively and smoothly, which will have a bad influence on the learning effect. The impact of resource learning on the learning effect The resource learning situation refers to the process in which learners obtain knowledge and information in virtual learning community courses while performing knowledge processing and reorganization. In the learning process, the higher the learner’s understanding of knowledge information and the degree of knowledge impression, the more beneficial the subsequent course learning will be effective and smooth. Therefore, the higher the learner’s understanding of knowledge information and the degree of knowledge impression, the more it will have a better impact on the learning effect. The impact of resource review on the learning effect Resource review refers to the process in which learners log in to the course again to review and consolidate the knowledge they have learned after completing the course in the virtual learning community. If the learner repeats the course review, it will better affect his own learning effect. If the learner rarely or even does not conduct the course review, it will not have an impact on his own learning effect or only have some small effects. Therefore, in learning, the higher the frequency of resource review, the better the learning effect achieved. The impact of interaction and participation on the learning effect Interactive cooperation refers to the activities in which learners communicate and exchange knowledge with other learners and teachers in order to complete tasks in the virtual learning community. It is divided into learners and learners, learners and teachers, and learners and teachers. The more interaction and cooperation in the virtual learning community, the greater the expansion of their knowledge and the deeper their mastery of knowledge and information. So as to have a better impact on your own learning effect. The effect of the completion of the learning task on the learning effect The completion of learning tasks refers to the situation of learners completing coursework and other tasks assigned by the teacher in the virtual learning community. Coursework and tasks can deepen the learner’s impression of knowledge information, and think about the knowledge information to organize Practical application in the future. Therefore, every completion of a coursework and tasks assigned by the teacher will further have a good effect on the learning effect. The impact of task completion enthusiasm on learning effect Task completion enthusiasm refers to the degree of willingness and interest of learners to complete coursework and the teacher’s assignment in the virtual learning community. Only if they are willing, will they be more active and serious to complete coursework and tasks, so as to achieve the desired effect of coursework and tasks.
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2.4 Zero Expansion Poisson Model Introducing covariates and applying them to actual research makes the analysis of influencing factors in various fields more comprehensive. The zero-expansion Poisson mixture distribution formula is as follows: P(X = x, φ, λ) = φ + (1 − φ)exp(−λ), x > 0
(1)
P(X = x, φ, λ) = (1 − φ)λx x! exp(−λ), x < 0
(2)
Here parameter φ is a zero expansion parameter. If the random variable X obeys the above zero-expansion Poisson mixed distribution, its expectation and variance are respectively: E(X) = (1 − φ)λ, Var(X) = E(X)(1 + λ − E(X))
(3)
This model is clear in expression, simple in calculation, and has outstanding advantages in many current analysis methods. It is called a more commonly used statistical model for processing zero-inflated data.
3 Experimental Research on the Relationship Between Learning Behavior and Learning Effect Based on Big Data 3.1 Experimental Subjects and Methods In this experiment, students from a certain school are used as the experimental objects, and the students’ learning behaviors are studied through questionnaire surveys, and the degree of influence of students’ learning behaviors on learning effects is analyzed, from the external environment to the individual students.
3.2 Data Collection In this experiment, team research was conducted, tasks were assigned, questionnaires were designed, distributed, and recycled. Perform statistical analysis on the collected questionnaire data to get the final experimental data.
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4 Experimental Research and Analysis of the Relationship Between Learning Behavior and Learning Effect Based on Big Data 4.1 Analysis of the Influence Degree of Learning Behavior on Learning Effect This experiment conducted an experimental research on the learning behavior model, mainly from four aspects: interactive behavior, pre-class preparation, after-class exercises and after-class development. The experimental results are shown in Table 1. As shown in Fig. 1, the interaction with teachers and classmates has the greatest impact on the learning effect, with an impact of 31.79%; the next is the behavior of after-class expansion, with an impact of 26.64; pre-class preparation and pre-class practice have an impact on The influence of learning effect is generally around 20% on average. In general, it is still necessary to have more exchanges and discussions with teachers and classmates in order to improve the learning effect. Table 1 Analysis of the influence degree of learning behavior on learning effect
35.00%
Interactive behavior
31.79
Preparation before class
23.15
Homework
18.42
After class development
26.64
Influence level
30.00%
learning result
Influence level (%)
25.00% 20.00% 15.00% 10.00%
31.79%
26.64%
23.15%
18.42%
5.00% 0.00%
Interactive behavior Preparation before class
Homework
Learning behavior
Fig. 1 Analysis of the influence degree of learning behavior on learning effect
After class development
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Table 2 Analysis of the influence degree of individual behavior
Influence level (%) Willingness to learn
23.34
Learning interest
43.75
Intellectual factors
32.91
Individual learning behavior
Willingness to learn
Learning interest
Intellectual factors
32.91%
Learning interest Willingness to learn
Intellectual factors
43.75% 23.34%
Influence level Fig. 2 Analysis of the influence degree of individual behavior
4.2 Analysis of the Influence Degree of Individual Learning Behavior on Learning Effect This experiment conducted experimental research through three aspects of students’ own learning willingness, learning interest, and intellectual factors. The experimental research results are shown in Table 2. As shown in Fig. 2, the individual’s interest in learning has the greatest impact on the learning effect, up to 43.75%, followed by their own intellectual factors, with a degree of influence of 32.91%, and finally the willingness to learn, with 23.34%. All in all, interest has a great impetus for learning.
5 Conclusion From the current situation, there are certain drawbacks in traditional online teaching, such as the lack of teacher monitoring, the loss of student networks, and the inadequacy of teacher guidance, which have a negative impact on students’ learning effects. A large number of studies have shown that as the main body of online education, all the learning behaviors and results produced by students during the entire learning process, these data information are very important basis for reflecting the individual characteristics of students. Therefore, we should improve online teaching methods, conduct research and statistics on students’ personal behaviors in online learning, and improve students’ self-adjustment ability, so as to reduce the occurrence of “net loss”. Based on big data technology, this paper analyzes various data of
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students’ personalized learning behaviors, and generates them instantly in the form of pop-up windows, which are used to guide students in the next stage of learning or to provide feedback on students’ learning status. This concise and intuitive form enables students to discover their own learning problems in time, to make up for the lack of teachers’ absence of on-site tutoring, and to point out the right direction for students’ learning. This research constructs and verifies the structural relationship between effective online learning behavior and its influencing factors, and clearly puts forward the main factors that affect students’ effective learning behavior in the online learning environment. Future research can join the empirical analysis of college students’ online learning, combined with the results of quantitative research, and continue to explore the completeness of the factors.
References 1. Heurn LV, Vermeulen RJ et al (2016) Feedback learning and behavior problems after pediatric traumatic brain injury. Psychol Med 46(07):1473–1484 2. Mahjoubpour B, Nasirzadeh F, Golabchi MMHZ et al (2018) Modeling of workers’ learning behavior in construction projects using agent-based approach: the case study of a steel structure project. Eng Constr Archit Manag 25(4):559–573 3. Kim MH et al (2016) A study on the influences of the self-leadership of college students majoring in beauty arts on their learning effects and college life satisfaction. J Korean Soc Des Culture 22(4):25–36 4. Li H (2017) Stochastic single-machine scheduling with learning effect. IEEE Trans Eng Manage 64(1):94–102 5. Nuraini F (2019) Intellectual intelligence, learning behavior and availability of educational means on intermediate accounting understanding with motivation as a moderating variable. Accruals (Acc Res J Sutaatmadja) 3(2):139–154 6. Jie YL, Shieh CJ (2016) A study on the effects of multiple goal orientation on learning motivation and learning behaviors. Eurasia J Math Sci Technol Educ 12(1):161–172 7. Kao CC, Luo YJ (2020) Effects of multimedia-assisted learning on learning behaviors and student knowledge in physical education lessons: using basketball game recording as an example. Int J Emerg Technol Learn (iJET) 15(1):119 8. Laer SV, Elen J (2019) The effect of cues for calibration on learners’ self-regulated learning through changes in learners’ learning behaviour and outcomes. Comput Educ 135(JUL):30–48 9. Tokan MK, Imakulata MM (2019) The effect of motivation and learning behaviour on student achievement. S Afr J Educ 39(1):1–8 10. Lee J, Sang KK, Moon CW (2017) Changes in learning behavior: the effect of learning in strategic alliances. Acad Manag Annu Meet Proc 2017(1):15156 11. Kim SR et al (2017) Analysis of multi-group structural relationship among transformational leadership, personal & team learning behavior and team effectiveness. J Agric Educ Human Res Dev 49(1):85–111 12. Nor NM, Nambiar R, Ismail K et al (2018) Effect of redesigned classroom on secondary students’ learning behaviour. Arab World English J 9(3):17–32
Construction of School Quality Assurance System Based on Big Data Analysis Yue Yang
Abstract In order to continuously improve the quality of education and teaching and better realize the goal of talent cultivation, we should combine education and teaching with big data analysis technology, adhere to the working principle of “student-centered, achievement oriented and continuous improvement”, improve the construction of education and teaching quality assurance system, and strive to build a long-term mechanism to improve the quality of talent cultivation, Form an internal quality assurance system guided by “national and social needs” and “student learning effectiveness”, with “743” as the main content and “monitoring and evaluation” as the focus. Keywords Big data analysis · Evaluation of education reform · The quality assurance system · The student centered
1 Introduction The Central Committee of the Communist Party of China and the State Council issued “China’s education modernization 2035" [1], which proposed to build the education quality evaluation and monitoring mechanism, and establish the whole process, allround talent training quality feedback monitoring system [2]. In 2021, the Ministry of education’s Department of higher education pointed out that this year, the evaluation center of the Ministry of education will start a new round of pilot work of audit and evaluation in due time. The plan for audit and evaluation of undergraduate teaching in ordinary colleges and universities proposed that the implementation of audit and evaluation should give full play to the role of third-party evaluation [3]. In the professional certification work, it is emphasized to build a graduate survey mechanism [4], apply the survey results to the continuous improvement of the professional Y. Yang (B) Shenyang Institute of Technology, Fushun, Liaoning, China e-mail: [email protected] Nueva Ecija University of Science and Technology, Cabanatuan City, Philippines © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_42
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personnel training process [5], and clearly put forward to analyze the employment development and ability accomplishment of graduates five years after graduation [6]. That is clearly pointed out that “the tracking investigation results and external evaluation of the quality of graduate training” are needed [7]. In the twenty-first century, China’s higher education is facing the challenge of the globalization of knowledge economy [8]. The most direct embodiment is the requirements for the teaching quality of colleges and universities. Only when colleges and universities adhere to the student-centered construction of internal teaching quality assurance system, can they cultivate more excellent comprehensive talents for the society, Only in this way can we give full play to the value engine function of colleges and universities in the process of the development of socialism with characteristics [9]. The construction of student-centered internal teaching quality assurance system in Colleges and universities can play the role of goal orientation, condition guarantee, incentive and self-monitoring. It is not only an important means to enhance the international competitiveness of colleges and universities in China, but also an important cornerstone to promote the sustainable development of colleges and universities [10]. Teaching quality is the lifeline of various activities in Colleges and universities. Without a perfect internal teaching quality assurance system, high-quality teaching cannot be generated automatically. Therefore, it is necessary to strengthen the research on student-centered internal teaching quality assurance in Colleges and universities.
2 Meaning of Big Data At present, there are many researches on big data in academic circles, but there is no unified and accurate definition. In the book big data, Tu Zipei, a famous scholar in China, made such an understanding: the so-called big data represents the general name of large-scale data that is beyond the traditional meaning scale, and it is difficult to capture, analyze and manage large-scale data with general software tools, and its unit is “Ethernet Festival”. The big data refers to not only the large capacity, but also the deeper meaning lies in the new knowledge and new value, the great development and the big technology, etc. brought by the massive data integration, analysis and exchange. Some scholars also put forward two concepts of big data when they study big data, one of which is that the huge data that cannot be processed by artificial and simple computer software is called big data. Another concept proposed is: to make statistics on a large amount of data information, and then find the law of value from the seemingly unrelated information.
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3 Construction Ideas Based on Big Data Analysis The main task of internal teaching in Colleges and universities is to cultivate talents. From the perspective of quality control, talent cultivation in Colleges and universities is similar to enterprise production. If the student-centered internal teaching quality assurance system in Colleges and universities wants to enhance the effect of talent training, it must focus on the three processes of “input, processing and output”, which is also the most basic task. The construction of quality assurance system based on big data analysis will be based on qualitative research methods, through data collection and analysis, combined with quantitative research. The research ideas are as follows: first, according to the collection and research of the relevant literature on the studentcentered quality assurance system, the evaluation index of the quality assurance system is preliminarily determined; Second, according to the evaluation index, the questionnaire is made and distributed to the students; Third, the questionnaire statistics, combined with the results of satisfaction survey, to evaluate all aspects of education and teaching in the whole process of students’ growth. This topic needs to be able to deal with the above research process on the basis of establishing a scientific and reasonable evaluation index system of the whole process of student growth.
4 Construction of Quality Assurance System 4.1 Construction Content Focusing on the orientation and purpose of the college, we should learn the advanced ideas and experience of the construction of teaching quality assurance system at home and abroad through training, lectures, self-study and other forms, and carry out the construction of quality assurance in the college’s education and teaching activities, so as to make every staff of the college master the basic knowledge of higher medical teaching and understand the development law of popular higher education, To understand the general trend of the reform and development of higher education in China, especially the innovative development concept of medical education, to form a scientific and correct higher education quality concept, to create a good quality culture atmosphere, to give full play to the enthusiasm of all staff, and to make them really participate in the whole process of college development and quality construction. In order to continuously improve the quality of education and teaching and better realize the goal of talent cultivation, Shenyang Institute of technology adheres to the working principle of “student-centered, achievement oriented and continuous improvement” in accordance with the working requirements of the development goal of “building the best applied technology university”, actively improves the construction of education and teaching quality assurance system and strives to build
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Fig. 1 Internal quality assurance system
a long-term mechanism to improve the quality of talent cultivation, It has initially formed an internal quality assurance system guided by “national and social needs” and “student learning effectiveness”, with “743” as the main content and “monitoring and evaluation” as the focus. As show in Fig. 1. The orientation of talent training objectives in Colleges and universities can reflect the characteristics and ideas of running schools, and also can clearly define the direction of running the school, which plays an important role in guiding and inspiring the development of colleges and universities. The internal teaching quality assurance system in Colleges and universities should define the orientation of talent training objectives, adhere to the guiding ideology of students as the center, formulate them in combination with social needs and the actual situation of campus, and ensure that the goal orientation can be based on the comprehensive quality of students in all aspects and the relevant national education laws and regulations. In formulating the target orientation of the professional level, we need to grasp the principles of social adaptation, market supply and demand and long-term planning, and refine the orientation of talent training objectives, and guarantee the quality of professional teaching mainly by the development of students’ specialty, so that the trained professional talents can be more in line with the target orientation of talent training. When the teaching mode is targeted, colleges and universities need to choose appropriate teaching behaviors and processes according to the internal education teaching concept, and enhance the flexibility, dynamic and effectiveness of teaching mode. The teaching mode of colleges and universities will be different according to the factors of specialty and type. For example, the application universities take the cultivation of applied talents as the main goal, the teaching mode needs to reflect the dynamic of the professional frontier, the teaching means need to pay more attention to practicality, fully consider the development needs of students, enrich the teaching mode in the form of combination of production and research to enhance students’ literacy.
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4.2 Establish the Quality Assurance Concept of “Student-Centered” In September 2017, the Ministry of education revised order No. 41 “Regulations on the management of students in ordinary colleges and universities”, which proposed for the first time that “students should participate in the management of colleges and universities in an appropriate way, and enjoy the right to know, participate, express and supervise the affairs related to the rights and interests of colleges and students”, indicating that higher education has gradually turned to a new perspective and mode of “student-centered”. Colleges and universities begin to focus on students’ learning effectiveness, development and needs, and collect all the teaching resources of the school to provide high-quality and efficient guarantee for students’ learning, promote students as the main body of teaching quality guarantee, and make them actively participate in the learning process and evaluation of learning effectiveness. The focus of evaluation changes from teachers’ teaching effect to students’ learning effect, which is mainly designed around what and why students learn (curriculum evaluation), how students want teachers to teach (Teaching Evaluation), and how students should learn (learning process and effectiveness evaluation), To promote students to provide reasonable learning information as an important basis for school improvement.
4.3 Horizontally Realize the Separation and Linkage of “Management, Operation and Evaluation” Three Types of Organizations, and Vertically Implement the Division of Labor Management of “School, College and Section” The college constantly strengthens the awareness of teaching quality. The supervision and audit department is responsible for the monitoring of teaching quality. It is equipped with a supervision team composed of full-time and part-time personnel, and initially constructs a teaching quality assurance system composed of internal quality assurance and external quality assurance. In terms of the construction of internal quality assurance system, a “two-tier and three-tier” teaching quality management system has been established. The “first tier” is the decision-making organization system of education and teaching quality, which is composed of School Teaching Committee, Secondary College Teaching Committee and teaching and research section. It is responsible for system construction, standard construction and other responsibilities “ The second level is the implementation organization of education and teaching quality management, which is composed of the academic affairs office and the evaluation center, the second level college, and the teaching and Research Office, and is responsible for implementing the corresponding policies and decisions.
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School level. The university has established a supervision and audit department and an office for undergraduate teaching qualification evaluation, and formed an organization separated from “management, operation and evaluation” of quality management and an operation mechanism of mutual linkage among departments. Through the implementation of a series of quality monitoring measures, the school effectively monitors the order of teaching operation process, the implementation of teaching norms and the quality of theoretical and practical teaching, and timely conducts situation analysis and information feedback, so as to promote the continuous improvement of existing problems; To ensure the strict implementation of the main teaching quality standards and the smooth and orderly operation of teaching. College level. Each teaching unit has set up a quality assurance working group headed by the director; Each teaching unit has also established a supervision group composed of experts to cooperate with the school and the unit to implement the quality management requirements, implement the unit’s education and teaching quality assurance work projects, carry out real-time supervision, timely discover and feed back problems, put forward suggestions to solve problems, and urge the continuous improvement of existing problems. At the level of teaching and research section. Establish a quality assurance working group headed by the director of the teaching and research section, implement quality control on the factors that affect the quality of the major and courses that the teaching and research section is responsible for, carry out investigation on relevant situations, analyze the causes of problems in major construction and course teaching, and organize and implement continuous improvement of existing problems.
4.4 Strengthen the Quality Monitoring and Evaluation, and Form the Working Mechanism of “Closed-Loop Operation and Continuous Improvement” According to their respective responsibilities, the teaching management department, the undergraduate teaching qualification assessment office, and the supervision and audit department collect and process relevant data through the continuous implementation of daily supervision, teaching evaluation, and special investigation, so as to form an analysis report on the special investigation, and provide the development trend of students’ and social satisfaction and teaching service requirements.
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4.5 Strengthen the Connotation Construction of Quality Culture, Promote Our Quality Behavior to Become Spontaneous Behavior, and Gradually Rise from Quality Control to Quality Culture As an important part of university culture, quality culture is the core and soul of teaching quality assurance system. The formation of quality culture can make the construction of teaching quality achieve twice the result with half the effort. Strengthen the quality subject consciousness of teachers and students, try to internalize the quality requirements in the consciousness of teachers, students and administrators, enhance teachers’ sense of identity with the school, cultivate students’ sense of participation, and guide teachers, students and administrators to work towards a common goal.
5 Conclusions To sum up, the internal teaching quality assurance system of colleges and universities should adhere to the guiding ideology of student-centered, clarify the basic tasks of the assurance system, follow the main principles of the assurance system, and actively build from the objectives, standards and systems, so as to ensure the healthy operation of the system and improve the teaching quality. Through the investigation and analysis of big data, the school has built an internal quality assurance system composed of seven systems, including “organization, objectives, standards, resources, process, monitoring and evaluation”. Taking the improvement and implementation of supervision and monitoring system and evaluation system as the focus of quality assurance work, taking “teaching, learning, management and construction” as the content of supervision and evaluation, the supervision and evaluation system of “supervision of teaching, learning, management and construction” and “evaluation of teaching, learning, management and construction” is constructed. From the aspects of students, teachers, school leaders and functional departments, society and so on, we should establish a continuous improvement mechanism of “evaluation feedback improvement” three-step cycle, and implement closed-loop control management. Acknowledgements This work was supported by LMJX2021066 and JG20DB333.
References 1. The State Council of the CPC central committee issued the general plan for deepening the reform of education evaluation in the new era
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2. The general office of the general office of the CPC Central Committee and the general office of the State Council issued the opinions on deepening the reform of the educational supervision system and mechanism in the new era 3. The Central Committee of the CPC and the State Council issued 2035 of China’s education modernization 4. Key points of work of Higher Education Department of Ministry of education in 2021 5. Examination and evaluation plan of undergraduate teaching work in general colleges and universities 6. Wen SW, Jia D (2020) Prototype of teaching early warning system of emotional tendency in big data scenario Design. Inf Commun 02:278–280 7. Feng Z, Li W, Shanshan S (2019) Early warning model of teaching quality monitoring based on evaluation system type research. Heilongjiang Sci 10(03):38–39 8. Wu S, Ge SS (2018) Teaching reform of mechanical manufacturing specialty in Higher Vocational Colleges under “made in China 2025”. J Xingtai Vocational Tech College 33(02): 5–7 9. Hu Q (2018) Evaluation of teachers and students based on intelligent educational administration system of digital campus Research on price model construction. East China Normal University 10. Yang G, Song C, Xiujin H (2018) Improving the teaching quality monitoring and evaluation of higher vocational education Thoughts and suggestions on price system. China Higher Educ Eval 15(04):34–38
Design of University Data Governance Process System Under the Big Data Environment Yao Yao
Abstract Data governance is the important means for fully excavating and realizing the value of big data. Based on big data technology, this paper designs the data governance process consisting of management domain, process domain, governance domain, technology domain, and value domain; based on the data life cycle supported by system and process specifications, the data governance system consisting of data service layer, data governance layer and data collection layer has been constructed; in order to improve the effect of data governance in universities under the big data environment, it is proposed to build the cross-departmental multi-party participation mechanism and strengthen the foundation strategies such as facility construction, strengthening data security management and following strict data standards. The research results of this paper solve the core problems of university data governance and can promote the transformation of university big data governance from “experience governance” to “intelligent governance”. Keywords Big data · Data governance · Process design · System design
1 Introduction With the rapid development of technologies such as cloud computing, Internet of Things, and artificial intelligence, human society has entered the era of big data. Data flow is the most typical feature of the era of big data, and data can be massproduced, utilized, and shared. Data has become an important capital in all fields of society and is driving the transformation of the times. Educational data resources are tools for systemic reforms in the education field. Countries are actively focusing on the collection and utilization of educational data to make scientific education decisions, optimize the allocation of educational resources, and innovate teaching systems. Data governance is the management of the full life cycle of data, including data collection, cleaning and conversion and other traditional data integration and Y. Yao (B) College of Software, Liaoning Vocational University of Technology, Jinzhou, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_43
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storage links. It also includes data asset catalogs, data standards, quality, security, data development, data value, and data services and applications, etc., the business, technology, and management activities carried out during the entire data lifecycle belong to the category of data governance. Data governance focuses on a set of management mechanisms for applying and managing data as data assets, which can eliminate data inconsistencies, establish standardized data application standards, improve data quality, realize data sharing, and use data as a valuable asset for the organization to apply to business, management, and strategic decision-making to give full play to the value of data assets. Big data can realize education customization and innovate traditional teaching models and learning mechanisms. Education in the era of big data realizes feedback learning, personalized learning and predictive learning. As the basic guarantee for the effective use of data, data governance has important value [1]: first, it is beneficial to improve the defects and deficiencies of the entire life cycle of education data. Including the redundancy of educational data resources caused by scattered data collection, the poor quality of educational data caused by lack of data benchmarks, and the hidden dangers of data security caused by the vague data sharing authority. Data governance can effectively avoid educational data information islands, improve the utilization rate of educational data, and highlight the true value of educational data. Therefore, key issues such as educational data collection, data access, data sharing, data reporting, and data security are more important than ever. Second, education data governance can forward-looking identification of potential data problems that may arise in the future. In an era of “everything is data”, the more data is collected, the more data variables will be faced, so there will be more data noise. Educational data governance can preprocess data problems in a timely manner and reduce the economic loss of later data governance and staff wastage. Third, educational data governance helps to clarify the responsibilities of all parties, enhance the transparency of data, and make relevant data problems have evidence and laws to follow, and they can be investigated in accordance with the law. Data governance in universities under the big data environment is applicationoriented, according to standardized data standards and methods, to obtain, organize, analyze and calculate data from different sources, and then explore the data variables in the process of teaching, scientific research, management and service. The relationship between education and big data is used to provide reliable decision support for teaching, management and service to drive the reform and innovation of teaching, management and learning. Through data governance, the original platform data of universities is comprehensively managed, and data quality is improved. While providing accurate data support for various business services of the school, it lays the foundation for data visualization analysis and big data analysis. Data governance can improve the ability to implement information standards, improve the openness of shared data, improve the governance of data quality, and improve the ability to accumulate historical data [2]. This paper studies the design of the university data governance process system under the big data environment, provides technical support for university data governance, and promotes the transformation of big data governance from “experience governance” to “intelligent governance”,
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which is conducive to scientific decision-making, efficient management, innovative services and speed in universities, which is respond to serving the innovation and development of universities.
2 Process of University Data Governance Under the Big Data Environment The university data governance process under the big data environment is composed of management domain, process domain, governance domain, technology domain and value domain, as shown in Fig. 1 [3]. For each part shown in Fig. 1, brief description is as follows: first, the management domain is mainly to determine the strategy, build the organization, formulate the system, and clarify the specifications. Setting up an organization is one of the most important tasks. The university data governance organization is composed of a data governance committee, a data governance center, a data business department, a data support department, and a data use department. Second, the process domin. In the process of data governance, it is carried out in accordance with the process of “analysis-design-execution-evaluation”. These four processes are also the cyclical improvement process, that is, each time the process area is executed, the data governance technology is improved. Third, the governance domain, which is mainly divided into master data governance, business data governance, and analytical data governance. Master data is based on the establishment of master data standards in all aspects of the master data life cycle. The master data life cycle includes the
Fig. 1 Process of university data governance under the big data environment
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generation, integration, storage, publish, maintenance, use and archiving. Fourth, technology domain, the main technologies used in data governance, including data architecture, management and control platforms, and governance tools. The data management and control platform has changed from the working environment of the data department to the data working environment of the entire university. Fifth, the value domain. The value domain realizes value creation such as data circulation, data services, and data insights through the effective arrangement of governance results and the construction of specific data products.
3 System of University Data Governance Under the Big Data Environment The educational data governance system is the complete structure composed of the educational data governance subject, the governance object, and the mutual operating mechanism between the subject and the object. The sound education data governance system is the basic guarantee for fully mining and exerting the value of education data, the effective method to effectively avoid data privacy issues, and the necessary strategy to give full play to the potential value of educational resources. The university data governance system in the big data environment is supported by the data life cycle and system process specifications, and consists of a data service layer, a data governance layer, and a data collection layer, as shown in Fig. 2 [4]. The brief description of each part shown in Fig. 2 is as follows: first, the data life cycle, the flow of data in the organization business under the big data environment, consists of six stages, including data collection, storage, processing, transmission, exchange, and destruction. The life cycle of a particular data is determined by the actual business scenario, and not all data will go through six stages completely [5]. Second, standardize the system and process. Through the formulation of systems, processes and specifications, relevant personnel can clarify the work content and work processes included in data governance during the entire life cycle of data generation, storage and application, and form the unified management system within the school. Third, the data collection layer, which collects data from various systems currently used in universities, including student systems, teacher systems, teaching systems, scientific research systems, financial systems, and other systems. Fourth, the data governance layer, is the core layer of the data governance system. It consists of data governance tools and data assets. Data governance tools are used to implement the data management system, realize data management automation, improve data management efficiency, ensure data quality, and achieve security data sharing. Data assets are data resources recorded in a physical or electronic way. Fifth, the data service layer, various data services developed on the basis of data governance, including decision support, multi-dimensional analysis, comprehensive evaluation, data derivation, comprehensive query and other services.
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Data service layer Multidimensio nal analysis
Comprehensiv e evaluation
Data derivation
Comprehens ive inquiry
Other service
Data governance layer Data assets
Data lifecycle
Data governance tools Data standard management
Metadata management
Data catalog
Data model management
Master data management
Data label
Data quality management
Data value management
Data map
Data security management
Data sharing management
Other assets
System, process and specification
Decision support
Data acquisition layer Student system
Teacher system
Teaching system
Scientific research system
Financial system
Other system
Fig. 2 System of university data governance under the big data environment
4 Strategy of University Data Governance Under the Big Data Environment In order to give full play to the role of university data governance in the big data environment, referring to relevant literature, this paper proposes the following strategies: (1)
(2)
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Build the cross-departmental multi-party participation mechanism. The implementation of data governance in universities requires the participation of the education department, the science and technology department, the student department, the finance department, the assets department, the library, the human resource department, the general service department, and the discipline construction department. The effect of data governance depends on all departments of the university, and strategic decision makers, business managers, and business operators are required to specifically promote data governance [6]. Strengthen infrastructure construction. Data governance relies on infrastructure construction. Universities should build advanced campus network infrastructure, put data center construction at the center, and build the first-class information environment. In the construction process, computing resources, storage resources, and network resources are effectively integrated to achieve the maximum sharing and utilization of resources [7]. Strengthen data security management. In the era of big data, data is facing problems such as data leakage, data storage and lack of quality of data security
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manager [8]. It is necessary to strengthen data security management, establish complete and systematic security strategies and measures, conduct comprehensive security management and control, and ensure the security of data assets in various means such as “storage, management, and use” through various links. Follow strict data standards. As the core technical specification, data standards are the link between business and data, and it is also the key link that determines the level of data governance [9]. Data standards are conducive to the aggregation, compatibility and connection of various types of data, ensuring the stability and reliability of data, realizing efficient flow and deep sharing of data, and better improving the level of data governance.
5 Conclusion Big data has become a hot issue studied by experts and scholars at home and abroad, and data governance is an important means for fully excavating and realizing the value of big data. Educational data governance is the responsibility of universities, and unified solutions and governance models are needed to protect and share data at different levels. Educational data governance is also a comprehensive discipline, which can combine various independently operated systems in universities to redefine the value and protection mechanism of data. The research results of this paper solve the core problems of university data governance, can give full play to the value of big data, and improve the scientific nature of decision-making. In the actual application process, it is necessary to combine the latest technology to deepen and expand the research content. Acknowledgements This work was financially supported by Science and technology research project of department of education of Liaoning province in 2019 (LNLG201902): Design and Research of University Data Governance Process System under the Big Data Environment.
References 1. Dan JF (2020) Research on education data governance framework in kentucky in the big data era. Master’s thesis of Southwest university 2. Zhu ZL, Wang LY (2019) Research on university data governance system in big data environment. J Chifeng Univ (Natural Science Edition) 35(5):49–51 3. itpub’ Blog, The importance of ideological and political education in universities. http://blog. itpub.net/69936596/viewspace-2715107/. 20 May 2021 4. Zhou W (2021) Big data-driven decision-making in universities: patterns, problems and optimization strategies. Res Educ Dev 41(9):78–84 5. Beijing Hyatt Management Consulting Co. (2020) Ltd, Six stages of data life cycle. http://www. iso27001.org.cn/fuwu/it/DSMM/show_620.html. 05 Feb 2020 6. Zeng K (2016) Research on big data governance framework. China CIO News 29(11):130–131 7. Peng F (2019) Research on infrastructure construction of University Data Center. Comput Knowledge Technol 15(19)14–15+19
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8. Fang X (2018) Analysis of data security management system in the era of big data. China Comput Commun 12(12):197–198 9. Wu G (2018) On the construction of big data governance system in universities. Educ Rev 34(7):65–68
Platform and Path of University Student Management Informatization Construction in the Big Data Environment Jian Wang
Abstract The management of university students in the big data environment is no longer the repetitive work and procedural process. Data information gradually replaces work experience. It is necessary to deeply explore the hidden information value behind big data to provide the reliable basis for student management. Based on the relevant technologies and principles of big data, this paper constructs the architecture of the university student management information platform in the big data environment composed of “user layer, service layer, application layer, persistence layer and data layer”; it is designed based on Hadoop’s big data storage technology, including NoSQL and NewSQL, solves the difficult data and scenarios of traditional relational databases; proposes the path for the construction of university student management information in the big data environment, to improve the scientific and effective management. Keywords Big data environment · Management informatization · Platform and path · Data storage technology · Frame structure
1 Introduction Student management is the important content of universities’ work, and it is also the basic work to maintain the normal teaching order in school and ensure the healthy growth of students. For a long time, universities have strengthened student management, provided timely and comprehensive education, care and protection to students, and promoted the healthy growth of students. In the era of cultural diversification, universities must recognize the urgency and importance of student management, establish the leadership responsibility system, improve various work systems, and perform in-depth, meticulous and solid management tasks to help students establish correct talent outlook, hardship and optimism outlook, love outlook, and career choice outlook, to develop good behavior habits, healthy and progressive spirit. The J. Wang (B) Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_44
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traditional face-to-face management method, the manager’s data awareness is not strong, and the lack of relevant data collection, arrangement, integration, storage and analysis capabilities, can no longer meet the needs of the information age. The development of mobile information technology and campus construction provide the basic platform for student management informatization. Universities must change management methods and improve the information and scientific level of student management. Big data refers to the large-scale database that can no longer be managed, stored, and analyzed using traditional database software. Compared with traditional data, big data has the characteristics of large capacity, fast speed and rich content, which has changed the ways and means for people to understand the world, and data information has gradually replaced work experience [1]. Through the processing and analysis of big data, we can find relevant links in a variety of data and accurately find valuable information. The massive information resources in the big data era have become irreplaceable core assets, providing the reliable guarantee for the management of college students, and the potential value behind it must be tapped to release huge potential [2]. Big data technology can develop and utilize the information value hidden behind the data, provide the reliable basis for university management, and improve management quality and management efficiency [3]. Universities must combine their own technical level, improve the construction of information infrastructure, strengthen the sharing, integration and analysis of data and information, improve the construction of standardized systems, and provide the good operation and maintenance environment for the data analysis of student management.
2 Frame Structure of University Student Management Informatization Platform in the Big Data Environment In order to improve the maintainability and expansibility of the university student management information platform in the big data environment, the layered architecture is adopted, as shown in Fig. 1. (1)
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User layer. The user layer is the application that presents data to users or processes user input. The users of the university student management information platform include students, teachers, counselors, and administrators. Through the unified interface provided by the user layer, enter the user name and password to enter the system. Each role has different permissions to provide a certain guarantee for data security. Service layer. The service layer is to build browser-based applications and provide enterprise-level information services. There are many software products available in the service layer, which are directly related to Web access performance. Currently the most representative web servers are IIS, Apache and Nginx. Apache currently has the highest market share, open source code, cross-platform and excellent security [4].
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Fig. 1 Frame structure design
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Application layer. The application layer is the software function provided for users, which forwards the user’s specific needs to the application server for completion, and feeds back the results to the user. The software functions provided by the university student management information platform include personal information, daily affairs, fee payment, student loans, club activities, work-study programs, physical and mental health, awards, comprehensive evaluation, and employment tracking. Persistence layer. The persistence is to store objects in a relational database. In order to improve the efficiency of storing data, the persistence layer is specially designed. Hibernate is the lightweight persistence frame. By encapsulating JDBC lightweight objects, it replaces CMP in the J2EE architecture using EJB, and completes persistence operations through object-relational mapping [5]. Data layer. The data layer is responsible for data storage and access. Data storage and access services such as database service, file service, cache service and search service are all deployed on separate server clusters [6]. The advantage of setting up the dedicated service layer is that the data access service is not interrupted, and the data needs to be synchronized and copied when data is written to increase the access speed and prevent data loss.
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3 Data Storage Technology of University Student Management Informatization Platform in the Big Data Environment The data storage system of distributed storage technology has high elastic and can realize the horizontal expansion of database capacity, which has become the main expansibility end of data storage technology in the big data era. One of the current distributed storage technologies is to derive related big data technologies around Hadoop to deal with data and scenarios that are difficult to handle with traditional relational databases, as shown in Fig. 2. SQL is the relational database management system, built around relational algebra and tuple relational calculus. SQL provides the wealth of programming interface tools, provides more options for user program design, system management and database management are simpler, users can easily publish data in the database to Web pages; the main problem of SQL is that it is difficult to expand and performance With the increase of the size of the database and the rapid decline, it cannot meet the number of transactions processed by modern databases per second, so two types of databases, NoSQL and NewSQL, have emerged. NoSQL is mainly used to solve the expansibility problem of SQL. It is built on the distributed system and is easy to expand and fragment. It can provide the more efficient solution than the SQL system without significantly losing stability. NewSQL is a relatively new form, using existing programming languages and previously unavailable technologies, combining the best parts of SQL and NoSQL, combining the ACID guarantee of SQL with the expansibility and high performance of NoSQL. Big data storage technologies based on Hadoop include NoSQL and NewSQL. Both types of distributed storage technologies are built through × 86 services.
Fig. 2 Data storage technology
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NewSQL is mainly oriented to structured data. The database type is relational, which can better support SQL, and has the advantages of good performance and high expansibility [7]. NewSQL technology is the resident memory of the main server module, which belongs to the memory-type distributed storage technology. Compared with NewSQL, NoSQL database technology based on Hadoop is mainly for unstructured data. The advantages of Hadoop data storage technology are distribution and parallelism. Distributed refers to distributing information such as files and data into multiple small parts, and then storing them on the hard disks of different nodes; parallel refers to the parallel implementation of multiple nodes. Read and write operations of information such as files and data. Compared with the traditional data storage data recovery system, the distributed parallel cluster storage system does not need to introduce new disks when recovering data. Data recovery is performed directly in the background, reducing the impact on the application server, and has the advantages of short time consuming and high efficiency.
4 Path of University Student Management Informatization Construction in the Big Data Environment In the big data environment, the construction of university student management information is not only conducive to comprehensively improving the management level, but also conducive to creating the high-quality learning atmosphere, demonstrating the student-oriented mission of universities, and enhancing the effect of teaching and educating [8]. In order to improve the construction level and practical application effects, and effectively solve various existing problems, referring to previous research results, this paper proposes the following paths: (1)
Optimize the awareness of student management in universities. In traditional university student management work, the efficiency of information mining and processing is relatively low, and it is difficult to take timely measures to properly solve the problem [8]. The management of university students in the big data environment is no longer the simplistic and process-based work. It needs to scientifically apply network equipment, obtain rich data information, and use big data analysis to carry out targeted work to make up for the shortcomings of traditional management. Student management personnel must establish correct management awareness, innovate management work models, improve the efficiency of student management work, ensure the main status of students, grasp student dynamics in time, and conduct scientific management based on student characteristics. In-depth understanding of students’ interests and concerns, so that student management work has the higher influence and the larger scope of influence. Establish the sense of service, emphasize emotional management, carry out self-management, and truly realize management education and service education.
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Improve the students’ information literacy. Information literacy should include information awareness, information ability and information ethics. Information awareness is the foundation, information ability is the core, and information ethics is the guarantee. The three form an organic whole [9]. Improving information literacy is the foundation of the reform of the talent training model, the prerequisite for the realization of education informatization, and the focus of education modernization. Information literacy is not only the manifestation of students’ personal learning ability, but also the important symbol of the degree of national modernization. The information literacy of university students includes [10]: clarify information needs and choose reasonable information acquisition methods; use the Internet to search for and identify information; use computers to process information and comprehensively use computer systems; use network information rationally and legally. At present, the phenomenon of university students’ online entertainment is relatively serious, and their ability to search and identify information is relatively weak. Universities need to build a student information literacy training system, attach importance to the training of student information knowledge and skills, increase courses in network and information technology, and carry out a variety of teaching practice activities to improve students’ ability to use network information to analyze and solve problems. Make full use of big data research results. Big data technology has been widely used in many fields of society and has great social application value. Big data consists of the basic elements of structured, semi-structured, and unstructured data. In the face of big data that changes from quantitative to qualitative, innovative algorithms and technologies are required to use identification, collection, cleaning, classification, storage, statistics, analysis, technologies such as visualization, artificial intelligence, and cloud computing can adapt to the huge changes in the amount of data. The management of university students in the big data environment must pay attention to information construction, focus on building big data centers, support digital transformation, deepen big data application research, create the big data research model that combines industry, university, research and government, and integrate big data. Application as the powerful driving force for the management of university students [11]. Specific research results include: data collection technology, the use of web crawler technology to collect and periodically capture all kinds of information; data storage technology, stores the collected data on the medium, which can be stored in the local database or distributed data storage; data processing technology, which processes the collected raw data to prepare for data analysis; data analysis technology [12], improves existing data mining and machine learning technologies, and breaks through object-based data connections and similarity connections and other big data fusion technology; data display technology, provides users with operating pages to achieve interaction with the system. Build the advanced student management information platform. Build the unified, efficient, complete, and real big data management platform to provide
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the basis for decision-making and improve the scientific level of decisionmaking. The construction of the platform shall widely apply big data technology, including data acquisition technology, data storage technology, data processing technology, data analysis technology and data display technology, etc.; it must make full use of the research results of information technology, including mobile communication technology, network transmission technology, data security technology and software development technology, etc. Software development technology is the most critical technology. It follows the ideas and methods of software engineering, and develops based on the multilayer structure in accordance with the process of system investigation, feasibility study, demand analysis, outline design, detailed design, database design, system testing and system maintenance. Realize the loose coupling between all levels, improve the expansibility and maintainability of the system. The information platform needs to achieve personalized services, through big data analysis, to obtain the personalized needs of students, push relevant educational content, and carry out comprehensive personalized guidance. Make effective use of the predictive function of big data to analyze students’ behavior, allocate resources scientifically and rationally, and avoid management confusion. Improve the organization of student informatization work. Organizational structure is the hub for universities to implement management informatization, and the complete organization is an important force for the advancement of school informatization. The construction of student management informatization in universities involves more people, more departments, more equipment, and more compatible software systems. Therefore, it is necessary to improve the organization of student informatization work to achieve unified coordination and comprehensive management. The organization is fully responsible for the overall planning of the informatization construction of student work management, extensively listens to the various opinions of the teachers and students of the school on the informatization construction, and studies the feasibility and advancement of the plan; invites outside experts to conduct research and judgment on related plans, and scientifically analyze major issues decision-making. The organization implements the team leader responsibility system, all tasks are assigned to specific responsible personnel, scientific rules and regulations are formulated, various tasks are regularly assessed, the informationization process of student management work is improved, and the big data analysis and integration capabilities of the student management team are enhanced [13], at any time to solve various problems arising in the process of construction, a variety of measures simultaneously to ensure the smooth progress of information construction.
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5 Conclusion The difficulty of student management in the big data environment has increased. It is no longer repetitive work and procedural processes. Data information has gradually replaced work experience. Full attention must be paid to informatization work, and student information should be registered, sorted, analyzed, and applied. With the help of informatization and big data technology, work can be carried out targeted to free managers from complicated and changeable affairs. Student management is not only the process of planning, coordination and control, but also the process of interaction between teachers and students. As university management moves toward informatization, big data technology is used for student management, fully mining the potential information behind the data, realizing the timeliness, efficiency and high quality of student management, better serving students, and helping students grow and become talents.
References 1. Zhang LL, Xu L (2021) Effective strategies of strengthening college students management under big data era. Heilongjiang Sci 12(1):142–143 2. Shi JF (2021) College student management under the background of big data. J Yanbian Educ Coll 35(1):114–116 3. He H (2021) Exploration on the construction path of university student management informatization under the environment of big data. Sci Technol Vis 11(9):168–169 4. Liu J, Yu (2020) Comparison and selection of mainstream Web server construction technologies. Comput Knowledge Technol 16(15):81–82 5. Tang WJ, Chen LN (2012) Performance optimization scheme research based on the hibernate lasting layer. Intell Comput Appl 2(1):56–58 6. Lonely Fang does not admire himself’ blog, Highly available website architecture: application layer, service layer and data layer. https://blog.csdn.net/en_joker/article/details/100049870. 14 Apr 2021 7. Yang WJ (2021) Research on distributed parallel cluster storage technology for big data. Electron Test 28(9):88–89 8. Wang ST (2017) Research on university student management informatization under the background of big data. Shanxi Agric Econ 35(23):125–125 9. Ma XL, Yang HJ (2021) On the promotion of college students’ information literacy based on collaborative construction theory. J Harbin Univer 42(5):137–140 10. Yu AB (2021) An empirical study and enlightenment of higher vocational students’ information literacy under the background of education information 2. 0. J Hubei Open Vocational Coll 34(8):60–62 11. Wang MM (2021) Information construction and strategies of university student management under the background of big data. Comput Network 47(8):34–35 12. Yin PP (2013) Network public opinion analysis system in the era of big data. Radio TV Broadcast Eng 40(7):44–47 13. He P (2021) The information construction of university student management in the era of big data. Educ Inf Forum 5(3):40–41
Construction of Course Achievement Evaluation System Based on Big Data Analysis Dan Tian and Ke Yang
Abstract The advent of the era of big data has brought a new development opportunity for higher education. Along with the increasingly mature analysis technology, it has further promoted the evaluation and standardization process of education and teaching. In this paper, from the perspective of designing three kinds of supporting relations of curriculum achievement evaluation, the construction scheme of achievement evaluation system is described by taking information courses as an example, and suggestions for continuous improvement of teaching content and methods are put forward. Keywords Course attainment · Big data analysis · Evaluation system · Student centered
1 Introduction In 2016, China officially became the 18th official member of the “Washington Agreement”, marking a big step forward in the opening up of China’s higher education to the outside world. The quality standard of China’s engineering education has achieved international substantial equivalence, and the quality assurance system of engineering education has been internationally recognized [1]. And the reasonable evaluation of curriculum achievement is an important part of guaranteeing the quality of engineering education in colleges and universities. In October 2020, the central committee of the communist party of China and the State Council formally issued by the “master plan for deepening the reform of new times education evaluation” [2], scheme, points out that the education evaluation is a matter of education development direction, to rigorous academic standards, improve the students’ academic schools of different types, the pass of export, perfect the procedure and the results of examination of organic combination of academic evaluation system, Strengthen classroom participation and classroom discipline examination, D. Tian (B) · K. Yang Shenyang Institute of Technology, Fushun, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_45
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guide students to establish a good style of study. We will strengthen professional construction, innovate evaluation tools, and utilize modern information technologies such as artificial intelligence and big data to explore ways to carry out whole-process longitudinal evaluation of students’ learning in all grades and horizontal evaluation of all elements of moral, intellectual, physical, aesthetic, and labor. In March 2021, the Ministry of Education, Ministry of Finance, National Development and Reform Commission made the construction of “double top” effect evaluation method (trial) [3], in the way forward on the basis of discipline, on the basis of the tradition and the development task, subject characteristics and the cross fusion tendency, set up normalized construction detection system, pay attention to check them and the final target to achieve degrees [4]. Colleges and universities have implemented the concept of “student-centered, output-oriented and continuous improvement”, steadily promoted the construction of first-class majors, enhanced the awareness of “quality”, promoted a “quality revolution” in colleges and universities across the country, and built a “quality China” brand of higher education.
2 Meaning of Big Data The term “big data” appeared in 1997, when NASA researchers Michael Cox and David Ellsworth first used the term to describe the data challenge that emerged in the 1990s, namely the huge amount of information data generated by supercomputers [5]. Since then, the concept and technology of big data have been developing continuously, especially with the rapid development of communication and computer industry. It has not only attracted extensive attention from researchers, but also been favored by the government and society. There is no consensus on the concept of big data, but there are two views that can explain the nature of big data. First point, “Big Data exceeds the capabilities of common hardware environments and software tools to collect, manage, and process data for their users in an acceptable amount of time.“ Another point, “big data refers to data sets whose size exceeds the collection, storage, management and analysis capabilities of typical database software tools” [6]. The application of big data analysis in education is reflected in the following aspects: to provide decision support for education; to assist education to achieve differentiated teaching; to provide data support for diversified evaluation; to provide resource integration for intelligent teaching quality monitoring.
3 Achievement Orientation and Achievement Degree Outcome orientation, namely education based on learning outputs, is the core connotation of engineering education certification. It serves the tenet of student-centered engineering education upward and provides a basis for the continuous improvement
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mechanism of engineering education downward [7]. At present, in the engineering education professional certification, various universities adopt curriculum a degree evaluation method to evaluate student learning outcomes [8], however, because of the lack of curriculum to achieve standardization degree evaluation method, course to achieve index evaluation method is inconsistent, too dependent on the final exam, ignore the process of evaluation problem and thus cannot of course completion. Curriculum achievement evaluation is the core link of engineering education certification. It details all kinds of abilities required by professionals and corresponds to the goal of a certain course. Through the examination and evaluation of the whole learning process of the course, it proves whether students’ abilities are achieved with quantitative data and documents. The main process of course achievement evaluation is composed of the following five links [9]: Determine the graduation requirement index points supported by this course; Determine the relationship between the course objectives and the supporting graduation requirements; Determine the course assessment and performance evaluation methods; Calculation and evaluation of course attainment; Curriculum achievement analysis and continuous improvement measures.
4 Construction of Course Achievement Evaluation System 4.1 Construction of Thinking Based on large data analysis of the construction of the information class curriculum degree evaluation system by adopting the combination of the qualitative and quantitative “way, first of all, based on the results orientation as the core, take the student as the center of engineering education professional certification related literature collection and research, combined with different kinds of information talent cultivation scheme analysis, classification and study of related courses, The evaluation index of course achievement evaluation system is preliminarily determined. Secondly, the evaluation indicators are analyzed and distributed to the relevant course teachers to calculate the course achievement and form the course achievement analysis report. Thirdly, the course achievement analysis report is statistically analyzed, and students’ learning in different courses is analyzed and evaluated based on the classification results of different types of courses, and the content and methods of course teaching are adjusted according to the results. This topic needs to deal with the above research process on the basis of the establishment of a scientific and reasonable course achievement evaluation index system can reflect the evaluation of the whole process of students’ learning.
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4.2 Construction of Content Curriculum system is a series of teaching activities designed and organized on the basis of the goal set for training talents [10]. As an important component of the curriculum system, the evaluation of the goal achievement degree is based on the ratio between the goal value achieved and the expectation value as the main evaluation point. In accordance with requirements of the shenyang institute of technology applied talents training goal, actively improve the course to reach a degree of evaluation system, to provide professional talent training plan formulation data support of continuous improvement, improving the quality of talent cultivation, initially formed in “three support” as the main content, to “continuous improvement” as the key point of course a degree evaluation system. As show in Fig. 1. The first support: Curriculum aim, and support between graduation requirements, it is an important part of course support the demand of talent cultivation content, manifests the graduation requirements, curriculum goals, the level of support, this support level is divided into three kinds of high, medium and low, high proportion of corresponding range 30–45%, corresponding proportion in the range of 25–30%, low corresponding proportion 10–25%, There are at least 3 goals for each course. Second kind of support: Process of curriculum evaluation and curriculum goals between support, this is an important part of course assessment support target content, reflected in different stages of the curriculum assessment score, curriculum objectives, support proportion, here in different stages of the course include students after Graduation Goals
Teaching Process
Evaluation of Learning Process
Course Objectives
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Course Completion Analysis
Expected Achievement Ratio
Course Objectives
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Fig. 1 Course achievement evaluation system
Assessment and Evaluation
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learning students in the process of teaching evaluation, teaching evaluation of two main parts, which strengthen the learning evaluation of students in the process of teaching content, And detailed, the “examination as the main body” into “learning process + examination”, will be assessed throughout the whole process of students’ learning. Third support: expect to achieve index and teaching quality continuous improvement between support, which is the main target, results oriented according to the course to reach a degree of analysis (i.e., expect to achieve index) for course construction of continuous improvement, to ensure the teaching quality of ascension and the ascension of student satisfaction, achieve teaching closed-loop management.
4.3 The Supporting Relationship Between Course Objectives and Graduation Requirements Information courses mainly support students’ ability to solve complex engineering problems and improve students’ ability to use practical tools. Defining the supporting relationship between course objectives and graduation requirements mainly includes the following points: (1)
Course objective design
Taking database principle and application course as an example, its teaching content mainly involves database design and the whole process of systematic database design and development using a certain DBMS, and its teaching objectives are designed as follows: Knowledge Objectives: Through the study of this course, students will be able to master the database design mode, decomposition and design, memorize the grammatical structure of SQL statements and database objects, and understand the methods of database management. Ability objectives: Adopt the teaching method of “project-oriented, task-driven”, integrate the teaching process and project development, and enable students to carry out database design, database object management, data operation and database maintenance, etc. Quality objectives: enable students to have good professional ethics, form the consciousness of maintaining database security, and have the spirit of teamwork and collective honor. (2)
Design of graduation requirement index points supported by the course
Taking Database Theory and Application Course as an example. Graduation requirements: Problem analysis: able to use the basic principles of mathematics, natural science and engineering science to identify and model engineering problems in the field of computer, and analyze general engineering problems in this field through literature research, so as to obtain effective conclusions.
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Index point: Able to analyze and compare solutions to general engineering problems in the computer field using scientific principles. Course Teaching Objectives: Teaching Goal 1—High; Teaching Goal 2—middle; Teaching Goal 3—Low.
4.4 The Support Design Between Curriculum Process Evaluation and Curriculum Objectives Objective a degree evaluation is a process of course teaching each link, and the evaluation of the curriculum implementation effect of the overall analysis, according to different teaching goal, the appraisal way to improve a youngster at the same time, the evaluation of each student’s learning effect throughout the teaching process, including the “students’ learning process evaluation and evaluation” the two main parts, The evaluation of students’ learning process includes students’ attendance, students’ homework completion, students’ project completion (or practical operation completion) and other different parts, and the evaluation of students’ assessment includes students’ final assessment (standardized assessment or non-standardized assessment), etc.
4.5 The Support Between the Expected Curriculum Achievement Index and the Continuous Improvement of Teaching Quality The teaching objective corresponds to the graduation requirement index points, and the achievement of which provides data support for the achievement of graduation requirements. Evaluate the achievement of course teaching objectives, analyze students’ learning effects, improve teaching links, teaching content and assessment methods, etc., for continuous improvement, and enhance the role of the course in talent cultivation. The calculation of course achievement degree is based on the expected value of each target point, and the achievement index between it and expectation is formed. According to the index of achievement degree, the realization of each teaching objective is analyzed, and the degree of students’ mastery of a certain knowledge point and the degree of mistakes are obtained. Targeting improvement methods and suggestions are put forward, and measures for continuous improvement are formulated.
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5 Conclusions In engineering accreditation system, curriculum evaluation is the core part of the graduation requirements to achieve degrees, is an effective support for cultivating goal, through the design of the three kinds of support relationship, combined with the course of diversified evaluation method of design and implementation for the process of teaching a degree of analysis and evaluation system, can effectively improve the teaching effect, achieve the construction of first-class courses, We will comprehensively improve the quality of training applied personnel. Acknowledgements This work was supported by LMJX2021100.
References 1. China’s engineering education has achieved international multilateral mutual recognition, China Education Daily (2016) 2. Hu Q (2018) Evaluation of teachers and students based on intelligent educational administration system of digital campus Research on price model construction. East China Normal University 3. Measures for evaluating the effectiveness of “double tops” construction (trial implementation) 4. The general office of the general office of the CPC Central Committee and the general office of the State Council issued the opinions on deepening the reform of the educational supervision system and mechanism in the new era 5. Zhiting Z, Demei S (2013) New paradigm of educational technology research based on big data. Electron Educ Res 10:5–13 6. Fati W, Zhijia M (2014) Construction and implementation path of learner personalization analysis model based on big data in electronic schoolbag. China Video Educ 3:63–71 7. Wenying M, Xiaohui Z, Yue S et al (2017) Evaluation design of course goal achievement degree based on OBE cultivation mode: taking “analog integrated circuit design” course as an example. Educ Teach Forum 21:108–109 8. Jianshu Z, Ruili G (2016) Evaluation reform of curriculum achievement under the background of engineering education certification. Higher Educ Forum 6:72–74 9. Qiang S, Meishu L, Limei W et al (2020) Research on evaluation and continuous improvement method of object-oriented and c++ programming curriculum achievement. Comput Educ 2:156–160 10. Zhao Y, Fu B, Li H et al (2018) Study on evaluation of achievement degree of graduation requirements of curriculum support. J Heilongjiang Inst Technol 1:62–67
Research and Application of Tax Classification Prediction Analysis Method Based on Big Data Technology Chao Pang
Abstract In China, tax is the main source of government revenue, which promotes the harmonious development of the national economy and plays an important role as an economic lever regulator in the process of economic operation. It is mainly reflected in the market economy system. The government can optimize the allocation of resources, adjust the economic structure and adjust the income distribution by collecting taxes. It can be seen that tax revenue is a necessary condition to ensure the healthy and good operation of the market economy and promote the continuous progress of society. Reasonable tax planning is based on scientific tax forecasting methods. Therefore, under the new situation, we must abandon the traditional base method to formulate tax planning, consider various tax modes on the basis of big data, and study a logical, efficient and accurate advanced tax forecasting method as soon as possible. This paper mainly studies the prediction and analysis method of tax categories based on big data technology. This paper puts forward the principle of tax forecast analysis for the prediction of tax revenue, expounds the role of tax forecast methods, and correctly grasps the dynamic changes by reasonably estimating the future tax revenue, which is of great value for the government to issue tax policies and formulate tax plans. In this paper, through data mining technology to understand the main types of tax, and the representative tax system method of this paper is analyzed, the results of the typical business tax, enterprise income tax and individual income tax in recent three years are compared with the actual results of the year, and the feasibility of the representative tax system method is analyzed. The experimental results show that the error of representative tax system is not high in enterprise income tax, and it needs to be improved in personal income tax and business tax. In terms of business tax, the error between actual business tax and predicted business tax in recent three years is about 5.22%; in terms of enterprise income tax, the error between actual business tax and predicted business income tax in recent three years is about 3.64%; in terms of individual income tax, the error between actual personal income tax and predicted personal income tax in recent three years is about 5.93%. C. Pang (B) Faculty of Economics, Yunnan University of Finance and Economics, Kunming 650000, Yunnan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_46
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Keywords Tax revenue · Tax category · Forecast analysis · Big data
1 Introduction At present, tax is not only an important means for the state to participate in the distribution of GDP and carry out macro-control, but also can raise financial funds for the government to provide public goods and services, and can adjust the distribution of interests among different economies [1, 2]. After the reform of tax sharing system in 1994, the average annual tax revenue increased by 17.13%, and has always occupied a dominant position in the total fiscal revenue [3, 4]. Since the establishment of the market economy system, tax reform and tax forecasting have been the focus of the government’s economic work, especially in the current period of national economic transformation [5, 6]. Tax is a complex product in the process of economic development. Accurate data analysis is very important for the scientific decisionmaking of tax departments. Moreover, there are many kinds of tax in China. If we want to make a reasonable and accurate prediction of tax, we have to make a classified prediction for each tax [7, 8]. It is of great significance to study the prediction and analysis method of tax categories based on big data technology, and to gradually expand the research on tax revenue capacity from one tax category to other taxes, so as to comprehensively establish the research system of tax revenue capacity [9, 10]. Since the research of tax forecasting method has been paid attention to in our country, many scholars have studied it and made some achievements. Zhou et al. Pointed out that theoretically, if we can get the tax payable of a certain taxpayer in the micro level and sum up the tax payable of all taxpayers, we can get the tax payable of the whole country. However, due to the lack of tax information of all taxpayers, it is impossible to obtain the national tax capacity by summarizing the tax payable of all taxpayers in the actual estimation, only through the macro data and using the principle of tax revenue capacity estimation [11]. Li Y and others pointed out that at present, the domestic research on tax revenue capacity is still concentrated on a few taxes, even if the taxes involved are only partially calculated for a few tax items, or theoretical discussion on the research model of tax revenue capacity of some taxes is carried out, and no empirical research and quantitative analysis are carried out [12]. This paper mainly studies the prediction and analysis method of tax categories based on big data technology. This paper puts forward the principle of tax forecast and analysis for the prediction of tax revenue, and expounds the principle of calculating by tax categories, the principle of determining the method by data, the principle of the importance of tax categories, and the principle of the direction of tax reform. This paper expounds the role of tax forecasting method, through reasonable estimation of future tax revenue, correctly grasp the dynamic changes, so as to have great value for the government to issue tax policies and formulate tax plans. In this paper, through data mining technology to understand the main types of tax, and the representative tax system method of this paper is analyzed, the results of the typical business tax, enterprise income tax and individual income tax in recent three years are compared
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with the actual results of the year, and the feasibility of the representative tax system method is analyzed.
2 Research on The Prediction and Analysis Method of Tax Classification 2.1 Principles of Tax Forecast Analysis (1)
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The principle of tax classification calculation China’s tax system is a dual main tax system with turnover tax as the main body and income tax as the auxiliary. It also includes resource tax and property tax. Different taxes adopt different tax rates. Even in the same tax category, different tax items may have different tax rates and tax calculation methods. Preferential tax policies are different in many ways, for tax revenue and industry. This determines that when we calculate the tax revenue capacity, we should calculate it according to different taxes and different income items. Moreover, in recent years, although the calculation of tax categories is not perfect, it has been effectively applied and achieved remarkable results in the calculation of tax revenue capacity of some tax categories in China. The principle of data determination The principle of data determination is that after the research of tax revenue ability has entered China, domestic researchers, when using the research results of western scholars, found that domestic and foreign researchers lack the data resources when studying the tax revenue capacity of western countries, Many studies are unable to carry out research because of the data is not good or the accuracy of data cannot be guaranteed. Because our country is still in the primary stage of social and economic development, in the previous years, due to the limitation of technology and policy in data collection and economic statistics, many economic statistics data are missing, and many methods cannot be used. This requires us to study the tax revenue capacity, first of all, we should choose the method and model of calculation according to the possibility, source and quality of data acquisition and the cost of these data collection. In this way, when using the methods of determining tax base, standardizing tax base, standardizing tax rate and representative tax rate, we need to select the data sources of various elements related to the tax revenue capacity. The model selection is determined by the selection of elements and data sources. Therefore, in general, when researching the tax revenue capacity, researchers should choose different models of tax revenue capacity according to the quality and characteristics of data. The principle of the importance of taxes Because different countries and regions have different economic structures and cultural traditions, we should, according to the tax revenue structure,
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according to the proportion of different taxes in the total tax revenue, and in combination with the contribution of the tax in different financial systems, the different degree of its impact on the economy and the level of social concern, Some taxes are selected for detailed analysis, while others are simplified. The principle of importance is also applicable when measuring the tax revenue capacity of different tax purposes under the same tax category. According to the income structure of tax items, researchers can focus on measuring the tax revenue capacity of main tax purposes. In the selection of estimation indicators and other influencing factors, researchers should also evaluate the importance of influencing factors for the measurement of tax revenue capacity, and exclude the secondary factors from the research model. The direction principle of tax system reform In recent years, China’s domestic tax reform is entering a crucial year. In the main direction of tax system reform, it is necessary to calculate the tax revenue capacity of the main taxes involved in the tax system reform. It is necessary to determine the applicable calculation methods and calculate the income capacity of each tax category and the tax effort of each region, so as to provide a theoretical basis for the tax system reform.
2.2 The Role of Tax Forecasting Methods In a narrow sense, tax forecasting is based on the comprehensive consideration of tax revenue influencing factors and statistical data, combined with the relevant theoretical basis to establish a forecasting model, and to judge the future trend of tax revenue. The broad tax forecast is not only the calculation of the quantity, but also the analysis of the characteristics of the tax source or tax base under the current tax system and policies, and infer the tax capacity according to the current tax policy requirements. It is of great value for the government to issue tax policies and formulate tax plans by reasonably estimating the future tax revenue and correctly grasping the dynamic changes. Tax forecasting is the basis of quantitative evaluation of the tax policies issued by the state, and it is also the main content of controlling tax risk and forecasting tax capacity. Tax forecast is not only the basis of the national budget, but also an important basis for the preparation of tax plans and the assessment of the government’s tax plans. At the same time, it is also the premise of the financial analysis at the end of the period and the formulation of the next financial budget. By analyzing the difference between the predicted tax revenue and the actual tax revenue, and combining with the comparison of the third-party information provided by banks, industry and Commerce and other relevant departments, if there is any abnormality, the tracking analysis is carried out to find and solve the problems, so as to improve the level of tax collection and management and improve the tax prediction system. The last function of tax forecasting is to make government affairs open. Open forecasting data and transparent forecasting procedures can effectively reduce risks, enhance the ability
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of government budget, and improve the image of the government in the eyes of the public.
2.3 RTS Forecast Analysis Method Under the current tax system structure, RTS calculates the expected income according to the average tax rate, and then compares the expected income with the actual income to get the tax effort index, and the expected income is the tax revenue capacity. When using the representative tax system method, it is necessary to first determine the representative taxes and calculate the average tax rate of each tax. In this way, the average tax rate of each tax can be estimated by establishing the regression equation of the tax level, tax base and control variables of each tax. In the representative tax system method, the tax rate refers to the standardized tax rate, which is the actual weighted average tax rate of each tax type and the average value of the ratio between the same tax base and the actual tax revenue from this tax base,the general calculation method is as follows: T R= B
(1)
Where T is the sum of all state tax revenues and B is the sum of all state tax bases. According to the application principles and elements of the representative tax system, the tax sources are classified. The tax capacity of a certain tax category has the following linear relationship: Ti = a + ri X i
(2)
Among them, ri is the standardized tax rate and X i is the standardized tax base. The treatment of tax rate and tax base is carried out according to the principle of representative tax system law. When the representative tax system method is used in the calculation of tax categories, it has the following effects on a single tax category or tax item: S Ji j ∗ S L i j (3) f 3 (S Mi j ) = Where S Ji j is the tax base or substitute tax base of the tax type, and S L i j is the statutory tax rate, standard tax rate or substitute tax rate of the tax type.
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3 Experimental Study 3.1 Subjects The research content of this paper is based on the big data technology of tax classification prediction analysis method, the research object is the tax types, as well as the representative tax system method. In this paper, data mining technology is used to study various types of tax, and experiments are carried out to analyze the feasibility of representative tax system.
3.2 Experimental Process Steps This paper mainly studies the prediction and analysis method of tax categories based on big data technology. This paper puts forward the principle of tax forecast analysis and expounds the function of tax forecast method. In this paper, through data mining technology to understand the main types of tax, and the representative tax system method of this paper is analyzed, the results of the typical business tax, enterprise income tax and individual income tax in recent three years are compared with the actual results of the year, and the feasibility of the representative tax system method is analyzed.
4 Experimental Research and Analysis of Tax Classification Prediction Analysis Method 4.1 Analysis of Tax Types To forecast tax revenue, we should first study the law of economic development, the business concepts, scenarios of various taxes and the whole process of tax generation. Different taxes involve different industries, nature and collection methods, and their related economic indicators are also different, so it is necessary to forecast different taxes separately. Therefore, this paper uses big data technology for data mining to collect and sort out the main tax categories. The results of tax categories are shown in Figure 1. From Figure 1, there are various types of tax, among which the most important are three types of tax, namely business tax accounting for 30.87%, personal income tax accounting for 27.59% and enterprise income tax accounting for 10.13%, the rest includes 6.31% urban maintenance and construction fee, 4.26% real estate tax, 2.24% stamp tax, 5.71% land value-added tax, 1.16% vehicle and ship use tax 3.26% of the deed tax and 8.47% of the other taxes.
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Sales Tax corporate income tax 8.47% 3.26% 1.16% 5.71% 2.24% 4.26%
individual income tax Urban maintenance and construction tax Property tax
30.87%
stamp duty
6.31%
Land value added tax
10.13% 27.59%
Vehicle and vessel use tax Deed tax other
Fig. 1 Analysis of tax types
4.2 Analysis of the Representative Tax System In this paper, the representative tax system method is selected to predict the tax according to the analysis method of tax classification. In order to prove the feasibility of the representative tax system method, this paper selects the business tax, enterprise income tax and individual income tax of the last three years for the experimental research. In this paper, the tax situation in 2018, 2019 and 2020 and the factors that need to be calculated are collected and sorted through data mining, and then put into the representative tax system method for prediction, and the results are compared with the actual results of that year. The data results are shown in Table 1. As can be seen from Figure 2, the actual value is slightly different from the predicted value. In terms of business tax, the error between the actual business tax and the predicted business tax in recent three years is about 5.22%. In terms of enterprise income tax, the error between the actual business tax and the predicted business income tax in recent three years is about 3.64%. In terms of personal income Table 1 Analysis of the representative tax system Sales Tax
Corporate income tax
Individual income tax
Actual value
Predictive value
Actual value
Predictive value 332.49
Actual value
Predictive value
2018
1034.78
1098.7
802.12
804.45
333.84
2019
1068.63
1123.88
915.84
863.95
383.52
368.38
2020
1186.12
1135.14
1024.73
973.77
478.12
413.86
C. Pang
type
420
number Fig. 2 Analysis of the representative tax system
tax, the error between the actual business tax and the predicted business tax is about 3.64%, In recent three years, the error between the actual individual income tax and the predicted individual income tax is about 5.93%. The error of representative tax system law in enterprise income tax is not high, and it needs to be improved in personal income tax and business tax.
5 Conclusions China’s tax system is huge and complex, different taxes and different economic indicators have deep-seated inevitable relationship, tax forecasting needs to carry out personalized forecasting analysis for different taxes. In this paper, through data mining technology to understand the main types of tax revenue, and the representative tax system method of this paper is tested, and the feasibility of the representative tax system method is analyzed, which provides a theoretical basis for the research of tax forecasting methods.
References 1. Oh K-W, Sung-Jong, et al (2017) Financial statement comparability as determinant of pre-tax income forecasts and implied corporate tax expenses forecast accuracy. J Tax Stud 17(3):251– 279 2. Eckhouse B (2017) GOP’s “cruel and unusual” tax plan cuts wind forecast in half. Environ Rep 48(44):1698–1699 3. Ye JL (2021) The effects of analysts’ tax expense forecast accuracy on corporate tax avoidance: an international analysis. J Contemp Acc Econ 17(2):100243 4. Mauler LM (2019) The effect of analysts’ disaggregated forecasts on investors and managers: evidence using pre-tax forecasts. Acc Rev 94(3):279–302
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5. Gupta S, Laux RC, Lynch DP (2016) Do firms use tax reserves to meet analysts’ forecasts? evidence from the pre- and post-FIN 48 periods. Contemp Acc Res 33(3):1044–1074 6. Begun JW, Trinh HQ (2019) Determinants of community-related expenses of US tax-exempt hospitals, 2013. J Public Health manag Prac: JPHMP 25(4):1 7. Stefan S, Joerg K (2018) Exploring adoption determinants of tax-subsidized company-leasing bicycles from the perspective of German employers and employees. Transp Res Part A Policy Practice 11(7):238–260 8. Kai SK, Bai S, Tejinder S et al (2019) Advancements and forecasts of electronic tax return and informational filings in the US. Int J Acc Inf Manag 27(4):00–00 9. Park J, Chee S, Shin JE (2016) The effect of corporate tax aggressiveness on analyst’ earnings forecast errors. Korean Manag Rev 45(6):1859 10. Balakrishnan K, Blouin JL, Guay WR (2019) Tax aggressiveness and corporate transparency. Acc Rev 94(1):45–69 11. Zhou D, Liu Z, et al (2018) A method to extract and eliminate TEM interference by metallic bodies in tunnel geological anomaly forecast. Geotech Testing J 41(1):17–30 12. Li Y, You X, Zhao J, et al (2019) Production forecast of a multistage fractured horizontal well by an analytical method in shale gas reservoir. Environ Earth Sci 78(9):1–20
Development of Rural E-Commerce Based on Big Data Analysis Technology Lina Xiao, Can Li, Huiyang Wu, Yalou Yue, and Li Li
Abstract With the rapid development of Internet economy, big data and mobile payment, the application of e-commerce has greatly improved the rural economy, and the development of rural e-commerce has become an important thrust of Rural Revitalization. The construction of smart countryside is imminent. Taking Honghu City in Hubei Province as an example, this paper analyzed the development status of rural e-commerce, investigated the existing problems in the development of rural e-commerce electronic trading market in Hubei Province by means of field investigation and questionnaire survey, and put forward the corresponding countermeasures and suggestions to provide some ideas for the development of Honghu smart countryside e-commerce, and at the same time to promote the healthy and sustainable development of e-commerce in other rural areas. Keywords Rural E-Commerce · Big data · Problems · Development strategies
1 Introduction Agriculture plays a fundamental role in the development of China’s national economy. In recent year, with the rapid development of Internet technology and information technology, more and more agricultural production is combined with ecommerce platform, and the rural economy has greatly improved. Data from the China E-commerce Research Center show that the number of rural e-commerce users increased from 177 to 222 million from 2014 to 2018 and remained stable. The transaction volume of rural e-commerce industry increased from 21.2 to 320.2 billion, and the number of express delivery in rural areas exceeded 15 billion, accounting for more than 20% of the total national express delivery business. There are 4310 Taobao villages and 1118 Taobao towns in the country, showing an explosive growth trend. Thus it can be seen that China’s rural e-commerce economy has entered a stage L. Xiao · C. Li · H. Wu · Y. Yue · L. Li (B) School of Management, Wuhan Donghu University, Wuhan, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_47
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of rapid development from the beginning [1]. With the rapid development of rural e-commerce, the long-term backward situation in rural areas has also been improved. Honghu City is a county-level city in the central and southern part of Hubei Province, which is subordinate to Jingzhou City. It is known as “the land of fish and rice” because of its superior climatic conditions and abundant natural resources. These conditions give Honghu City more advantages in rural development. However, Honghu’s rural e-commerce development is late, the development speed is slow, and there is a big gap with the development of urban e-commerce. Therefore, this paper focuses on the development status and problems of Honghu’s rural e-commerce development, and puts forward research countermeasures.
2 Development Status of Rural E-Commerce 2.1 Situation of the Number and Scale of Rural E-Commerce Enterprises in Honghu City In 2018, more than 300,000 people participated in e-commerce activities (shopping and sales) in Honghu City, and nearly 51% of the people participated in e-commerce activities. In 2018, there were 250,000 people shopping through platforms such as Taobao, with an average of 12 online purchases per person, for a total of about 5 million purchases. As of 2018, Honghu’s e-commerce has been developing steadily. According to statistics, Honghu Taobao.com, Alibaba’s merchants reach more than 580, 176 of which are Taobao stores and operating local specialties, in Alibaba wholesale online there are 22 merchants for production and processing enterprises, about 4800 people for e-commerce activities.
2.2 Situation of Honghu City Rural E-Commerce Infrastructure Construction With the implementation of the “Broadband China” strategy, China’s broadband network has covered most of the rural areas [2]. Honghu Telecom also pays close attention to the construction of telecommunications infrastructure. At the same time, all kinds of rural “Internet+” business development hardware platform has been completed. Honghu Telecom Branch has provided all the required communication network platforms and broadband pipeline facilities to build Honghu into a comprehensive demonstration county in rural areas. Therefore, Honghu city’s e-commerce infrastructure construction is relatively perfect, all regions have the network communication ability, farmers have been able to trade agricultural products through the e-commerce platform.
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2.3 Situation of Agricultural Products Electronic Trading Platform in Honghu City Honghu rural e-commerce relies on Alibaba and other platforms, without establishing its own third-party platform. This paper inquired and counted the relevant materials of Taobao and Alibaba’s Honghu e-commerce merchants, and found that there were about 280 Honghu e-commerce merchants selling agricultural products, but at present there is not a “golden crown” level merchant and 90% of the above merchants belong to Honghu agricultural products processing enterprises. According to the survey, only about 80 (less than 45%) of these merchants operate their stores normally and make profits through e-commerce.
2.4 Situation of Rural Logistics Transport in Honghu City The development of Honghu logistics and express industry shows a trend of rapid growth, which has met the basic logistics and transportation conditions of ecommerce consumption. According to statistics, there are 38 express companies registered in Honghu City. This paper investigated 6 large express delivery companies with more perfect logistics system and more outlets in Honghu City, the survey found that in 2019, six express delivery enterprises in the city completed 100,000 more pieces than last year, an increase of 7%; Delivery volume increased by 6%, an increase of 300,000 pieces; The number of employees increased by 12.5%2 from 800 at the end of 2018 to 900 at the end of 2019. Thus, Honghu express logistics industry development is very rapid (Table 1). Table 1 Situation of Honghu city logistics transportation
2018
2019
Packages received quantity
1.3 million pieces
1.4 million pieces
Packages delivered quantity
4.5 million pieces
4.8 million pieces
The number of employees
800 people
900 people
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3 Existing Problems of Rural E-Commerce Based on Big Data Analysis Technology 3.1 Lack of Rural E-Commerce Talents Through questionnaire analysis, we found that the ability to understand the operation of e-commerce is the most basic ability requirements of e-commerce activities, but the construction of rural technical talents team in Honghu City is still in the initial stage, and there is a lack of talents with such ability. From the perspective of the total labor force, Honghu City has a serious labor outflow phenomenon, and most of them are young people with computer knowledge, they go out to employment it’s bad for Honghu City’s e-commerce economic development. From the perspective of education, the number of rural netizens in Honghu area is relatively small, with low education level, and it is difficult to learn network technology due to the influence of traditional production mode and trading mode, and the number of rural netizens grows slowly.
3.2 High Logistics Costs and Low Distribution Efficiency Honghu City has no railway through, only national roads, provincial roads and waterways to connect the adjacent areas, and the city’s towns and villages only have village roads, traffic inconvenience, and thus high logistics costs. In addition, there are fewer express outlets in each village, and the outlets are far away from the county-level logistics centers, many logistics enterprises have kept a backlog of express deliveries phenomenon, and then transport them to the express outlets in each village after reaching a certain quantity, this leads to the particularly low efficiency of rural logistics distribution, which far exceeds the expected time of customers. Questionnaire survey showed that the villagers of Honghu City received express delivery time for more than 5 days.
3.3 The Degree of Agricultural Product Branding is Low Honghu City lacks national well-known brands, and the questionnaire results show that 60% of people believe that the low degree of agricultural product branding limits the development of rural e-commerce in Honghu City. Nowadays, the annual sales of Honghu featured agricultural products such as crayfish and lotus seeds are higher than those in other regions, but Honghu still has not formed a well-known brand, resulting in poor overall brand effect and lack of competitiveness. Honghu city’s most famous brand is Deyan aquatic products, Deyan aquatic products developed “Deyan aquatic products”, “Deyan lobster”, “Honghu fisherman” and a number of
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well-known international and domestic market crayfish and aquatic products brands [3]. As early as 1998, the company has exported crayfish to more than 30 countries and regions such as the United States, European Union, Japan, South Korea, etc., leading the domestic crayfish industry.
3.4 Electronic Trading Platforms Are not Perfect Honghu City has not yet established a relatively perfect city-level e-commerce platform, unable to quickly sell agricultural products, unable to get agricultural technical guidance and the latest policy trends on agriculture. The existing electronic trading activities of Honghu city are mainly concentrated in some industry websites, such as Honghu aquatic products merchant network, Honghu network, Honghu agricultural information network and so on. Although companies such as Deyan Aquatic Products Co., Ltd and Chenguang Industrial Co., Ltd have established their websites, Honghu City is unable to effectively organize enterprises in the city to carry out various commercial activities due to the lack of a mature electronic trading platform, and cannot give full play to the advantages of e-commerce. In contrast, the government of Suichang County in Zhejiang Province and local enterprises have jointly established an e-commerce platform with local characteristics, which can provide training for e-commerce newcomers and entrepreneurial services for e-commerce workers. As a characteristic mode, rural e-commerce in Suichang County has created a two-way service platform “Sui Internet” and “Ganjie Internet”, which is worthy of reference for Honghu Municipal Government [4].
3.5 Tourism E-Commerce Needs to Be Improved Some tourism companies in Honghu have ignored the potential benefits of ecommerce and failed to combine e-commerce with tourism. The survey shows that Honghu Tourism Investment Group Co., Ltd. has created its own tourism website, but the website only briefly describes some scenic spots, tourist routes and knowledge of Honghu City, and does not provide the introduction of the special agricultural products of Honghu City [5]. Moreover, the tourism website has very simple functions, slow content update speed and lack of interactive sections. According to the tour routes recommended by some companies in Honghu City, the main tourist attractions of the two-day tour are Wulinyuexi Peninsula, Qujiawan Town, Lantian Ecotourism Scenic Area, etc., without mentioning the Honghu characteristic agricultural products tourism project.
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4 Suggestions of the Development of Rural E-Commerce 4.1 Increasing Rural E-Commerce Propaganda, Training and Introducing E-Commerce Talents The development of rural e-commerce trading market cannot be separated from a large number of e-commerce talents. Whether it is the creation of agriculture-related websites or the information update and maintenance of e-commerce websites, there need to be enough e-commerce talents as the foundation. To this end, it is necessary to train a group of new farmers who understand agricultural e-commerce knowledge and computer technology knowledge and to introduce, retain excellent e-commerce businessmen for the development of Honghu e-commerce [6]. On the one hand, we should increase the publicity of rural e-commerce and change the traditional thinking of farmers. Relevant government departments extensively publicize e-commerce through TV, Internet and other channels, and conduct e-commerce training for farmers. On the other hand, the government makes good use of the appeal of the policy and to provide a good working environment, encourage young talents with professional knowledge to return to their hometowns and start businesses. At the same time, the government should encourage colleges and universities to carry out e-commerce related education courses, strengthen the study of basic knowledge of rural e-commerce, and cultivate qualified rural e-commerce talents.
4.2 Building Village-Level E-Commerce Service Stations and Improving the Rural E-Commerce Logistics System The village-level e-commerce service stations in Honghu City are currently facing the problems of small scale and fewer functions. Therefore, the rural areas of Honghu City should upgrade their service stations, expand their service stations, expand their coverage, and expand the functions of the service stations, so as to provide better services for e-commerce enterprises and farmers [7]. The development of Honghu City is inseparable from a sound logistics system. The government should expand the input to the construction of logistics in the rural areas of Honghu City, optimize and enhance the level of rural logistics, and reduce the cost of logistics. Improve the traditional rural logistics system, build a diversified rural logistics system, strengthen the construction of logistics bases, including rural storage, cold chain construction, reduce transportation costs, attract foreign investment; Integrate distribution center, trade center, express enterprises, supply and marketing cooperatives and other resources, improve the construction of the logistics system of Honghu e-commerce, promote the long-term and healthy development of rural areas.
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4.3 Strengthening Brand Building of Agricultural Products and Building Well-Known Local Brands Honghu agricultural products can only form a brand to stand out in the fierce competition in the e-commerce platform. The online sales of Honghu’s lotus seeds, lotus root belt, lotus root and other agricultural products have increased rapidly. The “green mud” lotus root store, known as “King of Honghu Lotus Root”, has an annual sales volume of hundreds of thousands of jin and its products are exported to more than 20 provinces and cities in China, which is well received in the country. “Deyan Aquatic Food Co., Ltd.”, a large agricultural product processing enterprise in China, is a national key leading enterprise in agricultural industrialization with outstanding industrialization. Honghu City wants to develop e-commerce, must play the leading role of these leading enterprises, build independent brand system, comprehensive consideration of the product characteristics of different regions in the city, through reasonable brand construction measures, to create a number of well-known local product brands [8].
4.4 Improving the Electronic Trading Platform System and Strengthening Information Construction Honghu Municipal Government has issued a series of documents about how to promote the development of agricultural products e-commerce, such as Honghu Alibaba rural Taobao project implementation plan, Honghu Municipal People’s Government’s opinions on speeding up the development of e-commerce. These documents have promoted the development of the “Internet+ Rural” economic model from both the policy and legal aspects [9]. On the one hand, Honghu City should continue to issue some preferential policies to encourage agricultural enterprises to carry out e-commerce; At the same time, standardize the e-commerce activities of agricultural products, strengthen the construction and supervision of the electronic trading platform system, safeguard the interests of enterprises, farmers and consumers, focus on the supervision of logistics, marketing, trading and other links. On the other hand, the cooperation between Honghu Municipal Government and Jingdong, Alibaba and other e-commerce platforms is also indispensable, which is conducive to shaping the e-commerce environment, promoting the development of “e-commerce from the mountains to the countryside”, and establishing a perfect e-commerce network system.
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4.5 Promoting the Construction of Tourism Resources and Optimizing the Tourism Model of E-Commerce Honghu tourism enterprises to strengthen contact with agricultural products, while on Honghu tourism resources development in order to strengthen the propaganda work of agricultural product, to do a good job of market research, according to tourists age structure, sex ratio, tourism intention, interests, food taste, ability to pay, etc., design corresponding travel websites. At the same time, tourism enterprises should strengthen the propaganda role of new media, and use Weibo, WeChat, live broadcast and other forms to promote tourism destinations and tourism products, so as to make the communication between the company and the tourism customers more smooth, so that users can more clearly understand the characteristic agricultural products of the tourist destination. Honghu government should vigorously publicize the “revolutionary base in Western Hunan and Hubei”, integrate natural landscape and folk culture, make use of red cultural resources, vigorously develop red tourism and fully tap tourism characteristics. So they can continuously attract more tourists, and create a good tourism experience, so as to bring moving flow and drive the sales of characteristic agricultural products [10].
5 Conclusions Under the strategy of rural revitalization, the development of smart countryside ecommerce in Hubei Province is the key to drive the economic development of rural areas. Taking Honghu City as an example, this paper puts forward the suggestions to develop the e-commerce platform in rural areas of Hubei Province, cultivating ecommerce professionals, improving the rural e-commerce logistics system, building a well-known brand of agricultural products, strengthening the construction of ecommerce information, and optimizing the e-commerce tourism model and so on. Acknowledgements This work was supported by the grants from Hubei Provincial Collaborative Innovation Centre of Agricultural E-Commerce (Wuhan Donghu university research [2019] No. 17 Document).
References 1. Su H, Cui K (2019) Accelerating the improvement of rural e-commerce service system. China Dev Obs 10:37–39+43 (in Chinese) 2. Feng G, Peng J (2020) Application and improvement of mobile digital media technology based on we-chat platform in e-commerce industry chain of agricultural products. Digit Technol Appl 38(7):222–224 (in Chinese)
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3. He X, Li G (2018) Thoughts on e-commerce of county agricultural products to promote targeted poverty alleviation—a case study of Honghu City. Mod Bus 02:57–59 (in Chinese) 4. Yi Y, Yu Y, Peng Y (2019) Promoting the optimized development of rural r-commerce platform—taking Xianning City of Hubei Province as an example. Comput Knowl Technol 15(34):259–261 (in Chinese) 5. Dun B, Liu Q (2019) How to develop and transform the rural characteristic economy in the era of internet plus agriculture—taking Honghu as an example. Technol Econ Guide 27(25):119 (in Chinese) 6. Zhang H (2020) Research on transformation and upgrading of modern logistics supply of agricultural products under the background of smart rural construction. Agric Econ 04:130–132 (in Chinese) 7. Lai Y, Wei G, Zhou H (2019) Construction of smart rural information platform based on logistics perspective. J Qingdao Agric Univ (Soc Sci) 31(03):34–39 (in Chinese) 8. Zhang B, Wang Z (2021) Optimization of e-commerce logistics system based on intelligent collaboration. J Commercial Econ 12:103–106 (in Chinese) 9. Gu J (2021) Big data processing method of e-commerce logistics based on Hadoop from the perspective of precise poverty alleviation. China Storage Transp 06:135–136 (in Chinese) 10. Wen X, Cheng L (2020) Path selection and mode analysis of e-commerce for characteristic agricultural products under the internet perspective. J Commercial Econ 13:89–92 (in Chinese)
Management Analysis of Human Resources Sharing Economy Platform Under Big Data Technology Ling Luo and Xiaohui Zhu
Abstract With the development of my country’s economic science and technology, and with the support of big data technology, the sharing economy model has achieved rapid development. The development of China’s sharing economy has provided society with new economic momentum. At the same time, it has also made a huge contribution to the promotion of popular employment and the utilization of social idle resources. In the future, with the further adaptation and follow-up of policies, the improvement of the compliance level of industry platforms, and the maturity of user popularity awareness, the sharing economy will also usher in a better development environment, but due to the blind expansion of such enterprises, Causing disorderly competition and lack of scientific management methods, there have been some issues that have received widespread attention from society. This article analyzes and discusses the problems and solutions faced by such emerging enterprises and new economic development models from the perspective of human resources, so as to promote the orderly development of social economy. Keywords Big data · Sharing economy · Human resource management
1 Introduction With the development of my country’s reform and opening for more than 40 years, social productivity has been greatly liberated and developed, information technology has been continuously improved, and the application level of Internet technology and computer technology has been continuously improved in recent years, which has promoted the development and progress of big data technology. This led to the start of the sharing economy. Since 2015, China’s sharing economy has begun to explode, and various sharing platforms have sprung up. The scale continues to expand, but problems such as brutal growth, fierce competition, industry irregularities, imperfect management methods, and short development cycles have emerged one after another, L. Luo · X. Zhu (B) Department of Management, Wuhan Donghu University, Wuhan, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_48
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which has led to a series of unpredictable problems in the development of the sharing economy [1]. This article mainly analyzes and discusses the problems existing in the development of the sharing economy from the perspective of human resource management.
2 Overview of Big Data and Sharing Economy With the rapid development of the Internet, the application of big data technology, the popularization of third-party payment and the reduction of cost, various sharing network platforms have been developed rapidly. From online creative design and marketing planning to catering and accommodation, logistics and express, capital lending, transportation, life services, health care, knowledge and skills, from consumption to production, sharing economy has penetrated into almost all fields [2].
2.1 Big Data Technology With the rapid development of economy and society, the continuous progress of science and technology, the frequency of communication between people is higher and higher, and the data information generated by human society is growing geometrically, resulting in the concept of big data [3]. Big data mainly includes a large number of structured and semi-structured data, which are usually stored in a complete database, and the calculation of these data often takes a long time and more money. Therefore, big data technology is often associated with cloud computing technology. Big data contains great value and is an important resource pool. It has become a breakthrough in the reform and development of all walks of life and has a greater role in promoting the development of human society.
2.2 The Concept of Sharing Economy In 1978, Marcus Felson, a professor of sociology at Texas State University, and Joe L. Spaeth, a professor of sociology at the University of Illinois, first formally proposed the concept of sharing economy. However, the real sharing economy is the sharing economy model formally proposed by Rachel botsman and Lu Rogers in the United States. It is also called collaborative consumption. In the “proposal of the Communist Party of China on formulating the 13th five year plan for national economic and social development” in 2015, China formally put forward the strategy of developing “sharing economy”. In the “2016 China sharing economy development report”, the concept of “sharing economy” is clearly put forward, that is to say, sharing economy
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Table 1 Development of China’s sharing economy from 2017 to 2020 Sector
Share economy market transaction volume (100 million yuan) 2017
2018
2019
2020
2020 Year-on-year growth (%)
Transportation
2010
2478
2700
2276
−15.7
Shared accommodation
120
165
225
158
−29.8
Knowledge and skills
1382
2353
3063
4010
30.9
Living services
12,924
15,894
17,300
16,175
−6.5
Shared healthcare
56
88
108
138
27.8
Shared office
110
206
227
168
−26.0
Production capacity
4170
8236
9205
10,848
17.8
Total
20,772
29,420
32,828
33,773
2.9
Data source National Information Center Sharing Economy Research Center
refers to the use of Internet and other modern information technology to integrate and share a large number of scattered idle resources. The sum of economic activities to meet different needs [4] (Table 1). From the above data, we can see that under the impact of the epidemic, 5 g, artificial intelligence, big data, Internet of things and other technologies have been more widely used, promoting the online and offline integration, accelerating the development of new formats and new models of shared services and consumption, and becoming an important force to enhance economic resilience and vitality.
3 Characteristics of Enterprise Human Resource Management in the Environment of Big Data and Sharing Economy Under the environment of big data and sharing economy, enterprise human resource management presents three characteristics: (1) the personnel allocation is open and mobile; (2) the semi contractual relationship is not restrictive; (3) the number of participants is large and the management is difficult.
3.1 Staffing is Open and Mobile In the mode of big data and sharing economy, enterprise human resource management not only aims at the operation and management personnel of sharing economy platform, but also aims at the resource providers who share idle resources [5] (Table 2).
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Table 2 Working hours distribution of drivers on different car hailing platforms Online car-hailing platform
Driver’s working hours Less than 4 h
4–8 h
8–12 h
12–16 h
16–20 h
Didi travel platform (%)
1.27
12.66
46.84
34.18
5.06
Other travel platforms (%)
3.53
18.24
50
24.12
4.12
Data source School of Social Sciences, Tsinghua University
From the above data, we can see that online ride-hailing drivers work longer hours and are under greater pressure. Middle-aged men are the main drivers of online ridehailing, but most of them have been in the industry for less than 3 years. Many novice drivers often feel the huge gap and choose to leave the car-hailing industry. Therefore, the entire online car-hailing industry is highly mobile.
3.2 The Semi Contractual Relationship is Less Restrictive In the mode of big data and sharing economy, idle resource providers and sharing economy platform are not labor relations in the traditional sense, but semi contractual or semi subordinate relations. The sharing economy platform is responsible for providing resource utilization opportunities for resource providers and performing the responsibilities of platform maintenance and interest protection, while the resource providers need to perform the responsibilities of following the platform rules and maintaining the image of the company and the platform. This contractual relationship leads to the poor constraint of the platform to the resource providers.
3.3 The Number of Participants is Large and the Management is Difficult Compared with the traditional enterprise organizations and units, the number of managers of the sharing economy platform is very small, but there is no upper limit for the number of people participating in the sharing economy platform [6]. Some large sharing economy platforms even have tens of millions of human resources suppliers, which makes it difficult to manage the human resources of enterprises in the sharing economy environment.
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4 The Influence of Big Data and Sharing Economy on Enterprise Human Resource Management The impact of big data and sharing economy on enterprise human resource management is as follows: (1) breakthrough of thinking cognition; (2) improvement of performance management mode; (3) innovation and breakthrough of management mode.
4.1 The Breakthrough of Thinking Cognition The traditional human resource management regards the employees as the appendages of the company. The employees must obey the arrangement of the leadership. The power of the enterprise is concentrated in the hands of a few people. The enterprise leaders do not know how to respect the employees and share. The enterprise human resource management emphasizes the humanized and rational team management. Improve employees’ right to know and voice in the organization, and establish a more collaborative, lasting and stable partnership. Fully release the innovation and creativity of organization members.
4.2 The Improvement of Performance Management The commonly used performance appraisal methods of enterprises in traditional economy include KPI key performance appraisal, BSC balanced score method, OKR target and key achievement method, 360° appraisal method. Enterprises in sharing economy mode change and innovate these appraisal methods to form their own performance appraisal methods, such as airbnb’s mutual evaluation mechanism and the company’s diversity evaluation system [7].
4.3 Innovation and Breakthrough of Management Mode Traditional human resource management mainly introduces and manages all-round talents from six major sectors [8]. In shared enterprises, resource providers and enterprises and platforms are simply contractual relations, not labor relations in the traditional sense. However, resource providers themselves and their service quality are important manifestations of corporate image, How to realize the management of resource providers through innovation and breakthrough of management methods is the future development direction of human resource management in sharing enterprises.
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5 Measures to Improve Human Resource Management System Under Big Data and Sharing Economy To continuously improve the human resource management system under the big data and sharing economy, we need to improve the recruitment and selection system, strengthen the training guidance, improve the performance management system, and improve the handling of employee relations.
5.1 The Breakthrough of Thinking Cognition In order to reduce the occurrence of safety accidents, travel companies must screen and eliminate the platform participants. In addition to the three certificate certification and information audit of drivers, they should also increase the qualification certification link composed of work ability, driving years and personnel quality screening. Accidents about riding or driving didi travel sharing car have occurred from time to time across the country. The reason is that some platform participants are included in the credit blacklist by banks, or have criminal records in public security organs, or have illegal records in transportation departments [9]. Because of the low entry threshold of didi travel company, this kind of personnel can smoothly enter the didi travel sharing platform, resulting in a mixed situation. In this regard, Didi travel company can effectively connect with the financial credit reporting represented by the bank credit reporting center, the criminal record and illegal behavior credit reporting represented by the public security organ, and other administrative departments, carry out social background investigation on the platform participants, exclude such personnel from the service scope, and no longer provide them with platforms or services.
5.2 Strengthen Training Guidance Since the travel company has designed many types of taxi services, the special train, express train and free ride can be trained by category. After the rectification plan was put forward, the travel company has taken different measures for the resource providers of these three types of services. For the free ride with frequent safety incidents, the travel company has taken the nationwide indefinite off-line training for the special train drivers, It includes software operation, service standard training, service standard participation, Star Award and income composition explanation. However, for the express resource providers whose platform accounts for 80% of the total, there is no perfect supporting measures, only non mandatory on-line training is launched [10]. Therefore, travel companies should also strengthen the training for
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express resource providers, Set up special training system, improve and perfect the corresponding training system. Travel companies can set a safety score coefficient range, and set the system to automatically issue system reminder messages to those participants who are about to be lower than the lowest score coefficient, so as to regulate the behavior of both parties, If the credit score of both users is lower than the minimum credit score, they will be informed through the network platform that they must participate in relevant training at their own expense and pass the training assessment before they can continue to use the platform.
5.3 Improve the Performance Management System Improving the performance management system is mainly reflected in two aspects: “first, it no longer only depends on the credit score to eliminate inferior service providers, but also speeds up the pace of survival of the fittest by improving the performance communication link, so as to gradually improve the performance communication system. Second, it is clear that the income is directly linked to the assessment.” Travel companies can implement priority and high-quality scheduling for personnel participating in performance appraisal through performance appraisal system. It can also be combined with the company’s salary incentive mechanism to give material and spiritual incentives to those who are satisfied with the results of performance appraisal. For example, the official certification of high-quality participants (high-quality drivers, civilized passengers) can be given to the travel company, so that these people can get higher income through the travel company platform, which can also improve their loyalty to the company and the platform. In addition, from the perspective of the company, such groups can better create a good corporate image and create higher value for the company.
References 1. Geissinger A, Laurell C, Öberg C (2021) Copycats among underdogs—echoing the sharing economy business model. Ind Mark Manage 24(4):96–98 2. Zhang Y, Xu S, Zhang L, Yang M (2021) Big data and human resource management research: an integrative review and new directions for future research. J Bus Res 133–135 3. Lei L (2021) Research on the characteristics of semi-contractual human resource management under the background of sharing economy: a case study of Airbnb. Int J Soc Sci Educ Res 4(5):78–83 4. Lei G (2021) Research on the innovation of enterprise human resource management mode in the era of big data. Front Econ Manage 2(5):121–122 5. Zeng J (2021) Application of Big Data Processing Technology in Human Resource Management Information System. J Phys: Conf Ser 1881(3):142–144
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6. Hung Yi T (2021) Research on the application of big data in enterprise human resource management. J Phys: Conf Ser 1744(3):112–115 7. Sun X (2021) Explore the management mode of human resources under the C2C sharing economy—take the case of Didi Chuxing. Acad J Bus Manage 3(1):99–104 8. Yuan T, Honglei Z, Xiao X, Ge W, Cao X (2021) Measuring perceived risk in sharing economy: a classical test theory and item response theory approach. Int J Hospitality Manage 24(6):96 9. Jessica B, Wang Y, Li S (2020) Big data governance and algorithmic management in sharing economy platforms: a case of ridesharing in emerging markets. Technol Forecast Soc Chang 36(8):161–165 10. Liao Y (2020) Research on human resource management innovation of small and medium-sized enterprises under the background of sharing economy. World Sci Res J 6(12):123–125
Smart City Medical Resource Allocation System Based on Big Data Xiaomu Yu and Xueqing Shi
Abstract To solve the problem of unbalanced allocation of medical resources in smart city, using big data technology to design a smart city medical resource allocation system. The system utilizes the Internet of Things to collect massive medical resource data, fully integrates data using multiple information technologies, provides and displays data analysis results. The system realizes the effective classification, management and reasonable allocation of medical resources in smart city and provides strong data support for the construction of smart city. Keywords Smart city · Big data · Medical resource · Allocation system · System design
1 Introduction With the rapid development and practical application of new-generation information technologies such as the Internet of Things, cloud computing, big data, 5G and artificial intelligence, informatization has gradually become intelligent [1]. The rapid development of network digital information technology will serve the intelligent construction of city better. The concept of smart city came into being, it is a new sustainable urban system based on geospatial data [2, 3]. As an important part of smart city, smart medicine is a medical proprietary concept that has emerged in recent years. It is composed of three parts: a smart hospital system, a regional health system and a family health system [4]. Smart medicine is built on the Internet of Things, gives full play to the advantages of the Internet of Things, establishes a regional medical information platform for health files, turns the interaction between patients, medical equipment, medical staff, and medical institutions into reality. Through the cooperation of big data, cloud computing, Internet of Things and other technologies, we can truly be patient-centered, keep medical staff, X. Yu (B) · X. Shi School of Health Care Technology, Dalian Neusoft University of Information, Dalian, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_49
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medical institutions, medical equipment and patients in close contact and realize the informatization and intelligent construction of medical services [5]. When smart medicine is combined with big data technology, the massiveness, diversity and reliability of big data will become one of the important starting points to promote the development of our country’s medical and health industry. Smart medicine and medical health will directly benefit from big data technology and will be the deeply integrated with big data technology. More application scenarios for smart medicine and smart health will be discovered, for example, high-definition video remote consultation, remote surgery, emergency rescue, AI-assisted diagnosis, remote medical teaching and so on, it brings new opportunities for the development of smart medicine [6]. The new crown epidemic that broke out in early 2020 also played a role in promoting the application and popularization of big data. Big data combined with 5G, AR/VR and other technologies helped medical institutions in various regions to fight the epidemic [7]. However, with the rapid development of science and technology in the medical field, while smart medicine has become a hot topic, problems related to the unreasonable allocation of medical resources in cities have gradually become prominent. At present, the regional distribution of high-quality medical resources in our country is uneven, patients are usually willing to choose tertiary grade A hospitals or specialist hospitals in large cities for treatment, resulting in overcrowded large hospitals and relatively vacant hospitals in small and medium-sized cities. Moreover, the problem has gradually spread from large cities to small and medium-sized cities. To improve the uneven allocation of medical resources, the government has also increased its investment. However, due to the increasing demand for medical resources, the problem of irrational allocation of medical resources still exists, which has become an urgent problem to be solved, and it has also become a core challenge for smart medicine and smart city construction [8]. To rationally allocate urban medical resources, including the construction of urban health service systems and medical infrastructure, construct a safe, convenient and efficient urban medical system. This paper uses big data technology to design a smart city medical resource allocation system.
2 Design Concept of Smart City Medical Resource Allocation System Big data can show the dynamic changes of urban medical resources, and it can also reflect the diversity of urban medical resource information. Big data technology will fully collect, mine, analyze and process fragmented and one-sided massive data, obtain high-value information resources from it and provide data support for the rational allocation of medical resources in smart city. However, if we rely solely on big data technology, it is still impossible to realize the real-time transmission and sharing of information resources. Therefore, while applying big data technology for
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information collection and exploration, it is also necessary to rely on the support of network technology and communication technology for data transmission and sharing, etc., through the collaborative application of multiple technologies to build a smart city medical resource allocation system. The system realize the data collection, storage, transmission, mining, configuration, confidentiality, resource sharing, etc. of medical resources [9], so that the data obtained from the system can provide support for the formulation of decisions related to the reasonable allocation of medical resources.
3 Architecture Design of Medical Resource Allocation System Through the Internet of Things technology, sensors and other equipment are placed in designated locations of medical institutions in each administrative area of the city, and the formed sensor network is used to build a medical resource information platform based on the data of various institutions. It provides real-time and accurate frontier information for rational allocation of medical services and infrastructure construction of medical institutions. The cloud computing platform is used to compress, transmit, store, process massive data and form classified data, so that the massive information data collected by the Internet of Things can be fully and effectively used. So that the medical resource allocation system can complete data analysis and rational allocate resources to better serve the construction of smart medicine.
3.1 Information Collection Part The information collection part is composed of sensing terminals related to the Internet of Things, including sensor networks, radio frequency identification, cameras, global positioning systems, readers and other sensing terminals. The function of the terminal is to collect and identify relevant basic medical information data, including outpatient and emergency personnel information, inpatient information, doctor information, medical institution information, etc.
3.2 Data Processing Part The data processing part is composed of communication network, internet, internet of things and cloud computing platform. The data mainly includes (1) Basic data of medical resources: Including the number of doctors in each medical institution, the scale of the building, the setting of departments and the number of open beds,
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the geographic location of the medical institution and the surrounding population data, etc.; (2) Medical business data: Including the number of outpatient and emergency visits of medical institutions, information on patients, hospitalization days and turnover, medical expenses and medical insurance related information [10, 11]. Among them, the change frequency of the basic data of medical resources is relatively low, while the medical business data is generated by expanding the basic medical data according to business needs, and the update speed is relatively fast. The data processing part is to fully integrate the collected basic medical data and medical business data, then check, clean and store the data, finally integrate it with the basic geospatial data of city and provide it to the application part for later intelligent analysis. Therefore, it can provide relevant evidence data on the allocation of medical resources. In addition, this part also has the function of transmitting information.
3.3 Application Part The application part is the goal of the medical resource allocation system. This part integrates, excavates, intelligently analyzes various aspects of medical resource information [12], and visualize the results with spatial data. It provides big data services to the government, the public, patients, and provides assistance for the rational allocation of urban medical resources and decision-making. Take smart medical treatment as an example, before going to the doctor, the patient can use big data analysis and recommendation functions to understand the configuration of medical institutions around the place of departure, including the size of medical institutions, the current flow of people, the types of diseases that medical institutions are good at, analyze the possible waiting time for medical treatment, plan the recommended medical institution, the travel route based on the condition of the patient’s disease and location data. At the same time, the application part has the functions of centralized management of data approval, operation and release. It is necessary to complete the data catalog extraction and management while ensuring the safe and stable operation of the system.
4 Function Module Design of Medical Resource Allocation System The functions of the smart city medical resource allocation system based on big data technology mainly include the following three aspects: (1) Urban medical resource data transmission and integration function, which can realize the exchange, integration, storage and transmission of massive urban medical resource data; (2) Effective classification, sorting and management of urban medical resource data; (3) Provide
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strong data support for realizing the rational allocation of urban medical resources and the construction of smart city.
4.1 Data Transmission and Integration Functions Data transmission and integration functions refer to the integration of structured and unstructured data related to urban medical resources through technical means such as data processing, comparison, cleaning, and integration, while completing the datato-information conversion, the dynamic configuration of different types of medical resource information is realized [13]. The function of data integration is mainly to process, integrate and correlate existing data such as urban geospatial databases and urban population databases, provide a more targeted and valuable data set for the subsequent allocation of urban medical resources. Data cleaning is to filter and clean the incomplete, error-containing and repeated data according to specific rules, perform data association according to the correlation and causality, develop targeted data integration and processing for different needs, mine the hidden information contained in the original data, build a new data set and provide it to the system.
4.2 Data Sorting and Management Functions Data sorting and management functions provide various resource configuration interfaces and services for the entire system, consists of interface services, data services, and thematic data mining services. Interface services include three types of interface services: basic, resource, and management. Mainly responsible for providing system application developers with the authority to call city medical resources data, integrating system services and applications. The work content of the data service is to use the interface to provide basic urban data for the allocation of smart city medical resources; thematic data mining is to provide specialized and targeted data analysis services for a certain field or a certain demand.
4.3 Data Display Function The interactive display function of the system is to display the collected and analyzed information resources and perform system operation and maintenance. It can provide services for patients and various medical institutions according to different needs [14]. The system data interactive display is the default display carrier of the system terminal sub-items. An interactive terminal can display different content, including static display and dynamic display. It can also be switched at any time by using
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somatosensory interactive action trigger mode. The effective integration of system interaction and display terminals can enhance the freshness of user’s operations and enhance user’s experience. At the same time, the interactive terminal has a built-in physiological information feature recognition function, which can provide intelligent recommendations based on the external physiological characteristics of the interactor and enhance the intelligence of the system’s interactive display.
5 Medical Resource Allocation System Testing Before the allocation system is used, various functions of the system need to be tested to ensure the smooth application of the system in the future. The test includes the integrated interaction between the functional modules, the performance of each part, the comprehensive performance of the system, etc., and needs to cover all the functional modules and key processes of the system.
6 Conclusions Smart city evolved from the Internet and are currently the highest stage of urban informatization. This paper designs a smart city medical resource allocation system based on big data technology, effectively integrates the Internet of Things and big data technology into the system. The system has a comprehensive, in-depth and thorough perception of the city’s medical institutions, it makes people’s medical treatment more convenient and efficient. The system reasonable, effective and correct allocation of urban medical resources, it can solve the problem of uneven distribution of medical resources to a certain extent, while completing the interconnection and intercommunication of urban medical information, realize the construction of a smart city with a virtuous circle of sustainable development.
References 1. Luo J (2021) Design of smart city public resource allocation system based on big data technology. Mod Electron Tech 44(2):122–126 (in Chinese) 2. Ferreira CMS, Garrocho CTB, Oliveira RAR, Silva JS, Cavalcanti CFMDC (2021) IoT registration and authentication in smart city applications with blockchain. Sensors (Basel) 21(4):1323 3. Kolesnichenko O, Mazelis L, Sotnik A, Yakovleva D, Amelkin S, Grigorevsky I, Kolesnichenko Y (2021) Sociological modeling of smart city with the implementation of UN sustainable development goals. Sustain Sci 3:1–19 4. Ge M (2019) Analysis and countermeasure research of global intelligent medical development. Chin Hosp Manage 39(4):43–45 (in Chinese)
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5. Muse ED, Barrett PM, Steinhubl SR, Topol EJ (2017) Towards a smart medical home. Lancet 389(10067):358 6. Price WN, Cohen IG (2019) Privacy in the age of medical big data. Nat Med 25(1):37–43 7. Alsunaidi SJ, Almuhaideb AM, Ibrahim NM, Shaikh FS, Alqudaihi KS, Alhaidari FA, Khan IU, Aslam N, Alshahrani MS (2021) Applications of big data analytics to control COVID-19 pandemic. Sensors (Basel) 21(7):2282 8. Yi M, Peng J, Zhang L, Zhang Y (2020) Is the allocation of medical and health resources effective? Characteristic facts from regional heterogeneity in China. Int J Equity Health 19(1):89 9. Li D (2019) 5G and intelligence medicine-how the next generation of wireless technology will reconstruct healthcare? Precis Clin Med 2(4):205–208 10. Yan X (2018) Government input and static and dynamic operational efficiency of health institutions in different regions: an empirical study based on the DEA-Tobit methodology. Nankai Econ Stud 6:95–113 (in Chinese) 11. Varabyova Y, Müller JM (2016) The efficiency of health care production in OECD countries: a systematic review and meta-analysis of cross-country comparisons. Health Policy (Amsterdam, Neth) 120(3):252–263 12. Liu C, Wang Y (2014) Research on medical data mining and its applications. J Biomed Eng 31(5):1182–1186 (in Chinese) 13. Wang F (2019) Exploration on optimal allocation of medical resources under hierarchical diagnosis system: based on medical big data information sharing mechanism. Chin Health Econ 38(8):45–47 (in Chinese) 14. Raidou RG (2019) Visual analytics for the representation, exploration, and analysis of highdimensional, multi-faceted medical data. Adv Exp Med Biol 1138:137–162
Hardware Design of Electronic Components Remote Test Adapter Based on Information Processing Xiran Liu
Abstract With the in-depth integration of a new generation of information technology and electronic manufacturing, electronic components are developing in the direction of miniaturization, and the difficulty of quality inspection has greatly increased. Aiming at the shape defects of electronic components, research the remote testing technology of electronic components based on information processing, design the test system adapter system, and realize the automatic detection of electronic components. The purpose of this paper is to study the hardware design of remote test adapters for electronic components based on information processing. This article first expounds the goal of adapter hardware design, explains the structure design of the adapter, related modules, and introduces the tools used in the development process. This article mainly examines the hardware of the power adapter from the test experiments of adapter adaptation function and adapter resistance operation. Experimental results show that the effective rate of the adapter hardware is as high as 99.8%, and has been maintained above 98.6%. Therefore, the adapter hardware in this study is qualified and meets the design standards. Keywords Electronic components · Remote testing · Adapter hardware · Quality testing
1 Introduction With the rapid development of information technology, the electronics industry has become the fastest growing industry in recent years [1, 2]. The huge demand for electronic products has driven the strong growth of the electronics industry. Electronic components are developing in the direction of portability, and the requirements for quality inspection and the difficulty of the process are gradually increasing [3, 4]. It is of great significance to establish a remote testing system for electronic components X. Liu (B) Changchun College of Electronic Technology, Changchun 130114, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_50
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based on information processing, and the design of adapter hardware can provide technical support for the system [5, 6]. Many scholars have discussed the inspection of electronic components and adapters. For example, Cai pointed out that the shape defects of the rubber pins on the aerospace electronic connectors will cause the electronic connectors to fail and affect the operation of the spacecraft [7]; Xu et al. analyzes the characteristics of different defects of chip capacitors, designs corresponding inspection procedures, detects black spots and scratches through Blob analysis, Liu et al. detects angular defects by separating the ceramic body and the electrode part [8]; emphasize the need to pay attention to the shape defects and shape quality defects of electronic components, otherwise it will significantly affect the electronic components [9]. The purpose of this paper is to study the hardware design of remote test adapters for electronic components based on information processing. This article first expounds the goal of adapter hardware design, and explains the structure design and related modules of the adapter. Then introduce the tools used in the development process. Finally, the hardware of the power adapter was tested mainly from the test experiments of adapter adaption function and adapter resistance operation.
2 Hardware Design of Electronic Component Remote Test Adapter Based on Information Processing 2.1 Adapter Hardware Design Goals Through the design of the electronic component remote test system, the following goals need to be achieved: 1.
2.
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The vertical expansion capability of the adapter. Vertical expansion means that the adapter can quickly support the new electronic component remote test system, without a lot of repeated development work, and only need to modify the functional modules of the new system to distinguish it from other traditional systems [10, 11]; Horizontal expansion design of the adapter. Horizontal expansion capability refers to the ability of the adapter to easily support new functional modules to expand the functions of the adapter. The adapter integrates the various modules of the dynamic unit test. If you need to add new functional modules in the dynamic unit test, such as the debugging module to be implemented in this article, the adapter can easily support it, and there cannot be a large number of existing codes; In terms of efficiency, the introduction of adapters cannot lead to a reduction in the efficiency of unit testing. First, compared with the design without an adapter, the unit test efficiency of the adapter cannot be reduced; secondly, as the number of remote test systems supporting electronic components increases, the efficiency of the adapter cannot be reduced accordingly;
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In terms of user experience, the adapter needs to have a good user experience. The adapter is tested as a unit A module in the process needs to provide a good interface to interact with other modules. At the same time, as the main module for realizing the dynamic unit test function, the adapter needs to provide users with good configuration items to conveniently configure the internal functions of the adapter; The adapter needs to have a good error capture mechanism and error statistics ability. A good error capture mechanism can facilitate developers and testers to quickly locate errors and improve the efficiency of development and testing; The structure of the dynamic unit test module related to the remote test system of electronic components is rationalized to facilitate the expansion of the new system support [12].
2.2 Structural Design of the Adapter 1.
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Interface layer The interface layer is responsible for managing the interfaces provided by the adapter to external modules, and implements functions including interacting with users, accepting and verifying user input, and calling the access layer to implement adaptation. Considering the functional modules that the adapter needs to adapt, the interface layer is responsible for unifying the external interfaces of these modules. The unification of the interfaces can reduce the interaction complexity between the various functional modules inside the adapter and the external modules under different electronic component remote test systems. The interface layer encapsulates the changes in the bottom layer of the adapter, provides constant interfaces to external modules, and serves as a bridge between the adapter and other modules. Data layer The data layer is responsible for managing all data members that need to be used in the internal function modules of the adapter. The unified management of data enables the internal functional modules of the adapter to be independent of other modules outside the adapter after the adapter is initialized, reducing coupling and increasing the maintainability and portability of the adapter. The data layer is the basis for decoupling each module inside the adapter from the external module. Access layer The access layer implements the main logic of the adapter’s adaptation system. The internal contains various functional modules that need to be adapted, including the specific implementation of interfaces such as preprocessing, cross-compilation, linking, execution, and result capture. The access layer is the main change of the adapter and the layer most closely related to the bottom layer.
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2.3 Design of the Internal Module of the Adapter Hardware The access layer is the main change in the hierarchical design of the adapter, and it is the main realization part of the remote test system for electronic components. Among them, the access layer continues to subdivide sub-modules. These sub-modules are modules closely related to the system platform in the dynamic unit test. This article calls them the internal modules of the adapter. The internal modules of the adapter are mainly divided into several modules such as cross-compilation, linking, execution, and result capture. If the program needs to be pre-processed during processing, the pre-processing module is also included. In the process of dynamic unit testing, the unit under test will be compiled, linked, executed, and result captured in order, and then the captured results will be handed over to the result analysis module to obtain coverage information of the tested unit. The internal module of the adapter needs to be adjusted after the introduction of the remote test system for electronic components. This article uses two ways to adapt the internal module of the adapter, which is based on the general template scheme design and the scheme design based on the strategy mode.
2.4 Design of Debug Module In the process of dynamic unit testing, the execution of the tested program is abnormal, such as the most common segfault, and the execution of the tested unit is terminated. To analyze the cause of the abnormality of the program caused by the test case, it is necessary to analyze the source code of the tested unit and the test case. In this case, the test case will not be saved, so the time to find an exception during the execution period can only be after the error occurs and before the next execution. In the process of automated unit testing, once an exception is found during the execution period, the automated testing process needs to be paused at a designated location, the debugging module is called, the cause of the program exception is found, and the rationality of the test case is further analyzed. After the unit test is completed, you can get the coverage information of the program and test cases. If you find that the path corresponding to a test case does not meet expectations, you also need to analyze the source code and find the cause in conjunction with the test case. In this case, you can directly call the debugging module to debug the program, which can analyze the program more conveniently and quickly.
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2.5 Detection and Positioning Algorithm of Electronic Components Based on Deep Learning Set the target component to be detected as A, and define the conditional probability of predicting that the target object is the specified electronic component A as 1, then the confidence level of A in the candidate box is 2: Con f = Pr(A|Object) × Pr(Object) × I OUPrT reduth
(1)
For each candidate box, predict the probability that it contains A and the position of the bounding box, then the output prediction value of each candidate box is: [X, Y, W, H, Con f (Object), Con f ]
(2)
Among them, X and Y are the offset of the center of the prediction frame relative to the cell boundary, and W and H are the ratio of the width and height of the prediction frame to the entire image. For each picture input, the final network output is a vector: M × N × B[X, Y, W, H, Conf(Object), Conf]
(3)
3 Experimental Research on Remote Testing Adapter Hardware of Electronic Components Based on Information Processing 3.1 Experimental Environment Hardware configuration: CPU is Intel Pentium; system environment: Ubuntu; development tool: Eclipse Luna.
3.2 Test Items 1.
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Adapter adaptation function The adaption function of the adapter in this article refers to the existence of the embedded adapter, which can support more than one type of embedded platform unit testing. The test task of this experiment is the coverage rate of the tested unit, and the adaptation function of the adapter is tested by checking whether it can produce a reasonable coverage rate. Hardware efficiency test
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Table 1 Partial function coverage information
Function name
Statement coverage (%)
Branch coverage (%)
MCDC coverage (%)
skipwh
100
100
64.3
prtrec
100
100
100
mrtansp
86.9
92.1
79.6
lightt
100
100
100
islow
100
100
100
Average coverage
97.38
99.02
88.78
Taking resistance as an example, after running the component for 5 months, the number of normal components is counted, and the efficiency of the adapter hardware is obtained, and it is judged whether it can be used in the remote test system of electronic components.
4 Data Analysis of the Hardware Design of the Remote Test Adapter for Electronic Components Based on Information Processing 4.1 Adapter Adaptation Function Data The device under test is set to the normal working mode, and the results of the adapter adaptation function test are shown in Table 1: The average sentence coverage, branch coverage and MCDC coverage of the adapter are 97.38%, 99.02%, and 88.78%, respectively. It can be seen from Fig. 1 that the adapter realizes the support for the remote test system of electronic components, and the average coverage rate meets the demand.
4.2 Resistance Test Taking resistance as an example, the statistical data after 5 months of operation is shown in Table 2: the number of components tested in January was 2400, and the number of normal components was 2392; the number of components measured in February was 2530, and the number of normal components was 2521. It can be seen from the data in Fig. 2 that the effective rate of the adapter hardware is as high as 99.8%, and has been maintained above 98.6%. Therefore, the adapter hardware in this study is qualified and meets the design standards.
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coverage
Statement coverage 100% 100% 100% 100%
Branch coverage 100% 92.10% 86.90% 79.60%
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MCDC coverage 100% 100% 100%
100%
100%
100%
64.30%
skipwh
prtrec
mrtansp
lightt
islow
Function name Fig. 1 Partial function coverage information
Table 2 Comparison of component measurement batches Month
Number of normal components
Total number of test components
Efficient (%)
1
2392
2400
99.67
2
2521
2530
99.64
3
2369
2400
98.71
4
2466
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98.64
5
2994
3000
99.8
Total number of test components
Number of normal components 3000 2994
5 2500 2466
Month
4
2400 2369
3
2530 2521
2
2400 2392
1 0
500
1000
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Number
Fig. 2 Comparison of component measurement batches
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5 Conclusion The next five years will be a critical period for the upgrading of my country’s electronic technology and electronic products. With the opening of a new round of industrial explosion and the adjustment and transfer of the global industrial structure, China’s electronics industry will be committed to explosive growth in the medium and long term. Driven by the non-innovation and upgrading of the industry, my country’s electronic manufacturing industry will gradually move towards more efficient, intelligent and modern development. Research on electronic component detection systems based on information processing technology has important research significance for the development of such technologies.
References 1. Senkondo W, Munishi SE, Tumbo M et al (2019) Comparing remotely-sensed surface energy balance evapotranspiration estimates in heterogeneous and data-limited regions: a case study of Tanzania’s Kilombero Valley. Remote Sens 11(11):1289 2. Lin AJ, Hsu CL, Chiang CH (2016) Bibliometric study of electronic commerce research in information systems and MIS journals. Scientometrics 109(3):1–22 3. Dixon BE, Zhang Z, Arno JN et al (2020) Improving notifiable disease case reporting through electronic information exchange-facilitated decision support: a controlled before-and-after trial. Public Health Rep 4:003335492091431 4. Mita Y, Kawahara Y (2017) 15-year educational experience on autonomous electronic information devices by flipped classroom and try-by-yourself methods. IET Circ Devices Syst 11(4):321–329 5. Berdahl EJ, Blandino M, Shanahan D (2017) Applying information theory to investigate music performance with continuous electronic sensors. Acoust Soc Am J 141(5):3875–3876 6. Zhou X, Rong C, Lu T et al (2016) Information functional theory: electronic properties as functionals of information for atoms and molecules. J Phys Chem A 120(20):3634–3642 7. Cai J (2020) Ship electronic information identification technology based on machine learning. J Coastal Res 103(sp1):770 8. Xu ZW, Mei W, Yu JQ et al (2019) Research on the lightning disaster risk assessment of electronic information system with intuitionistic fuzzy information. J Intell Fuzzy Syst 37(2):2043–2050 9. Liu Y, Yao L, Xiong W et al (2018) Joint kinematic and feature tracking of ships with satellite electronic information. J Navig 71(5):1178–1194 10. Laskar J (2016) The MTT-S electronic information committee-enhancing communication [MTT world]. IEEE Microwave Mag 17(9):66–69 11. Wu J, Jianhua L, Yang L et al (2018) Design of anti-radiation testing platform for electronic component. Annu Rep China Inst At Energ 00:239–239 12. Krylov VP, Bogachev AM, Pronin TY (2019) Deep level relaxation spectroscopy and nondestructive testing of potential defects in the semiconductor electronic component base. Radio Ind (Russia) 29(2):35–44
The Application of Virtual Reality Technology in Aerobics Training from the Perspective of Information Technology Tingting Gou
Abstract In all walks of life, aerobics teaching in colleges is one of the application areas. Based on an overview of the characteristics of the application of virtual reality (VR) technology in aerobics teaching in colleges, this paper analyzes the shortcomings of traditional Chinese aerobics teaching, and points out the effective application strategies of VR technology in aerobics teaching in the background of college information. Keywords Information technology · VR technology · Aerobics · Teaching training
1 Introduction VR technology is an emerging technology that uses computers to orderly and comprehensively process various digital, audio, text, image and other information carriers, so that these information carriers present a certain logical relationship [1]. It is a product of the development of computer technology. It first appeared in the 1980s and developed into the 1990s. It has become a teaching aid with a higher application rate [1]. VR is the abbreviation of VR, and also the abbreviation of VR technology. It integrates multiple sciences such as computer graphics, image processing and pattern recognition, and transforms the digital information processed by the computer into multi-dimensional information with various manifestations that people can feel. It is used through visual, audio, and tactile effects. In order to make a dynamic interactive response to the user’s control behavior. VR technology has been widely used in sports training, as shown in Fig. 1. In recent years, with the continuous deepening of teaching reforms, aerobics has been further popularized and improved in Chinese universities and has become a popular sport [1].
T. Gou (B) Chongqing Chemical Industry Vocational College, Chongqing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_51
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Fig. 1 VR system architecture in aerobics teaching
2 The Application Characteristics of VR Technology in Aerobics Teaching in Colleges Usually VR technology mainly shows the following three application characteristics in college aerobics teaching.
2.1 Integration After introducing VR technology to the teaching of aerobics in colleges, it collects and comprehensively organizes various related teaching materials, thereby enhancing the richness and completeness of teaching content.
2.2 Systematic A very important feature of using VR technology for teaching activities is that it must cooperate with the computer in the whole process, and a systematic teaching program is preset through the computer. In this teaching program, each teaching content arranged is not independent. The organizational structure of the system, but a logical relationship with other links, is an indispensable process in the system. For example, when teachers in colleges explain the various movements of aerobics, they generally develop a complete demonstration of aerobics and each step by step aerobics demonstration [2]. Then according to the system flow design of VR technology, these actions can be displayed and scientifically played reasonably, and finally the expected teaching effect can be realized.
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2.3 Non-linearity Chinese traditional aerobics teaching activities in colleges are generally completed through practical exercises. After applying VR technology to aerobics teaching activities, it uses computer hyperlinks to selectively place pictures, videos, text, audio, etc., to make the content of college aerobics teaching concise and easy to understand [2].
3 The Drawbacks of Traditional Aerobics Teaching 3.1 Insufficient Arrangement of Theoretical Courses At this stage, aerobics teachers in many colleges in China still one-sidedly believe that aerobics teaching is a highly practical subject, mainly based on practical exercises, so they generally do not pay attention to the theoretical teaching of aerobics. In traditional Chinese aerobics teaching activities, theoretical courses are obviously inadequate [3]. As a result, many students lack the basic theoretical knowledge of aerobics, and it is difficult to achieve good results of aerobics learning under the guidance of the professional theoretical knowledge of aerobics.
3.2 The Teaching Content is Boring In the traditional aerobics teaching activities of Chinese universities, the teaching content is divided into auxiliary teaching part, basic teaching part and extended teaching part. However, because the teaching hours of aerobics in most schools are insufficient and the students’ ability to accept it is poor, in actual teaching, many aerobics teachers only teach the boring basic teaching part, and rarely involve the interesting extended teaching part [3]. In this way, the classroom teaching of aerobics becomes dull, and the students’ enthusiasm for learning is not high.
3.3 The Teaching Mode is Backward Aerobics teaching in colleges is different from other knowledge-based subject teaching. It mainly takes body movement demonstration and practical exercises as the main teaching mode. In this traditional teaching model, teachers become the leaders of teaching activities, students become completely passive learners, and there is little communication between teachers and students. In the long run, it will severely limit the development of students’ aesthetics and the development of creative thinking [2].
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3.4 Lack of a Strong Professional Faculty College aerobics course is a sports elective course very popular among college girls, and it is also a sports course with the highest elective rate every year. However, at this stage, the aerobics teachers in many universities in China are not strong, especially those professional, compound and skilled high-quality aerobics teachers are even lacking. Many schools have too many students taking aerobics courses, and the fulltime aerobics teachers in this school cannot afford such a heavy amount of teaching [4]. They can only hire extracurricular fitness coaches to temporarily replace the lessons. This makes it difficult to guarantee the quality of aerobics courses. Some of the aerobics teachers in some schools are not highly specialized, they are not proficient in the application of VR technology, and some do not even have the ability to independently produce aerobics multimedia teaching courseware [4]. These are the teacher problems that need to be solved urgently in the teaching of aerobics in Chinese universities.
4 The Application of VR in the Teaching of Aerobics in Colleges 4.1 Motor Skill Training In the teaching of aerobics, sometimes the teacher’s explanations and demonstrations can’t really comprehend the main points, and the training will get half the result with half the effort. Some students with a higher level than others find it difficult to find opponents to improve their level [5]. At this time, if you use virtual Realistic technology can, based on the characteristics of immersion and interaction, enable students to devote themselves to the learning environment in a virtual environment, and can simulate different masters’ styles for targeted training, thereby improving students’ motor skills, as shown in Fig. 2.
4.2 Psychological Training Before the aerobics competition, students can be immersed on the scene according to VR technology, feel the atmosphere of the arena in advance, and train the players of stage fright in a targeted manner, so as to improve their psychological adaptability and resilience on the court, and achieve the expected results of the competition [5].
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Fig. 2 VR technology for motor skills training
4.3 Theoretical Knowledge Learning A wide range of aerobics theoretical knowledge, including the development history of aerobics, the basic theories of exhaust techniques, tactics, the theories and methods of aerobics teaching, etc. Simply relying on books and teacher explanations cannot make students deeply understand and understand aerobics sports, if you use VR technology to integrate theoretical knowledge and aerobics-related knowledge into the virtual cyberspace, let students experience the development process of aerobics in the virtual space of aerobics, and if they encounter something that is not easy to understand theoretical knowledge, students can use three-dimensional renderings to create realistic virtual people to perform visual exercises, and to understand theoretical knowledge more vividly and intuitively, thereby increasing their interest in learning aerobics and more conducive to aerobics teaching [6].
4.4 Virtual Exercise Experiment By performing aerobics exercises in the VR system, the system can make reasonable suggestions on the intensity and methods of the students’ training according to the students’ physiological reactions and the students’ goals, so as to promote healthy teaching of aerobics and reduce bodybuilding [6].
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5 The Application Strategy of VR Technology in Aerobics Teaching of Information Technology 5.1 University Leaders and Aerobics Teachers Should Attach Great Importance to Information Technology Teaching Aerobics courses in colleges are complex and leapfrog courses. It is not easy to learn. Especially for those students who lack dance knowledge and lack of extracurricular knowledge, it is difficult to learn aerobics courses. At this time, advanced and reasonable teaching methods have become very important for college students studying aerobics. VR technology is a brand-new teaching method. Therefore, college teachers should actively introduce VR technology into college aerobics teaching. Leaders of various universities must attach great importance to multimedia teaching [7].
5.2 Configure Advanced Software and Hardware Equipment to Create a Complete Information Network System The network environment of colleges is the prerequisite and foundation for the implementation of multimedia teaching in college calisthenics. Therefore, major universities must strive to create a sound and stable information network system to ensure the normal and smooth operation of the university information network at all times. The specific creation strategy can refer to the following two aspects [8]. First, it is equipped with advanced computer software and hardware facilities to provide a good teaching environment for aerobics multimedia teaching, so that students can directly access various aerobics teaching websites through the Internet when they are in class, and teachers can also use the Internet to study The extracurricular time provides students with a variety of teaching materials, which extends the teaching of aerobics in space and time [8]. Second, regularly maintain and scientifically manage the school’s campus network system to ensure that the campus network is unimpeded and provide network conditions for students to learn aerobics in the dormitory.
5.3 Strengthen the Construction of Aerobics Teacher Team and Improve the Professional Teaching Level of Teachers At this stage, the teaching level of aerobics teachers in many colleges in China is not very high, and some still have some difficulties in applying VR technology. This requires aerobics teachers themselves to continuously improve their overall quality and take the initiative to respond to the media in their daily work. In-depth study
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of technology in order to be able to use it flexibly as soon as possible [9]. At the same time, the school should also conduct professional VR technology application training for aerobics teachers, and conduct an assessment after the training. Only after passing the assessment, the aerobics teacher can continue to serve as the school’s aerobics teacher. In addition, the school can clearly stipulate in the form of formal documents that aerobics teachers should use VR technology to do the pre-teaching preparation work, and use VR technology in the classroom to learn some major aerobics theoretical knowledge and dance movements. Carry out content extension and knowledge extension [9]. Or ask the aerobics teacher to make some interesting and aesthetic aerobics PPT files for students to learn and observe. These can well improve the professional teaching level of aerobics teachers and the application ability of VR technology.
5.4 Change the Traditional and Backward Teaching Concepts and Actively Encourage Students to Explore Independently Different from other theoretical disciplines and logic disciplines in colleges, the aerobics course is a course with a strong artistic quality, a high degree of freedom, and a rich aesthetic. Beauty is ever-changing, and every student has his own unique understanding and appreciation of aerobics. Therefore, in the teaching activities of aerobics in colleges, teachers should completely change the traditional and backward teaching concepts and actively encourage students to carry out self-exploratory learning. That is to say, in daily aerobics teaching activities, after teachers have taught students the basic movements, they should allow students to learn and watch various aerobics operations independently, and allow students to independently choose the form of aerobics they are interested in. As shown in Fig. 3, instead of being forced by the teacher, some aerobics forms and movements are repeatedly trained [10]. Fig. 3 Students conduct independent exploration training under VR technology
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5.5 Appropriate Use of Multimedia to Further Strengthen Teacher-Student Exchanges VR technology is only an auxiliary teaching method of college aerobics teaching, not a leading teaching method. In college aerobics teaching activities, students are the main body of teachers’ teaching. Therefore, teachers should continue to strengthen teacher-student interaction and teacher-student communication on the basis of appropriate use of VR technology [10]. Discover the key points and good learning methods of aerobics in colleges, and continuously improve the teaching efficiency of aerobics courses in colleges. Don’t put the cart before the horse, pay too much attention to multimedia teaching, and neglect effective communication between teachers and students. That will make college aerobics teaching fall into a mechanized state of rigidity, and it will be harmful to college aerobics teaching [10].
6 Conclusion In summary, traditional aerobics teaching in Chinese colleges generally has problems such as unreasonable teaching design, boring teaching content, single teaching mode, and weak teachers. However, the application of VR technology to college aerobics teaching activities can be achieved through the integration, system and non-linearity of VR technology presents the teaching content of aerobics courses to students intuitively and vividly, which allows teachers to extend the teaching time and teaching space, thereby further enriching the various aerobics courses items of teaching content. This not only stimulates students’ interest in learning, but also improves students’ innovative ability. Therefore, college teachers must actively use a variety of effective methods and rational use of VR technology in aerobics teaching activities. Only in this way can the teaching quality of aerobics courses in colleges be effectively improved.
References 1. Li L (2015) The application of virtual reality technology in the teaching of aerobics in colleges. J Chifeng Univ (Nat Sci Ed) 12:180–183 2. Liu JP (2015) The application of virtual reality technology in college calisthenics teaching. J Chifeng Univ (Nat Sci Ed) 11(06):102–104 3. Wang FJ, Liu DB (2015) Analysis of the application of virtual reality technology in college aerobics teaching. J Jilin Radio TV Univ 14(06):34–35 4. Feng J (2016) Application of virtual reality technology in college aerobics teaching. J Hubei Correspondence Univ 9(16):132–135 5. Xu LJ (2017) Virtual reality technology and its application prospects in aerobics teaching. J Beijing Normal Univ 13:55–56
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6. Liu H (2018) Practice and thinking of physical education teaching model based on virtual reality technology. J Beijing Normal Univ (Nat Sci Ed) 12(11):192–194 7. Wang S (2016) The application of virtual reality technology in physical education and training. Shaanxi Educ 9(05):19–21 8. Guo H (2016) Research on the application of computer vision-based virtual reality technology in physical education. J Northwest Polytech Univ (Soc Sci Ed) 36(02):92–96 9. Yu QC (2015) Application of virtual reality technology in physical education. Anhui Sports Sci Technol 26(04):115–117 10. Zhu CT (2014) Discussion on the application of virtual reality technology in physical education. Audio-Vis Technol 5(27):184–185
Precision Design of Dongguan Intangible Cultural and Creative Products Based on Big Data Analysis Technology Dongning Chen
Abstract By studying on the status of intangible cultural and creative products in Dongguan, this paper explores the method of how to carry out accurate design of cultural and creative products of intangible cultural heritage in Dongguan with big data analytical technology. Based on the features of massive data, objectivity and accuracy of big data, this thesis conducts the deep analysis of the consumer demand to intangible cultural and creative products by taking big data technology; the uniqueness elements of intangible cultural heritage in Dongguan are excavated, those data information benefits the designers to optimate their designs when formulating marketing campaigns. By taking these measures, an outstanding brand of intangible cultural and creative products in Dongguan emerge, to empower the intangible cultural and creative products in conjunction with modern lifestyle, in return, to stimulate the modern transformation of intangible cultural heritage projects and drive economic development. The purpose is to provide remarkable reference for the design and research of cultural and creative products. Keywords Big data · Dongguan intangible cultural heritage · Cultural and creation · Precision
1 Background As an important part of Chinese traditional culture. Intangible cultural heritage embodies an essence of Chinese people’s wisdom and civilization, and has very important historical value, cultural value and artistic value for the continuation of national culture and national spirit. In 2016, the National Development and Reform Commission and the Ministry of Culture put forward tasks such as actively promoting the cross-border integration of cultural and creative product development [1]; In 2021, the Dongguan Municipal People’s Government made institutional constraints on the
D. Chen (B) Art and Design, Guangdong University of Science and Technology, Dongguan, Guangdong, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_52
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preservation of intangible cultural heritage [2]. These documents have fully mobilized the enthusiasm for the development and construction of intangible cultural and creative products in Dongguan, showing a good trend of modern transformation of intangible cultural heritage projects. Under this circumstance, the use of big data technology to analyze the design of cultural and creative products of intangible heritage in Dongguan can improve the creativities, to achieve a seamless integration between intangible heritage and digitalization age. Eventually, it is a fusion of technology, innovation and creativity of intangible heritage. In addition, it will help the cultural and creative product design of the intangible cultural heritage in Dongguan connect with the industry and achieve high-quality development in line with consumer demand, and enhance the value regeneration, development and innovation of the intangible cultural heritage in many aspects.
2 Concept on Big Data Big data serves as the product of development of the information age. “Big data” is a innovative processing measure with huge decision-making power, insightsdiscovering capacity and process-optimizing ability to adapt to those large-scale, highly-grow and diversified information assets [3]. Professor Victor Maier Schonberg from Oxford University put forward in his book Big Data Age that the core of big data is prediction, and the analysis of comprehensive data will be more scientific and accurate. Those who master data and data analysis methods will win in this age [4], which shows its power of big data. As recent years, IBM has put forward the “5 V” features of big data as (1) Volume (2) Variety (3) Value (4) Velocity (5) Veracity [5]. The strategic significance of big data technology is to professionally process some meaningful data, so that it is able to be applied in the direction of prediction and precision customization. By adopting big data analytical technique, we can make precise as well as integrated analysis of the elements of Dongguan intangible cultural heritage, consumer market, promotion and marketing, etc. through big data, and feed back to enterprises to provide accurate reference data for relevant creators. It can provide precise directions for the creators in the early stage of market research, product positioning, design direction, etc., avoiding the uncertainty of the designer’s personal subjective judgment, solving the current problems and creating more commercial value.
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3 Development Status of Cultural and Creative Products of Dongguan Intangible Heritage In May this year, Tea Garden Tour and Mojia Boxer were added to the representative items of Dongguan’s national intangible cultural heritage. So far, there are 10 items in total, 146 of which are listed at the municipal level or above, including 44 at the provincial level or above. These are very important intangible cultural heritage resources in Dongguan. Under the national attention to intangible heritage as well as the call of vigorously promoting cultural and creative policies, Dongguan government has strengthened the support and construction of intangible cultural and creative products. From 2017 to 2018, Dongguan Intangible Cultural Heritage Creative Products Competition was held for two consecutive sessions, enabling cultural creativity and promoting the modern transformation of intangible cultural heritage projects. In 2020, Dongguan opened the “Intangible Cultural Heritage Market City” WeChat small program e-commerce platform, there are intangible cultural heritage cultural creation, intangible cultural heritage food and crafts and other sections. The launch of “Intangible Cultural Heritage Market” Mini Program has expanded the sales channel and influence of cultural and creative products in Dongguan, but at the same time, it also produced lots of problems in the course of its rapid growth. According to the survey of the Dongguan Intangible Cultural Heritage Development ➁ investigated by Dongbao Think Tank, 57.06% of people think that cultural and creative products of intangible cultural heritage in Dongguan have not been well promoted, 44.42% of people think the practicability is low; 35.76% think the style is too old and conservative, 32.64% of people think the price is unreasonable, and 30.48% of people think that the homogenization is serious and they have no desire to buy, as shown in Fig. 1 [6]. From the above, problems can be summarized as follows:
Fig. 1 Photo provided by Dongguan Press Institute of public sentiment and think tank
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3.1 Insufficient Innovation Ability and Too Much Emphasis on Inheritance In the design process of intangible heritage products, besides the inheritance, the key is to redevelop and redesign it. Successful design of intangible cultural and creative products can revitalize the intangible heritage skills that are on the brink of being lost, while the profound historical and cultural deposits of intangible cultural and creative products has also been enhanced, further more to improve their style together with taste of products [7]. During the investigating process of the design of cultural and creative products in Dongguan, it can be found that most of the designs remain in the shallow level of creation, simply copying the original form to make a miniature version, or directly extracting the original pattern. Generally, too much attention is paid to the inheritance of intangible cultural, resulting in a lack of innovation. For example, in the WeChat mini program, the weaving skills of tea dolls and asters—primary color round plates and straw baskets, etc., are too conservative and outdated, resulting in poor sales. This too much attention to the inheritance of a single development model, lack of innovation, low practicability, resulting in many products are similar, it is very difficult to follow the changes in the market and the development of new era.
3.2 Inaccurate Positioning and Insufficient Publicity The research of on-sold intangible cultural and creative products in Dongguan finds that the consumer market they are facing is very vague, the overall price of the products is on the high side, and the quality of some products is rough, the price is not proportional to the quality, and the positioning is not accurate enough. For example, with a thousand corner lamp as the theme, the design team used nearly half a year’s time to create a thousand corner lamp metal three-dimensional puzzle, bracelet, teapot, tea leakage, accompanied by a gift box. Among them, the thousandcorner lamp metal three-dimensional puzzle was also selected as the top ten “new creations of intangible cultural heritage” in Guangdong in 2020. However, by the deadline of the author, 109 sets of small programs had been sold in the “Intangible Cultural Heritage Market”. The monthly sales volume of the best stores in Taobao Mall was only 18 sets, and the sales volume of many stores was 0. The total sales volume of e-commerce platforms was about 140 sets, and the investment was not in direct proportion to the return. The reason is that the price is too high, the promotion of the product is not enough, and the marketing is not accurate, which leads to the lack of competitiveness compared with the same type of products.
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3.3 Single Product Type and Serious Homogenization At present, the design of some intangible cultural and creative products in Dongguan follow its own trend too much. This types of products developed are relatively single, there is some imitation, and the lack of in-depth exploration of consumers’ preferences, behavior habits and needs leads to serious product homogeneity. Most of them are handicrafts such as decorations, mobile phone cases, cups, bookmarks and key rings, which are similar to other products in the market. They are difficult to attract consumers’ attention. In the process of experience, consumers are also difficult to feel the unique charm of Dongguan’s intangible cultural heritage culture, which is hard to impress consumers.
4 The Precise Design Strategy of Dongguan Intangible Cultural and Creative Products with Big Data Analysis Technique China vigorously advocates cultural prosperity, and cultural and creative trends are also set off around the country. To build up its own brand effect of intangible cultural and creative products in Dongguan, it is necessary to take good use of big data technology. A successful cultural and creative product needs various factors. By applying big data analysis technology, the consumer demands can be accurately realized the uniqueness of intangible cultural heritage elements in Dongguan can be excavated, and the marketing promotions can be effective. The specific process is demonstrated in Fig. 2.
Fig. 2 The application route of intangible cultural heritage products in Dongguan based on big data
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4.1 Focus on Consumers The design of Dongguan intangible cultural and creative products in the big data era needs to be consumer-oriented. Excepting pay attention to the intangible cultural heritage itself, it should also pay attention to the needs of consumers and the market development trend. Big data analysis technology supports to form a comprehensive, detailed and accurate insights into consumers. Consumers can be divided into four ages segments: children, teenage, grow-up people and elder people. Big data technology is used to accurately analyze their interests, habits, emotional needs and consumption power of different age groups to determine the positioning of intangible cultural and creative products as well as to make targeted designs. The company adopts the consumer-oriented thinking mode and takes good use of big data analysis technology to accurately insight into consumer demand, to lead demands for customized design, and to strongly support the decision makers in enterprises making highly objective, effective and precise market prediction [8].
4.2 Explore the Uniqueness of the Cultural Elements of Dongguan Intangible Heritage The intangible heritage in Dongguan is similar to that in other areas in China, and also has its unique cultural meanings. By taking big data analysis technology is to fully excavate the uniqueness of the cultural elements of Dongguan’s intangible heritages, so as to avoid the same type of intangible cultural heritages in different regions, easy to create its own characteristics. In addition, it can focus on collecting, processing and extracting information in terms of its style and pattern design, cultural meanings, and craftsmanship, those unique features information assist designers a deep understanding, enabling them to make innovation when designing new cultural and creative products for Dongguan city take those.
4.3 To Achieve Precision Marketing Promotion As the continuous growth and maturity of artificial intelligence technology, big data analytical technology has become a sharp tool for current promotion and marketing [9]. If the products of Dongguan’s intangible cultural heritage want to be favored by more consumers, they should be different from other competitive brands. The big data analytical technology ensure more accurate and scientific reference to make brand position and conduct marketing companions for Donguan cultural and creative products, the company built its own brand, established offline brand stores, and established official stores on platforms such as TaoBao, JD.COM and TikTok, so as to realize online and offline “double-line marketing”. Big data analysis technology is
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used to accurately locate audience groups, and targeted publicity methods are adopted according to the characteristics, hobbies, activity venues and shopping platforms of consumers of different ages to achieve precision marketing.
5 Conclusion Intangible cultural and creative products build a bridge between intangible cultural heritage and modern life [10]. The big data analysis technology analyzes sufficient data information to extract information of consumer segments, intangible cultural heritage elements, marketing promotion, etc. It can provide technical support for precision design of intangible cultural and creative products in Dongguan. Combined with the creativity of designers, it can not only produce high value-added intangible cultural and creative products but also maximize economic benefits.
References 1. State Council Several opinions on promoting the development of cultural creative products of cultural heritage units. 2016–5–16. http://www.gov.cn/xinwen/2016-05/16/content_5073762. htm 2. Dongguan Municipal People’s Government Office Notice on issuing the interim measures for the protection and management of intangible cultural heritage in Dongguan. 2021–01–22. http://www.dg.gov.cn/gkmlpt/content/3/3452/post_3452001.html#683 3. Li X (2018) Innovative development of archives management under the background of big data. New Bus Wkly 000(002):47 4. Schönberger VM (editor-in-chief), Cooke K (2013) The era of big data (trans: Sheng Y, Zhou T). Zhejiang People’s Publishing House, Hangzhou 5. No longer maintained. Big data 5V features. 2017–04–27. https://blog.csdn.net/arsaycode/art icle/details/70847184 6. A little information. Dongbao Think Tank Survey|Dongguan Intangible Cultural Heritage Development Report➁: when buying intangible cultural heritage products, boys pay attention to mission and inheritance, while girls prefer appearance and practicality. 2021-06-12. http://www.yidianzixun.com/article/0V1e8vjE?appid=s3rd_op398&s=op398 7. Sun C, Yang H, Ren Y (2019) Research on the inheritance and design of Funan willow weaving from the perspective of intangible cultural heritage culture. Art Educ Res 24:27–28 8. Liu X, Yu D (2017) The subversion and innovation of cultural and creative industries by big data technology. In: Cross-strait creative economy research report (2017) China conference. 2017–10–01 9. Shu K (2019) The role of artificial intelligence in inheriting and innovating traditional culture. People’s Forum 28:44–45 10. Zhang Y (2021) A brief discussion on new ideas of non-heritage cultural creation in Lingnan. Mod Ancient Cult Creation 01:81–82
Proportion of College Students’ Internet Education Data Based on Big Data Analysis Technology Yuxin Yang and Yong Cui
Abstract In recent years, the rapid development of information science and technology is also changing the way of life of human beings. Especially with the application of advanced technologies such as big data analysis technology, digital media technology and network technology to the field of education, not only is the form of education reformed, college students as one of the subjects of education have also been affected by many definitions. This article aims to study the proportion of college students’ Internet education (IE) data based on big data analysis technology. Based on data analysis technology, this article sorts out the general situation of college students’ IE, and summarizes the content and form of college students’ IE. This paper analyzes the problems and causes of IE for college students through a questionnaire survey, and discusses strategies for the active development of IE for college students from schools, teachers and college students themselves. Survey data shows that among the 508 college students, 86.02% of the students use Tencent Meeting; 72.44% of the students use Superstar Learning Link; 59.84% of the students use Wisdom Tree; 31.89% of the students use MOOC; 34.65% of the students use Nail nail. This shows that college students can actively use the Internet platform for learning and play the positive role of the Internet. Keywords Information technology · Internet education · College student learning · Data analysis
1 Introduction With the help of information technology and network platforms, “IE” breaks through the limitations of learning locations, provides learners with abundant learning resources, and triggers the reform of teaching models [1, 2]. On the one hand, because of the application of Internet big data cloud computing, educational resources are more abundant, and all educational resources around the world can be shared through Y. Yang · Y. Cui (B) School of Information, Southwest Petroleum University, Nanchong 637001, Sichuan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_53
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the Internet [3, 4]. On the other hand, college students can learn courses from the Internet at any time according to their own interests and needs, or they can choose to watch the key points of the courses they think according to their existing cognitive level and ability, so as to better play the role of the students themselves as the main body of learning [5, 6]. Therefore, the research on the proportion of college students’ IE data based on big data analysis technology is of great significance to college students’ learning. In the research of IE, many scholars have carried out research. For example, Firoozeh believes that the openness of IE resources exceeds the geographical restrictions and restrictive time management of schools [7]; Ekenze SO believes that “Internet+” can optimize the allocation of educational resources. To promote fairer education, respect individual differences of students, and meet individual needs of students [8]; Corcimaru A believes that the Internet enables students to access a wealth of educational resources, and the way to obtain knowledge no longer depends on teachers, which has changed the teaching of the teacher center [9]. This article aims to study the proportion of college students’ IE data based on big data analysis technology. Based on data analysis technology, this article sorts out the general situation of college students’ IE, and summarizes the content and form of college students’ IE. Through the questionnaire survey, the problems and causes of IE for college students are analyzed, and the strategies for the active development of IE for college students are discussed from the schools, teachers and college students themselves.
2 IE for College Students Based on Big Data Analysis Technology 2.1 Problems in IE for College Students Based on Big Data Analysis Technology 1.
The weakening of college students’ autonomous learning ability
There are still many teachers and college students who worry that after the combination of the Internet and college students, college students’ awareness of role models will be reduced [10]. Autonomous learning is to place students in a state of active and inquiring learning, and to guide students from fully receptive learning to learning to learn actively. Learning through the Internet is generally done spontaneously and automatically by college students for a certain purpose. However, in many offline education, the relatively closed educational space and the lagging and boring educational content cannot raise the interest of college students in active learning. If online education cannot be organically combined with offline education, college students will face the dilemma of lack of the guidance and supervision of traditional classroom teachers, which will greatly weaken the motivation
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of college students to learn actively. This has once again put forward requirements for the innovation of educational methods and content under the environment of “Internet + education” for college student education [11, 12]. 2.
Inefficient use of curriculum resources
Facing a large amount of educational content, some teachers will be confused when screening, and they are prone to difficulty in choosing. In the actual teaching process, some teachers are unable to effectively introduce and use the curriculum resources because of the difficulty in selecting curriculum resources. Mainly manifested in the following two points: One is the excessive introduction of online teaching resources. Some teachers have added too much curriculum resources, overwhelming the knowledge of textbooks. At the same time, too many course materials make classroom teaching time squeezed, and it is easy to cause classroom teaching content to be extensive but not specialized, and students cannot conduct in-depth classroom learning in a refined manner. The second is the fragmented presentation of classroom teaching resources. Many of the educational resources and videos on the Internet platform can be watched using fragmented time. A small piece of knowledge makes people’s memory and learning fragmented, which is in line with the systematic level required by the syllabus. There is a gap in sexual teaching. If the teacher does not integrate it and presents it to students, students will have fragmented learning and memory, and it is difficult to construct knowledge into their own knowledge system.
2.2 Strategies for the Development of IE for College Students 1.
College students should strengthen self-management and improve their independent learning ability
First of all, college students should have a strong self-time management concept. Reasonably arrange online time, determine self-learning goals, reject the interference of bad information, improve one’s self-monitoring ability, and the degree of completion of learning tasks is not high. A large part of the reason is that self-monitoring is not enough. Therefore, it is necessary to improve your time management concept. Have a positive attitude, correct learning attitude, treat learning with an optimistic and positive spirit, to achieve the purpose of improving self-management, and at the same time have the habit of making learning plans, and make practical long-term and short-term learning plans based on personal actual conditions. Second, we must clarify the motivation for learning. Clarifying the motivation of autonomous learning is the prerequisite for enhancing self-management awareness and enhancing autonomous learning awareness. Motivation is an internal driving force of an individual, which embodies the autonomy of behavior, runs through the entire learning process, and plays a central role. Different life experiences will produce differences in values and guide different learning behaviors, but the way to
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achieve learning goals is the same and the only way is to strive to obtain scientific knowledge, so it is necessary to optimize the ability of independent learning. Finally, it is necessary to make reasonable use of online learning equipment and online learning platforms to promote their own knowledge learning, relying on information technology, rationally using network resources to assist their own learning, and improve information processing capabilities by sharing and displaying learning results to achieve the construction of their own knowledge. 2.
College students should actively use the online learning platform to improve the ability of collaborative learning
First of all, college students should cultivate their own awareness of using network equipment for learning, improve their own awareness of online learning, and maintain communication with everyone frequently. When communicating online, they must express their own views in order to have a clearer understanding of things. Views, you will understand your own shortcomings, you will also see the shining points of others, and strive to learn from others. Second, we must focus on innovation and sharing on the IE platform. Some students have difficulty in language expression ability. When sharing and communicating, they can maintain feelings and exchange ideas with classmates. On the other hand, they can improve their language expression skills. While improving their own quality, they can also enhance their collaborative learning ability. Finally, in the era of “Internet + education”, time is extremely precious to most people. It is impossible to solve all problems face to face. Online communication becomes extremely precious. In the learning process, it is necessary to rely on the power of a group or peers. Help each other, through the establishment of discussion groups, discussion groups, etc., everyone coordinate and division of labor, use the online learning platform to search, transmit, share and exchange information to complete learning tasks. Therefore, college students should actively use the IE platform to improve collaborative learning capabilities. 3. 1.
Schools should build a perfect “Internet + education” environment and create a good teaching atmosphere Allocate high-quality teaching resources and optimize the “Internet + Education” environment
The degree of development of network information resources directly determines the smooth development of Internet learning. Compared with non-governmental training and education platforms, the teaching platforms and teaching resource sharing centers built by major universities are often more representative and authoritative, because the accumulation of educational resources and their teachers are often more advantageous. In colleges and universities, educators have stronger voice and dominance, which is conducive to giving play to the top-down guidance and education role of IE for college students. On the one hand, schools should provide students with a sound campus environment for online learning. On the other hand, it is necessary to introduce more high-quality information retrieval tools, integrate a library of shared resources, carry
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out the construction of high-quality learning resources, and increase investment in improving learning conditions. Build an online teaching platform to provide students with online and offline learning opportunities, enhance college students’ online information learning experience, and enjoy the fun of online learning. 2.
Develop a variety of technical means to strengthen Internet management
While paying attention to the use of Internet technology, colleges and universities should also pay attention to increasing the input of manpower. After the firewall is used to block and filter the software to play its role, the artificial screening is finally used, so that the Internet environment can be purified to the greatest extent, and thus it serves the IE of college students well. 3.
Improve educators’ ability to use the Internet
First, educators should strengthen the application of IE platforms. Large-scale online education platforms such as MOOC, XueXitong, and Wisdom Tree are important places for college students to learn extracurricular knowledge and obtain the information they need. Educators must first understand the basic information of college students’ needs, preferences, and psychological status in order to be targeted Educate them on the Internet. Second, educators should improve the means and skills of Internet communication. Educators must not only learn to use the IE platform, but more importantly, they need to learn the methods of communicating with college students on the Internet, such as college students’ study habits, online phrases, online pastimes, online learning methods, focus on hot spots, etc. It can shorten the distance with college students, and while keeping pace with the times, it also eliminates the barriers between college students and enhances the enthusiasm of college students for IE.
2.3 Clustering Algorithm in Big Data Analysis The use of clustering algorithms in big data analysis technology can improve the efficiency and robustness of data processing. At first, the algorithm selects K objects to represent the initial center of each cluster, and then divides the remaining objects into different clusters according to the minimum distance from the center of each cluster, and then recalculates the center of each cluster. This process is repeated until the criterion function converges. The algorithm is specifically described as follows: 1.
2.
Randomly select K objects to represent the initial cluster center of each cluster: Z 1 (1), Z 2 (1), . . . , Z K (1). Among them, the number in brackets represents the sequence number of the iterative operation. According to the principle of minimum distance, the remaining objects are divided into clusters to which K cluster centers belong, namely: D j (k) = min{X − Z i (k), i = 1, 2, . . . , k} = X − Z i (k), X ∈ S j (k)
(1)
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Among them, k is the sequence number of each iteration operation; K is the number of cluster centers. Calculate the mean value of each cluster center again: Z j (k + 1) =
1 Nj
X, j = 1, 2, . . . , k
(2)
X ∈S j (K )
Among them, N j is the number of samples in the jth category.
3 Proportion of College Students’ IE Data Based on Big Data Analysis Technology 3.1 Purpose of the Investigation Through the issuance and collection of questionnaires, this study conducted a survey on the proportion of college students’ IE data in order to obtain the following information: IE media frequently used by college students, and the time spent on IE by college students. On this basis, a deeper analysis of the main problems that exist in college students’ IE.
3.2 Survey Object This questionnaire group is mainly for undergraduates of H University. At the same time, this study selected 20 undergraduates with different professional backgrounds and different grades as interview subjects.
3.3 Questionnaire Preparation The first part is a basic survey, including college students’ gender, grade, major, the time they use the Internet every day, the time they use the Internet every day for study, and the purpose of the Internet. The second part is the main questionnaire.
3.4 Questionnaire Distribution, Collection and Analysis In this research, the final questionnaire will be distributed on a larger scale, mainly to the undergraduates of H University from freshman to senior. 540 questionnaires
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were actually sent, 526 were recovered, and 14 invalid questionnaires were deleted. In the end, 508 valid questionnaires remained, with an effective rate of 94.07%.
4 Analysis of the Proportion of College Students’ IE Data Based on Big Data Analysis Technology 4.1 Proportion of IE Media Frequently Used by College Students The IE media frequently used by the surveyed college students are shown in Table 1: Among the 508 college students, 86.02% of the students use Tencent Conference; 72.44% of the students use Chaoxing Xuetong; 59.84% of the students use Wisdom Tree; 31.89% Students use MOOC; 34.65% of students use Dingding. It can be found from Fig. 1 that the IE media most commonly used by contemporary college students are: Tencent Conference and Chaoxing Learning Communication. It also shows that the development of the Internet has brought great convenience to the study and life of college students, and college students can make full use of these online communication media. Table 1 Percentage of IE media frequently used by college students
IE media
Number of people
Percentage
Tencent conference
437
86.02
Chaoxing learning pass
368
72.44
wisdom tree
304
59.84
MOOC
162
31.89
Nailed
176
34.65
Other
34
6.69
Number of people
Percentage
Percentage: %
437 368 304 162 86.02
72.44
Tencent Chaoxing Conference Learning Pass
59.84 wisdom Tree
176 34.65
31.89 MOOC
Nailed
Fig. 1 Percentage of IE media frequently used by college students
34
6.69
other
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Table 2 Percentage of college students’ average daily IE time
Time
Go online
Learn
Less than 2 h
12%
66%
2–5 h
76%
34%
6–7 h
8%
0
More than 7 h
4
0
Learn
Time
More than 7 hours 6-7 hours
Go online
0
4
0 8% 34%
2-5 hours Less than 2 hours
12% 0%
76% 66%
50% 100% 150% 200% 250% 300% 350% 400% 450%
Percentage: % Fig. 2 Percentage of college students’ average daily IE time
4.2 Percentage of College Students’ Average Daily IE Time In the current era of “Internet + education”, what is the time spent online and the frequency of participating in IE for college students? Through questionnaire surveys and interviews to further understand the online time and online learning time of college students, we get Table 2: 76% of college students spend 2–5 h on the Internet on average every day, and 12% of college students spend less than 2 h on the Internet every day on average. But only 34% of students spend 2–5 h studying online. Figure 2 shows that college students have relatively strong willingness to learn independently on the Internet and have clear learning goals, but they need to be lacking in self-evaluation and supervision. They should be appropriately adjusted to achieve perfect performance. After interviews, it was found that when there is no learning task, the university is not willing to actively use online learning tools for knowledge learning, and cannot make good use of the IE platform for self-evaluation and self-monitoring of learning effects.
5 Conclusion Internet technology not only provides new opportunities and challenges for the development of IE for college students. Based on the big data analysis technology, the
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proportion of college students’ IE data is used as the research object. Opportunities and existing problems, and explained the reasons for the problems, and finally put forward countermeasures to promote the positive development of IE for college students.
References 1. Hu F, Hao C, Huo X et al (2020) Application research of big data analysis technology in oil and gas field development. J Phys: Conf Ser 1578(1):012033 (5pp) 2. Yadav VK, Medhavi S (2017) Adoption of internet based experiential learning by Indian international marketing education providers. Ital J Food Sci 27(3):310–319 3. Jabbour J, Dhillon HM, Shepherd HL et al (2017) Challenges in producing tailored internet patient education materials. Int J Radiat Oncol Biol Phys 97(4):866–867 4. Jafari J, Moonaghi HK, Zary N et al (2016) 3 Exploring educational needs and design aspects of I0n.ternet-enabled patient education for diabetics. BMJ Open 6(10):e013282 5. Famularo N, Kholod Y, Kosenkov D (2016) Integrating chemistry laboratory instrumentation into the industrial internet: building, programming, and experimenting with an automatic titrator. J Chem Educ 93(1):175–181 6. Perry J, Vandenkerkhof EG, Wilson R et al (2017) Development of a guided internet-based psycho-education intervention using cognitive behavioral therapy and self-management for individuals with chronic pain. Pain Manag Nurs 18(2):90–101 7. Nilchian F, Ghasemi L et al (2016) Internet patient education as topic pit and fissure sealants retinoic acid. J Dent (Tehran, Iran) 13(1):55–59 8. Ekenze SO, Okafor CI, Ekenze OS et al (2017) The Value of internet tools in undergraduate surgical education: perspective of medical students in a developing country. World J Surg 41(3):1–9 9. Corcimaru A, Morrell DS, Burkhart CN (2017) The internet for patient education on atopic dermatitis: friend or foe? J Am Acad Dermatol 76(6):1197 10. Rosenbaum AJ, Ellis SJ (2016) Response to “letter regarding: the internet for patient education: a friend or foe?” Foot Ankle Int 37(6):681–681 11. Denvir C (2016) Online and in the know? Public legal education, young people and the internet. Comput Educ 92:204–220 12. Choi M, Cristol D, Gimbert B (2018) Teachers as digital citizens: the influence of individual backgrounds, internet use and psychological characteristics on teachers’ levels of digital citizenship. Comput Educ 121:143–161
Gender Research in Literature Based on Big Data Technology Xun Wu
Abstract With the advent of the era of big data, the whole society has been impacted and influenced, which also indicates that there will be a new information technology revolution quietly rising. Big data technology is promoting the reform and continuous development in many fields. With the continuous updating and improvement of China’s Internet technology, a variety of Internet derivative technologies are emerging in endlessly, such as big data technology, which has been spread in all aspects of our daily life. Now big data technology has gradually penetrated into literary research, which is also a challenge and opportunity for literary research. In this paper, in order to explore the specific impact of big data technology on gender research in literature, we selected two literature websites as the experimental research objects. Finally, through the experimental data, we can see that big data can help literary creators effectively improve their search ability and search efficiency of literary works, the search efficiency of B literature website applying big data technology is basically more than 80%, the highest is 93%; while the efficiency of D literature website is only 70%, the lowest is 60%, which is a bit low compared with B website. Keywords Big data · Literature research · Gender differences · Influencing factors
1 Introduction The era of big data not only marks the renewal of technology, but also marks the innovation and improvement of human information processing mode, the improvement of thinking mode, and the development and extension of thinking depth [1]. People can use big data technology to quickly get the information they want from a large amount of information without spending a lot of manpower and material resources to find information. People understand things and make decisions through a variety of data. At present, the application scope and field of big data technology are very wide. Now relevant experts and scholars are also trying to apply big data technology X. Wu (B) Nanyang Technological University, Singapore, Singapore e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_54
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to literary works, trying to use this new technology to help literary works realize the transformation of creative thinking [2]. Lu et al. believe that the use of big data can help us make quantitative analysis of the objects we want to study, and mine hidden information from the complex data, so as to discover the development law of things [3]. Now a lot of topics about gender appear in the dust, so many literature works about gender studies have sprung up. However, due to the lack of sufficient market research in many literary works, many views are not supported by the reality, so they are criticized. Wang and Yao think that different genders have different views on different things, and many things can not be generalized, especially the views in some literary works are obviously lack of critical thinking [4]. Therefore, we should look at things dialectically. We can’t take things for granted, or just see one side of things and blindly believe in the information we want to see. The views on gender in literary works should be objective and without any prejudice. If the author creates articles with his own emotions, then the whole article has lost its objectivity from the beginning, and its authenticity is also questionable [5]. Therefore, this paper is to use big data technology to help literary creators in gender research, to quickly and accurately find the commonness and individuality between the two sexes, so as to help the authors dialectically look at problems and think about problems [6]. With the development and improvement of big data technology, great changes have taken place in the way we obtain information. Now many industries and fields are carrying out information technology reform, so as to update and upgrade the existing information system [7]. The practice of applying big data technology to the creation of literary works is of great practical significance, which can be a huge qualitative leap for the literary world, especially in many gender related literary studies, involving the use of big data. This shows that big data technology is gradually penetrating into the literary world, and this trend is still expanding. Although the application of big data in literary creation is still subject to some constraints and restrictions, we believe that these problems will be properly solved in the future, so that big data can create greater value for us [8].
2 Method 2.1 Big Data Big data is a new technology, and it is the most commonly used professional term in artificial intelligence industry. It refers to the data set that traditional software tools cannot capture, manage and process in a certain time. It is a massive, high-speed growth and diversified information source, and a new processing mode. Through this technology, relevant personnel will have stronger decision-making ability, insight and process optimization ability. The strategic importance of big data technology is not to capture massive data information, but to capture these meaningful data
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professionally. Big data is very important to all industries [9, 10]. The key to the industry profit is to improve the “processing ability” of data and realize the valueadded of data through “processing”. It is a huge data set, far beyond the scope of traditional database software to collect, store, manage and analyze it. It has the characteristics of large data volume, fast data flow, many data types and low density.
2.2 Literature Literature is a way and means of expressing objective world and subjective knowledge orally or in writing. It is necessary to point out that not all literature belongs to art. Only when a literary work is not only recording one thing or expressing something, but also when it is endowed with a new idea, people can feel the beauty of art through this new idea, and such literary works can be called literary art. Poetry prose, novel, drama, script, fable, fairy tale and other styles are important literary forms. Literature reflects the inner feelings and social life of an era and a region in different historical periods. Literature includes Chinese and literature, foreign language and literature, news and literature communication. Communication literature, as a branch of humanities, is a social top structure of philosophy, religion, law and politics. Another is oral literature, which is usually combined with music into lyric poetry. The earliest written literature form in China was the book of songs. Before Qin Dynasty, all works written in words were collectively referred to as literary works. Later, the traditional European literature theory can be divided into three categories according to the content and form: poetry, prose and literary drama [11, 12]. Modern film literature is a divided literature, it is a language art, is an important form of social culture and artistic embodiment. When the writer expresses his unique spiritual world with his unique language and art, it is a real work of literary significance. An excellent writer is a hero of the human spiritual world, a representative of national literature, and even represents the art and wisdom of a nation.
2.3 Gender Influence on Thinking Form From the characteristics of thinking form, image thinking is the performance of individual, abstract thinking is the closure of individual. Generally speaking, boys’ emotions are rough and flexible, easy to remember and flexible, pay attention to the analysis of conditions and conclusions, and can innovate the concept and judgment of abstract thinking; girls’ emotions, strong memory, tend to imitate and use, often only through mathematical image in the mind image of image thinking, but not good at exploring the essence of the problem.
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2.4 Stereotypes in Gender Gender role refers to the personality, attitude, value orientation and behavior characteristics formed by individuals according to their physiological characteristics under the influence of certain social and cultural gender norms. Most people in society have serious stereotype of gender, and some literary writers are no exception. Since ancient times, human beings have a great prejudice on the role division in language learning. They often feel that girls have an advantage over boys in language learning. Psychologists have learned a problem through research, and for a long time, there is a widespread bias against gender roles of men and women around the world. Most people think courage, wisdom, determination and perseverance are the qualities men possess, while women are fragile and emotional. It is in this rigid social environment that the female subjective consciousness is relatively weak compared with men. Some literary creators’ stereotype about gender consciousness is mainly influenced by their living environment and social environment. For example, we generally believe that boys are brave and girls are weak. In our childhood learning environment, the most prominent thinking setting is that most people think that girls are familiar with Chinese learning, their Chinese learning is regarded as a gift, while boys are students of mathematics and chemistry. It is very common for them to learn Chinese very difficult. Some experts and others have shown that people with gender stereotypes are more able to participate in the processing of gender schema automatically than those without gender stereotypes.
2.5 Relevant Algorithm Formula Involved in the Experiment in This Paper In order to verify the correlation between big data technology and gender research in literature, we use correlation algorithm, and when choosing data, we use error constraint formula. The following is the algorithm formula involved in this paper: CoV (X, Y ) V ar |X |V ar |Y |
(1)
N =k+
N0 − k 1 + (x/T ) p
(2)
y=d+
e−d 1 + (x/c)b
(3)
r (X, Y ) = √
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3 Experiment 3.1 Subject of Investigation We selected two literature websites B and D as the experimental objects. In the investigation of some gender related research in literature works in recent years, website B adopted big data technology; while D website used the conventional data query method in the investigation. Then we calculated the efficiency of four groups of inquiry on two websites, then looked up some topics about gender research, and randomly interviewed 200 passers-by’s views on gender research in current literature, of which half of the respondents were male and female, namely 100 boys and 100 girls.
3.2 Experimental Research Steps We recorded four groups of efficiency of the two literature websites, and then asked the interviewees about their satisfaction with the gender research in the current literature works, then recorded all the survey, and organized them into charts. Finally, we summarized and analyzed the results of these surveys.
4 Discussion 4.1 Efficiency of Searching for Two Literary Websites We can see from the above chart that the search and inquiry ability of B literature website is obviously stronger than that of D website. From Table 1, we can understand that the efficiency of B literature website is more than 80%, for example, the efficiency of the first group is 80%, the efficiency of the second group is 85%, the efficiency of the third group is 88%, and the efficiency of the last group is the highest, and the efficiency is 93%. But the efficiency of search and query of B website is basically below 70%, even when the efficiency is the lowest, it falls 60%. The efficiency of the Table 1 Efficiency of searching for two literary websites
B literature website (%) D literature website (%) Group one
80
65
Group two
85
60
Group three 88
70
Group four
67
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B literature website
100%
80%
D literature website 88%
85%
93%
80% 60%
65%
70% 67%
60%
40% 20% 0% Group one
Group two
Group three
Group four
Fig. 1 Efficiency of searching for two literary websites
first group and the fourth group is within the range of 60%–70%, and the efficiency of the two groups is 65% and 67% respectively. And we can see from Fig. 1 that the efficiency of B literature website is higher than that of D website. This shows that using big data technology can help us to find the information we need quickly, and can effectively improve the efficiency of query.
4.2 People’s Views on Gender Research in Literary Works From Table 2 and Fig. 2, it can be seen that men generally have higher views and evaluation on gender research in literary works than women. Among the female population participating in the survey, the number of women who expressed great support is 15, the number of support is 25, the number of people who express no feeling is 20, and the number of people who express opposition is 40. The number of men who expressed great support was 20, 35 for support, 25 for unconsciousness, and 20 for opposition. We can see from these data that women are twice as opposed to men, and that women are almost twice as satisfied as men. This shows that most women don’t feel very good about gender research in literature, and they are very low. Table 2 People’s views on gender studies in literary works
Female
Male
Very supportive
15
20
Supportive
25
35
Non-inductive
20
25
Against
40
20
Gender Research in Literature Based on Big Data Technology Fig. 2 People’s views on gender studies in literary works
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Male
female 20
against
40 25
non-inductive
20 35
supportive
25 20
Very supportive
15
0
10
20
30
40
50
5 Conclusions In short, we can see from this article that big data technology can help people find the information they want quickly and accurately, and can also screen the information they want. In this experiment, we can find that applying big data technology to gender research in literature can help the literary workers to better find the information they want and can be more effective to understand the different views of some things in the gender accurately and efficiently. We also look forward to big data technology to bring us more surprises and help in the future.
References 1. Mao P (2019) Empirical research on reader acceptance theory based on big data. Comp Study Cult Innov 003(011):58–59 2. Cao SR (2019) Big data analysis of ancient literature research—based on the research of the national people’s congress copy database from 2011 to 2018. Jianghuai Forum 295(003):172– 180 3. Lu L, Chen S, Li HY (2018) Research on the employment value orientation of college students under the background of big data—based on the difference analysis of gender and major. Mod Mark: Bus Ed 310(10):36–38 4. Wang L, Yao LI (2017) Research on teaching phenomena based on big data of classroom teaching behavior. e-Educ Res 038(004):77–85 5. Yu DL, Gao L, Sun JH et al (2016) Application of big data technology in health management. Chin PLA J Hosp Manage 23(001):44–48 6. Liu SC, Zhang DX, Zhu, Chao Y et al (2016) Thinking on big data technology in energy Internet. Power Syst Autom 040(008):14–21 7. Lionel M, Shu N, Jiang X, Tan HY (2016) “Popularization”—a new goal of big data technology development. Sci China Inf Sci 59(10):1–3 8. Zhang KL, Wang JW, Cao H et al (2016) Internet plus coal mining big data technology research and practice. Coal Sci Technol 44(7):123–128 9. Xu L, Jiang C, Wang J et al (2017) Information security in big data: privacy and data mining. IEEE Access 2(2):1149–1176 10. Akter S, Wamba SF (2016) Big data analytics in e-commerce: a systematic review and agenda for future research. Electron Mark 26(2):173–194
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11. Lovell SJ, Stone SF, Fernandez L (2016) The economic impacts of aquatic invasive species: a review of the literature. Agric Res Econ Rev 35(01):195–208 12. Cegan JC, Filion AM, Keisler JM et al (2017) Trends and applications of multi-criteria decision analysis in environmental sciences: literature review. Environ Syst Decis 37(2):1–11
Exploration of Accounting Information Construction Under the Background of Big Data Yanbin Tang
Abstract Under the background of big data accounting informatization, accounting informatization develops from paper-based to paperless operation with the help of the rapid development of informatization, which makes accounting work more standardized. This article analyses problems in the production of accounting information in the context of large data by means of bibliographic analysis, surveys of questionnaires and staff interviews. At the same time, he interviews 200-year-old accountants and 50-year-old accountants. The results show that the development of accounting information in the context of large data remains unclear in the classification of management accountancy data (30%); poor real-time performance of data (25%); unreasonable information transmission process (24%); poor information extraction ability, unable to tap the value of data (21%); among the 200 accounting managers and 50 accounting experts surveyed, they have a high level of financial expertise Knowledge accounted for 80%, with a high level of computer knowledge accounted for 85%, with a high level of information management knowledge accounted for 70%. According to the modern business system, the updating of accounting management has become an unavoidable trend. The accounting information system has therefore also shifted from traditional accounting to management, paying more attention to the actual state of business development and to achieving sustainable business development. Keywords Big data · Accounting informatization · Accounting management · Information construction
1 Introduction Nowadays, the state attaches more and more importance to the information of management accounting [1]. In recent years, the Ministry of finance has published several papers, emphasizing that management accounting information is the key direction of China’s accounting reform and development [2]. Although there are still Y. Tang (B) Jiangxi University of Applied Science, Nanchang, Jiangxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_55
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many views on management accounting in academic circles, in accounting practice, it is realized that the support of management accounting must be informatization to realize it [3]. In recent years, with the official introduction of the series of business documents of the application guide for management accounting, the integration of management accounting and information technology will be accelerated [4, 5]. There is a very close relationship between the development of accounting and economic development, and the improvement and development of accounting related to the development of the market economy. The accounting industry must constantly adapt to economic growth and change in order to adapt to the economic environment and the economic model of the new era [6, 7]. With the continual rapid development of the current society and the continuous deepening of information technology, there is no doubt that the arrival of the era of big data had some impact on traditional accounting information [8, 9]. With the continuous development of the era of large data, valuable accounting information will play a decisive role in the strategic analysis of the company. Therefore, in this case, it is very effective to study accounting information in the era of big data [10]. In the context of large data, this article analyses the problem of producing accounting information in the context of large data by means of a combination of bibliographic analysis, a questionnaire survey method and a personal interview method [11]. At the same time, it interviews 200 accounting managers and 50 accounting experts. It also discusses the application methods of enterprise management accounting tools and advances the problems existing in the development of accounting information in China The paper analyzes the problems and puts forward the corresponding reform measures [12].
2 Overview of Large Data and Accounting Information 2.1 Big Data Concept and Characteristics 2.1.1
The Concept of Big Data
Large data are also called huge data, huge data or large data. The volume of large data is so large that people can’t eavesdrop, can’t manage, to process and organise information within a reasonable period of time. However, large data do not only refer to large amounts of data, but also represent the potential value of data.
2.1.2
Features of Big Data
The characteristics of large data mainly include a large number of data, multiple types and fast processing speed. The amount of data appears mainly in large data. The data range exceeds the GB or TB measurement unit and the unit required for large
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enterprise data is already in EB. The other two outstanding features of the large data are the speed at which information is processed, which is also an important reason for the rapid development and popularity of large data in the context of information. Faced with enormous amounts of data, the efficiency of data processing is very important. Enterprises should first deal with internal data to ensure the integrity, consistency and compliance of internal information. Only in this way can enterprises make the right decisions.
2.1.3
Cluster Analysis
Cluster analysis is short for clustering. In essence, the set of data objects is divided into several parallel classes or clusters based on the similarity and non-similarity between the data. Finally, the clusters and clusters are independent of each other, but the elements within the cluster have very high similarity. The calculation method is as follows: Minkowski distance: dist(p) =
n
1p |xi − yi |
p
(1)
i=1
When P approaches infinity, which is called the Chebyshev distance, then: n
dist( p) = max|xi − yi | p i=1
(2)
2.2 Concept and Characteristics of Accounting Informatization 2.2.1
The Concept of Accounting Information
Accounting information is the combination of accounting and information technology. Enterprise financial information management is a new requirement of the information society for enterprise financial information management, and also the main measure to conform to the trend of information. It is the main channel for enterprise leaders to obtain relevant information in the network environment, which helps to enhance the competitiveness of enterprises, improve the ability of accounting management decision-making and the level of enterprise management.
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The Characteristics of Accounting Informatization
First, universality. Modern information technology has been widely used in accounting theory, accounting work, accounting management, accounting education and other fields, and formed a complete application system. Secondly, integration. Accounting information will reorganize the traditional accounting organization and business process to support new organizational forms and management modes such as “virtual enterprise” and “database”. The starting point and the foothold of this process are to realize the integration of information. It’s dynamic again. The dynamic characteristics of accounting information reflect the dynamics of accounting data collection and the actual time of accounting data processing Timely. Finally, it is gradual. Modern information technology has subjective initiative in the reconstruction of accounting model, however, the embodiment of plan is a gradual process. It should be divided into three steps: the first step is to adopt information technology to adapt to the traditional accounting model, that is, to establish an accounting information system. The second step is the mutual adaptation of modern information technology and traditional accounting model. The third step is to use modern information technology to reconstruct the traditional accounting model, form a modern accounting information system and realize accounting informatization.
3 Ideas and Methods 3.1 Research Ideas This article first provides a solid theoretical basis for research through bibliographic analysis. Secondly, use questionnaire and interview methods to carry out further investigation, analyse problems in the production of accounting information in the context of large data and present some countermeasures and proposals.
3.2 Research Materials and Experimental Design Based on the questionnaire survey and enterprise interview, a group of professionals was set up by our school of accounting and ACCA. In order to ensure that the design of the questionnaire is in line with the reality, we interviewed 200 accounting managers and 50 accounting experts to basically understand the application methods of enterprise management accounting tools. At the same time, we adjusted and determined the content of the questionnaire.
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4 The Result of the Problems in the Construction of Accounting Information in the Background of Big Data 4.1 Analysis of Accounting Information in the Context of Large Data The development of accounting information is currently facing many problems. Here we have carried out an analysis. The results are shown in Table 1 and Fig. 1. It can be seen from Table 1 and Fig. 1 that there are many problems in the development of accounting informatization.
4.1.1
Unclear Classification of Management Accounting Data (30%)
Different manufacturing industries have different products and different production and operation processes. For example, in discrete manufacturing and process manufacturing, different enterprises have different requirements for data acquisition. On the one hand, data input still depends on the front-line data operators, and management accountants need to judge the applicability of various data subjectively; on Table 1 Accounting informatization under the background of big data
Fig. 1 Analysis and research on accounting information in the context of large data
Problem
Proportion (%)
Unclear classification of management accounting data
30
The real-time performance of data is poor
25
Unreasonable information transmission process
24
Information extraction ability is poor, unable to mine data value
21
25%
24% 45%
30%
21%
Unclear classification of management accounting data The real-time performance of data is poor Unreasonable information transmission process Information extraction ability is poor, unable to mine data value
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the other hand, management cannot obtain useful information to support decisionmaking. The decision-making of enterprise management not only needs to collect and analyze the internal process information in real time, but also needs to obtain the relevant data of the external macro environment, market share and competitors to analyze and adjust the strategy. However, these external information is opaque and difficult to obtain. Even if the relevant data is obtained at a high cost, the business personnel do not know how to accurately enter it into the information system.
4.1.2
Poor Real-Time Data (25%)
The informatization construction of banking and insurance industry is relatively perfect. Due to the complex product manufacturing process and the incomplete digitalization and networking of production and manufacturing links, a lot of data cannot be collected and screened in time and can not be classified accurately, resulting in the difficulty of information extraction. For example, in the activity-based costing, it takes a long time to input written documents and then conduct manual accounting and management accounting information system at the same time, while small and medium-sized enterprises are more used to input data in the form of written documents due to cost reasons, so the degree of informatization is very low.
4.1.3
Unreasonable Information Transmission Process (24%)
Unreasonable design of production process will delay the transmission of information and reduce production efficiency. For example, in the traditional raw material purchasing process, if the inventory quantity is lower than the safety warning line, the system will send an alarm to the production department, and the production department will submit an application to the purchasing department after approval, and then the purchasing department will make a decision, and feed it back to the production department and the financial department. Because the factory detects that the inventory of raw materials is below the warning line, and then transmits the budget, production progress report, raw material demand and other data to the purchasing department, the process can be simplified. The purchasing department will place the order directly after the audit, and then send the purchase order to the financial department to improve the production efficiency.
4.1.4
Poor Information Extraction Ability, Unable to Mine Data Value (21%)
Enterprises produce massive data every day, but lack of effective theoretical basis and analysis framework, resulting in the information obtained can not be analyzed effectively. Most of the existing management accounting software is a general system, lack of professional software for specific industries, which makes the input–output
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model, linear regression and other management models and companies cannot be applied to practice.
4.2 Research on Accounting Management Team We interviewed 200 accounting managers and 50 accounting experts respectively, and investigated the accounting management team. The results are shown in Table 2 and Fig. 2. It can be seen from Table 2 and Fig. 2 that among the 200 accountants and 50 accounting experts surveyed, 80% have high level of financial professional knowledge, 12% have medium level and 8% have low level; 85% have high level computer knowledge, 10% have medium level and 5% have low level; 70% have high level information management knowledge and 20% have medium level, Low level accounted for 10%. In the context of big data, in addition to financial professional knowledge, financial personnel also need to have the ability to comprehensively use the knowledge of operation, product, technology, market, etc., skillfully use various information management tools (such as ERP system, database tools, etc.), strengthen the training of management accounting personnel, and adopt the internal and external training methods. Table 2 Research on the quality of accounting management team High level (%)
Financial expertise
80
12
8
Computer knowledge
85
10
5
Information management knowledge
70
20
10
Ability and accomplishment
Ability and accomplishment
Middle level (%)
Low level (%)
Information management knowledge
Low level
Computer knowledge Financial expertise
Middle level
High level 0%
20%
40%
60%
80%
Percentage Fig. 2 Analysis of the ability and accomplishment of accounting managers
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4.3 Addressing Problems of Manufacturing Accounting Information in the Context of Large Data 4.3.1
Speeding up the Construction of the Information Platform
The government should establish an information exchange platform, provide appropriate encouragement and support, improve the efficiency of resource integration, increase technological investment and achieve additional benefits. Simultaneously, whereas it is necessary to reduce the difficulty of independent research and development and to reduce the costs of research and development; whereas suppliers must also innovate in its structure; knowledge, improve the level of research and development and strengthen innovative ideas. Suppliers must not only introduce foreign advanced technologies, but also improve these technologies, establish demonstration projects, provide a theoretical basis and innovation for the production of corporate accounting information and progressively improve the accounting allocation system.
4.3.2
Establishment and Improvement of the Management Accounting System
For the development of modern enterprises, all management work cannot be separated from the system guarantee, The same applies to accounting management in the context of large data. To build a perfect internal and external control system, especially to play the role of social intermediary, will implement the supervision means more objectively and fairly. In the process of market economy development, enterprises have more rights to operate independently, and accounting management, as an important part of enterprise management, ensures the authenticity and integrity of accounting information, which is of great significance. Governmental services should also step up their efforts to regulate and manage to help businesses effectively implement accounting information in the context of large data.
4.3.3
Training High Quality Accounting Management Staff
As the accounting management personnel in the new era, we should improve the level of professional knowledge, improve computer knowledge and network knowledge, improve the pertinence of work, ensure the efficiency and quality of accounting management, and play the role of compound accounting talents; secondly, strengthen safety education. Due to the particularity of informatization and networking, network security issues can not be ignored, strengthen the professional ethics education of accounting personnel, establish a sense of responsibility in the work; thirdly, enterprises should pay more attention to accounting personnel, provide more followup education opportunities, not only can enrich the accounting theory knowledge,
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but also can strengthen the accounting computerization system, better serve the development of enterprises.
4.3.4
Innovation of Enterprise Management Mechanism
Big data points out new ideas for management accounting informatization, and the management mechanism of enterprises also needs to adapt to the new characteristics of the times. The traditional management methods and means need to be innovated, and the level of accounting management needs to be improved. Management accounting informatization involves accounting, management, information science, operations research and other disciplines, which requires continuous innovation of enterprise management mechanism.
5 Conclusions In the age of big data, big data can effectively promote the reform of accounting information technology, but there are many risks and problems in the development of computer accounting, such as the delayed construction of information platforms, inefficient protection of accounting information systems and imperfect legal systems. The development of IT for accounting management is a gradual process and must be improved and innovative. In the modern business system, Accounting information technology has become an unavoidable trend and a steady increase in the level of accounting engineering in our country will affect the computer of accountants. The level of application technology sets higher requirements, so the accounting information system has also shifted from traditional accounting to management, by paying more attention to the real state of business development and developing large-scale management software in a targeted way to better cover the business development needs and achieve the development of business sustainability. Acknowledgements (1) “Study on Identification and Countermeasure of Systemic Risk in Industry-University-Research Cooperation Projects”, Science and Technology Project of Jiangxi Provincial Department of Education in 2019, Issue number: GJJ191199; (2) “Strategy research and practice of integrating socialist core values into the whole teaching process of accounting specialty”, Teaching Reform Project of Jiangxi Provincial Department of Education in 2020, Issue number: JXJG-20-29-3; (3) Study on the Course Construction of < Comprehensive Simulation Training of Accounting > under the Background of “Double Ten Thousand Plan”, In 2019, Jiangxi Institute of Applied Science and Technology university-level teaching reform project general project, Issue number: JXYKJG-19-21.
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References 1. Wang, Luxiao, Huang, et al (2019) Construction exploration of Chongqing environmental supervision information system under the background of environmental protection vertical reform. J Phys Conf Ser 1213(5):52074–52074 2. Zhang Y, Dai S, Wu F et al (2019) Exploration on the construction of digital content security course under the background of “New Engineering Disciplines.” Int J Innov Educ Res 7(4):123– 129 3. Schreieder S, Soldatenkov A (2020) The Kuga–satake construction under degeneration. J Inst Math Jussieu 19(6):2165–2182 4. Lan Y, Li X (2021) A brief analysis of the application of enterprise’s internal accounting and financial management in computer. E3S Web Conf 235(24):03087 5. Yuzhe W, Weiwen Z, Jiahui S et al (2017) Smart city with Chinese characteristics against the background of big data: idea, action and risk. J Cleaner Prod 173:60–66 6. Pingping S (2017) Research on the innovation of enterprise employee incentive way management based on big data background. Agro Food Ind Hi Tech 28(1):1434–1438 7. Zhen-Hua L, Yao W, Zheng-Tian W et al (2008) Research on electronic voltage transformer for big data background. Symmetry 10(7):234 8. Nan W, Xiaochun S (2020) The influence and countermeasures of enterprise marketing activities under the big data background. J Phys Conf Ser 1684(1):012016 (5pp) 9. Gan C, Deng X, Cui L et al (2019) Construction of a high-density genetic linkage map and identification of quantitative trait loci associated with clubroot resistance in radish (Raphanus sativus L.). Molecular Breeding 39(8):116 10. Wu D, Jiang X, Huang J (2020) Research on promoting urbanization in western sichuan minority regions with ancient architecture planning based on big data analysis—taking Li county as an example. J Phys Conf Ser 1648(2):022039 (4pp) 11. Shulin M (2021) Research on problems and countermeasures of management accounting informatization construction in small and medium-sized enterprises. Acc Township Enterprises China (04):166–167 12. Wu X (2021) Research on accounting information construction in administrative institutions. Public Investment Guide 06:95–96 (in Chinese)
On the Path of Improving the Work Quality and Accuracy of College English Workers in the Era of Big Data Yue Li
Abstract With the development of the Internet age, big data has penetrated into all aspects of our lives. As a major base for cultivating college students, the use of big data in Colleges and universities cannot be underestimated. University workers must improve their work quality, equip with big data knowledge and skills, in order to improve the level of work accuracy. As a member of teaching English, College English workers (CEW) also need to improve their work quality in order to maintain their teacher status in the development of big data and keep pace with the times. This paper mainly studies the ways to improve the work quality and accuracy of CEW in the era of big data (TEOBD). This paper studies the current situation of College English teachers’ work quality, and then puts forward some strategies to promote the development of English teachers’ professional quality. Through questionnaire survey and chart analysis, this paper analyzes the cognition of CEW to improve their work quality, which affects the improvement of teachers’ work quality. The results show that 83 of them believe that advanced English teaching methods can improve their work quality, 54 believe that basic language skills can improve their work quality, and 79 believe that classroom teaching organization ability can improve their work quality, 43 workers think that teaching theory knowledge can improve their work quality, and 64 workers think that modern educational technology can improve their work quality. Keywords English workers · Work quality · Accurate work · Big data
1 Introduction In TEOBD, there is a huge stock of data in the field of education, and the resource characteristics of big data are more prominent, which has become an important basis for the development and utilization of education, science and technology and other fields [1, 2]. It provides data support and decision support for the development of Y. Li (B) School of International Education, Changchun University, Changchun, Jilin 130118, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_56
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education, and knowledge transfer, student service, learning optimization and other aspects have gradually become an important content and research field of education researchers [3, 4]. With massive data, relying on advanced big data mining and analysis technology, people can get accurate information through more direct, economic and environmental protection methods [5, 6]. The massive and complex information of big data affects people’s way of life and the way of understanding the world, and becomes a breakthrough point for people to master knowledge and create new value [7, 8]. English Teaching in Colleges and universities is also facing the impact of big data. It is very important for colleges and universities to obtain competitive advantage that educational information is reasonably managed and effectively applied. How to adapt to the current form of big data, make good use of big data technology, and find a suitable entry point in the education of CEW is an urgent problem to be solved [9, 10]. In the research on the path of improving work quality and accuracy level, many scholars at home and abroad have studied it and achieved some results. Flo E and others pointed out that English teachers’ information skills are constantly developing and dynamic, and professionalism is the outstanding performance of their development. The final result of professional development is to transform its external general ability into the continuous improvement of personal internal quality, which in turn promotes the optimal combination of teaching, scientific research and information technology, thus further promoting the improvement of teachers’ information skills [11]. Enrique A and others pointed out that the integration of information technology into teaching is the inevitable trend of education development in the future. Among them, the integration stage of information technology and English course teaching is not only to realize the implementation ability of information technology in teaching practice, so as to achieve the optimization of English classroom teaching effect, but also to explore a new mode of integration of information technology and English course in the integration of various information technology and English subjects, and creatively design and develop various integration environments [12]. This paper mainly studies the path to improve the work quality and accuracy of CEW in TEOBD. By studying the current situation of College English teachers’ work quality, this paper finds that the knowledge system of pre service learning and pre service training is incomplete, and the measures of on-the-job training are lack of effectiveness. Then it puts forward the strategies to promote the development of English teachers’ professional quality, correctly deal with teachers’ job burnout, increase the training opportunities for teachers’ professional development, do a good job in-service training for English teachers, strengthen post career learning, innovate the mode of collective lesson preparation, update the understanding of English Teaching, build an efficient learning cooperative team, and strengthen the knowledge reserve. Through questionnaire survey and chart analysis, this paper analyzes the cognition of CEW to improve their work quality, which affects the improvement of teachers’ work quality.
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2 Improving the Work Quality and Accuracy of CEW in TEOBD 2.1 The Current Situation of College English Teachers’ Work Quality (1)
(2)
The knowledge system of pre service learning and pre service training is incomplete Due to the imperfection of the teacher education system in China, most college English teachers are graduates of comprehensive universities. Most of them have only received basic computer knowledge before graduation, and have not learned any special work information literacy courses. Therefore, their overall work literacy is relatively weak. At present, the Ministry of education requires the pre job training of university teachers organized by all provinces, that is, the training of teacher qualification examination, which is not complete and can not effectively cultivate and improve their work quality. To effectively improve the information behavior of university teachers, we must take the advanced education theory as the guidance. Only when the educational theory is included in the teaching plan of post training for university teachers, and through the study of educational theory knowledge, can university teachers change their educational concepts, pay attention to the cultivation and improvement of the effectiveness of their information behavior, and consciously guide their information teaching behavior with educational information theory. The measures of on-the-job training are lack of effectiveness Although in recent years, colleges and universities are also promoting the construction of information campus, vigorously strengthening modern teaching facilities and implementing a variety of training projects, some English teachers use vivid multimedia teaching methods to make the original classroom explanation more attractive to students’ attention and easier to understand and accept. However, most of the teaching models adopted by College English teachers still follow the traditional teaching model, and students are still passive receivers to a large extent; teachers’ teaching is still an individual independent behavior, the teaching communication between teachers has not become more smooth and in-depth, and the integration of curriculum and technology has not been realized.
2.2 Strategies to Promote the Development of English Teachers’ Professional Quality (1)
To deal with teachers’ job burnout correctly and increase the training opportunities for teachers’ professional development
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Long term engaged in educational activities, teachers will inevitably produce psychological fatigue and job burnout, and enter a period of teachers’ job burnout. During this period, teachers are easy to produce negative emotions, their enthusiasm for work will be greatly reduced, their emotional alienation from students and teaching activities will occur, and their job satisfaction will also decline. Through the research, after the teachers enter the period of job burnout, the seriousness of the work will decline, and they will not consider the students’ various reactions of listening and learning, but just give lectures. Obviously, teachers at this stage not only have a negative impact on their own physical and mental conditions, but also hinder the development of students, which is easy to mislead their children. Due to the new curriculum reform and the continuous promotion of quality education, teachers are busy with the tasks of the new curriculum reform, such as learning new ideas and new teaching methods, strengthening their professional knowledge and skills. On the surface, the new curriculum reform not only brings pressure to teachers, but also gives them the motivation to study hard. However, this sense of urgency obviously brings a certain impact to the teachers in the period of job burnout. This part of teachers may participate in learning for herd psychology or other reasons, but when they are awarded senior titles, they will quickly release this pressure, which will affect the overall state of teachers. They will feel that once and for all, not for merit but for nothing, and begin to muddle along. Although this kind of situation occurs less, but in the reality of education process has highlighted the problem. It can be said that the new curriculum reform provides a new driving force for teachers in the period of job burnout, but once the goal is achieved, it will return to the symptoms of burnout. Therefore, to solve the problem of job burnout, we can not completely rely on the new curriculum reform. The root of it is to constantly urge teachers to actively promote their professional development and improve their comprehensive ability. However, teachers’ professional development not only requires teachers to reserve a lot of professional knowledge, but also requires teachers to participate in various training activities. Recently, it is not only the requirement of education administrators but also teachers to carry out professional development training activities. Of course, in the process of training, teachers’ professional development can not be smooth sailing, and they will encounter all kinds of knowledge, theoretical and even operational difficulties. Schools and teachers should dare to look directly at them and find ways to overcome them one by one. In addition, schools can provide more training opportunities for teachers in the period of job burnout. In this process, teachers’ thoughts, knowledge, psychology, morality and other aspects will be improved and sublimated, and then they can smoothly pass through the period of teachers’ job burnout. On the other hand, young teachers can also actively participate in training, constantly enrich their own knowledge, in order to continuously improve their teachers’ quality.
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(2)
(3)
(4)
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On the job training of English teachers and post career learning In the new century, more and more people begin to realize the importance of lifelong learning. As a front-line teacher, the disseminator of knowledge needs lifelong learning. Teachers’ participation in work does not mean the end of their learning career, but the beginning of their in-service learning career. However, due to the limited time and energy of teachers’ on-the-job learning, we need to carry out on-the-job training and on-the-job follow-up learning in a planned, organized, systematic and scientific way. Teacher training is an important way to improve the overall quality of teachers and speed up the construction of teachers. In the teacher training course, we should pay attention to the personality of different teachers, conduct a lot of educational experience exchange meetings, analyze and observe the teaching videos of excellent teachers, and put forward their own opinions and gains. In training, we should not only pay attention to the study of educational theory, but also pay attention to educational practice. We should carry out targeted and equal exchanges and cooperation, improve the enthusiasm of teachers, enable them to actively participate in them, jointly solve the problems of front-line teachers, constantly promote the professional development of front-line teachers, and constantly improve their comprehensive quality. In addition, English is a global language, lack of learning environment and atmosphere in China, English teachers need to constantly search English learning resources, make full use of the network, learn and understand all kinds of knowledge, in order to improve their professional quality. Therefore, the training of in-service teachers is of great help to teachers’ In-service Learning and is an effective way to promote teachers’ professional development. Innovating the mode of collective lesson preparation and renewing the understanding of English Teaching Teachers’ collective lesson preparation is actually an interactive process. Different teachers have their own advantages. Through collective lesson preparation, teachers and teachers have moderate communication, learn from each other, help each other, deepen the understanding between each other, and then improve the professional quality of teachers’ teaching. In addition, English teachers in rural high schools are already in a weak position in terms of objective conditions. Only by pooling the strength of all teachers can they form a force and achieve common development. Build an efficient learning cooperation team and strengthen knowledge reserve On the one hand, it can promote the personal development of teachers, find their own shortcomings with the help of other teachers, learn the advantages of others, and promote the professional development of teachers. On the other hand, a harmonious learning environment can make teachers feel happy physically and mentally, and participate in more learning work with motivation and passion. In most schools, the construction of teachers’ team is usually summarized from the aspects of teachers’ morality, teachers’ quality, teachers’ ability and teachers’ personality. At present, with the deepening of the new curriculum
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reform, every teacher is facing great challenges, and strive to become a practitioner of education in the new era. At the same time, the school is also facing great pressure, the need to build an efficient learning team. It can be seen that the construction of learning team is not only the objective need of school development, but also the internal requirement of teachers’ professional development. Therefore, school leaders should make a scientific evaluation of each teacher’s positioning to provide reserve forces for their development; every teacher should also pay attention to the strength of the team, deepen their knowledge reserves and teaching skills, and improve their professional ability and teaching level.
2.3 Questionnaire Survey Algorithm This paper mainly studies the path analysis of CEW to improve their work quality and work accuracy in TEOBD. In order to understand the cognition of CEW to improve their work quality and understand a relatively objective situation of work quality improvement, this paper collects data through questionnaire survey to analyze the cognition of CEW to improve their work quality. This paper uses the method of weighted summary, through the comprehensive questionnaire data to understand the improvement of work quality, the specific formula is as follows: A=
I
λi [
i=1
m j=1
λi j (
n
λi jk ai jk )]
(1)
k=1
At the same time, in addition to the information collection and processing of the questionnaire evaluation work literacy, the weighted summary statistics method is also used to process the feedback information of the collected work literacy, the calculation formula is as follows: S=
n
Q i Si (i = 1, 2, . . . , n)
(2)
1
By collecting and understanding the cognition of CEW to improve their work quality, this paper studies the cognition of CEW to improve their work quality, constantly innovates the talent training mode, and explores how to improve teachers’ professional quality.
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3 Experimental Study 3.1 Subjects This paper mainly studies the path to improve the work quality and accuracy of CEW in TEOBD. In this paper, through the form of questionnaire survey, 100 online questionnaires are distributed to 100 CEW to understand their cognition of improving their work quality.
3.2 Experimental Process Steps By studying the path of improving the work quality and accuracy level of CEW in TEOBD, this paper studies the current situation of College English teachers’ work quality, and understands that the knowledge system of pre service learning and pre service training is incomplete, and the measures of on-the-job training are lack of effectiveness. Then it puts forward the strategies to promote the development of English teachers’ professional quality, correctly deal with teachers’ job burnout, increase the training opportunities for teachers’ professional development, do a good job in-service training for English teachers, strengthen post career learning, innovate the mode of collective lesson preparation, update the understanding of English Teaching, build an efficient learning cooperative team, and strengthen the knowledge reserve. Through questionnaire survey and chart analysis, this paper analyzes the cognition of CEW to improve their work quality, which affects the improvement of teachers’ work quality.
4 Experimental Research and Analysis on Improving Work Quality and Accuracy of CEW in TEOBD 4.1 Understanding and Analysis of Improving Self Work Quality In order to understand the cognition of CEW to improve their work quality in TEOBD, this paper studies the types of CEW to improve their work quality through questionnaire survey. In this paper, through the form of online questionnaire, we collected 100 CEW’ questionnaires, and through data collection, we got their cognition of improving their work literacy. The results are shown in Table 1. As can be seen from Fig. 1, 83 of them think that advanced English teaching methods can improve their work quality, 54 think that basic language skills can improve their work quality, and 79 think that classroom teaching organization ability
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Table 1 The situation of CEW improving their work quality
Proportion
Advanced English teaching methods
Basic language skills
Classroom teaching organization ability
Teaching theory knowledge
Modern educational technology
83
54
79
43
64
Fig. 1 The situation of CEW improving their work quality
can improve their work quality, 43 workers think that teaching theory knowledge can improve their work quality, and 64 workers think that modern educational technology can improve their work quality.
4.2 The Influence on the Improvement of Teachers’ Work Quality In order to understand the improvement of teachers’ work quality, this paper collects 100 CEW’ questionnaires through the form of online questionnaires. Through data collection and sorting, we can get the situation that they think affects their work quality improvement. The results are shown in Table 2. Table 2 Influence on the improvement of teachers’ work quality
Proportion
Lack of training opportunities
Heavy burden on teaching
How to improve oneself in confusion
Incentive measures are not in place
Life is stressful
40
31
15
6
8
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Fig. 2 Influence on the improvement of teachers’ work quality
As can be seen from Fig. 2, 40% of the workers think it is lack of training opportunities, 31% think it is too heavy teaching burden, 15% think it is confused about how to improve themselves, 6% think it is the lack of incentive measures, and 8% think it is the pressure of life.
5 Conclusions At present, more and more attention has been paid to English workers, but the research on improving their work quality and accuracy is still insufficient. In this paper, through the research on the path of improving the work quality and accuracy level of CEW in TEOBD, we study the cognitive improvement of CEW’ work quality, constantly innovate the talent training mode, explore to improve teachers’ professional quality, better cultivate the talent quality in TEOBD, keep up with the times, and promote the development of college education level. Acknowledgements Projections: (1) No. 2018BS61, A study on core competence of English teachers at an architecture and engineer university in Jilin Province, Jilin Provincial Social Science Program. (2) No. JJKH20190888SK, An applicant research of core competence of English teachers at an architecture university for the poverty alleviation, The 13th Five-Year Social Science Program of Jilin Province Education Administration.
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3. Hodgkinson J, Koshiaris C, Martin U et al (2016) Accuracy of monitors used for blood pressure checks in English retail pharmacies: a cross-sectional observational study. Br J Gen Pract J Roy Coll Gen Pract 66(646):e309–e314 4. Li K, Xu T, Zhang Y, et al (2021) Deepening research on the distribution characteristics of interlayers in marine sandstone reservoirs. IOP Conf Ser Earth Environ Sci 692(4):042028 (6pp) 5. Li W, Wang X, Feng Q (2021) Final prediction of product quality in batch process based on bidirectional neural network algorithm. IOP Conf Ser Earth Environ Sci 692(3):032091 (6pp) 6. Zhang S, Dai Y, Wang X (2021) Research on the valuation of City enterprises based on ARIMA series prediction. IOP Conf Ser Earth Environ Sci 692(4):042121 (10pp) 7. Vaughn AA, Drake RR, Haydock S (2016) College student mental health and quality of workplace relationships. J Am Coll Health 64(1):26–37 8. Brooke J, Hammond A, Hirst G (2017) Using models of lexical style to quantify free indirect discourse in modernist fiction. Literary Linguistic Comput 32(2):234–250 9. Yu KA (2016) Effects of strategy-based instruction on Korean EFL college-level students’ English speaking abilities and their communication strategy use. J Linguistic Stud 21(1):165– 188 10. Pandu A (2019) Work-family conflict and family-work conflict and their effect on quality of life among leather industry workers. Int J Recent Technol Eng 8(4):3124–3132 11. Flo E, Husebo BS, Bruusgaard P et al (2016) A review of the implementation and research strategies of advance care planning in nursing homes. BMC Geriatr 16(1):1–20 12. Enrique A, Yassel R, Henning H et al (2016) Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences. Bioinformatics 6:821–827
New Tools for Macroeconomic Analysis in the Era of Big Data Juan Xie
Abstract As big data (BD) gradually penetrates into all corners of the national economy and becomes one of the increasingly important production factors, it will inevitably bring new impetus to my country’s economic operation and social development. Therefore, it is necessary to innovate the ideas and tools of macroeconomic analysis. Improve and optimize macroeconomic analysis methods. The purpose of this article is to study the new tools of macroeconomic analysis in the era of BD. This article starts with the basic theories of BD and the concepts of macroeconomic analysis, combined with macroeconomic information analysis methods, and systematically analyzes the service value of BD for macroeconomic analysis. Then it analyzes the new situation of the operation of the national economy in the era of BD and the application of BD in our country’s macroeconomic analysis. And from three aspects: establishing a national BD resource database, strengthening BD theory and technology research and development, and using BD to promote industrial innovation, proposed ideas for improving macroeconomic analysis, as well as suggestions for optimizing our country’s macroeconomic analysis structure. Survey data shows that the Internet, telecommunications, and financial industries are the industries with the largest investment scale in the BD IT application industry, accounting for 29.1%, 21.1%, and 17.6%. This shows that the government can use BD to increase Internet financing, telecommunications companies, data and other new business formats in the process of macro-control. Keywords Big data · Macroeconomic analysis · Macroeconomic control · Data analysis
1 Introduction In daily macroeconomic activities, a large amount of industrial economic information is generated, which reflects the changes in the national economic situation. J. Xie (B) School of Economics, Sichuan University, Chengdu, Sichuan Province, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_57
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By obtaining, analyzing and processing these industrial information, the national macroeconomic analysis and control are carried out, and the production departments and different links are dealt with [1, 2]. The era of BD has brought tremendous changes to the economic system, changed the way the market operates and the behavior of market entities, and subverted the traditional way [3, 4]. Its application is not only in the field of business models and ideological models, especially in the field of macroeconomic analysis [5, 6]. BD has both opportunities and challenges for my country’s macroeconomic analysis. On the one hand, it provides more convenient tools for national macroeconomic analysis; on the other hand, it puts forward higher requirements for macroeconomic analysis [7, 8]. In the study of new tools for macroeconomic analysis in the era of BD, many scholars at home and abroad have conducted various discussions on them, and have achieved good results. For example, Dieppe A started from the new normal of economic growth, the new normal of structural adjustment, the new normal of macro-control objectives, and the new normal of macro-policies analyzed the current macroeconomic situation and policies [9]; Cubells JF conducted a comparative analysis of national macro-control models and put forward relevant experience for reference. [10]; Ferraz L made a forecast and analysis of current and mid-to-long-term economic trends: low in the front, stable in the middle, and slow in the back, and proposed that loose monetary and fiscal policies should be adopted, social safety nets should be built, and policy reserves for responding to emergencies should be studied [11]. Based on a deep understanding of the relevant theories and connotations of macroeconomic analysis and BD, this article first expounds the relevant mechanism of macroeconomic analysis and forms an analytical framework. And under this framework, analyze the new characteristics and new environment of the operation of my country’s national economy in the era of BD, and then explain the application of BD in public utilities, national economic accounting and other fields in macroeconomic analysis, and how to use BD for macroeconomics analyse and elaborate. At the same time, this article uses empirical analysis to classify and analyze the inflation rate and corporate BD application data to test the actual application value of BD analysis. Finally, it puts forward countermeasures and suggestions for improving and optimizing my country’s macroeconomic analysis tools.
2 New Tools for Macroeconomic Analysis in the Era of BD 2.1 Application of BD in Macroeconomic Analysis (1)
Application in public utilities BD can guide innovation in public utilities. Through the analysis of big data tools, the government can not only provide timely feedback on public services, but also improve existing policy measures, thereby providing better services
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(2)
to the public sector and creating new value. Its application in the public sector involves many aspects, including public safety, public crisis management, food safety supervision, public health and medical care, and intelligent transportation. Application in the field of national economic accounting
(3)
The frequency and scope of the use of national accounting capital flows have been changed. First of all, through BD software, each item of the fund flow table is distinguished in detail, and the scope of fund flow accounting is applied to the three levels of macro, meso, and micro [12]. With the help of BD, not only can the analysis of capital flow be made more accurate, but also a panoramic picture of the capital flow of the entire national economy can be constructed. Secondly, from the perspective of the frequency of use of capital flow and the time series, traditional capital flow accounting is compiled in accordance with the annual time series, so the effect will be greatly reduced if the cycle is too long. However, capital flow accounting based on BD flow can greatly shorten the time period and can flexibly select the time period. It can not only calculate the capital flow that has occurred in the past, but also accurately predict the occurrence of future cash flow. Analyze economic operation through mobile terminal location positioning data
(4)
In the Internet age, mobile communication technology has also developed rapidly. With the popularization of smart phones, the offline behavior information of netizens is mastered by mobile operators through the location services of mobile devices. Mobile operators store information on the flow of a large number of people across the country. At the same time, combined with user registration information, they can perform detailed statistics and analysis on the flow of people across the country. Through long-term detection of population flow information between different regions, and then based on changes in information such as economic development, industrial structure, and population conditions in different regions, the trends in economic and trade exchanges, traffic conditions, tourism development and employment among different regions can be monitored. Carrying out economic operation monitoring and forecasting analysis based on search engine user demand data The first choice for netizens to find information is a search engine. By analyzing the keywords that netizens search for information, they can understand the real needs of netizens. The search index data of search engines reflects the behavior and psychological tendencies of most netizens to a large extent, and can provide important support for macroeconomic operation analysis and decision-making. Search index data has the advantages of large range, wide users, and many contents, and can more comprehensively reflect the needs of users, while ecommerce websites reflect only the unilateral needs of users. However, the search index cannot accurately reflect the user’s true intentions, and it is difficult to monitor the user’s buying tendency and potential.
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2.2 Macroeconomic Analysis Optimization and Countermeasures Under the Background of BD (1)
Establish a national BD resource database
(2)
The government can use the existing e-government website as the basis to integrate cross-departmental government data to cover most industries and fields of the national economic and social system, formulate data resource standards, establish a list of shared data resources, and build a national BD resource database. At the same time, integrate the scattered data resources in the society. Integrate the existing data centers of the government and society into a unified data center system to give play to the integration effect. In addition, make full use of existing resources to collect, analyze, integrate, and store Internet information, and integrate it into the data center, as a useful supplement to the national BD resource database. Strengthen the research and development of BD theory and technology
(3)
Strengthen BD basic theory and technical research, provide support for BD applications, and improve BD service capabilities. The BD industry involves computers, humanities and social sciences, economics and management, mathematical sciences and other disciplines. It studies the relationship network and chain application of data, and establishes a data-based interdisciplinary system. Research the theoretical systems and methods related to BD, and break through the technical bottleneck of BD development. Strengthen research in the fields of data collection, acquisition, analysis, processing, integration, storage, and at the same time do a good job of privacy protection and information security, closely link BD with various industries, and form mature BD solutions for various industries. Promote industrial innovation with the help of BD In the field of traditional industries, BD is used to promote the transformation and development of traditional industries such as handicrafts and agriculture, cultivate new economic growth points, and promote stable economic growth. Establish a national BD center, reduce the cost of government data collection, and create a good data information environment in the whole society. Use the Internet to cross-border integration to promote the application of BD in all aspects of the industry, such as technology research and development, manufacturing, operations, sales, etc., to promote the development, innovation, transformation and upgrading of traditional industries. In the field of emerging industries, increase the cultivation of new business forms such as Internet finance, digital enterprises, and data materials, improve the application capacity of industrial BD, tap the potential of industrial development, and use BD to promote reform and innovation, including technology research and development systems, operation mechanisms, and management Models, etc., to cultivate emerging industries into the country’s new economic growth point.
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2.3 Data Mining Algorithm for Association Rules Establish a framework model of the Markov logic network of association rules. The Markov network is a joint distribution model of random variables X = X 1 , X 2 , . . . , X n . It is composed of an undirected graph and a set of potential functions. Each random variable is a node on the graph. For each group, the node can be regarded as each item in the item set. The joint probability formula of the Markov network of each group in the Figure is: P(X = x) =
1 k φk (x{k}) 2
(1)
In Formula (1), x{k} represents the state of a random variable, and Z is the partition function, which is defined as (2) k φk x{k} Z= x∈χ
For a better understanding of the joint probability distribution of Markov network, the joint probability distribution represents DMN logarithmically linear mode, all the eigenvalues of the potential function Formula (1) of each group by the weighting Summing and exponentiating, we can get Formula (3): P(X = x) =
1 exp( w j f j (x)) 2 j
(3)
3 New Tools for Macroeconomic Analysis in the Era of BD 3.1 Research Methods (1)
Mathematical analysis
(2)
That is to use the corresponding data analysis tools to deal with the relevant characteristics of the research object from the perspective of quantitative analysis, so as to make correct explanations and judgments. In the research process of this article, in addition to permutation analysis based on the basic ideas of big data-related analysis techniques or algorithms, it also links the permutation analysis of related theories and concepts in the research of big data macroeconomic analysis with the statistical processing process to discuss the essential characteristics of the research object. Normative analysis and empirical analysis
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When analyzing the relevant characteristics of traditional macroeconomic analysis methods and BD macroeconomic analysis methods, this paper uses normative analysis methods to clarify the relevant advantages of BD macroeconomic analysis methods and to propose the necessity of its research; Empirical analysis is used to test the actual application value of BD analysis method when classifying and analyzing the data of enterprise BD application.
4 Empirical Analysis of New Tools for Macroeconomic Analysis in the Era of BD 4.1 Application Analysis of Industrial BD Based on Data Mining Technology Through BD analysis, it is concluded that the investment structure of China’s big data IT application industry accounts for the largest investment in the Internet, telecommunications, and financial industries, accounting for 29.1%, 21.1%, and 17.6%, respectively. The application of big data in transportation and government accounts for 9.4% and 8.7% respectively. The statistical results are shown in Table 1: As can be seen from Fig. 1, in the application of BD, the Internet, telecommunications and finance are industries with more outstanding investment potential. From the perspective of investment structure, banks have the highest proportion of BD application investment structure in the financial industry, followed by securities and insurance. Therefore, in the process of macroeconomic regulation and control, the government can use BD to promote industrial innovation, increase the cultivation of new business formats such as Internet finance, digital enterprises, and data materials, improve industrial BD application capabilities, and tap industrial development potential. Promote reform and innovation, including technology research and development system, operation mechanism, management model, etc. Table 1 Investment ratio of BD IT application industry
Industry
Percentage (%)
The internet
29.1
Telecommunications
21.1
Financial
17.6
Traffic
9.4
Government
8.7
Medical treatment
6.2
other
7.9
New Tools for Macroeconomic Analysis in the Era of Big Data Fig. 1 Investment ratio of BD IT application industry
the Internet telecommunications financial traffic government Medical treatment other
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6% 9%
8% 29%
9% 18%
21%
4.2 Analysis of Inflation Rate This article selects data from China’s 2010–2016 data sample. The inflation rate is usually calculated by the consumer price index CPI deflator, and the unemployment rate is based on the urban registered unemployment rate of the National Bureau of Statistics. The data comes from the Statistical Yearbook of the National Bureau of Statistics. The specific data is shown in Table 2. It can be seen from Fig. 2 that our country does not have a significantly high inflation rate to lower the unemployment rate, but a significantly higher unemployment rate results in a low inflation rate. Since 2010, the natural unemployment rate has been at a relatively high level of about 4%, and there is a continuous small growth trend. The main reason is that on the one hand, the international financial crisis in 2008 has sustained impact, and on the other hand, in response to the financial crisis, the economic growth is sluggish. And carry out policy reforms, such as supply-side reforms. Table 2 China’s inflation rate and unemployment rate from 2010 to 2016 (%)
Year
Inflation rate (%)
Unemployment rate (%)
2010
3.29
4.10
2011
5.39
4.10
2012
2.60
4.10
2013
2.60
4.05
2014
2.00
4.09
2015
1.40
4.05
2016
2.00
4.02
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Fig. 2 China’s inflation rate and unemployment rate from 2010 to 2016 (%)
Inflation rate (%) Unemployment rate (%) 3.29
2010
2010-2016years
2011
4.1
2012
2.6
2013
2.6 2
2014 2015 2016
4.1 5.39
4.1 4.05 4.09
1.4
4.05 2
4.02
unit: %
5 Conclusion The new wave of technology is swept, and the most typical feature is the introduction of BD. In the final analysis, BD information analysis is to better apply to the development of various industries and serve the macro analysis. BD application analysis in the Internet era opens up a new direction for industrial economic information analysis. Through BD macroeconomic analysis, it can effectively mine and integrate macroeconomic information, so as to better serve the industrial economic departments and macroeconomic decision-making. In the process of macroeconomic analysis, we must integrate the requirements of BD as soon as possible to make macroeconomic analysis more efficient and accurate, guarantee the professionalism and democracy of macroeconomic analysis, and establish a data-centric, multi-level system design, a legalized macroeconomic analysis system for the collection, utilization, and protection of decision-making information.
References 1. Ivanter VV (2018) Role of input-output model in macroeconomic analysis and forecasting. Stud Russ Econ Dev 29(6):581–583 2. Fried S (2018) Climate policy and innovation: a quantitative macroeconomic analysis. Am Econ J Macroecon 10(1):90–118 3. Nahar FH, Faza C, Azizurrohman M (2020) Macroeconomic analysis and financial ratios on Sharia Commercial Bank profitability: a case study of Indonesia. Ihtifaz J Islamic Econ Finance Bank 3(1):37 4. Ng WL, Wang YC (2020) Winners and losers of universal health insurance: a macroeconomic analysis. BE J Theor Econ 20(1):20180064.1–20180064.20 5. Chen SH (2018) International bond risk premia, currency of denomination, and macroeconomic (in)stability. J Publ Econ Theor 20(6):795–821 6. Singh G, Wilson A, Halari A (2019) The efficacy of macroeconomic policies in resolving financial market disequilibria: a cross-country analysis. Int J Financ Econ 24(1):647–667
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7. Bahadir B, Chatterjee S, Lebesmuehlbacher T (2018) The macroeconomic consequences of remittances. J Int Econ 111(3):214–232 8. Iti SH, Begen N (2019) Macroeconomic conditions at workforce entry and job satisfaction. Int J Manpow 40(5):879–893 9. Dieppe A, Georgiadis G, Ricci M, et al (2018) ECB-global: introducing the ECB’s global macroeconomic model for spillover analysis. Econ Modell 72(1):78–98 10. Cubells JF, Latorre MC (2021) Brexit deal done! A detailed micro- and macroeconomic analysis of its fallout. Econ Syst Res 5:1–26 11. Ferraz L, Ribeiro MB (2018) New tools for the CGE analysis of PTAs in the era of non-tariff barriers and global value chains: the case of Mercosur and China. Revista Brasilra de Economia 72(3):100–106 12. Huang L et al (2018) A new paradigm for accident investigation and analysis in the era of BD. Process Safety Progress 37(1):42–48
Application of “Big Data + Education” in the Construction of Curriculum Content System Xinyu Fu
Abstract It is of positive significance to guide the reform process of physical dance teaching materials, improve the quality of curriculum education, and realize teaching students in accordance with their aptitude and time. After a thorough study of the degree of attention to the sports dance network, based on the principles of combining basicity and flexibility, unifying practicality and advancement, and ensuring educational content, this article analyzes the curriculum content construction method that combines vertical construction and horizontal construction and integrates the subject logic and the demand logic, and puts forward the curriculum content system of the “three-tier structure” of public sports dance. Keywords Sports dance · Big data · Curriculum content system
1 Introduction Big data is a data collection with the main characteristics of large capacity, multiple types, fast access speed, and high application value [1]. In the era of cloud computing in which human society has entered information technology, it is an epoch-making landmark technology to classify, summarize and reorganize the massive, high-growth and diversified fragmented information by using special processing mode, and to analyze and extract high-value information that meets the needs of specific fields. Facts have proved that “big data + “ profoundly affects the innovation and development of all walks of life and all fields of society. Especially in the field of education, big data technology meets the development needs of education to keep pace with the times in the information age. From the beginning of the formation of human society, education has accompanied the growth of human beings and has gone through all stages of social replacement. While the content of education is constantly changing, people’s requirements for its timeliness, effectiveness and pertinence have X. Fu (B) Taizhou University, Taizhou, Jiangsu Province, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_58
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also become more and more relevant with the development of society. The introduction of big data technology in education not only allows people to learn from the past educational experience that is usually difficult to insight, it can also improve the current education to make it more accurate, scientific and efficient. What’s more, it can even clarify future development trends, so as to predict the result. The knowledge system of sports dance is supported by many disciplines, including sports training, anatomy, psychology, physiology, mechanics, musicology, fine arts, sociology, etc. It is shown with body movement and body display, and with unique and beautiful melody and pleasing dance costumes. It is an emerging movement that “puts emotions into actions”. It has sport cultural values such as fitness, pleasure, entertainment, competition, social interaction and aesthetic appreciation, and has a good foundation for social sports groups and development potential. The rich educational element contained in sports dance is its natural advantage from social sports to school sports, becoming one of the main courses of “sports man” in public sports in colleges and universities. In theory, it provides a diversified content system for the construction of the sports dance course. The content of the course directly determines what teachers “teach” and what students “learn” in the education process, and is the material basis for the cultivation of talents in colleges and universities. Therefore, screening, optimizing, and reorganizing the content of the curriculum to make it conform to the physical and mental characteristics of students, follow the scientific laws of education, and meet the development needs of the times, has always been the only way for emerging sports to enter the higher education system. However, because of the comprehensiveness of the subject field, the construction of the curriculum content system becomes difficult within the framework of the traditional curriculum reform technology, which becomes the main challenge that hinders the development of the teaching material of the sports dance and gives full play to the value of education.
2 Big Data Research on Attention of Sports Dance Baidu Index is based on the behavior data of a large number of users using mobile apps and PC clients to search on the Internet. Through scientific analysis and calculation, it reflects the degree of attention and network exposure of a certain keyword in the past period of time. Its main function is to gain insight into the specific information hidden in big data, and through mathematical quantification processing; it can intuitively reflect the hot spots of social concern and the interests and needs of netizens. This article uses “sports dance” as the search keyword, and selects the “monthly average search index” from May 4, 2012 to May 4, 2021 as the research object.
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Fig. 1 The time trend of “sports dance” being paid attention
2.1 Time Characteristics of Sports Dance Attention The time characteristics reflect the degree of attention and continuous changes of sports dance by the Internet through scientific calculation and analysis of the frequency and weight of sports dance search on the Internet (Fig. 1). It can be found from Fig. 1 that in the past ten years, netizens have paid 397 attentions to sports dance every day, showing a relatively regular ups and downs. In the past five years, the attention has been relatively high. Every year in mid-September, there is an explosive increase in attention heat. This time feature is closely related to the holding of most major sports dance events around this time, reflecting that netizens’ attention to sports dance is largely affected by competitive competitions, indicating that sports competitions have a guiding role in online attention. In addition, there will be a heat outbreak that is slightly lower than September every year around February, which is highly consistent with the start of the new semester of Chinese schools, indicating that in addition to sports competitions, school sports may be another important factor that promotes and influences sports dance attention. School students may be the main group that pay attention on the sports dance network.
2.2 The Characteristics of the Crowd that Pay Attention on Sports Dance Baidu Index finely portrays the gender, age, interest and other data of Baidu users searching for sports dance, and performs cluster analysis based on the common attributes of the population, and summarizes the distribution and arrangement of related information such as age, gender, and interest. It aims to explore the inner connection of sports dance attention with different population characteristics, and try to discover new rules or new methods (Figs. 2 and 3). Figure 2 shows that sports dance attention presents significant age and gender differences. The main groups of concern are young and middle-aged netizens. Among them, netizens aged 19 or younger who pay attention accounted for 32.3%, those
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Fig. 2 Population age and gender distribution
Fig. 3 Distribution of crowd interests
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aged 20–29 accounted for 36.16, and sites aged 30–39 accounted for 21.35%. The two groups of netizens under the age of 19 and 29 have both sports dance attention TGI indexes exceeding 100, 335.23 and 107.32 respectively, showing that young people have more sports dance attention, and youth groups are more willing to invest time and energy to understand sports dance. With age growing, this willingness will gradually fade. Among the following netizens on the entire network, female netizens accounted for 70.02%, with a TGI index of 140.81. It shows that, compared to men, sports dance is more attractive to women. It can be seen from Fig. 3 that in all major related hotspots, sports dance shows a higher-than-average network-wide attention (TGI > 100). Among them, a further breakdown of the field of educational training found that sports dance generally has more attention in the fields of school education, physical training, and skill learning (TGI > 100). a further breakdown of the field of medical and health, leisure and hobbies, it is found that sports dance has more than average attention in the fields of plastic surgery, dance, art, music and so on (TGI > 100). It can be seen that sports dance plays a pivotal role in promoting online attention in related fields, which shows that netizens have more hope to understand the demands and tendencies of related fields through sports dance.
2.3 The Demand Map of Sports Dance Attention The demand map presents the relevant needs of “sports dance” as the core shown by Baidu Index through the user’s search behavior before and after searching for “sports dance”. It is obtained by comprehensively calculating the degree of relevance between “sports dance” and specific keywords, as well as the search popularity of the specific keywords themselves. The distance between a specific keyword and the central word “sports dance” indicates the strength of the correlation between the word and “sports dance”. The shorter the distance, the higher the correlation. The diameter of the dot of a specific keyword indicates its search popularity. The larger the diameter, the higher the popularity. The color of the dot indicates the trend of search popularity, red means rising, and green means falling. Figure 4 shows that when netizens search for “sports dance”, the keywords with strong relevance are sports dance competition, quality outdoor training, physique lessons, gesture etiquette, dance rehearsal, etc. Commonly relevant keywords include body shape, shuttlecock, body shaping, modern social etiquette, sports dance papers, orienteering, Tai Chi soft ball, health Qigong, etc. Key words with weaker relevance include fancy rope skipping, English phonetics learning, China Sports Dance Federation, etc. This may be because certain concepts and cognitions of netizens are consistent in sports dance and related words, or sports dance and related words have similar effects to meet the specific needs of concerned netizens.
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Fig. 4 The demand map of sports dance attention
3 The Construction of the Content System of the Sports Dance Course 3.1 Basic Principles of Content Selection for the Sports Dance Course 3.1.1
Combination of Basicity and Flexibility
The main task of public physical education in colleges and universities is to enable students to master one or two sports skills, experience excellent sports culture, and develop social adaptability and lifelong sports learning capabilities based on this. Therefore, the content of the sports dance course should focus on basic special knowledge and skills, and consolidate the learning foundation of special sports quality and sports culture literacy. The curriculum should also appropriately expand the developmental content, balance the breadth and depth of the curriculum content, and reflect a certain degree of flexibility under the premise of ensuring the foundation.
3.1.2
Combination of Practicality and Advancement
Sports dance is a physical education course that focuses on physical practice. Even academic sports dance theories should be as close to students as possible, so that students can use the knowledge they have learned to solve specific problems in life and practice, achieve the goal of learning and the educational goal of using and enhancing students’ practical ability. Analyzed from the perspective of logic, practical course content must have a certain degree of advancement. The advanced nature of the content of the sports dance course is an important factor to ensure the effectiveness of teaching. Its comprehensive and diverse subject characteristics require that the content of the curriculum must pay attention to the cutting-edge information of the
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subject development, social needs, and constantly update the content elements so that the curriculum education can keep pace with the times.
3.1.3
Ensure the Educational Nature
“What kind of people to train, how to train them, and for whom?” is the fundamental problem that higher education needs to solve. “Educating people with morality” and “educating people with sports” are the fundamental tasks of public physical education in colleges and universities. Young people are in a period of value formation and establishment [2]. There is still a lot of room for shaping and development potential of personal character, and it also needs the nourishment and cultivation of education. The selection of the content of the sports dance course in colleges and universities should avoid going to two extremes: one-sided emphasis on the improvement of sports level or technical level, turning physical education into special training courses and quality exercises; or blindly catering to students’ individual pursuits or entertainment interests, turning them into sports games class, leisure and entertainment class [3]. The choice of course content should be educational. Through sports knowledge, you can appreciate sports culture, cultivate sports spirit, and build a strong physique, so that the Olympic spiritual monument shines in the sports courses.
3.2 The Basic Method of Constructing the Content of the Sports Dance Course 3.2.1
Vertical Construction and Horizontal Construction
Vertical construction refers to arranging the content of the course according to the order of the course teaching advancement, from shallow to deep, from simple to difficult [4]. Sports dance belongs to the science of sports, and its discipline is characterized by equal emphasis on theory and practice. For the construction of curriculum content in similar disciplines, a vertical structure of “theory-skills-theory-skills” is generally adopted [5]. The vertical construction focuses on the inner connection of the course content itself, and emphasizes the depth of the course content’s gradual development, which is more suitable for the construction of the professional course content of sports dance. Compared with the improvement of students’ skill level, the sports dance course in the field of public sports pays more attention to cultivating students’ ability to apply knowledge. Therefore, while there are limitations on the depth of vertical construction, horizontal construction should be used to expand the breadth of course content, so that the course content is closely linked to social practice and highlight the connection between subject knowledge and life experience.
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Subject Logic and Demand Logic
Subject logic refers to the construction of the curriculum content system based on the internal logical connection of the sports dance discipline knowledge system [6]. Basic theory set, basic skill set, general special theory set, general special skill set, advanced special theory set, advanced special skill set… are the more common building blocks of discipline logic [6]. The demand logic is to construct the curriculum content system according to the learning needs of the educational objects. Subject logic reflects the system structure of subject knowledge itself, which helps students to grasp subject knowledge clearly and comprehensively. The logic of demand reflects the learning motivation and value pursuit of students, conforms to the cognitive characteristics of students, and has a positive impact on improving students’ subjective initiative in learning [7]. Therefore, the construction of sports dance curriculum content system should pay attention to the balance and unity of subject logic and demand logic.
3.3 Concrete Construction According to the research on sports dance network focusing on big data, it can be seen that young student groups around the age of 20 are the main group that sports dance network pay attention to. Their attention to sports dance is mainly in the fields of education, sports, art, health, and entertainment. The core requirements for sports dance include: physique, shaping, technology, social interaction, etiquette, knowledge, competition, etc. The curriculum content system with a “three-tier structure” is constructed: the first layer, based on the internal logic of the sports dance discipline, is constructed vertically with “basic theory collection”, “basic skill collection”, “special theory collection” and “special skill collection”. The second layer, based on the practicality of the course content and the attention of students, is based on “origin and development”, “classification and characteristics”, “basic terminology”, “etiquette”, “clothing and appreciation”, “sports safety and prevention”, “ “body posture”, “basic actions”, “rhythmic actions”, “basic combinations”, “biological principles”, “sports and nutrition”, “fitness guidance and services”, “competition fields and organizations”, “rules and judgments” “cultural and social functions”, “special qualities”, “graded combination”, “performance combination” and other horizontal constructions. In the third layer, according to the general tasks and goals of the public physical education curriculum in ordinary colleges and universities, and in accordance with the implementation characteristics of the public physical education curriculum, the practical curriculum content in the “basic skill set” and “special skill set” is constructed vertically, highlighting the skill learning (Fig. 5).
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Fig. 5 The content system of the public sports dance course
4 Conclusion “Big data + education” is the general trend under the development opportunity of the times [8]. Based on big data technology, it is of practical significance to analyze the big data that school sports dance pays attention to, and to explore the hot spots surrounding sports dance that the current youth group pays attention to. Analyzing and summarizing massive amounts of relevant data, diagnosing academic conditions, giving students a visual presentation of their psychological characteristics, meticulously depicting students’ learning needs, grasping students’ interest trends, and providing targeted intelligent decision support are very helpful to students. At the same time, it is of positive significance to select appropriate sports dance education elements, build a general professional sports dance curriculum content system in colleges and universities, guide the reform process of sports dance teaching materials, improve the quality of curriculum education, and realize teaching students in accordance with their aptitude and time. After a thorough study of the degree of attention to sports dance networks, based on the principles of combining basicity and flexibility, unity of practicality and advancement, and ensuring educational content, the article analyzes the curriculum content construction method of integrating vertical construction and horizontal construction, subject logic and demand logic coordination, and puts forward the curriculum content system of the “three-tier structure” of public sports dance. Acknowledgements Foundation Project: Philosophy and Social Science Research Project in Universities of Jiangsu Province (Number: 2020SJA2137).
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References 1. CPC Central Committee and the State Council (2015) Action plan for promoting the development of big data 2. Dong H, Zhao E’, Zhang S et al (2018) General secretary Xi Jinping’s important speech during his inspection tour at Peking University aroused enthusiastic responses—to advance together with the new era. People.cn 3. Fu X, Yao Y (2020) Research on the reform of public physical education courses in colleges and universities from the perspective of curriculum ideology and politics. PR Magazine 06:50–51 4. Qian M, Mao Z, Zheng J (2019) The construction of fairness evaluation model of classroom teaching quality in university—based on the perspective of student evaluation. Edu Forum 34:64– 68 5. Na Z, Zhang Y, Yu L (2020) A comparative study of classroom teaching modes in “Internet + ”at home and abroad. Digital Edu 06(06):26–27 6. Fan X, Zhang N (2021) A research on the institutional logic and statute of first-class disciplines. J China Univ Mining Technol (Soc Sci) 23(01):64–74 7. Li Z, Jiao Y, Li J et al (2021) An analysis on the logical connotation of the construction and development of application-oriented universities. J Xinyang Agricult Coll 31(01):131–135 8. Ren Y (2020) Discussion on the innovation and practice of accounting education mode in universities under the mode of “The Internet and Big data.” Intell Comput Appl 10(04):246–247
Expanding the Direction of Economic Development by Improving the Utilization Rate of WR in the Age of Big Data Ru Ji
Abstract The rapid industrialization process has led to economic growth and increased the consumption of resources, followed by various environmental problems are becoming more and more serious. As a necessary guarantee for the survival and development of human beings, the quality of WR ecological environment has always been a hot topic. The distribution of WR in China is extremely uneven, and the per capita WR are insufficient. As a large water user, industry still has a big gap with developed countries in the efficiency of WR utilization. The results of this paper show that the coupling coordination degree of WR utilization and social and economic growth in our city has gradually improved between 2007 and 2016, the level of population, economy and WR development has tended to be consistent, and the system coupling coordination degree has increased from 0.05 to 0.87. The research shows that the steady growth of population will not increase the amount of water used, but the economic growth will increase the amount of water used, and the increase of water consumption will stimulate the population growth and benefit the economic development. Keywords Big data · Water utilization rate · Economic development · Dynamic relationship · Degree of coupling
1 Introduction China’s accelerated industrialization over the past 40 years has led to economic growth and completely changed the face of China, so all kinds of environmental problems brought about by industrialization are becoming more and more prominent. In the face of a series of environmental problems brought by rapid economic growth, it is of great practical significance to the rational utilization and protection of resources. Although our country has done a lot of work on the development of WR, so far, the phenomenon of WR shortage in our country still exists a lot. In addition, industry R. Ji (B) Haojing College of Shaanxi University of Science and Technology, Xi’an, Shaanxi, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_59
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is the main pillar of our economy, but the annual water consumption of industry is still high, the discharge of industrial waste water and the pollution degree of WR are increasing gradually. At present, the efficiency of industrial WR utilization is far lower than that of developed countries. Economic and social rapid development will inevitably lead to a huge demand for resources, WR as a basic resource, whether human survival or development are important [1–3]. In the era of increasingly prominent contradiction of resources, the contradiction between supply and demand of WR has been a major global problem, and all countries in the world have water crisis to varying degrees. According to the WR report issued by the United Nations in 2017, the global demand for freshWR will continue to grow for a long time to come, and the current use of freshWR is close to 70% for agricultural [4]. At present, nearly two thirds of the world’s population still lives in water-scarce areas, always facing water stress, while the world’s population is still relatively fast growing. In this context, how to use WR efficiently and improve water use efficiency as much as possible is the focus of global attention. From the current situation of WR utilization in China, there is also a serious mismatch between supply and demand, and the water crisis is becoming more and more severe. Under this situation, it is of great significance to analyze the situation of WR utilization and its problems and put forward practical policies and measures to improve efficiency. According to the research of scholars at home and abroad, economic development can not be separated from the development and utilization of WR, but the carrying capacity of WR in a certain region is a limited [5]. This paper will study the correlation and variation synergy of the internal elements of WR and economic system in our city, and study the coordinated development of WR utilization and economic growth in our city. The coupling coordination model is used to study the coupling coordination degree between WR utilization and economic development in our city, and the dynamic relationship between WR and economy is studied by autocorrelation model. On the basis of studying the relationship between WR and economic growth at home and abroad, this paper takes our city as the research object, discusses the relationship between WR and economic development from three aspects: population, economy and WR utilization. The coupling and coordination model is established to measure the coupling and coordination degree of WR and social economic system in our city. By using autocorrelation model, the dynamic relationship between WR utilization and social and economic growth in our city and three industries is analyzed.
2 Related Concepts 2.1 Measuring Water Resource Efficiency The depletion and scarcity of resources in the 1970s caused many scholars to study how resources constrain economic growth. Nordhaus, with the help of Solo model,
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natural resources are brought into the category of economic growth, and the impact of WR on economic growth is clarified. However, how to allocate resources in order to achieve the Pareto optimal allocation of resources, so that the allocation of resources can meet the needs of human development? Koopman first put forward the concept of technical efficiency in the study of effective combined production analysis, and defined that the technical efficiency is under a certain technology, if the input can not increase the output, that is, the input output is technically effective. However, Zhang Yuan, a Chinese scholar, thinks that WR efficiency is composed of three parts: allocation efficiency, technical efficiency and economic efficiency, and puts forward a method to calculate WR utilization efficiency. As the research progresses, scholars begin to pay attention to the [6, 7] of ecological environment of WR. By measuring the efficiency of industrial green WR, it is proposed that WR efficiency can be divided into WR production efficiency and use efficiency. In social activities, how to obtain the maximum output with as little input as possible is the most ideal state of human social production. Scholars at home and abroad in the measurement of water use efficiency, Using different index construction or measurement methods will produce different evaluation criteria. From the study of WR abroad, Thompson apply geography, geology and economics, The state of industrial water use in the United States, The main reason for the decline of industrial water use in five years is the technological progress of industrial water-saving recycling. Bithas uses DEA studies to compare water use in European countries, it is concluded that the way to realize the sustainable utilization of WR is to improve the utilization efficiency of WR. Bouman establishing the ratio method of water resource efficiency of single element between agricultural water resource utilization and agricultural product yield, measuring the efficiency of agricultural water use, the intervention measures of increasing the output of agricultural products and saving agricultural water are put forward. Moa and Liu using remote sensing data from parts of Hebei Province, Evaluation of crop yield, water use efficiency and water consumption in parts of Hebei Province, And put forward the suggestion [8] to the agricultural area WR management. Gregg analysis of water control policies in Austin, Texas, It is concluded that improving the utilization efficiency of WR is one of the important strategies to meet the long-term water demand of cities. Filippini and so on uses the random front-line analysis method to calculate the Slavonia WR utilization efficiency, and compared and analyzed the difference of water use between regions. And with the rapid development of the economy, the increase in industrial water consumption and the deterioration of water pollution, Scholars have made a lot of research [9] in industrial water saving and water use. Mortier studied industrial water management in the steel industry, It is suggested that environmental protection water and industrial water saving can improve the sustainable utilization of WR. Boix optimized the industrial water network by using the multi-objective mixed integer programming method. There are also some foreign scholars from the industrial water technology level research, such as Hoinkis and Libman proposed industrial water-saving technology [10].
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2.2 Coupling Coordination Studies The relationship between resource environment and economic development is not a contradiction, but a coupling and coordination relationship. By regulating the development direction of the two, they can eventually form a coordination system and develop in coordination. Therefore, coupling coordination theory has been widely used in the study of the relationship between resources, environment and economic development. At present, the coupling coordination degree analysis is mainly applied to the relationship between resources, environment and economic development, WR and social development and urbanization. The coupled coordination model developed IWAS-ToolBox such as Kalbacher for the analysis of hydrological systems in sensitive areas can be used to predict the availability and quality of WR when natural and socio-economic boundary conditions such as population growth, climate change and land use change change change change change in the future. Voisin and so on from the unidirectional economics foundation way constructs the surface resources management coupling model. Kaser establish the coupling model between groundwater and surface water, quantitatively analyze the interaction of groundwater surface water and evaluate the influence of groundwater on watershed process. China is in the stage of rapid development of social economy. Whether the utilization of natural resources is suitable for the development of economy is a hot research direction in China. At present, the commonly used methods of coupled coordination measure in China mainly include: comprehensive evaluation and metrological analysis, fuzzy and grey theory, system dynamics and so on. Comprehensive evaluation and metrological analysis are the most widely used because of their simplicity and accuracy. Through comprehensive evaluation and metrological analysis, Zhangka and others found that the coupling coordination degree between social economy and water and soil resources system in Liaoning Province is not high. Zhou Xupei and others used comprehensive evaluation and metrological analysis to calculate the coupling coordination degree between WR and social economy in Nanjing in recent years from a serious imbalance and recession to a barely coordinated development. The coupling coordination between regional WR and economic development in Yunnan Province is calculated by comprehensive evaluation and metrological analysis. The above results show that it is not only reasonable to use coupled coordination model to study the coordination degree between WR and economic growth, but also can provide a new research idea. Taking our city as an example, this paper analyzes the coupling and coordination of WR utilization and economic growth in our city by means of comprehensive evaluation and econometric analysis.
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2.3 Dynamic Relationship Research The overall development of the system can be preliminarily judged by calculating the coupling coordination between effluent resources and social economy, but further analysis is needed to understand the effect and degree of mutual influence of internal subsystems. At present, the main analysis methods of the relationship between resources and social economy are VAR model, decoupling theory, growth tail effect, Kuznets curve and so on. Compared with other models, autoregressive model has the advantage of dynamic relationship research, so autoregressive (VAR) model is used in this study. Abroad VAR widely used in regional economy, resources and economic development, industrial structure and other fields, and achieved good results. Chevallier and other self-return models of industrial production and carbon price are established. The research shows that the market macroeconomic activities may affect the lag of carbon price due to the specific system of environment. An autoregressive model is established to analyze the impact of external shocks on the economic shocks of East Asian countries. The research shows that oil prices and financial shocks have a great impact on them. On the basis of VAR model, Gupta and so on improve the research and development DSGE-VAR, combined with the economic characteristics of South Africa to study the influencing factors of regional economic impulse response in South Africa. The domestic VAR model has also been widely used in the analysis of the dynamic relationship between regional economy and resource utilization. Tang Hongsong uses the VAR model to analyze the relationship between population, economy, resources and virtual land import in China. Yang Qian uses the VAR model to think that the water use rate and space of agricultural use in China have mutual influence effect. To sum up, the relationship between WR utilization and economic growth is more complex and cannot be discussed separately. Therefore, the above scholars use the VAR model to study the relationship between resource environment and economic development, and have achieved certain research results. A VAR model is used to study the relationship between WR utilization and social and economic growth.
2.4 Relevant Formulas System integrated development evaluation Si =
(Ui − L i )(X i − Ti ) (Ui + L i − 2Si )X i + Ui Ti + L i Ti − 2Ui L i
Measurement of comprehensive development evaluation index
(1)
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i j S=
i= j
(Si + 1)(S j + 1) 2n(n − 1)
(2)
Coupling coordination model C = exp[−
3 λ=1
(Sλ − 1)2 ] 1
(3)
D = (C × T ) 3
(4)
T = αSλ1 + β Sλ2 + λSλ3
(5)
3 Study on the Impact of Improving WR Utilization in Big Data Era on Economic Development The relationship between WR and economic and social development is very close. WR are the guarantee conditions for economic and social development, and economic development is also a prerequisite for the development of WR utilization. The socalled coordinated development of WR utilization and economic growth is to analyze the cooperation and mutual adaptation between WR and social and economic subsystems, and then to provide decision-making services for the sustainable utilization of WR and the development of water measurement in the future.
3.1 Construction of Evaluation System The WR and social economic system are composed of many subsystems, the structure is complex, it is divided into three subsystems, the population subsystem selects 6 indexes, the economic subsystem selects 5 indexes, and the WR subsystem selects 10 indexes.
3.2 Evaluation Methodology (1) (2)
Systemic integrated development evaluation: standardized treatment, measurement of integrated development evaluation index; Coupling coordination model.
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Table 1 Comprehensive development evaluation index of WR and economic and social system 2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
S1 0.0000 0.0270 0.0534 0.1778 0.2306 0.2493 0.4097 0.4704 0.6076 0.7436 S2 0.0556 0.1521 0.1692 0.3183 0.1725 0.0761 0.0829 0.2529 0.5536 0.8571
Fig. 1 Comparison of comprehensive development evaluation index of WR and social economic system
Index
S3 0.0809 0.0757 0.0697 0.1395 0.0234 0.1184 0.2252 0.3157 0.5028 0.8927
1 0.5 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Year Population subsystem
Economic subsystem
Water resources subsystem
4 Analysis of Data on the Impact of Water Resource Utilization on Economic Development in Big Data Era 4.1 Integrated Development Evaluation Index According to Table 1, Fig. 1, Between 2007 and 2016, The evaluation value of the population subsystem in 2007 is close to zero, But then steadily increased year by year to the 2016 evaluation index of 0.74; The evaluation value of the economic subsystem fluctuates, From 0.06 in 2007 to 0.32 in 2010, The two years that followed, After 2013, the evaluation index rose to 0.86 in 2016; The evaluation values of the WR subsystem also fluctuate, Similar to the economic subsystem, It also rose from 2007 to 2010, 2011 as the turning point, The evaluation fell to its lowest level in ten years, The evaluation index then rose to 0.89 in 2016.
4.2 Coupling Coordination Measurement Table 2 shows that between 2007 and 2013, the coupling degree between WR system and social economy in our city is low, the overall trend is rising, but the has been in the low coupling stage. On the one hand, the development level of subsystems is low. The turning point began in 2014, and the system relationship developed into an antagonistic stage, but the economic development was still extensive. Then the gap between the level of social and economic development and the level of WR utilization is decreasing. In 2015, the initial coupling and merger reached high quality coupling in 2016.
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Table 2 Evaluation value of coupling and coordinated development of WR and economic society Coupling degree
Coupling level
Coupled cooperative
Evaluation level
0.0648
Low coupling
0.0543
Bad
2008
0.0805
Low coupling
0.0827
Bad
2009
0.0861
Low coupling
0.0916
Bad
2010
0.1524
Low coupling
0.1797
Poor
2011
0.1075
Low coupling
0.1236
Poor
2012
0.1114
Low coupling
0.1284
Poor
2013
0.1670
Low coupling
0.1999
Poor
2014
0.2707
Antagonistic stage
0.3062
Commonly
2015
0.5486
Preliminary coupling
0.5516
Commonly
2016
0.9070
High quality coupling
0.8682
Good
Fig. 2 The degree of coupling and coordinated development of WR and economic society
2016 2015 2014 2013 2012 2011 2010 2009 2008 2007
Year
Year 2007
0
0.2
0.4
0.6
0.8
1
Coupling Coupled cooperative
Coupling degree
From Fig. 2, we can see that between 2007 and 2010, the degree of coupling coordination of the system showed a steady upward trend. In 2010, the inflection point, the degree of coupling coordination decreased, and then the coupling coordination degree rises in a straight line after 2014. The system coupling coordination degree increased from 0.05 to 0.87 between 2007 and 2016, which indicates that the coupling coordination between WR and social economy in our city is improving continuously.
4.3 VAR Model Analysis Conclusion Based on the time series data of WR utilization and economy of our city from 2007 to 2016, the population, economy and water consumption VAR model of our city and the population, economy and water consumption of three industries were established. The dynamic relationship between WR utilization and economic growth and the population, economy and water consumption of three industries was analyzed by
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impulse response function and variance decomposition. The following conclusions were obtained. There is no long-term cointegration relationship between WR and total water consumption in our city, and the abundance and withered of WR will not directly affect the annual water consumption; There is a long-term cointegration relationship between population, economy and water consumption in the secondary industry of our city, in which the change of population and economy in the secondary industry is the Granger cause of the change of water consumption, and there is a long-term cointegration relationship between population, economy and water consumption in the tertiary industry of our city. After causality test, the change of water consumption in tertiary industry is not the main factor of population change, and the change of population is the Granger reason of water consumption change.
5 Conclusions This paper makes an empirical study on the relationship between WR and economic growth. The main work is to analyze the present situation of population, economy and WR utilization in our city, analyze the degree of coupling coordination between WR and social economy, and then analyze the dynamic relationship between WR and social economy through autocorrelation model and three industries. Acknowledgements The scientific research projects of education department of Shaanxi: research on wr utilization efficiency in western china based on dea and malquist index, project number: 20JK0077.
References 1. Tu Y, Chen K, Wang H et al (2020) Regional WR security evaluation based on a hybrid fuzzy BWM-TOPSIS method. Int J Environ Res Public Health 17(14):4987–4988 2. Xu YJ et al (2015) Advances in research on water and fertilizer coupling and its effects on rice growth and utilization rate of nitrogen. Agricult Technol 16(4):737–744 3. Li W, Wang B, Xie YL et al (2015) An inexact mixed risk-aversion two-stage stochastic programming model for WR management under uncertainty. Environ Pollut Res Int 22(4):2964–2975 4. Watkins DW, Mckinney DC (2015) Finding robust solutions to WR problems. J WR Plann Manage 123(1):49–58 5. Hartmann A, Goldscheider N, Wagener T et al (2015) Karst WR in a changing world: review of hydrological modeling approaches. Rev Geophys 52(3):218–242 6. Karthe D, Chalov S, Borchardt D (2015) WR and their management in central Asia in the early twenty first century: status, challenges and future prospects. Environ Earth Sci 73(2):487–499 7. Withanachchi SS, Houdret A, Nergui S et al 2015 (Re)configuration of WR management in Mongolia: a critical geopolitical analysis. Dtas.fyper.com 88(3):647–653
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8. Ruperez-Moreno C, Perez-Sanchez J, Senent-Aparicio J et al (2015) The economic value of conjoint local management in WR: results from a contingent valuation in the Boquerón aquifer (Albacete, SE Spain). ence of the total environment 532:255–264 9. Denooyer TA, Peschel JM, Zhang Z et al (2016) Integrating WR and power generation: the energy–water nexus in Illinois. Appl Energy 162:363–371 10. Lv Y, Huang GH, Li YP et al (2015) Planning regional WR system using an interval fuzzy bi-level programming method. J Environ Inf 16(2):43–56
Application of Artificial Intelligence Technology in College English Teaching Under the Background of Big Data Yuanyuan Chai and Na Liu
Abstract China enters a new era of deep integration of information technology and education, which leads the development of education. Big data and artificial intelligence are the emerging information technologies which have supported and promoted the reform and development of education. Based on the realistic requirement of college English teaching, how to improve the teaching effectiveness of the teaching mode with the aid of artificial intelligence technology; implement personalized, wisdom and interactive learning; cultivate students critical thinking ability, cooperative ability, thus realize the artificial intelligence in the effective use of English classroom become a key problem to be solved. This paper expounds the background of big data in the artificial intelligence application value in language learning, and then explores the application of AI in college English teaching from five aspects— listening, speaking, writing, translation and intelligent management respectively and finally expounds the big data under the background of AI technology innovation strategy in college English teaching. Keywords Artificial intelligence · Big data · College English · Teaching
1 Introduction The “Teaching Methods and Means”, part of College English Teaching Guide (2020 edition) advocates that teaching methods should focus on teaching methods and activities, but also to learning methods and activities. Big data, virtual reality technology, artificial intelligence technology and other teaching methods meet the effect of college students’ English learning [1]. The innovation of teaching based on teaching methods and means cause the college English teaching reform respectively. The implementation of the Action Plan for Informatization in Education 2.0 (2018) has made foreign language education experience two periods from “Internet + ” to “Artificial Intelligence + ”. The period of “Internet + Foreign Language Education” is Y. Chai · N. Liu (B) School of Foreign Language, Jingchu University of Technology, Jingmen, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_60
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hybrid learning based on online resources such as MOOCs. On the basis of “teach first, practice later”, the process of “learn first, teach later” is flipped.” “Artificial intelligence + foreign language education”, on the basis of “reverse process”, further realizes “learning based on teaching”, and enters into the intelligent foreign language teaching mode of systematic structural reform [2]. At present, the English teaching is shifting from the blended learning mode and the deep integration of college English teaching and artificial intelligence will become the new normal of language teaching which is at a critical stage of transformation and upgrading.
2 Artificial Intelligence and Big Data 2.1 Artificial Intelligence (AI) Artificial Intelligence (AI) is the Intelligence that is realized on the machine (computer) with the Artificial method. Generally speaking, artificial intelligence is to study how to make machines have the ability to listen, speak, see, write, think, learn, adapt to environmental changes and solve various practical problems. It can be roughly divided into three categories: one is to complete the storage, extraction and internal processing of information through the intelligent system; the second is to complete the symbolic processing of information through the intelligent ability; the third is to establish the program logic which is similar to the human behavior logic, and make use of this ability to answer or deal with the questions raised by human beings [3]. From the perspective of language learning, the function of artificial intelligence is more specific, such as language parsing technology, speech recognition technology, language translation techniques are more common. As the growth of the penetration of artificial intelligence, the application of these techniques in language teaching classroom is more widely, and is still in a growing process, and it has brought great opportunities for the innovation and transformation of language education methods. In the field of teaching application, artificial intelligence technology involves many concepts, such as (computer) highly intelligent, big data, machine learning, personalized learning, adaptive learning, intelligent learning, deep learning, learning behavior analysis, emotional computing and so on [4]. The 2020 Conference on Artificial Intelligence and Big Data in Education shows that artificial intelligence has achieved deep integration with education, and tens of millions of students are engaged in personalized learning based on big data.
2.2 Big Data Big Data refers to the integration and analysis of all existing Data without the use of random sampling analysis, which is a huge database collected from various sources
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in various forms. Big data can complete information processing within a short period of time that takes a lot of time for human beings. In other words, information that cannot be read by human beings can be obtained through big data. The relationship between big data and cloud computing is as close as the heads and tails of a coin. Big data cannot be processed by a single computer, so it is necessary to adopt distributed computing structures. Its characteristic is to carry out huge data mining, which cannot be separated from the distributed processing, distributed database, virtualization technology, cloud storage and other related contents expressed in the computer. Professor Yang Xianmin pointed out in his article “College Teaching Reform in the Era of Big Data”, big data drives educational reform and innovation, and teaching has entered a new era of data-driven, and diversified data collection technologies have promoted teaching to be data-driven [5]. He pointed out that when the teaching data continues to accumulate to a certain extent, the teaching system will become intelligent and even more intelligent.
3 The Application Value of Artificial Intelligence in College English Teaching Under the Background of Big Data First of all, most English teaching is still based on teachers’ instilling of relevant knowledge, which leads to students passively listening to the class and failing to understand and master relevant knowledge in time at the first time. In recent years, teachers have been teaching with the help of multimedia. Although the class is vivid, this teaching method lays emphasis on courseware explanation and ignores the realtime teacher-student interaction. As a result, students still passively accept knowledge and have little enthusiasm in learning, let alone to put the knowledge being learnt into practice. Teaching cannot form a good cycle. Appropriate and effective teaching methods should be used to reflect the teaching concept of teacher-led and studentoriented. The teaching method that teachers guide students to participate is worthy of praise and praise, which truly realize the complementarities and interactions between big data and artificial intelligence, so as to play the role of better teaching effect under “1 + 1 > 2” [6]. Secondly, language subjects not only have requirements for basic knowledge system, but also have obvious practical requirements. It is difficult for a classroom teaching to effectively expand the teaching scope. The requirements of different teaching content involved the use of an array of intelligent teaching means can open up new channels, consolidate the teaching purpose of cognitive structure, and realize the diversification of teaching methods and richness, which can not only provide good support for the students’ learning activities, but also help regulate the classroom teaching atmosphere, letting to greatly enhance teaching effectiveness.
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4 The Application Path of Artificial Intelligence in College English Teaching Under the Background of Big Data At present, college English teaching still pays more attention to speaking, reading, listening and writing courses which are all explained by teachers. However, there are many contents in college English textbooks. In order to catch up with the teaching progress, teachers can only choose important contents to explain. Classroom time is limited, and teachers leave fewer opportunities for cooperative learning such as discussion among students. In the teaching of listening and speaking courses, teachers select a part of listening materials for students to train, and then appropriately interspersed with listening exercises, this teaching method is more effective [7]. However, due to some students’ own limited vocabulary and some English teachers’ pronunciation which is not too standard, resulting in students’ low interest in listening learning. As for listening and speaking class, teachers spend a long time on listening practice, so they compress the time for oral practice. In addition, many students feel that their pronunciation is not standard and shy to speak English, which also leads to very dull oral English class. Reasonable teaching arrangement and the integration of advanced information technology inject more fresh “blood” into classroom teaching and guarantee the maximum of classroom teaching effect.
4.1 Listening Training—Use Corpus to Complete Automatic Resource Matching and Interaction Listening training is a basic part of college English teaching, which has a decisive influence on the construction of students’ English application ability. Traditional English listening mode is only mechanized training, lack of flexibility. This fixed English listening training mode, which makes students feel very tired and feel boring to learn English listening. As a result, students’ listening cannot be developed and improved. Therefore, based on the current combination of online and offline listening teaching in college English, artificial intelligence technology is a key channel to open the range of listening training resources. With the help of the existing corpus reserve, automatic matching and interaction can be completed, so that students can quickly acquire listening materials in line with their own learning needs from the huge English listening materials, and carry out targeted automated exercises with artificial intelligence devices according to the content of the materials. First of all, when students are faced with massive data, they can input their age, study period, English listening foundation, key training direction and other basic information in the online artificial intelligence system, and they can also intelligently screen and match the corresponding listening materials in the corpus. In order to reduce the burden of students’ listening practice, teachers can push relevant high-frequency words to individual students according to students’ listening
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levels, accompanied by the corresponding explanation of the use of words to help students deeply understand the meaning of the words. Secondly, in order to further enhance the interaction between offline classroom learning and situation, the automatic recognition function of artificial intelligence can also be further utilized. According to the learning needs, students can speak out the key words they want through artificial intelligence speech recognition, and the system can automatically screen and match relevant listening practice materials for students to use and enable students to gain a better learning experience in automatic and random language scenarios. For example, when the students say “artificial intelligence”, the corpus can automatically screen out the listening materials and relevant information related to “artificial intelligence”. Such as iFlytek E listening and speaking platform which let students learn in a free place, effectively inject vitality into students’ learning; let students in a very relaxed learning environment without pressure to learn. Using this platform for learning, students do not have to listen under the supervision of the teachers, reduce the pressure of students learning. Students can interact with the intelligent system, teachers and classmates, which greatly increases the listening training time and ensures enough listening training time. In order to test the learning effect of online platform, we conducted a survey, and out of 400 questionnaires, 387 were particularly supportive. This figure is already a basic representation of students’ attitudes towards online education.
4.2 Spoken Dialogue—Use Artificial Intelligence Robots to Carry Out One-To-One Dialogue Communication is the ultimate goal of language learning, and English learning is no exception. The ultimate pursuit of college English teaching lies in the construction of practical application ability of language. Oral English is the most critical content in English learning. Oral expression can fully reflect the level of students’ language use ability. Oral English expression level not only affects the effectiveness of students’ inter-lingual communication, but also objectively reflects the development of students’ English application ability. In the traditional college English classroom teaching mode, teachers ignore the creation of context and students also lack communication opportunities, resulting in the situation that students are shy to express themselves, or even express themselves unclearly. The AI technology has, to a large extent, broken the deadlock of the difficulty of dialogue organization in English classroom. Firstly, artificial intelligence technology can provide students with accompanying oral practice. The emergence of educational robots has built a more colorful English communication environment for students. Accompanied dialogues are of great help for learners to become more accustomed to using English. The educational robot has the ability of action and can follow the learners like playmates. Students can
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solve the problems of limited teachers and students’ insufficient mentoring abilities by establishing a one-to-one dialogue with an artificial intelligence robot. It can create diversified dialogue scenarios according to different learning needs, which can provide students with sufficient opportunities to practice oral English. In this process, it can fully and effectively relieve the tension when students talk with real people, and help students achieve better oral English fluency practice and improvement. And in the dialogue practice, it can also analyze the students’ thinking time and the actual situation of communication, and provide them with vocabulary hints and pronunciation correction, so as to ensure the accuracy of the use of language expression. After the dialogue is completed, the robot can simulate the role of a teacher, summarize and evaluate the students’ oral practice, and give them specific suggestions. This way of oral English practice can reduce the embarrassment of some students who are not willing to speak English, but also enhance the effectiveness of students’ oral English practice, and stimulate the potential of students’ learning. Human–robot interactive dialogues in today’s oral English teaching should promote the robot teaching indepth development in order to achieve the full and effective development of oral English teaching which will enhance students’ abilities to use English and improve their English level. Secondly, intelligent technology can be used to carry out group dialogue exercises with students. In the traditional English classroom, the main difficulty that teachers encounter in teaching is that students’ oral English level will decline due to the lack of practice. The main reason is that students lack the opportunity of oral expression outside the classroom and lack of practice objects. In the view of above problems, the application of artificial intelligence technology can become an auxiliary tool for students’ oral practice in class and outside class. In the classroom, the intelligent robot can play the role of assistant in the activities, and set up dialogue topics or fixed sentence patterns for teachers and students, etc. At the same time, the intelligent robot promotes the in-depth communication between teachers and students, so that teachers can have a more comprehensive understanding of students’ oral English ability and lay a good foundation for subsequent learning.
4.3 Translation Exercise—Using Cloud Platform Technology to Achieve Key and Difficult Breakthroughs English translation is a language conversion process based on sufficient word and sentence accumulation and listening practice, which has high requirements for learners’ grammar application level, real-time parsing ability and organization and expression ability. It has become an important issue in the teaching process to how to improve the accuracy of English translation. The practice of translation teaching under the background of artificial intelligence can enrich and expand more teaching resources on the basis of the content of textbooks, and give students a more real and efficient sense of learning experience.
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First, in the past English translation, teaching is based on the content of the teaching materials. Teachers rarely engage in extracurricular activities. The diversity function of artificial intelligence can integrate a lot of Internet resources on translation into English teaching practice, to make students quickly into the language environment and learn a lot of present popular elements. Through artificial intelligence, we can find the English materials we need or are interested in and then carry out translation practices. It can not only enrich the contents and forms of students’ translation, but also greatly increase the interest and immediacy of translation practices. Personal translation is boring and inefficient. Artificial intelligence system carries out cooperative learning, through the formation of student teams to discuss the topic of translation, not only stimulate the enthusiasm of students in translation, but also improve their own language organization ability and critical thinking ability through mutual learning. Second, the cloud platform application supported by artificial intelligence can bring new channels for English translation teaching. By setting translation tasks on the intelligent cloud platform, artificial intelligence can be used to track each student’s translation and view the formative assessment report, so as to timely and accurately grasp the characteristics and problems of students’ translation and realize personalized guidance. The emergence of English translation software and platform based on cloud services can help students solve translation difficulties. Students can search translation materials on the platform, solve their own difficulties in sentence understanding, language processing and language application, and enhance the practicality of translation learning. To a certain extent, it enhances students’ learning confidence and stimulates their interest in in-depth learning. Third, in the teaching practice of the past, the classroom teaching time is limited. Students complete homework after class quality and cannot get effective guarantees. However, teachers can make use of the advantages under the background of artificial intelligence, and diversified functions of artificial intelligence to arrange translation tasks and practical trainings for students without the limitation of time and space, so that students can practice independently after class, complete translation practice after class, and exercise students’ English translation ability. Teachers can evaluate the completion of students’ homework in real time and give feedbacks. For example, teachers can monitor students’ learning on the cloud service platform and track the progress of students in completing translation tasks, understand the development of learning situation, establish the files of students’ translation learning, analyze students’ behaviors, and give feedback to students in time. Using the platform to discuss solutions with students can achieve accurate and targeted evaluation. Online guidance for students’ translation work can make artificial intelligence have a positive impact on the practical teaching of English translation in colleges and universities.
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4.4 Writing Guidance—Use the Automatic Correcting Function to Check and Fill Gaps Writing is an important link for students to exercise their expression level of words and sentences and use of grammar. The traditional English writing teaching classes are often trapped in a narrow theme, revised too subjective. As a result, it is difficult for students to objectively grasp the advantages and disadvantages of their English writing. First of all, under the current information platform, big data sharing provides extensive materials for college English writing, such as writing cases, teaching videos, expert lectures, news reports, learning methods and so on. English writing instruction in using the artificial intelligence technology is mainly with the help of the framing function. Students choose their own topics according to their own thoughts and then do their writings under the technology support system which gives full play to the function of frame building, and provides a general frame template combined with the theme and basic ideas, as well as relevant vocabulary and sentence patterns for reference, so that students can follow the guidance of the frame, form a clearer logical chain of writing, and achieve the training purpose of deepening expression. Secondly, the essay automatic correcting system can carry on a very comprehensive analysis to the student’s English composition, make the basic judgment to the student’s article structure, can be timely revised in the spelling mistakes and many grammar mistakes, and timely put forward the revision advice. Introducing an automatic online grade service tools to English writing teaching can give full play to its individuality, instant feedback, comments, and the advantages of online modifications, which let the students get writing fun in the process of multi-draft revision, realize self-exploration and self-innovation construction, enhance the sense of self-efficacy of their own writing ability, and improve the motivation and writing ability of English independent writing [8]. Teachers can make use of the automatic feedback mechanism, to manually revise twice and give further teaching feedbacks. Teachers can also master the learning situation of students according to the formative assessment report given by the system and carry out course improvement at any time, which has profound significance in improving teaching methods and improving teaching system. Intelligent English correcting learning platforms such as correcting website have been widely promoted in many colleges and universities. This intelligent teaching platform can provide timely and effective feedback to the English compositions uploaded by students, and objectively evaluate the English compositions of students.
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4.5 Intelligent management—to Provide More Diversified Process-Based Teaching Evaluation Artificial intelligence is used at teaching management including test and evaluation, teaching management. The use of AI can provide more diversified process teaching evaluation, make the evaluation means more abundant, which make the evaluation more scientific and accurate in the process and the result respectively, so as to provide students with a comprehensive learning diagnosis. It can also realize the unification of teaching scale and individuation with timely and accurate learning intervention. For example, in the virtual learning space Super Star Learning Connect, teachers can create classes, share self-made micro-lesson teaching plans and supplementary learning materials. It can also assign homework; carry out teaching activities such as sign-in, discussion and Q&A in the virtual class. Teachers can organize online exam at the end of the semester. The platform would record of each student’s answers according to “off” times. Teachers can monitor multiple classes at the same time; send warnings to “leave” (cheating) students, or “a key roll”. In addition, the platform’s machine grading also greatly reduces the teacher’s grading burden. In addition to course and class management, the platform can also provide process-based evaluation based on students’ attendance, homework completion quality, micro-class viewing time and other big data formation. Teachers can master students’ daily learning conditions through quantitative data, so as to carry out precise learning intervention. Students are encouraged and urged to realize personalized learning through learning diagnosis and class ranking issued by the master platform. A similar platform is “WeLearn” of FLTRP, which can monitor and evaluate students’ online learning throughout the whole process, and is a tool for the effective implementation of blended teaching. The “Sentence Database Correcting Net” has provided a powerful online intelligent thesis revision platform. The teachers create classes on the correcting network, upload composition training, and set deadlines and grading criteria for submission in the background, and also the scoring criteria (such as National CET-4 and CET-6, Professional CET-4 and CET-8, IELTS criteria), and upload the samples. The platform generates an essay number or a QR code, and students upload essays online through class tasks on the platform or by searching the essay number/QR code. The “Regrading Net” is based on the big data technology, from which the students’ compositions are commented on sentence by sentence, involving word spelling, grammar and syntax structure, punctuation marks, etc. [9]. The advanced replacement words are suggested for some basic words, and multiple synonyms are listed. College English classes generally have a large number of students, and college teachers have heavy teaching tasks, so it is difficult for them to correct all students’ compositions from details, let alone give targeted one-toone guidance. The correcting software with artificial intelligence is a powerful and effective supplement to English writing teaching.
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5 Innovative Strategies of AI Technology in College English Teaching Under the Background of Big Data 5.1 Integrate Teaching Resources and Innovate Teaching Models First of all, learning resources can be highly integrated. The integrating use of big data can obtain, analyze process, process useful resources, and screen valuable English learning software for students’ reference and make full use of digital platforms and mobile information terminals, such as MOOC, Netease public courses, TED lecture videos, etc., which can enrich teaching methods. Integrating in-class data and after-class data can innovate teaching model, create dynamic English classroom, and achieve the purpose of improving teaching effect [10]. Second, apart from the existing data resources and college English teachers’ teaching experience in the integration of teaching resources, it can also be used for classroom teaching content analysis and processing. We can build a team of teachers who are good at making online courses. On the basis of analyzing the teaching data of artificial intelligence and summarizing the previous experience, we should try the best to enrich the materials and select the best ones, so that students can get a better experience on online learning. Creating online boutique network classes actively can bring students specialized network curriculum contents, which make the accumulation of knowledge and ability of ascension. The high-quality goods net classes can also be promoted as models in other colleges and universities which can not only promote the courses but also achieve academic exchanges. As a result, it can be better to strengthen the effect of the courseware making, so as to optimize the teaching processes, and improve the level of instructional designs. Third, to highlight the teaching of AI technology advantages, it can further emphasize the cooperation and interaction in learning in the late English hybrid teaching courseware making. The indwelling greater interaction space can be used to stimulate the individual subjective initiative, more increase the dominant sectors dominated by the practice of the students, such as interactive dialogue link, link of real-time translation, etc., so as to achieve the purpose of strengthening the training effect. College English teachers aim at cultivating students’ language application ability, optimizing English curriculum design and increasing the proportion of oral English teaching. Introduction of mobile learning APPs enhances online interaction. The various multimedia technologies, such as mobile learning APPs and other information tools used in teaching process enrich teaching content and enhance teacher-student interaction. Taking the intelligent speech recognition and oral diagnosis platform VTalking as an example, students can download the VTalking software on their mobile phones, and teachers can carry out activities such as oral homework and large-scale oral examination to assist in the flipped classroom. Teachers can also choose other suitable mobile APPs according to the actual situation, such as English Fun Dubbing, Fluent
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English speaking, Microsoft Small English and other human-computer interaction softwares to assist teaching and also provide online guidance.
5.2 Optimize English Learning Methods and Transform Teachers’ Functions First, it enriches students’ language input and output path; Second, English penetrates every corner of students’ study and life, breaks the limitation of learning space and time, constructs the real environment of language communication as a result it improves the accuracy of students’ language expression. According to their own existing knowledge, the students complete English listening, speaking, reading and writing exercises with the help of online learning platforms. And then uploading learning outcomes to the system through the evaluation provides feedback to students, makes them master English autonomous learning problems in time, and maintains the correctness of the direction of learning [11]. At the same time, the use of intelligent products to monitor and track students’ learning progress and actual situation in real time, so as to fully understand students’ learning preferences and needs, adjust the learning methods of students who are less accepted in class, and customize personalized learning programs for students. When students are confused about learning, teachers can also keep communicating with students on the platform to answer their questions, further improve and optimize students’ independent learning and effectively improve learning efficiency to achieve the best learning quality [12]. Artificial intelligence can create a better teaching environment for teachers and replace the leading function of teachers in the traditional teaching model which is focus by the teachers, create more space for teachers’ classroom teaching, and help them out from the heavy teaching work, put more effort into training students’ professional abilities and accomplishments so as to provide help and support action from students’ all-round development. At last it can achieve the goal of cultivating talents with high ability and quality.
5.3 Improve the Teaching Evaluation System and Set Multiple Assessment Indicators Reconstruction of teaching evaluation system, setting diversified evaluation indexes and further improving teaching quality are the current evaluation system of applying artificial intelligence to college English teaching under the context of big data. Under the help of the network platform, teachers make full use of large data, which can divide students’ learning into multiple assessment contents from which we can obtain traces left by the students’ online activities and effective grasp the students’ learning habits and learning effect and also provide them with timely feedback and help.
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Teachers should be based on the characteristics of college students’ learning, make full use of the advantage of the large data. For example, big data technology can analyze a student’s learning track and interest points according to their online activity records, test their mastery of vocabulary, sentence patterns, grammar, knowledge and other aspects according to their online answer records, and help students solve their doubts by answering questions left in the discussion area online at fixed time points. The teacher also can set up various forms of learning task for all the students’ classroom participation and learning situation assessment in class. The upload task can be evaluated through mutual evaluation between teachers and students, self-evaluation, and group evaluation. In this way, through multi-dimensional and diversified mixed evaluation, it is helpful to realize the truest, most objective and most comprehensive teaching evaluation. In order to ensure that the assessment results are more fair and effective, it can truly reflect students’ learning situation and English application level, and help students’ complete targeted improvement. In this way, students’ differences and needs can be timely taught and feedback can be given. Students’ enthusiasm can be improved, and learning objectives are clearer. It can comprehensively measure the teaching quality and teaching effect, so as to create a foundation for subsequent teaching improvement.
6 Conclusion In a word, the advent of the era of big data brings great opportunities and challenges to English education. For the opportunity, it provides amount of resources for college English teaching, which has improved the motivation of college students. English education will also be increasingly personalized under the influence of big data. College English teaching and the characteristics of big data fully integrating together innovate their own teaching ideas and promote the development of modern college English teaching methods. The rapid improvement of big data analysis capability in the Internet era and the emergence of application algorithm platforms such as Baidu, Tencent and iFlytek have continuously promoted the in-depth deployment of “AI + ” applications. Intelligent Classroom, Intelligent Translation Machine, Intelligent Man-machine Dialogue Software, Intelligent Writing Correcting Software and Intelligent Management System Serving foreign language teaching are only the beginning of intelligent language learning [8]. The progress of artificial intelligence technology realize the personalized, wisdom, interactive learning, which fully improve students’ learning efficiency, let the students better use the fragment time for language learning, cultivate the students’ thinking ability, practical ability, cooperation and communication ability, so as to help students establish the awareness of lifelong learning. As for the challenges, educators should combine the development situation of contemporary society to innovate the concept of English teaching. At present,
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colleges and universities have gradually built and improved the campus network platforms. Teachers should re-establish new teaching thinking platforms such as large network and large classroom, so as to take maximum advantages of advanced technology. The teaching space of teachers should no longer be confined to the classroom, but combine with the learning needs and personality characteristics of students, which can well mobilize the enthusiasm of students for learning [7]. College English teachers should keep up with the pace of the times, improve their professional quality and information technology ability, improve students’ ability and meet the basic needs of students. College English teachers should not only make scientific use of big data technology, but also pay attention to improving their ability to use information technology. Acknowledgements This work was supported by the Intercultural Communication Research Team of Jingchu Institute of Technology, under Grant No. TD202102, Philosophy and Social Science Research Project of Education Department of Hubei Province (20Y191) and Education and Teaching Research Project of Jingchu University of Technology (JX2021-36).
References 1. Yu HL (2020) Implementing the college English teaching guide and innovating teaching methods and means. Foreign Lang 5:10–16 2. He JH, He JS (2020) The theory and paradigm evolution of college foreign language education information in 70 years. Audio-visual Teaching of Foreign Languages 1:17–23 + 3 3. Institute of Intelligent Learning, Beijing Normal University (2016) White paper on development of global educational robots, 2017 4. Cheng JL (2020) On the embodiment and application of artificial intelligence technology in foreign language teaching. J Beijing Int Stud Univ 2:14–25 5. Zhang J, Huang JS (2019) Big data driven foreign language teaching and research innovation: a review of foreign language teaching and research innovation seminar in the context of big data and the first love the future foreign language education technology innovation workshop. Audio-visual Teaching of Foreign Languages 3:116–120 6. Guo XP (2018) Design and practice of college English speech course under mixed teaching mode—a case study of Inner Mongolia Normal University. J Inner Mongolia Normal Univ (Education Science Edition) 31(3):87–90 7. Wang YY (2018) Discussion on the reform of college English teaching under the background of artificial intelligence and big data. Overseas English 15:80–81 8. Chen CM (2019) Application of artificial intelligence in english writing—a statistical analysis based on CNKI. J Leshan Normal Univ 34(7):105–111 9. Yang XQ, Du YC (2015) Research on the teaching mode of college English independent writing based on the regrading network. Audio-visual Teaching of Foreign Languages 2(126):17–23 10. Yang HA (2016) New model of college English teaching in the era of big data. China Educational Information 15:80–82 11. Chen JL (2017) Reconstructing the new paradigm of foreign language teaching in the era of big data. J Social Sci 12. Jin Y (2020) Exploration of English teaching innovation from the perspective of 5G+ artificial intelligence. Theo Res Pract Innov Entrep 3(7):67–68
Innovative Development and Practice of University Intellectual Property Management Based on Big Data Algorithm Shi Hang and Dan Ying Wu
Abstract With the gradual popularization of education and the continuous improvement of educational quality in colleges and universities, scientific research projects are an important part of improving self-competitiveness in colleges and universities. The establishment of a sound scientific research management system is conducive to laying the foundation for the development of scientific research. When the scientific research project is carried out, more and more data and records are produced, and the huge amount of data needs to be supported by the data mining algorithm, so as to realize the innovation of scientific research management. Keywords Data mining algorithm · Scientific research management in colleges and universities · Association rules · Cluster analysis
1 Introduction In the application of data mining algorithm, teachers can extract useful data from scientific research management system, find out the hidden laws of data, traditional methods cannot meet the requirements of data processing, and data mining technology can face massive data. Improve the efficiency of scientific research. Relying on data mining algorithm to carry out scientific research management is helpful to improve the application value of scientific research management system, improve the level of scientific research management, and optimize the work flow of scientific research management in colleges and universities.
S. Hang (B) · D. Y. Wu Guangzhou Xinhua University, Gaungdong 510520, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_61
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2 Research on Data Mining Algorithm and Specific Functions 2.1 Summary of Data Mining In fact, data mining is not a simple subject content, which contains knowledge in many fields, such as database, artificial intelligence, machine learning and so on. Academic circles think that database is to extract useful information from massive incomplete, random and fuzzy data, but this information is not obvious, but implicit in the data center, people extract information through data mining, and use data analysis tools to observe valuable information or ignored information.
2.2 Common Data Mining Algorithms and Functions Data mining is to use a certain algorithm to search for valuable or hidden information content in massive data, combine artificial intelligence, mathematical statistics, machine learning and other fields of knowledge, and give play to data mining technology and related functions. The details are as follows:(1) Association rule analysis, which is an important function of data mining algorithm, that is, finding frequent itemsets from a given data set, X and Y in the database represent attribute values, and under association rules, When the data satisfies the X condition, it must satisfy the Y condition. At present, data mining technology will be widely used in business and finance to process massive data information and extract valuable content from it. (2) Regression pattern analysis, unlike association rule analysis, is to predict continuous values to mine valid data. For example, a certain working life, personal resume, educational background and other conditions of a known enterprise, the approximate range of its annual salary, the application of regression model can achieve the determination of the scope. In the function of the data mining algorithm, the more known conditions, the more information excavated, and the more accurate the results are. (3) Clustering analysis means that data with a high degree of similarity are summed up together and grouped together into one category, and similar data are found from data sets by cluster analysis, and then divided into different data groups. In cluster analysis, clustering algorithm is used to detect the data, to judge the hidden attributes behind the data, and then to divide the data into several similar groups, which is convenient for subsequent data processing and research [1].
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3 Current Situation of Scientific Research Management in Colleges and Universities Under the background of information age, the progress of science and technology makes the management of many fields develop towards the direction of intelligence and digitization, especially in the management of scientific research in colleges and universities. At present, some schools attach too much importance to teaching management and ignore the importance of scientific research management, which makes it difficult to improve the level of scientific research management and related technologies. For example, scientific research management statistics still rely on traditional manual work methods, not only time-consuming, but also increased the pressure on managers. In the statistics of the scientific research achievements of the teachers of each department, most of the managers do a good job in the registration and induction of the information of each department, record all the teachers’ scientific research projects, the number of papers published, the winning information, and then input these data into the school scientific research management system, and collect the data uniformly. This kind of data collection method is inefficient. When the tutor is on a personal business trip, it is easy for the manager to miss this part of the tutor research project, which leads to incomplete data statistics. Not only that managers sometimes collect data, but simply collate and summarize the data. Because of their own level constraints, they can not standardize the operation of scientific research results, and the statistical data may lack authority. It is impossible to provide reference for later data prediction and analysis. In addition, the school lacks the feasible software in the scientific research management, each school’s scientific research direction is different, in the management, the school should combine own development present situation, selects the scientific research quantification software as far as possible, Ensure the regularization of scientific research data statistics. In order to improve the quality of scientific research management, schools should pay more attention to it and rely on high technology to apply data mining algorithm to scientific research management system [2].
4 Application of Data Mining Algorithm in Scientific Research Management of Universities in New Period 4.1 Application of Data Mining Techniques in Project Establishment and Feasibility Assessment The orderly development of scientific research management in colleges and universities needs to be based on specific scientific research topics. After selecting the topics, the feasibility of the research is evaluated and analyzed. The establishment and evaluation of scientific research projects is an important task of scientific research
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management. At present, the domestic application examination and approval system is used to establish scientific research projects. After the personnel of scientific research institutions have applied, the competent department of science and technology has screened all applications, and through the evaluation and demonstration of experts, the specific undertaking units of scientific research projects have been selected. At this time the project was formally established. The project will involve many aspects, such as application unit, fund organization arrangement, competent unit, subject reviewer, project research field and so on. Although the scientific research management department has been set up in the school, the database and the scientific research management system have also been established. The system includes the process of project application and evaluation, and makes automatic statistics on the upload and result of project declaration documents. Make the traditional scientific research management work gradually turn to information. In order to further improve the quality of management, we should dig into the existing data, find out the hidden valuable information, make full use of this information, give guidance to scientific research projects, and maximize the use of limited resources. Save scientific research funds on the premise of ensuring research results. In the process of scientific research and project establishment, we should make rational use of data mining technology, excavate all kinds of factors encountered in the application, find out the potential rules, and provide reference basis for the construction of the following index system and the determination of the selection method. By using data mining technology, using Apriori algorithm to mine valuable data, finding association rules in data, and making objective evaluation on the feasibility and rationality of scientific research project based on rule analysis, It also provides help for the orderly development of subsequent projects [3].
4.2 Application of Data Mining Technology in Project Management In the scientific research management of colleges and universities, project management is the key link. In order to improve the management level, it is necessary to make full use of data mining technology. In the information age, network technology is becoming more and more popular, many scientific research institutions begin to create information management systems, which cover subject projects, scientific research personnel, project implementation, research conditions and so on. In order to find out the rules in the shortest time, data mining technology should be used to analyze information and help scientific research projects to carry out smoothly. In the past, the scientific research management system has a single function, only the functions of information deletion, query and statistics. Although it can promote the progress of the project and manage the use of funds, most of the routine affairs are oriented to the system managers and do not think from the perspective of project managers and decision makers. Therefore, we should give full play to the important
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role of data mining algorithm, extract and analyze historical data, obtain valuable content from it, adopt the function of database online analysis and processing in data mining technology, guide managers to observe data from many angles. Find out the internal correlation of subject information, in order to find problems and solve problems effectively.
4.3 Improved Apriori Algorithms for Scientific Management 4.3.1
Define Mining Tasks
All along, the scientific research level of colleges and universities is an important standard to measure the quality of learning. Many schools have realized digital management mode, covering a large number of scientific research data in the system, applying Apriori algorithms to do scientific research management work well. Data in the scientific research management system of colleges and universities are multi-dimensional, and Apriori algorithms should be used to define mining tasks. The specific mining tasks are as follows: (1) mining teachers’ papers, understanding the number of papers published and winning information, mining the internal relationship with teachers’ own quality and paper publication, and using data such as teachers’ personal situation table and scientific research paper situation table to promote data mining speed. (2) Mining the situation of teachers publishing works, looking for the relationship between teachers’ own quality and works, applying the teacher situation table and scientific research works table in the scientific research management system, and looking for the correlation after the data is combined. (3) Mining the situation of teachers’ scientific research projects, looking for the relationship between project types and teachers’ own qualities, and preparing for the next data preprocessing.
4.3.2
Data Preprocessing
In the data preprocessing, these data come from the scientific research papers, scientific research works, teaching staff situation table in the scientific research management system. After the data preprocessing, the data that can be excavated can be obtained to ensure the quality of data mining. In data cleaning, the noise data and irrelevant data records are cleaned up, the abnormal data are ignored first, and then the non-teacher administrative staff are cleaned up. When data conversion, the abstract concept is used to replace the low level data object and complete the data generalization. For example, the teacher age attribute can reflect more content, as shown in Table 1, the teacher age is transformed into the attribute, and the age stage information is obtained. Summary: in a word, in the optimization of scientific research management system in colleges and universities at present, we should fully combine the background of big data era, apply data mining algorithm, and maintain the application value
562 Table 1 Changes in teacher age
S. Hang and D. Y. Wu Date of birth
Age division
≤ 1964
Old age
1962–1979
Middle East
≥ 1980
Green
of relevance with the help of big data technology. Multi-angle analysis of data, systematic management of scientific research data according to the characteristics of mining algorithm, integration of data into the data set, for the future school scientific research work in an orderly manner to provide reference materials to ensure the effectiveness of data management, It also provides support for the rationality of decision-making and avoids the problem of isolated information.
References 1. Hongmei G, Long W (2021) Application of data mining in educational administration [J].] in colleges and universities technology and markets, vol 28(04), pp 131–134 + 137 2. Yue X (2018) Application. Research on the design of management system for higher vocational scientific research in NET and data mining technology [J].]1 Comput knowled Technol 14(24):48–50 3. Chenzhi Z (2018) Design and research on scientific research management system of colleges and universities based on data mining technology under J. Big Data Environ Netw Security Technol Appl 05:29–30
The Big Data for Comprehensive Evaluation of GIS Engineering Based on Data Envelopment Analysis Lansheng Xu
Abstract Internet plus has opened a new era. The systematic use of big data has penetrated into every aspect of society, business and personal life. In this context, the establishment of a sound evaluation index system and scientific evaluation method of geographic information engineering is of great significance to the construction and development of Geographic Information Engineering in China, as well as the rational use of human, material and financial resources to develop and utilize information resources and give priority to the development of important geographic information engineering projects, It is of great significance to ensure that the completed system can bring good economic and social benefits into play. This is not only the need of China’s economic and social development, but also the inherent need of the development of geographic information engineering. According to the development and operation of Geographic Information Engineering, the establishment of a perfect evaluation index system is a prerequisite for scientific decision-making, measurement and guidance of the sustainable, coordinated and efficient operation of the information system, The comprehensive analysis and evaluation of Geographic Information Engineering with perfect comprehensive evaluation model is an important guarantee to ensure the healthy development of Geographic Information Engineering along the right direction and with appropriate scale and speed. Keywords Geographic information engineering · Comprehensive evaluation · Big data · Evaluation index system
1 Introduction In the twenty-first century, the modern information technology, which is centered on big data, has caused great changes in social and economic structure, production organization and even lifestyle, and profoundly changed the world. Information has become one of the three social pillars in parallel with material and energy. Information L. Xu (B) West Yunan University, Lincang 677000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_62
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industry has become the main driving force of economic growth in all countries, and information technology has become the focus of international economic competition and comprehensive national competition. The reason why information technology can develop rapidly has become the main factor to promote social progress, on the one hand, it is due to the influence of the world economic globalization; On the other hand, it provides a variety of tools and means to promote economic development [1]. With the application of Geographic Information Engineering, GIS technology has become an important tool for urban planning, facility management and engineering construction. At the same time, it has entered into military strategic analysis, business planning, mobile communication, cultural education and even people’s daily life. Its social location has changed significantly, which is recognized as the pillar industry in the twenty-first century. But when reviewing the rapid development of GIS, we must also clearly see the problems in the construction of GIS.
2 The Necessity of Comprehensive Evaluation of Geographic Information Engineering The geographic information engineering, which is expensive, should be treated carefully, so that it can play a real role in promoting social progress and economic development. “Strategic management and project management will play a key role in dealing with the changes in the global market,” said David Crand, an American scholar. For Geographic Information Engineering from planning, pre research, planning, project approval, design, construction to operation, strict evaluation and monitoring is needed to facilitate reasonable project approval, strict organization and proper development, and achieve good benefits. With the application of GIS technology from the traditional GIS system of single department to the network GIS Engineering at the national level and globally, the construction of geographic information engineering has more and more ability of business, industrialization and information service. Geographic information engineering combines spatial information with information in many fields and time scales, such as humanities, economy, etc., integrating the mainstream technologies of many disciplines such as Earth Science, information science, computer science, space exploration and digital communication, management science, economic and Humanities Science, and covers all industries related to space location, Therefore, it can be regarded as an open and complex large system. For this open and complex system, how to organize the specific work of participants and how to integrate these work into a practical system with reasonable technology, economic cost, short development cycle and coordinated operation needs the overall coordination personnel to have a deep understanding of the engineering construction rules of many related or similar systems in the past, And we can control the system fully on the basis of understanding the system rules, so as to realize the transformation of geographic
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information engineering technology into the real productivity on the basis of the combination of science and technology and economy. From the experience and lessons at home and abroad, it can be seen that the key to the success or failure of GIS construction and application is not only determined by technical factors, but also restricted by economic factors and social factors. Therefore, how to evaluate the benefits of GIS accurately and make the project run efficiently has become a common concern of all walks of life. In most cases, the failure of the project is ultimately manifested as cost overrun and delay in progress. A study on the cost estimation of software engineering shows that 63% of large software projects exceed the pre estimated cost. Although it is impossible to guarantee a good evaluation mechanism, the construction of geographic information engineering will be successful, but if the project decision and management are improper or there is no awareness of this, the probability of project construction failure will be greatly increased. In the decades of rapid development of geographic information engineering technology, although the design, development and organization management methods are becoming more and more front-end, comprehensive analysis and Research on Key Technologies of standardization, standardization and commonality in all links of engineering construction are still insufficient. The main table is that the consideration of technical details in engineering construction is often focused on a link, For example, how to organize data, which knowledge proportion is used to constitute the development team, etc., but it often ignores the factors that may be caused by each link, and lacks the understanding of the six elements of human, material, equipment, finance, task and information in the construction of the project. Today, many people still have a direct and perceptual understanding of the role of geographic information engineering. At present, there is no complete index system and scientific evaluation method for evaluating GIS. Although the direct influence of each component of a project may not be obvious, the interaction between them usually produces strong and unpredictable behaviors. Therefore, the author thinks that a certain comprehensive evaluation mechanism should be introduced into the construction of geographic information engineering to understand the restriction, and the optimal operation of the system under the restriction can be obtained by the ranking and evaluation results of the comprehensive evaluation.
3 Geographic Information Engineering Evaluation Process System 3.1 Methodology of System Engineering The main research objects of methodology of system engineering are: the formation and development of various system engineering methods, basic characteristics, application scope, the interrelationship among methods, and how to construct, select
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Fig. 1 Hard system methodology
and apply system methods. Professor Qian Xuesen pointed out that “system engineering is a scientific method for planning, research, design, manufacturing, testing and using of organizational management system, and a scientific method of universal significance to all systems”. It is a problem that must be solved first to control and evaluate a geographic information project, how to establish a project, how to design and develop, what work stages and steps should be followed, how to operate and maintain it. The application of system engineering methodology is helpful to analyze the order of project implementation. According to the system thinking, the system engineering method can be divided into hard system method and soft system method. Among them, the method that can solve problems by using conventional mathematical model represented by traditional operational research and system engineering is called hard system method, as shown in Fig. 1; The method of focusing on human factors and considering human world view and values in order to deal with soft problems including human is called soft system method.
3.2 Analysis of the Characteristics of Geographic Information Engineering As a very complex system engineering, geographic information engineering involves a wide range of issues, there are a lot of qualitative and quantitative organizational coordination and management decision-making problems, which are all through the various levels and links of geographic information engineering. For the four elements that geographic information engineering must have, namely technology, data, personnel and coordination organization, people often pay more attention to technology when the project is implemented [2]. Because data is the blood of the system, it also attaches great importance to data, but often ignores the latter two factors, and the result is often established—a demonstrative system, lacking practicability and application background, It is not even more of the vitality and development power of the system. During the whole project implementation, the coordination among the superior decision makers, technical personnel organization, user demand survey, etc., the fund allocation and distribution involved in the construction of project planning, system analysis, system design, system implementation and maintenance, and the allocation of personnel at different levels are involved in the
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Fig. 2 Characteristics of geographic information engineering
construction of project planning, and the allocation of personnel at different levels, and it is not related to “human” and “coordination organization”, Under the premise of certain technology and data guarantee, it is “human” and “coordination organization” that ultimately determines the success of geographic information engineering. Geographic Information Engineering has the following characteristics (as shown in Fig. 2). 1. High Complexity The development of geographic information engineering itself is a kind of comprehensive high technology, which involves geography, surveying and mapping remote sensing, computer science, communication technology, economic applied mathematics, management science, behavioral science and other disciplines, and should take corresponding measures against the changes of environmental conditions at any time. With the expansion of scale and the development of technology, the complexity of the system is increasing [3]. 2. Strong Uncertainty The development of geographic information engineering has a strong scientific research color, and the achievements of the project are mainly manifested as intangible assets software system and data. There are many uncertain factors in the process of the project, which affect the cost and completion time of the project; The key is to affect the success or failure of the whole project, which reflects the great risk of geographic information engineering. 3. Strong Timeliness The development of geographic information engineering generally costs a lot of human, material, financial and time resources. The workload of a geographic information engineering development often reaches hundreds of person years. The longer the project cycle, the more chance of unpredictable events caused by environmental changes, and the greater the deviation from the original goal. 4. Emphasis on Teamwork Geographic information engineering project is an intelligence intensive and laborintensive project, which is most affected by human resources. The structure, sense
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of responsibility, ability and stability of project members have a decisive impact on the quality and success of the project.
4 Comprehensive Evaluation of Geographic Information Engineering System evaluation generally has the following elements: evaluator, evaluation object (including system structure, evaluation index and weight relationship), evaluation objective, evaluation principle and strategy. Therefore, the system evaluation can be described in the following mathematical form: Z = {E, P{O, W, S}, Q, F}
(1)
Among them, Z stands for system evaluation, e stands for evaluator, P stands for evaluation object (including system structure s, evaluation index o and weight relation w), O stands for evaluation objective, f stands for evaluation principle. System objective and its structure are abstract systems based on system analysis. Single evaluation refers to the subjective measurement and evaluation of single index. Comprehensive evaluation is to evaluate and measure the operation of the system as a whole based on the analysis of single index of the system, emphasizing the overall optimization of the system. Compared with the general system evaluation, the comprehensive evaluation of Geographic Information Engineering emphasizes the following points [4]. (1)
(2)
(3)
(4)
Integrity. It deals with the problems from a comprehensive and holistic point of view, and evaluates the operation of the system in an all-round and multi-level way. Multi angle. It is necessary to measure and evaluate the system from the aspects of economy, technology, society, environment, policy and so on, and deal with the subjective utility of the evaluation target of the system by those uncommensurable indexes. Therefore, the evaluator is required to have the comprehensive ability of multi-faceted and multi domain knowledge. Multi angle is the most significant difference between comprehensive evaluation and single evaluation. Systematic. Because the comprehensive evaluation is the overall operation of the evaluation system for the target, and the comprehensive evaluation is greatly affected by the evaluation mode, only the close combination of many aspects can the comprehensive evaluation be successfully completed. Therefore, it is necessary to take the system theory as the guidance and use the system engineering theory and method to carry out the comprehensive evaluation. Subjectivity. Although the basic requirement of the comprehensive evaluation is the objectivity of the evaluation results, the problems studied in the comprehensive evaluation are mostly non quantitative, uncertain and uncommensurable factors, which can not be solved by the mathematical model with
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strict theoretical basis. Therefore, it is inevitable to make the comprehensive evaluation subjective and arbitrary.
5 Conclusion The purpose of comprehensive evaluation is not comprehensive evaluation but comprehensive evaluation. It is to promote the process control and improvement of geographic information engineering construction organization, provide decision support information for project management, enhance the strength, effectiveness and efficiency of project management. The construction process of geographic information engineering is constantly improved and optimized. The comprehensive evaluation process is a sub process of the project construction process, and it is also a process of continuous optimization. Different levels of project process have different process management requirements. The problem driven comprehensive evaluation process should be developed to meet the new requirements, which requires continuous improvement of the comprehensive evaluation process according to the implementation.
References 1. Jifa G (1995) System methodology of WSR. Transp Syst Eng Inf 1(3):25–28 2. Kefa G, Fei G (1998) On the methodology of physical one thing, one man theory system from the perspective of management science. Theo Prac Syst Eng 8:1–4 3. Dongqiang G (2000) Quantitative model of information system evaluation. China management science 4. Junpeng G, Youhua W, Hanhua L (2003) Improved DEA model and its application in regional industry economic evaluation. Appl Syst Eng Theo Method 12(2):174–176
Application of Web Crawler Technology Based on Python in Big Data Environment Junling Pan
Abstract With the transformation and upgrading of social economy and the application and development of high and new technology, it is an important trend of the development of China’s higher vocational education to carry out the extension of higher vocational college diploma education to undergraduate level vocational education or even higher level vocational education, which is also the highlight of the construction of modern vocational education system. Undergraduate vocational education has the basic attributes of higher, technical and regional. Based on Python data crawler, this paper studies the application of data crawler in the demand of undergraduate vocational education. Keywords Python data crawler · Undergraduate level · Vocational education
1 Introduction With the transformation and upgrading of social economy and the application and development of high and new technology, higher requirements are put forward for the comprehensive quality and technical skills of industrial employees, which urgently needs the further improvement of the talent training level of vocational education. Therefore, the development of higher vocational undergraduate education has become an important strategic topic in the field of education. which pointed out the development goal of Vocational Education: “by 2020, meet the needs of economic society for the development of high-quality workers and skilled talents”. In February 2014, Premier Li Keqiang presided over an executive meeting of the State Council to study and deploy the work of speeding up the development of modern vocational education system, and proposed to “open up the rising channel from secondary vocational school, junior college, undergraduate to postgraduate”. Therefore, it is a strategic choice for the connotation development of Higher Vocational Education J. Pan (B) Shandong Communication and Media College, Jinan City 250200, Shandong Province, China Krirk University, No. 3 soi Ramintra 1, Ramintra Road, Anusaowaree, Thailand © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_63
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in the new era to deeply promote the reform of higher vocational undergraduate education and build a modern vocational education system. It provides new ideas, new methods and new ways for the reform and development of vocational education. This series of national policy documents provide unprecedented opportunities for the development of undergraduate vocational education in China [1]. Vocational education is not a college mode of preparatory education, nor is it a development mode of vocational education, which has become the consensus of people. However, the school running ideas and development mode that conform to the laws of vocational education and have characteristics are still in the exploration stage. Vocational education is employment education. Vocational Education held by industries and enterprises, and to implement school enterprise cooperation and work study combination in vocational education. Undoubtedly, it is a kind of work policy thinking, and it lacks some theoretical basis and demonstration. Therefore, this paper attempts to explore the needs of undergraduate vocational education from the perspective of Python data crawler.
2 Python Crawler Technology 2.1 The Concept of Python Crawler If the Internet is compared to a big spider web, the data will be saved on all nodes of the spider web. Reptiles use small spiders to crawl their prey along the web (data). Crawlers require websites to obtain resources, analyze and extract useful data. Register memory. Technically, it refers to the behavior of requesting from the website through the browser program. HTML code / JSON data1 binary data replied from the website (as shown in the figure), put the video locally, extract the necessary data and save it.
2.2 Basic Process of Crawler How do users access network data? Method 1: submit the application browser → download the web page code → explain on the page. Mode 2: simulate browser transmission request (introduce web page code) → extract useful data → save in database or file. What reptiles need is mode 2. (1)
Request
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When you send a request to an object web site using the HTP library, the request is sent. The request includes request header, request body and so on. Defect of request module: unable to execute JS and CSS code. Get response content If the server can respond normally, you can get a response including HTML. JSON, image, video, etc. Analysis content HTML data analysis: regular expression (re module), third-party analysis library, beautiful soup, pyquery, etc. JSON data analysis: JSON module. Binary data analysis: record files in WB mode. Data storage
Database (mysql, mogdb. Redis). File–save. PS: after receiving the response, the browser will analyze its content and display it to the user. The crawler class makes a request in the browser and extracts useful data after receiving the response.
3 The Necessity of the Development of Undergraduate Vocational Education 3.1 The Need of Perfecting Modern Vocational Education System Compared with developed countries, vocational education in China started late, with backward concepts and incomplete vocational education system. For a long time, as one of the types of higher education, higher vocational education has been confined to the category of specialized training, and the majority of higher vocational graduates lack accurate and broad channels to improve their academic qualifications. After a few graduates enter ordinary undergraduate colleges and universities through the way of upgrading from junior college to undergraduate, they often accept the traditional undergraduate general education. Although they have achieved the improvement of academic qualifications, they do not have the ability to develop stronger professional ability. Therefore, the higher vocational education itself should have its own development channel, and improve the academic level within the system, that is, not only the specialized education, but also supplement the undergraduate and graduate level vocational education, so as to form a complete modern vocational education system and meet the requirements of talent development [2].
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3.2 To Meet the Needs of Industrial Restructuring In this era of rapid economic development, the industrial structure is rapidly adjusted, transformed and upgraded, and the knowledge and technology intensive industries occupy the peak of the market. Accordingly, the corresponding personnel training standards also need to be improved. Statistics show that the proportion of senior workers in China’s technical talents accounts for about 5%, which is far from 30 to 40% of the technical talents in developed countries. There is a serious lack of highlevel technical talents, showing a “structural shortage” situation, which is obviously difficult As an important support for industrial talent allocation, vocational education should enhance the training level and undertake higher standard talent training tasks, which is the inevitable choice for vocational education to adapt to the adjustment of industrial structure and reasonably allocate talents [3].
3.3 To Meet the Needs of the Internationalization of Vocational Education At the end of 1960s and the beginning of 1970s, many countries began to try to develop the undergraduate level vocational education, and give the graduates the Bachelor of technology degree. University of Applied Science and technology, Polytechnic College, community college, TAFE College, University of technology and science have been established in various countries. In the 1980s, these colleges and universities promoted vocational education from undergraduate level to master and doctoral level, and have become an important institution to cultivate high-tech skilled talents at the graduate level. Nowadays, the vocational education in developed countries has clear levels, orderly communication, and the vocational education system is becoming more and more perfect. It can be seen that the continuous improvement of vocational education quality and level is the international development trend. In China, in addition to a large number of independent technical colleges (universities), technical colleges under comprehensive universities and other undergraduate Vocational Colleges in Taiwan.
4 The Demand of Undergraduate Vocational Education There are five differences between undergraduate level vocational education and Application-oriented Undergraduate Education: first, different educational ideas. Application oriented undergraduate education emphasizes professionalism, while undergraduate level vocational education emphasizes professionalism. Second, the training faces different. Application oriented undergraduate education is oriented to professional fields, and undergraduate level vocational education is oriented to
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Fig. 1 System architecture of Python data crawler
professional posts (groups). Third, the specialty characteristics are different. The specialty setting of application-oriented undergraduate education is still subject oriented, while the specialty setting of undergraduate vocational education focuses on demand-oriented and vocational education characteristics. Fourth, the curriculum system is different. The curriculum system of applied undergraduate education is “t” type, emphasizing practice. The curriculum system of undergraduate vocational education is “II” type, emphasizing double pillars (professional education and vocational training). Fifthly, different employment orientation. Applied science education emphasizes employment orientation, and undergraduate vocational education emphasizes career orientation. The architecture of undergraduate vocational education system based on Python data crawler technology is shown in Fig. 1.
4.1 Cooperative Education Cooperation in running a school means joint training. It refers to the mode of running a school in which high-quality vocational education institutions cooperate with ordinary undergraduate universities on a certain advantageous counterpart specialty to jointly cultivate talents. Students can receive both general education and complete vocational training, and obtain the undergraduate degree and degree certificate issued by ordinary universities after graduation. This kind of path can be divided into two types: one is the “3 + 2” (vocational 3, general 2) segmented training mode represented by Shandong, the other is the joint training mode represented by Jiangsu.
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These two kinds of training modes have their own advantages and disadvantages. The advantage of Shandong mode is to open up the upward flow channel of higher vocational graduates, but there is a problem that the later theoretical knowledge covers the early practical skills, and the long-term separation of theory and practice is bound to affect the discipline construction of undergraduate level. Jiangsu mode puts all the students enrolled in the second batch of undergraduate courses into vocational colleges for four years, avoiding the risk of theory and skills mismatch, but there are also two problems: one is whether the vocational colleges can be competent for undergraduate teaching tasks independently, and the other is whether the ordinary undergraduate colleges with the right to issue degrees can effectively evaluate the quality of talent training in vocational colleges. The author puts forward the improvement ideas and suggestions for the above two kinds of school running modes: segmented training should update the training form. The traditional “3 + 2” (vocational 3, general 2) training mode, which is similar to that of upgrading from junior college to undergraduate, is changed to “1 + 3” (general 1, vocational 3). This segmented training mode is more in line with the talent training logic of theory before practice, which avoids the recessive transition of later education, and also enables undergraduate colleges and vocational colleges to show their strengths in talent training. Joint training should avoid the situation that vocational colleges take over the power of education after recruiting high-quality students with the reputation of undergraduate colleges. Undergraduate colleges and universities should actively participate, establish an effective leadership and cooperation mechanism, ensure the sharing of educational resources and the supply of high-level “Double Teachers” team, and ensure that the colleges and universities are located in the same area, which is convenient for supervision and management [4].
4.2 Independent School Running Independent school running refers to the way in which undergraduate or vocational colleges independently organize undergraduate level vocational education, cultivate applied, technical and complex talents, and issue academic degrees and degree certificates. In practice, the current national policy is to stop the upgrading of vocational colleges, and to advocate the transformation of local newly established universities to application-oriented universities. Undoubtedly, the construction of a number of Application-oriented Colleges and universities can directly serve the regional industrial revitalization and economic development, but also conducive to the integration of higher education resources, optimize the structure of higher education, and improve the attractiveness of vocational education. It is a good choice to enhance the undergraduate level of Vocational Education in the short term. But in the long run, the shortcomings are obvious. For example, the enrollment of application-oriented universities is still limited to the second and third batch of undergraduates, and the upward flow of secondary and higher vocational graduates is still blocked; After the transformation, independent undertaking of higher vocational education means
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abandoning academic education and keeping pace with vocational education, and major changes need to be made in the professional system, curriculum content, teaching methods, teacher structure, software and hardware supporting, etc. whether application-oriented colleges can independently run vocational education is still in the wait-and-see of the community.
5 Conclusion Python is an open source language that can be used free of charge and can contribute to the production and maintenance of their own code and program libraries. In fact, many people spend a lot of time and energy in the company to expand and improve python. This is an important reason why language is so attractive in the community. The development of undergraduate level vocational education is not an instant thing. Many experiences remind us that in the process of running a school, we must adhere to the talent training concept of application-oriented, technical skill oriented and compound type, and not deviate from the goal of training talents for regional economic development; The quality of vocational education can only be improved if we insist on the major construction, especially the major related to emerging industries; In addition, we should explore and improve the enrollment system; Improve the qualification certificate system and degree awarding system; Local governments should ensure policy support and financial input. We do not rule out that any mode may be an ideal choice for future undergraduate vocational education, but the premise is that the choice of path must be adapted to local conditions and constantly revised in practice.
References 1. Junqing R, Qi W (2013) Development of undergraduate vocational education: historical investigation, current situation analysis and path selection. Vocational Education Forum (4) 2. Design and implementation of poplar Python proxy IP directional collection crawler (2019) China New Commun 21(01):35–36 3. Gongsheng M (2015) Research on the development path of undergraduate level higher vocational education in Guangxi. Guangxi: Guangxi University 4. Qing L (2014) Thoughts on the path of developing undergraduate vocational education. China Education J 7:12–22
Intelligent Algorithm Big Data Analysis for the Construction of Smart Campus Feng Liao
Abstract China has entered the era of big data, and certain changes have taken place in the field of education and teaching. This paper will focus on the application of intelligent algorithm big data analysis in the construction of smart campus, so as to ensure the scientificity and effectiveness of the construction of smart campus. This way can combine the intelligent algorithm big data with the construction of smart campus. On the basis of the construction of smart campus, it can provide good teaching conditions and teaching environment for students, and then cultivate high-quality comprehensive talents for the society. Keywords Intelligent algorithm · Big data · Smart campus
1 Introduction The cloud computing mode of mass storage and on-demand computing impacts the traditional data center construction in Colleges and universities; The mobile trend represented by intelligent terminal, mobile Internet and Internet of things not only impacts the construction of campus network, but also reverses the thinking and direction of information system construction; The social network, which integrates communication, cooperation and sharing, has a huge impact on the traditional manager centered information system after replacing the campus BBS; Big data, characterized by massification, diversification and rapidity, has attracted great attention of all parties because of its high added value. In China’s “education informatization 2.0 action plan”, it is pointed out that Internet education is the main development direction of the future teaching field, and it is necessary to use the Internet to promote the integration and application of educational resources, so as to achieve innovation and improvement [1].
F. Liao (B) Jiangxi Technical College of Manufacturing, Nanchang 330095, Jiangxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_64
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2 Smart Campus System 2.1 Application Value of Intelligent Algorithm Big Data Analysis Big data contains a lot of data information, and in a certain period of time to achieve data information extraction, management and processing work, the data information contained in it will be integrated into more efficient and complete data information resources. Intelligent algorithm is a modern algorithm, which has been used for more than 20 years in China. At present, intelligent algorithm can be roughly divided into evolutionary algorithm and swarm intelligence algorithm. Intelligent algorithm big data is widely used in China. As a key field of development and construction in China, education needs to meet the requirements of the times and keep up with the pace of development in the process of actual development. This way can ensure the good development of the whole field of education. This paper will study the application of intelligent algorithm big data analysis in the construction of smart campus, optimize and improve the smart campus system by using intelligent algorithm big data, and promote the good development of smart campus construction.
2.2 Smart Campus System Construction Smart campus is a new type of campus management system based on the current era. In the process of practical implementation, it can combine spatio-temporal geographic information, building information model and other technologies to achieve a full vision of campus service platform from the aspects of campus security. In this platform, campus staff, campus events, campus buildings and campus assets can be managed to avoid the phenomenon of business and information island in the information construction of colleges and universities. Using artificial intelligence technology to analyze it and predict the sensitive events that may appear in the campus, including the analysis of data information and human behavior. The information exchange technology in the intelligent campus integrated management system can establish the exchange system between the various information sources in the campus. On the basis of this platform, the construction information related to the intelligent campus can be integrated, and the unified construction goal can be formulated, which can realize the unified management of the campus resource construction data, and make the data information more intuitive and convenient It is effectively applied in the construction of smart campus system [2], as shown in Fig. 1.
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Fig. 1 Smart campus
3 The Application of Intelligent Algorithm Big Data Analysis in the Construction of Smart Campus 3.1 Construction of Intelligent Management System Campus management is the key content of the whole campus construction, use the cloud service platform to integrate and manage various application services in the system. This way can change the phenomenon of data island in traditional education, and then reduce the maintenance cost and security risks, and promote the good interaction between teachers and students. The system can be roughly divided into the following aspects in the actual construction: (1)
(2)
According to the comprehensive level of teachers, the specialty of students and the distribution of classrooms, the educational administration management system can use the information system to arrange and adjust courses intelligently, use the students’ search for course information and select course information for big data analysis, and timely adjust the course arrangement of students in the next academic year, so as to realize intelligent scientific research application, approval, project approval and cooperation, Improve the management level of the whole educational administration. In collaborative office management, university staff need to cooperate with other universities or enterprises. For example, in the process of travel application, the applicant can submit by filling in the application form online. The examination and approval personnel will conduct the examination and approval work as soon as they receive the message push, and predict the financial funds. The staff of the educational administration office can adjust classes and work together to realize the efficient and flexible development of the whole management, and then realize the intelligent development of the office.
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(3)
Asset management, the use of Internet of things technology, the establishment of smart campus network, classrooms, libraries, dormitories and other assets to carry out effective management, if the equipment failure can be perceived in the first time, improve the quality of students’ learning and living environment [3].
3.2 Establish Big Data Early Warning Platform Campus early warning can realize the stable development of the whole campus. By using cloud storage and big data analysis technology, we can collect and analyze the students’ real life and attendance information, and determine the students who do not conform to the corresponding rules according to the analysis results, which can help teachers achieve accurate and efficient early warning analysis and complete early warning. In this process, it can be analyzed from the following aspects: (1)
(2)
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(4)
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Plan implementation early warning, training for fresh students, including learning practice, social practice and military training, etc., the above training content is recorded in the students’ daily files, which can play the role of timely reminder. Graduation qualification examination, one year before the formal graduation of students, teachers need to remind students to complete the unfinished plan according to the actual situation of students, so as to ensure that students have the graduation qualification and can graduate smoothly. Early warning of project funds, targeted reminders for the person in charge of the project, and the formulation of a sound project construction plan before the formal implementation of the project can avoid the chaotic phenomenon of acceptance before the project acceptance, so as to ensure the effective operation of the project. Early warning of style of study, good style of study is the main content of smart campus construction, we can use intelligent algorithm big data to establish a good style of study early warning system. For example, according to the actual attendance situation of students and the dormitory return rate, remind the students who violate the regulations and do not meet the standards. Through the intelligent algorithm big data analysis, we can find the style of study problems in the campus in time, remind as soon as possible, and complete the intervention. Employment early warning, students need to enter the society after graduation, use the early warning system to count the employment rate of students, and analyze the demand and development trend of today’s employment market, so as to improve the consistency between students and social employment market which is shown in Fig. 2.
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Fig. 2 Smart classroom
3.3 Form Smart Teaching Classroom Classroom education is the core content of university construction. Therefore, in the process of building an intelligent campus, we should focus on intelligent education and improve the education level of the whole campus. Education is the basis of campus construction. Therefore, we have changed the traditional education method and the concept of intelligent education application, which can transform students’ passive learning into active learning. For example, using Internet of things technology and mobile Internet technology to build a comprehensive education model on the Internet, and carry out educational practice for students through intelligent login before class. After class, students can use computers and other intelligent devices to study independently. Enrich the educational methods of the whole class and expand the teaching content. The evaluation system can improve students’ learning efficiency from multiple aspects, such as the use of teaching time and technology.
3.4 Expand the Integrated Data Source Data is the core part of big data analysis of intelligent algorithm. In the process of building smart campus, it is necessary to ensure the comprehensive and effective data sources and realize the effective integration of data sources. In order to ensure the actual application effect of big data of intelligent algorithm, it is necessary to realize data sharing from the perspective of application integration. The basic purpose of data management is to make full use of big data technology, carry out a comprehensive and effective analysis of the whole teaching system on campus, and then formulate the strategies for improving the management level. After the above work, the establishment of integrated data integration platform is the main content of data management. Data management is a necessary condition in the construction of the whole intelligent campus. Through data management, data information in daily
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work can be accumulated gradually, which provides data conditions for big data analysis of intelligent algorithm in the future. It can be seen that data source is the main content of the construction of smart campus. In the process of building smart campus with big data of intelligent algorithm, relevant personnel need to fully recognize this problem, realize the effective integration and expansion of data resources, ensure the good application of data resources and give full play to the value of digital resources, Improve the effect of big data analysis of intelligent algorithm, and ensure the construction level of intelligent campus [4].
3.5 Providing Intelligent Services The main purpose of intelligent service is to take teachers and students as the basic, and provide more convenient and efficient services for teachers and students. At present, the main way is to establish the account number, the teachers and students log in the account, which can realize the inquiry and borrowing of library information, complete the course selection, appointment and course payment. For example, in the process of granting the subsidy for poor students, the number of takeout of student points and dining level of canteen can be analyzed by using big data technology as the basis for the final payment of the grant. In addition, according to the attendance records and out of office records, the hidden dangers of safety are determined and an early warning information system is established. In addition to the above aspects, intelligent algorithm big data analysis can also analyze the students’ learning features, learning characteristics, work and rest time and living habits. The students with the same comprehensive characteristics can be divided into a dormitory, which can reduce the probability of contradiction in daily life of students and improve the comprehensive level of daily life. In the aspect of school enterprise cooperation, we can also use big data of intelligent algorithm to analyze and apply. In this process, the following issues should be noted: (1)
(2)
To ensure the relationship between big data of intelligent algorithm and intelligent campus platform, intelligent campus platform is an important part of the whole campus management, so the two must keep close relationship. Select the appropriate big data platform analysis tools to support app access, TV and LED display, etc., so that it can respond to
4 Conclusion Through the above research, we can see that the application of intelligent algorithm big data analysis in the construction of smart campus can form a comprehensive and systematic University smart campus system, and carry out research from the aspects of educational data resources, educational management system, data analysis organization, school enterprise cooperation, etc. Promote the good development of
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the whole university smart campus construction, and ultimately achieve the purpose of forming a complete smart campus system. Acknowledgements Provincial key project of science and technology research project of Jiangxi Provincial Department of Education in 2019 GJJ191422 Optimization and Practice of Smart Campus System in Higher Vocational Colleges under the Background of Teaching Diagnosis and Reform
References 1. Ying W (2018) Problems and countermeasures of smart campus construction in Chinese universities China New Commun (14):104 2. Lei S (2018) Research on the construction of smart campus in higher vocational colleges supported by big data analysis platform. Education modernization 7:133–135 3. Changtao Q, Lin S, Kunfeng L (2017) Research on the construction of intelligent campus in Universities under the background of Internet plus. Neijiang sci Technol 8:70101 4. Lei X (2017) Research on smart campus planning and design based on big data. Information Technol Informatization 8:145–148
The Construction and Research of the Platform of Intelligent Sharing Laboratory Based on Big Data Yanling Luo, Jiawei Wan, and Shengqin She
Abstract It is the primary task of colleges and universities to cultivate undergraduate applied talents under the background of popularization of higher education in China. According to the characteristics of engineering education, colleges and universities should build an intelligent sharing laboratory platform with students as the main body according to their own characteristics and objectives and regional advantages, and solve the contradiction of shortage of experimental teaching resources and low utilization efficiency in local universities, The more important aspect is to improve the students’ ability of engineering practice and employment competitiveness. Based on the analysis of the Popularization Background of engineering undergraduate education, this paper explores the operation mechanism of establishing shared laboratory through the traditional concept, optimizing the experimental teaching resources, and exploring the new mode of laboratory construction in local engineering colleges and universities from the practical experience and teaching results. Keywords University Laboratory · Intelligent sharing · Practical ability · Creative spirit
1 Introduction Facing the rapid development of information society and increasingly tense employment pressure, colleges and universities are vigorously adjusting the talent training strategy, and training senior professionals with innovative spirit and practical ability, social competitiveness and international competitiveness. In order to be competitive, Y. Luo (B) · J. Wan · S. She Wuhan Technology and Business University, Wuhan 430065, China e-mail: [email protected] J. Wan e-mail: [email protected] S. She e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_65
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the students in Colleges and universities must have the ability to do it themselves. In the digital and information age, if they do not do it and can not do it, nothing will be done. And the practical ability of dare to do and can do is just cultivated in the laboratory. The laboratory of colleges and universities is an important base for experimental teaching, scientific research, cultivation of students’ innovation ability and improvement of students’ comprehensive quality; It is the main place to incubate high-level research results and serve economic construction. It is an important sign of undergraduate teaching level to build an open and shared intelligent experimental teaching platform and to highlight the cultivation of practical practical ability of engineering college students. Experimental teaching in Colleges and universities is the most direct and key link to cultivate students’ innovative spirit and practical practical ability. Its advantage is to combine the theory and practice they have learned, The cultivation of students’ innovative spirit and knowledge utilization ability can help students to use limited funds on the blade. Through reasonable integration and optimization of experimental resources, the utilization rate can be improved to reduce waste, and cross integration of disciplines can be promoted, which is conducive to the improvement of teaching and scientific research level and the efficiency of running a school. Intelligent sharing laboratory has both knowledge inheritance and practical practice, and follows the cognitive rules of human beings, so that students can discover and understand scientific theory and test the theoretical knowledge obtained in scientific experiments. But the current university laboratory has not fully played an important role in cultivating students’ innovative spirit and practical ability because of the lack of experimental hours and closed management [1]. Therefore, it is necessary to establish the corresponding open intelligent sharing laboratory platform, strengthen the research of experimental teaching reform and establish an open reservation sharing platform. The construction of open sharing system of university laboratory has great significance and profound influence on the cultivation of students’ innovative spirit and practical ability.
2 Intelligent Shared Laboratory Analysis 2.1 Objectives and Principles of Open and Shared Laboratories The opening of university laboratory should aim at cultivating students’ scientific creative thinking and practical ability, and at the same time, we should implement the teaching principle of teaching according to their aptitude and personality cultivation while paying attention to the coordinated development of knowledge, ability and quality. Zhang Xiaoli puts forward the principles to be followed in the construction of open engineering laboratory: first, hardware construction is equally important
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as software construction, second, human factor plays a decisive role, third, professional education and teaching promotion should be fully attached importance, fourth, attention should be paid to the standardization and standardization of laboratory construction, and the benefit of construction investment should be improved; fifth, the open construction should be adhered to “. There are also two basic principles that managers propose to adhere to in the construction of open laboratory. One is to adhere to the principle of “people-oriented and service students”, that is, the humanoriented thought and the idea of convenience and service for students are embodied in the construction of laboratory resources and rules and regulations; Second, adhere to the principle of “open innovation and teaching according to their aptitude”, that is, teaching students according to their aptitude and various forms in the content of the laboratory, and focusing on training students’ awareness of innovation and entrepreneurship and practical ability.
2.2 Factors Restricting the Open Sharing of Laboratories From the implementation of open and shared laboratories in Colleges and universities, the main factors are imperfect management mode, incomplete system construction, lack of fund investment guarantee and unreasonable evaluation system. The opening of experimental time and space is relatively easy to realize and maintain, but the implementation of open content is relatively difficult. The average student resources, especially laboratory site and material resources, caused by the expansion of enrollment in Colleges and universities have been seriously reduced, the quality of experimental teaching is not high, the laboratory construction is not suitable for the expansion of scale, the investment in funds is insufficient, the experimental equipment is backward and maintenance is difficult, the guidance of the experimental teachers is insufficient, and the students’ initiative participation is not high, Many factors such as the lack of attention of experimental courses restrict the open sharing of laboratories. From the perspective of the allocation and use of laboratory resources, the problems of insufficient demonstration, repeated purchase and unreasonable distribution of experimental equipment in the purchase will affect the development and expansion in the future. There are also some problems in management ideas and systems. The use of experimental equipment is in an independent and closed state, which restricts the open sharing of experimental equipment [2].
3 Construction of Intelligent Sharing Laboratory Platform It is necessary to build an intelligent shared laboratory platform in Colleges and universities, strengthen the application and cutting-edge of the laboratory, inject advanced engineering ideas, keep up with the pace of the development of talent demand of the times, and highlight the characteristics of close coupling with regional
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industrial characteristics and students’ active practice. Laboratory teaching resources include hard resources, including financial resources, material resources, human resources, as well as soft resources, including curriculum, results, information and management resources. To build an open and sharing platform for laboratories, we need not only a certain material and economic foundation, but also the necessary knowledge structure, ability structure, innovative thinking and energy input of teachers and students, It is more necessary to reform the management system and update the management methods.
3.1 Management Mechanism Construction of Open Sharing Platform Management concept and system are the hidden resources in laboratory resources. Scientific and orderly management of laboratory and the establishment of effective operation mechanism of opening, competition, flow, sharing and cooperation are the important guarantee for the full opening and sharing of laboratory. It is necessary to coordinate the contradiction between limited resources and the realization of the overall goal with high efficiency, so as to make the best use of people, materials and money. (1) System management. After the opening of the laboratory, the group and nature have changed. The system construction is a strong guarantee for the smooth operation of the opening and sharing of the laboratory. The opening needs the policy support of the school, and there are rules to follow. Rules and regulations regularize the work of laboratory staff and standardize the behavior of those who work, research and study in the laboratory, so as to ensure the safe realization of laboratory opening and sharing. The good work discipline and code of conduct of the laboratory also have a strong influence on the students. In the process of standardizing the construction of the laboratory open sharing platform, we should form a normal management system, adhere to the management of the University and the hospital, make the laboratory management standardized, institutionalized and scientific, strengthen the management through the system, clarify the responsibilities, improve the safety management responsibility system, formulate corresponding incentive policies, and fully mobilize the enthusiasm of all staff, Make the limited resource allocation more effective, high operation efficiency, and ensure the continuity of laboratory opening and sharing. (2) Construction management. In the planning and construction of laboratory opening and sharing, it is necessary to take overall consideration, follow the principle of partial obedience to the whole, short-term obedience to long-term, strengthen the plan management, process management and supervision summary of laboratory opening and sharing platform construction, so as to ensure the orderly construction and sustainable development of the platform. In the laboratory planning, we should stand at the height of the
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school, make a comprehensive planning, ensure the smooth implementation of the laboratory opening and sharing, and maximize the overall benefit of investment [3].
3.2 Establishment of Open Sharing of Curriculum Project Resources In order to build an open and sharing platform for laboratories, the opening of experimental course resources is the first step. Experimental curriculum resources are multi-source and hierarchical. According to the characteristics of different stages and different disciplines, we should carry out flexible and diverse experimental courses in and out of class, actively carry out the construction of “shared Experimental Courses”, create appropriate problem situations according to the characteristics of engineering students, stimulate their curiosity and thirst for knowledge, and experiment is no longer just a theoretical verification of a course, Instead, it expands the integration of curriculum content, strengthens the interdisciplinary penetration and knowledge integration among various disciplines, implements the operation mechanism of both teaching and scientific research through the open sharing of curriculum resources, improves the efficiency of laboratory use, and reflects the value of students’ active development. (1) The goal of experimental curriculum system. The setting of experimental courses should achieve three goals: first, to cultivate students’ interest in engineering majors; The second is to enable students to have the skills and experience to solve practical problems through experimental learning and related methods training; The third is to internalize the necessary knowledge and skills into theoretical literacy and practical ability. Through the integration, optimization and reconstruction of the experimental curriculum system, the goal of cultivating applied talents can be achieved. The design of the experimental curriculum should meet the training requirements of students’ ability to discover, analyze and solve problems, and the school should offer high-level experiments. According to the school’s own orientation and talent training objectives, we should break through the traditional concept and system barriers, build a scientific and reasonable practical teaching curriculum system, build a three-dimensional experimental teaching and training system with interdisciplinary integration, increase students’ experimental interest and knowledge, strengthen the combination of teaching, scientific research and practice, and cultivate students’ engineering application ability Practice innovation ability, social practice ability. (2) Establishing multi-level experimental curriculum resources. The experimental course system of modern engineering universities is mainly divided into three levels, as shown in Fig. 1. The first level is the basic verification course, the second level is the design comprehensive course, and the third level is the scientific research innovative course. Based on the principles of thick foundation, multi direction, wide field and heavy skill, the experimental teaching courses
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Fig. 1 Open, multi-level and autonomous sharing platform for different grades and different interests
should be focused on the pertinence and application of the contents, the cultivation of students’ practical application ability should be emphasized, and the students should be encouraged to design and choose the experimental courses according to their personal interests, which is beneficial for the students to expand their vision and understanding to the general engineering field under the background of modern large-scale engineering, From the simulation type to the actual combat type in the process of experimental practice. Experimental course teaching is a process to enable students to put theory into practice, emphasizing the combination of theory and practice, knowledge and practice, and cultivating the ability to analyze and solve practical problems [4].
4 Human Resource Optimization of Sharing Experimental Platform High quality teachers are the basic to cultivate high-quality students. To realize the open sharing of laboratories, the establishment and sharing of human resources is the most critical factor. The key factor of grasping good people is the premise of living things, managing wealth and reasonable planning. The idea that “talent resources are the first resource” is established. The open and sharing of laboratories puts forward higher requirements for the teachers. A reasonable echelon structure, excellent quality and special-purpose engineering education teachers team are established to improve the teaching level of the laboratory in an all-round way, cultivate the leaders and the middle backbone of the laboratory construction. The team has the following aspects: education background, age, and so on. The structure of Title tends to be more reasonable and younger, which makes the construction of laboratory team enter a benign cycle. It is an important link to realize the sharing of laboratories to create fair competition mechanism, encourage the enthusiasm and enthusiasm of the laboratory staff, improve their theoretical knowledge and experimental quality, build a shared “learning community”, improve the utilization of teachers, and build a platform for the development of talents.
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4.1 High Level Leader Team Focusing on core talents and improving the core competitiveness of laboratories, experts or professors with innovative and changing thinking and spirit, high academic level, advanced manufacturing industry and rich management and engineering practice experience should be the leaders of laboratory construction. They are important human resources for centralizing the wisdom of the team and organizing and integrating the laboratory team, and can bring up a echelon and form an advantage, It provides each member of the team with a platform and opportunity to show their personal wisdom and ability. Besides, centralized management must give full play to the initiative of discipline leaders. The open sharing of laboratories requires leaders to have economic minds, be able to get out of school and contact and cooperate with local industry widely, and give full play to the best benefits of open and sharing of laboratory resources.
4.2 Flexible Mobile Team We should break the mode of lifelong system of Posts and titles, introduce the mechanism of talent flow, and enable the flexible employment system of “not seeking ownership, but for what to use, and more for what to do”. We will not restrict the introduction, trust and use of new people, attach importance to the cultivation and use of young people, encourage laboratory staff to study and study, and actively participate in production practice and scientific research through the combination of “production, study and research”, Improve their own professional skills and technological development capabilities. We will build learning teams, break through the traditional “unit” framework, invite experts and scholars from all disciplines, fields and industries to communicate, visit or discuss at school, and report to the school. We can learn from all available concepts and experiences through the opening of the platform.
5 Conclusion Building intelligent shared laboratory platform is conducive to the transformation of engineering teaching from “professional education” to “quality education”, expand students’ professional vision, realize the integration of “teaching” and “learning”, promote students’ independent practice, fully tap the potential of integration of “production, study and research”, assume the function of social science and technology services, and realize resource sharing, joint research and achievement sharing, It is helpful to maximize the allocation and utilization of resources and the comprehensive benefits of the laboratory.
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Acknowledgements Guiding project of scientific research plan of Hubei Provincial Department of education in 2019: Research on intelligent sharing laboratory based on iLab structure (Project No. B2019279).
References 1. Jiashou D, Wengui Z (2004) Laboratory management. University of Electronic Science and Technology Press, Chengdu 2. Jiaqing H (2008) local universities from the perspective of popularization. Guangxi Normal University Press, Guilin 3. Jianyi K, Kuisheng C (2008) Teaching theory and practice of Applied Talents Training. Hubei people’s publishing house, Wuhan 4. Li H (2007) Research on University Asset Management. China University of science and Technology Press, Hefei
Research on Big Data Encryption Algorithm Based on Data Redundancy Elimination Technology Guojing Chen
Abstract Data has the characteristics of high speed, high value and high efficiency. In recent years, with the rapid development of cloud computing, Internet of things and social network technology, the amount of data carried by push network has increased dramatically. The traditional encryption storage technology and management methods have been difficult to meet the requirements of large data capacity, storage efficiency and security. In the big data environment, the data security and privacy protection of users are closely related to the academic circles. Keywords Big data security · Data adder (AES) mode
1 Principle Analysis of Big Data Encryption Technology 1.1 Basic Introduction The research of big data security mainly comes from three fields: scientific and engineering computing, e-commerce and Internet. Starting from the basic characteristics of big data, this chapter discusses the general encryption model of big data, and analyzes in detail the technical principles, advantages and disadvantages of four kinds of big data encryption schemes based on modern cryptosystem, bioengineering, attribute base and parallel computing technology. Based on the detailed analysis of the four V characteristics and general encryption model of big data, this paper analyzes in detail the encryption principles, advantages and disadvantages of four existing technologies, namely, big data encryption technology based on modern cryptosystem, big data encryption technology based on bioengineering, big data encryption technology based on basic attribute and big data encryption technology based on parallel computing [1–3]. The future development direction of big data
G. Chen (B) Chongqing Energy College, Chongqing 402260, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_66
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encryption technology is prospected, which lays the foundation for the follow-up research of big data encryption technology and encryption algorithm.
1.2 Principle and Analysis of Three Big Data Encryption Technologies Key space refers to all possible value ranges of key. The key space of a good encryption algorithm should be large enough to resist exhaustive attack. ECC encryption algorithm can with encryption algorithm the 1024 bit RSA encryption algorithm. In the preprocessing stage, ECC encryption algorithm with 192 bits key length. Therefore, the key space of the preprocessing phase depends on the key space of ECC encryption algorithm. After preprocessing, the key space of big data encryption algorithm depends on the key length of Rijndael algorithm, that is, the key space is 2128, 292 and 256. Taking the 192bits key as an example, if the theoretical timeconsuming calculation is carried out with the “Tianhe-1” computer of 10 billion times/s, the time-consuming is about 1 = 202/(1 × one hundred × three thousand and six hundred × twenty-four × 365) ≈ 1.9 × ten ° Billions of years [4, 5]. At the same time, the inherent characteristics of Rijndael algorithm and the introduction of initial vector make the design scheme have strong resistance to differential attack and statistical attack.
2 Research on Big Data Redundancy Elimination Algorithm 2.1 The Basic Principle of Data De Duplication Algorithm Technology is committed to saving On the other hand, the data is compressed effectively by data De duplication, which reduces t thus reducing the bandwidth consumption. Ratio (DER) is measured by the ratio of bytes in before data De duplication and bytes out after data De duplication, as shown in Eq. 1: DE R =
Bytes I n Bytes Out
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Although the data reduction rate shown in equation has compressed the duplicate data between data blocks and metadata overhead is not considered. However, the metadata overhead in the data De duplication system can not be ignored. The researchers propose a modified formula of data reduction rate, as shown in Eq. (2):
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Fig. 1 Complete file detection scheme process
DE R =
DE R 1+ f
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where f is the cost of metadata size, and its calculation method is as follows: f =
Metadata Si ze Average Chunk Si ze
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2.2 Complete File Detection Scheme Whole file detection (WFD) technology takes single file as granularity to find duplicate data, that is, each complete file is partitioned as an object. When the experimental data, the hash value of the whole file is calculated first, and the hash value of the file is compared with the previously stored hash value; if it is the same, the file is replaced with a pointer, but the actual storage is not carried out, otherwise the new file is stored [6–8]. The main flow of full file detection scheme is shown in Fig. 1. Full file detection technology has two advantages, that is, in the ordinary hardware environment, the computing speed is faster, and it can detect all the identical files in the data set, which can save a lot of storage space. However, there are two shortcomings in this method, which are large data sets, large range and technology [9].
3 Theoretical Basis 3.1 Data Preprocessing Duplicate data detection detects the data to be encrypted and removes the duplicate data from the data [10–12]. Among them, the of duplicate data is stored in hash table, the number of duplicate times of the i-th file or data block is represented by NRD (number of duplicate data), and the size of data is represented by ofd2 (the original
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d); the hash table is initialized, and the complete file detection algorithm is used to detect duplicate data in the data with a single file as granularity, Through SHA-1 and calculating the hash of the file respectively, the hash function value obtained is compared with the value already stored in the hash table. If the match is the same, replace the file with the pointer to the stored file; if the match fails, store the file and repeat the steps. Until all the files are detected, the data without duplication in the complete file detection technology is re filed [13]. The CDC block algorithm is used to start from the file header, and the data in the fixed size sliding window is regarded as each part of the file. In each position of the window, Rabin fingerprint algorithm calculates a fingerprint of the data in the window. When the fingerprint meets the matching conditions, the position of the window is regarded as the boundary of the block. Repeat this process, all file data is divided into blocks [14].
3.2 Experimental Results and Analysis Because big data usually has the characteristics of large quantity, diversity and low density, and the data itself has a certain randomness, AES advanced encryption standard is used to encrypt the preprocessed big data information, which makes the new scheme have strong resistance to differential attack and statistical attack; meanwhile, the amount of summary information of big data is small, and ECC encryption algorithm is used for encryption, As shown in Fig. 2. The new scheme solves the problem that some or even all plaintexts are lost due to slow encryption speed and large segmentation granularity in big data encryption algorithm, and effectively realizes the secure interaction of big data information on the network platform [15]. Fig. 2 Experimental results and analysis
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4 Concluding Remarks Basic characteristics of large quantity, diversity, high speed and low density, which are usually semi-structured and unstructured. Because the existing encryption algorithms and management methods do not combine the characteristics of big data itself, it is meet the requirements. Therefore, the research on big data encryption algorithm and redundancy elimination technology is of great significance and value. Based on the in-depth analysis of the basic characteristics of big data and existing encryption technology, this paper explores a big data redundancy elimination Bloom filter elimination and analyzes and verifies the effectiveness of the algorithm through experiments. Acknowledgements Research and Practice on the training mode of industry-University-Research collaborative Innovation talents based on Ali Cloud big data Technology and Application in Higher Vocational Colleges,No 192097.
References 1. Xiaoqiang Z, Mengmeng W, Guiliang Z (2012) New progress of image encryption algorithm. Comput Eng Sci (recommended article of the second National Postdoctoral Forum on Computer Science) 34(5):1–6 2. Yizhe Z, Mengmeng W, Guiliang Z (2013) Research on an improved algorithm for reducing the misjudgment rate of fingerprint image. Comput Eng design. 34(3):993–997 3. Duan Y (2012) Lang Bo’s extended ciphertext policy attribute based encryption mechanism. J Huazhong Univ Sci Technol (NATURAL SCIENCE EDITION), 40(1):113–115 4. Su J, Cao D, Wang X, et al. (2011) J Softw J (6):1299–1315 5. Mao X (2019) Analysis of new data encryption algorithm in big data environment. Inf Commun (11) 6. Li Q, Chen L, Feng M, Li C, Fang J (2017) Analysis of several typical data encryption algorithms. Inf Syst Eng (11) 7. Zuo X, Tan H (2017) Analysis and comparison of common data encryption algorithms. J Luohe Polytech (02) 8. Li S (2007) Analysis of popular data encryption algorithm. J Liaoning Commun Coll (02) 9. Liu Z (2020) Application of big data encryption algorithm in data security protection. Electron Test (12) 10. Ren H (2016) Overview of data encryption algorithms. Electron world (18) 11. Li P (1990) A data encryption algorithm and its implementation. Microcomput Appl (06) 12. Zeng L (2017) Research on new data encryption algorithm in big data environment. Sci Technol Bull (06) 13. Wang M, Hao Y, Chu Y (2014) Data encryption algorithm with privacy protection function. Comput Eng Appl (23) 14. Feng K (2020) Research on risk monitoring system of big data platform based on Hadoop. Autom Technol Appl (09) 15. Yan P, Zhang L (2020) Analysis and research on intelligent characteristics of traffic big data based on Hadoop platform. J North China Univ Technol (NATURAL SCIENCE EDITION) (03)
Design of Big Data Management and Analysis Platform Based on Knowledge Map Yong Chen, Wenjun Xue, Leiyu Wang, Yiyang Li, and Xin Xing
Abstract Semantic knowledge mapping is widely used in search, question answering and analysis scenarios, which needs scalable storage mode and distributed parallel query support. In this paper, we design a distributed aggregate storage model with the characteristics of balanced storage load distribution and local node aggregate storage under the big table model, and a distributed parallel query engine with distributed parallel computing query tree based on group by model. Experimental results show that the storage mode and query engine designed in this paper have good horizontal scalability. Keywords Knowledge mapping · Storage · Query · Horizontal scalability
1 Introduction In 2012, Google realized semantic matching search through knowledge map, which changed the traditional search method based on character matching. Knowledge mapping is a kind of directed graph structure, which describes the entities and concepts in the real world and the relationship between them. Search engine can use knowledge map to expand the semantic of query keywords, so as to improve the Y. Chen (B) University of Science and Technology of China, Hefei, Anhui 230026, P.R. China e-mail: [email protected] W. Xue · L. Wang · Y. Li · X. Xing North Automatic Control Technology Institute, Taiyuan, China e-mail: [email protected] L. Wang e-mail: [email protected] Y. Li e-mail: [email protected] X. Xing e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_67
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search quality [1]. In addition to intelligent semantic search, knowledge mapping is also widely used in Q & A and intelligence analysis scenarios to improve the accuracy of Q & A and make deeper analysis and decision on intelligence. In order to support intelligent search, automatic question answering, intelligence analysis and other applications, the storage and query of knowledge map need to have good horizontal scalability [2, 3]. The knowledge of knowledge map is formed by various data fusion and processing, which is related and orderly. The knowledge in the knowledge map is usually stored in the form of triples, and some knowledge and relationships will always be stored, while some invalid knowledge and relationships need to be deleted or modified in time. The updating and modification of knowledge in knowledge map follow certain principles, so that the addition of new knowledge and the updating of old knowledge will not cause the change of knowledge map structure, new knowledge will be accepted as much as possible, and many old knowledge will be kept as much as possible. Large scale knowledge map has a large number of entities and relationships, and the relationship level is deep. When querying, the knowledge map needs to query the corresponding entities or multi-level relationships in the knowledge map timely and accurately according to the user’s query conditions, so as to ensure the user’s experience [4]. Therefore, knowledge map not only needs reasonable storage mode and appropriate storage medium to ensure good storage and access performance of knowledge map, but also needs good scalability and high availability, which not only ensures the stability and rich knowledge of knowledge map system, but also does not affect the system operation efficiency and the ability of data manipulation and management. At present, relational database and non relational database are commonly used to store access knowledge map [5].
2 Related Technology and Research Apache spark is a popular fast general computing engine for large-scale data processing. Compared with MapReduce framework, spark can save the output results of jobs in memory without re reading and writing to disk. At the same time, spark uses the unified abstract RDD (resilient distributed dataset) data structure, which makes spark have more methods than MapReduce, At the same time, it can also deal with different big data processing scenarios in a basically consistent way [2]. RDD is the core data structure of spark. In fact, simply speaking, RDD is a simple set of distributed elements [6]. It is an immutable data structure after creation and supports cross cluster operation. All the work in spark is described in two ways: 1. Create a new RDD or transform an existing RDD. 2. Call some operations in RDD to calculate the result. There are two operations in RDD: 1. Transformations. Transformations can only return a new RDD. 2. Actions. Actions can return the result value or store the result. RDD uses a lazy loading mode, that is, only when the actions operation is executed, the calculation will be started, and the transformations operation will be executed. Spark will not execute the transformations operation,
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Fig. 1 Flow chart of RDD calculation
but only record the operation. Only when the actions operation is encountered, the calculation will be started. Figure 1 is the calculation flow chart of RDD. This chapter is mainly divided into two parts. The first part mainly introduces the related theory of knowledge map storage query by the data model of knowledge map and query language of knowledge map. The second part mainly introduces the related technologies of big data storage and processing, such as mongodb, neo4j, HBase and spark [7–9]. In the later chapters of this paper, we will compare the performance of the above technologies, design the storage mode and distributed parallel query engine to meet the scalability of knowledge map storage query performance, and finally implement the knowledge map storage access prototype system based on big data platform and compare the performance.
3 Design of Distributed Parallel Query Engine for Knowledge Map Storage Access System In order to meet the needs of large-scale knowledge map query scalability, the a distributed parallel query scheme to speed up the query by scanning the query tree query performance map [10]. This chapter mainly introduces the design of two knowledge map distributed parallel query engines in group by mode. Based on the distributed aggregate storage model proposed in the previous chapter, this chapter designs a query engine MQE (memory iteration query engine) using distributed memory iteration technology and Iqe (inverted index query engine) combining
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inverted index technology and coprocessor technology. Based on spark computing model, MiQE iteratively queries entities represented by abstract sets in cluster memory by filtering and linking operations. Iqe is to query the entities corresponding to query conditions in parallel in the cluster by inverted index and coprocessor. The above two distributed parallel query engines are designed to reading knowledge map and speed up the query speed of knowledge map by reducing disk IO and parallel query.
3.1 Query Engine MiQE Based on Distributed Memory Iteration Technology When storing, entities and relationships are stored in HBase in the data storage layer in the designed distributed aggregation storage mode. When querying, the data is first loaded into spark memory in the data processing layer, and then spark queries and returns the results according to the query condition operator. Based on the distributed aggregation storage mode, is shown in Fig. 2. MiQE can have good parallel query performance [11]. The main reason is that when spark loads HBase data, it takes the region of HBase as the unit, that is, the number of regions represents the number of spark partitions. At the same time, because the entity types stored in the region have the characteristics of local aggregation, on the premise that the cluster itself has good performance, MQE design scheme can not only avoid the long time of shue3 caused by data skew, but also find the entities that meet the query conditions in parallel for all regions at the same time, so as to improve the query performance.
Fig. 2 IIQE design scheme
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3.2 Join Query The connection query based on Iqe design is similar to that based on MQE design. Because HBase itself has no connection method, it needs to design connection operation [12]. Iqe uses hash join method to connect entities. Hash join is to take one of the two tables as a hash table, and then scan the other table. In the process of scanning, it is necessary to check whether there is any content that meets the equivalence condition with the hash table. When the result set is large, hash join is more efficient than nested loop join. For the hash join of a table, this paper uses the result of the first single entity query as the hash table, then compares the result of the second entity query with that of the first entity query, and so on, and finally obtains all the entities that meet the join query conditions. In order to do the hash join operation, we need to save entity 1 in the format of < entity 1 object value, entity 1 Object > and entity 2 in the format of < entity 2 subject value, entity 2 Object > for the connection query pseudo code based on the query scheme of HBase coprocessor. Then we can judge whether the entity 2 subject value and entity 1 object value are equal to do the hash join.
4 Conclusion In the face of more and more knowledgelarge-scale k and query performance, and provides basic storage and query performance support for the application of largescale knowledge map. In the e-learning environment, in order to help teachers teach and students learn better, researchers have gradually realized that it is very important to model knowledge and organize and manage resources effectively. Knowledge modeling based on domain ontology is an important form of building conceptual structure and forming the relationship between related knowledge. The relationship between knowledge is the basis of organizing resources and learning knowledge path planning. In the network learning platform, although there are better multimedia presentation and perfect online interaction, the learning effect of learners is still unable to be effectively guaranteed. Advanced technology does not bring high efficiency and high quality learning results for learners.
References 1. Wang L, Li J, Shen Z (2015) Overview of RDF data storage and query technology based on NoSQL. Comput Appl Res (5):1281–1286 2. Renwu W, Yi Y, Xuping Y (2016) Research on the construction of Chinese business knowledge map based on deep learning and graph database. Library Inform (1):110–117 3. Hitzler (2012) Fundamentals of semantic web technology. Tsinghua University Press 4. Huan C, Lin S (2016) Spark best practice. People’s Posts and Telecommunications Press
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5. Ren M (2021) Research on the construction of agricultural big data analysis platform. South Agric Mach (02) 6. Bao Xun Ge, Zhang JM, Zhang Ji, Shang Tian ting (2021) Design and implementation of power big data intelligent analysis platform. Commun Power Technol (02) 7. Xu H (2020). Research on network education data analysis platform based on cloud computing. Comput Prod Circ (06) 8. Yang X (2020) Research on the construction and application of big data analysis platform. Information and computer (theoretical Edition) (09) 9. Che H (2020) Design and implementation of intelligent traffic situation analysis platform based on big data. Radio Telev Netw (07) 10. Zhu Z (2020). Application experience of big data information analysis platform in library management and service. LAN Tai Wai (25) 11. Wang Shuguo, PI Zonghui, Fu Wenhao (2020) Design and implementation of university big data analysis platform. Inform Commun (09) 12. She X (2017). Research and development of game operation analysis platform based on big data intelligent marketing cloud. Electron Technol Softw Eng (23)
Application of Artificial Intelligence in Computer Network Technology in Big Data Era Zhenhui Shan
Abstract Computer network technology has become a widely used modern technology in the development of modern society, which has a good role in promoting the development of many industries and fields in the society, so it is necessary to promote the better application and development of computer network technology. In the current era of big data, in the application and development of computer network technology, more and more modern technical means can be integrated, and artificial intelligence is a common one, which plays an irreplaceable role and value in improving the level of computer network technology, so it is necessary to grasp the application of artificial intelligence reasonably. Keywords Computer network technology · Artificial intelligence · Application
1 Introduction Compared with foreign and domestic data mining technology, in 1993, NSFC supported the research in this field for the first time. At present, major universities are also competing to carry out the research on data mining technology and the optimization and transformation of its algorithm. Although it is far from the international research level, it has also made some achievements, such as: Professor Shi bole of Fudan University has made remarkable achievements in knowledge discovery in relational database, and the knight system successfully developed by Nanjing University. Although it has achieved satisfactory results in theory, it is not satisfactory in practical application. Few successful examples have been heard, and it has not formed a relatively complete force. Generally speaking, it is still in the experimental stage and has not been applied to the actual production. Because data mining technology can bring competitive advantage and huge economic benefits for enterprises, some well-known companies begin to position their development direction in the ranks Z. Shan (B) Heilongjiang Province Harbin City, Harbin Institute of Information Technology, Harbin 150431, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_68
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of data mining. Research related development software and tools. Among them, the most famous are net perception, accue and wed trend. Among many companies, IBM is in the forefront of research and development with its unique intelligent miner mining tool [1]. The Ministry of education starts the undergraduate teaching evaluation, and the teaching quality evaluation of colleges and universities is often held in universities. In the past, the work of school teaching evaluation generally focused on the macro or meso level evaluation, for teachers and teachers’ personal teaching quality evaluation. The research on evaluation methods is relatively less, most of them are manual operation mode, and completely adopt the manual data collection evaluation and processing, such as the display or academic staff in all levels of research. Evaluation methods and means, not only a heavy workload, low efficiency, evaluation results are not satisfactory reliability and effectiveness. Once faced with more evaluation objects and large sample statistics, it is difficult to achieve the expected goal, and a small amount of data processing can still be carried out; It is particularly urgent and important to develop a simple and efficient teaching evaluation tool. It is of great significance to establish an online teaching quality evaluation system based on campus network. Teachers’ teaching quality evaluation system “in the process of classroom teaching quality evaluation of campus network”, in the classroom teaching of students’ evaluation of teachers’ quality, teachers and teaching staff can not only improve the efficiency of teaching management, but also continue to accumulate evaluation data, providing data guarantee for future data analysis. Such a system can not only achieve paperless data acquisition process, but also be able to deal with large data.
2 Overview of Big Data Technology and Artificial Intelligence 2.1 Overview of Artificial Intelligence For artificial intelligence technology, its main use is computer technology and modern communication technology. On the basis of the effective application of computer, it can realize the imitation of human thinking and various behavioral abilities. On this basis, it can combine with relevant computer programs to effectively process various data. In the process of practical application of artificial intelligence technology, it shows the characteristics of humanization [2].
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2.2 Determination of Data Mining Target In order to meet the long-term development of the country, the enrollment scale of colleges and universities is expanding year by year, and the education methods are flexible and diverse. Most colleges and universities are facing the contradiction between the sharp increase in the number of students and the shortage of teaching resources. At the same time, some institutions of colleges and universities are constantly reforming and changing, which has brought unprecedented development and challenges to the teaching management of colleges and universities. In such an environment, how to get the maximum development at the minimum cost has become a new problem to be solved. Generally, teachers will accumulate a lot of data in the process of teaching implementation, but now the processing method of these data is still in the primary stage of data backup, inquiry and simple statistics, so it is not able to deeply tap the potential value of these data. So how to excavate the value of these data, and how to use these data to rationally evaluate some aspects of teaching objectively, will be the focus of our research. For the data warehouse in Colleges and universities, because of its large scale, it can meet the needs of customers by separating the storage management part, application processing and client application of the data warehouse. Therefore, it is particularly important to adopt the C three-tier structure. This three-tier structure mainly includes: client layer based on workstation, middle layer based on server and third layer 1 based on host; The host layer is mainly responsible for managing data sources and converting the optional data sources; The service layer realizes the operation of data warehouse and data mart software, and stores the data in the data warehouse; The workstation of client layer will run the application program of query and report generation, and also store the partial data dumped from data mart or data warehouse.
3 The Necessity of Using Artificial Intelligence in Computer Network Technology 3.1 Advantages of Artificial Intelligence Artificial intelligence in advanced computer to achieve human simulation learning, but combined with the actual situation analysis, through the network technology to achieve intelligent transformation of database, improve the level of system management. The rapid technology provides a new development application of artificial intelligence, which is also the main direction of future development.
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3.2 Fuzzy Information Processing Ability Artificial intelligence technology is reasonably applied to practice. It can process fuzzy and uncertain information according to the actual situation. It will not be processed according to the previous accurate calculation method and will not be limited by fixed thinking. It is similar to human thinking. It adopts fuzzy processing method, which can simulate human thinking mode, processing of information, It can better promote the improvement of information processing level. The determination of equivalent weight U of control output. In theory, the number of weight u depends on the number of observations. According to the control rules, the output weight algorithm synthesis rules of fuzzy controller can be obtained: u = l · M
(1)
Orenz-96 model is a strong nonlinear chaotic model, which roughly simulates the evolution process of uncertain meteorological variables along the latitude circle, and is often used to verify the effectiveness of data assimilation algorithm. Because there are 40 state variables in the z-96 model, different combinations can be made in the study: d xi = −xi−2 xi−1 + xi−1 xi+1 + xi + F dt
(2)
the error operator to test the performance of the assimilation method: n 1 R M S Ea = ( (x − xi )2 ) n t=1
(3)
4 Effect Analysis of Artificial Intelligence Technology Applied to Computer Network in Big Data Era 4.1 Artificial Intelligence Can Improve Information Security Under people’s life has changed greatly. But in the Internet age, human resource sharing will also bring great challenges to information security. From the current analysis of the actual situation, a lot of information leakage events, leading to the hidden danger is very huge. artificial intelligence and advanced technology put forward the corresponding solutions.
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Fig. 1 Network security schematic diagram in the era of artificial intelligence
Artificial intelligence is a new application in network security. Figure 1 is a schematic network security in the era of artificial intelligence. (1) intrusion detection of the Internet of things. It is predicted that by 2020, the number of devices connected to the Internet in the world will reach more than 50 billion. By then, it can be said that everything is interconnected. However, due to the limitations of software and hardware conditions, many devices do not have the most basic network security protection capabilities [3]. The prediction model based on artificial intelligence can automatically reside and work on all kinds of devices, real-time intrusion detection and protection against malicious program attacks. (2) Protecting files from network attacks is the main means of attacking all kinds of files, including all kinds of office files and PD files, which are vulnerable to illegal attacks. Using the standard model of artificial intelligence and fuzzy processing ability, we can easily analyze and obtain various types of characteristics of suspicious files, including the slightest code conflict, so as to prevent file network attacks. (3) Quantitative risk how to quantify the network risk has always been a technical problem, which is due to the lack of historical data and more variables. With the help of artificial intelligence data model and fast and accurate parallel computing ability, massive data in real time, generate prediction results, and obtain accurate network quantitative risk assessment results.
4.2 Artificial Intelligence Technology is Applied to the Field of Network Management and System Evaluation The reasonable use management has made great progress, which of human technology and telecommunication technology. The rational use of artificial intelligence technology can realize the joint application of expert database and problem processing, can achieve efficient system management, and improve the overall application effect. With the development of the times, the network changes very fast and has strong liquidity. There will be many constraints in the network management and system evaluation. Artificial intelligence can deal with these problems, and it will be more professional and effective. Artificial intelligence technology used in computer network security management will also produce very good results, can
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form a perfect security technology system, according to the requirements of system information screening processing, can be efficient and stable inspection, make the operation more convenient [4]. This way of access, and also can effectively open the network access management, can prevent the problem of network damage, and can also prevent adverse effects on the computer network. In addition, the artificial intelligence security system can create a way to block the spread of the virus and avoid hacker attacks. Even if the firewall is broken, it will form a new barrier and improve the effect of network security.
4.3 Key Technologies of Data Warehouse DW (data warehouse) is not only a structure and method, but also a technology. What are the key technologies of data warehouse? 1. Determining the size of data granularity refers to the data or refined combination 2. The data warehouse stored in the data warehouse contains a large number of data tables. What kind of storage granularity do these data tables use to store information in systemsmany. Designers to determine which data level granularity standards, which will seriously affect the data warehouse data storage and query data warehouse, and further affect the design quality of the system, is able to meet the needs of end-user analysis. Determining the size of data is based on the design of data warehouse. It will be very easy to determine the design and implementation of data granularity. 2. Whether it is database or data warehouse, optimization is to improve the efficiency of data warehouse, so we need to adopt some special optimization measures to save storage space, speed up response, reduce maintenance costs and so on. There are three main technologies used. (1) Filter irrelevant modification operations on the source site. (2) Maintainability. (3) Multi view optimization. 3. Data warehouse maintenance large data warehouse, data life cycle is very long, so it will give data warehouse update and maintenance of high requirements. In general, the maintenance and update of data warehouse are mostly carried out at night, but for multinational companies, there is no real time to refresh and maintain the data warehouse. And in the refresh, once there is a mistake or failure, it will bring serious consequences to the enterprise and affect the business action. For large data warehouse maintenance, the following key technologies are involved: (1) effective calculation of cube; (2) incremental update maintenance; (3) index optimization; (4) fault recovery. 4. Data integration data integration is the decomposition process of logic, for different data warehouse products, the specific implementation is very different. In the process of integration, we need to consider the following aspects: (1) Pattern matching for those real-time data, or data closely related to time, the interval of automatic collection is relatively close, and the collection frequency is relatively high; However, the time of manual data entry is relatively scarce; Because
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the transaction time is not equal interval, and the data recovery time is historical. If metadata is used to explain the matching of these different patterns, the pattern matching error caused by data integration can be avoided (2) In each update of data redundancy, there may be multiple attributes in a log file that affect the result at the same time, and the attributes that affect the same result will be more easily associated, so those attributes that can be derived from other attributes are considered as redundant attributes (3) Data value conflicts in different data sources, the attribute values of the same entity may be different, such as data type, quantity unit, coding and so on, which need to be standardized and unified.
5 Epilogue With big data technology, has made great progress, which can promote the improvement of people’s quality of life and efficiency, but there are also many problems. Therefore, people should pay attention to artificial intelligence technology, but can not rely too much on it to achieve steady development and promote the long-term development of human society. With the development of higher education informatization, various management information systems and the accumulation of teaching materials, data mining technology will be more in-depth and extensive in Colleges and universities. Data mining technology in teaching management, especially in the teaching quality evaluation system, can also provide some data to support university managers, to help managers make effective and reasonable decisions.
References 1. Xi J (2020) Research on application of artificial intelligence in computer network technology based on big data era. Electron Compon Inform Technol 4(08):66–67 2. Xu S, Sun Y, Liu J, Gao Y (2020) Research on the application of artificial intelligence in computer network technology in the era of big data. Netw Secur Technol Appl (08):112–113 3. Zhang D (2020) Discussion on the application of artificial intelligence in computer network technology in the era of big data. Comput Knowl Technol 16(21):169–170 + 174 4. Bin Y (2020) Application of artificial intelligence in computer network technology based on big data era. Commun World 27(07):213–214
Application of Big Data Electrical Automation Technology in Electrical Engineering Qiang Jiao
Abstract Electrical automation technology can effectively improve the work efficiency of electrical engineering, reduce labor costs and improve economic benefits. With the development of big data, cloud computing and artificial intelligence technology in China, electrical automation technology has been improved and applied under related technologies. How to improve the operation reliability of equipment to ensure the security and stability of power system has become a research topic of great concern in the power industry. In addition to using the equipment with high quality and good stability, improving the equipment operation reliability can also be achieved by improving the equipment health assessment ability and equipment fault diagnosis level. At the same time, due to the complexity of the power system, it is difficult to extract valuable information from the data. In this paper, combined with big data, deep learning and other related content, the equipment health status assessment and fault diagnosis are studied and analyzed. Keywords Electrical automation · Big data · Artificial intelligence · Electrical engineering
1 Introduction Electrical automation technology has been applied and developed in China as early as the 1950s. In the 1990s, with the rapid development of MCU technology, electrical automation technology has been more widely used. The automation technology of electrical system is developed on the basis of integrating computer technology, application communication technology, motion control technology and system automatic protection, which has a strong comprehensive technology of multidisciplinary combination. With the development of big data technology, electrical automation technology based on big data technology in the implementation of equipment operation data detection, real-time collection of detection system data, at the same time to Q. Jiao (B) Henan Vocational College of Industry and Information Technology, Henan 454000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_69
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achieve data acquisition, processing, fault analysis and the corresponding potential operational risk to make a forecast alarm and other aspects of better performance. Under the development trend of artificial intelligence, electrical automation is more intelligent to the system processing after combining with artificial intelligence. Artificial intelligence technology is mainly used by engineers to simulate the operation logic of computer according to human thinking, so that the machine can achieve the same effect as human operation, so that artificial intelligence can make the system more stable and efficient. This paper mainly summarizes the technical characteristics of electrical automation, studies the application of electrical automation in electrical engineering, and studies the latest development direction and technical characteristics of electrical automation.
2 Research on Equipment Life Cycle Theory and Big Data 2.1 Research on Life Cycle Management of Equipment Assets In the field of economy, assets generally refer to the resources owned or controlled by the enterprise itself, which can produce certain economic benefits in the future. By analogy with the definition of assets, data assets can be defined as data resources that are controlled by enterprises or organizations and are expected to bring economic benefits or value. Data asset management is a data management concept based on data assets. The emergence of data asset management has changed the previous concept that “data is only a by-product of business activities”. With the development of big data, data is regarded as a new type of asset like money or gold for management and use. But not all data can be called assets. Only those data that can be controlled, measured and realized by enterprises can be called assets. Generally, the process of realizing variable attribute mining of data and reflecting data value is called data capitalization [1]. The core of data asset management is to capitalize the data, that is to say, data is regarded as the core assets that can bring economic benefits to enterprises like physical assets, knowledge assets and talent assets. Through the construction of a perfect management framework, the data can be managed, so as to better deal with the challenges brought by the data change in the era of big data [2].
2.2 Big Data Platform and Architecture In the era of big data, data asset management is the necessity of the development of information construction, and also the basic way to carry out big data analysis and realize data-based operation. In the face of the growing mass of data, only
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through data asset management can we better organize and manage data and provide support for enterprise analysis and operation. At present, enterprise data is mainly composed of structured data, unstructured data and massive real-time data. Faced with the growing mass of big data, it is urgent to integrate the data of various systems, comprehensively mine the potential value of data, and use big data technology for analysis and processing, so as to enhance the value of enterprise data assets [3].
3 Research and Application of Equipment Life Cycle Management 3.1 Evaluation Standard of Equipment Health Status Generally speaking, when the measured value of monitoring parameters exceeds the specified threshold range or some specified limit values, it can be considered that there is an abnormal change in the operating state of the unit. When there is abnormal situation, it is not sure that the equipment has failed, or it may be the result of some changes in some operating conditions of the unit, which causes the change of monitoring parameter value. On the contrary, when the monitoring data is within the normal operation threshold, the unit also has abnormal conditions. Therefore, another criterion of evaluation can be introduced: relative evaluation standard. Relative evaluation standard is a method for the same monitoring and measurement of the same unit, and the normal value in a certain period is regarded as the standard value or health value, and then the actual value is compared with the standard value, so as to evaluate and analyze the operation condition of the unit [4]. According to the statistical theory, in the case of large sample conditions, the mean value of the sample can be regarded as the theoretical mean value. Therefore, when the unit data accumulation is large, the mean value of the characteristic quantity can be used as the reference value of the operation health evaluation: X=
(x1 + x2 + · · · + xn ) n
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The structure of hydraulic turbine is complex, and the random error of datum value is composed of many factors, such as fluctuation, random interference and so on. However, the random error generally has the characteristics of statistical distribution and obeys the normal distribution law. Therefore, the alarm threshold range can be determined by combining with the Leiter criterion: X c = X ± 3σ
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For each characteristic quantity of the unit, its reference value and warning interval can be constructed as its health samples through historical operation data. If the
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equipment operates normally, the probability of the data in the health sample range is 99.73%. If the measured data of the unit exceeds the healthy range of the sample, the probability of abnormal equipment is 99.73%. Therefore, from the perspective of statistical theory, it is feasible to use this method to construct the equipment health benchmark and warning interval.
3.2 Research on Hybrid Prediction Model Based on SVR and ARIMA There are many researches on the prediction of time series data, including the auto regressive moving average (ARMA) model, the autoregregive integrated moving average (ARIMA) model 4,48 and other linear prediction models, including support vector machine (SVM)0.50, neural network 12 and other nonlinear prediction models. ARMA and ARIMA models based on autoregression are suitable for linear time series prediction, and s V m and neural network are suitable for nonlinear prediction. However, due to the limitations of various models, the single prediction model has a large prediction error, which can not meet the demand of prediction. Therefore, a combination prediction model 1534, which combines ARIMA model with SVR (Support Vector Regeneration), is proposed in this paper. Through the linear sequence predicted by ARIMA model, the nonlinear residual sequence is obtained by making the difference between the actual value and ARIMA prediction results, and then the SVR model is used to predict the nonlinear residual sequence, Finally, the linear series of ARIMA prediction and SVR nonlinear residual sequence are added to obtain the final prediction sequence, so as to improve the prediction accuracy. The model is shown in Fig. 1. Fig. 1 A hybrid prediction model of SVR and ARIMA
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4 Application Prospect of Electrical Automation Technology 4.1 Joint Development of Electrical Automation Technology, Big Data and Cloud Computing Technology There are a lot of real-time data to be collected and processed for equipment operation and power grid operation data. In this way, the online monitoring system can synchronously realize data collection and processing, electrical fault analysis and processing, and make corresponding self inspection report. And put forward higher requirements for danger warning and notice and warning. Generally, large capacity data storage device is required to keep the historical data of electrical equipment operation. The online monitoring system operates normally, and the analog quantity is sampled at 36 points per cycle. If there is an abnormal situation, 128 points sampling is used. In this way, each online monitoring system has at least three fault reports. Therefore, there is a large amount of data information, which needs the help of big data and cloud computing technology.
4.2 Development of Electrical Automation Technology and Artificial Intelligence The development of artificial intelligence has been widely concerned in recent years, and its application in electrical automation technology has also been put forward. Its main characteristics are as follows: (1) Optimize the design of electrical products. The traditional design relies on the design experience of engineers, and the work is cumbersome and inefficient. Artificial intelligence can make the process more intelligent and improve the design efficiency; ➁ It is more efficient for fault handling. Although the traditional electrical automation technology can effectively reduce faults and accidents, it is still not enough. Artificial intelligence algorithm has many advantages in the prevention and maintenance of faults and accidents. For example, in transformer fault detection, the traditional way is to collect transformer gas, which needs to be resisted by a series of anti-interference methods. For example, in the aspect of hardware improvement, the main power supply line and signal line can be set separately to weaken or even eliminate the potential formed by the grounding of each equipment in a large range. In addition, isolation transformers can be used. Secondly, although the improvement of hardware equipment can achieve the purpose of weakening the interference source to the workshop electrical control system, the improvement of hardware facilities alone is not enough to resist the influence of interference, there will still be more interference to the equipment. Therefore, it is necessary to start with the configuration and design of the software to resist the interference of the interference source to the electrical control system of the workshop, so
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as to ensure the reliability of the power supply and distribution system. For all kinds of anti-interference factors, through the design and transformation of the software, we can realize the functions that need to play in the actual process, eliminate the interference source, and improve the security of the system. This measure not only helps to simplify the circuit design of hardware, but also is a simple, fast and reliable method to save capital to a large extent.
5 Innovation Way of Electrical Engineering Automation Control Work 5.1 Realize the Visual Management of Electrical Engineering Automation Control Work The visual management subsystem of electrical engineering is completed by the cooperation of the front-end and back-end technologies. The vector map or image electronic map of electrical engineering can be added through relevant science and technology for later operation, and the map can be enlarged, reduced and dragged. The AP application interface can also provide small controls such as eagle eye map and bookmark editing, which greatly enhance the user’s experience. Through these basic map operations, the operation of electrical engineering can be clearly managed, In the process of the actual operation of the electrical insulation platform, the staff should first determine the installation method and location of the insulation platform combined with the actual work. After that, the workers need to wear a complete set of personal insulation protection equipment, with the insulation platform as the main insulation, the personal insulation protection equipment and insulation shielding work as the auxiliary insulation, and the workers directly contact the live equipment to replace the metering device. The advantage of using this method for live replacement of metering device is that it is more flexible and easy to operate. Its disadvantage is that the insulation platform can only be used as the main insulation of the staff, which can provide the working platform for the staff, and can not play a good auxiliary role as a tool, and it can not support the complex live working.
5.2 Operation Method of Insulated Operating Rod Insulated operating rod method is that workers do not directly contact the conductor in the process of operation, but use a variety of insulated tools to carry out live working across a certain distance. There are some shortcomings in using insulated control rod operation method, that is, the staff use tools instead of hands to work, and can not effectively complete the long-distance, complex and meticulous work. The insulating operating rod method mainly uses insulating tools as the main insulation
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method, and personal insulating protective equipment as the auxiliary insulation method. The workers generally use the foot buckle and lifting plate to the working position, and then fasten the safety belt to keep a safe distance from the electrified body. Although the staff on the pole are wearing insulating shoes, other parts are still in contact with the pole, so the role of insulation protection can be ignored, and the staff can be regarded as always in the state of ground potential. The advantage of insulating operating rod is that it can use foot buckle and lifting board to climb. This method can effectively solve the problem of difficult operation in some special areas.
5.3 Combination of Insulation Platform Operation Method and Insulation Operating Rod Operation Method The combination of insulation platform operation method and insulation operating rod operation method can form complementary advantages. By combining the two methods, the workers on the insulation platform can cooperate with the workers on the operating rod to work directly on the charged body, which can effectively improve the efficiency and quality of live replacement of metering device. Taking one meter for one household as an example, the installation of one meter for one household can effectively improve the power supply reliability of our people, greatly improve the level of urban electrification, and build a stable and harmonious electricity environment in our country. In addition, by taking the measure of one household meter, it can effectively solve the problems of low-voltage distribution network, meters and meter boxes in many public institutions and residential areas, such as years of disrepair, line aging, small diameter and electricity leakage. At the same time, it can also greatly reduce the efficiency and quality of live wire replacement work, and reduce the capital cost of the work.
6 Conclusion The interference problems caused by the formation of interference factors will affect or hinder the operation of power supply and distribution system to a certain extent. At the same time, the operation fault of power supply and distribution system will do useless work in the process of system operation, which will increase the workload and hinder the normal operation of equipment. Therefore, it is very important to study and apply anti-interference technology in power supply and distribution system. The common interference sources of power supply and distribution system include power supply interference, line interference and electromagnetic field interference. According to this, the power supply and distribution system is transformed and rectified, and then filtered and stabilized. The AC lead-in line with large conductivity
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and wide diameter is used, and the twisted pair with small torque and suitable length is used as the DC output line, and the power supply line is monitored in real time; In the transmission line, the transmission medium twisted pair and coaxial cable composed of hollow outer cylinder copper net and cable copper core on the central axis should be selected as far as possible to achieve the effect of strong anti-interference ability and stable and safe data transmission process. This method not only helps to reduce the risk factor in the process of centralized power supply, but also helps to reduce the impedance of the current in the public power supply, thus greatly increasing the safety and reliability of the power supply process. The reasonable design of grounding and appropriate shielding means can prevent the interference of electromagnetic field to the operation of equipment to a great extent, so that the power supply and distribution system can have a strong ability of anti electromagnetic interference and operate safely and reliably.
References 1. Zhaoping C (2015) Application analysis of intelligent technology in building electrical engineering. Doors Windows 2:67–68 2. Zhang X, Ma Q, Gao J (2015) Application of intelligent technology in electrical engineering automation control. Sci Technol Outlook 25(5):94 3. He X (2018) Discussion on the application of intelligent technology in electrical engineering automation control. Comput Knowl Technol 14(4):146–147 4. Jian C (2016) Application analysis of intelligent technology in electrical engineering and automation. Enterp Technol Develop 35(4):74–75
Operation Analysis of Financial Sharing Center Based on Big Data Sharing Technology—Taking SF Express as an Example Chengwei Zhang, Ge Guo, Weiqi Rao, and Xinyan Li
Abstract With the advent of information and digital age, the company’s economic development scale is getting bigger and bigger, and its development is getting faster and faster. While the number of company businesses is gradually increasing, they are also becoming more and more complex. We can use information technology to integrate business and improve operational efficiency. Therefore, a new model of process reengineering of the financial management system-the financial sharing center came into being. According to statistics, more than 400 companies among the world’s top 500 companies have prepared or have run financial shared service centers. The financial sharing center can not only reduce costs for enterprises, but also create profits for enterprises. This article takes SF Financial Sharing Center as an example to introduce its operating mode. Keywords Financial shared service center · Financial management · SF express
1 Introduction 1.1 Research Background With the continuous improvement of the international informatization level and the rapid development of the world economy, the increasing commercial trade also puts forward higher requirements on the financial management system of enterprises. Under such circumstances, many large and medium-sized enterprises usually have many problems, and the difficulties caused by financial management make the overall work efficiency of the enterprise inefficient. In order to solve these problems, the centralized management form of the financial shared service center came into being. The management model of the financial shared service center has comprehensively improved the quality and efficiency of corporate financial C. Zhang · G. Guo (B) · W. Rao · X. Li School of Management, Wuhan Donghu University, Wuhan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_70
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management, and is an important means for the transformation and development of financial management under the new situation. As early as 2013, the Ministry of Finance of our country promulgated the “Enterprise Accounting Informatization Work Norms” clearly pointed out: large enterprises should explore the use of information technology to gradually establish financial shared service centers. Since then, the construction of the domestic financial sharing center has developed rapidly [1–4]. For logistics enterprises, information construction is the key to the development of modern logistics industry. Therefore, the financial shared service center is imperative for the logistics industry. Since its establishment, SF Express has developed rapidly and is a leading representative express company characterized by “fast delivery”. In recent years, SF Express has been able to develop rapidly due to the dividends of e-commerce. Taking into account the rapid increase in the number of SF Express’ logistics business, coupled with the wide range of SF Express’ express delivery business, branch offices are scattered in various regions of the country. SF Express’s management and control capabilities are restricted. In order to change the status quo, SF Express has established a financial shared service center. This financial management model solves the problems existing in various financial processes of SF Express. So as to reduce the operating cost of the enterprise and meet the business needs of SF Express [5–8].
1.2 Research Significance First of all, to help accountants to achieve a change in thinking. After ERP goes online, the thinking of financial people needs to be changed. FSSC is a company’s “professional” financial management model, which came into being in the transformation of ERP’s technical process. It requires accounting to focus on the “business cycle” and the smooth connection of the entire process steps from front-end business to back-end finance, instead of focusing on the accounting records of a single business transaction after the fact.. Secondly, provide support for corporate financial transformation. The process of applying “Internet + “, “big data”, “cloud computing”, and “sharing” to financial management has resulted in the “financial sharing” model. The organization and management model of the Financial Shared Service Center has brought a revolution in the financial field. Over the years, market competition has become increasingly fierce, and corporate pressure has increased. More and more companies have joined the ranks of financial sharing services, providing more and more materials for the study of financial sharing centers. However, the financial sharing center does not have the best for different industries, only the most suitable. This article takes SF Express as an example to provide a reference for other similar companies in their financial transformation [9]. Finally, this article has certain reference value for the upgrade and improvement of the financial shared service center of SF Express in reality. With the updating of new technologies, more new technologies are gradually replacing manual operations,
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and financial sharing should be in a trend of continuous innovation. Undoubtedly, various problems will be encountered in the process of innovation. Analyzing the financial shared service center of SF Express can make its financial shared service center better innovate, and enable SF Express to maintain its leading position in the logistics industry [10].
1.3 Status of Domestic and Foreign Research 1.3.1
Research Status Abroad
Gunn and others put forward the concept of financial sharing earlier. They believe that shared management is an innovative management concept that can improve competitive advantage by centralizing and integrating resources. The IMA “Management Accounting Announcement” (2010) pointed out that the Financial Shared Service Center is an internal organization within the enterprise and is an independent part of the operation. Will Seal and Ian Herbert [3] pointed out that the financial sharing center can not only help enterprises integrate their businesses and provide support internally, but also create value externally.
1.3.2
Research State in China
Yanjun [4] believes that the design of the financial sharing center should meet its financial strategic goals. On the basis of achieving goals, combine business and human resources. Hui [5] pointed out the need to perform performance management on the financial sharing center. Only in this way can we provide better services and improve work efficiency.
1.3.3
Literature Summary
Scholars focused on the definition of the financial shared service center, the process reengineering of information technology, the problems that occurred in the operation of the financial shared service center, and the cost-effectiveness. But it did not combine the process reengineering and performance evaluation of the financial shared service center for analysis. Therefore, the research on the process innovation of the financial shared service center under the background of big data is conducive to enriching the depth of financial management theory in shared services. Second, financial sharing is an innovation in financial management methods. The essence is also the reengineering of the process. A case study of the operation of the corporate financial shared service center under the background of big data provides some basis for the theoretical research of financial management.
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2 SF Financial Sharing Center 2.1 Introduction to SF Express SF Express was established in 1993 with a registered capital of approximately 150 million yuan and its headquarter is located in Shenzhen. Since its establishment, SF Express has been developing steadily and is a leading representative of express delivery companies characterized by “fast delivery”. Due to national policies, the transformation of financial accounting and the internal requirements of SF Express Group, SF Express established a financial shared service center in 2015.
2.2 The Construction Goals of SF Express Financial Sharing Center The construction goal of SF Express Financial Shared Service Center is to optimize the financial management process. Before the sharing model of SF Express, financial personnel in different regions had deviations in their understanding of the financial system. Problems such as inconsistent accounting treatment standards, inconsistent process operations, and inconsistent travel subsidy standards often occur. And in terms of expense reimbursement, salespersons often need to go to each audit port for offline audit item by item, and work efficiency and work quality will be affected to a certain extent. After the establishment of the financial shared service center of SF Express, the financial management process will be sorted out. First, abolish the system that conflicts with the group’s regulations. Secondly, a unified system specification, reimbursement standards and accounting standards have been established, and the personnel of the Financial Shared Service Center have been uniformly trained. Open up all system interfaces and write standardized operating procedures. Not only that, the establishment of the financial sharing center has made the communication between the financial department and the business units smoother. The financial department can better regulate the financial work in response to the feedback from the business unit. Moreover, in the financial management analysis work, you can also find the business unit to consider the problem based on the situation learned in the communication, so that the financial decision-making and business decision-making of the enterprise can be more scientific and reasonable.
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Accounts Receivable Management and Accounting
Financial Statements
Accounts Payable Management and Accounting
Basic Financial Work
General Ledger Accounting
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Reimbursement
Collection and Payment Business
Tax Invoicing Money Management
Fig. 1 Basic financial work of SF express financial shared service center
2.3 The Construction History of SF Express Financial Shared Service Center 2.3.1
Establish a Standardized Basic Financial Processing Process
In order to unify the basic financial work of SF Express, it is first necessary to establish a standardized basic financial processing process. Realize that the basic financial work of SF Express Group’s branches is concentrated in the financial shared service center for processing. This centralized processing method improves the status quo of low-end repetitive work performed by financial staff. For example: accounts receivable management and accounting, accounts payable management and accounting, expense reimbursement, collection and payment services, tax invoicing and other basic financial tasks. After SF Express has established a standardized basic financial processing process, these businesses can be concentrated in the SF Express Financial Shared Service Center for processing (Fig. 1).
2.3.2
Cooperate with UFIDA to Establish a Shared Platform
The construction of SF Express Financial Shared Service Center is based on the sharing platform of UFIDA, which builds an information platform that meets its own characteristics. The information systems used by the shared platform of SF Express
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Financial Shared Service Center include financial ERP platform, professional financial system, and financial shared professional system. Among them, the financial ERP platform is mainly to generate general ledger. The professional financial system includes fund management, budget management, consolidated statements, expense amortization, analysis and modeling, etc. Financial sharing professional systems include imaging systems, professional platforms, quality management, performance management, electronic invoice management, electronic file management, contract management, online accounting, etc. The establishment of the information platform and the realization of its functions are the prerequisites for the operation and management of the SF Express Financial Shared Service Center. SF Express Financial Shared Service Center successfully built an information platform and realized the normal functions of the information platform. The sound operation of the information platform of the SF Express Financial Shared Service Center has improved the efficiency of SF Express’s operation and management. Improved the service quality of the SF Express Financial Shared Service Center and helped SF Express’s management to make effective decisions (Fig. 2).
NC System Professional Financial System
Integraon with Core Business Systems
Money Management
Budget Management
Consolidated Statement
Cost-sharing
Analycal Modeling
Financial Shared Operang System
Financial ERP Plaorm
Fixed Assets (FA)
Accounts Receivable (AR)
General Ledger (GL)
Report
Accounts Payable (AP)
Imaging system
Operang plaorm
Quality Control
Performance management
Electronic invoice management
Electronic file management
Contract management
Staff credit management
Online reimbursement
Fig. 2 Shared platform of SF express financial shared service center
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3 Achievements in the Construction of SF Financial Sharing Center 3.1 The Operating Costs of SF Express Continue to Decrease The establishment of the SF Express Financial Shared Service Center has improved the financial working mechanism. This enables SF Express to obtain effective information when obtaining the most valuable information for the logistics industry in the era of big data, avoiding repeated information purchases. Effective use of funds to reduce the operating costs of SF Express. The SF Express Financial Shared Service Center can further increase the utilization rate of SF Express’ financial information sharing, maximize the dynamic sharing of information transmission, and give play to the backing value of financial management in SF Express’ market competition and financing. After the establishment of the financial shared service center, SF Express’ operating costs, especially management expenses, have been continuously reduced. The administrative expenses in 2016 were 40.11 million yuan, while the administrative expenses in 2019 fell to 10.32 million, a decrease of 74.27%.
3.2 The Work Efficiency of SF Express Continues to Improve After the establishment of the Financial Shared Service Center, SF Express’ financial management has been unified. SF Express Financial Shared Service Center has improved the status quo of low-end repetitive work performed by financial staff. Centralized management simplifies the work process, is more conducive to the standardization of business processes, improves various financial management tasks, and avoids inconsistent implementation in various regions. It has realized the automation of financial accounting mechanism and the integration of financial analysis, reducing the disadvantages of reduced work efficiency due to different professional qualities. Secondly, the SF Express Financial Shared Service Center keeps abreast of financial information such as accounts receivable, accounts payable, bond relationships and capital increase and decrease of each branch company, avoiding the occurrence of idle funds. The establishment of the SF Express Financial Shared Service Center has reduced the process of project funding declaration, enabled the project funds to arrive on time, and improved work efficiency. After SF Express established the financial shared service center, the number of financial personnel handling business has been reduced by 89 people compared with before, and the total cost has been saved about 2.83 million yuan.
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3.3 The Management and Control Capabilities of SF Express Continue to Improve The establishment of the Financial Shared Service Center changed the traditional financial management mode of SF Express in the past. This real-time monitoring of the financial shared service center model has enabled SF Express Group to continuously improve its management and control capabilities of its branches. SF Express Financial Shared Service Center has standardized procedures, operating standards, work efficiency and risk management. This has changed the current financial management status of SF Express and strengthened the group’s management and control capabilities. Secondly, it changed the way that SF Express submitted financial data of each branch in the past. The birth of SF Express Financial Shared Service Center enables SF Express to control the financial status of its branches in real time. When a branch of SF Express needs to face emergencies or internal faults, SF Express can make timely remedies based on the financial status of the branch reflected by the Financial Shared Service Center. In addition, the centralized management of cash transactions and bank transactions in the SF Express Financial Shared Service Center has reduced liquidity collection and payment activities and improved the stability of the company’s capital. Through the direct monitoring of the SF Express Financial Shared Service Center and the bank, the internal financial information management and the control of financial activities of SF Express will be improved.
4 Comparison of SF Financial Sharing Center and Meicai Financial Sharing Center In 2015, SF Express optimized the basic financial work process and prepared for the establishment of financial sharing. Finally, SF Express selected the location of the financial sharing service center in Wuhan. Beginning in July of the same year, SF Express took only 11 months to successfully collect basic financial tasks such as accounting, payables, payments, and statements to Wuhan Financial Sharing, and completed the collection of hundreds of financial account sets across the entire network at that time. In the past two years, the number of shared business orders of SF Express has grown by an average of 36% annually and a cumulative increase of 85%, while the number of financial sharing personnel has been reduced by 25%. That is, when only 75% of the people are used to do the original 185% of the workload, the unit efficiency has been increased by 145%, and the quality has also been significantly improved. The financial sharing model is very much in line with the current development stage of SF Express. Meicai Financial Shared Service Center was established on September 23, 2016. Through the introduction of SSC information tools, the integration and coordination of business and finance are realized, and the matching and closed loop of “logistics, information flow, bill flow, and capital flow” are realized. After the establishment of
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the Meicai Financial Sharing Center, the operating cost was reduced by 30%, and the efficiency of business processing was significantly improved. In the first half of 2020, the total number of audited orders was 54,000, compared with 23,981 in the first half of last year, a growth rate of 125%. At the same time, in the first half of 2020, the cost of each reimbursement form decreased by about 50% compared with the first half of 2019. The financial shared service center is very suitable for large-scale enterprise groups such as SF Express and Meicai with large business volume and business coverage in Guangdong. If you want to solve the current problems in financial management, those similar enterprise groups may wish to consider establishing a financial shared service center.
5 Conclusion Nowadays, financial sharing services are still of strategic significance for the transformation of financial management and the improvement of the classification of financial indicators. The financial shared service center is a management model that adapts to the times, which is of great benefit to the development of large enterprise groups. Through the analysis of this article, I hope it will be helpful to the future development of SF Express Financial Shared Service Center, and hope to provide certain reference value for other enterprise groups to implement and improve the Financial Shared Service Center. Acknowledgements This work was supported by the Youth fund project of Wuhan Donghu university (Wuhan Donghu university letter [2020] Document).
References 1. Robert WG, David PG, Robert F (1993) Shared services: major companies are reengineering their accounting functions. Manag Acc 18:22–28 2. American Institute of Management Accountants (2012) Management Accounting Bulletin. First Series. Beijing: People’s Posts and Telecommunications Press 3. Seal W, Herbert I (2014) A knowledge management perspective to shared service centers: a case study of a finance SSC. Emerald Group Publishing Limited 4. Yanjun G (2017) Research on enterprise financial process reengineering embedded in cloud computing. Accounting Friends 7(3):40–42 5. Hui Z (2019) Research on the construction of performance management system of financial shared service center. Chinese Academy of Fiscal Sciences 6. Lingling H (2020) Research on the construction of financial shared service center of F company. Zhengzhou University 7. Lansheng L (2021) Discussion on financial sharing in the era of big data. Finan Econ 03:100– 101 8. Shu D (2021) Problems and countermeasures in the initial stage of financial shared service. Technol Market 28(01):180–181
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9. Ying Z (2020) Research on the optimization of W company’s financial shared service center. Zhengzhou University 10. Zhiyu L (2021) Analysis of new trends in financial management of commercial banks. Econ Manage Digest 02:18–19
Application and Practice of Big Data Analysis in Enterprise Brand Marketing Management Yishu Liu
Abstract Data information, further analysis and clear objectives, extract and output effective information, and provide an important reference for marketing. With the support of large-scale data analysis technology, the design of marketing strategies can better meet the needs of consumers, and more operable and accurate market trends and trends. In order to study the application and practice of enterprise brand marketing management under big data technology, we select four enterprises with similar brand values from this city and divide them equally into two groups, two enterprises in group A use traditional methods for brand marketing management and two enterprises in group B use big data technology for brand marketing management. Over time and with the progress of the experiment, we found that there were significant differences between the two groups of enterprises. The experimental results showed that brand awareness of group A increased by 20% after one month, 8% after the third month, and finally 40% at the end of the experiment. At the same time, brand awareness in Group B increased gradually to 45% from 22% a month later, which is significantly greater than Group A. Therefore, using big data technology can not only improve consumer satisfaction, but also enhance the brand awareness of enterprises. Big data can help enterprises better adapt to market needs and achieve long-term development. Keywords Big data technology · Brand marketing · Marketing management · Brand awareness
1 Introduction Big data not only collects large, efficient data, but also improves people’s understanding of the organic combination of data, technology and applications [1, 2]. Y. Liu (B) Business School, Xiamen Institute of Technology, Xiamen 361000, Fujian, China e-mail: [email protected] Chinese Graduate School (CGS), Panyapiwat Institute of Management, Nonthaburi 11120, Thailand © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_71
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First, the object of large-scale data analysis is large-scale data collection, lacking large-scale data [3, 4]. Large data is not the accumulation of large amounts of data, but the collection of related data is analyzed through the correlation between these data. Therefore, the correlation and structure of data are the main differences between large-scale data and large-scale data; secondly, large-scale data technology mainly comes from a wide variety of large data, which is different from large-scale with the rapid absorption of useful information technology [5, 6]. The biggest difference is that big data includes data processing. In order to get useful information from a large amount of data, people concerned should adopt a flexible and distributed processing method. Finally, in big data applications, big data technology refers to the collection of specific data, the analysis of specific technologies and the derivation of useful information [7, 8]. With the development and popularization of portable smart terminal devices and the popularity of the Internet, data in the era of big data is not only structured data, such as text, language and images, stored in traditional data storage, but also semistructured. Semi-structured data from sources such as mobile phones and the Internet of Things is characterized by a wide range of sources, abundant quantities and huge value. As a result, the application of big data has penetrated into all areas of operation and industry [9, 10]. It has gradually become an important basis for enterprises to forecast the market, make decisions, and understand competitors. For enterprises, the arrival of the big data era has also changed the business model and management of enterprises. Looking at recent research and business practices, it seems that the business community has reached a point where it does not talk about big data, trade, or new business models. Although this trend is accompanied by tremendous data advantages, there are still some areas to discuss, such as intensive data thinking based on sample analysis, which may further reveal market trends. This paper divides four enterprises of the same type into two groups, one using traditional methods for brand marketing and the other using big data technology for marketing. Except for the different methods used in the experiment, all other data are identical. Finally, it is found that using big data can improve the customer satisfaction and brand awareness of enterprises.
2 Big Data and Brand Marketing 2.1 Big Data Technology (1)
The role of big data Big data analysis technology is the product of the development of network technology and plays a role of marketing leverage to a certain extent. Data analysis mainly uses technical means to analyze a large amount of complex data information to further clarify the purpose of analysis, so as to develop and extract effective information as an important marketing reference. With
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the support of large data analysis, strategic marketing planning is more in line with the needs of consumers and more accurately records trends and growth trend market share. To strengthen the application of large-scale data analysis technology in marketing, enterprises can analyze the historical data generated by consumers’searches and purchases to derive personal preferences and consumer demand products, further predict consumers’ future behavior and needs, and provide consumers with accurate product information. Cluster analysis Cluster analysis is short for clustering. In essence, the set of data objects is divided into several parallel classes or clusters based on the similarity and nonsimilarity between the data. Finally, the clusters and clusters are independent of each other, but the elements within the cluster have very high similarity. The calculation method is as follows: Minkowski distance: dist ( p) =
n
1p |xi − yi |
p
(1)
i=1
When P approaches infinity, which is called the Chebyshev distance, then: n
dist ( p) = max|xi − yi | p i=1
(2)
2.2 Brand Marketing (1)
Concept
(2)
Brand marketing has its unique main product advantages in marketing. It is the basis for consumers to buy enterprise products and services, the representative of enterprise culture, and the main activity of marketing. After defining the trademark status of the company’s products, the operators should further scientifically and effectively segment the market, select one or more segments as the target market, and concentrate limited human and material resources for centralized marketing. At the present stage, enterprises only pay attention to the extraction of market value and market position, in order to achieve longterm growth and help enterprises to achieve a sustained favorable position in the market. Effect First, if the focus is on the marketing strategy name, the name of the enterprise, and the improvement of product service, the enterprise can quickly understand the needs of consumers and make them the first psychological position
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for consumers to purchase products. Once the market image is established, the basic competitiveness of the company can be effectively improved. For example, Li Ning brand is positioned as the first sports brand in China, which is directly different from international sports brands Nike and Adidas, and buyers have a clearer goal in the market. Secondly, the company trademark has become a part of the company’s important assets, representing the company’s cultural concept and service quality. The ultimate goal of consumers’ pursuit of brand is to better meet their own consumption needs. Consumers’ recognition of trademarks depends on their confidence in the brand name. Once consumers start to focus on the brand, it means that the value of the brand has increased greatly. The main reason is that the consumption level has changed greatly. Consumers have changed from the state of living and clothing to the development of independent consumption brand, that is, to meet their own personal consumption needs.
3 Experimental Objects and Processes 3.1 Experimental Objects We rank certain types of SMEs in our city and compare their brand value. Then, four small and medium-sized enterprises distributed in the average value are randomly arranged and combined, and they are divided into group A and group B. two enterprises in group a use traditional methods for enterprise brand marketing management, and two enterprises in group B use big data technology for enterprise brand marketing management.
3.2 Experimental Processes (1)
Method selection The evaluation method based on distributed is designed. MapReduce is used for distributed calculation. At the same time, various graphs are drawn according to the index data to facilitate users’ more intuitive analysis. In view of the brand value evaluation, according to the selected statistical analysis norm index, the brand value is analyzed horizontally, vertically and the validity of the test paper. Aiming at the process analysis of the promotion of brand awareness, AHP combined with data mining association rules is selected for mining analysis to find out the factors influencing brand awareness. Aiming at the analysis of consumer behavior, the gray clustering analysis method is used to evaluate the process of consumer behavior, so as to analyze consumer behavior.
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Experimental steps The experiment lasted for six months, in which the students in group a carried out enterprise brand marketing management according to the previous marketing methods of other enterprises, while the students in group B used big data technology to analyze and calculate on the basis of traditional methods. One month, three months and six months after the start of the experiment, the brand value of the two groups of enterprises was calculated and compared. Among them, the content of evaluation includes consumer satisfaction and brand awareness.
4 Comparison of Experimental Results Between Two Groups The core of large-scale data technology is consumers, so when analyzing and applying big data, we must highlight the centrality of consumers and organize marketing activities based on 4P theory. For consumers, there are a lot of consumer goods of the same type. In the face of tens of thousands of products, it is difficult for consumers to make accurate judgments, consumers will invest more time to understand in the market process. Different brands pay more attention to consumers’ demand preferences, choices and behaviors. Consumers evaluate the value of products and create scenarios for consumers’ needs. These data come from consumers. Once you have the data, follow the concept of consumer orientation, conduct scientific and reasonable data processing and analysis, be in the position of consumers’ Reflection on marketing, and conduct more accurate marketing based on 4P marketing theory and dynamic innovation. With big data analysis technology as the support of 4P theory, brand marketing always takes consumers as the center. Product design inspiration comes from consumer demand, product pricing refers to the ability and choice of consumers, and expand consumption channels according to consumers’ preferences. It is up to consumers to make different promotion plans, so that consumers can experience and participate, confirm the primary value of consumers, truly understand customers as God, and take customers as the center. In the implementation of enterprise data, we must pay attention to data security, and effectively protect the privacy of consumers.
4.1 Comparison of Consumer Satisfaction Between Two Groups of Enterprises In the development of the new period, the consumption standard has changed significantly. The psychological age, consumption demand, personality hobbies, education level, financial income, education level of consumers are different, making a product
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Table 1 Comparison of consumer satisfaction survey
Three months later (%)
Six months later (%)
A
42
64
71
B
51
73
86
A
B
100%
86% 73% 64%
80%
Percentage
Fig. 1 Comparison of consumer satisfaction between two groups of enterprises
A month later (%)
60%
71%
51% 42%
40% 20% 0% A month later
Three months later
Six months later
Time
can no longer meet the needs of all consumers in the market. Therefore, companies should use large-scale data technology, information technology and high-tech equipment to detect hidden consumer data and recommend related products to consumers according to the following requirements. With the consumer demand, we can help consumers control their goods better. In this experiment, the consumer satisfaction of two groups of enterprises, A and B, is shown in Table 1 and Fig. 1. From the data comparison in Table 1 and Fig. 1, we can see that group B enterprises have better consumer satisfaction than group A enterprises. One month after the start of the experiment, the satisfaction of group A was 42%, group B 51%, and after three months, there was no significant change between the two. However, after the end of the experiment, the satisfaction of group A was 71%, group B was 86%, and the difference increased from 9 to 15%.Therefore, using big data technology for enterprise brand marketing management can better enhance consumer satisfaction.
4.2 Brand Awareness Comparison Between Two Groups of Enterprises With the continuous growth of the economic income of consumers and the improvement of consumption level, people begin to purchase products according to their own preferences, and gradually form a new consumption trend. Therefore, companies should formulate different marketing strategies for different types of consumers. Once consumers agree with the name in their minds, they will think the brand is of good quality and people will rush to buy it. When supply is below demand, the market value of the brand will increase significantly. Because well-known brand products represent the reputation of the company, the quality of products and the service
Application and Practice of Big Data … Table 2 Comparison of brand awareness survey
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A month later (%)
Three months later (%)
Six months later (%)
A
20
28
40
B
22
31
45
Fig. 2 Comparison of brand awareness of two groups of enterprises
A
B
Percentage
50% 40% 30% 20% 10% 0%
A month later
Three months later
Six months later
Time
of products, when consumers purchase products of known brands, they can minimize market decision-making time and meet the needs of consumers. Therefore, we compare the brand awareness of the two groups of enterprises, and the comparison results are shown in Table 2 and Fig. 2. From the data analysis in Table 2 and Fig. 2, we can see that the brand awareness of Group A increased by 8% after one month and 40% at the end of the experiment. At the same time, brand awareness in Group B increased gradually to 45% from 22% a month later, which is significantly greater than Group A. Therefore, the use of big data technology in Group B is more powerful to improve the brand awareness of enterprises.
5 Conclusion With the continuous development of the times, the application of large-scale data technology is becoming mature and universal. As a new idea and thinking mode, big data analysis plays an important role in marketing. It’s really time to market and big data in the name of marketing. In this context, it is necessary to explore the value of large-scale data analysis in market management and specific implementation strategies. Using large-scale data analysis technology, we can achieve the precise commitment to the needs of consumers, accurately describe the consumer groups, and carry out high-quality marketing. In the process of enterprise marketing management, a large amount of data affects the enterprise marketing management system, and increasingly presents business value that cannot be ignored. As an important resource, big data has gradually penetrated into all aspects of life. The application of big data in enterprise marketing management can not only make the business activities of enterprises run smoothly, but also promote the development of national economy.
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According to the actual situation of market development, enterprises can formulate important data marketing strategic plan in advance, and make full use of the market opportunities it brings.
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Implementation Evaluation System of Land and Spatial Change Planning Based on Big Data Xiong Wang, Liang Qin, and Qiancheng Luo
Abstract Land use change is a hot research area in recent years. In the process of vigorous development and construction, the blind expansion of the city is easy to cause waste of resources, idle land, duplication of construction, system and reality are not coordinated and other problems. This paper mainly studies the design of the evaluation system for the implementation of land resource use planning based on big data. This article will space consistency into the general land use planning implementation evaluation, on the basis of scholars at home and abroad related research achievements, building space consistency measurement model and the classification standard, and the process of implementation of general land use planning implementation result evaluation and comparison analysis, based on the construction of resources of the land use planning implementation evaluation system. Keywords Big data · Land spatial change · Land resource use · Evaluation system
1 Introduction In recent years, big data, cloud computing and other new technologies and ideas have developed rapidly. They have gradually become a new driving force and new direction for the development and construction of information technology in China, bringing about comprehensive changes and reforms in the way the country thinks, behaves and governs. Through “using data to make decisions, to speak, to manage and to innovate”, it has exerted an all-round impact and a deep shock on social structure and social concepts. Meanwhile, it has innovated the mode of government governance and improved the government’s ability of macro-control, market supervision, public service and social management. With the continuous advancement of X. Wang (B) · L. Qin · Q. Luo College of Resources and Environment Economics, Inner Monglia University of Finance and Economics, Huhehaote 150100, Inner Monglia, China e-mail: [email protected] X. Wang College of Land Science, China Geoscincese University (Beijing), Beijing 120000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_72
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information transformation and the diversified needs of land services, the number of spatial information services is increasing day by day. These information services, on the one hand, come from the utilization of data in the big data resource pool, and the data in the cloud pool is packaged in the form of Web services, which can be easily called by other users in the network. On the other hand, it comes from the development of spatial information applications, that is, a series of tools and services with data processing capabilities developed for specific land businesses. In order to meet the needs of big data analysis, quite a few of them are services with large number analysis capabilities developed based on Hadoop or Spark [1, 2]. In terms of application, data service mainly provides data resources for users, while tool service mainly provides analysis function. Because of its certain business pertinence, tool service presents the characteristics of poor reuse. If multiple services with single functions are combined, more complex spatial information applications can be dealt with in the form of multi-service cooperation, and the reusability of a single service will also be improved [3]. In Western countries with rapid development, the study on evaluation of planning implementation started early and fully, and its theoretical system is relatively advanced (originated from urban land use at the earliest). However, the research is mostly focused on urban planning, and evaluation is mostly used for the research on planning compilation and technology [4]. With the development and maturity of this perspective in the implementation of urban land planning, foreign scholars have broadened their studies and studied the overall planning of regional land use. At present, the important achievements in the evaluation system of foreign planning implementation are planning objective evaluation and planning impact evaluation. In the twenty-first century, research on planning evaluation has become increasingly rich. 3S technology has been introduced, and the ecological influence of planning and the sustainable use of land resources have become the focus of evaluation research, and theoretical research has begun to broaden [5]. In this paper, according to the actual situation, the construction of basic geographic information database, construction of national spatial change of land use planning implementation evaluation system, on the basis of the development of five typical applications, strengthen the function of application service, speed up the co-construction and sharing mechanism of data between government departments and the informationization process, for the local economic development and urban management to provide effective decision aid.
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2 Land Resource Utilization Planning Implementation Evaluation System 2.1 Spatial Consistency Evaluation Model 2.1.1
Evaluation Model
Spatial consistency evaluation model is a model that evaluates the implementation status of spatial dimension of general land use planning by selecting research areas and research periods, and selecting specific methods and standards to evaluate the implementation process and results of planning [6]. The spatial consistency evaluation model can clearly show the problems existing in the implementation process and results of the planning, which has a good reference significance for the implementation of the current round of planning and the compilation of the next round of planning, and plays an important role in promoting the economic and social development and ecological protection in the study area [7]. The spatial consistency evaluation model for the process of planning implementation is to compare the plots that have changed from the early stage of the study to the planning goal. The inconsistencies are eliminated to obtain the degree of consistency with the planning goal, which reflects the degree of consistency between the plots change and the planning comparison. Taking construction land and agricultural land as an example, T1 represents the initial stage of evaluation and T2 represents the time point of evaluation. ArcGIS software is used to superposition T1 and T2 to obtain T1 → T2, namely the changes of land types during the evaluation period. T1 and T2 shows that there are four kinds of circumstances, the green area of farmland is constant, the blue areas into agricultural land, construction land yellow area agricultural land into construction land, construction land in the red areas is constant, the change of land types of regions, namely into agricultural land and agricultural land into construction land for construction land in the planning process consistency to research object. The spatial consistency evaluation model of planning implementation results is to measure the implementation of planning at the evaluation point, that is, to measure the degree of consistency between the actual situation of land use and the planning goal in terms of quantity and spatial position [8]. There are three situations in both the evaluation time point and the planning goal: Increasing, constant and reduce evaluation point and planning objectives superposition analysis on ArcGIS software, get nine different situation, the classified summary for the three conditions, namely A conform to the planning (including the present situation and the change of the planning in sync on the quantity and space, which is 1 increase at the same time, 2 remain the same, 3) at the same time. B Not in line with the plan (the status quo changes but not consistent with the plan, that is, 2 the status quo increases but the plan remains the same, 3 the status quo increases but the plan decreases, 7 the status quo decreases but the plan increases, 8 the status quo decreases but the plan remains the same); C Not implemented (the status quo has not
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changed and the planning is inconsistent, i.e. 4 the status quo is unchanged but the planning is increased, 6 the status quo is unchanged but the planning is reduced) [9].
2.1.2
Grading Standards
Based on the spatial interpretation of the above spatial consistency model, the spatial consistency evaluation of the master land use plan includes three aspects, that is, whether the quantity, land type and spatial location are consistent with the planning. Let the spatial consistency of the planning implementation process be P1, and the time span is from the initial evaluation to the evaluation time point, i.e., T1 → T2. The basic idea of measurement is P1 = 1—the part that has changed but does not meet the planning goal/planning goal. The measurement formula of its spatial consistency is as follows: P1 = 1 −
C1 G1
(1)
P1 is the spatial consistency of land type map spots in the process of planning implementation; C1 is the area that does not meet the planning objectives in the land type map spots that have changed during the implementation of the plan; G1 represents the total area of land planning. Let the spatial consistency of the implementation result of the planning be P2, and the time node of the evaluation be T2. The basic idea of measurement is as follows: P2 = (current area—area changed but not in line with the planning—area not implemented in the planning)/planned area. The measurement formula of its spatial scale is as follows: P2 =
X2 − C2 − NC2 G2
(2)
2.2 System Architecture and Module Design 2.2.1
System Architecture
The overall structure of the system is mainly divided into four layers. The first layer is pre-processing such as data collection, unified format, classified storage, etc., which is mainly divided into GIS data and non-GIS data. GIS data includes vector layer data (including commercial points, settlements, roads, rivers, administrative centers, etc.) and raster data (land use classification atlas, general planning map, etc.). Non-GIS data include demographic, economic and other statistical tabular data and planning related map files.
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The second layer is database layer, including SDE database and non-spatial database tables. GIS data is stored in SDE database, and data records are stored in common database tables. The third layer is the middle layer, including the software middle layer and the model layer, which are based on the database layer to realize data analysis and scheduling. Among them, the software middle layer is mainly ArcGISServer, which serves as the carrier of map data for the client to call, display and analyze. The model layer mainly consists of predictive analysis model and GIS-based index analysis model, which is realized through Python script [10]. The fourth layer is the application layer, which mainly realizes the display of data, including the display and operation of basic maps, statistical analysis and chart display of data, thematic display of GIS data, prediction analysis and result display analysis, index analysis and display.
2.2.2
Functional Modules
This paper implements the following five modules in the application layer: basic map operation, statistical analysis, index calculation, thematic map display and prediction analysis module. (1) Basic map operation: This module mainly realizes the management function of data layer and data query, map zoom, distance, recognition and other basic functions. The map data of this module are mainly thematic data, such as road layer, water system layer, educational facility point layer, etc. Layer management is mainly aimed at thematic data management. It can display or close a layer, adjust the display order of layers, and open the items of keywords contained in the layer property sheet according to keyword query and highlight them. (2) Statistical analysis: This section is an application layer based on the layer management function. You can create perspective and pivot tables for the properties of open layers. Sum, average, maximum, minimum, and other operations on one or more columns. You can do the same for tabular data. (3) Indicator analysis:This part mainly calculates the index of land spatial change based on layer and table data. Indicators such as dynamic attitude, compactness, coordination with population and economic development of land are mainly calculated. (4) Thematic map: This part mainly produces thematic maps such as hierarchical color, single value map and heat map based on layer data. (5) Predictive analysis: This part is divided into model training and prediction. In the model training part, the year of input training data can be changed and the data at different intervals can be selected for training. The trained model is the probability model of land use conversion. In the prediction module, different models can be selected for prediction and cycle time of CA model can be set [11, 12].
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3 Experimental Land Resource Use Planning 3.1 Data Sources This study takes administrative districts and communities as the evaluation unit. The data are mainly from statistical yearbooks, visits to relevant departments and field surveys. However, when collecting data, the index data of some administrative villages could not be obtained. In order to ensure the accuracy of the data, this study used interpolation method to calculate the missing data. Intrapolation is a kind of approximate calculation method based on the function value of the unknown function at some points in the interval, using the relation of equal ratio, using a group of known function independent variables and the corresponding function value to calculate the unknown function value.
3.2 Data Preprocessing 3.2.1
Calculation of Evaluation Indexes
The collected original data is used to calculate the value of each evaluation index through the calculation formula of the above indexes, and the original index data table is obtained through statistical sorting, so as to facilitate the next processing.
3.2.2
Standardized Data Processing
In order to reduce their differences and follow the comparability of evaluation indexes, the dimensionless processing of evaluation indexes should be carried out due to the disunity of dimensions and units in the original data. In this paper, Zscore standardization method is used to conduct dimensionless processing on the data of each evaluation index. Z-score standardization method is widely used in data standardization processing and can uniformly transform data of different orders of magnitude into the same order of magnitude. Raw data were standardized by public display (3) through SPSS19.0: Z=
x−μ σ
(3)
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3.3 System Performance Test Performance test mainly tests the response speed of the page, the response of the page includes the first screen response time and jump response time. Test the response time of ten page openings and jumps on client A and client B, respectively.
4 Simulation Test Results 4.1 Spatial Consistency of Planning Implementation Results As shown in Table 1 and Fig. 1, the planned area of agricultural land in a certain area is 82,478 hectares, the unplanned area is 18,364 hectares, and the spatial consistency is 0.9214. The planned area of construction land is 12,992 hectares, the unimplemented area is 1223 hectares, and the spatial consistency is 0.8725. The planned area of other land is 33,247 hectares, and the unplanned area is 703 hectares, with a spatial Table 1 Spatial consistency of planning implementation results
Planning area
Unapplied area
Spatial consistency
Agricultural land
82,478
18,364
0.9214
Construction land
12,992
1223
0.8725
Other land
33,247
703
0.9412
90000
0.96
80000
0.94
70000
Area (hm2)
50000
0.9
40000
0.88
30000 0.86 20000 0.84
10000 0
0.82 Agricultural land
Construction land
Other land
Land type Planning area
Unapplied area
Spatial consistency
Fig. 1 Spatial consistency of planning implementation results
Spatial consistency
0.92 60000
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Response time
Fig. 2 System page response time test
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1.2 1 0.8 0.6 0.4 0.2 0 Home page response time
Subpage response time
Page classification Client A
Client B
consistency of 0.9412. The above data show that there is still a large gap with the planning target, and it is necessary to correct the land types that do not conform to the planning in the follow-up planning, implement the land types that have not been implemented, and implement the land types that have not been implemented in strict accordance with the planning target.
4.2 System Response Time Test As shown in Fig. 2, the system page response time was tested in this paper. The average response time of client A’s home page is 1.32 s, and the average response time of sub-page jump is 0.55 s. The average response time of client B’s home page is 0.82 s, and the average response time of sub-page jump is 0.37 s. The response time of both the home page and sub-page jump of the two clients is less than 2 s, indicating that the system designed in this paper meets the demand of response time for daily use.
5 Conclusion In recent years, emerging technological concepts represented by big data and cloud computing have developed rapidly and become the new driving force and new direction for the development and construction of national information technology. However, most of the land government systems still adopt traditional technologies, and the application achievements of big data in the land and resources field are still relatively rare. In order to promote the application of big data technology in the territorial field and study the feasible scheme of integrating big data technology into the construction of territorial thematic system, this paper is written. This paper designs and implements the land spatial change analysis system, which is based on Web front-background mode. The system includes data display, data statistics, spatial analysis, data query (spatial data and non-spatial data), display of relevant important indicators, prediction and analysis of future urban spatial layout and other
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functions. Based on the data layer, the basic map operation, statistical analysis, index calculation, thematic map and prediction analysis modules are designed. And for the system and each module design test cases and the function and performance of the system test, the results show that the system function runs well, the performance can meet the requirements of use.
References 1. Gupta S, Qian X, Bhushan B et al (2019) Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective. Manag Decis 57(8):1857–1882 2. Hegde R, Niranjana et al (2018) Site-specific land resource inventory for scientific planning of Sujala watersheds in Karnataka. Current Sci: A Fortnightly J Res 115(4):644–652 3. Peters CB, Schwartz MW, Lubell MN (2018) Identifying climate risk perceptions, information needs, and barriers to information exchange among public land managers. Sci Total Environ 616–617:245–254 4. Yachnin J (2017) 3 Western groups unite to promote ‘common sense’ land policy. Greenwire 9–10 5. Abdelrahman M, Arafat SM (2020) An Approach of agricultural courses for soil conservation based on crop soil suitability using geomatics. Earth Syst Environ 4(1):273–285 6. Mogaji KA, Omosuyi GO, Adelusi AO et al (2016) Application of GIS-based evidential belief function model to regional groundwater recharge potential zones mapping in hardrock geologic terrain. Environ Process 3(1):93–123 7. Tommaso R (2016) Dynamical landau theory of the glass crossover. Phys Rev B 94(1):14202– 14202 8. Cheng W, Gill GS, Ensch JL et al (2018) Multimodal crash frequency modeling: multivariate space-time models with alternate spatiotemporal interactions. Accident Anal Prevent 113:159– 170 9. Huang J, Frimpong EA (2016) Limited transferability of stream-fish distribution models among river catchments: reasons and implications. Freshw Biol 61(5):729–744 10. Ma C, Min Z (2018) A GIS-based interval fuzzy linear programming for optimal land resource allocation at a city scale. Soc Indic Res 135(1):143–166 11. Tong S, Feng Z, Yang Y et al (2018) Research on land resource carrying capacity: progress and prospects. J Resour Ecol 9(4):331–340 12. Leenhardt P, Stelzenmueller V, Pascal N et al (2017) Exploring social-ecological dynamics of a coral reef resource system using participatory modeling and empirical data. Marine Policy 78:90–97
Research on Intelligent Tourism Resource Management Based on Big Data Technology Yufang Wang
Abstract In order to use big data technology to do a good job of intelligent tourism resource management, this paper will carry out related research, mainly discusses the basic concepts of big data and big data technology, and then points out the problems of intelligent tourism resource management. Finally, the system design is carried out around big data technology, and the resource management can be done well through the system. The use of big data technology makes intelligent tourism resources more abundant, but also more effective management of various resources, is conducive to the allocation and use of intelligent tourism resources, can improve the quality of tourism service. Keywords Big data technology · Intelligent tourism · Resource management
1 Introduction Under the background of the vigorous development of intelligent technology, this technology has entered many fields and shown good application value, making each field take on a new look and have a broader development prospect, among which the tourism field is a big “beneficiary”. Therefore, the intervention of intelligent technology has realized intelligent tourism. People can provide diversified intelligent tourism services to tourists through this technology. This practice has been very popular in the current tourism industry. But the establishment of the intelligent tourism also brings some problems, including how to intelligent tourism resource management is a more representative, so that modern professional field, should be intelligent tourism resources management, big data technology is the main means of enterprises should introduce the technology, the development of the technology features, so as to achieve the purpose, it is necessary to research.
Y. Wang (B) Jilin Business and Technology College, Changchun 130507, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_73
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Basic Concepts of Big Data and Big Data Technology
1.2 Large Data Essentially big data is huge data integration, internal data information not only large scale, has a wide range of complex correlation between these data and information at the same time, if you can sort out related data information accurately, can make accurate judgement on the issue, for example of tourist services in tourism service data information, clear tourists favorite services, Then strengthen the service strength of the project, can open more service interfaces, to achieve targeted services. However, it is worth noting that, as a large data integration, big data itself does not actively display relevant information, and users need to take the initiative to mine. The huge amount of data information, rich data information types and complex correlations make it difficult for users to mine by ordinary means, let alone carry out tracking analysis. Therefore, how to play the role of big data is a problem that relevant users need to focus on [1].
1.3 Big Data Technology With big data, data technology refers to have the ability to the value of big data technology, is a new concept, namely mentioned above (1.1), large data itself will not take the initiative to display information, require users to adopt the method of targeted to give play to the role of big data, and these methods is implemented by the big data technology. At present, the commonly used big data technology is the intelligent technology, this technology has the identification function of information, thus the big data within the relevant data can be defined, in accordance with the definition of data information is related to the event to be analysed, if related to the corresponding data extracted information, according to the data information of the associated order to analyze the events. In tourism services, for example, the intelligent technology can identify tourists, again on the basis of tourists information node, to judge other base node related information, including the timing of the tourists to enjoy the service, choice of services, such as tourist road map, according to these information can provide personalized service to tourists.
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2 Intelligent Tourism Resource Management 2.1 Difficulty in Data Collection and Management The necessary premise that intelligent tourism resources management is to obtain resources, and resources is the principal means of information gathering, the intelligent tourism resources for more information resource, managers need to get these resources, build resource data, then continue to dig through the intelligent technology, the information collection is will. At the same time, the efficiency of information collection is related to the updating frequency of resource big data, and the collection results affect the quality of resource big data. If the efficiency is slow, the service strategy adjustment may not be timely; if the quality is poor, the service quality cannot be guaranteed, indicating that the information collection needs to be managed. But in reality, a lot of tourism enterprises to realize the importance of information collection and management, but without good management methods, the reason is that intelligence sources of tourism resources and its own frequency of updates very fast, so ordinary method is difficult to complete information acquisition, prone to the lack of efficiency, and also it is difficult to confirm the information collected quality, This makes service policy setting blind [2].
2.2 Difficulty in Processing Data Information After completion of the data information collection enterprise need to deal with data and information, the main work includes: the first pretreatment, namely, remove all the data information of useless, such as low quality, repeat bad data information, leaving only valuable data information, this could make big data resources more careful, can reduce the difficulty of the follow-up work; The second data corresponding to the relationship, that is, a single data information is not representative event, so the enterprise continues to get all the event related data information, and arrange the data information classification, corresponding relation between the known data, data between groups, the relationship represents the development direction, is advantageous for the enterprise to establish logical analysis. From here you can see, data information processing is an important job, has an indispensable position, but in the face of huge amount of data and complex data relationships, modern enterprises in the use of artificial or some simple technology process, therefore, because of the method is not applicable, so difficult to deal with data and information, or slow down the efficiency seriously, is not conducive to intelligent tourism resources management quality [3, 4].
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2.3 Single Idea of Data Information Analysis As intelligent tourism resource management, need to analyze data after data processing, but the analysis method of many enterprises are single, such as an enterprise in data analysis, generally used statistical thinking is analyzed, the statistics all the business service frequency, available every quarter of the most popular with visitors services, The service was then vigorously developed. Statistical thinking is, indeed, data information analysis method, but companies should be aware of the connotation of the different data information is varied, can form the analysis of the different ideas from different angles, if has always been in accordance with the single idea is analyzed, and cannot give full play to the role of data, may lead to the enterprise service development trend of simplification, this can have an adverse effect to the enterprise sustainable development. Therefore, in the modern perspective, it is necessary for enterprises as intelligent tourism resource managers to analyze data information through different ideas, so as to establish more management directions and guide the diversified development of enterprise intelligent tourism services. This is very important [5].
3 Design of Big Data Intelligent Tourism Resource Management System In order to do a good job of intelligent tourism resource management through big data technology and solve the problems in the current work, it is necessary to design relevant systems before setting policies, so that policies can be set according to system functions. The design of big data intelligent tourism resource management system in this paper is divided into three steps, as follows.
3.1 Framework Design Big data intelligent tourism resource management system generally includes three levels, design the framework of the physical layer, communication layer, terminal, respectively, including the physical layer is composed of relevant physical device, the device has the function of information collection, to be able to record each of the tourists on the tourism data information, and information acquisition methods for the physical equipment in Table 1. The communication layer is mainly responsible for sending the information collected by the physical layer to the terminal layer so that the terminal layer can process the data information. Terminal layer will be in accordance with the default logic to deal with data and information, at the same time using smart technology data information identification, classification, after the completion of the analysis, know the demand of the tourists, etc., according to the
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Table 1 Physical layer equipment and information collection mode Physical layer device name
Information collection method
On-site self-service equipment Record each step of the visitor’s selection of self-service items, or information entered by the visitor on the device interface Field monitoring equipment
Taking pictures
Fig. 1 System framework topology diagram
analysis result choose the default service strategy, strategy will again be sent via communication layer to the physical layer, make the physical equipment correct response for tourists [6]. At the same time, if found the default terminal layer has no strategy to meet the demand of users in service strategy, will send the result to the human side, on the one hand, let the human to provide services, on the other hand make artificial rich default service policy, which have played an important role intelligent tourism resource management, every resource in accordance with the standard way of operation, can quickly and accurately provide the corresponding service. Figure 1 shows the framework design topology.
3.2 Functional Design Based on the basic framework of the system to design relevant application function, the application functions are divided into two kinds, one in the service of resource managers, a second service to tourists, all kinds of application function connotation is very rich, and there is no pattern, you just need to design according to the actual
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Table 2 Common tourist application functions The name of the function
An overview of the
Route, route inquiry
Used to understand the length of the journey, planning the route
GPS positioning
Find the destination location, used to support the distance, route inquiry function operation
Attractions query
Understand the distribution and scenery of tourist destinations
demand, but any application function design must be based on the technology of data, That is, any function must be supported by big data technology to operate, otherwise it is easy to appear the phenomenon of inaccurate function service. First on the first application function, after analyzing the needs of most of the resource managers generally to information scheduling, namely because managers cannot directly to a large number of tourists to the statistical data from the so only with the aid of technology of data processing, get relevant statistical results, so the managers to manage of tourism resources, must be scheduling results, in this way can we have a clear direction. In order to meet this demand, the system should have the corresponding scheduling function, and the functional operation should be as simple as possible. In this system, the manager only needs to input the keywords of the scheduling target, and then the corresponding statistical results can be seen after confirmation. Second for the second application function, because the needs of different visitors vary, so cannot treat as the same, so to tourists weather information query demand analysis as an example, the tourists tourism travel must consider the weather factors, if the weather is bad, the inconvenience, but tourists itself does not get the weather information at any time, Therefore, there should be a special channel to meet the needs of tourists to inquire about weather information. In this condition, the system should have the weather information statistical functions, and under the action of tourists will present statistical results show visitors, such as visitors click on the “weather query” button, the system will show the future for a period of time the weather information, and under the impetus of the intelligent technology, after every time visitors enter the system will automatically display the weather information, provide personalized service to tourists. Table 2 is the second type of common application functions for reference only.
3.3 Algorithm Design In order to exploit the effect of big data through big data technology, the coefficient system must have the ability of data information identification, which is realized by the algorithm of data information feature recognition. There are many common data information feature recognition algorithms, this paper mainly choose the BIF algorithm, MIFS algorithm, will compare the two algorithms, choose one more suitable for this system algorithm. First is a BIF algorithm, the algorithm is the main
Research on Intelligent Tourism Resource … Table 3 MIFS algorithm steps
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The serial number
Content
1
Access to the object
2
Individual optimal eigenvalue calculation
3
Feature matching
object of the data information as evaluation function, applications will be the first to feature candidates as evaluation function to calculate all objects, can get the corresponding evaluation function, and then according to the descending order, choose several subsets are lined up in the characteristics of the former group, subset with characteristic value, according to the characteristic value can identify the data information. The advantage of BIF algorithm lies in its simple idea and simple application, but it also has obvious defects. The results do not consider the correlation and redundancy between features, so the results are prone to anomalies and can only identify data information with obvious features. See Formula 1 for the BIF algorithm. Second is MIFS algorithm, the algorithm formula 2, this is a kind of feature matching principle for feature calculation method, the calculation of the optimal characteristics to calculate all project alone, the most striking feature), then in accordance with the principle of matching degree, choose features the most similar constitute a set of data information, so you know which data information similar features. The application of this algorithm is complicated, but the correlation and redundancy between features are fully considered. By comparison, the data analysis objects in this paper are relatively complex, and the correlation and redundancy between features are obvious. Therefore, MIFS algorithm is the main choice. The steps of this algorithm are shown in Table 3. J ( f ) = α · g(C, f, s) − δ
(1)
where, f is the original feature, and C is the category knowledge of each subset of f . J ( f ) = I ( f ; C) − β
I ( f ; s)
(2)
s∈S
where, s is the combination of all subsets, and β is the penalty factor.
4 Conclusion To sum up, in the face of the three problems in intelligent tourism resource management, the design of relevant systems using big data technology can solve the current problems. That is to say, the physical layer of the system can solve the problem of information collection and facilitate the formation of big data, while the subsequent
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intelligent logic can synchronously complete data information processing and data information diversification analysis, so the system can help enterprises to do a good job in resource management.
References 1. Xiao G (2020) Research on intelligent management platform for energy enterprises based on big data technology. J Phys Conf Ser 1549:042039 2. Zhao Y, Zhang J, Xiang S et al (2021) Research on intelligent analysis technology of network security risk based on big data. J Phys Conf Ser 1792(1):012036 3. Li Y, D Yu (2021) Research on intelligent prescription review system based on medical big data. J Phys: Conf Ser 1744(4):042084 4. Zhang X, Wang Y (2021) Research on intelligent medical big data system based on Hadoop and blockchain. EURASIP J Wirel Commun Netw 2021(1):1–21 5. Su H, Meng X (2021) Research on the development of rural E-commerce based on smart tourism. J Phys: Conf Ser 1915(3):032007 6. Garcia-Lillo F, Claver-Cortes et al (2018) Mapping the “intellectual structure” of research on human resources in the “tourism and hospitality management scientific domain” Reviewing the field and shedding light on future directions. Int J Contemp Hospital Manage 30(3):1741–1768
Research on Sales Forecasting Method Based on Data Mining Zhihua Gan
Abstract In recent years, with the continuous improvement of the socialist market economic system and development, the enterprises face more and more fierce market competition, and in the face of increasingly fierce market competition environment, enterprise want to win the market, gain a competitive advantage, to win more customers in the market, it must be able to at the lowest cost and efficient way to provide the product or service to clients, In order to achieve such a goal, we must accurately grasp the needs of market customers. With the rapid development of modern network information technology, market information is omnipresent. Enterprises must have a complete set of sales forecasting methods to help them better grasp the needs of market customers, timely and accurately control the needs of customers and the direction of market development, and these must rely on accurate forecasting and data mining technology. Based on this, this paper takes FMCG industry as an example, and takes data mining technology as a breakthrough to study the innovation of sales forecasting methods. Keywords Data mining · Sales forecast · FMCG Economic globalization thorough development accelerated the modernization of the transformation and development of market economy, especially the network era and with the market information is to let today’s market competition is becoming increasingly fierce, modern enterprise want to occupy the market through competition, to win more customers, will have to be combined with their products and services and market development trend, In the most efficient way and the lowest cost to provide their products and services to more customers, and these must be built on the basis of accurate sales forecast. On the other hand, the network information age brings and the impact on the market, enables the enterprise to grasp the historical sales data more and more, in the face of huge data cluster, traditional sales forecasts have been unable to adapt to today’s market development, the enterprise must be combined Z. Gan (B) Maanshan Teachers College, Maanshan 243000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_74
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with the more advanced technology of data mining, comprehensive analysis of the market sales data, So as to provide support for enterprises to develop a more scientific sales strategy. Article based on this background, with FMCG market as the research object, combined with the corresponding data mining technology, put forward a kind of based on comprehensive consideration of external variables accurate sales forecast method, hope to be able to through the sales forecast method, more accurate historical sales data and the combination of sales strategy in the future, to the enterprise market sales for accurate prediction of the future [1].
1 Preparations for the Application of Data Mining in Sales Forecasting Data mining is a systematic process that usually involves three stages, as shown in Fig. 1. As can be seen from the above figure, the process of data mining needs to go through three processes: preparation, information mining and result evaluation. The quality of the final data result and the quality of the mined information are closely linked. The first stage is to accurately define, process and express the data information that needs to be mined. Through this process, it can use specific data mining technology. This link is the most critical one, which is directly related to the final interpretation and analysis of the results. On this basis, the preparation of data mining also includes the following aspects [2]. Fig. 1 There are three stages of data mining
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1.1 Data Cleaning Generally speaking, the historical sales data of an enterprise mainly comes from the heterogeneous operation database, but the data in this database is not completely accurate. The most common problems are incomplete data, inconsistent data and repetitive data, and the data in this database is collectively referred to as “dirty data”. This kind of data will not have a positive impact on data mining, and even affect the whole process of data mining and the final accuracy, the most common is the unreliable data output. Through data cleaning, the problem of inconsistent data can be better solved by filling in the blank values, so as to “clean” the “dirty data”. For the sales forecast of an enterprise, data cleaning can be carried out before or after the sales data is entered into the database [3]. Data cleaning technology includes three methods, namely rule-based cleaning method, visual cleaning method and statistical method. The first method is to comprehensively evaluate each data information of the field by means of meta-knowledge of the field definition domain, constraints and relations with other fields. The second method is to reflect the effective contour characteristics of the data cluster by means of graphs, so as to identify those data as “dirty data” more clearly. The third method is to make up for the lost data with the help of statistical methods, and can also correct the wrong data timely and accurately [4].
1.2 Data Integration and Exchange The deep mining of sales data with the help of data mining technology usually requires merging two or more data stores and converting them into a form suitable for data mining. For businesses, in the historic sales data in the cluster, we often find a phenomenon, namely data attributes represent the same concept, often have different names in different data, and the result will lead to don’t understand the database produce inconsistent phenomenon and redundant phenomenon, and contains a lot of this kind of data will reduce the performance of the process of data mining, The process of data inheritance and exchange is to avoid this phenomenon and improve the accuracy and efficiency of data mining to the maximum extent. Usually sales data integration needs to consider the identification of sales entities and correlation analysis between sales data, as well as numerical conflict detection and processing problems. The so-called sales entity identification problem is to reasonably match the entities from different information sources; The problem of correlation analysis is to detect data redundancy more accurately with the help of correlation analysis. In the process of data conflict detection and processing, the redundancy detection at the tuple level can eliminate the heterogeneity in data semantics to the greatest extent. The process of data integration and exchange involves
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smoothing, aggregation, data generalization, normalization, data attribute construction, etc. Through these processes, the data can be transformed into a more suitable form for sales mining.
1.3 Data Reduction If a large data cluster is used for data analysis in the process of data mining, it will inevitably take a long time to carry out relatively responsible data analysis or data mining for a large data cluster. However, mining such a large data cluster is usually unrealistic or even impossible. Therefore, we need to make use of the data reduction technology. The so-called data reduction technology is to reasonably “compress” the huge data cluster and reflect it in the form of “reduction”. The data cluster after the data protocol is relatively small, but the integrity of the original data will not be lost, but the reduced cluster is more suitable for mining. Data code technology contains the gathered data cube, dimension reduction, data compression, and the numerical method such as compression and concept hierarchy and processes, the methods and process includes the characteristics of the party, compress the search space of heuristic algorithm, wavelet change, main component analysis (PCA, and logarithm linear regression model, multidimensional factors such as technology. Figure 2 is the sales forecasting method of regression combination model based on data mining technology.
2 Data Mining Tools for Enterprise Sales Forecasts Figure 2 sales forecasting methods, for example, for some FMCG enterprise, combining with data mining technology to form a reasonable promotion commodity sales forecast, you first need to combine the characteristics of FMCG industry and business requirements, in Fig. 2 sales forecast method, based on the combination of time series model and multiple regression model to do the sales forecast.
2.1 Pre-Processing of Sales Data First sales data pretreatment is needed to build the model according to the needs of the business data cluster, the process is different from the time series data, this process needs the enterprise comprehensive analysis of the historical sales data in the report information, and then the enterprise wide promotion factor into the data table, data processing elements of the object for the promotion date, promote sales, sales promotion factor three dimensions, The details are shown in Table 1.
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Fig. 2 Sales forecasting method based on data mining technology of regression combination model
Table 1 Corporate promotion calendar Object
Jan
Feb
Mar
Apr
May
Jun
Sales theme New Year special sales promotion
Lantern Festival Promotion
Goddess Day Promotion
New product launch promotion
Brand promotion and promotion
Trump product promotion
Promotion time
2.16–2.22
3.4–3.10
4.15–4.18
5.6–5.7
6.2–6.4
The full 88 minus 18
The full 119 The full minus 40 119 minus 50
The full 199 minus 40
The full 199 minus 30
1.20–1.24
Sales efforts The full 99 minus 40
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2.2 Multiple Regression Model Training This process is mainly to train the data cluster with the help of the multiple regression model, and the final model parameters and coefficients of each factor can be obtained through the multiple regression model obtained by training. Then, according to the demerits of sales pretreatment, the influence coefficient of promotion factors is extracted. Training through multivariate regression model can be seen that different levels of promotions will produce what kind of impact on sales, specific values, thus draws the regression coefficient and regression coefficient, the greater the indicates a factor in the process of sales will have different effect on sales, the changes of factors change the bigger size effects on sales. For example, the regression coefficient is 23, indicating that sales in the corresponding promotion mode can bring 23 sales increase for the enterprise in one day.
2.3 Multiple Regression Fitting The multiple regression parameters can be obtained through the training of multiple regression model. On this basis, we can assume that there is no historical data cluster to produce the promotion situation, which will affect the use of multiple regression fitting to obtain the non-promotion influence sequence, that is to say, the original sequence promotion influence is removed.
2.4 Sales Mix Forecast The time series prediction under the multiple regression model is mainly the regression prediction data based on the assumption that there is no sales promotion event. After all, there will be sales promotion phenomenon in the real historical sales data of any enterprise, and sales promotion will affect the sales volume of the enterprise. However, the assumption we adopted here is that there is no sales promotion, so when other sales variables do not change, promotion factors need to be included in the multiple regression model to evaluate the influence of enterprise sales promotion methods. In this way, we can see the influence factors of promotion data and get different promotion influence coefficients. Based on the above process, the sales forecasting method without promotional goods can be obtained, as shown in Fig. 3.
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Fig. 3 A sales forecasting method without promotional items
2.5 Data Mining Process for Sales Forecast First of all, data mining is needed after data selection and data preprocessing. Before data mining, data mining model must be constructed. Typically, you can start with a random sales amount as a seed, and then divide the sales data into two parts, the training set and the test set, which are used to construct and evaluate the built model, respectively. You then need to use data mining tools to test the quality of the selected data, compare the final results from the different tools, and then build a more accurate sales data model from which to draw. Secondly, after the completion of the construction of the data model, it is necessary to verify the conclusion of the final model to determine whether the final conclusion is the normal business development of the enterprise, that is, whether the final promotion method is suitable for the enterprise to expand the market, sell products or services. If there are errors in the final mining results, it is necessary to find out whether there are problems with the model in time. At the same time, it is also necessary to re-mine the data and select a more suitable model.
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3 Conclusion To sum up, in the present situation of overwhelming data information, data mining technology not only brings benefits to the sales forecast of enterprises, but also poses a challenge to modern enterprises. The rapid development of information technology has greatly influence for many industries, enterprise information for the sales data of control directly affects the sales of the enterprise, and it is this on the one hand, make enterprise for sales forecast a particular focus on the effect of application of data mining technology in the sales forecasting has become a necessary choice of modern enterprise, for modern enterprise, Must pay attention to this new type of data mining technology, with the help of data mining technology, enterprise in-depth analysis of the historical sales data, hidden in the deep mining valuable information of the company sales data, timely discover and grasp the information, and help enterprises to better control the market dynamics and the sales prospects of the enterprise, management for the enterprise to provide a more scientific basis for the decision-making of sales. Acknowledgements This paper supported by academic funding project for top talents of disciplines (majors) in Colleges and universities of Anhui Province in 2020 (gxbjzd2020042) and "Marketing teaching team" of Anhui teaching team in 2020 (2020jxtd258).
References 1. UCF (2020) Research on supermarket sales data prediction based on regression algorithm. Inform Technol Informationization (05):39–41 2. Wang X, Wang X, Guo R, Liu X, Han Y (2020) The sales forecast model of garment enterprises based on grey theory. Silk 57(02):55–60 3. Jianfeng Y, Guoying Z (2019) FMCG industry sales forecast based on data mining. Appl Microcomputer 35(07):143–147 4. Minbo L, Haipeng W, Songkui C, Chang L (2017) Industrial big data analysis technology and tire sales data forecasting. Comput Eng Appl 53(11):100–109
Study on Learning Path Selection of English Writing Based on Big Data Technology Han Wu, Wei Xiong, and Sen Hong
Abstract Advent of the era of big data for many fields have produced great impact, education field also have started to use big data such as modern science and technology to promote teaching reform, especially in the aspect of English teaching, The continuous development of modern network technology and its deep integration with the field of education have brought great changes to contemporary English teaching, which has exerted a certain influence on teachers, students, English teaching methods and learning environment. As an important link in English teaching, English writing is directly related to the internalization of students’ theoretical knowledge of English and the improvement of their ability to apply the subject. The improvement of students’ English writing ability requires not only the accumulation of vocabulary and the change of teaching methods, but also the innovation of English writing learning paths with the help of big data technology. Taking college students as an example, this paper first makes a brief analysis of the current situation and problems of English writing teaching for college students, and on this basis, focuses on the study of the learning path of English writing by combining big data technology. Keywords Big data · English writing · Learning path In recent years, with the continuous development and popularization of Internet technology and related applications, modern society has gradually entered a new era of big data. The emergence of cloud technology and the development also brought new development for college English writing teaching based on, for example by means of the big data technology makes English writing system can help teachers improved English article review it speed, and some of the online writing system can also download and easier storage, analysis, more conducive to the integration of teaching and analysis of the corpus of English article, For students, it can help them to consult, review and summarize repeatedly, and gradually improve their English writing ability. Of course, the advent and development of the era of big data and related technologies bring both opportunities and challenges to English writing. On H. Wu · W. Xiong · S. Hong (B) Jiangxi University of Engineering, Xinyu 338000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_75
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the one hand, big data technology can provide more resources for English writing teaching, such as English writing resources, writing auxiliary tools and writing evaluation, to help students improve their writing ability. Of course, this is also a brand new challenge to the traditional English teaching model [1].
1 Current Situation and Problems of English Writing Teaching in Contemporary Colleges and Universities 1.1 English Teachers Are Relatively Deficient in Classroom Writing Teaching Methods But in terms of English writing teaching, not only involves the accumulation of basic knowledge of English and to use, but also including thinking of building in English writing, so that English writing is one’s deceased father grind the innovative application of teachers’ teaching methods and teaching tools, plus English writing will be more boring than Chinese writing, so one can imagine the difficulty of the teaching process. Therefore, teachers must be good at applying more innovative and reasonable teaching methods to improve students’ writing ability. However, according to the current situation of college English teaching, teachers still adopt the traditional writing teaching mode, that is, teachers give specific writing topics, students write independently, and teachers evaluate students’ English articles. It can be seen from this process that the “didactic component” occupies a large proportion and does not reflect the subjective initiative of students [2].
1.2 English Writing Teaching Resources Are Relatively Scarce At present, in the process of English teaching in domestic colleges and universities, teachers basically use the content of the textbook to guide students to write, and the content of the textbook is mainly based on the basic knowledge of English, but in addition to English writing, there is also a need for rich writing materials. Therefore, there are fewer available materials in modern college English textbooks, and students cannot accumulate more writing materials solely by relying on textbooks. Such a situation will not only affect the improvement of students’ writing ability, but may even make students fear of English writing. Moreover, the lack of English teaching resources will make students from different regions unable to enjoy the same educational resources, and even affect the fairness of education [3].
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1.3 English Writing Teaching Lacks Pertinence Sketch English writing level of ascension in addition to teaching methods and teaching resources, also need to be targeted teaching, teachers should condition for students to choose targeted teaching methods, better compensate for the deficiency in students’ writing in English, but because most of the lack of necessary communication between teachers and students, Therefore, teachers’ understanding of students is only limited to classroom assignments and writing articles, so they cannot truly grasp the comprehensive English writing level of students, and thus fail to develop targeted teaching programs. It is precisely because of the lack of targeted teaching that students will have many problems in English writing. For example, the most common types of mistakes are words, grammar, punctuation, sentence structure, etc. Figure 1 shows the types of errors in English writing of college students surveyed by a website: Firstly, taking sentence structure as an example, common errors in sentence structure include sentence fragment error, non-parallel structure error and single order error in English composition, as shown in Fig. 2: As can be seen from Fig. 1, among the types of writing errors, “writing style errors” account for the largest proportion, including phrases, passive voice, references, etc., as shown in Fig. 3: Fig. 1 Types of mistakes in English writing
Contextual spelling The word
strip structure grammar
punctuation
Writing style
22%
32%
14% 2%
9% 21%
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H. Wu et al. Wrong word order
Non-parallel structure error
Sentence fragments
37% 61% 2%
Fig. 3 Wrong type of writing style
References are unclear
The phrase a long
The passive voice is wrong
6%
27% 67%
2 Cultivation of Students’ English Writing Ability Driven by Big Data Technology 2.1 Teachers Should Update Their Teaching Concepts in Time To deepen the learning ability of English writing based on big data technology, first of all, English teachers must change their teaching concepts and roles, have a full understanding of the content of English writing teaching, and at the same time understand the characteristics of students. On this basis, they should actively organize students to carry out English writing and give students maximum guidance in writing [4]. Secondly, teachers must pay attention to the improvement of their own ability, and gradually master the compound English knowledge structure, including English language professional knowledge, teaching science knowledge and basic humanities knowledge.
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In addition, teachers should constantly improve the application ability of information technology, correctly understand the era of big data, strengthen the application awareness of big data, and gradually improve the ability of data analysis. Therefore, colleges and universities should organize teachers to carry out educational technology training regularly, so as to improve teachers’ technical ability and data application ability. Of course, the application of big data involves computer network technology, so teachers should strengthen the cooperation and communication with technical personnel, have a certain understanding of big data technology, and be able to give full play to the application value of big data.
2.2 Strengthen the Construction of English Writing Teaching Resources Teaching resources are an important guarantee to improve students’ English writing ability. English teachers, students and big data technology providers should all pay attention to the construction of English writing teaching resources to maximize the integration of resources related to English writing. The construction of English writing resources based on big data technology must be combined with the learning process of students. In particular, personalized writing materials should be provided to students from the use of English writing resources and personalized writing guidance should be pushed to students in time. At the same time under the big data technology of English writing ability should also pay attention to study for students with a certain activity, at the same time, the whole track students for the usage of English learning resources, collect the data information, students use the resource through the use of resources in the process of analysis, a more comprehensive grasp student resource requirements, so as to provide students with personalized services. For example, in the process of English writing, the English corpus should be constantly improved to better diagnose students’ language ability. Meanwhile, personalized resource services can be provided for students through the collected data and information.
2.3 Scientific and Reasonable Application of English Writing Teaching AIDS English teaching should be good at using some scientific and reasonable English writing auxiliary tools, while combining with the English teaching content to develop some new auxiliary tools, and in the teaching process to actively guide students to use the writing auxiliary tools. Can, for example, to use some of the existing resources of network technology, is used in the interaction of social network, social software and support software, English writing corpus into student writing teaching, the further
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advantage of a variety of teaching methods to promote learning, on the basis of the applications can also use a specific real-time tracking of students’ learning situation, More comprehensive collection of students’ learning data, convenient for teachers to adjust teaching methods, timely update teaching resources.
2.4 Conduct Diversified Assessment of Students’ English Writing Scores With the help of big data technology, the real-time online English teaching model can be used for diversified assessment of students’ writing performance. In the era of big data, through the centralized analysis of data information, it is possible to conduct diversified assessment. The assessment subjects are more diversified and the methods are diversified, and the specific elements of the observed objects can be timely fed back. Big data intelligent system, teachers’ teaching methods and processes, and students’ learning should all be involved in the diversified assessment. For example, the intelligent writing system built with the help of big data technology can conduct personalized assessment from the dimension of language. Teachers can improve teaching methods and guide students’ English writing based on the feedback information of the system. Students can interact with each other in the intelligent writing platform and provide help with their English writing language, writing content and vocabulary. Of course, in addition to the evaluation of English writing scores, it should also include the evaluation of students’ learning habits, learning methods and learning content, and timely update the online content according to the classroom teaching content.
3 The Design of English Online Writing Teaching Path Based on Big Data Technology The English online writing teaching path proposed in this paper based on big data technology can be divided into four stages, as shown in Fig. 4: Based on the above four stages to build online English writing teaching system, the system is mainly based on cloud computing, corpus technology of on-line automatic correcting system in English writing, with the analysis of big data, English article grade evaluation criteria to deep mining, and then to improve students’ English Fig. 4 The four stages of big data-driven English online writing
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writing ability and cultivate provide full technical support and personalized guidance, The main function of the system is to use big data technology to automatically generate diagnostic reports on students’ writing articles, and then teachers can help students improve their writing ability according to the writing diagnostic reports.
3.1 English Writing Preparation Stage First of all, the teacher uses the “homework retention” module in the system to assign writing content for students. At the same time, the teacher can also set additional conditions for writing according to the classroom teaching content, such as writing paradigm, article correction method (that is, according to what way students’ article is corrected, For example, system correction or system + teacher correction method), time limit requirement of article writing, mutual evaluation requirement of students, similarity test of article writing (i.e., checking the students’ writing articles and setting the allowed repetition rate of article writing), etc. On the other hand, teachers can upload some English reading materials related to writing in the “Forum” module of the system, and upload discussion topics and related learning activities in the “Communication and Interaction” module. Teachers can publish English writing topics, directions and other relevant requirements in the “Teacher’s Website” module after class. Meanwhile, they can also provide students with search directories and related topic contents related to article writing. Second, students can timely understand the requirement of English writing, with the help of system requirements related literature search and writing by reading reference, and then enter the “interaction” in the module, discuss the material with teachers, classmates and writing themes, such not only can enrich student writing content, also can strengthen the communication between teachers and students; Moreover, students can obtain more writing materials with the help of the system, and form their own writing ideas in the way of learning logs and learning records. The system will leave data for each operation of students, so as to provide data support for teachers to master students’ learning trends and situations.
3.2 English Online Writing Stage Students complete the online writing tasks in the system. Before the completion time set by the teacher, students can repeatedly check the articles they need to submit. In the process of English writing at the same time, students can also query system provided by the corpus of this point, to retrieve the statements in the article, whether language, language or grammar errors or inappropriate place, of course system can also collect students submit different versions of the English composition, record every submission time, according to the periodical situation of the students to give writing suggestions.
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3.3 Language Feature Analysis Stage This stage mainly analyzes the writing characteristics of students, including the following two aspects: (1) students’ compositions are analyzed based on the system’s individual language ability. Students’ essays are evaluated from the perspective of vocabulary, sentence fluency and structural integrity, and an analysis report of students’ language ability is generated automatically. (2) The commonality analysis is carried out on the language of students’ groups. Teachers can grasp the common language characteristics of a student or some students’ groups in time with the help of the “commonality analysis” function of the system.
3.4 Feedback Stage of English Writing Teaching System based on big data and English language corpus system, the students complete the thesis compared with corpus, from the article words, sentences and discourse, and the content of four aspects to give the most scientific evaluation feedback, timely find out the students article appear grammar mistakes, such as then there will be wrong again feedback to students, students to the content of the modified according to the feedback, Then submit it again, until there are no more grammatical errors, the system will approve the composition, and then submit it to the teacher.
4 Conclusion In conclusion, the application of the big data technology can provide powerful data for online students English writing and technical support, provide students with a more personalized learning guidance, at the same time can also help teachers a more comprehensive understanding of the students’ writing characteristics and learning situation, ways to enrich the students’ English writing learning, improve the learning efficiency.
References 1. Yunsong G (2021) Reflection on the reform of college English writing teaching in the era of big data. Overseas English 12(09):102–103 2. Juanjuan G (2021) Feedback effectiveness and optimization strategy of online English writing evaluation system. English Square 34(13):87–89 3. Wanming G (2021) A brief analysis of the reform of high school English writing teaching in the era of big data. Academia Edu 8(13):91–92 4. Changsheng L (2021) Research on teaching methods of high school English writing in the era of big data. English Middle School Students 21(14):33
Data-Driven Co-design of Communication, Computing and Control for Smart City
The Development Trend of International Economy and Commercial Industry Based on the Analysis of Internet of Things Technology Ping Wang
Abstract Science and technology are now evolving, the Internet is also evolving, and the impact of the Internet is increasing. On the one hand, it has not only a major impact on people’s lives, it has a major impact on the international economy and the commercial sector, and in such circumstances this article has initiated research into the development of the international economy and the commercial industry in connection with the Internet. First of all, we are analysing the development of the Internet in my country. On the other hand, we have collected import and export data from my country over the last five years. In this case, it may reflect the development of my country’s international economy and trading industry on the one hand. Finally, the results of this article show that, with the development of science and technology, the spread of the Internet is increasing. In 2019, the rate of penetration of the Internet in my country has reached 64.5% and with development. Total imports and exports from my country have also increased. It has grown for years. In 2019 my country reached the total volume of imports and exports of 30,417 billion RMB. This shows that the development of the Internet has encouraged the development of the international economy and commercial industry. Research in this article will provide some reference value for the development of the international economy and commercial industry. Keywords International economy and the commercial sector · Internet penetration rate · Total import and export volume · Industry development trend
1 Introduction With the rapid development of science and technology today, the explosion in information has given us opportunities and challenges. The international economy and commercial industry have the most extensive and obvious influence. One of the areas P. Wang (B) Henan College of Industry and Information Technology, Zhengzhou 454000, Henan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_76
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of [1, 2]. At the moment, with rapid economic globalisation and the rapid development of science and technology, global economic trends tend to trade between countries [3, 4]. The number of trade cooperation between countries is increasing and their importance for international trade has also increased. In these circumstances, the development of the international economy and commercial industry is particularly important [5, 6]. Society is developing and the Internet is developing; in these circumstances, it is natural that the international economy and the commercial sector are becoming more and more diversified. Because it has introduced trade rules and business organisations [7, 8]. On the other hand, the Internet can increase the security of the international economic and trade index. Moreover, operating costs can be compressed in the middle. As a result, the surplus from the international economy and the commercial sector will increase [9, 10]. Internet connection of great importance for the development of the international economy and the commercial sector to examine the development of the international economy and commercial industry. The research in this article has, on the one hand, carried out an appropriate deepening of the international economy and the commercial sector and its industry. In addition, in order to study the development of the Internet in my country, we have analysed the extent of Internet users and the degree of Internet penetration in my country over the last five years. But this article also organizes my country’s import and export data over the last five years so that it can reflect the development of my country’s international economy and industry on the one hand. On this basis we are the economy and the commercial industry was summed up.
2 Internet and International Economy and the Commercial Sector 2.1 International Economy and the Commercial Sector International Economy and Trade Connotation The international economy and the commercial sector are trade in goods and services between different countries and regions for their own needs and purpose. Transport of goods and labour services. In terms of composition, the international economy and the commercial sector include imports and exports. International economy and the commercial sector essentially reflect the providers and spatial embeddedness of international economic and trade services. In other words, it means that both parties are engaged in international trade or international trade, which means that the trade activities related to local economy and international trade are completed by the enterprises themselves, Whether it is undertaken by international economic and trade buyers alone, or by specialized trade intermediaries embedded in a specific economic space for production enterprises.
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International Economy and Trade My country’s international economies and trade sectors are different: according to the different commercial characteristics, they can be divided into trading companies and foreign trade and export companies. The source of the goods should also indicate the sales target and sales plan of the product and generate a certain profit. My country’s trading companies are primarily concerned with trade, with low added value potential, while my country’s domestic products are dominated by foreign investment. As the largest developing country, there are many trading companies in my country; among many trading companies, small trading companies do not have much room for development, while large trading companies have more room for development.
2.2 Impact of International Economy and the Commercial Sector Industry It simplifies the Trade Process and Improves the Trade Efficiency In the context of the Internet, the greatest impact on the international economy and the Commercial sector is the simplification of the trade process. The traditional international economic and trade process includes many links, in conjunction with the tax, customs, goods inspection and other parts, making the entire country’s trade process complicated, long operation and a high error rate. The most important thing is that it requires a lot of manpower, Material and economic resources for completing the entire trade process and efficiency is not high, this is the most worrying aspect of trade and country. The development of cross-border e-commerce has completely changed this inefficient mode of operation. In the context of the Internet, trading entities and relevant parts shall use EDI technology to complete pre-trade preparation, negotiation and signature of contracts and performance by means of non-meeting electronic data transmission. The whole process is free of time and space constraints and transactions can be made. Multilateral, long-distance and automatic transactions simplify the transaction link and reduce transaction costs. As regards the marketing and promotion of products, e-commerce can also promote products through online marketing, which saves time and money, reduces marketing costs and improves the efficiency of international trade. It has Promoted the Diversification of International Trade Business Entities The Internet age is an era of information explosion and it is also an era based on the Internet platform for economic development. Each company can rely on its own advantages in information technology to dominate industry and each person can rely on its own professional sector. With excellent technology for independent entrepreneurship via the Internet, a large number of virtual e-businesses emerged. These virtual electronic companies use Internet technology to connect individual companies engaged in foreign trade activities to: form a community of stakeholders with a division of
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labour, a distribution of benefits, additional benefits and a distribution of resources; to achieve market functions that an individual foreign trade company cannot achieve and to promote China’s foreign trade strategy. Improve and upgrade the standard. Promote the Development of Trade in Intangible Products and Services Traditional international trade is mainly based on trade in material products, i.e. goods. Due to the specificity of the product of intangible products and the trade in services, their share in international trade is extremely low. Until the advent of electronic commerce, intangible products such as classical music, dramas, cinematographic and television works, music and other cultural goods, are spread throughout the world through the form of connection or compression, satisfying the intellectual and material life of consumers. Desire also contributes to the exchange and dissemination of global culture.
3 Data Sources and Research Methods (1)
(2)
(3)
Research ideas This article analyses for the first time the scale of Internet users and the rate of Internet penetration in China over the last five years, to reflect the development trend of the Internet. Secondly, this article classifies China’s import and export data over the last five years, reflecting the international economy of China and the development situation of commercial industry. Finally, we have summarised the development trend of the international economy and commercial industry in the context of the Internet. Data source The data used in the analysis of the development of the Internet in this article are derived from the Internet development statistics in China and the source data for the five-year analysis of import and export data shall be derived from Chinese import and export statistics over the years. Research methods (i)
(ii)
Literature analysis In this paper, through online and offline and many other ways, a wide range of domestic and foreign scholars’ research theories and research results are collected, and the screening and analysis are carried out to lay the theoretical foundation for this study. Data analysis In this paper, a series of relevant data are listed, and through calculation and integration, the results are obtained, and then the phenomenon is analyzed. This method is more scientific and can more specifically reflect the current situation and future development trend of things, so as to make corresponding adjustments and countermeasures.
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Longitudinal analysis This article uses the method of time analysis to compare and analyse the development of my country’s Internet over the last five years and import data, and exports of my country over the last five years to reflect developments in my country’s Internet and imports and exports.
4 Analysis on the Development Trend of International Economy and the Commercial Sector Industry Under the Background of Internet 4.1 Analysis of the Development of the Internet Based on the analysis of the development of the Internet, the scale of Internet users and the Internet penetration rate in China in recent five years are shown in Table 1 and Fig. 1. From Table 1 and Fig. 1, we can get a lot of information: firstly, the number of Internet users in my country 2015 68,826 million, and the degree of Internet penetration 50.3%. In 2016 the number of Internet users in my country reached 73,124
Particular year
Scale of Internet users (10,000 people)
Internet penetration rate
2015
68,826
50.3%
2016
73,124
53.2%
2017
77,198
55.9%
2018
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59.6%
2019
90,400
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20000 0
Penetration rate
Ten thousand people
Table 1 Scale of Internet users and Internet penetration rate in China
0.00% 2015
2016
2017
2018
2019
Particular year Scale of Internet users (10000 people)
Fig. 1 Development of Internet in recent five years
Internet penetration rate
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million’s, and the degree of Internet penetration was 53.2%. In 2017 the number of network users in my country 77,198 millions’, and the degree of Internet penetration was 55.9%. In 2018 the number of network users in my country 82,851 million, and the degree of Internet penetration was 59.6%. In 2019, the number of network users in my country has reached 904 million, and the rate of Internet penetration is 64.5%. You can see that with the development of science and technology, the number of network users in my country is gradually increasing, and the spread of the Internet is also increasing.
4.2 Analysis on the Development of International Economy and the Commercial Sector Industry Under the Background of Internet Import and Export Data Analysis. The results are shown in Table 2 and Fig. 2. It can be seen from Table 2 and Fig. 2 that China’s total import and export volume is growing from 2015 to 2019, of which, the total import and export volume in 2015 was 23,140.3 billion yuan, and the balance of import and export was 3593.3 billion yuan. In 2016, the total import and export volume was 23,184.1 billion yuan, and Table 2 Import and export data in recent five years (unit: 100 million yuan) Time
Total imports and exports
Total exports
Total imports
Import and export balance
2015
231,403
133,668
97,735
35,933
2016
231,841
132,329
99,512
32,817
2017
267,001
149,590
117,411
32,179
2018
293,624
160,299
133,325
26,974
2019
304,217
169,411
134,806
34,605
RMB100mn
400000
Total imports and exports Total imports
Total exports Import and export balance
300000 200000 100000 0 2015
2016
2017
Time Fig. 2 Analysis of import and export data in recent five years
2018
2019
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the balance of imports and exports was 3281.7 billion yuan. In 2017, the total import and export volume was RMB 26,700.1 billion, and the balance of import and export was RMB 3217.9 billion. In 2018, the total import and export volume was 29,362.4 billion yuan, and the balance of imports and exports was 32,697.4 billion yuan. In 2019, the total import and export volume is RMB 30,421.7 billion, and the balance of import and export is RMB 3460.5 billion. Development Trend of International Economy and Trade Industry under the Background of Internet. The trend of economic globalization In the global environment, China should gradually optimize the business philosophy and innovative management mode of Chinese enterprises with the concept of win–win cooperation, and improve their own ability and quality. The marketing team can better serve the needs of enterprise development and improve the competitiveness of the company. Under the background of Internet, the development of international economy and trade industry tends to globalization. The diversification trend of trade pattern The traditional mode of international trade is to confirm the quality of products, reach the cooperation intention with partners and the person in charge of on-site inspection, and then sign the contract and product production and transportation. However, with the development of Internet technology, trade between countries can also be carried out through the Internet platform. The company can find suitable partners in the Internet information resources database and issue the invitation letter for cooperation. Then, the two sides can negotiate the specific cooperation plan and finally complete the transaction. The development of science and technology provides a new way for trade exchange, breaks the space restriction between countries, and realizes the diversified trade mode. The trend of trade liberalization With the development of economic globalization and the rapid development of science and technology, economic exchanges between countries in the world are increasingly frequent. As a result, with the expansion of international trade, trade has become more free. With the development of international economy and the commercial sector, countries have relaxed the traditional binding force of international trade according to the needs of world economic development. Enterprises of various countries can break through the limitation of domestic market, seek the cooperation of excellent foreign enterprises and absorb advanced production and operation cooperation from abroad. The growth rate of international trade shows linear growth At present, international trade has entered a period of crazy growth. According to statistics, as of 2014, the number of people with mental disorders has increased by about 23, breaking the highest growth rate of Global trade in recent years. Due to the frequent political and cultural exchanges among countries, economic exchanges have been promoted. Regional economic cooperation is more prosperous Many big trading countries play an important role in regional trade. The members of the trade zone respect and benefit each other, seek common economic growth points, pay attention to large
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markets outside the region, and enhance their ability to adapt to the international economy. A more advanced international trade structure From the perspective of promoting domestic economic development, countries should actively adjust the industrial structure and optimize the industrial layout. For example, in recent years, the proportion of service industry, manufacturing industry and transportation industry in the whole industrial structure has increased significantly, indicating that the economic structure of international trade in the future will be more advanced.
5 Conclusions The Internet has had a major impact on people’s lives; many industries have been affected by the network, and this applies in particular. This article begins to examine the development of the international economy and industry in the context of the Internet. By analysing the development of the Internet in China and the import and export data over the last five years, this document reflects the development of the international economy and industry in China. The document summarises the opinions on the international economy and the commercial sector in the light of the Internet and the development of industry. It can be said that the development of the Internet promotes the development of the international economy and industry.
References 1. Balli HO, Balli F, Tsui WHK (2019) International tourism demand, number of airline seats and trade triangle: evidence from New Zealand partners. Tour Econ 25(1):132–144 2. Hong S, Oh SH, Sim SG (2018) Imperfect labor mobility and the trickle-down effect in international trade. J Korea Trade 22(1):68–83 3. Barua A, Ghosh P (2017) Factor specificity and wage inequality in a developing economy: the role of technology and trade in Indian manufacturing. Int Rev Econ Finance 52:77–90 4. Clark P, Hussey I (2018) Fair trade certification as oversight: an analysis of fair trade international and the small producers’ symbol. New Political Econ 21(2):1–18 5. Chodor T (2019) The rise and fall and rise of the trans-pacific partnership: 21st century trade politics through a new constitutionalist lens. Rev Int Political Econ 26(2):232–255 6. Allee T, Elsig M (2017) Veto players and the design of preferential trade agreements. Rev Int Political Econ 24(3):1–30 7. Kyzym MO, Kramarev HV (2020) Methodological approach to estimating and analyzing international trade in manufactured goods in value added terms in Ukraine and countries of the world. Prob Econ 2(44):119–129 8. Ye N, Wang Y (2019) A push over trade barriers: firms access to external finance and their sales hierarchy. Chin Econ 52(6):464–487 9. Oban O, Onifade ST, Yussif ARB et al (2020) Reconsidering trade and investment-led growth hypothesis: new evidence from Nigerian economy. J Int Stud 13(3):98–110
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10. Javed S, Khan MS, Farooqi AR (2020) Impact of population, trade openness, education, life expectancy and gross capital formation on the economy of Indian sub-continent. Int J Psychosoc Rehabil 24(6):2020
Interface Usability of Video Sharing Websites in the Internet Era Meiping Dai
Abstract The rapid development of the Internet has provided more possibilities for enriching people’s lives. The emergence of video-sharing sites in the Internet era has opened up a new mode of video dissemination for video sites. The development quality of the website interface relates to the ease of use of the video sharing website and the quality of user experience. The purpose of this article is to study the interface usability of video sharing websites in the Internet era. This article first attempts to point out the interface usability principles and evaluation indicators for the interface characteristics of the video sharing website, and draws a usability evaluation table suitable for the user-side interface. This paper analyzes the characteristics and requirements of video sharing websites, and completes the usability evaluation of the M video website interface based on the usability test method, and grasps the user’s feedback on the usability of the video sharing website interface based on experience. The test results show that the average task operation time of user 1 on the first and second editions of the interface is 28.3 s and 25 s, respectively; the average task operation time of user 2 on the first and second editions of the interface is 29.7 s, 26.3 s, respectively. It can be seen that after the improvement of the user-based interface usability evaluation of the second version, the operation efficiency of users to complete the target requirements is greatly improved. Keywords Video sharing · Usability · User experience · Interface design
1 Introduction The rapid development of web2.0 and streaming media technology gave birth to video websites [1, 2]. In the web2.0 era, most network users upload their original content to the network platform for other users to watch or share [3, 4]. Therefore, the most significant feature of Web2.0 is the UGC (User Generated Content) model, and video sharing websites are based on this model [5, 6]. With the development M. Dai (B) Wuxi Vocational Institute of Commerce, Wuxi 214153, Jiangsu, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_77
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and application of video sharing websites being liked by more and more people, the usability of the website interface should meet the needs of more users [7, 8]. Regarding the research on the usability of video sharing websites and interfaces, many scholars at home and abroad have conducted various discussions on them. For example, Kiet research on short video content analysis algorithm based on deep learning [9]; Anabela et al. method of using the model will The design process of the user interface is divided, and the abstract design model and the physical design model are output [10]; Deepak et al. believes that the design of the mobile phone interactive interface can no longer be designed from the perspective of the aesthetic effect or the technical structure of the mobile phone [11]. This article first conducts a systematic study on the characteristics, interface requirements, and usability evaluation of video sharing websites in the Internet era, and summarizes the usability principles of user interfaces, from the object usability of the interface, the usability of the interface context, the usability of the interface operation, and the interface. The content usability is obtained from the four dimensions of the interface usability evaluation of the video sharing website. Then it analyzes the characteristics and functional requirements of the video sharing website, including video management, user management, social management and recommendation functions. Finally, through experimental investigations, we have grasped the user’s evaluation of the usability of the video sharing website interface based on experience.
2 Interface Usability Analysis of Video Sharing Websites in the Internet Era 2.1 Characteristics of Video Sharing Websites in the Internet Era As UGC short videos are loved by more and younger people, video sharing websites will have the following characteristics in the video-on-demand system: (1)
(2)
(3)
The cost of video traffic is high. Because video belongs to streaming media content, it will occupy a large amount of limited bandwidth resources on the network. On the one hand, it will cause the quality of service of the entire network to decrease, and on the other hand, it will also face high traffic charges. The geographical distribution of users is relatively scattered. Because of my country’s vast territory, users may come from different provinces. For example, the video uploader is located in southern China, and the corresponding users who watch the video may be from northern China. Therefore, the storage location of the video is directly related to the playback quality of the video. The on-demand traffic of hot videos may increase sharply. Major events may cause the broadcast volume of some videos to increase sharply. Video sharing
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(4)
689
websites need to cope with the sudden explosive increase in click-through rate [12]. Video interviews are uneven. Based on his analysis, Italian economist Pareto concluded the two-eighth law, that is, in any group of things, the most important part is often only 20%, and the minor part is often Occupies 80%. In video sharing websites, the playback of videos will also have such unevenness. Because people are more inclined to watch high-quality videos, there may be a small part of the video with high playback volume and most of the video playback. The phenomenon of a small amount.
2.2 Interface Usability of the Video Sharing Website (1)
Requirements for the interface of the video sharing website (i)
(ii)
(iii)
(iv)
(v)
Video management Video management mainly includes video-related functional requirements. Provide users with functions such as playing videos, uploading videos, deleting videos, and video post-processing. Search function The search function mainly includes the search-related functional requirements in the short video sharing platform. Provide users with functions such as searching users and searching videos. Among them, the video search function can also be divided into subdivision functions such as search by video subject and search by video tag, providing users with more comprehensive search function services. User management function The user management function mainly includes the functional requirements related to the user’s basic management. Among them, it includes providing users with basic functions such as login, registration, and logout. And provide users with the function of viewing personal homepage and viewing personal information. In addition, users can manage their own basic information, including but not limited to modifying their own password, mobile phone, email, etc. Social management function The social management function mainly includes the user’s socialrelated functional requirements. Its core needs to provide users with a circle of friends function. Specifically, users can see all the video updates posted by friends they follow in the circle of friend’s page. In addition, it provides users with social functions between users, including following/unfollowing, chatting, blocking and other functions. And, provide users with video-related social functions, including comments, likes, sharing and other functions. Recommended features
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The recommendation function mainly includes the functional requirements related to the recommendation. Mainly provide users with short video recommendation function and user recommendation function. According to the user’s preferences, the appropriate algorithm is used to intelligently recommend the user, so that the user can get a better experience. (2)
Evaluation of interface usability Through the research on the usability of the interface, we get the usability of four dimensions, namely: usability of the use object, usability of the context, usability of the operation behavior and usability of the interface content. Corresponding design principles are put forward for each dimension. These principles can be used as indicators for evaluating the usability of public terminal interfaces, forming a public terminal interface usability evaluation table to guide the work of public terminal interface usability testing. (i)
(ii)
(iii)
Object availability of the interface Most novice users have no learning experience in the operation of public terminal interfaces. Differences in language and cultural background will lead to differences in cognition and behavior. This requires that the interface has good learnability and can provide usability for users with different backgrounds. At the same time, paying attention to the capabilities of special populations can improve the overall satisfaction of the public terminal interface. Contextual usability of the interface In the context of public use, factors that need to be considered include terminal location, environmental impact, user privacy exposure, and the impact of context follow-up. A reasonable location can effectively guide users to use self-service terminals, avoid environmental interference, protect user privacy information, and avoid interference with user operations following the situation. These are all effective measures to improve user satisfaction. Operational usability of the interface The user’s low-frequency use reduces the chance of learning the interface, and the memory of the interface is very low. If not used for a period of time, it will cause the loss of short-term memory. The shortness of operating behaviors has a greater relationship with efficiency. Time is very important and sensitive to every user waiting in front of a public terminal. Sometimes it is just a few minutes of waiting for the operation to delay planning and scheduling. The reduction of operating time can improve the efficiency of the service, thereby improving the usability of the interface. Providing clear operating directions and reducing fuzzy choices can also reduce the time spent by users, thereby improving efficiency. In addition, the puzzling special operation logic of the interface brings many obstacles to the user. The location of the card
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(iv)
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slot, the sequence of card verification, and the number of repeated operations will all affect the probability of interface errors and thus affect the usability of the interface. The usability of the content of the interface The unchangeable interface content determines that the interface content will be used for a long time. If the content of the system’s initial settings is difficult for users to perceive and understand, it will affect the learnability of the interface, and the time the user spends on information acquisition will be prolonged, and the longer the process, the easier the user will choose to give up the operation. In particular, the professional descriptions used in the interface will hinder users from learning the interface, thereby affecting usability. In addition, the unreasonable organization of the interface information architecture will also cause time delays for users to understand and operate. Simple functional layouts may result in low utilization. Effective use of information organization logic that conforms to user behavior habits should be used to design public terminal interface content. When the user is overwhelmed by operating difficulties, the information restoration of the system provides the user with an opportunity to re-operate, and at the same time prepares for the next user’s operation to avoid being affected by the result of the previous operation.
2.3 Mean Algorithm for Website Analysis The goal of the improved algorithm for distance measurement and the traditional fuzzy C-means algorithm is also to minimize the objective function. First, the Mahalanobis distance formula must be used to calculate it, as shown in Formula (1): di2j (x j , vi ) = (x j − vi )T
−1 ij
(x j − vi )
(1)
After such replacement, the objective function of the improved algorithm for distance measurement becomes the following form: Min J (U, V, X,
)=
n C i=1 j=1
u imj (x j , vi )T
−1 ij
(x j , vi )
(2)
Among them, 0 ≤ u i j ≤ 1, i = 1, 2, 3, . . . n. This is also a condition that needs to be met for the fuzzy clustering algorithm, which means that the sum of the membership degrees of any data object to all cluster centers is equal is 1.
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3 Interface Usability Test of Video Sharing Websites in the Internet Era 3.1 Test Purpose First, obtain user feedback data through the user’s interface experience evaluation of the M video sharing website, and compare the usability of the first version of the website interface according to the feedback data. After the test, organize records and analyze the data to understand that the user is in the test to discover the usability problems in the interface of the website and complete the usability evaluation of the interface.
3.2 Test Object A total of 6 people are invited for this test, with no major limitation, aged between 20 and 35 years old.
3.3 Test Steps The test allowed the test subjects to perform task operations on the paper prototype to simulate real scenes: T1 was to find the video with the highest view of the specified user, T2 was to publish a self-made video, and T3 was to share a specified video with Moments. Record the valid data of the relevant tester during the test, as well as the suggestions and data processing and analysis provided by the tested object. The test metrics mainly include task success and completion time.
4 Interface Usability Analysis of Video Sharing Websites in the Internet Era 4.1 Interface Task Operation Time Before and After the Improvement Three users under test completed the specified three tasks on the video sharing website interface of the first version and the second version (see 3.3 for details), and recorded the time of completing the tasks in the interfaces of the two versions, and the results are shown in the Table 1 shows: the average task operation time of user 1 on the first and second edition interfaces is 28.3 s and 25 s respectively; the average
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Table 1 Interface task operation time before and after improvements Operation task
User 1
User 2
User 3
Version 1
Version 2
Version 1
Version 2
Version 1
Version 2
T1 (s)
42
38
45
39
40
37
T2 (s)
13
9
10
8
15
7
T3 (s)
30
28
34
32
34
30
Average time (s)
28.3
25
29.7
26.3
29.6
24.7
task operation time of user 2 on the first and second edition interfaces is 29.7 s and 26.3 s respectively; the average time for user 3 to operate tasks on the first and second versions of the interface is 29.6 s and 24.7 s respectively. It can be seen from Fig. 1 that the task operation time of the test object is significantly shortened in the second version. It can be seen that after the user-based interface usability evaluation of the second version is improved, it greatly improves the user’s operational efficiency in fulfilling the target requirements, solves the user’s cumbersome operation of the video website interface, and improves user satisfaction. User 1 Version 1 User 2 Verion 1 User 3 Verion 1
45
42 38
39 40
User 1 Verion 2 User 2 Verion 2 User 2 Verion 2
37
34
unit: s
30
13
T1
32
28
15 9 10 8
7
T2
Operation task
Fig. 1 Interface task operation time before and after improvements
T3
34 30
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4.2 Success Rate of Task Completion Before and After Improvement Within the specified time, take the same task, test the other 3 testers, record the success rate of the tested user’s task completion, and get the data shown in Table 2: the average success rates of user 1’s task operations on the first and second versions of the interface are 85% and 99.3%; the average success rates of user 2’s task operations on the first and second versions of the interface are 93% and 99.3%; the average success rates of user 3’s task operations on the first and second versions of the interface are 92.7% and 100%. In combination with Fig. 2 and Table 2, it can be seen that the success rate of each user’s task execution has been significantly improved, and the operational efficiency has also been correspondingly improved. Table 2 Success rate of task completion Operation task
User 1
User 2
Version 1
Version 2
Version 1
T1 (%)
86
84
T2 (%)
87
100
User 3 Version 2
Version 1
Version 2
96
100
94
100
95
100
89
100
T3 (%)
82
96
89
98
95
100
Average (%)
85
93.3
93
99.3
92.7
100
User 2 Verion 2 User 2 Verion 2 User 1 Verion 2
User 3 Verion 1 User 2 Verion 1 User 1 Version 1 100% 95% 98% 89% 96% 82% 100% 89% 100% 95% 100% 87% 100% 94% 100% 96% 84% 86%
Operation task
T3
T2
T1
0%
20%
40%
60%
Success rate Fig. 2 Success rate of task completion
80%
100%
120%
Interface Usability of Video Sharing Websites in the Internet Era
695
5 Conclusion In the Internet age, the interface of video sharing websites is changing the lives of the audience. The goal of a good interface design is to make the audience’s operation and use of the system more comfortable and natural. The applicability of the system and the ease of use also involve the usability of the interface. Therefore, improving the usability of the interface is an important way to achieve the goal of interface design. This paper studies the interface usability of video sharing websites in the Internet era, and analyzes the relationship between the characteristics of video websites and the usability of their interfaces, and derives the corresponding demand analysis for short video sharing websites based on the usability of the user interface. The object usability of the interface, the usability of the interface context, the usability of the interface operation and the usability of the interface content are the four dimensions of the interface usability evaluation of the video sharing website. Then it analyzes the characteristics and functional requirements of video sharing websites, including video management, user management, social management and recommendation functions. Based on the usability test method, this paper completed the usability evaluation of the M video website interface, and grasped the user feedback on the interface usability based on the experience. Combining the two results, we have mastered the ingenious features and optimization results of the video sharing website interface.
References 1. Jardine G, Lombardo NT, Jarvis C et al (2018) An evaluation of educational neurological eye movement disorder video posted on internet video sharing sites: comment. J Neuroophthalmol 38(1):1 2. Shen Y, Jiang C, Quek T et al (2016) Device-to-device-assisted communications in cellular networks: an energy efficient approach in downlink video sharing scenario. IEEE Trans Wireless Commun 15(2):1575–1587 3. Oh S, Baek H, Ahn JH (2017) Predictive value of video-sharing behavior: sharing of movie trailers and box-office revenue. Internet Res 27(3): IntR-01-2016-0005 4. Hossain IT, Malik HH, Iqbal SS (2016) An evaluation of educational neurological eye movement disorder videos posted on internet video sharing sites: comment. J Neuroophthalmol 36(3):352 5. Sevenoaks H, Houghton N, Mehta J (2016) Sharing CT images via smartphone video. Ann R Coll Surg Engl 98(03):232–233 6. Huesch M, Chetlen A, Segel J et al (2017) Frequencies of private mentions and sharing of mammography and breast cancer terms on facebook: a pilot study. J Med Internet Res 19(6):e201 7. Errata to “Modeling, analysis, and implementation of universal acceleration platform across online video sharing sites”. IEEE Trans Serv Comput 11(4):740–740 8. Doyle C, Jackson D, Loi S et al (2016) Videoconferencing and telementoring about dementia care: evaluation of a pilot model for sharing scarce old age psychiatry resources. Int Psychogeriatr 28(09):1567–1574 9. Kiet Q (2016) Remote virtual supervision system. Eur J Soil Sci 67(1):79–89
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10. Anabela C, Jesús GZ (2020) Food resource sharing of alder leaf beetle specialists (Coleoptera: Chrysomelidae) as potential insect–plant interface for horizontal transmission of endosymbionts. Environ Entomol 49(6):6 11. Gupta DK, Tata BVR et al (2018) Optimization of a spatial light modulator driven by digital video interface graphics to generate holographic optical traps. Appl Opt 57(28):8374 12. Salvucci DD (2001) Predicting the effects of in-car interface use on driver performance: an integrated model approach. Int J Hum Comput Stud 55(1):85–107
The Realization of the Practical Ability Training System of Media Talents in OBE Mode Based on Network Information Technology Chunjie Han
Abstract With the development of social economy, the development of network information technology is getting faster and faster, and the competition in the network media industry is becoming more and more fierce. This kind of competition not only exists in China, but also fiercely in the world. International competition is not only conducive to the development of the entire network media industry in China, but also very important to the development of network media companies themselves. The current domestic online media market continues to open up to the outside world, and a large number of foreign online media companies have been introduced. This situation has brought development opportunities for our country’s online media companies, and at the same time brought corresponding fierce competition. With the advent of the era of smart media, the traditional media talent training model must be innovative in order not to be out of touch with the needs of the society. Improving the training of their practical ability is a top priority. This article discusses the practical ability of media talent training from various aspects such as the formulation and modification of talent training programs, strengthening the reserve of dual-qualified talents, building internship base construction, and improving the quality of graduation design. This article believes that the practice ability training of media talents should be improved in an all-round way based on the “output orientation” of the OBE concept. Based on this, a media talent practice ability training system under the OBE model is proposed, and the practice ability training of media talents under the OBE model is proposed. The system comprehensively sorts out the mixed information and reflects the data to make management transparent, rational and informatized, thereby improving the efficiency of talent training. Research shows that the average value of Transaction per second is 65.21, which shows that the number of transactions per second processed by the system can well simulate real information query use cases. The average transaction response time is 84.21 s, which is too long. But if the peak response time of 170 s is not considered, the system transaction processing time will eventually stabilize between 30 s, which meets the performance requirements.
C. Han (B) NanJing XiaoZhuang University, Nanjing 211171, Jiangsu, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_78
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Keywords Network information technology · OBE model · Media talents · Talent practice · Practical ability training
1 Introduction The demand for employment in the media field has undergone tremendous changes with the development of various media, and the training of media talents will inevitably undergo changes and reforms. In order to better adapt to social needs, the most urgent matter is to improve the proportion and quality of the practice part of media talent training [1, 2]. Although on the one hand, media majors are more popular among candidates, the employment rate is not ideal, even far below the average level, and graduates’ satisfaction with majors is not high [3, 4]. From the perspective of research time, domestic research on media talent training has shown a growth trend since 2009, and reached a peak in 2013, indicating that the media talent training model has gradually attracted the attention of researchers in recent years [5, 6]. Based on the analysis of the current situation and problems of the training model of media professionals in our country, Heller proposed to establish a “modern apprenticeship” talent training model for the art design major, and discussed from the aspects of function, constituent elements, etc. [7]. Dauletbekova—Doctor believes that there are many aspects of the current media majors in the target positioning of talent training, the reform and implementation of teaching concepts, the curriculum system and content, teaching management mechanism, professional practice teaching, and the construction of a double-qualified teaching team. Inadequate, and put forward countermeasures and suggestions on how to train creative, highquality and compound media professionals in higher vocational education under the background of creative industry [8]. This article discusses the practical ability of media talent training from many aspects such as the formulation and modification of the talent training plan, strengthening the reserve of dual-professional talents, building the construction of practice bases, and improving the quality of graduation design.
2 Realization of the Practical Ability Training System for Media Talents Under OBE Mode 2.1 Shortcomings of Media Talents’ Practical Ability Training (1)
Teachers are mostly theoretical
The media academy is a liberal arts college, and the source of teachers is mainly “humanities teacher type, media professional transfer, and college graduate research
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young scholars. Among them, the proportion of teachers with media experience is less than 20%. At present, the starting point for recruiting talents in domestic colleges and universities is doctoral degree, so the overall result is that teachers are mostly theoretical teachers. In the process of teaching and educating people, there are more theoretical aspects”. This kind of teacher composition has led to a relatively theoretical education model. For many practical processes Teachers cannot give corresponding guidance for the specific difficulties encountered in the process [9]. (2)
The proportion of practice in the talent training program is not high
There are more theoretical courses in the professional talent training program. This status quo has a lot to do with the majority of theoretical teachers. In the process of revising the talent training program, in order to ensure that teachers have classes, it is inevitable that the teachers will have classes. In reality, there are few teachers who can guide practical teaching and have practical experience, which leads to the lack of practical courses in the training program. Taking Nanjing Xiaozhuang College as an example, the school requires the lowest proportion of practical courses in the training program for liberal arts talents it is 15%. This ratio is relatively low for the majors of the School of Journalism and Communication with strong practicality. This status quo cannot keep up with the pace of the times to adapt to social requirements and strengthen the training of students’ practical skills [10]. (3)
Insufficient media internships on and off campus
Many media students have never done internships in the social media before graduation, nor have they internships in the corresponding media on campus. These internship opportunities are very important and beneficial for improving students’ professional practical ability. Exercising in the media internship will allow students to quickly familiarize themselves with the process of the media and master various equipment to learn how to operate. Quite a few students despise their studies and are not motivated enough to practice part-time [8]. (4)
The conditions of experimental training are backward
In the process of cultivating media talents, practical ability is very important, but there must be corresponding experimental training conditions. Otherwise, students will not have a place for internships, and many things learned are on paper. For example, whether the laboratory conditions can keep up with the changes in the media and development, many school laboratories are computer rooms, students cannot experience the latest media development and changes in practice, or the experimental conditions corresponding to the old set of courses. The laboratory construction lacks professionalism and forward-looking, which seriously affects the quality of practical teaching [11].
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2.2 Suggestions on the Cultivation of the Practical Ability of Media Talents Based on the OBE Model The OBE concept originated from the basic education reform movement in North America in the 1980s. It values the actual needs of the society for talents, emphasizes the scientific arrangement of teaching time and teaching design around learning and production, and the traditional domestic emphasis on the systematic and complete subject knowledge system. The positive design thinking of teaching design based on sex is two completely opposite thinking. This model emphasizes that the curriculum setting is for the output of results, so it is of great help to the applied majors of local colleges and universities. Especially the media majors, which belong to the liberal arts majors, but also require a strong time ability, especially to change the teaching mode. The author believes that it can be strengthened from the following aspects: (1)
The talent training plan focuses on the practical curriculum arrangement for output enhancement
The talent training plan determines the direction and quality of talent training. It is the general outline of talent training. To strengthen practical training, we must first start with the formulation of the talent training plan. Based on the OBE concept, to improve the practical ability of media talents, the talent training plan should focus on industry arrange for the promotion of practical courses. Strengthen collaborative education, strengthen the cultivation of students’ practical ability, deepen the integration of industry and education, strengthen cooperation with industry enterprises and local governments, develop and introduce enterprise practical courses and crossborder courses, and promote teaching to closely follow production practices and technological progress; make full use and development Practical teaching resources inside and outside the school, optimize practical teaching content, increase the total number and proportion of graduation design in the graduation link, establish a talent training process guided by improving practical ability and collaborative education as a way to realize the professional chain, industry chain, and curriculum content and professional standards, teaching process and production process docking. (2)
Graduation design incubation in the form of workshops
Generally speaking, a workshop is a small group of people with experience in a particular field, who work together to discuss a topic through a variety of activities, discussions and short talks under the guidance of a keynote speaker. The instructor of college graduation thesis (design) can conduct graduation design guidance in the form of a workshop. The graduation thesis (design) of senior students is a compulsory course for them, and everyone must complete it. The instructor can lead several students to form a workshop, carrying out graduation design creation, and finally incubating excellent graduation works. Excellent graduation design works can often also be a strong proof of students’ ability to show themselves when they are employed. For example, several students in a workshop team work together to complete the shooting of a micro film or documentary, and present the finished product to the
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employer when choosing a career. It can explain the professional quality and practical ability of a media graduate, and it can also encourage students to form a team to participate in the Internet + competition for graduation design. (3)
Introduce industry teachers to carry out project-based teaching
Industry teachers are on the front line of media, and will bring the latest information on industry media changes to college classrooms, guide them to project-based teaching, and result-oriented. It is a good compensation for most of the theoretical teachers in media colleges and inspires students to improve the practical ability of Students plays a very important role, which can stimulate their curiosity and interest in the most cutting-edge media changes and increase their love for the profession. Taking the School of Journalism and communication of Nanjing Xiao zhuang University as an example, according to the actual classroom effects and the results of student evaluations, high-level external teachers from the industry are relatively more popular with students, and professionals in the industry are willing to attend classes in colleges and universities. Based on a kind of enthusiasm for teaching, their teaching content comes from the first-line experience of the media. They have practical blessings and are grounded to attract students, increase their interest in learning, and broaden their horizons. Some external teachers are also quite personal and personal, and they are often more popular Students welcome. In the auditing education evaluation, there are statistics and measurement of dualqualified talents. I personally think that this has played a very good guiding role. In the process of promoting construction by evaluation, it also makes the evaluated school leaders and managements the reserve and introduction of qualified talents has been further strengthened and understood. It is believed that it will have a good boost to the dual-qualified talent reserve of colleges and universities, and it will also play a good role in the introduction of dual-qualified talents in media colleges. (4)
Transform internship into cooperation to realize media product release
Based on the concept of the OBE model, the School of Media can cooperate with the internship unit during the internship, and finally realize that the results of the internship work of the students can be released in the form of media products. Many internships have no goals, but only to complete the internship credits. For internships, there are internships oriented to the goal of media product release, which will inevitably increase students’ interest and motivation for internships, thereby improving students’ professional practical ability. The School of Media can fully carry out its own media construction in the school. Newspapers, magazines and TV stations are all good carriers. Students are generally more interested in participating, but in order to further enable more students to contact the media on campus and carry out majors. Skills training can achieve this goal through elective course credit requirements. Taking the School of Journalism and Communication of Nanjing Xiaozhuang University as an example, students have to choose a course of one credit for newspapers, electronic magazines, and Internet TV stations in the college. The combination of theory and practice can indeed improve students’ professional practical ability.
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2.3 Design of the Practical Ability Training System for Media Talents Under OBE Mode The users of this system are divided into teachers, students, and administrators. Each type of user has its own corresponding authority and corresponding function. Generally speaking, it consists of homepage, daily summary, weekly report, project information, personal files, and resource sharing. It is composed of three modules, covering all aspects of online communication between students and teachers during their stay at school. (1)
Home page module
After the students log in to the system, the homepage shows two major panels of the memo, namely Today’s Affairs and Completed, plus the notification panel and calendar for teacher notification information, and hot information. (i)
(ii)
(iii)
(2)
In the memo module, the main process is that students click the to-do button to enter the schedule page, and arrange their schedule in advance. When the time is up, their tasks will be displayed in the Today’s Affairs on the home page of the system. Check the management, and the completed tasks of the day will be displayed in the completed. Teachers can view the student’s schedule for themselves in the drop-down menu of student management in the student plan. After entering the page, you need to select the student’s name to enter the corresponding student plan list. The time management carried out by the students has laid the foundation for the usual learning efficiency. The teacher can check the students’ plans and arrangements at any time, which invisibly urges the students to study efficiently. In the notification panel, the teacher publishes information on the notification page on the teacher side, and the notification content is displayed in the notification panel on the student side. Hotspot information. The title and connection of this module are provided by the teacher, which can be added or deleted. The permission of the published hot content can be selected, and it is divided into viewing within this group and viewing across the department. The student end uses the title content sent by the teacher to link to the relevant website. Provide a centralized platform for students to understand the frontier dynamics of the field. Daily summary module
The daily summary details page is divided into the display section of the new summary and the submitted summary. The new summary consists of two parts: the title input box and the content input area. The submitted summary can be viewed, edited and deleted. After entering the system, the teacher can view and process the files submitted by the students through the daily summary of the student management drop-down menu. All the daily summaries will not be displayed on the teacher page, but the content of the last three days will be displayed to reduce the clutter of the
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information on the teacher page. The name of the student who submits the content will be displayed directly after the file, and the name will be directly given if it is not submitted. If the list is not submitted, the teacher can clearly see the submission status of the students. (3)
Weekly report module
The weekly report is divided into the new weekly report and the submitted weekly report display page. In the new weekly report, one more attachment is added than the daily summary, which is composed of three parts: the title bar, the attachment upload button, and the content input area. Because students often forget to submit weekly reports in daily life, the system will send reminders to the corresponding students before submitting the weekly reports, and the weekly reports are submitted on Saturdays and Sundays, and submissions are not allowed at other times, which further enhances the efficiency of weekly reports. On the teacher page, only the submissions of the last three weeks will be displayed. If the teacher needs to find other weeks, you can query the conditions at the top of the page. (4)
Project information module
The teacher creates a new project, and the specific content of the added project includes the project name, project description, project personnel, person in charge, and start and end dates. After the project is established, the project information will appear on the student page, and it will clearly show whether the current status of the project is in progress or completed in all projects. Each project contains project dynamics, project tasks, project personnel, project basic information, and project files. In the project dynamics, students and teachers can leave messages and exchanges in this area respectively. Project tasks are assigned to students by the teacher or the person in charge. If a task arrives, the relevant student’s page will display the content of the task, and there is a button for submitting the file. After completing the task and submitting the file, the teacher will display this task corresponds to the file submitted by the person, and then the feedback will be completed or rejected. (5)
Personal file module
This module is divided into research directions and course information, used to manage your own personal files. After students click to enter the corresponding page, a page for creating a new folder will appear. You can click New as needed, and then you can import files into this folder. (6)
Resource sharing module
The previous project information and personal files are private, and this module is aimed at resource sharing in the same teacher group, and is also divided into project resources and course resources.
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2.4 Resource Modeling of the Practical Ability Training System for Media Talents The resource pool is a ledger for measuring the physical resources of each node. The establishment of a resource pool will help to fully control the real-time situation and allocation of resources. In this project, non-preemptive resource scheduling is used, and the description is as follows: RMPi = RMcpu , RMram , RMdisk , CNM, VN
(1)
RMi = RMcpu , RMmem , RMdisk , CNM, VN
(2)
RMALLi = RMcpu , RMmem , RMdisk , CNM, VN
(3)
3 Implementation of the Practical Ability Training System for Media Talents Under OBE Mode 3.1 Background Development Technology Under the OBE mode, the media talents practice ability training system uses PHP as the development language of the system. PHP is an open source, server-side scripting language. The grammar combines the characteristics of C language, Perl, and Java, and is widely used by developers. Its main function is to develop dynamic and interactive Web server applications. With the emergence of PHP, it can become a substitute for ASP or JSP. The PHP language can be integrated into HTML documents, and this code runs much more efficiently.
3.2 Database The data storage of this system uses MySQL database, which is an open source relational database management system, which uses a structured query language for management. Its small size, fast running speed, open source and other characteristics make MySQL database the object of choice for many developers. And MySQL is written in C and C++, which not only has good portability, but also supports connection operations in several languages, such as PHP, JAVA, C language, Python, etc., so that different languages can establish an effective database with the database.
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4 Implementation Test Analysis of Media Talents Practice Ability Training System Under OBE Mode 4.1 Stress Test The system has been subjected to 3000, 5000, and 7000 visit stress tests to verify the gradient performance of the system for different user visits. And select the core function point of the practice ability training system for media talents in the actual application process under the OBE mode: the frequency of use and the highest system performance requirement: the full-text retrieval of research information as a test sample for performance testing. As shown in Table 1 and Fig. 1, through the analysis of the stress test chart, when the access volume of the system increases or decreases, the concurrent response of the system will actually increase stepwise, but even under 7000 accesses per second, the system’s the response time is also good. The actual response time is no more than 3 s, which can fully meet the requirements. From the side, it can be verified that the system can fully meet the user’s access needs. Analyzing the number of processing per second of the system, the actual processing situation will not change significantly due to the increase of the CPU utilization of the system, and the performance is good. Table 1 Stress test statistics (S)
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Response time
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Fig. 1 Stress test statistics
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Table 2 Corresponding time for querying physical objects Elapsed 0:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 scenario time mm/ss Average response time
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Average response time 200 150 100 50 0 -50
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Elapsed scenario time mm/ss
Fig. 2 Corresponding time of system transaction processing
4.2 Platform Processing Performance For this system, the system processing transaction performance was tested, and the results are shown in Table 2. It can be seen from Fig. 2 that the average value of Transaction per second is 65.21, which shows that the number of transactions per second processed by the system can well simulate real information query use cases. The average transaction response time is 84.21 s, which is too long. But if the peak response time of 170 s is not considered, the system transaction processing time will eventually stabilize between 30 s, which meets the performance requirements.
5 Conclusions Guided by the OBE model, through the revision of talent training program, incubation of graduation design in the form of workshop, introduction of industry teachers for project-based teaching and change of internship cooperation into realization of media products, through organizing outbound research, introducing experts for training, conducting intra-institutional seminars and guiding the creation of graduation design, the college has built consensus within the college, put the practical link in a strategic level, formed the concept and atmosphere of theoretical teaching and we will strive to build a reasonable knowledge structure and ability structure for students, to build a
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scientific and systematic experimental teaching system that integrates theoretical and practical teaching, in-class and out-of-class teaching, multi-level, multi-module and interconnected, and to improve the professional practice ability of media students.
References 1. Li Q (2021) Analysis and practice on the training of key ability of students majoring in electronic information in higher vocational education. Proc Comput Sci 183(4):791–793 2. Kohmura Y, Nakata M, Kubota A et al (2019) Effects of batting practice and visual training focused on pitch type and speed on batting ability and visual function. J Hum Kinet 70(1):5–13 3. Rosen P (2019) The emperors soul. Children’s Book Media Rev 40(3):118–118 4. Rebouillat S, Pla F (2019) A review: on smart materials based on some polysaccharides; within the contextual bigger data, insiders, “improvisation” and said artificial intelligence trends. J Biomater Nanobiotechnol 10(2):41–77 5. Kapuci´nski G, Zhang N, Zeng L et al (2021) Effects of crisis response tone and spokesperson’s gender on employer attractiveness. Int J Hospital Manage 2021, 94(3):102884. 6. Robin P (2020) Creative media workers as representatives to actualize the tagline of “independent Dan trusted.” Diakom Jurnal Media dan Komunikasi 3(2):138–147 7. Heller R (2020) The editor’s note: the economics of talent development. Phi Delta Kappan 102(4):4–4 8. Dauletbekova Z, Yelubayeva P (2020) Talent development and excellence experimental system of learning the culture of dialogue speech through 4C modeling skills in education. Talent Dev Excell 12(2):3051–3060 9. Hartati S, Safitri D, Marini A et al (2020) Talent development and excellence bullying behavior in early childhood: study at early childhood education institution in East Jakarta in Indonesia. Talent Development and Excellence 12(2020/1):55–63 10. Fang YC, Chen JY, Zhang XD et al (2020) The impact of inclusive talent development model on turnover intention of new generation employees: the mediation of work passion. Int J Environ Res Public Health 17(17):6054 11. Sami Y, Eid A, Noor M et al (2020) Talent development and excellence internal public relations practice and job satisfaction of academic staff in higher academic institution. Talent Dev Excell 12(3s):797–805
A Design for Software Management Architecture and Software Management Information System in Time of Open Source Xiao Wang, Li Wang, Yindong Li, Rong Wu, and Wenbo Sun
Abstract Open source software can be modified, invoked, distributed, etc. within the scope of license authorization. Open source licenses are authorized in various ways, and there are risks of improper citation or misquote, authorized contagion or forced open source, patent defense or patent decoy, export control or weak autonomous controllability, code quality and security issues, etc. To control the risks and challenges, we design software management architecture that can meet both open source and closed source software, which is based on Multi-dimensional management architecture including following dimensions: Life Cycle, Organizational, construction, Technical control, Industry and Ecological. We also develop software management information system, and use technology to enforce management. The structure of software management information system including following layers, the business layer, the application layer, the data layer and the technical layer. There is a need in meeting operational compliance requirements. In order to meet regulation need, there are following parts of risk management which are audited: risk control planning, risk identification, risk analysis, risk Response, risk Monitoring. Keywords Open source software · Risk control · Software management architecture in multi-dimensional
1 Introduction Open source software [1] has significant advantages in terms of cost compared to traditional commercial software. The development of open source software is less difficult than commercial software, effectively enhancing the flexibility of the system [2]. From the perspective of risk management [3], there are some items which need to attention: intellectual property issues, technology export control issues, quality X. Wang (B) · L. Wang · Y. Li · R. Wu · W. Sun Information and Telecommunication Branch, Inner Mongolia Power (Group) CO., LTD, Hohhot 010010, Inner Mongolia, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_79
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Fig. 1 The outline design for closed-loop system-based control system architecture
and security issues [4] exist to varying degrees. Especially in Improper citation or misquote, Licensing contagion or forced open source, Patent defense or patent decoys, Export control or weak autonomous control, Outstanding code quality and safety issues. The Outline Design for closed-loop system-based control system architecture [5] conclude input factor which is work task, Interference factor which is Industrial Ecology, transfer factor which is System Design, human resource management (HRM) and Technology, Feedback factor which is Evaluation, output factor is Work Results (Fig. 1). To control risks, we design software management architecture, and software management information system, finish Compliance Audit project. The following text is all about this.
2 Design for Software Management Architecture 2.1 Multi-dimensional Management Architecture According to the existing information system stock and increment, combined with the coexistence, mixed use of open source and closed source, according to the principle of “open and closed integration”, the software management system is upgraded and partially reconstructed, and a unified management system is built to adapt to the parallel application of closed source software and open source software. The management model is designed for multiple dimensions [6], the specific dimensions are: life cycle dimension, organization construction dimension, technology management dimension, industry and ecological dimension.
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2.2 Life Cycle Dimension Information system life cycle mainly includes: planning, feasibility study, design and selection, development and implementation, operation and maintenance, upgrade and transformation, and exit. The above phases include pre-research, evaluation, selection, import, maintenance and upgrade. When introducing open source software, the main control points are: architectural conformity, technical feasibility, north– south/east–west compatibility in network and security, advancedness, open source software life cycle, etc.
2.3 Organizational Construction Dimension Open source software risks are mainly in intellectual property, network security, and supply chain. When conducting control, the legal group (patents, soft documents, trademarks, etc.), network security group (vulnerabilities, reinforcement, code audit), technical group (development and implementation, operation and maintenance), and economic group (cost assessment, supply chain assessment) should be established correspondingly. At the same time, the inter-group work coordination mechanism and information sharing mechanism should be established.
2.4 Technical Control Dimensions Mainly in technical specifications and standards, mainly including: media (code) sources, technical application level and autonomous control requirements, network security measurement and security reinforcement, technical library, technical review guidelines, PoC guidelines, open source (custom) branch control, technical condition evaluation, technical process evaluation, technical result evaluation, etc.
2.5 Industry and Ecological Dimension Building an open source software application ecosystem with peer companies. Joining open source communities as needed to ensure reliable sources of software or code. Join intellectual property cross-licensing alliances to reduce the probability of intellectual property disputes and the risks arising from them. Join the cyber security intelligence network to share all kinds of software intelligence and shorten the window of high-risk operation. Building an open source software supply and demand ecosystem with upstream and downstream companies. Join industry alliances as
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needed to ensure extensive engineering validation of software or code. Join open source ecological organizations to share open source management experience.
3 Design for Software Management Information System 3.1 Overall for Software Management Information System The structure of software management information system including following layers, the business layer, the application layer, the data layer and the technical layer [7]. The business layer is belong to IT service system, which is out of the scope of this article. The application layer including four Dimension management, Life Cycle, Organizational construction, Technical control, Industry and Ecological. The data layer including database and data source which is combined with cloud source [8] and network is called IT technical platform.
3.2 Application Design for Life Cycle Dimension Management According to management specifications and demand research instructions, we develop input items and tools such as job responsibilities, systems and processes, technical specifications, resource usage, etc., and output evaluation results to form management closed-loop and other functions.
3.3 Application Design for Organizational Construction Dimension Management According to the open source software risk management team job description and requirements research report, the developer qualifications, selection procedures, scope of work, job evaluation, etc., output evaluation results and other functions.
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3.4 Application Design for Technical Control Dimensions Management According to the open source software technical management work standards and demand research report, design the technical specification, technical process, technical evaluation and other processes, form the workflow and output the evaluation results. Docking control tools, mainly including: unified media (code) management tools, unified media (code) automated audit tools, unified media (code) leakage tools, CVE/CPE/NVD intelligence transfer and comparison tools, media (code) official compatibility list library, PoC test environment, architectural control and evaluation tools.
3.5 Application Design for Industry and Ecological Dimension Management Develop data interface with industry alliance members and share data. This includes software resources such as source code, programs, documents, configuration items, software data such as defect situation, repair situation, emergency control situation, operation index, version and patch information, life cycle plan, and related knowledge base.
3.6 Design for IT Technical Platform The interaction mode between users and system is browser-system, the portal is established with four main functions: Account Authorization Audit Authentication. Firewall and IPS/IDS in border between application server and database server, the data interaction between internet and intranet must need cross-net action through a net gap device which is made by two firewalls with different structure. The core of technical platform is the detect engine, which including following items: (1) composition analysis, (2) license analysis, (3) security analysis, (4) ecological monitoring, (5) basic information monitoring, etc. The composition analysis, including identification, component version, source, etc., to help administrators manage the information of the source in enterprise’s software warehouse. License analysis which is used in searching license detail, including name, classification statistics, contagious grading statistics, to help administrators manage the information in order to avoid the misuse of license. Security analysis is detect the vulnerabilities of source components, including the statics of vulnerabilities, names, etc., to help administrators manage the security risks of software.
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Table 1 Comparison table of risks before and after project implementation (high risk) Time
Risk control planning
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Risk monitoring
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Medium risk
High risk
High risk
High risk
Medium risk
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Low risk
Low risk
Medium risk
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Low risk
Ecological monitoring, which are supported by artificial intelligence technologies, big data, etc. especially in field of containers, microservices, RDBMS, key-value DB, kits of development, etc. Basic information including development language, software license, the ownership of intellectual property and rights, etc. The change of basic information which is made by owner should be sent to administrator in order to help him to make suitable decision.
4 Compliance Audit 4.1 The Overview of Compliance Audit After project implementation is completed, IT auditors are invited to conduct risk management audits in the areas of risk management planning, identification, risk analysis, risk response, and risk monitoring. The comparison table of risks before and after project implementation conclude Table 1. According the results form the table, it can be included that the risk level in risk control planning, risk identification, risk analysis, risk response and risk monitoring is degraded after implementation.
4.2 Risk Control Planning Based on regulations, management plans, management documents, combined with environmental and organizational factors, mapping and combing risks, organizing developers, legal staff, etc. to prepare open source software risk management content in the risk management plan. Pay attention to open source software risks, prevent and effectively resolve them. In the risk management work, especially in the risk management plan, setting security requirements, security design, security coding, security testing, security release, and security response support the full coverage of risk management.
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4.3 Risk Identification Checking management plans and documents, internal/external contracts or agreements, set meeting agendas in context, organize expert groups to map risk sources such as use of open source code, open source software, and open source licensing policies, update risk registers and revise risk reports. Also completing document updating. For example, in the architecture design phase. Organize developers and legal staff to review the system architecture design plan and open source agreements, and identify the risks of the open source software products or referenced codes involved.
4.4 Risk Analysis Based on the management plan, documents, combined with the actual environment, through data collection and analysis, expert discussion, etc., the risk is classified and graded, the documents are revised and the risk report is prepared. Adopt simulation, sensitivity analysis, decision tree analysis, impact diagram and other methods to revise the risk report. For example, during the code development stage, establish an open source software source library, complete open source citation tests on the developed code and open source license impact analysis reports, compile relevant chapters in the test reports and continuously track and supervise the rectification. Relying on our own or cooperative security labs in net security field, we complete penetration testing and code security analysis to continuously and dynamically verify and evaluate the security of open source software.
4.5 Risk Response We compile plans considering threat response, opportunity application, and emergency management, and develop multiple options and corresponding cost options for decision makers. At the same time, the impact on schedule, cost, resource scheduling, and procurement due to risk management and plan implementation is forewarned, and the baseline of scope, schedule, and cost is pre-adjusted. Preparing risk reports. For example, we analyze the GLP-type infectious open source license terms, ASPLtype patent defensive terms, etc., and implement avoidance through technical or legal means.
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4.6 Risk Monitoring Performing techno-economic analysis of risk response process and completing related behavioral audits. Simultaneous completion of document revision. For example, improving the open source software intelligence collection system (the contained CVE/CPE system and NVD system), collecting and informing horizontally about open source protocol changes, 0Day vulnerabilities, etc., and conduct emergency response.
5 Conclusion In the enterprise application environment, open source software relative to closed source software, the main advantages are: (1) improving software development flexibility, suitable for dev-ops environment. (2) Reducing information systems, especially software development costs, conducive to the promotion of cloudbased services. (3) Open source ecological access threshold is low, conducive to market regulation. (4) Products transparency, can be transformed into a large space, conducive to independent and controllable. At the same time, code citation, licensing, patent regulation and other commercial issues, export controls and other international relations issues, code quality and security and other information security technology issues are still widely present and profoundly affect the use and development of open source software. According to the rules of the overall construction of information systems, the first should be in the preparation of information technology planning, enterprise-level architecture design and other aspects of top-level design to fully consider the use of open source software or code, control. Secondly, the project construction and implementation phase should be controlled in accordance with PMBOK [9] related methods. Again, closed-loop management should be carried out in accordance with ITIL [10] rules in the operation and maintenance phase to achieve process traceability. Finally, the ISMS [11] system knowledge accumulation and enrichment work should be done. In addition, industries related to national security and people’s livelihood and enterprises whose overseas business is regulated should do a good job of COSO [12]/COBIT [13] system self-audit and external audit.
References 1. Open source: What is open source? https://opensource.com/resources/what-open-source 2. Zhou X (2009) Research on the application of enterprise service bus (ESB) in SOA. Dalian Maritime University, In Chinese 3. PMI: Risk analysis and management: a vital key to effective project management. https://www. pmi.org/learning/library/risk-analysis-project-management-7070
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4. NIST software quality group, source code security analyzers. https://www.nist.gov/itl/ssd/sof tware-quality-group/source-code-security-analyzers 5. Closed-loop Anomaly Detection and Resolution Automation project: Choosing a closed loop pattern and logical architecture https://inform.tmforum.org/ai-data-and-insights/2021/03/cho osing-a-closed-loop-pattern-and-logical-architecture/ 6. Grossman CL (2007) Multidimensional management. PM Netw 21(4):20–21 7. OpenGroup, ArchiMate: relationships between core layers. https://pubs.opengroup.org/archit ecture/archimate3-doc/chap12.html 8. Microsoft: What is cloud computing? A beginner’s guide. https://azure.microsoft.com/en-us/ overview/what-is-cloud-computing 9. PMI: PMBOK® guide and standards. https://www.pmi.org/pmbok-guide-standards 10. The Knowledge Academy, ATO of AXELOS Limited. what-is-itil https://www.itil.org.uk/ what-is-itil 11. ISO/IEC 27001 information security management. https://www.iso.org/isoiec-27001-inform ation-security.html 12. COSO: About us. https://www.coso.org/Pages/aboutus.aspx 13. ISACA: A right-sized governance solution … tailor-fit for your enterprise. https://www.isaca. org/resources/cobit
Business Model Innovation Mechanism and Value Creation Effect of Data-Driven M&A—Case Study Based on Alibaba Xingrui Yang and Erna Qi
Abstract Objectives: In order to systematically reveal the black box between M&As, business model innovation and value creation in the context of big data. Methods: To analyze the vertical evolution process and mechanism of business model innovation of M&As in the context of big data, nested case studies are chosen. Results: For exploratory M&As, Alibaba adopts novelty-oriented business model innovation mechanisms; For exploitative M&As, Alibaba adopts efficiency-oriented business model innovation mechanisms. For the former, Alibaba expands new business in content; in terms of structure, it improves the value chain, innovates key processes, expands the value network, meets customer value propositions, optimizes profit models and customer interfaces, and has a higher degree of integration in governance. For the latter, Alibaba strengthens existing businesses in content; in terms of structure, it optimizes the customer interface, meets customer value propositions, and expands the value network and has a lower degree of integration in governance. Conclusions: Both exploratory and exploitative M&As could achieve value creation through specific business model innovation mechanisms. Exploratory M&As are suitable for novelty-oriented business model innovation, exploitative M&As should carry out efficiency-oriented business model innovation. Keywords Big data · M&A · Business model innovation · Value creation
X. Yang (B) School of Business, Huanggang Normal University, Huanggang 438000, Hubei, China e-mail: [email protected] School of Management, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China E. Qi Shi Liangcai School of Journalism and Communication, Zhejiang Sci-Tech University, Hangzhou 310018, Zhejiang, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_80
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1 Introduction Human society is gradually entering the era of digital economy from the age of industrial economy. At present, big data has penetrated into every business in every industry, and has gradually become an important production factor for enterprises; enterprises can use network information technology to mine data value, carry out innovation insights, product development, and service provision, and form a datacentric core competitiveness. At the same time, the subversive transformation of data processing costs to transaction costs has changed the boundaries and organizational forms of enterprises, leading to horizontal, vertical, and cross-border development of enterprises, causing reshaping from organizational boundaries to value boundaries. As a new economic resource, big data can promote business model innovation and change the relationship between producers and consumers, because data insight and analysis can capture consumer behavior and better understand customer value propositions [1, 2]. In order to maintain or establish a competitive advantage, many companies continue to initiate mergers and acquisitions (M&As). For example, Alibaba spent 115.075 billion yuan to M&A 31 companies in 2017. And it has become China’s largest data provider and service provider. In the field of M&A research, scholars have not reached a consensus on whether M&A can create value. However, for Alibaba, M&As are accompanied by rapid corporate growth and value creation. How does Alibaba promote its exploration and exploitation M&As to achieve value creation through business model innovation in the background of big data? In order to systematically reveal the black box between corporate M&As, business model innovation and value creation in the context of big data, this article intends to select China’s largest and most representative big data platform company Alibaba as the research object, to explore the business model innovation mechanisms and value creation effects of six data-driven M&As, and systematically deconstruct the key dimensions, construction logic and operating mechanism of data-driven M&A business model innovation; and try to provide reference for other companies implementing data-driven M&As to carry out business model innovation and improve value creation effects [3–5].
2 Research Methods This article aims to analyze the vertical evolution process and mechanism of business model innovation of corporate M&As in the context of big data, so nested case studies is chosen. Following the extreme and enlightening principles of the case study, this article chooses Alibaba as the research sample. In order to avoid the deviation caused by impression management and retrospective interpretation, this paper adopts a triangular verification strategy to collect data and information. The data sources mainly include: (1) Internet data, such as company websites, annual
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reports, leadership interviews, news reports Etc.; (2) Literature research materials. As a rapidly growing company known for its business model innovation, Alibaba has attracted many scholars to conduct research on it. During the research process, we collected more than 100 scientific research papers and monographs. (3) First-hand information of the company. Through field surveys, participatory observations and semi-structured interviews with Alibaba staff and platform users, we have obtained first-hand information on Alibaba’s M&As and business model innovation [6–9].
3 Case Discussion and Analysis 3.1 Business Model Innovation of Exploratory M&As (1)
(2)
(3)
Typical cases of exploratory M&As Case 1: Alibaba M&A AutoNavi Map. In May 2013, Alibaba acquired 28% of AutoNavi Map for US$294 million and became AutoNavi’s largest shareholder; in 2014, Alibaba spent US$1.4 billion to achieve a wholly-owned acquisition of AutoNavi Maps. Case 2: In March 2013, Alibaba spent US$506 million on a strategic investment in UC browser; in December 2013, Alibaba further increased its holdings of UC and paid US$180 million in cash. So far, Alibaba holds 66% of UC Browser; in June 2014, Alibaba completed its wholly-owned acquisition of UC Browser. Case 3: In October 2014, Alibaba invested US$1.088 billion to acquire a 16.5% stake in Youku Tudou; In October 2015, Alibaba acquired all the outstanding shares of Youku Tudou for US$26.60 per share. The business model innovation orientation of exploratory M&A Alibaba’s exploratory M&A are the implementation of its “cloud + terminal” strategy. With the acquisition of AutoNavi Maps, Alibaba can accumulate more geographic location data and win a high-gold bargaining chip for O2O competition; after the acquisition of UC browser, Alibaba has obtained the underlying platform and entrance of UC mobile terminal; after the acquisition of Youku Tudou, Alibaba has obtained Youku Tudou’s PC and mobile video playback channels; the acquisition of “terminal” resources has enabled Alibaba to fill the gap in the video terminal, and provide an opportunity for it to build new activities and adopt new methods of connecting activities and governance activities. Alibaba needs to allocate resources through novelty-oriented business models so as to seize business opportunities and build competitive advantages [10–12]. The business model innovation mechanism of exploratory M&A Business model innovation is the design of using business opportunities to create value, including three aspects: content, structure and governance; content refers to the choice of activities, that is, what activities to do; structure refers
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to the connection of activities, that is, how to connect activities; governance mainly refers to who is engaged in the activity, that is, who will do it. For exploratory M&As, Alibaba mainly adopts novel-oriented business model innovation. For example, after the acquisition of AutoNavi Maps, UC Browser and Youku Tudou, Alibaba’s mobile Taobao business has developed rapidly. It uses innovative content formats, effective the mobile interface and intelligent personalized recommendations optimize the shopping experience to drive user engagement. At present, mobile Taobao has become a powerful content ecosystem, and Tmall’s functions are not limited to distribution platforms. It can empower brand merchants to gain insights into existing customers and untouched new customers through marketing tools and consumption big data. Alibaba’s novelty-oriented business model was designed to implement the “cloud + terminal” strategy, connecting the newly acquired AutoNavi map, UC browser, and Youku Tudou with existing e-commerce platforms, Alipay, life services and entertainment projects. Then the resources of each platform can be shared, the structure of the value chain is optimized, the efficiency of platform services can be increased, and the operating cost of the platform has been reduced. After the innovative design of the business model, due to the optimization of the platform service interface and the increase in the number of users, Alibaba’s core position in the entire Internet value network has been strengthened. In terms of governance, Alibaba had a higher degree of integration for the purpose of these three acquisitions by Alibaba is to serve its own “cloud + terminal” strategy.
3.2 Business Model Innovation of Exploitative M&A (1)
(2)
Typical cases of exploitative M&A Case 4: In May 2014, Alibaba spent US$249 million to acquire a 10.23% stake in Singapore Post; in July 2015, Alibaba spent US$138 million again to increase its stake in Singapore Post to 14.51%. Case 5: In August 2015, Alibaba spent US$4.5 billion to acquire 19.99% of Suning Yunshang (formerly Suning Appliance) and became Suning’s second largest shareholder. Case 6: Alibaba acquired a 51% stake in Lazada in April 2016 and increased investment in June 2017. So far, Alibaba’s shareholding in Lazada has reached 83%. The business model innovation orientation of exploitative M&As The purpose of Alibaba’s exploitative M&As is to expand the operating boundaries and improve the exploitation efficiency of e-commerce platforms. For example, the acquisition of Singapore Post is to cooperate with e-commerce to provide complete e-logistics solutions; the acquisition of Suning is to improve the offline layout of e-commerce and improve the operational efficiency of the supply chain, and strengthen the shaping of Alibaba’s offline image and its influence in fourth-and fifth-tier cities; the acquisition of Lazada
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is to expand the boundaries of e-commerce operations and develop potential cross-border e-commerce opportunities. Alibaba expects to achieve efficiency improvement and cost reduction through the reorganization of externally obtained resources. Therefore, it needs to allocate resources through efficiency-oriented business model innovation. The business model innovation mechanism of exploitative M&As For exploitative M&A, Alibaba mainly adopts efficiency-oriented business model innovation. For example, after the acquisition of Suning Cloud Business, Alibaba digitized physical stores, inventory, mobile payment and supply chain systems, which improved the user experience of goods and services, and improved the operational efficiency of merchants and supply chains. Alibaba’s efficiency-oriented business model innovation design follows the logic of online, offline and overseas linkage, and is equipped with an efficient and fast logistics system; after the redesign of the business model, the e-commerce platform service interface is more convenient and the operational efficiency of value chain is improved, the value proposition of Alibaba customers is further satisfied, the boundary of Alibaba’s value network continues to expand, and the value network position of e-commerce platforms is further consolidated. In terms of governance, Alibaba had a lower degree of integration, even if Alibaba’s holding of Lazada has reached 83%. Figure 1 shows the business model innovation mechanism and value creation effect of Alibaba’s data-driven M&As.
Fig. 1 Alibaba’s data-driven M&As business model innovation mechanism and value creation effect
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Table 1 Overview of Alibaba’s main business performance 2015
2018
Monthly active users
289 million active users 617 million active users
GMV of China’s retail commerce platform
2443.7 billion yuan
Digital media and entertainment business
In the fourth quarter of fiscal 2017, an increase of 234% over the same period last year
International e-commerce business
In the fourth quarter of fiscal 2017, an increase of 312% over the same period last year
GMV of Suning Tmall flagship store
In the fourth quarter of fiscal year 2017, an increase of 2 times over the same period last year
4820 billion yuan
Innovative business based on YunOS system Increased 88% in the fourth quarter of fiscal year and AutoNavi map 2017 over the same period last year
3.3 The Value Creation Effect of Data-Driven M&As Kitching [12] pointed out that the key to measuring the success of an M&A business is to determine a comprehensive performance measurement method, and this method must link the management’s level of satisfaction with the current operating performance with the initial acquisition motivation, that is, the basis for judging whether the merger is successful or not is the degree of realization of the acquisition motivation. On the whole, Alibaba’s “cloud + terminal” strategy has been implemented and successfully achieved the leap from PC to mobile. In FY2015, Alibaba Mobile’s monthly active users increased to 289 million, and Taobao for mobile terminal continued to top the list of China’s most popular mobile e-commerce applications. In response, Alibaba CEO Lu Zhaoxi said: “This year we consolidated leading advantages in the mobile field … the business has maintained strong growth, and the data reflects the advantages of our ecosystem and a solid business foundation”. In FY2018, Alibaba Mobile’s monthly active users increased to 617 million, successfully completing the mobile transformation (Table 1). In terms of performance, Alibaba’s M&As have been successful: in fiscal 2015, Alibaba’s China retail commerce platform’s merchandise transaction volume (GMV) reached 2443.7 billion yuan. In fiscal 2018, Alibaba’s China retail platform GMV was 4820 billion yuan. Regarding this achievement, Alibaba CEO Zhang Yong said: “This is due to the strong growth of core e-commerce business, and investment in projects with growth potential in the past few years”. For the M&As in those cases, Alibaba also achieved initial results in the 2017 fiscal year: In the fourth quarter of fiscal year 2017, the quarterly revenue of the international retail e-commerce business reached 2.429 billion yuan, a year-on-year increase of 312%. The total annual active buyers of AliExpress and Lazada reached 83 million; the quarterly revenue of the digital media and entertainment business was 3.927 billion yuan, an increase of 234% over the same period last year; quarterly revenue of innovative projects and other businesses was 919 million yuan, an increase of 88% year-on-year. This part of the
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revenue was mainly driven by revenue from the YunOS system and AutoNavi maps; The GMV of Suning Tmall flagship store in the quarter is three times that of the same period last year.
4 Conclusions This article has conducted research on 6 M&As of Alibaba in recent years and found that: (1) For Alibaba, whether it is motivated by exploration or exploitation, its data-driven M&As have achieved value creation. For M&As with big data platforms, M&As motivated by exploring new and heterogeneous big data resources can expand the boundaries of the organization, expand the existing big data and knowledge reserves, and lay the foundation for the implementation of differentiated strategies, and improve customer stickiness through innovations in business models such as optimized profit models and convenient service interfaces. M&As motivated by the exploit of existing big data resources can make full use of their own big data analysis and processing capabilities, and improve the service efficiency of the value chain through the optimization and integration of resources, so as to meet the value proposition of customers better and further improve the company position in the value network. (2) M&As motivated by exploration should carry out novelty-oriented business model innovation. After the merger, enterprises should expand new businesses in terms of content; In terms of structure, enterprises should improve the value chain, innovate key processes, expand the value network, satisfy customer value propositions, optimize profit models and customer interfaces; and in governance, enterprises should have a higher degree of integration. (3) M&As motivated by exploitation should carry out efficiency-oriented business model innovation. After M&As, enterprises should strengthen existing businesses in terms of content. In terms of structure, enterprises should optimize the customer interface, satisfy customer value propositions, expand the value network; enterprises should have a lower level of integration. The research conclusions of this article extend the existing research on value creation of M&As. It further reveals the conditions for big data-driven M&As to achieve better value creation effects: appropriate business model innovation. At the same time, the conclusions of this article also deepen our understanding of big data resources. Big data is not only a new and important resource, but also a special resource. Big data platform resources enable companies to have strong innovation insights and dynamic capabilities; because companies can accurately perceive M&As opportunities through data analysis, quickly acquire external resources, and then reconfigure and deploy the acquired resources to update the original resource and capacity foundation of the enterprise. Acknowledgements Ministry of Education Humanities and Social Sciences Project (18YJC630226); Hubei Province Soft Science Project (2019ADC075); Hubei Provincial
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Department of Education Humanities and Social Sciences Project (19D096); Dabie Mountain Tourism Economy and Cultural Base Project (201926503).
References 1. Ruosen Y, Xiangyang Q (2018) Strategic analysis of Chinese operators’ digital transformation in the era of digital economy. China Soft Sci 328(04):177–187 2. Provost F, Fawcett T (2013) Data science for business: what you need to know about data mining and data-analytic thinking. O’reilly Media, Cambridge 3. Choi S, Mcnamara G (2018) Repeating a familiar pattern in a new way: the effect of exploitation and exploration on knowledge leverage behaviors in technology acquisitions. Strateg Manage J 39(2):356–378 4. Zott C, Amit R (2007) Business model design and the performance of entrepreneurial firms. Organ Sci 18(2):181–199 5. Mitchell D, Coles C (2003) The ultimate competitive advantage of continuing business model innovation. J Bus Strateg 24(5):15–21 6. Zott C, Amit R (2008) The fit between product market strategy and business model: implications for firm performance. Strateg Manage J 29(1):1–26 7. Christensen C, Alton C, Waldeck A (2011) The new M&A playbook. Harv Bus Rev 89(3):48–57 8. Eisenhardt M, Graebner E (2007) Theory building from cases: opportunities and challenges. Acad Manage J 50(1):25–32 9. Yin K (2003) Case study research: design and methods. Sage Publication, Thousands Oaks, pp 1–116 10. March G (1991) Exploration and exploitation in organizational learning. Organ Sci 2(1):71–87 11. Amit R, Zott C (2010) Business model innovation: Creating value in times of change. Social Sci Electron Publishing 23(23):108–121 12. Kitching J (2001) The theory and practice of parsimony analysis. Oxford University Press, Oxford, pp 1–228
Design and Implementation of Enterprise Accounting Supervision Platform Based on Big Data Jiuyun Ma
Abstract At present, our country’s economic and social development has undergone an unprecedented high-speed development after reform and opening up. However, in this situation, some undesirable phenomena have also appeared. For example, some companies conceal information when they conduct statistics and report on company accounts. In order to solve these phenomena, relevant standards of accounting supervision have been gradually established. This article mainly studies the design and implementation of the enterprise accounting supervision platform based on big data. This article introduces the current research status of domestic accounting supervision, and also briefly expounds the basic concepts and characteristics of the accounting supervision platform. This article analyzes the requirements of the enterprise accounting supervision platform, mainly introducing the feasibility analysis and functional requirements of the system. This article has completed the test of the system. Through the inspection of the system environment and key function modules, it is judged whether the system design meets the requirements of the expected system functions. Through test experiments on the platform, it is found that the pass rate of the test cases of each module is above 97.5%, and when the number of concurrent users is within 1000, the response time is below 0.35, it shows that the performance of the accounting supervision platform designed and implemented in this paper can meet the work requirements of corporate accounting supervision, and most of its module functions can reach the expected value. Keywords Internal control · Accounting supervision · Platform design and implementation · Big data
1 Introduction At present, the phenomenon of untrue accounting information in domestic enterprises is very serious. Quite a number of companies tend to have some fraudulent accounts J. Ma (B) Aba Normal College, Aba, Sichuan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_81
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when they issue accounts and accounting information, especially in places where the economy develops relatively fast, the distortion of accounting information is particularly serious [1, 2]. The establishment of the accounting supervision system is still a very long process [3]. Based on the information-based enterprise business process processing method in the era of big data, this paper designs and implements a big data-based enterprise accounting supervision platform in order to solve the phenomenon of untrue internal accounting information of the enterprise. Many scholars have conducted research and analysis on the accounting supervision of enterprises. For example, Jiang Yunxia believes that the establishment of internal control and supervision mechanisms for corporate accounting is of great significance to the long-term development of enterprises, and is the only way for enterprises to obtain higher economic benefits [4]. She also believes that the internal control and supervision mechanism of corporate accounting can improve the accuracy and effectiveness of corporate financial statement data, provide an effective data basis for corporate leaders to make decisions, provide supervision of corporate economic activities, and promote corporate sustainable development. Ju Yeon Lee, Joo Seong Yoon and Bo-Hyun Kim proposed the architecture and system modules of a big data analysis platform for smart factories of small and medium-sized enterprises, as well as a data analysis library that provides unified information [5]. Based on the analysis of the status quo of the development of domestic accounting supervision, this paper conducts a preliminary study on the design of a financial management information system suitable for all types of enterprises, and designs and implements an enterprise accounting supervision platform based on big data, which is important for the internal supervision of enterprises. The accounting supervision platform designed in this paper contains many functions, including data management module, report application module, account management module, short message notification module, personnel quality module and system management module. This paper conducts performance testing and functional testing on the accounting supervision platform, and uses the testing technology in software engineering to do a more scientific and reasonable test on the platform to ensure that the system can run stably and efficiently, and to verify whether the development achieves the expected goals.
2 Research on Design and Implementation of Enterprise Accounting Supervision Platform Based on Big Data 2.1 Accounting Supervision System 1.
Basic theory of accounting supervision (1)
Concept
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With the development of the economy and the development of various economic activities, accounting supervision has gradually been given a broad and narrow meaning [6]. In a narrow sense, accounting supervision refers to the supervision and control of various accounting activities carried out by accountants to achieve the expected goals stipulated by national policies and regulations [7, 8]. This is actually part of the company’s internal oversight. Broadly speaking, accounting supervision is multi-level, multi-level and multi-faceted. It not only includes cooperating external agencies or company accounting personnel and the public’s supervision of the economic activities of the enterprise, but also includes the supervision of the company’s accounting personnel and external agencies by the person in charge of the enterprise [9, 10]. Constitute Currently, accounting supervision mainly includes three aspects: internal supervision, social supervision and government supervision [11]. The platform studied in this paper is mainly used to realize the internal supervision of enterprises. The internal supervision of an enterprise can also be regarded as one of the methods of internal control. The main functions are: company accounting personnel, cooperative accounting institutions or audit institutions shall supervise and manage all relevant personnel, processes and data of the company’s economic activities in accordance with relevant national policies and regulations.
2.2 Research State in China At present, the accounting supervision level and status quo of most companies in China are not optimistic. Most companies excessively pursue the company’s production, efficiency and market share, while ignoring the importance of accounting supervision [12]. In addition, due to the limitation of personnel quality and enterprise scale, its accounting supervision system is not sound enough, and the problem of insufficient accounting supervision is also prone to appear. At present, most of the accounting supervision platforms used by many enterprises in our country are accounting-centric general accounting software, rather than professional accounting supervision platforms suitable for our company. With the rapid development of my country’s economy and the popularization and scale of enterprise internal information management, the accounting supervision needs of most enterprises have reached a new level, which also brings huge business opportunities to the current domestic enterprise accounting supervision platform.
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2.3 Overview of Accounting Supervision Platform 1.
2.
Basic concepts The main function of the accounting supervision platform is to supervise the economic activities carried out by the enterprise and the information resources of the parameters in the process of management activities. Through the platform, it is possible to clearly understand whether there are errors or violations in the economic activities carried out by the enterprise. Basic features Accounting supervision activities run through all departments of the enterprise and are an indispensable activity in the enterprise. The accounting supervision platform should have the characteristics of standardization, comprehensiveness and rigor.
2.4 Demand Analysis 1.
2.
Feasibility analysis In order to develop and implement a monitoring platform, the design ideas, methods and plans of the operating platform must be considered. In addition, consideration should also be given to whether the platform can be realized by using existing technologies, whether the economic benefits of the platform exceed its development costs, and whether the operating plan of the design platform is feasible. Only through feasibility analysis of the platform to be developed can a fully functional supervisory platform be built. Through analysis and research, it is found that the accounting supervision platform designed in this paper is technically, economically, operationally and legally feasible. Functional requirements analysis Specifically, the functions of the accounting supervision platform should include a data management module, a report application module, an account management module, a short message notification module, a personnel quality module, and a system management module.
The data management module is mainly for scientific and effective management and processing of a series of data related to the flow of funds within the enterprise. The report application module mainly includes functions such as generation of new reports, query of existing reports, analysis of existing reports, and report warnings. The main function of the subject management module is to manage the information of the supervised subjects, and at the same time it also has the function of importing and exporting subject data. The SMS notification module mainly realizes the real-time transmission of information, and informs the supervisors of information updates and changes in a timely and convenient manner. The personnel quality module is mainly for the evaluation and training of accounting-related personnel. The system
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management module mainly includes functions such as system user management, system log management, system data backup and recovery.
3 Experiments on the Design and Implementation of Enterprise Accounting Supervision Platform Based on Big Data 3.1 System Test Experiment 1.
Test purpose and environment (1)
(2)
2.
Testing purposes System testing can check whether the system has reached the expected function, discover unknown problems, to ensure software quality, and provide a convenient reference basis for system upgrades and maintenance. Before deploying the system, it is necessary to find all defects and optimize them to reduce system maintenance costs and improve product quality. Test environment According to the characteristics of the system development and design, the processor uses Intel Core i7-980 @ 3.33 GHz, the memory size is 8G, and the hard disk size is 1 T; the operating system is Windows 10; Oracle 11g is used as the database management system; the Web server is Tomcat 8.5.
Testing method (1)
(2)
System function test The system function test mainly adopts the black box test method. Use input data to interact through the system interface to check whether the test result is consistent with the expected result, so as to determine whether the function of each module of the system is realized. The main testing methods used are: use correct test cases to verify whether the system can complete normal processing operations; use wrong test cases to verify whether the system can complete error handling operations; check whether the page jump between pages can be implemented normally; check the degree of connection between the system function modules. System performance test Through processing a large amount of data in the long-term uninterrupted operation process, to test the performance index of the system server. The test indicators are as follows: detect the data load capacity of the system and the operating status of the system under load; detect the
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mean time to failure of the system after 2 weeks of continuous operation; check the operation of the system in different network environments. 3.
Test content (1)
(2)
System function module test In the process of implementing unit testing for each functional module interface of the accounting supervision platform, the items that need to be tested are mainly the number of parameters of the test object, the order of selecting test items, the attributes of the tested item, and the detection of the test item being tested. Whether the input-only variables have been modified in the middle, but also pay attention to the definition of the global variables of the tested item and whether their usage is consistent in the various functional modules of the accounting supervision platform. Performance testing The main purpose of this test is to evaluate the comprehensive performance of the system designed and developed in this research in terms of the number of concurrent users, the number of non-concurrent users, and the system response time.
3.2 Statistical Methods and Formulas Used in This Article In the functional test of this article, the results are statistically analyzed, and the average calculation and percentage calculation are mainly used. 1.
The mean calculation is to calculate the average response time of the test cases that achieve the expected result. The formula is: n s=
2.
i=1 si
n
(1)
Percentage calculation is to calculate the proportion of test cases that achieve the expected results in each module. The formula is: Passing rate =
Number of test cases passed Total number of test cases
(2)
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4 Experimental Analysis of Design and Implementation of Enterprise Accounting Supervision Platform Based on Big Data 4.1 System Function Test In the process of system function testing, 100 test cases are provided for each function of each module, the test cases of each module reaching the expected results are counted, the pass rate is calculated, and the response time of the test cases passed by each module is counted. The specific test results are shown in Table 1. As shown in Fig. 1, the pass rate of test cases for each module is above 97.5%, and the average response time is below 0.25 s. During the test, it was found that the response time of the function that requires connection query will be longer than that of other functions. According to the above description, it is found that most of the Table 1 System function test analysis table Module 1 Module 2 Module 3 Module 4 Module 5 Module 6 Passing rate Average response time
98.23 0.106
97.68 0.194
Passing Rate
99.5
98.12
99.12
0.108
0.197
98.69 0.230
Average Response Time
99
98.35 0.217
0.25
Passing Rate(%)
98.5 0.15 98 0.1
97.5 0.05
97
96.5
0 Module 1
Module 2
Module 3
Fig. 1 System function test analysis chart
Module 4
Module 5
Module 6
Module
Average Response Time(s)
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J. Ma Number of concurrent users
Response time (s)
Application server CPU usage (%)
100
0.071
0.59
200
0.077
0.66
300
0.081
0.71
400
0.086
0.76
500
0.094
0.84
600
0.11
0.92
700
0.17
0.99
800
0.21
1.06
900
0.29
1.15
1000
0.34
1.23
functions of each module designed in this article have reached the expected value, only a small part of the functions need to be repaired, and the understanding query of the database also needs to be optimized.
4.2 System Performance Test The performance test of the platform system is generally carried out by using responsive software. This study uses the concurrent user simulation test tool Loadrunner to perform the test. In the process of simulating the continuous increase of users, the response time of the system is tested. The specific test results are shown in Table 2. As shown in Fig. 2, when the number of concurrent users is less than 1000, the response time is less than 0.35, and the CPU usage of the application server is less than 1.3%. It can be concluded that the performance of the accounting supervision platform designed and implemented in this paper can meet the work requirements of corporate accounting supervision.
5 Conclusions With the development of computer technology and automation level, the accounting supervision work of various enterprises and institutions has long since got rid of the traditional manual mode and entered the era of computer processing. This puts forward higher requirements for accounting workers and also requires accounting supervision software. The development puts forward higher requirements. At present, most of the accounting supervision systems in the software market cannot meet the requirements of professional small and medium-sized enterprises. The accounting
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Application Server CPU Usage
0.35
1
0.25 0.8 0.2 0.6 0.15 0.4
0.1
0.2
0.05 0
Application Server CPU Usage(%)
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0.3 Response Time(S)
1.4
0 100
200
300
400 500 600 700 800 Number of Concurrent Users
900
1000
Fig. 2 System performance test analysis chart
supervision platform for small and medium-sized enterprises developed and designed in this paper is based on the analysis of the cross-level supervision process of small and medium-sized enterprises, the main content of accounting supervision is divided into several modules, the functional framework of each module is designed, and a detailed database is established. The structure meets the requirements of accounting supervision for small and medium-sized enterprises, forming a software engineering system with clear functions, clear relationships, standardized structure and wellexpanded software.
References 1. Rahimimovaghar V, Rasouli MR (2021) Published validated study and restricted supervision are two confounding factors in implementation of C-spine rule. BMJ 339(44):729–729 2. Sari DAA, Latifah E (2021) Revitalization of traditional fisheries rights of indigenous people in sustainable fisheries management in Indonesia. IOP Conf Ser Earth Environ Sci 724(1):012117 (7 pp) 3. Gardi B, Hamza A, Sabir Y et al (2021) Investigating the effects of financial accounting reports on managerial decision making in small and medium-sized enterprises. Turkish J Comput Math Educ (TURCOMAT) 12(10):2134–2142 4. Lee JY, Yoon JS, Kim BH (2017) A big data analytics platform for smart factories in small and medium-sized manufacturing enterprises: an empirical case study of a die casting factory. Int J Precis Eng Manuf 18(10):1353–1361 5. Yunxia J (2018) How to strengthen the internal control and supervision mechanism of enterprise finance and accounting. Chief Finan Officer 014(018):85–87
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6. Nagyová A, Paaiová H, Markulik T et al (2021) Design of a model for risk reduction in project management in small and medium-sized enterprises. Symmetry 13(5):763 7. Secinaro S, Calandra D, Biancone PP (2020) Reflection on coronavirus accounting impact on small and medium sized enterprises (SMEs) in Europe. Int J Bus Manag 15(7):48 8. Kantun S, Djaja S, Kartini T (2019) Analysis of fixed assets accounting implementation in micro, small and medium enterprises (MSMEs) units in Jember. IOP Conf Ser Earth Environ Sci 243(1):012064 (6 pp) 9. Nufus EH, Zuhroh I, Suliswanto MSW (2021) Analysis of COVID-19 impact on micro, small, and medium enterprises (MSMEs) credit distribution in East Java Banks. J Account Investment 22(2):342–360 10. Yin X, Zhang G, Ji X et al (2017) Design and implementation of a big-data-based university library cloud service platform. C e Ca 42(6):2463–2468 11. Adedeji EA, Zaynab S (2020) Evaluation of accounting system used by small and medium scale enterprises in Odo-Ona Nla/Idi Ayunre Ward. Adv Soc Sci Res J 7(7):256–237 12. Widyaningdyah AU (2019) The needs of information technology based accounting record in Indonesia small and medium enterprises (SMES). Int J Adv Res 7(5):1141–1151
Research on VR Data Visualization Design Based on Usability Principle Zheng Lu, Yaxin Li, and Qiannan Liang
Abstract With the development of VR technology, people began to display the visually processed data under the virtual reality scene, users can more easily extract data information and get an interactive experience through this immersive way. This article relies on the ten usability principles of human–computer interaction proposed by Nielsen, combined with the current data visualization design cases in the VR scene, the line-of-sight design and the data information design level. Analyze, put forward design ideas for its development from the aspects of light, motion, color, sound and other audiovisual methods through comparison and induction. Keywords VR (virtual reality) technology · Data visualization · Usability principles · Audio-visual techniques · Design ideas
1 Research Background 1.1 Concept and Development of Data Visualization and VR Technology With the progress of the times, digital technology based on computer science continues to affect people’s daily lives. Data visualization is a design form that displays relatively complex and abstract data through visualization in a way that is easier for people to understand. Effective visual information forms can help people observe, research, explore, understand, and discover large-scale and complex data information simply and effectively, so as to more comprehensively and in-depth discover the hidden characteristics and laws of the information. From business dashboards, medical and health visualization to industrial development, scientific research, etc., data visualization covers a wide range of application scenarios [1, 2]. Z. Lu · Y. Li (B) · Q. Liang School of Art and Media, China University of Geosciences, Wuhan, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_82
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Fig. 1 2015–2020 China’s VR user scale forecast
It is iteration in the field of visual communication and has a huge impact on our relationship with information [3, 4]. VR, also known as virtual reality technology, first appeared in the United States. In the early 1980s, the founder of the American VPL company Lanier first proposed VR technology [5, 6]. It is a comprehensive use of computer graphics systems and various reality and control interface devices, which are used to give users a simulated immersion in vision, hearing, smell, taste, touch and other senses in an interactive three-dimensional environment generated on a computer. Sensory technology. So far, these videos and photos have been viewed more than 580 million times [7– 9]. As a new generation of information interaction technology, with its continuous development and improvement in recent years, it has quickly been widely used in various fields and industries. According to statistics, as of 2018, the output value of the entire virtual reality industry reached 5.2 billion U.S. dollars, and it is predicted that it will even reach 45 billion U.S. dollars by 2025. In my country, the number of users under VR experience has increased from 480,000 in 2015 to 3.405 million in 2017, an increase of 2.925 million in three years. It is predicted that the scale of China’s virtual reality consumers will exceed 24 million in 2020 (Fig. 1).
1.2 Data Visualization Design in Virtual Reality Scenarios With the development of society, the total amount of data in various aspects is also growing rapidly. In 2004, the total amount of global data was 30 EB, in 2006 it increased to 161 EB, and a year later, it increased to 7900 EB. It is estimated that the total amount of data this year will exceed 35,000 EB. Faced with such a huge volume and high-dimensional information, the traditional visualization method that relies on the original two-dimensional carrier will become difficult to process. The virtual reality technology can convert a large number of letters and numbers into various
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three-dimensional images that are easier to understand than the original data, and allows participants to check these “visible” data with various virtual reality input devices, so with the continuous innovation of VR Development has provided new research space and display methods for data visualization. Under the VR scene, with the help of the immersive, interactive, and conceptual features of virtual reality technology, users can have a more realistic experience feedback mechanism and have a more intuitive experience of data information. As a result, more and more designers have begun to combine VR technology with data visualization to build data visualization in the virtual reality space. However, this kind of visualization of data in a virtual environment is not only aimed at the processing of huge data itself, but also requires the establishment of a human–machine communication mechanism between users and the virtual space, so that users can gain a sense of experience.
2 Literature Review 2.1 Nielsen’s Ten Usability Principles The Nielsen Ten Principles are ten usability principles proposed by Jakob Nielsen, Ph.D. in Human–Computer Interaction in 1995. These principles can be used to evaluate the quality of user experience. Designing with usability principles can not only facilitate the user’s use process, enhance the user’s intimacy with the software, improve user satisfaction, but also optimize the production and design costs in the development process. The specific content of the principle includes the following aspects: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Principle of system visibility Environmentally appropriate principles Controllable principle Flexible and efficient principle Principles of consistency and standardization Principle of assisting memory The principle of error prevention Principles of aesthetics and design simplicity Principle of fault tolerance Principles of humanized help
The usability principle is a criterion for the use of human–computer interaction mechanisms proposed by Dr. Nielsen. As an important theoretical basis for product and user experience design, it provides an important reference standard for designers when designing related man–machine interface interactions. The experience of VR is also derived from the interaction mechanism established by the user and the virtual
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reality space. The user needs to be in the virtual reality space and obtain the information characteristics of the environment to obtain a sense of user experience. Therefore, the ten usability design principles are also important for constructing communicable VR scenes.
2.2 VR Design Principles Jeremy Bailenson, a professor of communication at Stanford University and the founder of the Virtual Human–Computer Interaction Laboratory, proposed in his new book “Experience On Demand: What Virtual Reality Is, How It Works, and What It Can Do” Three principles of VR design are discussed, namely “Ask yourself, is this VR technology used?”, “Don’t make people feel uncomfortable”, and “Pay attention to safety.” Here he emphasized that VR technology should be used in practical and useful scenarios and can bring good help to people. Users should be smooth in their use process, and finally safety in the use process is also important. Factors fully considered. For the design and construction of VR scenes, many domestic scholars have also put forward many design principles such as limitations, three-dimensional space specifications, interaction modes with the environment, movement methods, spatial sound effects, health and safety, etc. Based on Nielsen’s usability design theory, this research establishes an association model between users, data visualization, and VR technology (see Fig. 2), combining the current virtual space interface design principles and data visualization design cases, from the space scene Analysis of design, data information design, and line-of-sight guidance design provide ideas for future data visualization design and development in VR scenarios. Fig. 2 User association model diagram
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3 Data Visualization Design in VR Scenarios 3.1 Space Scene Design The spatial scene is the modeling element of virtual reality and the display environment of visual data. Good scene design can set off and present the data content, and the data content itself also exists as an important factor in the scene. Therefore, VR scene modeling based on the usability principle is an important prerequisite for data visualization. The AI-based analysis platform Virtualitics (VIP for short) provides a space for data visualization in virtual scenarios through machine learning and immersive visualization experience (see Fig. 3). In the future, Virtualitics can become a common thread between IoT, blockchain and social media technologies. A good virtual space system should be in accordance with natural logic. The data in the scene and other auxiliary information that need to be supplemented can be used for the user’s self-selection based on the text or graphics that the user can recognize. In the design process, the designer should focus on how to let users better understand the visual data model, reducing excessive irrelevant elements in the scene and user interference factors outside the design purpose. Among them, controlling the brightness of the light in the space scene is a common means to achieve this goal. For example, in the Virtualitics analysis platform, the data is in the space in the form of higher color brightness, and the surrounding dark environment plays a role in highlighting the data, allowing users to pay more attention to the data information instead of distracting the attention from the surrounding environment. Go in. In addition, according to the environment-appropriate principle, scene switching methods such as fading in and fading out to control the brightness of the light are also adopted to avoid the dizziness and discomfort caused by the audience due to the rapid change of the picture or the rapid transition.
Fig. 3 Data visualization scene under the virtualitics platform
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Fig. 4 Visualized histogram in lookVR
3.2 Sight-Guided Design The immersive viewing environment surrounded by 360° in the VR scene makes the audience no longer restricted by the traditional two-dimensional plane. Based on the flexibility and efficiency in the usability principle, users have the option of viewing visualized data from different angles autonomously. The American data visualization analysis company Looker has carried out a bold practice in the form of virtual reality data visualization in its product lookVR, using histograms as the basis of visual design, making histograms a “mountain”. Users can climb upwards by wearing VR glasses and operating handles, and finally stand on top of the histogram and overlook the visualized global content (Fig. 4). Time and space create an atmosphere, movement guides vision, and the user’s visual perception can be guided by the movement of scene elements. In a static environment, our vision is often attracted by moving objects in space. Therefore, in the VR space, designers need to mobilize the subjective initiative of participants through reasonable exercise effects, so as to effectively receive visual data information and further enhance user immersion.
3.3 Data Information Design According to the consistency and standardized design principles proposed in the usability principle, for the visualization of data sources in the virtual scene, in addition to the design principles on the two-dimensional plane, the use of colors in the space also reflects the characteristics of the data and obtains user feedback. In Adobe’s design case (shown in Fig. 5), the horizontal similar projects use the same hue data column chart, while the general explanatory text or auxiliary text is white or gray. This design method of the same color allows users to view Even if there is no explanatory note, the project data can have a rough psychological expectation classification, and this is also the presentation of the principle of assisting memory or humanized assisting design in the process of human–computer interaction. In addition to the use of hue, the control of color brightness and saturation in data items is equally important. The position with higher brightness or saturation can naturally attract
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Fig. 5 Project Newview virtual reality data visualization application launched by Adobe
the user’s attention. Therefore, the relative environment can be used to illustrate the saturation and higher color of the important visual data items that need to be highlighted, thereby deepening the user’s memory. Help users better obtain more important data information in the environment. In the design of data information in the virtual reality space, there is another point that is not available on the traditional plane, that is, the integration of sound information into the scene. Sound can convey information to users through different senses, and both the fault-tolerant principle and the humanized help principle in the usability principle can also be easily completed by sound: when the user performs a wrong operation, the user will give a sound effect, or part of the data Information is conveyed in the form of sound, which greatly reduces the user’s visual fatigue.
4 Conclusion Data visualization interface design under virtual reality has gradually become a new direction worth exploring in the future development of visual communication. Its spatial scene, sight guidance and data information processing are all important factors that designers need to consider. Based on the interactive usability principles proposed by the predecessors, designers can make reasonable planning through light, motion, color, and sound, and present data information in the VR scene so that users can gain a sense of experience and play the role of data visualization (shown in Fig. 6). Functional utility achieves greater social practical significance and value. In the “13th Five-Year Plan” outline, it is clearly proposed that “strongly support the innovation and industrialization of emerging frontier fields such as virtual reality”, which supports the integration of VR technology and data visualization in terms of industrial policies. It is believed that in the near future, this emerging design form can achieve further breakthroughs and better serve the public, which is worthy of the joint efforts and expectations of all sectors of society.
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Fig. 6 VR data visualization architecture
References 1. Zhang M (2001) Virtual reality system. Beijing Science Press 2. Li J (2016) Information visualization design. Beijing People’s Posts and Telecommunications Press 3. Nielsen J (United States) (2019) Ten usability principles of human-computer interaction. http:// www.woshipm.com/pd/3628716.html 4. Lei W (2018) Research on the development process of data visualization. Electronic Technology and Software Engineering 5. Pinyi S, Feng Z (2016) Discussing data visualization in the era of big data. Design 6. Xi M (2016) The value and prospects of visual design in the era of big data. Modern Business 7. (America) Lie S, Noah L, Zhu H, Li M (2011) The beauty of data visualization (Translated). Machinery Industry Press 8. Arnheim R (German) (1984) Art and vision. China Social Sciences Press 9. Xie X (2006) Visual interaction in visual communication design. Suzhou Human Studies
Supply Chain Finance and Internet of Things Technology Longzhen Zhou
Abstract Supply chain financial business is a short-term financial business which commercial banks mainly provide chattel mortgage loans to upstream and downstream enterprises based on the core enterprises. China’s commercial banks are limited by information asymmetry and traditional service mode. Under the prompt expansion of information technology, their service efficiency and the means of risk resistance are short of improvement. Therefore, the launch of Internet of things technology can solve risk caused by the difficulty of real right supervision, and has a positive impact on cost and risk. Keywords Internet of things · Supply chain finance · Credit risk management
1 Introduction Nowadays, the competition between enterprises and products is becoming more and more competitive between supply chains. Therefore, the supply chain financial business comes into view. It is a kind of financial service that commercial banks take the core enterprise as the axis and through managing logistics and Cash flow of allied enterprises, the unmanageable risk of isolated firm is reshaped into the controllable risk of the entire chain [1]. But the commercial bank’s low-level information technology capabilities restricts the further development of this business. Therefore, the improvement of information technology is the only manner to carry out supply chain financial business. The Internet of things is a new technique which is extended On the basis of Internet. Through combining various sensing devices and the network, the connection of people, machines and products is realized [2]. Many scholars believe that Internet L. Zhou (B) School of Business Administration, Dongbei University of Finance and Economics, Dalian 116000, Liaoning, China e-mail: [email protected] Dongbei University of Finance and Economics, Dalian 116000, Liaoning, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_83
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of things technology will change the financing environment and mode of small and micro enterprises. Therefore the need of small and micro enterprises can be responded easily in financing. This text will study the role of Internet of things technology in supply chain finance.
2 Literature Review Domestic scholars have studied the role of Internet of things technology in supply chain finance for a long time. Hongjun Xu believes that it regulates chattel pledge, and then solves the regulatory problem of chattel pledge [3]. Wei Dong uses it in the visualization of logistics activities. Through intelligent logistics system of it, it can realize the integration and sharing of logistics statistics, and improve service ability of commercial banks in the market competition [4]. Baojia Zhang and others believe it can improve the supply chain links and improve the information quality of logistics financial markets [5]. Yongzhang Gong combines the Internet of things with supply chain management and logistics technique to achieve pledge supervision, and suggests the integration of supply chain financial logistics operation management platform in order to strengthen supply chain financial logistics [6]. Aidong Wu and others believe it will enhance the innovation of financial services in China [7]. Weizhe Guan and others said that it accelerate the transformation of commercial bank financial service industry to intelligent finance [8]. Ruibo believes that Internet of things technology can greatly reduce the credit risk and management costs [9]. Kailong found that the application of it in China’s supply chain financial business is still in the first stage, but technique has achieved the effect of reducing the risk [10]. Yamei Pan believes that it is conducive to the cost savings and risk aversion of commercial banks in supply chain finance business [11]. Obviously, above research focuses on the role of Internet of things technique in cost reduction, innovation and risk aversion in supply chain finance business. This article will also explore the role of it in financial supply chain from the perspective of cost and risk.
3 Definition and Status of Supply Chain Finance Supply chain finance is that banks contact central company and upstream and downstream firms, control risks, provide flexible use of financial products and services, take funds as a solvent in the supply chain, and increase liquidity. On the one hand, it effectively inject funds into the relatively weak upstream and downstream supporting small and medium-sized enterprises to solve the financing difficulties of small and medium-sized enterprises and supply chain imbalance; on the other hand, Supply chain finance integrates credit into the purchase and sale behavior of enterprises.
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Fig. 1 Flowchart of supply chain financial services
There are three main organizational models of financial supply chain, including logistics enterprise leading model, enterprise group cooperation model and commercial bank service model [12]. This article mainly discusses it under the commercial bank service mode, and the main process of supply chain financial business with commercial bank as the core, as shown in Fig. 1. In chain, commercial banks are not only center of capital turnover, but also the center of information interaction. Logistics and order flow achieve two-way transmission. Banks make up the lack of information of small and medium-sized enterprises. On the other hand, under the foundation of big data, banks reduce cost of obtaining information, broaden access to information, through the enrichment of individual information, realize unsecured pure credit. Through supply chain financial management, commercial banks promote long-term strategic synergy. However, non-standard system, the difficult evaluation of credit rating, the difficult supervision of fraud behavior, the isolated island of internal information, the nontransmission of enterprise credit, the difficulty of recording real right information and the risk of performance make the bank not perfect in the application of chain in order to avoid the hazard of non-performing loans. Therefore, improving information quality and information acquisition effect is the only way to improve the level of chain.
4 Application and Impact of Internet of Things Technology in Supply Chain Finance 4.1 Definition of Internet of Things Technology The Internet of things refers to the real-time acquisition of data of objects or processes through various information sensors and sensing technology, through accessible internet, to accomplish the connection. The Internet of things takes the Internet as the information carrier and connects the related physical objects. It can be subdivided
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Fig. 2 Internet of things service architecture
into four affiliations: identification, perception, processing and information transmission. The key technologies of the four links are RFID (RFID), sensors, cloud computing and intelligent chips. The Internet of things connects objects and objects, connects people, establishes a platform for information exchange and sharing, and realizes the real-time tracking of related objects, which will have a great impact on the complete record of registration and transfer of real right information. This paper will discuss the impact of technique on supply chain finance from the perspective of cost and risk (Fig. 2).
4.2 Cost Perspective The impact of the introduction of it on the cost of chain business makes it known in the following aspects:
4.2.1
Saving of Supervision Cost of Real Right
The demand of chattel pledge loan market in China is large, especially the small and medium-sized enterprises with high chattel account for this kind of loan urgently. Technique can fully supervise flow of space, using sensing technology, positioning technology, so that make visual and controllable management of logistics process.
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This not only helps banks supervise the logistics and inventory of enterprises, but also reduces the possibility of fraud and reduces the manpower and material resources invested in the supervision of real rights.
4.2.2
Production Cost Savings
The cloud computing and sensor technique can alleviate “delay effect” in the financial chain of supply chain, effectively avoid the “bullwhip effect” caused by the guarantee of production and reduce the additional cost of material transportation and storage process, give more liquidity and reduce operational risk. RFID technology and EPC technology are used in all links of the supply chain to track each product in real time, forming a smooth flow, so that the impact of the time lag effect becomes controllable.
4.3 Impact on Information Search Costs Supply chain finance takes core enterprises as the axis and transfers capital liquidity to other firms, which can enhance long-term strategic synergies between small enterprises and central enterprises, and enhance the competitiveness of the supply chain. Therefore, with the application of technology, various companies in the supply chain have alleviated time lag benefits, improved production efficiency and quality, which ensured long-term strategic partnerships between companies and saved the cost of searching for new partners. In addition, banks can obtain logistics information in the process of distribution and transfer through it, and effectively supervise the authenticity of transaction information between enterprises. Traditional supply chain financial management relies on the bank’s original information system. The bank entrusts professional logistics companies to supervise the pledged movable assets of the upstream and downstream industrial chains, while logistics companies generally appoint special personnel to supervise on-site. This type of model is not only costly, inefficient, and easy to breed Moral insurance such as repeated mortgages. The Internet of Things technology replaces manual labor, real-time supervision and dynamic tracking of the logistics process, and saves the employment costs of entrusting professional companies to collect information. However, the information collection of the Internet of Things technology requires sensors and the Internet, so the management and operation models of related enterprises must be improved. The installation of professional equipment such as sensors or the assistance of thirdparty companies to operate the Internet of Things technology will increase the cost of corporate information search. As a result, the impact of the Internet of Things technology on the cost of searching information in supply chain finance, on the one hand, reduces the supervision costs of hiring logistics companies, and on the other hand increases the installation costs of enterprises using Internet of Things technology (Fig. 3).
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Fig. 3 “Internet of things + supply chain finance” financing structure
4.4 Impact of the Internet of Things on the Risks of Supply Chain Financial Operations The impact of the introduction of technology on the hazard of chain financial business is mainly known in the following aspects:
4.4.1
Impact on Credit Risk
Credit risk means the risk of the counterparty’s failure to perform due debts, resulting in the possibility of losses to the bank, investors or counterparty. First of all, it searches
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for information in all links of the supply chain, enabling the upstream and downstream of the industrial chain to obtain product information in real time, greatly reducing the possibility of false orders and reducing the impact of credit risk on transaction contracts. Then, core enterprises can share logistics information in real time with the help of it to avoid difficulty in proving the authenticity of the contract to the bank, improve the bank’s credit to the core enterprise, and reduce the bank’s credit risk to a certain extent. Finally, the Internet of Things technology is characterized by the interconnection of all things, collecting material information repeatedly for a long time, forming an information collection system that is difficult for market entities to improve, accumulating credit data of various enterprises, and to a certain extent, restricting the tendency of enterprises to default and reduce Market credit risk.
4.4.2
Impact on the Risk of Information Transmission
The technique relies on the network to dynamically upload the data of each link in the supply chain to track each product, forming a smooth flow of information for enterprises in chain, and reducing the outwork of time lag. A unified information system not only improves the sensitivity of information transmission, but also increases the difficulty of forging information, improves the level of information interaction between enterprises, and optimizes production and operation models.
4.4.3
Impact on the Bank’s Risk Control System
The credit statistics collection simplifies the bank’s forecasting procedures for each loan and optimizes business efficiency. It alleviates uncertainty risk caused by information asymmetry and reshapes the bank’s risk management system. In terms of information acquisition, it supervises the real storm of information, production process, inventory link, sales situation of the upstream and downstream SMEs in the industry chain. In terms of information collection efficiency, the Internet of Things effectively integrates real “things” and “information”, cross-validates the transaction data of SMEs in the supply chain, effectively eliminates the “noise data” that affects the credit qualifications of SMEs, and improves the entire The quality and credibility of the information delivered by the supply chain. Banks use IoT credit data to accurately measure the default probability and loss rate, pre-lending investigations, in-lending credit management, and post-lending loan flow tracking, real-time overall risk, and improving the level of bank risk control.
5 Conclusions The problem of information asymmetry in supply chain financial management will make enterprises and banks face greater risks in transactions and lending. The Internet
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of Things, as an effective technical means, can provide information on the capital flow, logistics and information flow of supply chain nodes. Effectively integrate and monitor in real time, reduce the degree of information asymmetry in the bank’s risk management process, enhance the bank’s ability to extend credit to SMEs, expand the business scope of bank supply chain financial services, and help SMEs in the supply chain solve their financing difficulties. Thereby making the capital operation of the entire supply chain more transparent and efficient.
References 1. Xue Y (2021) Block chain technology drives the innovative path of commercial banks to carry out supply chain finance business. Southwest Finan 2:38–48 2. Jia Y (2010) Application of internet of things technology in environmental monitoring and early warning. Shanghai Constr Technol 1:65–67 3. Xu H (2010) A study on the supervision of Chattel Pledge based on internet of things. Mod Commer Ind 24:327–328 4. Dong W (2014) Analysis on the application of internet of things in logistics finance industry. Shandong Univ 4 5. Zhang B, Shao H (2015) On innovation and audit supervision of logistics finance from the perspective of internet of things. Neweconomy 14:66–67 6. Gong Y, Liu F, Pang R et al (2017) Financial logistics supervision of supply chain based on internet of things technology. China Sci Technol Forum 6 7. Wu A, Chen Y (2012) An international comparison of innovative power mechanisms in financial services based on internet of things. J Tianjin Univ Finan Econ 1:36–42 8. Guan W, Sui L, A study on the innovation of intelligent finance based on internet of things. Sci Technol Innov Bull 119–121 9. Guo R, Chen Y (2014) A study on the model of internet of things applied to financial services. Exp Technol Manag 11:137–139 10. Guo K (2015) Supply chain finance under technology mall modernization. 2:148–150 11. Pam (2017) Effects and countermeasures of internet of things technology on supply chain financial business. Shandong Soc Sci 6:32–136 12. Xie S, He B (2013) Analysis of three typical patterns of international supply chain finance. Econ Theory Econ Manag 4:80–86
Analysis of “National Animation” Driven by Big Data Technology Hong Li
Abstract With the penetration of visual cultural concepts and the advent of the era of digital information technology, the film and television industry has become one of the most promising industries in the cultural and creative industries. However, in the era of globalization, the “nationalization” of Chinese cartoon art is not just a propaganda slogan, it is necessary to integrate the inherent cultural elements of the Chinese nation into animated film and television works to make it richer in Chinese national style. This is a major development trend of domestic film and television today. This article focuses on the analysis of ethnic animation driven by big data technology. First, we have a simple understanding of some theories of ethnic animation, and then analyze the current situation of ethnic animation. In order to analyze ethnic animation in depth, this article collected 2016–2020. The national animation box office data found that the box office of national animation is increasing year by year. This shows that the prospects of the national animation industry are very good. In order to understand how to make the national animation better develop, this article has conducted a report on the animation professional teachers. A simple questionnaire survey yielded the results. Among the measures given, most people think that the domestic original model should be adhered to, and they should break away from the foreign animation style, accounting for more than 41%. Keywords Big data · Ethnic animation · Animation film and television · Cultural elements
1 Introductions With the continuous internationalization of cultural and creative industries, film and television animation, as the tertiary industry, has become an economic and cultural pillar recognized by China and the world [1, 2]. In Japan, the United States and Europe, they have established their own animation industry chain and have gained a H. Li (B) Xiangsi Lake College, Guangxi University for Nationalities, Nanning 530008, Guangxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_84
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firm foothold in the animation industry [3, 4]. As a developing country, China should seize this opportunity to create miracles with our thousand-year-old local cultural materials. With the reform of the economic system, Chinese animation has absorbed foreign elements from artistic styles, institutional models, and technical means, and some changes have taken place [5, 6]. At the same time, government departments have successively introduced a series of strong support policies for the animation industry, making China’s animation industry start to develop [7, 8]. In the study of “national animation”, some researchers pointed out that without a solid humanistic background and rich cultural resources, film and television animation cannot have good creativity, and there will be no lively characters, grand plots, and emotional spatial scenes as support. In other words, the cultural habits involved in film and television animation play a vital role in the whole process [9]. Some researchers also pointed out that the “localization” of Chinese animation films is based on tradition, and one cannot blindly learn while imitating, inherit while not forgetting to innovate. Combining the characteristics of nationalization with modern humanitarianism and a deep understanding of traditional culture, and incorporating Chinese-style humor and wisdom into it, can we get out of the road of creating cartoons with Chinese characteristics [10]. Some researchers have also pointed out that customs not only affect the theme of animated films, but also subtly influence the collective aesthetic orientation of the creators and audiences of animated films. Because of the unification of people’s aesthetic psychology and aesthetic taste, the public and creators are willing to accept this popular customization model [11]. Some researchers have conducted research on the vitality of national animation, and proposed that in order to cater to the tastes of the Chinese people; most Chinese animation chooses traditional folk tales and classic literature in the choice of themes. The end of the film always emphasizes the theme of sublimation to reflect the educational significance. Although the appearance of these films has met the psychological needs of some audiences, once the innovation is over, the psychological needs of the audience will change. Excessive emphasis on educational significance makes most viewers feel a little disgusted and even reluctant to watch it again. The lack of audience creativity in animated films is hindered by ideological rigidity [12]. This article analyzes and researches “ethnic animation” driven by big data technology, summarizes several manifestations of ethnic elements in animation on the basis of relevant documents, and then analyzes the status quo of ethnic animation, in order to further understand some of the situation of ethnic animation a simple questionnaire survey was conducted for animation students and teachers, mainly around some suggestions for the development of national animation.
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2 Ethnic Animation Research 2.1 The Embodiment of Ethnic Elements in Animation 1.
2.
Nationalization of characters Among the domestic animated films in China, the most fascinating and emotional focus of the entire animated film is the character design, which is also one of the centers of public discussion. Chinese animators design and inherit Chinese traditional art, have a strong feeling for Chinese traditional art, and incorporate this feeling into the character creation. Emphasizing the expression of “deep writing” and “very rhyme” is the main creative purpose of Chinese animation when creating characters. Controlling and expressing the inner spirit of the person is the key to the success of shaping the character’s image. Animation in our country pays attention to the exploration of modeling rules from traditional art and culture to character image modeling. For example, when designing a portrait, an animator does not need to use anatomy as a theoretical basis, but only relies on visual intuition to design. Paper-cutting art The art of paper-cutting often expresses people’s inner world in various forms in movie cartoons, showing people’s most basic aesthetic concepts and spiritual character, and has a distinct artistic style. The art of paper-cutting has two characteristics in scene design: one is carving, which means cutting too much material in order to maintain the final shape. The other is connection. This method mainly uses points, lines, and faces to connect intelligently to the final shape. In simple terms, the design method of paper-cutting art usually combines the shapes in the paper-cutting with the method of connecting the joints of the shadow puppet game. In this way, there will be no shaking during human operation, and the animation film image effect can be more vivid and free.
2.2 The Development Status of Ethnic Animation The gradual deepening of reform and opening up and the gradual strengthening of global integration have caused many foreign film and television animations to flood into the Chinese market, bringing some opportunities and challenges to the Chinese film and television industry. In order to be able to compete with foreign film and television animations, a large number of film and television animations have been created. However, many film and television animations attach great importance to speed, resulting in low production level, not only the production quality is not high, but also the animation in our country does not make good use of China’s local culture, the commercial atmosphere is very strong, the creative techniques are also very old, and the national characteristics are not fully reflected. This limits the development of my country’s film and television industry to a certain extent. For those cartoons
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that are deeply imprinted with national colors, they are not only classic, but also unforgettable. For example, excellent domestic animations such as “Journey to the West” and “Mulan” have been deeply rooted in our national culture and have received wide acclaim and praise. In recent years, film and television animation with ethnic cultural elements has shown a rapid growth trend. Essentially, the organic integration of national elements and vitality makes the vital role of film and television with national characteristics more in line with the aesthetic needs of our masses. Their welcome has broad room for development. Therefore, at this stage, how to achieve the deep integration of Chinese national elements and contemporary animation art has become an urgent issue before relevant personnel.
3 Investigation on the Status Quo of Ethnic Animation 3.1 Purpose of the Investigation This article mainly focuses on the analysis of national animation. Firstly, it analyzes the proportion of the current animation market based on the big data of animation film and television, and then conducts a questionnaire survey on the measures to develop national animation at this stage.
3.2 Data Sources 1.
2.
Establishment of the survey site This survey is aimed at the development of ethnic animation measures. In order to reduce the difficulty of carrying out survey activities, this survey is mainly carried out in this city. In order to facilitate the development of survey activities, and to ensure that the survey results are supported by enough data, it is determined the survey was conducted in universities in this city, and three universities with different reputations were randomly selected for the survey. Since this activity is mainly aimed at universities in this city, the results are not universal, so the results this time cannot explain other regions the influence of the integration of national elements into the improvement of animation professional production and education capabilities. Determination of relevant parameters The establishment of the number of questionnaires is the most basic step of the survey activity, because the number of questionnaires is related to the validity of the survey results. If the number of questionnaires is set too low, the results of this survey will be questioned because the base of the data is not large enough, and the results of the survey are not large enough. It is universal. The number of questionnaires is set too high, and the difficulty of the questionnaire
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survey activity increases. Therefore, the number of questionnaires this time is set to 200 according to the minimum sample size proposed by the experts and the technical conditions of this survey. The distribution process of the questionnaire The issuance of this questionnaire is mainly divided into two stages. The first is the issuance of the questionnaire, and the second is the recovery of the questionnaire. In order to ensure that the results of this survey have greater authenticity, the recovery of the questionnaire will be completed after the questionnaire is issued. Recovered in the next six days, given time to fill out the questionnaire completely. 189 questionnaires were recovered, and the recovery rate this time was 95%.
3.3 Data Processing 1.
2.
When performing correlation analysis on the collected data, the data must be classified and sorted. This will not only increase the utilization rate of the data, but also promote cross-data analysis. Therefore, the main consideration is the completeness and accuracy of the data. First of all, about data integrity. When the questionnaire is delivered to the sample subject for completion and collection, some sample items are arbitrarily completed, or their selection cannot be completed, which will cause some data sorting problems, but because the retrieved data accounts for the majority, so deleting the lost data means deleting the lost data. Secondly, the precision and accuracy of the data. When conducting an audit, the main consideration is to check whether these data are inconsistent with other choices, or the principle that conflicts with it should be selectively removed but retained as much as possible. The main meaning of a correlation relationship in the objective correlation analysis method is to generally refer to a certain relationship between various objective phenomena, but they are not strictly corresponding to each other in quantity. There are two main forms of determining the relevant properties of objective phenomena here: qualitative analysis and quantitative analysis. The main purpose of qualitative analysis is to rely on the scientific theoretical knowledge and practical experience of the researcher to accurately determine whether there are correlations between various objective phenomena. Or what kind of factor, the subjectivity of this analysis method is relatively strong. Among them, the commonly used calculation formula is expressed as: (x − x¯ )(y − y¯ )/n S2 xy = Sx Sy (x − x¯ )2 /n (y − y¯ )2 /n n xy − x y r= √ 2 n x − ( x¯ )2 (n y2 − ( y¯ )2 )
r=
(1) (2)
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4 Result Analysis 4.1 Market Share of Animation Type Through the analysis of the box office of national animation and foreign animation from 2016 to 2020, the relevant data is shown in Table 1. It can be seen from Fig. 1 that domestic national animation has been growing year by year, from 310 million yuan in 2016 to 2 billion yuan in box office in 2020, an increase of 80%. This shows that the development prospects of national animation are relatively good. Table 1 The proportion of the animation market
National animation box office
Foreign animation box office
2016
3.1
13.3
2017
4.1
10.3
2018
5.2
11.0
2019
11.0
19.5
2020
20.0
22.0
National Animation Box Office
Foreign animation box office
25
amount
20 15 10 5 0 2016
2017
2018
year
Fig. 1 The proportion of the animation market
2019
2020
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Table 2 Development strategy of national animation A college (%) B college (%) C college (%) Insist on the inheritance and innovation of national culture
34
35
33
Strengthen the integration of digital technologies
21
24
25
Consolidate the development model of original 45 animated films
41
42
suggestion
Consolidate the development model of original animated films
Strengthen the integration of digital technologies
Insist on the inheritance and innovation of national culture 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
percentage C college
B college
A college
Fig. 2 Development strategy of national animation
4.2 Development Strategy of National Animation Based on the above, we can see that ethnic animation is a potential industry. How to develop better in the country. Based on this, this article conducted a simple questionnaire survey of people in related industries. The results of the survey are shown in Table 2. It can be seen from Fig. 2 that in the measures given, most people believe that the domestic original model should be adhered to and that they should break away from foreign animation styles, accounting for more than 41%.
5 Conclusions In the context of mutual exchange, integration and rapid development of world cultures, the nationalization of Chinese animation films is an inevitable trend in the development of Chinese animation films. The early domestic animation films
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inspired Chinese animation to continue to advance and adhere to the process of nationalization, which means to better inherit Chinese traditional culture, that is, to explore new national elements and national characteristics while consolidating the national cultural foundation of Chinese animation films. At the same time, the growth of Chinese animation nationalization must keep pace with the times and look at the development of animation nationalization from a global perspective. With the rapid development of digital technology today, animation film and television should be based on promoting national culture and national spirit, have the courage to continuously reform and innovate, learn and learn creative concepts and develop excellent foreign animation experience. Movies continue to play and upgrade traditional painting and digital technology, fundamentally revitalize the national animation industry, and promote the development of national animation films. Acknowledgements Class A, Undergraduate Teaching Reform Project of Guangxi Higher Education in 2021, “Research on Improvement of Production and Education Integration Ability of Animation Specialty Based on PJBL Concept”, 2021JGA404.
References 1. Yu R, Wu C, Yan B et al (2021) Analysis of the impact of big data on e-commerce in cloud computing environment. Complexity 2021(2):1–12 2. Peng QI, Zhang W (2017) Expression of national cultural elements in ancient costume animation movie scene. J Landscape Res 03:45–49 3. Won, Dae, Jung (2016) Trans-identity of Chinese animation: Calabash Brothers and Jungle Master. Glob J Cinema China 3(1):75–86 4. Ahn S-B (2016) The uncomfortable desire lying latent in the storytelling of Super Dimension Fortress Macross—from the perspective of pacifism, culturalism, and sentimentalism. J Popular Narrative 22(2):107–137 5. Thalmann NM, Thalmann D (2019) Editorial issue 30.2. Comput Animation Virtual Worlds 30(2):1–2 6. Park Y, Rhee G et al (2017) A study on the selection criteria of media for the textbook: based on the review of domestic and foreign media rating systems. Cartoon Animation Stud 47:295–333 7. Vaughan N, Dubey VN, Wainwright TW et al (2016) A review of virtual reality based training simulators for orthopaedic surgery. Med Eng Phys 38(2):59–71 8. Yorozuya CM, Liu Y, Nakajima M (2017) Region classification and image compression based on region-based prediction (RBP) model. ITE Tech Rep 22:13–18 9. Lindner C, Rienow A, Juergens C (2019) Augmented reality applications as digital experiments for education—an example in the Earth-Moon System. Acta Astronaut 161(Aug):66–74 10. Dohee, Kim, Byung et al (2016) The effects of the experience of Korean wave contents on country image, contents satisfaction and loyalty—a focus on the potential consumer of the new Korean wave in Europe. Korean J Bus Adm 29(12):1871–1894 11. Lawonn K, Glaber S, Vilanova A et al (2016) Occlusion-free blood flow animation with wall thickness visualization. IEEE Trans Visual Comput Graphics 22(1):728–737 12. Berney S, Betrancourt M (2016) Does animation enhance learning? A meta-analysis. Comput Educ 101(Oct):150–167
Data Management Strategy Based on Edge Computing Zaiyi Pu
Abstract In today’s society, the acquisition and processing of information has increasingly become a complex and difficult problem. How to improve the level of these information management and how to enhance the operating efficiency of the information system is now an urgent solution. As one of the important methods, edge computing (EC) has been proposed and has been widely used in practical research fields. It can not only provide help for a series of related operations such as the extraction and classification of data understood in the traditional sense, but also in many related operations. All aspects play a key role. At the same time, the use of this technology can effectively improve system performance and reliability. In order to better provide users with better services and meet their needs for network application system performance, this paper studies data management strategies. In this paper, a data management strategy based on EC is researched through data analysis method, experiment method and comparison method. Experimental research results show that the data management of EC has 98% accuracy. This article takes Xgboost’s human behavior recognition system as an example for verification. Keywords Data management · Edge computing · Cloud computing · Distributed storage
1 Introduction In today’s big data era, people’s demand for processing massive amounts of information is getting higher and higher, and how to quickly and accurately obtain this effective information is an urgent problem to be solved at present. Therefore, in order to ensure that the data management strategy can be carried out efficiently, it is necessary to have an in-depth understanding and research on it. The web algorithm environment established on the basis of EC theory, fusion method and information Z. Pu (B) Education and Information Technology Center, China West Normal University, Nanchong 637009, Sichuan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_85
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retrieval theory can improve the performance of computer network. And the framework can also help people use Web-shinet in their work. We need to make complex and changeable web pages richer and more complete, etc. to improve users’ browsing speed on the Internet and make web pages more practical. This requires the use of EC for data management. There are many researches on data management strategies based on EC. For example, Kumar N. proposed that the possibility of replacing traditional power grids with smart grids in the near future is increasing. Because the output of current data analysis is sometimes overloaded and there is a significant delay, it will affect the implementation of the solution [1]. Li C. believes that while cloud computing brings a paradigm shift to computing services, there are also some inherent problems in its nature. Therefore, he conducted a complete and up-to-date review of the edgeoriented computing system through the management methods and design goals of the edge-oriented computing system [2]. Nonaka J. said that the supercomputer produces a large number of simulation results, and most of these data sets are stored in the file system. They investigate the trend of computer data generation by analyzing some operation log data files [3]. The main research content of this paper includes the related technology of EC, the analysis of data management strategy, the research of related algorithms and the design of data collection and processing system. After this series of research and analysis, Xgboost’s human behavior recognition system is tested. Finally, the experimental results are obtained.
2 Data Management Strategy for Edge Computing 2.1 Related Technology 1.
2.
Mobile EC technology Mobile EC provides IT service environment, CC and storage functions in the radio access network (RAN). Its goal is to reduce network latency and improve user experience [4]. MEC is based on a virtualization platform and complements NFV. MEC also helps to meet the expected requirements of 5G in terms of throughput, network latency, scalability, and automation [5]. Fog computing technology Features: It operates independently of other parts of the network and can access local resources at the same time; MEC is close to the information source, which helps to obtain and analyze the key information in big data; the edge service runs close to the terminal equipment, which can greatly reduce the delay [6]. Fog computing is a new type of network computing mode that focuses on providing services to mobile smart terminal users. At present, the domestic understanding of fog computing and related research are still in the exploratory
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stage, and the research results are relatively few. Fog computing is a distributed service computing model that extends the computing, storage, and network services provided by CC to the edge of the network, making the service closer to the end user. Fog computing not only includes upward and downward gateways, but also includes devices for temporary data calculation and storage. It can prejudge the data to be processed. If the amount of data is large or needs longterm storage, the layer directly forwards the data to the cloud data center for processing. CC is mainly composed of high-performance computing equipment, which makes the overall computing power stronger. Fog computing emphasizes the number of computing devices, and makes up for the shortcomings of fog nodes’ weak computing power through numerical advantages [7]. Features: Fog nodes are geographically dispersed and consume less power; they are close to end users, reducing network latency; only necessary information is sent to the cloud, reducing the burden on the core network; widely distributed and multiple fog nodes have the same service; support high mobility. EC technology Communication companies hope to use EC technology to further tap the value of network connection equipment, and improve their technological status in the consumer Internet of Things and Industrial Internet by strengthening the relevant performance of the access side network. The overall framework of EC includes the release of edge-side dedicated network hardware devices for smart manufacturing or IoT scenarios, the use of software-defined technologies to create an IOx application framework, reorganization of edge-side capabilities, and the introduction of “Fogdirector” products to systematically manage EC application services. Relying on the DevNet developer community to provide API, SDK, etc. to cultivate the industrial ecology [8].
2.2 Data Management Strategy Metadata management has an extremely important impact on the performance of distributed file systems. Therefore, designing a good metadata management strategy and applying it to the actual system will significantly improve the performance of the system. 1.
Design goals (1) (2)
(3)
High performance. For user requests, the MDS cluster must be able to respond in a timely manner and provide very fast metadata access speed. Flexible. When users request to modify the attributes of a directory, they should try to avoid affecting the subdirectories and files contained in the directory. Consistency. In the MDS cluster, a copy of some metadata is stored at the same time, and the metadata and multiple copies may be distributed on different MDSs. In order to ensure that all users see the same global
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(4)
2.
The main metadata management strategy (1)
(2)
3.
4.
5.
namespace, it is necessary to maintain metadata and its copy makes their changes as synchronized as possible [9]. Scalable. In actual use, the metadata management system often needs to add or remove MDS, both of which will cause a large amount of metadata to be migrated. A certain strategy must be adopted to store as much metadata as possible. The location does not change, and a balance is reached between system performance and consumption caused by expansion.
No segmentation strategy. This type of metadata management strategy does not divide the global namespace. On the contrary, it stores the entire namespace and metadata in one MDS in the MDS cluster, or puts the entire namespace and metadata and its copies in multiple locations. Advantages: complete storage locality, easy maintenance of metadata consistency, high cache utilization and hit rate, and will not affect the files and subdirectories it contains [10]. Disadvantages: It is easy to be overloaded, costly, low concurrency, and cannot be expanded by adding servers. Static subtree splitting strategy. This type of metadata management strategy divides the global namespace into multiple subtrees through the configuration when the MDS cluster is started, and each MDS stores one or more subtrees. A wellknown distributed file system that uses a static subtree splitting strategy is NFS. Advantages: It has better storage locality, light network load, and attributes such as modifying the name or permissions of the parent directory. Disadvantages: not evenly distributed, high cost, increased hard disk I/O times, low concurrency, and not easy to expand.
Dynamic subtree segmentation strategy This type of metadata management strategy eliminates the shortcomings of some static subtree splitting strategies. It delegates and authorizes different subtrees in the global namespace to different MDSs, and the subtrees can be based on the workload distribution in the MDS cluster Migrate from the overloaded MDS to the lighter-loaded MDS. Static hash strategy This type of metadata management strategy distributes all metadata to different MDSs by calculating the hash value of a certain mark (file name, path or other mark) of the file. For example, if the file /home/usr/test.txt is calculated by the hash function, its hash value is 4, and the client will directly send a request message to MDS4 [11]. Lazy Hybrid (LH) strategy
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In the LH strategy, there are not only directory metadata and file metadata, but also the directory hierarchy to provide semantic operations of the file system. This type of metadata management strategy calculates the hash value through the full path name of the file, and distributes the metadata to a specific MDS based on the hash value.
2.3 Related Algorithms 1.
K-means clustering algorithm In order to improve the accuracy of the data management strategy, it is helpful for collecting historical access data of content with similar access characteristics to the content object to be calculated as a training set. the clustering sample data set is Y = Suppose2 yk |yk ∈ s , k = 1, 2, 3, . . . P , where yk is a two-dimensional vector, which represents the access feature of the content Qk in the current time period [t − t, t]. We use the static popularity of Qk in the entire MEC system ek and the rate of change of visit volume fk is used to describe this visit feature, that is, let yk = (ek , fk ) among them, ek =
N
en,k
(1)
n=1
N fk =
n=1
Mn,k (t) − N
N
n=1
2.
n=1
Mn,k (t − t)
Mn,k (t)
Mn,k (t) represents the number of times the content Qk is accessed in bn in the current time period [t − t, t]. CC layer delay calculation In the CC layer, suppose there are a cloud servers for data processing, and the total data processing volume is s. For cloud server k, assuming that the amount of data it needs to process is ks and its computing capacity is kw, its calculation delay can be expressed as: Ckcom =
sk (k = 1, 2, . . . , m) wk
2.4 Design of Data Acquisition and Processing System Based on EC 1.
(2)
System requirements design The main requirements of the data collection and analysis system are:
(3)
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(1)
(2)
(3)
2.
3.
System hardware architecture design In order to be compatible with more external devices, it is necessary to choose a suitable microprocessor as the core part of the overall system-EC equipment. System communication scheme design In order to ensure the operation of the entire EC network, the following requirements are imposed on the communication scheme: (1) (2) (3) (4)
4.
Data collection function According to the algorithm and the actual application environment, the data required by the system is collected. Data preprocessing (DP) In order to ensure the consistency of the data as much as possible, it is essential to correct data. Data analysis function In response to the challenges of big data mining, a distributed big data analysis and mining platform needs to be established [12].
It can be developed in use; A strong reliability; Protection of data and information; Ensure the normal operation of each communication network.
System key module design (1)
(2)
(3)
Design of data acquisition module based on edge device According to the design goals, this system needs to be applied to physical data collection scenarios and network data collection scenarios respectively. Physical data collection: Reserve a CC interface and a data preprocessing interface to facilitate the coordination of this module with other functional modules. On the other hand, in addition to the data collected by the sensor, the current status and control functions of the sensor can also be sent to Cloud, so that the CC center can grasp the actual collection status. Network data collection: Use the collaborative work of multiple edge devices under the unified command of the cloud center to divide a large workload capture task into multiple small tasks to be completed by each edge, similar to a multi-threaded processing module. Design of various types of DP modules It needs to be designed separately for the preprocessing of physical data and network data. Physical DP: The main content of DP includes data cleaning, integration, transformation and specification. Network DP: Use Python to do DP. CC collaborative data analysis module design When the DP module on the edge device completes data cleaning, in addition to saving the data locally, it will also upload the data to the cloud
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for the next step of data analysis. The functions of the data analysis module include commonly used data statistical analysis and the construction of data modules combined with machine learning algorithms. The statistical analysis function is independently completed by the CC center, and the data model construction is composed of edge devices and CC centers: the CC center is responsible for the training and correction of the model, and the edge devices are responsible for the testing and use of the model.
3 Experimental Research on Data Management System for IE Plore 3.1 Experimental Background The rapid growth of network information resources has made traditional data acquisition modules unable to meet people’s requirements for obtaining useful information. As the basis and important part of data analysis, the ability to automatically acquire required web content is particularly important. Because the capture of network data requires edge devices to be connected to the Internet, and it is inevitable to use the compilation environment to analyze and process the collected data when extracting information from web page data, this system is in the current module and subsequent data processing modules. Still choose Raspberry Pi as the edge device in this scenario.
3.2 Detailed Design and Implementation of Webpage Acquisition Module 1. 2.
3. 4.
Analyze the format of its related website first, and find a suitable automated crawling method. Each web page on the Internet corresponds to a unique URL. The information it contains indicates the location of the file and how the browser should handle it. Therefore, when collecting web page data, the URL corresponding to the target web page can be used to complete the collection task. After the Raspberry Pi receives the ID range of the captured documents sent by the CC center, the first ID of the range is set as the initial ID. When the loop reaches the last ID of the interval, it jumps out of the loop and performs the next step of data cleaning and data analysis.
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Table 1 Comparison of algorithms in UCI data sets
F-measure
Accuracy
Operation hours
Decision tree
0.9322
0.9326
27.98
GBDT
0.9437
0.9425
34.25
Random forest
0.9253
0.9361
46.57
Naive Bayes
0.9332
0.9522
35.48
Xgboost
0.9549
0.9839
17.62
Fig. 1 Comparison of algorithms in UCI data sets
operation hours Xgboost
0.9839
F-measure
17.62
0.9549
Naive Bayes Algorithms
Accuracy
35.48
0.9522
0.9332
Random forest
46.57
0.9361
0.9253
GBDT
34.25
0.9425
0.9437
Decision tree
27.98
0.9326
0.9322
0
10
20
30
40
50
Units
4 Test Analysis of Human Behavior Recognition System Based on Xgboost 4.1 Algorithm Comparison Under UCI Data Set Select several commonly used classification algorithm models from the sklearn library under Python, and compare and test them with Xgboost. The results are shown in Table 1. Through the analysis of Fig. 1, we can learn that, Xgboost runs faster than other basic algorithms, and it has greater advantages in precision. Since Xgboost’s regularization of the self-model is more sufficient than other models, the generalization ability of this type of model is stronger.
4.2 UCI Data Set Training Effect Since Xgboost comes with cross-validation, we can use the corresponding library function to complete the model test. The results are shown in Table 2. It can be seen from Fig. 2 that the prediction accuracy of the existing model for the training data is about 97.6%. If the number of iterations of the decision tree is infinite, the accuracy of prediction will reach 100%. Therefore, it is necessary to use edge algorithm for over fitting correction.
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Table 2 UCI data set training effect 1
Test-merror-mean
Test-merror-std
Train-merror-mean
Train-merror-std
0.023511
0.001097
0.019187
0.001657
2
0.020164
0.000852
0.015632
0.001632
3
0.018932
0.000937
0.014098
0.001325
4
0.018513
0.000724
0.013579
0.001213
0.025 0.02
Test-merror-mean
Test-merror-std
Train-merror-mean
Train-merror-std
0.023511 0.019187
0.020164
Fruits
0.015632 0.015
0.018932
0.018513
0.014098
0.013579
0.01 0.005 0
0.001097 0.001657 1
0.000937
0.000852 0.001632
0.001325 3
2
0.000724 0.001213 4
Times
Fig. 2 UCI data set training effect
5 Conclusion In recent years, society has gradually entered the “big data” era, and the emergence of CC has increased the demand for big data processing and application capabilities. However, due to the high hardware requirements of CC, this computing architecture cannot be widely promoted. How to effectively use existing resources to obtain data has become a prerequisite for restricting the development of big data services. Aiming at some shortcomings and shortcomings of CC itself, this paper implements a data collection and processing system based on EC, and designed a data management system. Acknowledgements Financial support was received through the Excellent Talent Foundation of China West Normal University (No: 17YC497).
References 1. Kumar N, Zeadally S, Rodrigues J (2016) Vehicular delay-tolerant networks for smart grid data management using mobile edge computing. IEEE Commun Mag 54(10):60–66
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2. Li C, Xue Y, Wang J et al (2018) Edge-oriented computing paradigms: a survey on architecture design and system management. ACM Comput Surv 51(2):39.1–39.34 3. Nonaka J, Inacio EC, Ono K et al (2018) Data I/O management approach for the post-hoc visualization of big simulation data results. Int J Model Simul Sci Comput 9(3):1840006.1– 1840006.16 4. Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39 5. Oezsu MT (2016) A survey of RDF data management systems. Front Comput Sci 10(3):418– 432 6. Abiteboul S, Libkin L, Martens W et al (2018) Research directions for principles of data management. ACM SIGMOD Rec 45(4):5–17 7. Bill C (2017) Computer integrated manufacturing: the data management strategy. J Oper Res Soc 41(1):89–90 8. Daraio C, Lenzerini M, Leporelli C et al (2016) The advantages of an ontology-based data management approach: openness, interoperability and data quality. Scientometrics 108(1):441– 455 9. Tudoran R, Costan A, Antoniu G (2016) Overflow: multi-site aware big data management for scientific workflows on clouds. IEEE Trans Cloud Comput 4(1):76–89 10. Chaudhary R, Kumar N, Zeadally S (2017) Network service chaining in fog and cloud computing for the 5G environment: data management and security challenges. IEEE Commun Mag 55(11):114–122 11. Ceri S, Kaitoua A, Masseroli M et al (2017) Data management for heterogeneous genomic datasets. IEEE/ACM Trans Comput Biol Bioinf 14(6):1251–1264 12. Sun Z, Bi X, Wu W et al (2016) Array organization and data management exploration in racetrack memory. IEEE Trans Comput 65(4):1041–1054
Construction of Parallel Corpus of Foreign Publicity Based on Computer-Aided Translation Software Meng Sun
Abstract Foreign publicity plays an important role in increasing the visibility of a country and companies at home and abroad. The application of big data, computeraided translation, and machine deep learning contributes to the translation of foreign publicity materials. This has made a parallel corpus of foreign publicity a hot spot of research in recent years. This paper introduces the construction of a parallel corpus of foreign publicity in detail and discusses how to apply computer-aided translation software to the construction of foreign publicity corpus, to improve translation efficiency. The alignment of a parallel bilingual corpus of foreign publicity lays the basis for building a bilingual parallel corpus. The main task of building the foreign publicity corpus is to build an English Chinese parallel corpus with sentence-level aligned according to the characteristics of Chinese and English. Its functions include the alignment of English and Chinese sentences, screening and storing databases, and browsing, retrieving, and exporting the corpus. This research is of great significance for building a corpus that meets the specific needs of foreign publicity. Keywords Computer-aided translation · Translation software · Parallel corpus · Foreign publicity
1 Introduction Nowadays, the construction of parallel corpus based on computer-aided translation software has a great influence on the English-Chinese bilingual conversion [1]. There are four types of bilingual parallel corpora: English to Chinese translation, Chinese to English translation, and a comparative study of English-Chinese translation and English-Chinese translation [2]. However, research on the construction of parallel corpora based on computer-aided translation software is not much. But because in some special fields of parallel corpora, and its biggest is the point-to-multipoint M. Sun (B) Department of International Studies, Changchun Humanities and Sciences College, Changchun, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_86
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translation mode, it can be applied to the analysis and research of many different corpora, and this is only suitable for use in literary works. Very few are used in non-literature works [3, 4]. The important role of a parallel corpus in English translation has been widely recognized. Today, the construction of my country’s parallel corpus has matured day by day, and it also has certain advantages in terms of quantity [5]. However, with the continuous progress of society and the improvement of translation accuracy, its defects have become increasingly prominent. So in terms of computer-based auxiliary translation software, how can people improve the usage and the popularity of future development direction [6]. And because the translation software does not need to be installed, the translation function is powerful, and it is convenient and free. Translators and teachers and students engaged in translation work in colleges and universities can quickly master the use of computer-based auxiliary translation software after reading the relevant operating instructions, even if they have not been trained in a professional system [7]. With the continuous development of science and technology, more and more abundant network resources provide a rich corpus for computer-assisted translation software, and also provide convenience for translation teaching. Relatively speaking, students majoring in translation should have the habit and ability to use computer-assisted translation software. Later, to improve this translation problem, some language experts established a corpus here and collected all the language materials that appeared and stored them in the database as the data source of the translation software. The corpus includes all the classics, slang, etc. [8, 9]. In this way, the translation software can have reference objects when translating, and can better translate the article to prevent errors. However, the results obtained in this way are still not ideal [9, 10]. Because the knowledge stored in the corpus is too large, sometimes, if the translation software is to translate it, it takes a lot of time to search for close words and sentences, sometimes because there are too many close words and sentences, there may be problems due to data corruption [11, 12].
2 Algorithm Establishment 2.1 Application of Improved Particle Swarm Algorithm in a Parallel Corpus Database data volume constraints S=
I
q(i)
(1)
i=1
In the formula: S is the planned data storage volume in the scheduling period.
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Database extraction volume constraints qmin ≤ q ≤ qmax
(2)
In the formula: qmin and qmax represent the minimum and maximum allowable flow of the database. Maximum data power constraint N ≤ Nmax
(3)
where Nmax represents the maximum allowable output power of the database. Blade angle adjustment constraints αmin ≤ α ≤ αmax
(4)
Hin ≥ 19
(5)
Constraint
where Hin is the amount of data stored in the database.
2.2 Application of PCA Mathematical Principles in Corpus Construction After maximizing the data mapping, the variance formula can be obtained as: 1 T w (xi − x)2 m − 1 i=1 m
max w
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The average vector of all data involved in dimensionality reduction. The matrix can get an optimized objective function through linear transformation, which is min tr W T AW , w
s.t, W T W = I
(7)
where tr is the trace of the matrix; A is the covariance matrix. The A expression is as follows: 1 (xi − x)(xi − x)T m − 1 i=1 m
A=
(8)
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2.3 Corpus Analysis Based on the Computer-Aided Translation Its purpose is to improve the process of software development, develop high-quality products, and promote the success of the overall project. At present, software measurement is divided into two factions. One faction believes that software can be measured, and the other faction believes that software cannot pass measurement analysis. However, the current mainstream of research believes that it can be measured, so we believe that in the current situation, the complexity of software is not very high, and it can be measured and analyzed. If possible, we hope that software can always be measured and analyzed. Because measurement analysis is equivalent to a planning book composed of software, we can control the software in real-time through real-time measurement analysis, which can help us make better software for services to achieve success. Computer-aided technology is a kind of service related to the use of computer technology and the Internet. We call their shared resource pool the cloud. Computeraided technology integrates many data computing resources and realizes automatic management through the environment so that resources can be provided quickly. Computer-aided technology is not a new technology, but a new concept. Its core is the Internet as the center, which provides fast and safe storage services. At present, computer-aided technology and cloud computing have become a new era-the cloud era. And computer aid has become a new revolution in the computer field since it was proposed ten years ago. And because of his appearance, the whole society has undergone a new change. The characteristic of this technology is that it can be virtualized and does not require physical control.
3 Modeling Method 3.1 Construction of the Optimal Model Based on Grey Relational Theory 3.1.1
Original Data Transformation
The two main ways to unify the dimensions of data are: Initial value changes: X(1) i (k) = Average transformation:
X(0) i (k) X(0) i (1)
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3.1.2
X(0) i (k) (0)
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Xi
Calculate the Correlation Coefficient
Using Xi ((1)) (k) as the bus, calculate the difference ij (k) of each sequence at the corresponding time: (1) k = 1, 2, . . . , i = j ij (k) = K(1) ik − XKjk
(11)
Find the minimum min and maximum max of |ij (k)|. Correlation coefficient ξij (k): ξij (k) =
min + ηmax ij (k) + ηmax
(12)
Among them, η ∈ [0,1], usually 0.5.
3.1.3
Calculate the Comprehensive Correlation Degree γi of Each Sequence Xj: γi =
n
βj ξij (k) j = 1, 2, . . . , m
(13)
k=1
According to the comprehensive relevance ranking, the one with the largest relevance value is the preferred solution.
3.2 Analysis and Data Processing of Experimental Design The experimental data in this article are mainly based on various English-based documents and new articles that have not yet been collected. The main reason why the experiments in this article are all in English is that the translation software formed by using different algorithms can more clearly see the data comparison after the corpus is constructed. The main purpose of the collected articles for the experiment is to observe the efficiency of the two translation software. The structured data is constructed through the potential external publicity network corpus. Therefore, the method of automated information extraction is to try to find the established patterns and can use these patterns to dig out more data to support the external Promote the construction of parallel corpora. First, the query data is
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handed over to the search engine of the translation software, and the corresponding translation result is usually returned. Each translation result is also called a record because these translation records are predicted on the Internet through computerassisted translation software. Obtained over the air, so this kind of record can also be called a network translation record.
4 Evaluation Results and Research 4.1 Data Table Analysis This time, by measuring the performance of computer-aided translation software based on different classifiers in the construction of foreign publicity corpus, the effect of the entire parallel corpus system is evaluated. With the increase of features, the accuracy of the translation system of different classifiers increased from 73.32 to 95.71%. Among them, S is the iron proof of repeated words, L is the length feature, and G is the frequency feature. The main analysis results are shown in the data presented in Table 1. To improve the classification performance of the overlapping word measurement, in the process of calculating the translation score, we not only use a bilingual dictionary but also use a set of equivalent translation pairs extracted from parallel corpus, which makes up for the fact that bilingual dictionaries cannot include all the shortcomings of the entity. Based on the data shown in the tree diagram in Fig. 1, the method in this article can only obtain parallel resources with a distance of 1. Figure 1 shows the location Table 1 The influence of translation software of different feature classifiers in the corpus
3000 2500
Feature analysis
Accuracy (%)
S
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S+L
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S+L+G
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Number of parallel sentences in translation software
1500 1000 500 0
1
35
39
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79
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3
5
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Other
Fig. 1 Parallel distance distribution of data resources in translation software
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102% 100% 98%
Particle swarm
96% Particle improvement algorithm
94% 92%
Distributed improved algorithm
90% 88%
Old translation correct rate
New translation accuracy rate
Comprehensive comparison
Fig. 2 The correct rate analysis of the changes in the basic translation efficiency of different algorithms in the foreign publicity corpus
statistics of 3000 randomly selected bilingual parallel resource corpus pairs based on computer-aided translation Ruan Ji. It can be seen from the figure that parallel resource pairs with a distance of 1 occupy the majority, so the method in this article can In the computer-assisted software in the external publicity of the parallel corpus, most of the internal parallel resources, but the recall rate of the resource acquisition system in this article still has some room for improvement. As shown in Fig. 2, the three algorithms of ant colony improvement algorithm, particle improvement algorithm, and distributed improvement algorithm are used to calculate the translation of English translation software respectively, and the data is modeled and analyzed to observe that their original translation is accurate. And make a comprehensive judgment on the accuracy of new text translation. It is found that the translation of the three original English texts is 100% completed. This is because the corpus has the same original reference, but the translation accuracy of the new text is different. Among them, the particle improved algorithm has the highest translation accuracy rate, the ant colony improved algorithm has the second-highest translation accuracy rate, and the distributed improved algorithm has the lowest translation accuracy rate. Therefore, it can be seen that in the case of computerassisted translation software changing it in different ways, the construction of the parallel corpus of foreign propaganda is also more refined, which has a very important effect on all aspects of communication between my country and foreign countries.
5 Conclusion In general, although the construction of other English-Chinese bilingual parallel corpora has been used for reference in the construction of the foreign propaganda corpus and the characteristics of various languages have been taken into account, it
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is still unavoidable that users will have various kinds of problems in the actual use process. To have a better user experience and increase the influence of the comprehensive foreign publicity bilingual parallel translation corpus, in the initial use, it is necessary to collect the discovered problems and focus on solving them, to provide a reliable basis for the later research on the foreign bilingual parallel corpus. With the continuous popularization of computer-aided technology, the development of software related to the construction of parallel corpora is also underway. In recent years, many software related to text processing and comparative translation have been developed at home and abroad, which has played an important role in the construction of parallel corpus and made the research of parallel corpus more intuitive.
References 1. Wu H (2021) Multimedia interaction-based computer-aided translation technology in applied English teaching. Mob Inf Syst 2021(5):1–10 2. Wang X (2021) Building a parallel corpus for English translation teaching based on computeraided translation software. Comput Aided Des Appl 18(S4):175–185 3. Excell D (2019) Some challenges in using computer-aided translation tools to facilitate second language fluency in education. Ann Emerg Technol Comput 3(2):22–31 4. Jiang H (2021) Coastal atmospheric climate and artificial intelligence English translation based on remote sensing images. Arab J Geosci 14(6):1–13 5. Zorrilla-Agut P, Fontenelle T (2019) Modernising the EU s IATE terminological database to respond to the challenges of today’s translation world and beyond. Terminology 25(2):146–174 6. Ginsburg A, Ben-Nun T, Asor R et al (2019) D+: Software for high-resolution hierarchical modeling of solution X-ray scattering from complex structures. J Appl Crystallogr 52(1):219– 242 7. Park C, Lee D, Kim W et al (2019) Wide image stitching based on software exposure compensation in digital radiography. J Korean Phys Soc 74(11):1067–1072 8. Levchuk P, Roszko D, Roszko R (2020) Multilingual corps institute of Slavic studies, Polish Academy of Sciences—CLARIN PL. Polish-Lithuanian Parallel Corpus “2” and Polish-Ukrainian Parallel Corpus. Language Classic Modern Postmodern 6:306–170 9. Griffin J (2019) Three parallel crossings in Corpus Christi. Undergr Constr 74(6):32–33 10. Rana T (2020) EX-MAN Component model for component-based software construction. Arab J Sci Eng Sect A Sci 45(4):2915–2928 11. Zhou R, Lu X, Zhao HS et al (2019) Automatic construction of floor plan with smartphone sensors. J Electron Sci Technol 17(01):15–27 12. Zhang H, Yuan X (2020) An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling. Neural Comput Appl 32(6):1581– 1592
Research and Design of Physical Survey Data Analysis System Under the Background of Big Data Jing Wang
Abstract At this stage, with the development and progress of society, people are paying more and more attention to their own physical health and gradually attaching importance to various sports. In the field of physical measurement, big data has many advantages such as enhancing exercise effects, improving human health, and reducing sports health data management costs. It can quickly process and analyze a lot of diverse and complex forms of data. The article analyzes the big data significance to the physical measurement data analysis system, studies the key points of system construction, and conducts an in-depth study on the design of the physical measurement data analysis system under the big data background. Keywords Big data · Physical measurement · Data analysis · Analysis system · Design
1 Introduction With the big data development, it has been continuously applied to various industries, creating usable value for various industries, changing the management of enterprises and society, and at the same time, it has also changed people’s traditional thinking and effectively improved the work efficiency of various industries. Especially in the field of physical measurement, there is a lot of relevant data with application value, which is a prerequisite for the big data application [1].
2 Big Data Regarding big data, it refers to the use of brains and tools to acquire, process, manage, and collect data. It can mine new systems, tools, and models. It can also use its unique J. Wang (B) Chongqing Chemical Industry Vocational College, Chongqing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_87
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distributed computing model to explore, acquire, and collect [1]. And the law of processing information. Big data has the three characteristics of totality, confounding, and relevance. It is a collection of all data, and can correlate and emphasize the relationship and law between various variables. Due to the existence of a large number of different types of data, it is relatively mixed. In addition, big data also has four characteristics, namely, large quantity, wide variety, fast speed, and difficulty in judging whether the data is true or false. Due to the huge amount of data in big data, traditional data storage and management models are difficult to meet the current status of data development, and data management solutions need to be innovated and expanded [1]. With the development of society, many forms of data such as graphics, voice, and video have appeared. Using traditional management models to analyze and process a lot of complex data, work efficiency is relatively low, and it is difficult to achieve effective data processing.
3 Application of Big Data Analysis in the Field of Physical Measurement 3.1 Promote Innovation As far as people’s physical fitness monitoring projects are concerned, they are completed by taking sample surveys of the people. The purpose is to enable the country to better understand the people’s health. At this stage, applying big data in the field of physical testing, in physical fitness monitoring, it is possible to use physical fitness testing for all eligible subjects, which can not only ensure the accuracy of the test data, but also effectively improve the persuasion of the test results. In addition, it is possible to make full use based on the actual conditions of different regions and the actual health of people [2].
3.2 Improve Data Integration Efficiency There is a lot of data and information in the field of sports and health. As the human body is constantly in motion, the sports environment is also changing at any time. In addition, the health of the human body is also affected by many factors such as physical and psychological, and social adaptability. Most of the data to measure these factors are scattered and not centralized, and the relationship between the various data is not obvious [2]. However, after big data applying in the field of sports, a sports and health database can be created. After a lot of data in this field is effectively integrated, it is stored in the database, which not only improves the efficiency of data integration, but also allows a lot of data to be used. Perform analysis, study the connections between various data, and tap the value of the data.
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3.3 Improve Athletic Ability With the society development, people are paying more and more attention to health. The big data application in the field of sports can provide people with better and more suitable exercise methods, and improve people’s usual physical exercises and human health [3].
3.4 Provide Personalized Services There are a large number of complex and diverse data in the field of sports. People can understand their physical condition and the sports environment they are in based on their own health data. Because big data technology has the advantage of in-depth data mining, it can mine the connection between various data, as well as the connection between various data and data in other fields [3]. In addition, an application model of sports health data can also be established to provide people with relevant personalized services.
4 The Application Challenges of Big Data in the Field of Physical Measurement First of all, in the collection and monitoring of health data. The factors that affect sports change dynamically, it is very difficult to collect health data. It is also a difficulty in the big data application in the field of sports, and it faces many challenges. In the data collection process, there is currently no terminal equipment that does not require professional operation and can be used by everyone [4]. Secondly, in terms of data sharing. At this stage, China’s establishment of big data management in the field of sports is in its infancy. As data cannot be circulated among medical institutions, data sharing is difficult to achieve [4]. Although China has developed in sports health in recent years, most of them are centered on physical examinations, and sports health services based on big data are few. Finally, in terms of data integration. Sports health data includes various types of data, physical health data, and personal medical data. However, different institutional systems have different storage standards for sports health data, resulting in diverse forms and different structures. In addition, due to the traditional way of recording sports health data, the data recording is incomplete, the data information is missing, and there are too many differences between the data [5]. These factors increase the difficulty of data integration and seriously affect the sports and health data. Integration.
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5 The Application Strategy of Big Data in the Field of Physical Measurement 5.1 Increasing Data Development Efforts When big data is applied in the field of sports, technicians need to increase the development of equipment and application software for collecting data in the field of physical measurement, expand the collection of sports health data, and make full use of related technologies to expand the database used for sports health data storage capacity [5]. At the same time, it is also necessary to increase the research of technical personnel on data processing and analysis technology in the field of sports, improve the speed and accuracy of data processing, and realize in-depth mining of sports and health data.
5.2 Building a Service System As far as the sports and health field is concerned, there are many industries involved, such as sports training, health care, and sports fitness and other related industries. The data collection and management work required by the sports and health field should be actively supported. Within each industry, the corresponding physical measurement database can be established, which can increase the communication and contact between the industries while also realizing data sharing, providing data resources for the application of big data [6].
5.3 Training Professional Talents The application of big data in the field of physical measurement is a very huge technical project. The theoretical knowledge and technology involved are very complex, especially in data processing, which mainly includes data collection, data processing and analysis, and data management [6]. Therefore, relevant departments should train professional big data talents. Enterprises can carry out relevant training activities according to their own needs. Schools can set up courses related to big data according to the needs of social development, and cultivate big data that meets the conditions through various methods [7].
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5.4 Formulate Relevant Development Policies In physical testing related fields, from data collection, data processing, to data application and final data management, all processes need to be carried out in accordance with related processes and regulations [7]. Therefore, the application of big data in the field of physical testing requires strict formulation of a scientific and reasonable policy to ensure the smooth progress of the entire work process. On the basis of policy formulation, the specific details of the big data application process should be clarified in detail. Gradually standardize the application of big data in the field of physical testing.
5.5 Effectively Guarantee Data Security With the rapid popularization and application of the network, people are paying more and more attention to data security. Since data is related to people’s privacy, a lot of personal data can predict individual lives [8]. Therefore, in the special field of physical testing, it is necessary to attach great importance to the security and confidentiality of data. If the physical test data is leaked, it will cause serious economic losses and negatively affect the society.
6 Design of Physical Measurement Data Analysis Platform Under the Background of Big Data 6.1 Design the Overall Architecture The physical measurement data analysis platform adopts advanced computer information technology, communication technology, sensor technology, artificial intelligence, etc. to effectively integrate for the management and control of physical information, focusing on the coordination between people, vehicles and roads, forming a conducive to an integrated transportation system that improves the environment, saves energy, and protects safety [8]. The physical measurement data analysis platform is designed with a hierarchical structure model. According to the requirements of big data construction, the entire platform includes three levels: data perception, resource layer, and application layer. The main task of the data perception layer is to collect traffic information, and the resource layer is designed to manage traffic. Field data; the application layer is designed to be responsible for real-time scheduling of physical measurement resources [9]. The physical measurement data platform system designed this time can meet the needs of collection, storage, scheduling and data processing. The specific architecture is shown in Fig. 1.
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Fig. 1 Overall architecture of physical measurement data platform design
6.2 Resource Layer Analyze from the physical measurement data storage, use data warehouse and mining technology to realize the storage and analysis of big data. Among them, the data warehouse technology can meet the requirements of the physical measurement data platform to process massive data. The technology relies on the preset storage mode to extract, call, and process the heterogeneous data in the physical measurement database according to the data structure data [9]. At the same time, the data is stored in the data warehouse according to the preset warehousing model, and the data storage and mining architecture under the physical measurement data platform designed with the help of data warehouse technology.
6.3 Application Layer Design Use SOA to realize the design of the application layer of the physical measurement data platform system, as shown in Fig. 2. This layer mainly contains three sub-modules: (1) Application realization module: This module is designed to complete data scheduling and realize corresponding functions in time with the help of logic programming; (2) Application process Module: The big data scheduling process relies on professional BPEL tools to schedule various resources; (3) Special scheduling module: The main task of this module is to convert a custom scheduling process into a BPEL process. Relying on the application layer of SOA service design [10].
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Fig. 2 Design of the application layer of the physical measurement data platform system
6.4 Data Presentation Layer The presentation layer in the physical measurement data platform system is the interface where users directly participate. Users can browse various physical measurement information data by relying on browsers, tablets, mobile phones and other terminal devices [10]. The main task of this layer is to ensure the user’s interaction with the entire system. Therefore, it is equipped with a concise appearance, interface framework, and unit controls.
7 Conclusion In summary, big data’s ability to analyze data and data mining enables effective processing of data in sports and health, which has changed the previous traditional data management methods, and can also establish sports and health data sharing platforms to realize various industries. Sports and health data sharing between. At the same time, people can also choose their own form of exercise to exercise, improve their exercise ability, and promote their physical health.
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References 1. Dong ShY, Zou W (2018) Application research of big data in the field of physical measurement. Sports Sci Technol Lit Bull 16(05):158–160 2. Sun WF, Zhao M (2018) Research on the development trend of sports training in China under the background of big data. Hubei Sports Sci Technol 14(02):169–171 3. Ju XT, Zhang BQ (2020) Design of analysis and processing system for college students’ health data based on big data. Autom Technol Appl 7(04):172–176 4. Li HG (2020) Analysis of the current status of adolescents’ physical health and its implementation strategies from the perspective of healthy China. Sci Technol Inf 4(35):233–235 5. Zhang XY, Li YH (2019) Research on the construction of the posture evaluation system of private college students from the perspective of big data. J Higher Educ 12(14):44–46 6. Yang ShM (2019) Development and research on the data integration system of college students’ physical fitness test. Sichuan Sports Sci 5(06):31–33 7. Liu YT, Guo H (2018) Design of cloud platform system for physical fitness test based on big data. Electron Technol Softw Eng 13(04):194–196 8. Fan Y (2018) Research on student physical health information management in big data environment. Autom Instrum 9(04):58–60 9. Chen ShP, Zhang ZhJ (2016) The impact of school sports policy attitudes on the test data of college students’ physical fitness standards. J Chengdu Sport Univ 11(02):110–115 10. Jiang Y (2018) Research and design of adolescent physical health data analysis and processing system from the perspective of big data. Shandong Inst Phys Educ 10(01):50–53
The Application of Computer Video Image Technology in Track and Field Training Wei Li
Abstract Under the contemporary background, with the development and progress of society, many opportunities are ushered in. The two notable features of the current era are the integration of the world economy and the rapid development of video and image technology represented by computers, video and image technology, and some advanced technologies. The technology should be fully utilized to enrich people’s lives and studies, but there are still imperfect problems in the application of video image technology in sports. And video image technology is rarely used in track and field teaching and training. Nowadays, video image technology has increasingly integrated with people’s lives. Video image technology is slowly being integrated into daily life and into physical education and training. Keywords Video · Image technology · Track and field · Project training
1 Introduction With the development of economy and the improvement of people’s living standards, the scope of application of video and image processing technology in life is becoming wider and wider, so people pay more attention to the application and development of image and video processing technology in the new situation [1]. Digital video and digital images have higher resolution than traditional images and videos, and are easy to handle, easy to operate and organize. However, in the application of video image processing technology, due to operational technical problems or objective factors, etc., it will bring some negative effects to the application of video image processing technology, which reduces the level and quality of the processing technology [1].
W. Li (B) Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_88
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Fig. 1 Image compression technology
2 The Development of Video Image Processing Technology The process of video image processing involves the acquisition, transmission, arrangement, display, and playback of video image data, among which these processes together form an overall cycle of the system, which can operate continuously [1]. The most important thing in the scope of video image processing technology is to include video image processing technology.
2.1 Image Compression Technology Image compression technology is one of the most important aspects of video image processing technology, which requires great attention. First of all, it is necessary to make it clear that image processing relies on image processing algorithms and processing effects, as shown in Fig. 1. The image processing method determines the quality and level of image processing technology, and affects the overall operating efficiency to a certain extent [2]. At present, the application of image processing technology mainly adopts technology types such as JPEG, M-JPEG, MPEG4 and H.264. These types include their own performance and advantages, so it needs to be based on the performance and the performance index of the system determines which technical method to choose [2].
2.2 Video Image Processing Technology Although the current video image processing technology has been greatly improved and developed, it is difficult to change the quality of the poorer image in the later
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processing technology. In many cases, the resolution of the video acquisition equipment (camera, camera, camera, etc.) is low, and the captured image is not clear, so it is difficult to make up for this defect in processing [2]. The HDR-SRl2E/SRllE camera produced by Sony now includes the application of CMOS image sensor, which can be installed at 45° on the basis of the traditional pixel arrangement.
3 Application of Video Image Technology to Track and Field Training 3.1 Modern Video Image Technology Has Obvious Effects on Track and Field Training And it has a huge promotion effect. The colorful network world brings people a wonderful life environment, giving today’s students a high-quality source of information and a colorful life. Teachers can increase the vitality of the classroom and improve training through video image technology and multimedia. The relationship between video image technology and track and field training is quite close [3]. On the one hand, video image technology can greatly promote track and field training. On the other hand, track and field teaching and training need the support of video image technology, as shown in Fig. 2.
Fig. 2 Application of video image technology in track and field stadium
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3.2 Advantages of Video Image Technology in Track and Field Training 1.
2.
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Preparing lessons before class is more convenient. The integration of video image technology into the classroom will add high-quality lesson preparation resources to the classroom. Video image technology has more resources, which can further optimize course resources, improve the classroom atmosphere, and lay a good job for teachers to attend classes more efficiently [3]. Promote the classroom atmosphere during class. Many scholars have inherited and reformed traditional education [3]. However, the advantages of video image technology are obvious, which can achieve better teaching and training results, and can achieve better results. Better display of action details. New educational technologies such as multimedia can improve teaching quality, increase teaching methods, and improve training levels. With the help of electronic teaching media, with the help of multimedia, a decomposition process of sports actions that can be repeatedly displayed can be constructed structure [4]. It is convenient for students to clearly see the essentials of technical actions. In traditional education and traditional training, physical education teachers and coaches often show themselves to students. Because there may be too many students in the class, it takes a lot of time and energy to demonstrate and correct student actions [4]. With the help of video images the integration of technology into track and field teaching and training saves time and efficiency. Teaching and training will be more standardized, which can further improve the effect of teaching and training. In teaching and training, physical education teachers using these technologies through network multimedia and other technologies will not only facilitate teaching and training, but also teach and train students to be full of interest [5]. Increase interest in physical education. “Interest is the best teacher” is a wellknown saying that we are familiar with. People are interested in things they are interested in, and their efficiency in doing things will increase accordingly. For example, in the track and field training course, if the teacher training combines video and image technology, it can better show the content of the track and field sports, such as the action essentials, action techniques, and action methods under certain conditions [5, 6].
4 The Effect of Video Image Technology in Track and Field Training Video image technology can enrich teaching and training. Arouse students’ interest in learning. The following effects can be achieved by using new educational technology.
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4.1 Improving the Model of Lesson Plans Before teaching and training, in order to improve the learning effect, the teacher will prepare the courseware before the class. In order to continuously improve, it is inevitable that there will be revisions. If the revision is not good, the template will be deleted and deleted [7]. The computer format can be as you want, and if there is an error, if you want to modify the template, you will not repeatedly modify the template like the traditional lesson preparation. This way, the operation is convenient, and the result is that the entire lesson plan looks neat and beautiful [7].
4.2 Increase Teaching Training and Resources to Make Teaching and Training More Convenient When writing teaching plans, traditional teaching and training generally use blackboard tools to prepare lessons, and achieve intuitive effects. After video and image technology is integrated into education and training, there are many databases and picture libraries on computers and multimedia [7]. Teaching with more training resources, many problems in teaching and training can be solved easily, making teaching and training more convenient.
4.3 Make the Teaching and Training Process More Vivid and Concrete Video and image technology has comprehensive processing capabilities in education. This method of auxiliary teaching and training can integrate text, sound, and images. The situation is more vivid and lifelike. The use of modern technology teaching can allow students to enter the situation [8]. It integrates well with sports games, sets up situations in sports games, and adds some animal sounds to increase students’ interest. So as to make teaching and training more novel, vivid and concrete (Fig. 3).
4.4 Better Evaluation System The teaching system includes pre-class, in-class, and after-class. However, video image technology plays a great role in three aspects: before class, during class, and after class. Video image technology can be used to record in teaching and training. In order to find out that the teaching and training are insufficient, students are prone to errors, etc., to improve the teaching and training system, to improve the teaching
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Fig. 3 Application of video image technology system in track and field teaching and training
and training evaluation system, and to make the evaluation system more perfect [8], as shown in Fig. 4.
Fig. 4 Video image technology used in the judgment of track and field competitions
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5 Suggestions on the Application of Video Image Technology in Track and Field Training 5.1 Continuously Improve Teachers’ Video Image Technology Level Video image technology has so many advantages, teachers and coaches should also continue to absorb the essence of them, in order to improve the quality of teaching and training, schools should organize more experts to the school to give lectures to improve the level of the school’s teachers’ video images [9]. Organize teachers to go out to learn to absorb high-quality curriculum resources, improve the level of teaching and training, and promote the smooth development of school physical education courses. Improve the education and training level of this school, and continuously improve the video image level of teachers [9].
5.2 Schools Should Increase the Use of Video Image Technology in Track and Field Teaching and Training Since video image technology has so many advantages, video image technology should be used more in the course of track and field teaching and training to give full play to the advantages of video image technology to meet teaching needs and teaching and training requirements. Multimedia facilities should be added to teaching to popularize video and image technology into track and field sports [9].
5.3 Schools Should Increase Support for the Application of Video Image Technology in Track and Field Classrooms and Training 1.
2. 3.
Schools should strengthen the importance of video image technology in track and field teaching and training, guide students’ interest in physical education, and make video image technology more popular in track and field classes [10]. Schools should increase sports activities and cultivate students’ hobbies in various ways, so that more students love sports activities. Multimedia teaching optimizes the way of education to a large extent and makes up for the shortcomings of traditional education in a great way [10]. Therefore, we must use more video and image methods to improve teaching and training modes.
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5.4 Problems that Should Be Paid Attention to When Integrating Video Image Technology into Track and Field Training Courses The integration and integration of video image technology and physical education is a major direction that the education layer in the new era urgently needs to study and pay attention to. It is necessary to pay attention to its organic combination, and its role cannot be ignored or exaggerated [10]. The issue of goodness should be mastered. Otherwise, the poor combination of the two factors will seriously hinder the development of informatization to a certain extent, and the poor combination will also lead to a serious departure from the direction of combining modern technology and track and field teaching and training. Video image technology has obvious advantages, and video image technology should be allowed to play to its level in track and field training [10].
6 Conclusion 1.
2.
Video image technology is integrated into track and field training. It has obvious advantages and is loved by teachers, coaches and students. Video image technology can improve the quality of teachers’ teaching and training to a certain extent, and can also actively improve students. The interest in class, optimize the quality of the class, and increase the atmosphere of the class. Video image technology meets the needs of the times. After investigation, it is found that video image technology can bring a lot of convenience and cater to the needs of track and field teaching and training. The questionnaire survey shows that both middle schools, universities, and even primary schools are using the advantages of video images, actively cooperating with teaching and training, and making full use of resources.
References 1. Zhou YL, Yuan H, Li B (2013) “Video image processing technology” course design research. Public Secur Educ 13(09):62–64 2. Yuan TG (2011) Application and innovation of motion video image multi-processing technology system in the field of track and field scientific research. Beijing Sport University 3. Tan BH, Ren LX (2008) Research status of automobile collision avoidance system based on video image processing technology. J North China Inst Aerosp Eng 11(05):7–10 4. Yang ZhF, Yang ZhG (2011) Analysis of the application of video image technology in college physical education. Technol Econ Market 10(09):114–115 5. Li YA (2011) Theoretical research on applying video image technology to optimize physical education in colleges and universities. Sports Sci Technol Lit Bull 19(07):53–54
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6. Wang TH (2011) The significance and role of video image technology in college physical education. Mod Mark Acad Ed 12(06):235–238 7. Zhan ChJ, Chen XH (2011) Research on the application status of video image technology in college physical education. Hubei Sports Sci Technol 30(02):233–235 8. Yan LL (2017) Discussion on the application of video image technology in college physical education. J Jilin Radio Telev Univ 11(03):80–84 9. Zhang W (2011) The method and practice of integrating video image technology with physical education. J Henan Urban Constr Univ 20(11):90–92 10. Wang T (2019) The application of multimedia technology in college physical education. Inf Rec Mater 20(04):153–154
Formation Control of UAV Swarm Based on Virtual Potential Field and Virtual Navigator Peng Zhang, Huidong Huangfu, Jianhua Zhang, Jianglong Zhou, Haiyan Chen, Wei Tao, Jiachen Shen, Yijie Ding, and Kang Su
Abstract In UAV research, formation control of UAV swarm is an important prerequisite for the mission of UAV swarm. To solve the above problems, this paper proposes a UAV swarm formation control method of UAV swarm based on virtual potential field and virtual navigator. By setting up a virtual navigator, establish the virtual potential field between each node of the swarm and between each node of the swarm and the virtual navigator. After the potential field is superimposed, the UAV swarm reaches the desired position under the effect of the virtual potential field, forming a preset formation. The simulation results show that the UAV swarm can quickly form a preset formation when the initial position is random. Keywords Formation control · UAV swarm · Virtual potential field · Virtual navigator
1 Introduction With the continuous development of the UAV application field, the tasks performed are more and more extensive, and the UAV is gradually developing from a single platform to a multi-platform swarm [1]. UAV swarm have the characteristics of distribution, self-organization, collaboration, stability, etc. And have good adaptability to the environment [2]. In the UAV swarm, the UAVs communicate with each other to share information, expand the perception of the environmental situation, realize coordinated task assignment, coordinated search, reconnaissance and attack, which can effectively improve the survivability, detection capabilities and reliability of the UAV, complete the complex task that a single UAV cannot complete [3]. When studying the problem of swarm formation control, only relying on local rules to control the swarm is difficult to make the swarm emerge in the direction people hope, and individuals in the swarm interact with other individuals as well as with the external environment. Therefore, we can introduce the concept of potential P. Zhang (B) · H. Huangfu · J. Zhang · J. Zhou · H. Chen · W. Tao · J. Shen · Y. Ding · K. Su Northwest Institute of Nuclear Technology, Xi’an 710024, Shaanxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_89
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field in physics into the swarm, use the artificial potential field control method, and use the potential function to simulate the internal and external effects that affect individual behavior. Individuals in the swarm act under the action of the potential function, thus relying on the potential field function realizes the control of the swarm. When using the artificial potential function method to design the individual behavior function, the artificial potential function can be designed on demand, but the convergence of the swarm needs to be ensured. Therefore, some special functions are usually selected. For example, a potential function can be selected, which is called the virtual potential field method [4–8].
2 Formation Control Model of UAV Swarm Based on Virtual Potential Field and Virtual Navigator When the virtual potential field method is applied to the UAV, the UAV obtains the corresponding formation under the gravitation and repulsion of the potential field. The gravitation of the target point to the UAV fly towards the target, the repulsion is used to avoid collisions between UAV nodes and avoid obstacles. The UAV swarm moves based on rules. All UAV nodes should obey these three basic principles, namely avoid collision, speed matching and keep swarming, and it can be illustrated by the R-A model [9] in Fig. 1. The outer circle of Fig. 1 is the sensing range of node i, and the inner circle of Fig. 1 is the repulsion zone of node i. It provides repulsive force to all other nodes in the repulsion zone of node i, and all nodes that is in the sensing range zone of node i should try to adjust the distance from node i in order to avoid collision, the Fig. 1 R-A model
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repulsion zone is represented as Rri = xi |0 < di j < dr , xi ∈ Rn ; The ring area farthest from node i is called the gravitation zone, and all nodes in this zone is affected by the will be attracted i, then the gravitation by the gravitation of node zone is represented as Rai = xi |dm < di j < da , xi ∈ Rn ; the ring-shaped zone between the repulsion zone and the gravitation zone is called the balance zone, which means that the distance between node i and othernodes is appropriate, this zone is represented as Rmi = xi |dr < di j < dm , xi ∈ Rn . dr , dm , da , di j represent the distance between the node i and the boundary of the repulsion zone, the balance zone and the gravitation zone, di j represents the distance between node i and node j. Suppose there are M nodes in the swarm, X = (x1 , x2 , . . . , xi , . . . , x M ) represent the position vector of the UAV swarm, V = (v1 , v2 , . . . , vi , . . . , v M ) represents the speed vector of the UAV swarm. It is known that xi ∈ R n and vi ∈ R n are the position vector and the velocity vector of the UAV node respectively. The motion equation of the UAV node i is as follows: x˙ = vi
(1)
vi should satisfy the constraints: vi =
vi vi ≤ Vmax vi Vmax vi vi > Vmax
(2)
In Formula (2), Vmax represents the maximum speed of the node. According to the R-A model, the velocity vector of the UAV swarm is affected by three potential field forces, assuming that vir , vim , via is the velocity component of vi generated in the repulsion zone, the balance zone and the gravitation zone, respectively. It can be expressed as follows: ⎧ r d +k vi = ki j ( dirj +kiijj − 1)2 0 < di j < dr ⎪ ⎪ ⎪ j∈Rr ⎪ ⎨ m vi = vj dr < di j < dm j∈Rm ⎪ ⎪ a ⎪ 1 1 ⎪ di j ( da −d − da −d ) dm < di j < da ⎩ vi = ij m
(3)
j∈Ra
In Formula (3), ki j is the parameter that control the strength of the repulsive force between the UAV node i and the UAV node j, and its value is much smaller than dr . From Formula (1), we can infer that in the process of controlling the flight of the UAV swarm, the velocity equation of vi can be obtained. Before constructing the velocity equation of vi, two definitions are provided as follows: Definition 1: Speed-parameter The Speed-parameter is the strength of the force generated by the virtual potential field function that can be adjusted. The letter ‘a’ represents the Speed-parameter. This means that the Speed-parameter will have an impact on the size of the swarm.
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Definition 2: Balance-distance Assuming that there is a constant δ, when the distance between one UAV node and another UAV node is δ, the gravitational forces and repulsive forces between the two nodes are equal, then δ is called the Balance-distance between two nodes. A virtual navigator [4, 10] is set in the swarm to determine the location of the swarm. Therefore, there are two forces on the UAV node, one is the force of the virtual navigator on the node, and the other is the force of the other nodes in the swarm. The magnitude and direction of the resultant force are related to the distance between the node and other nodes and between the node and the virtual navigator. According to the swarming and collision avoidance rules, the velocity equation of Vi is established as follows: M
vi = g i x i − x L + g i j x i − x j , i = 1, . . . , M
(4)
j=1, j=i
The motion formula of node i can be expressed as: M
x˙i = g i x i − x L + g i j x i − x j , i = 1, . . . , M
(5)
j=1, j=i
In Formula (4) x i ∈ R n , x j ∈ R n , x L ∈ R n represents the positions of node i, node j and the virtual navigator respectively. g i (·) represents the relationship function between node i and the virtual navigator, and g i j (·) represents the relationship function between node i and other nodes. The functions g i (·) and g i j (·) are established based on gravitational force and repulsive forces, which are expressed as follows:
δi xi − x L 1− i g x − x = −ai i x − xL x − xL
δi j xi − x j g i x i − x L = −ai i 1 − x −xj xi − x j i
i
L
(6)
(7)
In Formula (6) and Formula (7), ai ∈ R + represents the Speed-parameter, which is used to adjust the strength of the potential field function of Vi . δi ∈ R + is the Balance-distance between node i and the virtual navigator. When x i − x L > δi and
i i L navigator on node i is gravitational force; g x − x < 0, the force of the virtual when x i − x L < δi and g i x i − x L > 0, the force of the virtual navigator on node i is repulsive force. δi j represents the Balance-distance between node i and node j, then δi j = δ ji can be obtained. The swarm forms a potential field function according to the values of a and δ. Under the control of the potential field function, the swarm will move in self-organization. The gravitational force/repulsive force between members mainly affects the formation configuration of the swarm. The gravitation force/repulsive force between the
Formation Control of UAV Swarm Based on Virtual Potential Field … Fig. 2 Schematic diagram of UAV swarm formation
801
δ1 δ12 = δ 21
δ 13
41
δ3
δ
32
=δ
=
4
δ
23
δ1
δ2
δ4
δ 24 =
= δ 31
δ 42 δ34 = δ 43
node and the virtual navigator mainly provides the configuration constraints of the swarm and guides the overall movement direction of the swarm. For an example that a drone swarm with four members and a virtual navigator, the schematic diagram of its formation is shown in Fig. 2. In Fig. 2, the four boxes marked with a number represent the UAV node, and the red dot in the upper right corner represents the virtual navigator.
3 Simulation and Analysis In the simulation, the UAV swarm is composed of 6 UAVs and a virtual navigator. The preset formation of the UAV swarm is shown in Fig. 3, and the 6 green squares represent the UAV nodes, the red circle represents the virtual navigator, the UAVs and the virtual navigator form a regular triangle formation. First, set the Speed-parameter of Formula (6) to 5 and the position of virtual navigator to (35, 35, 30), which is also the target point position. After the simulation is completed, the initial and final position coordinates of the 6 UAVs are shown in Tables 1 and 2, respectively. When T = 0 s, the position distribution of the UAV in the three-dimensional space is shown in Fig. 4. From the figure, it can be seen that the initial positions of the 6 UAVs are randomly distributed. Figures 5, 6 and 7 show the position distribution of the UAV swarm at t = 1.6 s, 3.8 s and 6.5 s. Fig. 3 Preset formation of drone swarm
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Table 1 The initial position coordinates of the UAV swarm UAV1
UAV2
UAV3
UAV4
UAV5
UAV6
x(i)/m
UAV (i)
2.59
12.36
17.63
19.71
20.46
10.75
y(i)/m
25.06
15.39
15.91
11.31
28.61
24.17
z(i)/m
8.10
30.06
30.21
26.62
21.84
16.61
Table 2 The final position coordinates of the UAV swarm UAV (i)
UAV1
UAV2
UAV3
UAV4
UAV5
UAV6
x(i)/m
38.29
42.23
46.67
36.54
38.92
44.12
y(i)/m
37.47
40.69
43.14
35.83
37.62
41.88
z(i)/m
30.59
30.06
30.21
26.62
21.84
16.61
Fig. 4 Initial positions distribution 60
40
z/m
UAV Virtual Leader
20
0 60 60
40
y/m
40
20
20 0
x/m
0
From Figs. 4, 5, 6, 7, 8 and 9, it can be seen that the 6 UAVs are in random positions at t = 0. When t = 1.6 s, the UAV swarms begin to gather into a swarm and move toward the virtual navigator. When t = 3.8 s, the UAV swarm reaches the desired position and forms a preset formation with the virtual navigator. From the simulation results, it can be seen that under the control of the potential field function, the UAV swarm can quickly gather from the dispersed state into a formation state, and can keep the formation configuration unchanged when moving in the direction of the virtual navigator and finally reach the desired position. The motion trajectory diagram of the UAV swarm in the three-dimensional space and the motion trajectory diagram in the x–y plane are shown in Figs. 8 and 9, the red dot represents the position of the virtual navigator, 6 curves of different colors represent the movement trajectories of 6 UAVs. It can be seen from Figs. 8 to 9 that the initial movement of the 6 UAVs is irregular, when they begin to form a swarm
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Fig. 5 Positions distribution at t = 1.6 s 60
UAV
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Virtual Leader
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Fig. 6 Positions distribution at t = 3.8 s 60
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formation, they fly to the virtual navigator neatly and move in the same direction. The trajectory diagram of the UAV swarm shows that the 6 UAVs quickly formed a formation, and kept the formation configuration unchanged during the movement, and finally reached the target position, realizing the formation control of the UAV swarm.
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Fig. 7 Positions distribution at t = 6.5 s 60
z/m
40 UAV
20
Virtual Leader
0 60 60
40 40
y/m 20
20 0
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Fig. 8 The trajectory diagram of the UAV swarm in three-dimensional space
4 Concluding Remarks This paper is based on the virtual potential field and set up a virtual navigator, using the principle of potential field superposition to realize the UAV swarm formation control. And the correctness and feasibility of this method are verified by simulation analysis. This method overcomes the shortcomings of the traditional UAV formation control method that the formation speed is slow and the accuracy is not high. The
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Fig. 9 The trajectory of the UAV swarm in the x–y plane
next step of research will be transferred to the formation control problem in the UAV swarm movement and the problem of the swarm formation change.
References 1. Niu Y, Xiao X, Ke G (2013) Operation concept and key technical analysis of UAV swarm. Natl Defense Sci Technol 34(5):37–43 2. Qiu H, Duan H (2014) From collective flight in bird flocks to unmanned aerial vehicle autonomous swarm formation. Chin J Eng 39(3):317–322 3. Lyu Y, Liu L, Yang X, Yao J, Yang Y (2019) Formation control of UAV swarm combining artificial potential field and virtual structure. Flight Dyn 37(3):43–47 4. Olfati-Saber R (2006) Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Autom Control 51(3):401–420 5. Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res 5(1):90–98 6. Reif JH, Wang H (1999) Social potential fields: a distributed behavioral control for autonomous robots. Robot Auton Syst 27(3):171–194 7. Howard A, Matari´c MJ, Sukhatme GS (2002) Mobile sensor network deployment using potential fields: a distributed, scalable solution to the area coverage problem. In: Distributed autonomous robotic systems, vol 5. Springer, Berlin, pp 299–308 8. Spears WM, Gordon DF (1999) Using artificial physics to control agents. In: International conference on information intelligence and systems. IEEE, New York, pp 281–288 9. Couzin ID, Krause J, James R et al (2002) Collective memory and spatial sorting in animal groups. J Biol 218(1):1–11 10. Yu H, Wang Y, Liu L (2006) Control of flocking motion of the flock with a leader based on dynamic topology. Syst Eng Electron 28(11):1721–1724
Application of Computer Image Processing Software in Interior Design Qing Li
Abstract With the development of computer software technology, computer image processing software has played a very important role in the process of interior design. It can provide creativity and color display for graphic design, and improve the aesthetics of interior design. For interior designers Generally speaking, it is a very important auxiliary tool. This article introduces the development and types of computer image processing software in interior design, mainly taking the application of photoshop as an example, explaining the steps and application effects of its image processing, and giving some opinions on how to promote its application in graphic design And suggestions. Keywords Computer image processing software · Interior design · Photoshop With the development of computer technology and the progress of image processing technology, computer image processing software has begun to be used in all walks of life, and has achieved outstanding results. In terms of interior design, image processing technology can already make all-round system adjustments to design drawings, and even outline decoration renderings, and fully adjust the light and dark colors of the renderings. Therefore, it has become more and more popular among interior designers. In the interior design process, the computer’s two-dimensional drawing capabilities can greatly improve the efficiency of our drawing work. Compared with paper drawing, computer image processing software can achieve the accuracy and modifiable requirements of drawings, and even provide follow-up Provide the basis for 3D modeling. In the 3D modeling stage, the designer can easily perform structural perspective on each surface of the object, modify and create the material and lighting of the miniature model, and can also reasonably arrange the location of the furniture and the type of lighting. And the distribution of light and shade, etc., to help designers further improve the design level, and more convenient to show design solutions to Q. Li (B) Shandong Huayu University of Technology, Dezhou, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_90
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customers. If the designer can add some dynamic expression units on this basis, there will be better design effects. For example, image processing software can be used to vividly express the motion state and walking effect of the object, which will make it more Realistic, make customers more substituting, and increase customer satisfaction. In summary, computer image processing software is of great significance to interior design. It is necessary for us to understand the relationship between image processing software and interior design, so that we can better carry out interior design work and improve the design level [1–3].
1 The Application of Computer in Interior Design Similar to foreign computer-aided design, my country’s computer-aided design also originated in the 1970s. In the 1990s, people began to pay attention to software development in graphics, and computer-aided design technology began to spread in our country and gradually became a trend. After nearly 30 years of development, today, my country’s computer-aided design has been divided very systematically. From computer design sketches to computer room virtual reality, people have been able to use computer-aided interior design work very proficiently.. In the process of computer interior design, the commonly used computer sketching tools include SketchUp and 3DHome. This computer software can enable people to express the design and expression of the preliminary plan very squarely [4]. It is simple and easy to learn, and can complete the preliminary plan in a very short time. Design is an excellent tool for designers to express their design ideas. However, there are still relatively few research topics on the combination of computer technology and interior design, and there is still a lack of systematic guidance methods that can enable designers to fully grasp relevant technologies. Therefore, by understanding the influencing factors of computer technology in interior design, can we in turn promote the common progress of our interior design work and computer software technology, and can more conveniently explore a computer interior design theory suitable for social needs and realize social value [5, 6]. In the interior design process, the later decorative design is the key to interior design, and the overall design level of the renderings directly reflects the designer’s design ability. How to better handle the contrast of light and dark colors and color rendering of the renderings, and how to better add ornaments to increase the breath of life, are all issues that designers need to consider in the design process. In the later software processing process, we can make full use of Photoshop to solve these problems. For example, we can emphatically consider the ratio between the background and the three-dimensional model, handle the angle relationship between the scene and the perspective view, and pay attention to the consistency of size [7, 8].
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2 Types of Computer Software in Interior Design Work AutoCAD is the most commonly used drawing software in our interior design work. It is a product developed by Autodesk in 1982 and the following decades. After dozens of updates and upgrades, it has now developed into the best software in the graphics field, and has gradually been widely used in architectural design, civil engineering, chemical machinery and other industries, and is deeply loved by designers. At this stage, AutoCAD already has the function of 3D drawing, capable of 3D modeling and model rendering, which brings great convenience to practitioners. When using 3DMax, you can directly import the AutoCAD surface map, and on this basis, it can be very convenient to process, for example, you can directly perform coloring. It’s worth mentioning because 3Dmax Simple operation, powerful functions, and many plug-INS have become the most commonly used 3D design software for designers [9]. Vray renderer is another excellent lighting calculation renderer, its program is small, but the function is extremely powerful. It is fully compatible with various applications, such as 3DMax, etc. It can be very convenient to build models in 3DMax and quickly render it, especially with the addition of VrayMilWr that is per material and VrayMap texture material [10, 11]. PhotoShop is also one of Adobe’s most famous images processing software. It is quite different from the AutoCAD application field. It mainly processes pictures. It can complement AutoCAD in the interior design process. It integrates scanning, editing, modification, Image production, advertising creativity, image input and output are integrated, which can enhance the effect of insufficient performance in the 3Dmax rendering to bring the audience a more vivid, natural and real picture [12]. Finally, Premiere Pro video editing software is sometimes used in the actual interior design process. Its picture quality is very high, and it is compatible with other Adobe software, and is currently widely used in the field of video editing. The focus of computer interior design lies in computer two-dimensional, three-dimensional drawing and picture processing. It is necessary to conduct in-depth analysis. Pay attention to the detailed analysis of the pros and cons of the design plan in combination with the characteristics of the building structure, and make scientific research on each element of the design plan as much as possible. Judgment of sex, only by fully grasping the layout and structure can we make excellent design works. Computer interior design is a transformation of the traditional industry and has injected new vitality into the development of the interior design industry.
3 The Design of Interior Renderings In the post-processing process of interior design renderings, we must first clarify the definition of interior renderings. When interior designers carry out interior design,
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they need to display the two-dimensional or three-dimensional structure of the interior through computer image processing software, and comprehensively display the appearance, structure, color, light and shade, texture, and materials of interior decorations and furniture. The renderings show customers their design concepts and value points, and let customers feel the beauty of the design. Customers can experience the beauty of the design through the renderings displayed by the design, and can clearly feel the types, materials and effects of interior decoration. For example, using Photoshop as the processing software, designers do not need to do it overnight, they can fully combine various factors, and have enough time to pick the essence and remove the dross. When the user is not so satisfied with the design plan, he can immediately modify it according to the user’s concept. This requires the designer to show the user’s needs in an all-round way. Only in this way can the customer be satisfied [13]. In addition, when the Photoshop software is used for post-processing, the user’s needs can also be displayed in advance to meet the actual needs of the user, which not only saves the waste of time, but also saves the waste of materials, and avoids rework in the later decoration process. It should be noted that we have to strictly post-process the renderings.
3.1 Evaluation of the Renderings In the post-processing process of interior design, the first thing to do is to evaluate the renderings. The problems targeted by the design renderings need to be clarified, and all problems in the design process need to be comprehensively analyzed and processed. Only in this way can the problem be solved. For example, when processing an image that needs to be rendered, you need to observe the image in detail. Different observation angles have different problems. For example, you can observe the advantages and disadvantages of contrast, color matching, or structural layout, and strive to make the design images more vivid and not dull. In the design process of the living room, a photosensitive template can be used on the sofa to enhance the brightness [14].
3.2 Starting from the Overall Rendering When adjusting the renderings, you need to start from the whole and grasp the relationship between the whole and the parts. It is necessary to make the parts fine and detailed, but also to make the whole harmonious and beautiful, and don’t make the parts out of touch. In addition, it is necessary to pay attention to the color tone of the interior, and the texture of the decoration can be improved to a large extent through reasonable matching. Therefore, it is necessary to carefully arrange the modeling colors and the screen tailoring. Take photoshop as an example, it can play a big role in the post-processing of interior design. First of all, in terms of adding scenery,
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Fig. 1 The decoration effect after matching lighting, color and shadow
photoshop can strictly compare pictures, and then process pictures from different sources. Sometimes the pictures downloaded directly from the Internet cannot be used directly, and they need to be cut out or processed in other ways. This prompts software users to master the use of Photoshop proficiently, so that the pictures can be perfectly integrated into the entire design work. Users like some furniture with brighter colors and high reflectivity. We need to add shadow or reflection effects in the design, so as to increase the realism of the design and make it easier to impress customers. For example, in the following renderings, through the combination of colors, lighting and shadows, the whole decoration effect is softer and more comfortable (Fig. 1). In short, photoshop can make the effect in terms of configuration additions. The picture is more real and natural. Secondly, it is more convenient to make light effects. In the previous light effect treatment process, many people used 3DSMAX for processing, but there was a disadvantage of taking a long time. The use of photoshop software can greatly reduce the design time. For example, photoshop has unparalleled advantages in the production of indoor main light sources and wall arc light sources. The steps of making light effects are also very convenient. Only the required layers are required. Fill the position with the light effect and the color of the light source, and then use the layer mask color dodge to complete the corresponding effect. In terms of color adjustment, photoshop has been very successful in adjusting color and light effects. Transparency, saturation, and color selection can all be adjusted. Finally, the production of mosaic materials. Although many mosaic materials can be found through the material library at this stage, most of the mosaic materials actually needed are designed and made by the designer. Using image processing software can help the designer complete the idea of parquet materials, which can be continuously revised and improved. If the designer is proficient in the production skills of parquet materials, then it is a very quick and simple thing to design beautiful parquet materials.
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4 Concluding Remarks In the process of interior design, computer image processing technology has played a very important role. 3DMAX, photoshop, AutoCAD and other software are more and more widely used by designers, making design work easier and more convenient, and users can feel more intuitively. The designer’s concept and a clearer understanding of the results of the decoration can reduce the cost of communication. In short, in interior design work, graphics processing software is very necessary to bring better visual effects to the public, and also play a due role in advertising and dynamic display, which further promotes graphics and image processing. Promotion of software. Acknowledgements Public Visual Art, Humanities and Social Science Research Base of Shandong Huayu Institute of Technology (No. 6).
References 1. Wolper VE (2020) Photograph restoration and enhancement: using adobe photoshop CC 2021 version. Mercury Learn Inf 2. He J (2020) Interior decoration design and construction technology of hardcover room. J Phys Conf Ser 1649(1) 3. Qiu Z (2018) Analysis of the integration of indoor ecological landscape design and interior decoration design. In: Proceedings of 2018 4th workshop on advanced research and technology in industry applications (WARTIA 2018). International Information and Engineering Association: (Computer Science and Electronic Technology International Society), vol 6 4. Liu L, Shen W, Bai Y (2018) Interior decoration design based on AUTO-CAD. Advanced science and industry research center. In: Proceedings of 2018 3rd international conference on electrical, control and automation engineering (ECAE 2018). Advanced science and industry research center: science and engineering research center, vol 4 5. Yang Y, College of Design Guangzhou Academy of Fine Arts Guangzhou, China (2010) Application of artistic wallpaper in soft interior decoration design. In: ASME Press (ed) Proceedings of the international conference on optimization design (ICOD 2010). ASME Press, pp 68–72 6. Rohde L, Jensen RL, Larsen OK, Jønsson KT, Larsen TS (2021) Holistic indoor environmental quality assessment as a driver in early building design. Build Res Inf 49(4) 7. Wang R, Zhu Q (2021) Application analysis of BIM technology in green intelligent building design. IOP Conf Ser Earth Environ Sci 768(1) 8. Carvalho JP, Bragança L, Mateus R (2021) Sustainable building design: analysing the feasibility of BIM platforms to support practical building sustainability assessment. Comput Ind 127 9. Gao X (2021) Application of computer-based simulation technology in green building design. J Phys Conf Ser 1744(2) 10. Feng W, Yin D (2021) Research on design and construction key points of HVAC engineering in civil engineering under computer-aided technology. J Physi Conf Ser 1744(2) 11. Zhang X (2021) Energy-saving design of civil engineering buildings based on FPGA and embedded system. Microprocess Microsyst (prepublish)
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12. Guo D, Onstein E, La Rosa AD (2020) An approach of automatic SPARQL generation for BIM data extraction. Appl Sci 10(24) 13. Wang M, Yang Q (2020) Green building design based on 5G network and Internet of Things system. Microprocess Microsyst (prepublish) 14. Crawford RH, Bunster V (2020) A web-based tool for streamlining environmental decisionmaking in building design. IOP Conf Ser Earth Environ Sci 588(2)
The Application of Computer Technology in the Development and Analysis of Enterprise Intellectual Capital Kunseng Lao and Yixing Zhou
Abstract In today’s society, with the rapid development of computer technology, the use of computer technology has a huge impact on business operations, especially Internet enterprises. In the informatization construction of the Internet industry, the role of computer technology is irreplaceable. The wide application of computer information technology in the workplace of the entire enterprise has greatly improved the quality and efficiency of the work of the enterprise, and has reduced the investment cost of the entire enterprise. At the same time, it also promotes the effective and reasonable allocation of human resources within the enterprise and the improvement of corporate social responsibility awareness. New computer technologies have continued to emerge in recent years: blockchain, big data, Internet of Things, artificial intelligence, etc. This requires enterprises to have sufficient intellectual capital to survive fierce competition. Intellectual capital like human resources has become the key to enterprise survival in the fierce industry competition, and it is also one of the important factors for enterprises to achieve their social responsibilities. Therefore, how to use computer technology to improve the quality of enterprise intellectual capital and promote the development of enterprise intellectual capital is particularly important. Based on the use of computer technology in Internet companies, this paper analyzes the impact of enterprise intellectual capital and social responsibility on enterprise performance in the context of continuous updating and development of computer technology. Keywords Computer technology · Intellectual capital · Enterprise performance · Internet industry
K. Lao (B) · Y. Zhou Macau University of Science and Technology, Macau, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_91
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1 Introduction For Internet enterprises, intangible intellectual capital is the key to their survival [1]. Most of the products of enterprises are built on virtual networks, and intangible assets occupy a dominant position among Internet enterprises. New computer technologies have been emerging in recent years: blockchain, big data, Internet of Things, artificial intelligence, etc. This requires enterprises to have enough intellectual capital to survive the fierce competition. However, behind the prosperity of the Internet industry, there are also many problems. There are many places that the existing regulatory forces cannot take into account, and what follows is a bad social impact.
2 Research Background For Internet enterprises, intangible intellectual capital is the key to their survival. Most of the products of enterprises are built on virtual networks, and intangible assets occupy a dominant position among Internet enterprises. For example, the development of Internet products requires talents who understand computer-related knowledge, and the technology mastered by these talents is part of the enterprise’s intellectual capital. Moreover, new computer technologies have been emerging in recent years: blockchain, big data, Internet of Things, artificial intelligence, etc. this requires enterprises to have enough intellectual capital to survive the fierce competition [2]. However, behind the prosperity of the Internet industry, there are also many problems. There are many places that the existing regulatory forces cannot take into account, and what follows is a bad social impact. For example, the sharing economy “grows savagely” under the power of capital, and the excessive hype of the sharing economy by the domestic media attempts to cover up its poor service level and lax operation and supervision [3]. In the second half of 2017, a large number of enterprises using the sharing economy as a gimmick emerged, absorbing a large amount of “deposits” invested by users in the name of “sharing”. After the enterprises run away, the user’s deposit cannot be returned, which brings great losses to the user’s property safety.
3 Research Purpose At present, there is almost no research on the relationship between the intellectual capital of Chinese Internet enterprises and corporate performance. After the background introduction of the previous part, we have learned that because Internet enterprises have penetrated into the society and are closely connected with people’s daily
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lives, Internet enterprises should also bear the burden of society responsibility. Liu [4] once discussed the relationship between smart capital and social responsibility for commercial banks [4]. Therefore, this paper attempts to combine the two factors of corporate intellectual capital and social responsibility, and discuss the changes in corporate performance under the combined effect of the two. This paper took Internet companies listed on China’s A-shares as a research sample, used scientific methods to measure smart capital and enterprise social responsibility, and obtained reliable conclusions through empirical analysis, and provided preliminary suggestions for the better development of Chinese Internet enterprises in the future. At the same time, it is hoped that the research conclusions of this paper can provide reference for future related research.
4 Research Methods This paper divided the variables into dependent variables, independent variables and control variables. The choice of variables was done by summarizing the relevant factors related to the impact of smart capital on corporate performance and the impact of social responsibility on corporate performance in the relevant literature. Related research hypotheses.
4.1 The Relationship Between Human Capital and Corporate Performance People are an important factor that constitutes an enterprise, and talents are the guarantee for the efficient operation of an enterprise. The formation of the core competitiveness of an enterprise is inseparable from human capital, and enterprises with rich human capital are more likely to obtain long-term economic benefits [5]. Enterprises can help employees to grow better through regular training of employees. The knowledge, skills, and experience of employees are an important part of the enterprise’s human capital. Every investment in employees will further optimize the enterprise’s human capital and various abilities of employees will also improve the output efficiency of the enterprise. This put forward the first hypothesis of this paper: H1: There is a positive correlation between human capital and enterprise performance.
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4.2 The Relationship Between Structural Capital and Corporate Performance Structural capital plays an important role in maintaining the healthy and stable development of an enterprise. The structural capital of an enterprise includes the structure of the enterprise’s culture, superior-subordinate relationship, and the enterprise system. If an enterprise has a good structured network, its employees will have a better working environment, which can not only significantly improve the work efficiency of employees, increase the enterprise’s output, but also reduce the enterprise’s operating costs [6]. In addition, if the enterprise has a reward and punishment system, it can further stimulate the potential of employees and help them grow better. For Internet enterprises, the repetitive calculation of product updates is very fast. From collecting product usage information, listening to user feedback, to understanding user needs, adding or deleting product functions, if the organizational structure of the enterprise team is inefficient and communication. If the work is not smooth and cannot be completed in a short time, the enterprise’s products may be replaced by other products on the market. Therefore, a good organizational structure is very important for Internet enterprises. This put forward the second hypothesis of this paper: H2: There is a positive correlation between structural capital and enterprise performance.
4.3 The Relationship Between Relationship Capital and Corporate Performance As a component of intellectual capital, relational capital is embodied in the relationship between the enterprise and the external environment. This relationship is the interactive relationship between the enterprise and the government, customers, suppliers and other stakeholders [7]. The survival and development of an enterprise cannot do without coordination and interaction with external relations. For Internet enterprises, relationship capital is also very important. From the user’s point of view, listening to users’ suggestions in a timely manner can help enterprises make better products. From the supplier’s point of view, due to the huge number of Internet products, if the enterprise does not have a good relationship with the supplier, its products may be uninterested. Therefore, in order to gain market share, product promotion costs on major network platforms are indispensable. This put forward the third hypothesis of this paper: H3: There is a positive correlation between relationship capital and enterprise performance.
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4.4 The Relationship Between Social Responsibility and Corporate Performance An enterprise’s active fulfillment of its social responsibilities will send a positive signal to all sectors of society to show that it is trustworthy, so as to obtain support and improve the enterprise’s reputation. This behavior is conducive to the sustainable development of the enterprise and improves its financial performance. In today’s Internet age, the speed and wide scope of public opinion dissemination have significantly accelerated this effect. An enterprise’s negative news may immediately cause sharp fluctuations in its stock price. This put forward the fourth hypothesis of this paper: H4: There is a positive correlation between social responsibility and enterprise performance.
4.5 The Model The model was set as: ROEit = β0 + β1 H C E it + β2 SC E it + β3 RC E it + β4 C S Rit + β5 S I Z E it + β6 L E Vit + μit Among it: β0 is the constant term;βi is the model regression coefficient; µ is the error term; i = 1,2,3……N, representing the i-th company; t = 1,2,3……N, representing the year of the sample.
5 Empirical Analysis This paper referred to the list of “Top 100 Chinese Internet Companies” released by the Internet Society of China from 2013 to 2017, and selected 51 Internet companies listed on China’s A-shares as the research sample. For the investigation of social responsibility, the professional evaluation system of corporate social responsibility reports on Hexun.com was used as an indicator to extract the ratings of the corresponding companies. Most of the other financial data was collected from Bloomberg and CSMAR database. Some missing data was collected from the financial reports of various listed companies collected by Sina Finance. This paper selected the relevant data of the sample companies from 2010 to 2016 for a total of 7 years. Due to the late launch of some companies, only part of the time period data could be collected. These data formed an unbalanced panel data composed of 352 observations.
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Table 1 Descriptive statistics Minimum value
Maximum value
Mean value
Standard deviation
ROE
− 0.53
0.85
0.10
0.10
HCE
− 1.94
8.97
1.99
1.08
SCE
− 1.49
8.87
2.53
1.37
RCE
− 1.37
37.87
4.87
5.01
CSR
− 18.22
72.51
27.27
13.89
SIZE
18.09
25.64
21.42
1.29
LEV
0.01
0.78
0.31
0.19
5.1 Descriptive Statistics It can be seen from Table 1 that the standard deviation of ROE is only 0.1, which shows that the performance of the sample enterprises is very close to the average. Among the capital increase coefficients of smart capital, the value-added coefficient of relational capital has the largest change, with the maximum value reaching 37.87, the minimum value is − 1.37, and the standard deviation is 5.01, the largest of the three coefficients. It can be seen that the influence of relational capital on firm performance is very unstable. The maximum value of social responsibility is 72.51, the minimum value is − 18.22, and the average value is 27.19. This shows that the social responsibility of various enterprises is quite different, and the standard deviation is 13.89, which shows that the social responsibility of Chinese Internet enterprises is uneven, and it also shows that most the social responsibility of Chinese Internet enterprises is at a relatively low level according to Hexun.com’s rating standards. From the perspective of the standard deviation of enterprise size and asset-liability ratio, the scale and assetliability ratio of the sample enterprises selected in this experiment are very close to the average level.
5.2 Correlation Analysis From Table 2, it can be roughly seen that the maximum correlation coefficient does not exceed 0.7, so there is no obvious collinearity problem between the variables, and it will not affect the regression results. The three elements of smart capital and social responsibility are significantly positively correlated with enterprise performance, among which human capital has the greatest impact on enterprise performance. Therefore, the hypothesis of this paper is basically verified. However, the enterprise size in the control variable has a significant negative correlation on performance, and the effect of enterprise assetliability ratio on performance is not significant.
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Table 2 Correlation analysis ROE ROE
1.000
HCE
0.584**
HCE
SCE
RCE
CSR
SIZE
LEV
1.000
0.0000 SCE
0.475** 0.0000
0.0000
RCE
0.251**
0.443**
0.193**
0.0000
0.0000
0.0003
0.224 **
0.122*
0.280**
0.051
0.0000
0.0215
0.0000
0.3343
SIZE
− 0.288**
− 0.153**
− 0.046
− 0.177**
LEV
0.029
CSR
0.0000 0.5868
0.457**
0.0040 − 0.115* 0.0311
1.000 1.000
0.3872 − 0.152**
1.000 0.425**
1.000
0.0008
0.0000
− 0.194**
0.189**
− 0.575**
0.0002
0.0003
0.0000
0.0041
1.000
*
At the level of 0.05, the correlation is significant ** At the level of 0.01, the correlation is significant
5.3 Regression Analysis From the results of the mixed regression, the value of F statistic is 78.13, the value of P is significant, and the value of R 2 is 0.5761. In the acceptable range, the regression result fits well. All coefficients except RCE are significant at the level of 0.01. Both the human capital appreciation coefficient and the structural capital appreciation coefficient have a significant positive correlation with corporate performance, which is consistent with the assumptions H1 and H2 . The coefficient of HCE is 0.038 and the coefficient of SCE is 0.019, which shows that among the three elements of smart capital, human capital has a greater impact on corporate performance than the other two factors. In addition, the social responsibility and corporate performance that this article focuses on are also significantly positively correlated, which is consistent with Hypothesis H4 (Table 3). It can be seen from Table 4 that after the fixed effects regression, the R 2 of the model rises from 0.5777 to 0.6444, which shows that the model’s fit has improved. The p value of the F test in the last line is 0.0000, so the null hypothesis is strongly rejected H0 : all u i = 0, indicating that each individual should be allowed to have its own intercept term. Therefore, a fixed-effect model should be selected in the mixed regression model and the fixed-effect model. After random effects regression, the model’s R 2 = 0.64, which is basically the same as the fixed effects model’s R 2 . The results of the random effects model show that human capital, structural capital, and enterprise social responsibility are all significantly positively correlated with corporate performance, but relational capital still has no significant correlation with enterprise performance. Subsequently, the
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Table 3 Regression analysis Variable
Coefficient
Std. error
t-statistic
Prob.
HCE
0.0381696
0.0041326
9.24
0.000
SCE
0.0187422
0.0030851
6.08
0.000
RCE
− 0.0003026
0.0008134
− 0.37
0.710
CSR
0.001956
0.0003032
6.45
0.000
SIZE
− 0.0430678
0.0037605
− 11.45
0.000
LEV
0.2024558
0.0237116
8.54
0.000
C
0.7877265
0.0755351
10.43
0.000
Weighted statistics F(6, 345)
78.13
R-squared
0.5761
Prob. > F
0.0000
Adj R-squared
0.5687
Table 4 Fixed effects regression Variable
Coefficient
Std. error
t-statistic
Prob.
HCE
0.0402669
0.0056542
7.12
0.000
SCE
0.0275246
0.0048029
5.73
0.000
RCE
0.0012154
0.0011936
1.02
0.309
CSR
0.0009456
0.0003375
2.80
0.000
SIZE
− 0.0478352
0.0047163
− 10.14
0.000
LEV
0.3180815
0.0335289
9.49
0.000
C
0.8477311
0.996011
8.51
0.000
Weighted statistics R2 : within
0.6442
F(50, 295)
4.91
R2 :
0.4535
Prob. > F
0.0000
between
R2 : overall
0.5452
LM test is performed on the individual effects of the random effects model. The original hypothesis is H0 : σu2 = 0. If H0 is rejected, it means that there is a random disturbance term u i that reflects the individual characteristics in the random effects model, that is to say, in the mixed regression and random effects mode, the latter should be selected (Table 5). The results in Table 6 show that the p-value is 0.0000, which strongly rejects the null hypothesis that “there is no individual random effects”, so the random effects model should be selected.
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Table 5 Random effects regression Variable
Coefficient
Std. error
t-statistic
Prob.
HCE
0.0399514
0.0046607
8.57
0.000
SCE
0.0240238
0.0037318
6.44
0.000
RCE
0.0004588
0.0009759
0.47
0.638
CSR
0.0013324
0.0003119
4.27
0.000 0.000
SIZE
− 0.0466387
0.0040673
− 11.47
LEV
0.2670783
0.0275624
9.69
0.000
C
0.8408519
0.0836935
10.05
0.000
Weighted statistics R2 : within
0.6400
Wald chi2(6)
551.61
R2 : between
0.4982
Prob. > chi2
0.0000
R2 : overall
0.5644
Table 6 LM inspection
6 Conclusion This paper referred to the definition of Internet enterprises by the Internet Society of China, collected data on 51 representative companies listed on A-shares from 2010 to 2016, and obtained the final conclusion after analyzing the panel data. The research results show that human capital and structural capital in smart capital have a positive impact on enterprise performance, and enterprise social responsibility also has a positive impact on enterprise performance. After introducing the interaction item of relationship capital and social responsibility, it is concluded that relationship capital and enterprise performance are significantly positively correlated.
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6.1 Human Capital is the Main Influencing Factor The empirical research shows that among the three components of corporate intellectual capital, human capital and structural capital have a significant positive correlation with enterprise performance, and human capital has a greater impact on enterprise performance. This conclusion is the same as most previous studies on other industries. It shows that no matter what type of enterprise, human capital is always an important factor that cannot be ignored. Moreover, the regression coefficient of human capital is the highest, which shows that human capital is the most influential factor in intellectual capital. The introduction of excellent talents, improving the life and welfare of employees, and creating a good working environment for employees can significantly improve enterprise performance.
6.2 The Influence of Structural Capital is Slightly Weaker, But It Cannot Be Ignored Structural capital is the basis for human capital to be effective, and the investment of structural capital by enterprises can significantly promote the improvement of enterprise performance. The good management structure and cultural atmosphere of Internet companies can significantly improve employees’ enthusiasm for work and increase employee output efficiency, which can play an icing on the cake for enterprise performance. In the long run, enterprises with rich structural capital can also attract more outstanding talents, which will promote the further growth of the enterprise.
6.3 Relationship Capital Interacts with Social Responsibility Different from the conclusions of other similar studies, without considering the interaction, another element of intellectual capital has no significant relationship between capital and enterprise performance. Considering the interaction, there is a significant positive correlation between relationship capital and enterprise performance. Since Internet companies are based on the Internet, they are very different from traditional enterprises, especially in terms of operation. Nowadays, many Internet products have changed people’s lives and become a part of people’s lives. Therefore, users are very sticky to Internet products, and it is difficult to imagine what life is like when people leave the Internet. It can be seen that the relationship between Internet enterprises and various stakeholders is very complex, and it can be inferred that the relationship capital and social responsibility of Internet enterprises overlap in some aspects, and the two interact to a certain extent.
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6.4 It is Necessary to Pay Attention to the Impact of Social Responsibility on the Enterprise The influence of Internet enterprises is enormous today. Due to the characteristics of the Internet, Internet enterprises are often born with the attributes of communication media, and the performance of enterprise social responsibilities is more easily known to the public [8]. Enterprises with high social responsibilities are more likely to gain people’s goodwill, and the society’s recognition of enterprises will also be higher. On the contrary, if an enterprise’s social responsibility is poor, it will be attacked by public opinion immediately, which will have an impact on the enterprise’s business. Enterprise social responsibility is closely related to the relationship capital in intellectual capital, and good performance of social responsibility is easier to strengthen the enterprise relationship capital. The improvement of enterprise social image is more likely to be recognized by stakeholders, thereby improving financial performance.
References 1. Huang S (2020) The rise of smart capital-first of the series of research on intellectual capital. Mod Account 05:06–09 2. Zhao Y (2013) The surreal significance of Hanno Roberts’ smart capital. Shanghai Hong Kong Econ 08:60–61 3. Liu M (2021) The development of sharing economy from the perspective of new institutional economics. Jiangsu Commercial Forum 06:26–30 4. Liu B, Liu J, Li Y (2013) An analysis of the management quality theory of the senior management team. Leadership Sci 25:51–52 5. Yan X (2021) How to exert the agglomeration effect of human capital in the upgrading of industrial structure: Take Chongqing as an example. J Chongqing Univ Arts Sci 40(02):56–65 6. Zhu L (2021) The adjustment and optimization strategy of enterprise human resource management structure based on big data. Mod Marketing 15:162–163 7. Deng L, Huang J, He J (2021) Research on the impact of relationship capital on the innovation performance of enterprises—the intermediary effect of supply chain collaboration. Technol Innov Manage 42(02):183–189, 204 8. Shiyi X, Chen Y (2021) Institutional environment, corporate social responsibility performance and violations of listed companies. Finance Account Monthly 03:127–134
Design and Implementation of a Blockchain-Based International Trade Stable Digital Currency Issuance System Xin Wang
Abstract With the development of Internet technology, digital currency is playing an increasingly important role in the field of international trade. The emergence of digital currency makes it play an increasingly important role in the fields of electronic payment and financial services. Blockchain technology enables paperless transactions and data sharing. At present, blockchain technology has become one of the research hotspots in the international financial field. Due to the vigorous development of international trade, the demand for digital currencies is also increasing. To this end, this article designs a digital currency issuance system based on blockchain technology for the stability of international trade. This article mainly uses the data analysis method to investigate and analyze the current situation of international trade, then uses the data collection method to understand the blockchain in depth, uses the experimental method to design and test the digital currency issuance system, and finally interviews the sampled users for questionnaires survey. Experimental research results show that 86.3% of people believe that the test content of the system is relatively comprehensive and has a certain significance for the stable issuance of digital currency. Keywords Blockchain · International trade · Digital currency · Issuance system
1 Introduction With the rise of blockchain technology, in the era of economic globalization, business has transcended national boundaries. However, in international trade, both importers and exporters are likely to have different concerns, which hinder the smooth progress of the transaction. Therefore, for the stability and security of international trade, there is an urgent need to standardize the transaction and issuance of digital currencies. Therefore, the design and development of the digital currency issuance system is actually the icing on the cake for the development of trade. X. Wang (B) Huizhou Economics and Polytechnic College, Huizhou, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_92
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Many people have studied the design of digital currency issuance system based on blockchain. For example, Chai H. proposed that digital currencies have received widespread attention from central banks in recent years. As an important financial infrastructure, the UnionPay network is expected to provide effective support for the construction and operation of the digital currency system. Based on the UnionPay network, he systematically proposed a digital currency information system from the three aspects of digital currency account opening, exchange and use, and further elaborated the operating mechanism of the digital currency information system prototype. He also discussed how to apply blockchain technology to digital currency, such as using distributed ledgers for digital currency verification and registration [1]. Dilhani I believes that digital payment systems are an evolving field today, and seamless digital currencies have recently been enhanced. Therefore, although digital payment systems based on cryptocurrencies have many benefits, their adoption and dissemination in the field of universal payment platforms have been greatly hindered. The blockchain architecture is widely regarded as a promising mechanism to support the management of cryptocurrency-related transactions. However, due to various security threats and the existing high computational cost or domain-dependent prevention mechanisms, ensuring the security of digital payment transactions is a challenging task [2]. The main content of this article is to explain the development status of international trade, introduce in detail the level, category and key technology of blockchain technology, analyze the needs of the business module, function, performance and other aspects of the digital currency issuance system, and then carry out the system design and experiment. In the end, it is concluded that the system needs to be improved.
2 Blockchain-Based Digital Currency Issuance System 2.1 The Development Status of International Trade At present, the adverse impact of the financial crisis on international trade has gradually become prominent. The financial crisis has a greater impact on the real economy, and under economic globalization, the occurrence of financial crises can be said to be commonplace. Developed countries are still centers of international trade. Although our country’s international trade continues to grow in the cracks, among the major trading economies, the developed countries are still the main ones. Affected by free trade agreements, in today’s international trade, all major countries have sought the dominance of regional trade. As the threat of the financial crisis continues to emerge, countries have adopted different forms of trade protectionism in order to protect or inhibit the development of certain countries. The generation and sharpening of tariff barriers and technical barriers are also not conducive to the steady development of international trade [3, 4].
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2.2 Introduction to Blockchain Technology (1)
Blockchain level
The first is the data layer. The data layer encapsulates the underlying data in the form of blocks, which mainly implements functions such as data storage, accounts, and transactions. The second is the network layer. The blockchain network is decentralized or weakly centralized. The network layer includes P2P networking mechanism, data dissemination and verification mechanism, etc. The third layer is the consensus layer [5, 6]. The consensus layer encapsulates the consensus algorithm of each network node and is the core of the blockchain. The basic principle can be simply expressed as formula (1). R AN D(g, k) ≤
N N R AN D(g, k) ≤ c c
(1)
N represents a very large number, and c is the difficulty value of the block header member. RAND is a conceptual function. The fourth layer is the incentive layer. The incentive layer integrates economic factors with blockchain technology, including the issuance of rewards and distribution mechanisms. The fifth layer is the contract layer. The contract layer is the operating environment of scripts or smart contracts, and is the prerequisite for blockchain programming. Finally, there is the application layer. The application layer directly faces users and is a specific application of the blockchain. (2)
Blockchain category
In order to adapt to different application scenarios, blockchain technology also presents a differentiated development trend. According to different architectures and application scenarios, they are usually divided into three categories: public chains, alliance chains and proprietary chains [7, 8]. (3)
Key technology of blockchain
Blockchain technology is a technology synthesized by computer classic technology, and its key technologies mainly include consensus algorithm, smart contract technology and encryption technology. (1)
Consensus algorithm
The POS algorithm is developed on the basis of POW. It no longer completely relies on hash operations to compete for the system’s accounting rights, but uses the size of the token rights owned by nodes to compete for the accounting rights. It is based on the assumption that people with more tokens are less likely to damage the system, while people with fewer tokens are unlikely to have enough rights to damage the
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system. The concept of CoinAge is introduced in the POS consensus mechanism, and its calculation formula is as follows: Coin Age = Coin Amount × AgeCoin Age = Coin Amount × Age
(2)
Among them, CoinAmount is the amount of currency held, and Age represents the time of holding the currency. (2)
Smart contract
The emergence of the blockchain has greatly enriched and developed the smart contract technology. The blockchain smart contract is defined as a computer program that can be automatically executed once it is successfully deployed, enabling the blockchain system to program and manipulate data flexibly. (3)
Encryption technology
The public key used in the asymmetric encryption process is mainly used for encryption and made public, while the private key is mainly used for decryption and is kept secret. Blockchain systems usually use asymmetric encryption technology to determine the identity of participants and their permissions. The data processing of the hash algorithm is one-way, which can verify whether the data has been modified. The basic principle of digital signature is to add a piece of information after the data unit as a proof of the sender’s identity [9, 10].
2.3 System Requirements Requirement analysis is a key process of software engineering. It plays an outline and guiding role and provides guidance for the orderly and correct development of the entire software. Demand analysis mainly analyzes the user’s interaction, business process, and program feasibility. A correct understanding of business scenarios can develop a software system that meets business needs. Therefore, detailed demand analysis is essential [11, 12]. (1)
Functional requirements
After comparing various issuance systems and analyzing the functional modules of the blockchain-based digital currency issuance system, combined with the characteristics of the blockchain, the functions should include user management, user registration, digital currency issuance application, information review, and information modules such as query and information verification. In terms of user roles, it is divided into ordinary users and institutional users. The functional requirements of this system are shown in Fig. 1.
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User Registration General User
Release View
System Notice Currency Issuance Application Institution Registration, Institutional Users
Application Review
Announcements
Fig. 1 System function requirements
(2)
Performance requirements
The system is used by the People’s Bank of China currency issuance library. The operation, operation management, and maintenance in the design should follow the relevant regulations of the issuance office. In the currency management of the issuance library, management should be intelligent, real-time, and quantitative, so that the issuance library achieves a higher level and meet the following performance requirements. Operation interface: The system has a friendly interface, which is conducive to the administrator to perform various operations; Operating environment: A good system requires sufficient operating space. Since the currency management system requires a large amount of data for processing, only by ensuring that the system runs fast and the data transmission is smooth, can it provide fast and smooth services for the issuance library management work; Simple operation: The system is easy to operate and highly oriented, which can ensure that most users can easily conduct various information inquiries and business transactions; System functions: In addition to providing the necessary basic functions such as positioning, display, and report generation, the system also needs to provide other related additional functions, which will provide great convenience for subsequent function upgrades. (3)
Non-functional requirements (1)
Transaction performance. The digital assets in this system conduct crossinstitutional transactions through the blockchain basic platform, and the performance requires the system to realize real-time transaction reconciliation. At the same time, on the basis of a transaction throughput of no less than 100 transactions per second, the system needs to support
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(2)
(3)
(4)
(5)
the highest possible transaction performance to meet the needs of high concurrency in the process of multi-institution collaboration. Security. The system needs to ensure the security of digital assets and user operations. Super administrators can perform contract management and institution registration, but they should not have the authority to operate assets and ordinary users to avoid affecting institutions and assets. In addition, the user’s assets can only be controlled by the user through the private key, and other people have no authority to perform any operations on the assets. Accuracy. Ensuring the accuracy of the issuance and trading of various digital assets is the guarantee for the accuracy of the digital asset trading system. Each transaction needs to ensure the accuracy of the transaction data, and the transaction rollback needs to be performed when the execution of the transaction fails. Flexibility The system needs to flexibly meet the needs of various users, so that various institutions can issue digital assets according to their own needs. At the same time, super administrators need to realize the management of member institutions to meet the needs of the expansion of alliance members. Interface requirements. The system interface should be simple in design. Except for super administrators, users should not feel the existence of blockchain as much as possible in use, so as not to cause difficulties in understanding for users, so that users can quickly get started with the application. At the same time, when an abnormal situation occurs in the system, a user-friendly prompt needs to be given.
2.4 System Design (1)
Business function module design
As an application client, the business system connects users with the blockchain, and aims to allow users to complete the storage of digital currency on the blockchain through simple operations. (1)
User module design. The user is the main participant and builder of the system. A good user module design can make it more convenient, fast and safe for users to register, log in, and view personal information. Therefore, a good user module design is necessary and necessary. At the same time, because the blockchain is used as the underlying storage, the user is the user of the business system and the account that exists on the blockchain, and the user’s information security must be guaranteed. In order to ensure that the private key on the user’s blockchain is not leaked, this system will generate the blockchain account offline by the user on the client when the user registers, save it by him, and upload the personal address and public key to the background and save
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(2)
(3)
(4)
(2)
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it to the back-end database. At the same time, in order to prevent users from forgetting their passwords, the system will integrate the mailbox function. The user’s registered account must be filled in the mailbox and activated by the mailbox to register successfully. Issuing module design. The issuance module is the core business model of this system. This system is designed for two types of users, ordinary users and institutional users. Ordinary users use their own private keys to sign personal deposit certificates and upload them to the blockchain. The initiator of the transaction is an ordinary user, and the recipient is an institutional user. The user can also choose whether to encrypt or not, if the encryption will use the personal private key and the organization public key to obtain the negotiated key through the Diffie-Hellman algorithm for AES symmetric encryption. Nonencrypted data can be viewed by users on the entire network. Encrypted data can only be decrypted by the individual who uploads the certificate and the organization that accepts the certificate, and other users cannot decrypt it. Announcement module design. There are two user roles in this system, ordinary users and institutional users. In order to facilitate communication between institutions and ordinary users, an announcement module is added. Institutional users can publish announcements, and ordinary users can receive system announcements. Block information module design. Every business request on the blockchain is reflected in a transaction, and a certain transaction fee needs to be paid in every transaction. The unique blocks and account systems on the blockchain require users to be able to view them. The data of the blockchain is transparent, because any user can view any data of the blockchain. Issuance and transaction module design
The issuance transaction back-end subsystem in this system is composed of a web server, a database server and a file server, and the blockchain subsystem is composed of multiple blockchain nodes to form a distributed cluster. In order to reflect the equal status among the alliance institutions, all institutions currently only deploy one blockchain node, and each node has the same rights. The system operation structure is shown in Fig. 2. This system adopts an architecture model that builds a transaction back-end subsystem on the basis of a distributed blockchain subsystem. It has the following advantages: alliance agencies are connected through the blockchain subsystem, and the transaction back-end web service of a single institution will not affect the block if the transaction back-end web service fails. The chain subsystem will not cause any impact, that is, it will not affect the normal execution of transactions by other institutions and ensure the safety of the overall operation of the system. The blockchain subsystem adopts a distributed architecture model, which implements node error tolerance through a Byzantine fault-tolerant consensus mechanism within the scope of fault tolerance, strong security and stability, that is, it can avoid single node failures causing system downtime. Scalability is a challenge commonly faced by the
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Super Administrators A
Web Server
Institution Blockchain Node
Firewall
General User
C
E
Database Servers
B
D
File Servers
Fig. 2 Blockchain digital asset trading system operating structure
application of blockchain technology, and the transaction back-end subsystem of this system and Blockchain subsystems can perform business processing, so that the system can classify and process according to the importance of the business, thereby improving the scalability of the system.
3 Implementation of Digital Currency Issuance System 3.1 System Deployment In the process of system deployment, the compiled image file is used to build a multi-node blockchain subsystem on a physical machine, and on the basis of it, the deployment of the back-end transaction subsystem is completed. All servers in the system use the CentOS7.0 operating system and are deployed in the internal LAN environment. The blockchain subsystem deploys four blockchain nodes, and the publishing backend subsystem deploys two web service nodes.
3.2 System Function Test System testing is the key to ensuring system quality. It is the final review of requirements analysis, design and coding. In the process of system development, if an error is not corrected, it will often cause very bad consequences. Therefore, if errors are found, they must be corrected. This is the purpose of debugging.
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3.3 System Performance Analysis The main indicators of the system test are the transaction processing rate of the system and the average transaction delay during concurrent transactions. By setting different transaction request rates, testing the average transaction delay and transaction processing rate of the blockchain system, and testing the overall performance of the system.
4 Design Analysis of Digital Currency Issuance System Based on Blockchain 4.1 Blockchain Technology Platforms With the rapid development of blockchain technology, its technology platform also shows a diversified development trend. Choosing a suitable blockchain technology platform can often make the developed application system more secure and efficient. Considering the operational stability of the system, this article only selects the mainstream open source blockchain platforms Bitcoin, Ethereum and Hyperledger Fabric for analysis. By comparing the architecture model, consensus mechanism and applicable scenarios of the three, we select a blockchain basic platform suitable for the digital asset trading system. The characteristics of the three types of blockchain platforms are shown in Table 1. According to Table 1, because the bitcoin platform does not support smart contracts, it is difficult to implement complex digital asset transaction services, so it is impossible to develop and design digital currency issuance systems and transaction systems on its basis. At the same time, the platform relies on tokens (Ethereum) to maintain the operation of the system, and it is difficult to meet the requirements of the alliance’s digital asset transactions that do not rely on tokens and the throughput of hundreds of transactions per second. The Fabric platform is an alliance chain Table 1 Comparison of mainstream blockchain platforms Framework type
Whether a token is required
Are smart contracts supported
Consensus mechanism
Operation environment
Applicable scene
Bitcoin
Yes
No
POW
Built-in scripting engine
Public-owned chain
Ethereum
Yes
Yes
POW/POS
EVM
Public chain/alliance chain
Fabric
No
Yes
PBFT/KAFKA
Docker
Alliance chain
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Table 2 System deployment configuration information Blockchain test environment
Machine name
Hardware configuration
IP distribution
Vp1
CPU: Intel Core i7-6700 Memory: 16G network card: 100 Mbit/s Hard disk: SATA500G
10.3.11.185
Vp2 Vp3 Vp4
Release background test environment
Server1 Server2
10.3.12.187 10.3.13.189 10.3.14.183
CPU: Intel Core i7-6700 Memory: 8G network card: 100 Mbit/s Hard disk: SATA500G
10.3.12.193 10.3.13.195
architecture that supports smart contract technology and basically meets the needs of cross-institutional transactions of digital assets between alliance institutions.
4.2 System Specific Deployment Configuration Information As shown in Table 2, the blockchain test environment is equipped with 4 machines for testing. The hardware configuration is basically the same as that of the release back-end subsystem test environment. And the IP address allocation of both belong to the local area network.
5 Conclusion With the advent of the digital age, people’s demand for digital assets has become higher and higher, and major companies have increasingly relied on digital asset trading systems. Blockchain has the characteristics of contactlessness, scalability, and openness. At the same time, it can realize multi-node exchange to reduce costs, and it has made a great breakthrough in the scope of application. Although the blockchain digital currency issuance system designed in this article can realize the standardization of digital currency issuance and the safe flow of funds across institutions, due to time constraints, there are still some areas in the system that need follow-up improvements. However, I hope that the system can be practically applied. The overall architecture of the system includes multiple aspects such as blockchain technology, encryption technology, web development, operation and maintenance deployment, etc. It is difficult for individual energy to complete the stability and good usability of the entire system. I hope that the design of this system can finally be opened sourced and supported by other developments, and then the system can be further developed and practically applied.
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References 1. Chai H, Sun Q, Zhou Y et al (2020) Design of a digital currency information system based on the UnionPay network. Front Eng Manage 7(4):471–484 2. Dilhani I (2017) Transaction verification model over double spending for peer-to-peer digital currency transactions based on blockchain architecture. Int J Comput Appl 163(5):24–31 3. Jun W (2019) Ethical reflection on digital currency payment based on blockchain technology. J Kunming Univ Sci Technol Soc Sci Edn 6:18–23 4. Zhenhua Z (2019) Thoughts on the development of central bank digital currency based on blockchain technology. China Inf Technol 305(09):98–99 5. Jiteng Z (2019) The reform of the international monetary system from the perspective of blockchain and super-sovereign digital currency: taking the innovation and attempt of E-SDR as an example. Int Outlook 6:20–45 6. Zhang J, Wang Z, Xu Z et al (2018) Supervisable digital currency model based on blockchain. Comput Res Dev 55(10):2219–2232 7. Yanchen Z, Liru C (2016) Analysis of digital currency and traditional currency based on blockchain technology. Enterprise Herald 000(019):188–188 8. Jiteng Z (2020) The reform of the international currency system from the perspective of blockchain and super-sovereign digital currency. Acad Abstr Liberal Arts Colleges Univ 1:122–123 9. Lei Z, Zongwei G, Jiaren G (2019) Research on the issuance mode and risk control of digital currency. Wuhan Finance 231(03):57–63 10. Changwei T (2020) Analysis of the status quo and challenges of digital currency development under blockchain technology. Eng Manage Sci 2(5):15–16 11. Li S (2017) Research and thinking on digital currency based on blockchain technology. Finan Technol Times 000(012):36–38 12. Ying H (2019) Overview of the development of digital currency based on blockchain technology. China Financial Comput 6:78–81
Cross-Cultural Communicative Competence Based on Computer Aided Testing Aizhen Zhang
Abstract In recent years, cross-cultural communication has played an important role in language acquisition. Improving students’ cross-cultural communication skills has become one of the focus of language teaching. It is very important to cultivate students’ cross-cultural communication skills, and it is also important to test cross-cultural communication skills. Evaluation can test students’ learning situation, and can also produce a certain feedback effect on teaching methods. This article focuses on the computer software-based auxiliary test of cross-cultural communication ability. First, through the literature research method, it explains the advantages of computer-aided testing and the factors that affect the cross-cultural communication ability test with the help of computers, and then uses the questionnaire survey method to investigate the computer-aided testing of cross-cultural communication ability. Analyze the auxiliary effect of the cross-cultural communication ability through the results. The survey results show that after joining the computer-assisted cross-cultural communicative competency test, the completion rate of short answer questions is not very high, accounting for about 46%, and the second is the omission of answers, accounting for about 32%. The reliability of the test results is not very high. Keywords Computer · Auxiliary testing · Cross-Cultural · Communicative competence
1 Introductions With the advent of the era of globalization, mutual exchanges and mutual trade between different countries, regions and organizations are increasing [1, 2]. In this case, communication between different cultures is inevitable [3, 4], and it is becoming more and more frequent. The ability of cross-cultural communication has become the basic quality of modern talents, so it has become a research hotspot at home and abroad [5, 6]. A. Zhang (B) Henan Finance University, Zhengzhou 450046, Henan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_93
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In the study of inter-regional cultural communicative competence, some researchers have started from the philosophical perspective of the inter-regional cultural theory proposed by Habermas and its construction of inter-regional cultural communicative competence, according to the existing cross-cultural competence [7, 8]. The core of the theory of interculturalism will be emphasized in the dimension of intercultural technical ability. At the same time, the method of quantitative and confirmation and qualitative coordination is used to examine the dimensional perspective of students’ cross-cultural competence from the perspective of cross-culturalism, and verify the rationality and effectiveness of cross-cultural competence. Finally, a set of cross-cultural competence recommendations and strategies for current Chinese college students is proposed [9]. Some researchers pointed out that conceptual ambiguity and lack of measurement tools are the two main problems in the process of cross-cultural research in our country [10]. Globalization and new media can provide some new suggestions and change the factors and factors that affect their development [11]. The abc model can help deepen the understanding and understanding of the concept of cross-cultural communicative technology capabilities, and then establish a comprehensive evaluation theoretical framework and performance evaluation system for cross-cultural communicative technology capabilities based on China’s national conditions, and cultivate students’ cross-cultural communicative skills lay a good foundation [12]. This article focuses on the research of computer-assisted testing of intercultural communicative competence. First, the literature research method explains the advantages of computer-assisted testing and the influencing factors of computer-assisted cross-cultural communicative competence testing, and then uses questionnaire survey method to add computer-assisted intercultural communicative competence investigate the results of the test, and analyze the feasibility of computer-assisted testing of cross-cultural communicative competence through the results.
2 Computer-Aided Testing and Cross-Cultural Communicative Competence Research 2.1 Research Method (1)
Internet investigation method
Internet research methods are an effective supplement to traditional research methods. It is a research method that uses Internet technology to collect, record, organize and analyze relevant data. This article uses web search as a pre-processing method for the next questionnaire survey to make the questionnaire distribution more targeted and effective.
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Questionnaire survey method
The main purpose of using the questionnaire survey method is to understand the effects of the computer-assisted cross-cultural communication ability test in universities in this province. Through the questionnaire survey, we can intuitively discover the students’ enthusiasm for answering questions after joining the computer-assisted cross-cultural communication ability test in universities and the impact on the test results after joining the computer-assisted cross-cultural communication ability test, which provides a data basis for statistics and analysis.
2.2 Advantages of Computer-Aided Testing (1)
Information collection is more comprehensive.
In addition to recording the intermediate and final results, you can also create appropriate data to collect more data and information related to the problem-solving process and strategies, such as information on the student’s answering process, including the type, frequency, and sequence of each question. The length of the response time, the number of times the keyboard is typed, and the movement or alternation between questions assess the state of the student. (2)
Different evaluation goals make the evaluation more efficient.
It not only evaluates students’ problem-solving ability, but also evaluates the evaluation effect of the entire project. The application of advanced software development tools and network computers has improved the efficiency and effectiveness of evaluation, for example, improved the ability to control static and interactive problems, aroused the interest of students and collected more information about the problemsolving process. From a management point of view, management is simpler and cheaper. Since the hard disk can copy test questions, testing institutions do not need to print, distribute and recycle test papers; it is easier to design test questions, for example, it is easy to assign different questions to students in the same examination room. Issue test questions; directly electronically grade students’ answers, freeing teachers from daily document evaluation. (3)
The scope of monitoring or inspection is wider.
First, check the student’s response time. Through program control, you can monitor and limit the time students spend on specific topics. For example, students are required to explore a complex problem situation within a set time; secondly, the solution to dynamics will add a problem state. Talent test; again, to help students learn in the test, for example, to provide clues when the student first tries to answer an incorrect question.
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Realize the test problem.
Reliable and relatively complex problems, especially those that require the respondent to report and discover related information through direct interaction, can be implemented on a computer system, which has become the main feature of computer-aided testing.
2.3 Influencing Factors of Computer-Assisted Cross-Cultural Communicative Competence Testing (1)
Static
The most important thing is to show the solver whether the initial information about the problem is comprehensive. The first question refers to a question with a unique goal and complete information with clear meaning, such as the fastest driving route. The questionnaire clearly defines the initial state, target state and allowable functions. Another problem is vaguely defined, usually involving multiple conflicting goals. The whole process of achieving one goal is likely to greatly slow down the speed of the other goal. The solver must make the correct decision and Weigh the priorities between achieving multiple goals, and so on. Therefore, we have to determine the “best” route between the two locations, then they should be considered the shortest route, the fastest route, which route to go directly to, or the one that has changed the least in time one route? The correct answer to this question is usually not the only one. (2)
Student’s application ability
The ability of students to use computers will also have an impact on the evaluation results of students’ answers. Computer-aided testing needs to be tested in the software operating environment. In principle, the students’ ability to operate software up to 40–60% will have little effect on the results. In principle, computer-aided testing requires very simple computer skills (such as the speed of text input, drag-and-drop, and click), but it will be a challenge for students to focus on the dynamic and up-todate problem tests. Dynamic problems and interactive ability will affect the process and results of students’ problem solving.
2.4 Computer-Aided Test System Algorithm (1)
Artificial fish school algorithm
The basic idea can be expressed as: placing artificial fish randomly in the data field and simulating artificial fish schools in the exploration area to find the best answer through food, grouping, back-end collisions, etc.
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The size of the target exploration area is M-dimension, and there are N artificial fishes. The status of each artificial fish can use the vector (x1, 2,…, xM), xi (i = 1, 2, …, M) Reflected as the optimized variable for the artificial fish. Artificial = f (X) represents the feed concentration of each artificial fish at the current position, Y represents the value of the objective function; di represents the interval of each artificial fish, and Visual represents the perception range of the artificial fish. Step represents the maximum moving length of the artificial fish. Trynum represents the maximum number of food behavior tests; δ represents the overcrowding factor. (1)
Foraging behavior
If the position of the artificial fish is now Xi, choose any position Xj within its perception range. If the group similarity between the two is Yi < Yj, then proceed further in this direction according to formula (1). If it is not, please go to any position within the perception range Xj to guess whether the conditions are met. After the Trynum time is repeated, if the condition is still not met, the artificial fish will move one level arbitrarily according to formula (2). x(i|next) = xi + Rand().step.
x j − xi x j − xi
x(i|next) = xi + Rand().step
(1) (2)
Among them, () obeys the distribution U(0,1). (2)
Group behavior
Assuming that the current state of the artificial fish is Xi, within its perception range (dij < Visual), find the number of its partners nf and the center position Xc, according to the formula Yc/nf > δYi. Indicates that there is an optimal solution around the partner, then move forward one level in the direction of the center position Xc according to the formula, otherwise, apply foraging behavior. x(i|next) = xi + Rand().step. (3)
xc − xi xc − xi
(3)
Rear-end collision
Assuming that the current state of the artificial fish is Xi, in its perception area (dij < Visual), explore as the biggest partner, and if Yj/nf > f , the environment is a bit similar. If the surrounding environment is not very crowded, you can go forward or eat according to formula (4), otherwise perform a rear-end collision. x(i|next) = xi + Rand().step.
x j − xi x j − xi
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Behavior selection
In the artificial fish school algorithm, when the artificial fish cannot find a better position, it will randomly select a state in the field of view, and then switch to that state, which is the default food search behavior. The choice of random behavior increases the universality of the group, so that the artificial fish will not fall into an endless loop, thus jumping from the local extreme value.
3 Investigation of Cross-Cultural Communicative Competence Based on Computer-Aided Testing 3.1 Purpose of the Investigation The questionnaire survey method is used to investigate the effect of adding the computer-assisted cross-cultural communication ability test, and the feasibility of the computer-aided cross-cultural communication ability test is analyzed through the results.
3.2 Questionnaire Survey (1)
Survey object
This article is mainly a survey of the effects of joining the computer-assisted crosscultural communication ability test. Therefore, the subjects of this survey are teachers and students of major universities. Random surveys of 3 university teachers of different popularity and levels After the questionnaire survey, the 3 universities were marked as a, b, and c universities. Due to the limitations of this research condition, this questionnaire is only a survey of graduate students in 3 colleges and universities. The research results still have certain technical limitations and cannot fundamentally represent our participation in computers from other provinces, cities and regions across the country. The effect of assisting the cross-cultural communication ability test. (2)
Issuance of questionnaires (1)
The minimum distribution of questionnaires According to the minimum sample size formula in statistics, this article sets the confidence level of the questionnaire to 80%, and the allowable error does not exceed 8%. Calculate the minimum sample size as
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(3)
ta 2p
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2
=
1.645 2X 0.075
2 = 120
(5)
This questionnaire survey is completed in two stages. The first stage is the distribution of questionnaires. According to the minimum sample size, the number of questionnaires distributed this time is 180. The second stage is the questionnaire recovery stage, which will be recovered after 6 days. It was 167, and the response rate of this questionnaire was 95%. In order to test the reliability and stability of this survey, the variance of the questionnaire results was first calculated, and then the reliability of the returned questionnaire was tested by the method of “half reliability” test. Using formula (6) to calculate the reliability coefficient, the correlation coefficient of the questionnaire is r = 0.883. According to the theories and methods of modern scientific research, when the reliability of a test reaches 0.80 or more, it can be regarded as a test with higher reliability. The test results confirm that the questionnaire is reliable. 2 r = 1 − S (1 − r1 ) S 2 n
(6)
3.3 Data Processing (1)
(2)
Enter the questionnaire data into the EXCEL software, and establish a data table about the students’ enthusiasm for answering questions after joining the computer-aided cross-cultural communication ability test and the impact of the computer-aided cross-cultural communication ability test on the test results. In order to obtain the real questionnaire data, the data must also be tested. In EXCEL, the Grubbs method is used to eliminate abnormal data. Import the processed data in EXCEL into SPSS software, and use SPSS software to analyze the data.
4 Analysis of Survey Results 4.1 Students’ Enthusiasm for Answering Questions After Joining the Computer-Assisted Cross-Cultural Communicative Competence Test The questionnaire survey is conducted to investigate the students’ enthusiasm for answering questions after joining the computer-assisted cross-cultural communication ability test. The results of the survey are shown in Table 1.
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Table 1 Enthusiasm of students
A school (%)
B school (%)
C school (%)
Up
41
43
46
Same
34
32
30
Decreased
24
25
24
60%
percentage
50%
41%
43%
46%
40%
34%
32%
30%
30% 24%
25%
24%
20% 10% 0% Up
Same
Decreased
degree A school
B school
C school
Fig. 1 Enthusiasm of students
It can be seen from Fig. 1 that after joining the computer-assisted cross-cultural communication ability test, 46% of students and teachers believe that the enthusiasm for answering questions has increased, and 32% of students and teachers believe that it is the same as before.
4.2 The Impact of Joining the Computer-Assisted Cross-Cultural Communicative Competence Test on the Test Results A questionnaire survey was conducted to investigate the impact of joining the computer-assisted cross-cultural communication ability test on the test results. The results of the survey are shown in Table 2. It can be seen from Fig. 2 that when the computer-assisted cross-cultural communicative competence test is added, the completion rate of short-answer questions is not very high, accounting for about 46%. Secondly, the question is missed, accounting for about 32%. The reliability of the test results is not very high.
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Table 2 Impact of test results A school (% ) B school (%) C school (%) The completion of the essay question is not high 45
46
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More missed questions
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Limited question types
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23
22
22% 23% 22%
problem
Limited question types
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More missed questions
48% 46% 45%
The completion of the essay question is not high
0%
10%
20%
30%
40%
50%
60%
percentage C school
B school
A school
Fig. 2 Impact of test results
5 Conclusions Language ability and cross-cultural communication ability belong to two different aspects, and the language ability of a student cannot be used to evaluate his/her crosscultural communication ability. Through the cross-cultural communication test, the knowledge and skills department checks the students’ cross-cultural communication ability, and the emotional part is the emotional attitude and communication potential during the check.
References 1. Bowling NA, Hershcovis MS (2017) Research and theory on workplace aggression||crosscultural differences in workplace aggression. https://doi.org/10.1017/9781316160930(10): 245-268 2. Saadi H, Mirzayi K, Movahedi R (2016) Barriers of the development of web-based training in agricultural higher education system in Iran: a case study of Hamadan Bu Ali Sina University, Iran. Educ Inf Technol 21(1):53–70
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3. Vorobyov, N (2019) A cross-cultural comparison of communicative patterns in bilingual and monolingual mother-child dyads in the United States and Thailand. Alpenglow Binghamton Univ Undergr J Res Creative Activity 5(1):5 4. Al-Khawaldeh N (2016) A pragmatic cross-cultural study of complaints expressions in Jordan and England. Int J Appl Linguist English Liter 5(5):197–207 5. Mongillo G (2019) Primary teachers’ use of communicative strategies for linguistically diverse learners: a cross-cultural case study. Lang Literacy Spectrum 29(1):5–5 6. Netz H, Lefstein A et al (2016) A cross-cultural analysis of disagreements in classroom discourse: comparative case studies from England, the United States, and Israel. Intercultural Pragmatics 13(2):211–255 7. Vyushkina E (2016) Legal English through movies: development of professional communicative competence. Stud Logic Grammar Rhetoric 45(1):253–263 8. Oakley G, Pegrum M et al (2018) An online Chinese–Australian language and cultural exchange through digital storytelling. Lang Cult Curriculum 31(2):128–149 9. Glasner T, Van D, Belli RF (2016) Calendar interviewing and the use of landmark events—implications for cross-cultural surveys. Bulletin De Méthodologie Sociologique Bms 115(1):45–52 10. Coldwell DAL (2017) Custom and moral sentiment: cross-cultural aspects of postgraduate student perceptions of leadership ethicality. J Bus Ethics 145(1):1–13 11. Davey G, Mcdonald AS, Hirisave U et al (2016) A cross-cultural study of animal fears. Behav Res Ther 36(7–8):735–750 12. Grossmann KE, Grossmann K (2016) The wider concept of attachment in cross-cultural research. Hum Dev 33(1):31–47
E-commerce Development of Characteristic Agricultural Products Under the Background of Computer Science and Technology Lina Xiao, Can Li, Rui Wang, Shucai Mei, and Li Li
Abstract With the development of computer science and information technology, e-commerce of characteristic agricultural products has become an important driving force of “Rural Revitalization”. In recent years, with the establishment of e-commerce cluster, the introduction of advanced e-commerce platform and the creation of characteristic agricultural products brand, Hubei Province has made great efforts to develop e-commerce of agricultural products. This paper mainly studies the current situation of Hubei agricultural products e-commerce development under the background of computer science and technology. Taking Xishui County as an example, this paper conducts questionnaire survey, analyzes the problems existing in e-commerce of agricultural products, and puts forward suggestions for innovation of e-commerce system of agricultural products in Hubei Province, in order to promote and guide the healthy and sustainable development of “Rural Revitalization” and e-commerce in other rural areas of Hubei Province. Keywords Computer science and technology · E-commerce · Characteristic agricultural products · Development strategies
1 Introduction At present, with the development of computer science and information technology, the scale of e-commerce transactions in China has reached 37.21 trillion Yuan in 2020. However, the development of rural e-commerce is relatively backward. In China’s new economic environment and social background, rural e-commerce is becoming the backbone of rural poverty alleviation, but also is an important bridge to achieve rural revitalization strategy. Therefore, it’s urgent to promote the development of rural e-commerce and improve the rural e-commerce service system. We find that the research views of Chinese scholars on rural e-commerce in recent years mainly include: L. Xiao · C. Li · R. Wang · S. Mei · L. Li (B) School of Management, Wuhan Donghu University, Wuhan, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_94
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On the important of establishing China’s rural e-commerce service system, Su Hongjian and Cui Kai believed that accelerating the improvement of rural ecommerce service system is of significance to the adaptation of the industrial environment in the Internet era. It’s of great significance to promote the integration of e-commerce technology and rural circulation system [1]. On the development of e-commerce technology of agricultural products in backward areas in China, Wen Xiaosen and Cheng Le took Shaanxi Province as an example, by investigating the demand and marketing situation of characteristic agricultural products, they explored the path and mode suitable for the development of e-commerce of characteristic agricultural products [2]. On the e-commerce development of agricultural products in the East and West of China, based on the data of Alibaba platform and using statistical model, Wang Haina studied the differences of e-commerce development between eastern and western agricultural products, pointed out that the eastern agricultural e-commerce was better than the western agricultural e-commerce, and put forward that the government should continue to strengthen the policy orientation to promote the development of e-commerce of western agricultural products[3]. As above, China attaches more importance to rural e-commerce, and takes appropriate measures to solve the development dilemma of rural e-commerce at present mainly focusing on infrastructure construction, brand effect, marketing channels, talent training and policy support and other aspects.
2 Development Status of Rural E-commerce in Hubei Province Xishui County is located in eastern Hubei Province, and belongs to Huanggang City. The county is mainly agricultural and rich in land resources, with a total land area of 2,926,676.7 mu, of which 2,300,366.25 Mu is agricultural land, accounting for 78.60% of the total land area, making it a major grain producing county in China.
2.1 Development Situation of E-commerce Trading Platform At present, Xishui County lacks independent agricultural e-commerce trading platform, mainly bases on the third-party platform, such as Taobao. In 2017, 65 new village Taobao e-commerce service stations were built, covering more than 200 administrative village sites. At the same time, with the signing of rural Taobao, Cainiao logistics also followed the landing, and opened up the last kilometer from the county to the rural logistics through the “T + 1” mode. At the same time, the government of Xishui County is vigorously developing the construction project of e-commerce gathering area. Xishui County has a provincial
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electronic science and technology park—Xigu Electronic Science and Technology Park, which has been put into use with a total investment of 1.8 billion yuan. In addition, a site is being selected for the construction of Xishui County e-commerce town.
2.2 Development Situation of Agriculture-Related E-commerce Enterprises At present, there are many large agricultural e-commerce companies in Xishui County, including Xishui Zhonghe advantage agricultural products e-commerce Co., Ltd., Xishui gujingshan agricultural products e-commerce Co., Ltd. and other enterprises with a registered capital of more than 500,000, such as Xishui Wangda e-commerce Co., Ltd. At the same time, Xishui County has established E-Commerce Association and agricultural products exchange industry association to promote the information construction of agricultural e-commerce enterprises. Xishui county government also signed an agreement with Alibaba to vigorously develop rural Taobao businesses such as Taobao entering villages, and create local characteristic agricultural products.
2.3 Construction Situation of Logistics Platform and Information Platform Currently, Xishui County has more than 30 e-commerce logistics platforms and more than 70 village Taobao e-commerce service stations. At the same time, large logistics platforms such as Shentong, ZTO and YTO all have their sites, but they are all concentrated at the county level. In 2017, with the support of the government, the three-level system of county, town and village was started to be built, and the e-commerce logistics system is gradually being established. From the perspective of computer science and technology, the construction of rural informatization in Xishui County started earlier. In March 2001, Huanggang city established an information center, which is mainly used to organize the promotion and application of rural informatization technology. In 2019, the website of hu’ao village, Lanxi Town, Xishui County will be opened, striving to build a demonstration village of modern rural information construction.
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3 Problems of E-commerce Development of Characteristic Agricultural Products This paper conducts a questionnaire survey on the farmers, employees of e-commerce enterprises and residents in Xishui County. A total of 155 questionnaires have been issued and 150 of them have been recovered. The specific survey results are shown in Table 1.
3.1 Characteristic Agricultural Product Competitiveness Is Weak There are 7 kinds of characteristic agricultural products in Xishui county, and more than 100 kinds of characteristic agricultural products. According to the survey results, 22.6% of people think that the characteristic agricultural products in Xishui county were weak in competitiveness. After analysis, the main reasons are as follows: First of all, there is no well-known brand of characteristic agricultural products. There are many manufacturers selling agricultural products, and they form their own brands. In terms of packaging and sales of agricultural products, they act independently, resulting in consumers lack of brand loyalty [4]. Secondly, the county has no large-scale independent agricultural products e-commerce trading platform, mainly Taobao and other third-party platforms.
3.2 Rural Informatization and Digitalization Need to Be Improved With the construction of Xishui County rural informatization demonstration village, some progress has been made in the rural informatization project and digital project in Xishui County, but there are some problems, such as insufficient development Table 1 Problems of rural e-commerce development in Xishui County Questions
Number
Proportion (%)
Characteristic agricultural product competitiveness is weak
34
22.6
Rural informatization and digitalization need to be improved
32
21.3
Development of agriculture-related e-commerce enterprises is slow
28
18.6
The e-commerce service platform lacks consumers
19
12.6
Rural grassroots logistics system and e-commerce system is not perfect
37
24.9
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of information resources, information barriers between different towns and villages, and difficulty in coordinated development. The survey results show that 21.3% of respondents believe that the digitalization degree of rural informatization in Xishui County still needs to be improved. Analyzing the reasons, the main reason is the lack of scientific planning, and different progress between different regions, resulting in the construction of information and digital blindness and lag, which is not easy to form a sustainable development [5]. In addition, Xishui County is short of rural information and digital talents. There are many senior middle schools in Xishui County, but most of the students leave for the provincial capital or neighboring cities to develop, leading to a shortage of relevant talents.
3.3 Development of Agricultural E-commerce Enterprises Is Slow According to the survey, 18.6% of respondents believe that the development of agricultural-related e-commerce enterprises in Xishui County is slow. This paper investigates three large-scale agricultural product planting enterprises in Xishui County. As shown in Table 2, the registered capital of these large-scale agricultural product planting enterprises in Xishui County is relatively low, and he earliest batch of agricultural farming enterprises was established in 2010, but the overall number of agricultural farming industry is still small, and there is a lack of leading enterprises. In terms of business types, vegetable planting is the main business in Xishui County. Although there are characteristic agricultural products in Xishui County, the brand effect has not been established. In terms of e-commerce sales enterprises, Xishui County mainly consists of small and micro enterprises. Most of the existing enterprises have registered capital of less than 100,000 yuan and were established around 2016. The development time is short, and the business types are relatively simple, mainly clothing and daily necessities, and the number of e-commerce enterprises related to agriculture is extremely rare. Table 2 Survey on the development of agricultural product planting industry Company name
Established time
Registered capital (million)
Management type
Qingquan Lvhe vegetable professional cooperative
2010.10.28
370
Vegetable cultivation
Spring vegetable planting professional cooperative
2018.07.06
500
Vegetable cultivation
Ruifeng agricultural planting cooperative
2016.03.31
358
Field husbandry
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3.4 E-commerce Service Platform Lacks Consumers In terms of e-commerce service platform, Xishui County has signed an agreement with Alibaba to establish a village-level platform based on Taobao’s three-level system of counties, towns and villages. However, there are still the following problems: First of all, most of the platform staff in the village-level system are farmers with low knowledge level, lack of relevant operation theory and experience, and lack of talents in relevant fields. Secondly, most of the products sold are daily necessities, which are relatively rare and unable to meet people’s high-level needs. At the same time, it is difficult to guarantee the quality of products, which affects the user’s consumption experience. Finally, according to the 2018 ranking of China’s countylevel city consumption power index, Xishui County ranks 365th in the country with 5.18,the consumption level of Xishui County is low, and the residents’ knowledge level is not high. They have little contact with the Internet, and have not formed the awareness of e-commerce, which leads to the lack of consumers on the e-commerce service platform.
3.5 Rural Grassroots Logistics System Is not Perfect A complete e-commerce industry chain should have three aspects: logistics, capital flow and information flow [6]. In terms of the grassroots logistics system in Xishui County, restricted by the economic level, the overall development of the logistics industry is slow, and the logistics system is not sound enough due to the lack of corresponding infrastructure construction. In terms of the e-commerce system, the E-Commerce Association of Xishui County was established in Xishui County in September 2018. In 2014, we created Xigu Electronic Science and Technology Park, however the e-commerce system of Xishui county is also not perfect.
4 Development Strategies for Characteristic Agricultural Products Under the Background of Computer Science and Technology 4.1 Creating Standardized Production of Agricultural Products and Enhancing the Brand Value At present, the production, processing and final sales of characteristic agricultural products in Xishui County are completed in different factories with different standards, and there is a lack of unified standards. Meanwhile, the production technologies are also different. Sellers have no specific and unified industrial standards, so
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they can only rely on the market for screening, thus the labor rate cannot be effectively improved, therefore, effective industry standards should be produced and published by local authorities and implemented in every step of the process.
4.2 Accelerating the Introduction of Advanced E-commerce Service Platforms In terms of e-commerce service platforms, Xishui County can learn from Sichuan’s “Renshou Model” and improve it’s own e-commerce system by introducing advanced e-commerce platforms [7]. A good e-commerce platform and a sound e-commerce system will support local e-commerce enterprises of characteristic agricultural products and support the rural e-commerce service system in Xishui County. In terms of agricultural e-commerce enterprises, the Xishui government should strengthen the support for agricultural e-commerce and stimulate the development of local e-commerce enterprises through appropriate preferential policies; The government should perfect the e-commerce related laws, supervise the standardization construction of the industry, seal up the manufacturers that do not meet the quality standards, and optimize the industry environment; The government should also cultivate talents in the field of e-commerce to achieve sustainable development of rural e-commerce [8]. By establishing a three-level grass-roots e-commerce service system at county, township and village levels, the rural e-commerce service system in Xishui County will be thoroughly opened up, promoting the growth of local economy and broadening the income channels in rural areas.
4.3 Intensifying the Promotion and Taining of E-commerce in Rural Areas By improving residents’ awareness and recognition of e-commerce, Xishui County can provide a better development environment for e-commerce. On the one hand, by training leaders in rural areas or setting up rural e-commerce service platforms such as “Taobao Shop”, rural personnel can be transformed into a member of the e-commerce chain. On the other hand, the government should strengthen the training of e-commerce talents, so that fresh talents continue to join the rural e-commerce in Xishui County. The basic business model of rural e-commerce cluster development in China is relying on professional services and infrastructure provided by external suppliers and the government [9]. Xishui County can vigorously develop under the specific environment, such as farmers (farm) + companies + network providers (Zhejiang
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Suichang Model) or farmers (farm) + network providers (Sichuan Renshou Model) and other emerging e-commerce models.
4.4 Improving the Modern Logistics System for Agricultural Products In order to promote the development of rural e-commerce, the informatization construction of agricultural products logistics is an essential link. In other fields of our country, POS technology, EDL technology, bar-code technology and other modern logistics information technology has been progressing steadily, but the rural e-commerce field has not been fully developed, which also leads to the failure of integration of all links in the agricultural products industry chain, and delays the development trend of rural e-commerce [10]. The government of Xishui County needs to lay a solid foundation for rural ecommerce and improve logistics infrastructure, such as improving road conditions, promoting urban–rural integration, and mobilizing road construction personnel and communication engineering personnel to complete the construction of “access to every village” and improve the penetration rate of network broadband, so that rural e-commerce has have the ability to thoroughly launch to guarantee the sustainable development of rural e-commerce.
5 Conclusions In the context of computer science and technology, Hubei Province should strengthen the innovation of characteristic agricultural products e-commerce service system to promote the sustainable development of rural economy. This paper analyzes the problems of e-commerce development in Hubei Province, and puts forward the following strategies: improving industry standardization, creating brand effect, and enhancing the competitiveness of characteristic agricultural products; Rigorously introducing ecommerce platform, vigorously supporting local e-commerce enterprises, improving the county e-commerce service system; Actively innovating e-commerce model, learning domestic advanced rural e-commerce cases, increasing training to improve residents’ awareness of e-commerce; Improving the modern logistics system of agricultural products, so as to create a perfect e-commerce cluster. Acknowledgements This work was supported by the grants from Hubei Provincial Collaborative Innovation Centre of Agricultural E-Commerce (Wuhan Donghu university research [2019] No. 17 Document)
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References 1. Su H, Kai C (2019) Accelerating the improvement of rural E-commerce service system. China Dev Observ 10:37–39+43 (in Chinese) 2. Xiaosen W, Le C (2020) Path selection and mode analysis of E-commerce for characteristic agricultural products under the internet perspective. J Commer Econ 13:89–92 (in Chinese) 3. Haina W, Xu C, Yinsheng Y, Yanliang Y (2020) Differences between the East and the West in the development of E-commerce of agricultural products. Stat Decis 36(02):93–96 (in Chinese) 4. Qiaoyun L, Xiaoman S (2021) Current situation, dilemma and mode innovation of E-commerce poverty alleviation in Hubei Province 42(05):11–14 (in Chinese) 5. Hao X, Ying S, Chi Z (2019) Influencing factors and income effect of farmers’ participation in E-commerce of agricultural products. Bus Econ Rev 20(03):89–101 (in Chinese) 6. Yi Yang YuYu (2020) Development status and countermeasures of rural E-commerce in Wufeng County. Value Eng 39(01):18–19 (in Chinese) 7. Kunxiang D, Wenhu H (2016) Innovation oriented rural E-commerce cluster development— based on the analysis of Suichang model and Shaji model. Issues Agric Econ 37(10):60– 69+111(2016).(in Chinese) 8. Guangliang F, Jinxiang P (2020) Application and improvement of mobile digital media technology based on we-chat platform in E-commerce industry chain of agricultural products. Digital Technol Appl 38(07):222–224 (in Chinese) 9. Ting W, Qianzhan F (2017) Development path of E-commerce of characteristic agricultural products. J Nanyang Inst Technol 9(01):53–55 (in Chinese) 10. Yang Y, Yu Y, Yayan P (2019) Promoting the optimized development of rural R-commerce platform—taking Xianning City of Hubei Province as an example. Comput Knowl Technol 15(34):259–261 (in Chinese)
Graphic Design Understanding the Application of Computer Graphics and Image Processing Technology in Graphic Design to Improve the Employment Rate of College Graduates Ling Fu and Bei Gong Abstract With the continuous growth of national economy and the continuous innovation of science and technology, computer technology has been integrated into daily life and work and become an indispensable part. Among them, computer image processing technology is often applied in graphic design, which not only brings more creativity and color display for graphic design, but also brings more creativity and color for graphic design. At the same time, reflected in the past few years, college student’s employment problem is also helpful. For this reason, based on the understanding of graphic design, this article applies computer Graphics and Image Processing Technology (CGT) to graphic design, and explores the effect of this application on increasing the employment rate of college students (CS). Keywords Graphic design · Graphics and image processing technology · Computer technology · Graduate employment rate
1 Introduction In the past few years, due to the continuous popularization and promotion of higher education, the number of college graduates has gradually increased year by year, and CS’ choice of career and employment has become a general problem of the society. Every year, during the graduation season, job-related topics such as “the hardest year for CS to find a job (CSJ)” and “the hardest season for CSJ” spring up like mushrooms after a spring storm on the Internet. There is also a picture of people crowded in the job market. In the face of more and more year by year the employment of CS employment group “employment difficulties” more prominent. The phenomenon L. Fu Gongqingcheng College of Nanchang University, Nanchang, Jiangxi, China B. Gong (B) Gongqing College of Nanchang University, Nanchang, Jiangxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_95
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of “difficult employment” for CS and “difficult employment” for enterprises exists at the same time. Among CS, the joke that “graduation means unemployment” is widely circulated, which reflects the current situation of CS’ difficulty in employment from the side. Since entering the twenty-first century, China’s economy, science and technology into a period of rapid development, China’s computer more and more popular, basically every family will basically have a computer in the existence. The use of computer processing technology in graphic design is also constantly flooding around people. For example, the two-dimensional code that can be seen everywhere today and the beauty and image repair software that people often use are all the manifestations of this application in their life. The use of computer processing technology in graphic design can improve the ability of CS, thus effectively increasing the employment rate of CS. With the development of The Times and the arrival of the information age, The use of computer processing technology in graphic design has a greater impact on improving the employment rate of college graduates. Therefore, many field experts also put forward their own views. Some research groups say that computer graphics and graphics image processing are significant advances in computer technology. It began to develop in the 1980s and has been widely concerned and applied in the future development process. CGT have become one of the important aspects of modern computer applications. It is based on the basic composition and function of computer graphics and CGT [1, 2]. The other team said that this technology is to a large extent conducive to improving people’s image processing technology and level, from the previous single change, to the current matting, changing the bottom and other functions. In this way, the success rate of handling affairs in any industry will be improved accordingly, and the normal life will be enriched, and people’s spiritual level will be better improved, bringing more colorful colors [3, 4]. So, as technology advances, computer graphics and graphics are becoming more and more important in different industries. Song Na and Du Bo say people’s lives have changed as Internet technology has accelerated. Therefore, more attention should be paid to the use of computers. It is necessary to strengthen the application of computer auxiliary software, grasp opportunities, strengthen their own ability, and cultivate the hands-on ability of CS, so as to better present their own talents, so as to obtain better employment positions and make contributions to the society [5, 6]. Thus it can be seen that with the fierce competition of employment in the society, the older generation gradually withdrew, and CS gradually grew up to be builders of the society. CS for future national and society’s expectations and the nucleus, should be to undertake the mission of history, become the main force of new era tide [7], so we for university students graduate in CGT in graphic design has carried on the questionnaire investigation and study CGT in graphic design is conducive to the improvement of employment.
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2 Method 2.1 CGT Computer graphics is the discipline that explores how digital computers can be generated, machined in special ways, and rendered visually useful. The research content of computer graphics involves software and hardware technology, including the knowledge of algorithm, imaging principle and so on. Its research mainly focuses on the generation of graphics and images, the accuracy of the representation of the target and the calculation of the degree in accordance with the facts. It can be described in detail from the following aspects. According to its graphics machine graphics fundamental elements generate calculation, the structure of the graphic elements change, by dispersed, gentle join together, all the curve in the plane or a small part of the change, from a 2 d plane arrived in 3 d, show its technology, real time graphic display, showing its presence graphics generated calculation, and it is difficult to identify clouds of flowers and plants and other landscape simulation of production and fictitious reality three-dimensional visualization of high dimensional data field or more. The former objectively exists in the real world, while the latter is considered [7]. The computer makes a connection between the two. With the development of image processing technology, the application of image processing is becoming more and more extensive. Its graphics processing is the process of presenting, revising, perfecting and keeping records of concepts or describing and describing spatial patterns or data. Its most important functions include: structural changes of images, such as mathematical rotation around an axis, movement without changing the size, and scaling down, etc.; It makes it possible to increase the intensity of the image, return to its original appearance, analyze its essence and connection, and separate the whole. The curve and surface of the figure are fitted. Eliminated hidden lines and hidden faces in the image. Computer graphics production style. The corresponding color design, shadow surface adjustment and stripe reflection are carried out [8, 9].
2.2 Graphic Design Graphic design, also known as visual communication design, regards vision as a method for mutual communication and expression of ideas and thoughts. Through innovation in different forms and combination with written forms of signs, pictures and languages, visual expression can be used to express ideas and messages. Graphic designers use specialized skills such as sorting, layout, and computer software to achieve the purpose of a creative project. Graphic design is generally a process of making (designing) and doing well. Common applications include logos, published products, printed advertisements, posted advertisements, advertising signs, graphic elements of websites, and packaging of finished products [10]. For example, the
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finished product may be wrapped in a container with the manufacturer’s logo on the surface or with carefully arranged literature and particularly attractive colors. The appearance, size, or figure of a particular color style. Integration is an obvious feature of its design, especially in the use of existing raw materials and other concepts.
2.3 Computer Aided Technology and Employment Rate of CS The current computer level is also one of the important conditions for enterprises and employers to choose graduates. Because of the continuous progress of information technology, with the production technology and the level of office automation gradually in-depth research and widespread, computer aided technology has become a talent training must have. In today’s information technology integrated into life, from the moment you open your eyes, a variety of information will flood your eyes, such as brand logo, billboards, etc., which are the embodiment of its processing technology in graphic design. The application of computer graphics and image processing software in graphic design can effectively expand the central idea of its design, enrich the design effect, deepen its creativity and internal meaning, and better achieve the desired purpose. It can be seen that, with the rapid development of economy, the society has increasingly strict requirements on relevant employees. In the fierce social competition, CS with these abilities increase their weight to be approved by employers, thus improving the employment rate and not being eliminated easily.
3 Experiment 3.1 Subjects The object of this survey is the graduates of art design major in a university. The research scope of this paper is the graduates of art design major in a university. They are divided into experimental group and control group according to their familiarity with CGT. There were 30 people in each group. We use the way of questionnaire survey on the employment of these two groups of graduates, and through the employment situation to compare and analyze the employment rate of the two groups of graduates.
3.2 Experimental Design The main purpose of this project is to present the current employment situation through the survey data, to reflect the real employment situation through the
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survey data, and to investigate the impact of computer processing technology on the employment of CS in graphic design.
3.3 Analysis and Processing of Experimental Data Mathematical statistics: use Excel data processing software to conduct statistical processing of relevant data, and present in the form of charts. The formula is as follows. SU M I F $A$2 : $G$2, H $2, A3 : G3 SU M I F $A$2 : $G$2, H $2, A3 : G3 (1)
4 Result 4.1 Degree of Interest in CGT of Two Groups of Graduates In questionnaire survey, we know the two professional level of students’ interest in computer graphics image processing technology, according to the survey data show that the experimental group, 26.7% of students said they are not interested in its processing technology fully, said 30% of students interested in computer graphics image processing technology to compare, said 40% of students are interested in, and 3.3% of the students said not clear; In the control group, 73.3% of the students expressed no interest in computer processing technology, only 13.7 and 6.6% of the students expressed interest and great interest in computer processing technology, 6.7% of the students were not very clear. The comparison of the two groups of CS’ interest in CGT is shown in Fig. 1.
4.2 Cognition and Understanding of the Application of CGT of Two Groups of Graduates On “the use of CGT in cognitive” and “what do you think the understanding of the CGT in the survey, we found that the experimental group of students to answer these two questions to seems to be more scientific and rational, two groups of students, the experimental group 30 students mostly said computer processing technology is convenient, work efficiency be accelerated. Computer technology in the application of graphic design, the effect is also very ideal. Computer graphic design can complete all kinds of design work, to maximize the customer publicity design
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Fig. 1 Comparison of interest in CGT
requirements, to complete the design work with high standards. Graphic design has become simpler with the help of computer technology, and numerical calculation and line planning have produced more perfect results. Although graphic design is limited to two-dimensional space, its rich design content, various design means and various materials make the effect of expression different. In contrast, among the 30 students in the control group, many students’ cognition of the use of computer processing technology is confined to the narrow level of modifying pictures to make them more beautiful. And in the understanding of computer processing technology, the majority of the two groups of graduates agree that now computers are now common, but also people often encounter, contact. Big society. Some people need to know some basic computer skills. The application cognition and understanding of CGT of the two groups of graduates are shown in Fig. 2.
Fig. 2 Cognition and understanding of the use of computer processing technology
Graphic Design Understanding the Application … Table 1 Employment situation of the two groups of graduates
865
Unemployed (%)
Internship (%)
Positive (%)
Experiment group
10
26.66
63.34
Control group
20
33.34
46.66
4.3 Combine the Current Employment Situation of the Two Groups of Graduates The specific employment situation of the two groups of graduates investigated in this paper is shown in Table 1. As shown in Table 1, the employment rate of the experimental group is 10% higher than that of the control group, indicating the advantages of the experimental group in the control group. Make it faster to get jobs. 68% of the control group. It shows that after entering the workplace, more attention should be paid to the practical ability of graduates. At this time, it is not only necessary to be able to write, but also to do, and be able to complete the tasks assigned by the superiors. At this time, it reflects the power of computer processing technology. Computer processing technology can be more efficient to complete, with simple operation, to better reflect the desired graphic design. It can be seen from Table 1 that the experimental group is more able to survive in the fierce social competition. Therefore, the application of CGT in the plane is conducive to improving the employment rate of CS.
5 Conclusion It is a common phenomenon for CS to find a job and get employed. Every year, with the increase of the number of graduate students, the employment problem and a series of social contradictions become more and more prominent, which is conducive to the development of society to enhance the ability and competitiveness of CS. Computer technology has been widely used since its inception. Its technology plays a positive role in promoting the progress of our society. At the same time, social progress also ensures the continuous development of computer technology. Computer processing technology is a very practical and forceful computer-aided software in the current computer high-speed development and generalization. The society has a high demand for students with solid basic knowledge, high comprehensive quality and strong ability of art design. The use of computer processing technology in graphic design is an advantage. Computer processing technology is simple and efficient. The previous complex operation is replaced by simple operation, and the effect is better than before, beyond the initial expectation. Therefore, the application of CGT in graphic design is a skill that can improve the ability of CS. Master this skill, you can do things
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better, better spare time to learn things, and enrich and improve your own quality and ability. So as to effectively improve the employment rate of CS.
References 1. Wang P, Wang S (2020) Computer-aided CT image processing and modeling method for tibia microstructure. Bio-Design Manuf 3(1):71–82 2. Jelinek A, Zalud L, Jilek T (2019) Fast total least squares vectorization. J Real-Time Image Proc 16(2):459–475 3. Ebrahimi A, Loghmani GB (2019) B-spline curve fitting by diagonal approximation BFGS methods. Iranian J Sci Technol Trans A Sci 43(3):947–958 4. Shrivastava N, Bharti J (2020) Automatic seeded region growing image segmentation for medical image segmentation: a brief review. Int J Image Graph 20(3):2050018 5. Zhang H (2018) Employment assistance in urban China: a qualitative study from the youth recipients’ perspective. Children Youth Services Rev 2018:521–527 6. Hohmeyer K, Kopf E (2020) Caught between two stools? Informal care provision and employment among welfare recipients in Germany. Ageing Soc 40(1):162–187 7. Amit A (2018) Application of machine learning to computer graphics. IEEE Comput Graph Appl 38(4):93–96 8. Petkovi´c I, Herceg D (2017) Symbolic computation and computer graphics as tools for developing and studying new root-finding methods. Appl Math Comput 295:95–113 9. Williams K (2017) A graphic look at Katherine Young: allowing people to see information [amperes: current affairs from around the world]. IEEE Women Eng Mag 11(2):7–9 10. White M (2018) My favorite invisible color. Jewelry Art 72(1):4–4
Analysis and Research on the Mode of International Trade Practice Combined with Exhibition and Sales Under the Development of Internet Wei Bai, Deyang Zhang, and Xue Bai
Abstract In recent years, with the rapid development of computer technology and Internet, e-commerce was born in this context, and has been widely used in international trade. Through cross-border e-commerce and other network platforms, it has played a positive role in promoting the development of China’s international trade, and affected the traditional exhibition sales mode. However, there is little research on this field at present. Therefore, this paper puts forward the analysis and research of international trade practice combined with exhibition mode based on the development of Internet. In this paper, based on the Internet era of international trade and the development trend of the mode of exhibition and marketing, and further discusses the important impact of the Internet on international trade. Through the research, we can see that the new form of international trade exhibition mode plays an important role in promoting the development of China’s foreign trade. Although there are still many deficiencies in the current stage, the combination of international trade under the network can further broaden the external sales channels of enterprises, which is conducive to the enterprises to develop overseas markets. According to the relevant survey conducted in this paper, the Internet international trade joint Fair held in city B for four consecutive years has achieved excellent results, of which the export volume increased from 27% in 2016 to 68% in 2019, indicating that the mode can promote the city’s foreign trade and play a positive role in the development of the international trade industry. Keywords Internet technology · International trade practice · Exhibition model · E-commerce platform
W. Bai (B) · D. Zhang · X. Bai Haojing College of Shaanxi University of Science and Technology, Xi’an, Shaanxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_96
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1 Introduction With the rapid development of computer and network technology, the application of Internet is more and more extensive, and has penetrated into the field of international trade. Internet plus is also an important factor affecting the development of international trade. “Internet plus” international trade is also developing [1–3] to a higher level of international trade informatization. It fills the gap between countries and integrates geographically dispersed markets into global virtual markets. It has expanded the content and form of international trade, deepened the international division of labor, and made great changes in the theory and practice of international trade [4, 5]. The Internet has changed the operation mode, process and business model. The upgrading of China’s intermediate industry and the development of the Internet affect the trend of industrial upgrading. In particular, the Internet is leading the industry to a new level of quality improvement [6–8]. As a part of modern service industry, China’s exhibition industry is undergoing adjustment. From the economic point of view, the more developed the city, the faster the development of exhibition industry. Generally speaking, this is a trend of rapid development. At present, e-commerce has become an important platform for international trade, and has become the main form of international trade [9, 10]. This paper deeply studies the specific application of China’s exhibition and marketing mode in international trade, and understands that under the background of Internet, China’s international trade mainly focuses on e-commerce network marketing, which is represented by cross-border e-commerce platform. The traditional mode of exhibition and sales cannot play a full role in this aspect. Therefore, this paper puts forward the analysis and Research on the combination of international trade practice and exhibition sales mode under the development of the Internet, hoping to combine the advantages of the current network, effectively combine international trade and traditional exhibition and marketing activities, and establish a new international trade exhibition mode based on the Internet, so as to promote the development of China’s international trade. According to the actual needs of the existing international trade, combined with the advantages of the Internet, this paper puts forward targeted improvement measures. It has widened many channels for small and medium-sized enterprises to carry out international trade, especially for the development of the Internet. According to the relevant survey conducted in this paper, we can see that the number of participating enterprises and the transaction volume of international trade exhibition and sales activities combined online and offline have made a great breakthrough. The analysis shows that the combination of Internet cross-border e-commerce and traditional exhibition and sales mode is a mainstream development trend of international trade in the future, and the corresponding laws and policies should be improved as soon as possible to promote the sound development of international trade.
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2 International Trade Exhibition Under the Background of Internet 2.1 Internet Plus Overview Internet plus is a new form of Internet development. With the popularization and development of the Internet in various fields of society, especially the development of mobile Internet technology, the Internet has evolved into a new knowledge-based society. At the same time, the evolution of the Internet form makes the Internet and new communication technology constantly evolve and develop. The Internet, big data, cloud computing and other new information technologies emerge as the times require, and become the embodiment of the extension and development of the Internet. The evolution and development of Innovation 2.0 in the knowledge society gave birth to a large number of Internet innovation platform enterprises. In order to make full use of the new information technology and stimulate the economic and social market vitality, the state has issued a series of incentive measures.
2.2 Necessity of International Trade Innovation in the Era of Internet Plus The advent of the Internet era has a profound impact on the development of international trade. International trade has ushered in new opportunities and challenges, showing new trends and characteristics. With the outbreak of the global economic crisis in 2008, along with the global economic recession, international trade also has a serious decline. In order to protect their own economic development, countries will inevitably breed more trade protectionism, the form of international trade protectionism will be more high-end, and trade barriers will be set higher. International trade must integrate into the new normal of “Internet plus” development. The Internet plus “one belt, one road” is the aim of international trade. The purpose is to use the relatively high-quality domestic and international leading Internet power to accelerate the improvement of domestic efficiency, quality and marketing ability, and enhance the international influence of the whole domestic industry.
2.3 Concept of Exhibition Cultural activities such as exhibitions refer to the material exchange activities in geographical space. It has a certain scale, regularly organized in fixed places, organized businessmen from different regions. The exhibition industry discussed in this paper mainly refers to the exhibition. The exhibition industry includes not only
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economic and trade exhibitions, but also domestic trade fairs and other exhibition markets in China. But it also includes non-economic exhibitions, such as cultural exhibitions. In this paper, it refers to the economic exhibition. In China, economic exhibition is a very important marketing channel, which has more research value.
2.4 Development Mode of E-commerce in Exhibition and Sales The development of exhibition sales e-commerce needs business innovation, management innovation and technology innovation. First of all, we should use e-commerce to participate in the technological innovation of network services. Online exhibition and sales service refers to the offline exhibition which combines online exhibition and sales through the integration of exhibition resources. Online buyers and exhibitors can provide product matching, transaction matching, scene matching and other valueadded services. Online service exhibitors can scale up. It can help website suppliers and buyers increase trade opportunities and increase export turnover. In addition, it will combine online and offline services for exhibitors, including online and offline exhibitors, on-site buyers and online buyers. This is to establish a new service model.
3 Research on the Actual Effect of International Trade Exhibition Mode The purpose of this survey is to further understand the actual operation effect of the Internet-based International Trade Exhibition mode and its specific impact on international trade. We have conducted a survey on the online and offline activities of B city for four consecutive years. The survey data are from the official release of public information, collected by this paper. It is understood that city B will hold international trade fairs every year, through the exhibition mode to gather participating businesses to sell goods. The city’s exhibition and marketing activities have a certain foundation, during the conference attracted a large number of enterprises to participate in, and played a positive role in promoting trade. Due to the rapid development of e-commerce in recent years, it has further changed the traditional mode of exhibition and sales, from the pure offline mode to the current online and offline combination mode. Based on the statistical analysis of online and offline international trade exhibition activities in B city in recent four years, this paper has a certain representativeness and can better reflect the actual effect of the current international trade exhibition mode based on Internet technology.
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4 Discussion 4.1 Results and Analysis (1)
Investigation and Analysis on the number and types of participating enterprises
The statistical combination of Table 1 and Fig. 1 is obtained from public data. It can be seen from Table 1 that in the past four years, international trade fairs in city B have attracted a large number of enterprises to participate, and the enthusiasm of enterprises’ participation is also increasing year by year. Among them, the number of central enterprises increased from 8 to 52, and the number of well-known brands increased from 42 to 315. Combined with Fig. 1, it can be found that the type of enterprises participating in the exhibition and sales has changed from the original large-scale enterprises to the present small and medium-sized enterprises. Among them, the proportion of large enterprises has changed from 64 to 22%. The analysis shows that the current international trade exhibition mode based on Internet e-commerce is more conducive to small and medium-sized enterprises, and it is more helpful for small and medium-sized enterprises to open up the international market. (2)
Analysis of trade in exhibition and sales activities
Table 1 Statistical analysis on the popularity of exhibitors in 2016–2019
Particular year
Central enterprises
Famous brand
General enterprises
2016
8
42
1625
2017
15
88
1847
2018
29
137
2253
2019
52
315
2698
Large enterprises Small and medium-sized enterprises
100 90 78
Proportion (%)
80 70
64 58
60 50
56 44
42 36
40 30
22
20 10 0 2016
2017
2018
2019
Year
Fig. 1 Statistical analysis of the proportion of participating enterprises in 2016–2019
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Large enterprises
Small and medium-sized enterprises
100 90 80
73 68
Proportion (%)
70 57
60
48
50
43
40
32 27
30 20
16
10 0 On-line
Scene
Internal transaction Index
Foreign transactions
Fig. 2 Statistical analysis of trade types of trade fairs in 2016 and 2019
According to the public trading data, the results of Fig. 2 are sorted out. It can be seen from Fig. 2 that the proportion of online transaction amount has increased year by year, from 16% in 2016 to 57% in 2019, which is mainly due to the rapid development of e-commerce. More and more enterprises trade through online trading channels, and online trading mode is becoming a major international trade mode. In the trade volume of the exhibition, the export volume increased from 27% in 2016 to 68% in 2019. The rapid growth of the proportion of export value further shows that the international trade exhibition and sales mode based on the Internet has played a positive role and played an important role in promoting.
4.2 Opportunities for Enterprises in the Internet Plus Era The opportunities brought by the Internet plus era background are mainly reflected in two aspects: on the one hand, the changes in the international trade environment provide broad space for the development of enterprises; on the other hand, the changes in the international trade situation also promote the development of enterprises. Starting from the new environment of international trade, “Internet plus”, as a new form, breaks through the limitation of time and space, and creates a modern national trade mode environment for international trade. The emergence of the Internet has
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saved time and cost for international trade. It can also be said that the Internet has effectively optimized the allocation of resources around the world and strengthened the ties between countries. The new mode of international trade creates a new transaction mode for small enterprises, namely e-commerce mode. Consumers can directly complete relevant transactions through online shopping or online payment. As long as consumers are satisfied or satisfied with the product, the transaction can be successful. With the development of e-commerce model, it can also be carried out between consumers and consumers. At present, many enterprises have established e-commerce platform. For enterprises, e-commerce mode is very suitable for the development of enterprises, mainly because the e-commerce platform is easy to operate, easy to pay and relatively low cost, which provides a broad space for the development of micro enterprises.
4.3 New Measures of International Trade Practice Based on “Internet Plus” (1)
Strengthen the information authentication of both sides of trade
There are also some drawbacks in international trade based on “Internet plus”, for example, it is impossible to determine the authenticity of enterprise information, which brings great risks to both sides of the trade. Therefore, if we want to make international trade develop smoothly under the background of “Internet plus”, we must establish a verification and verification mechanism so as to verify the authenticity of information through effective channels. (2)
Ensure the security of electronic payment
At present, Alipay, WeChat payment and other electronic payment methods are gradually replacing cash payment, and also changing the payment method of international trade market in a wider range. However, various types of financial fraud and trade fraud also follow. Therefore, in order to ensure the stable development of international trade in the network era, we must improve the security of electronic payment. (3)
Strengthen the supervision of enterprises
For both sides of the trade, the management mode and operation level of the other party’s enterprises are directly related to their economic interests. In addition, the relevant national departments can also use the network platform to effectively supervise foreign trade enterprises, and conduct statistical analysis of big data, so as to provide scientific and accurate reference for China to formulate international trade policies.
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5 Conclusions With the rapid development of Internet technology, the traditional production and sales mode has been impacted unprecedentedly and also affected the field of international trade. This paper believes that the cross-border e-commerce and other Internet trading platforms play an important role in the existing international trade, and play a better role in making up for the shortcomings of the traditional sales model, while the traditional sales model is declining. Therefore, the combination of Internet technology to carry out international trade online and offline exhibition is a mainstream development trend in the future. It can give full play to the advantages of the exhibition mode and meet the needs of the times. The research in this paper has a good reference for the application of the exhibition mode in national trade under the background of the Internet, and has made a contribution to the research in this field. Acknowledgements Teaching reform project of education department of Shaanxi province: research on the teaching reform of international trade based on PBL model—taking the course of export commodity exhibition and negotiation as an example, project No.: 19BY142.
References 1. Wang Z, Chen C, Guo B, Yu Z, Zhou X (2016) Internet plus in China. It Prof 18(3):5–8 2. Yuezhou C (2016) Opportunity and challenge in the innovation and pioneering work of “internet plus” action: analysis in the perspective of technological revolution and technical-economical pattern% and the analysis from the perspective of technology-economic paradigm. Seeking Truth Acad J 043(003):43–52 3. Liu SM, Kim Y (2018) Special issue on internet plus government: new opportunities to solve public problems? Gov Inf Q 35(1):88–97 4. Zhang R, Liu J (2015) On the core competence of chinese manufacturing industry under the new formats of “internet plus”—a case study on furniture industry. J Serv Sci Manage 08(6):886–893 5. Xin J, Desheng Z (2017) China sports tv under “internet plus” background: reform, commonality and public interest of communication% China sports tv under the background of “Internet +”—Communication reform, publicity and public interest. J Wuhan Inst Phys Educ 051(006):24–28 6. Neary JP (2016) International trade in general oligopolistic equilibrium. Rev Int Econ 24(4):669–698 7. Davis AF, Moeltner K (2015) Human capital formation and international trade. BE J Econ Anal Policy 15(3):1067–1092 8. Spiller LPT (2015) Product diversity, economies of scale, and international trade. Quart J Econ 98(1):63–83 9. Akerman A (2018) A theory on the role of wholesalers in international trade based on economies of scope. Canadian J Econ/Revue Canadienne Déconomique 51(1):156–185 10. Adao R, Costinot A, Donaldson D (2017) Nonparametric counterfactual predictions in neoclassical models of international trade. Am Econ Rev 107(3):633–689
The Application of Computer Network Technology in University Library Mingqiu Yang
Abstract From ancient times to the present, great people have emphasized the importance of reading. Reading brings people not only the inner cultural accomplishment, but also the elegant outer conversational temperament. Every university has at least one library, and each library collects, summarizes and manages valuable books and documents, not only paper books, but also a computer network-based document resource library. These resources are provided to teachers and college students free of charge. The library is a treasure house of knowledge, and it needs personnel management. Therefore, computer technology is particularly important. Computer technology can help students better search for the books they need, and can store a large amount of information, including student loan and return information. Computer technology can also assist library management staff in classifying, purchasing, counting, and cataloging books. In this study, a survey method is used to survey 1000 college students. The results show that these college students go to the library once a week. Among them, 5% of the students go to the library to study every day. The survey results also show that about 80% of students borrow 7 books every month. Keywords Computer technology · University library · Application analysis · Management classification
1 Introduction Books are the best friends of life. They can not only bring endless knowledge to mankind, but also broaden people’s horizons and add fun to life. School education is inseparable from books. The establishment of university libraries meets the needs of teachers and students for daily course study and extracurricular reading. The development and application of computer technology has made university library resources more abundant, management more convenient, and greatly reduced the M. Yang (B) Jilin Engineering Normal University, Changchun, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_97
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workload of library administrators. As we all know, a university library has a large number of books and literature resources. It is a big project to manage and classify these resources. It takes a lot of staff time to sort and classify. Therefore, it is necessary to use computer technology to assist library management. The staff come to complete this series of work. After reading a large number of references, the research work of other scholars is organized as follows. The research focus of Jane Igie Aba et al. is the application of computer technology in the library reference services of universities and research institutes in north-central Nigeria. Using survey research methods, 32 libraries participated in the research. The survey results show that university libraries are to a certain extent computer technology is used for reference and consulting services, and there are problems such as abnormal power supply, insufficient infrastructure, and shortage of funds in university libraries [1]. In the study of Moonjung Yim and others, the comprehensive evaluation method of the Namibia regional library was introduced. Their research purpose is to evaluate the formativeness and summary of the library by solving the evaluation problems of library services, use and operation. Performance, showing how to design a mixed-method assessment to examine the performance of a multi-faceted library, and explain how the assessment design allows for information complementarity [2]. Klymchuk I. and others improve the productivity and quality of libraries and information services by creating, using, and integrating electronic resources and library process automation, creating electronic catalogs and digital documents to provide users with access to different types of library information resources [3]. Ebijuwa A. S. et al. used a descriptive survey research design, and the results showed that computer self-efficacy improved academic electronic library resources. Therefore, it encourages library management to provide necessary support and training for undergraduates to acquire technical skills [4]. Chandrappa research explored the importance of MARC21 control domain and its effective implementation in Karnataka University Library OPACs, and found that the library online operational amplifier under study rarely pays attention to the control domain, and 001 and 003 are widely used. With two tag numbers, he strongly recommends that libraries should fill in the data required for the control field when cataloging their records [5]. Combining with the foregoing, this article has made further improvements to the application of computer technology in university libraries after the results of other researchers. The computer technology involved in this article will more efficiently help students find out the specific floors and shelves of the books they want to read more quickly and accurately. At the same time, it can also help librarians to quickly manage books, simplify book classification, sorting and catalog. Large amounts of data stored by computers can be extracted faster, which is more convenient than traditional paper recording methods.
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2 Proposed Method 2.1 The Role of University Libraries As mentioned earlier, every university has a library. The library has many functions and functions. It contains many types of books and teaching aids, such as reference materials for subjects such as advanced mathematics, English, and physics; there are classics and novels that can be read outside of class; there are also e-books that can be viewed on the computer [6]. All kinds of books like this can not only help us to better learn and absorb the knowledge taught by teachers in class, but also add a piece of fun to our after-school life. The university library has already divided functional areas such as study room, reading room, reading room, electronic reading room, etc., so that college students can better choose the functional areas they need for study [7]. The self-study room provides a quiet learning environment, where you can study more attentively, complete after-school homework and preview in advance; in the reading room are rows of bookcases filled with books arranged in order, college students can learn according to the computer Find the target quickly by looking up the serial number of the book on the Internet; the reading room is suitable for college students who like to recite and recite, where they can read aloud, because the walls and doors of the reading room have a strong sound insulation effect, so there is no need to worry about disturbing students in other study rooms; the electronic reading room has a large amount of literature and book resources, which can not only check books and literature written by domestic authors, but also search for literature resources on the Internet [8]. Therefore, every university must build a library.
2.2 Computer Technology A computer is an electronic device that integrates network, computing, media, and other technologies. It has the functions of data storage and modification, and realizes the calculation of related logic and data. Computer technology can be divided into many types, including computer system technology, computer equipment technology, computer component technology and computer assembly technology [9]. The computer system is composed of various components. These components have rich technical content. This article mainly uses various component technologies of computers, which play a key role in the classification and retrieval of books and access to archives.
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2.3 Advantages and Disadvantages of Applying Computer Technology to University Libraries Advantages. The use of computers in university libraries saves library management staff’s working time and improves their work efficiency. In the past, recording materials was a complicated and error-prone task in university libraries. Various information of this book, such as title, author, classification and storage location, are recorded in paper files, which is not only difficult to find, but also difficult to save [10, 11]. Therefore, the appearance of computers can help university librarians to manage libraries. College students cannot do without the library, whether it is in study rooms such as study rooms and reading rooms, or the books and documents collected in the library. When college students need to borrow a book, they can not only use the computer to find the specific storage location of the book, which saves a lot of time, but also complete the borrowing and returning procedures through the borrowing system without going through the management staff [12]. The storage function of the computer is also very powerful. Some books and documents are stored in the electronic reading room of the university library in the form of e-books, which can be read and downloaded by college students. College students can log in to the main page of the library to stay at home. You can enjoy the fun of reading. Disadvantages. Even though university libraries have many advantages when used in university libraries, everything has two sides. Computers are inseparable from power sources. When the power system of the university library stops operating, the lending system goes on strike. The computer also needs to be checked and maintained by professionals on a regular basis.
3 Experiments 3.1 Research Purpose The university library has been built since the establishment of the school, in order to provide teachers and students with more learning materials and a rich and colorful after-school life, so that college students can swim in the ocean of knowledge. But sometimes the utilization rate of the library is not as high as imagined. This study mainly understands the utilization rate of the library by college students.
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3.2 Questionnaire Survey Method Questionnaire Design. According to the research needs of this article, with reference to the principle and method of making the questionnaire, through the analysis of reference documents, combined with the reality of this research, the overall framework of the questionnaire is determined, and the questions are designed accordingly. Then, the questionnaire was revised and improved repeatedly to ensure the validity of the questionnaire. Issuance and Recovery of Questionnaires. A class meeting is organized by the counselors of each department, and the monitor of each class is responsible for issuing the questionnaire, and every student participating in the class meeting is required to fill in and submit the questionnaire carefully. A total of 1000 students from each department participated in the class meeting. After inspection, each questionnaire was deemed valid.
3.3 Algorithm Recommendation Bayesian algorithm: Bayes’ theorem is a theorem about the conditional probability (or marginal probability) of random events a and b, where P(A|B) is the probability of a occurring b. Assuming that B1, B2… are some possible premises of a certain process, then P(Bi) is the pre-estimated probability of each premise occurring, called the prior probability. If this process yields the result A, the Bayesian formula provides us with a new method of evaluating the preconditions based on the appearance of A. P(Bi|A) is not only a new understanding of the probability of occurrence of the premise Bi, but also a posterior probability. The formula used is as follows: P (A ∩ B) = P (A)*P (B|A) = P (B)*P (A|B)
(1)
P (A|B) = P (B|A)*P (A)/P (B)
(2)
The variance formula used when processing the data in the collected 1000 questionnaires is as follows: (x1 − M)2 + (x2 − M)2 + (x3 − M)2 + · · · + (xn − M)2 n
(3)
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4 Discussion 4.1 The Number of Times College Students Go to the Library Every Week From Table 1 and Fig. 1, it can be seen intuitively that every college student goes to the library at least once a week. Among the 1,000 college students surveyed, 20 go to the library to study only once a week, accounting for the total number of participants in the survey. 2%; The second-to-last percentage in this survey is the number of people who go to the library every day, which is 5%; among the survey results, the percentage of people who go to the library 4 times a week is the largest, which is 33%; the proportion of people who go to the library 3 times a week ranks second, at 27%; the proportions of people who go to the library 5 and 6 times a week are 19% and 6% respectively; those who go to the library 2 times a week. There are 80 people, accounting for 8% of the total number of people surveyed. In summary, the university library is fully utilized. Every university student goes to the library to study every week, and 5% of them go to the library every day. Table 1 The number of times college students go to the library each week
Number of people
The proportion (%)
One time
20
2
Two times
80
8
Three times
270
27
Four times
330
33
Five times
190
19
Six times
60
6
Everyday
50
5
Fig. 1 The number of times college students go to the library each week
One time Five times
Two times Six times
Three times Everyday
Four times
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4.2 The Number of Books Borrowed by College Students from the Library Each Month The library can not only learn to read books in the library, but also borrow them, but different university libraries have certain requirements for the maximum borrowing time of each book. If the maximum time is exceeded, you can choose to continue to borrow or return the books. It can be seen from Table 2 and Fig. 2 that the 1000 college students surveyed borrowed a certain number of books every month, and nearly half of them borrowed 7–9 books every month. This part of the population accounted for 42% of the total number of 420 people. The number of people who borrow 9–11 books every month ranks second, accounting for 29%; the number of people who borrow 1–3 books and 4–6 books every month ranks fourth and third, and the proportions are respectively 9% and 13%; among the college students surveyed in this survey, 70 people1 borrow more than 11 books every month, and this group accounts for 7% of the total number. In summary, the ability of college students to learn independently needs to be improved, and they should read a certain number of books every month, whether they are related to learning or famous novels that enrich their after-school life. Table 2 The number of books that college students borrow from the library each month
Number of people
The proportion (%)
1–3
90
9
4–6
130
13
7–9
420
42
9–11
290
29
More than 11
70
7
7%
Fig. 2 The number of books that college students borrow from the library each month
29%
9%
13% 42%
1~3
4~6
7~9
9~11
More than 11
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5 Conclusions The university library is a treasure house of knowledge. Contemporary college students should learn to explore the various functions of the library, and apply what they have learned by constantly tapping the wealth inside. In the age without computers, library management staff need to spend a lot of energy to sort out books, go through procedures for borrowing and returning, extracting and archiving a series of complicated tasks. With the emergence of computer technology, library management methods are constantly updated. This article mainly explores the application of computer technology in university libraries. The computer can take on the functions of user login, inquiry, borrowing and returning, and management in the library. The computer can store a large amount of data. Each student has his own account, which records the reading, borrowing, and other records in detail. Library managers can use computer technology to effectively classify and archive books, saving a lot of time. Therefore, computer technology plays an important role in university libraries, and both students and management staff should make good use of computer technology.
References 1. Aba JI, Idoko N, Akor PU (2017) Application of computer technologies to reference services in University and Research Institute Libraries in North Central Nigeria. J Balkan Libr Union 2017(2) 2. Yim M, Fellows M, Coward C (2020) Mixed-methods library evaluation integrating the patron, library, and external perspectives: the case of Namibia regional libraries. Eval Program Planning 79 3. Klymchuk I, Stadnichenko O, Yaremko I et al (2021) E-democracy implementation in the process of stimulating the country socio-economic and socio-political development. Turkish Stud Inf Technol Appl Sci 2021:130–139 4. Ebijuwa AS, Mabawonku I (2019) Computer self-efficacy as a predictor of undergraduates’ use of electronic library resources in federal universities in South-west Nigeria. Global Knowl Memory Commun 68(10) 5. Chandrappa RNV, Harinarayana NS (2021) Use of MARC21 control field data in University Library OPACs in Karnataka: a study. Annal Libr Inf Stud 68:21–27 6. Fidler B, Acker A (2017) Metadata, infrastructure, and computer-mediated communication in historical perspective. J Am Soc Inform Sci Technol 68(2):412–422 7. Nazim M (2021) Analysing open access uptake by academic and research institutions in India. DESIDOC J Libr Inf Technol 41(2):108–115 8. Du C (2021) Research on the evaluation of University library’s reading promotion mode based on computer new media. J Phys Conf Ser 1827(1):012189 9. Li X (2021) Design of next-generation virtual library for free movement in a 360° perspective. Turkish J Comput Math Educ (TURCOMAT) 12(6):303–307 10. Meher SS, Ravi J, Celik ME et al (2021) Superconductor standard cell library for advanced EDA design flow. IEEE Trans Appl Superconductivity 99:1 11. Medawar K (2021) Setting up a new library: planning, challenges, and lessons learned. A case study about Qatar national library. Int Inf Libr Rev 2021(5):1–20 12. Capdarest-Arest N, Navarro CE (2021) Promoting health data fluency skills by expanding data and informatics work in libraries: the role of a health library informaticist. Med Ref Serv Quart 40(1):130–138
The Development Trend of Computer Network and University Library Yanqiu Wang
Abstract With the continuous deepening and promotion of the new technological revolution based on computer network technology, computer network technology has had a huge impact on all fields of society. As a preservation institution of information resources in all walks of life, the impact of computer network technology on the library is more intuitive and concrete. Especially college libraries, college libraries are important units of teaching and scientific research in colleges and universities, which can provide teachers and students with teaching and learning materials and information, which is very useful. This article first analyzes the application status and existing problems of computer network technology in university libraries, and then proposes new improvement measures for the application of computer network technology in university libraries. Finally, a certain domestic university that has applied computer network technology is proposed. As the subject of the survey, the library conducted a questionnaire survey on 200 students in the library. The results of the survey showed that after the application of computer network technology in university libraries, the efficiency of students searching for materials has been improved compared with the past. Students think that computer network technology is of great significance to the development of university libraries. Keywords Computer network · University library · Improvement measures · Development significance
1 Introduction Throughout the development history of libraries, libraries have increasingly become the forefront of the application of new technologies. The library industry has been constantly using new technologies to improve library management and promote the
Y. Wang (B) Jilin Engineering Normal University, Changchun, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_98
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upgrading of library services. University library is the information center of university teaching resources, which provides information resource guarantee for university teaching and scientific research. Therefore, as a knowledge center and a highly developed data information distribution center, university libraries should be at the forefront of the application of new technologies in libraries [1]. The introduction of computer network technology has gradually changed the way university professors and students request information, and has changed the concept of management and the service method of university libraries. In order to meet the ever-changing information environment and user needs, university libraries need to update their development thinking. The service model is updated on the traditional basis, new service content is constantly developed to meet the information needs of teachers and students, and the use of new technologies to be applied to the future development of university libraries is strongly supported [2]. When we pay attention to, explore and apply computer network technology, we must also keep our minds awake and develop a systematic library new technology application plan [3]. Therefore, through the investigation and analysis of the application of computer network technology in university libraries, this article puts forward the challenges and problems faced by university libraries in the application of computer network technology, and attempts to formulate countermeasures for the application of computer network technology in university libraries. The application and development of computer network technology in university libraries provide some reference. Chinese scholar Huang Yun believes that as a particularly important functional organization in university education, university libraries have developed rapidly in recent years. At present, there are still many shortcomings in the development of university libraries, and they are restricted by many factors. How to commit to better development of university libraries is a question that every school should consider [4]. Ren Zelian believes that with the continuous development of network technology, the traditional development model of university libraries has gradually become history, and the development of university libraries in the new era of network environment is facing huge challenges. Therefore, how college libraries can play their unique advantages in the network environment and how to improve their competitiveness in the industry has become a topic of discussion [5]. Li Aiying believes that in today’s computer network environment, many industries have used the wave of computer networks to achieve changes, gain benefits and promote social progress. As a training ground for various talents and an educational place for the country’s future pillars, colleges and universities need to keep pace with the times, analyze the traditional shortcomings of college libraries as soon as possible, and integrate the Internet model [6]. At present, the technical environment faced by university libraries is undergoing tremendous changes. Researchers in the library field are actively exploring the application of computer network technology in libraries. Computer network technology has caused tremendous changes in various functions of the library. The update and application of computer network technology are promoting the continuous development and innovation of various industries in the society. Facing the impact of intensified competition and diversified needs of students and teachers, university
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libraries must adjust their functions and positioning, and transform to provide information based on collections. As the goal of the traditional service concept, in the new environment, college libraries should clarify their core competitiveness and educational value based on their own positioning, goals, and resource ownership, and play the greatest role in college teaching [7]. As an important part of modern university education, university libraries play an irreplaceable role in document delivery, student education, entertainment and leisure. With the expansion of university libraries in the field of big data analysis, processing and application, university libraries have become the role of university learning centers, information centers, education centers, reading centers, and community cultural centers. While maintaining the vitality of the sustainable development of university libraries, it is also necessary to assume the mission of the existence value given to university libraries by the times.
2 The Development Trend of Computer Network and University Library 2.1 Analysis of the Development Research Trends of Domestic and Foreign Libraries With the advent of computer network technology, almost all libraries inside and outside are networked. In other words, the construction of digital libraries has become an unstoppable trend. Therefore, the literature or research report on the construction of digital library has undoubtedly become the main theme of the library development research literature [8]. Under this general trend, countries around the world have begun to push digital libraries to a climax. Due to different situations, the initial priority and level gap, the United States is in a leading position in the construction of digital libraries. In the United States, the construction of digital libraries started very early. It has not only developed a relatively complete standardization system and engineering specifications, but also has accumulated rich experience and has become a model for other countries to follow. Macroeconomic research mainly starts from the national profile, studying the strategic strategy, development plan and macroenvironment construction of my country’s library information development. The table includes monographs, documents and technical reports, etc., and puts forward many points according to China’s national treaties. It deserves people’s attention. Proceeding from the technical characteristics of digital libraries, and based on the principle of adapting the library’s operating mechanism to the technical level, we will discuss the reform of our library system in order to give full play to the advantages of information technology [9].
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2.2 The Application Status of Computer Network Technology At present, the rise of computer network technology is a new technology that has emerged with the development of technologies such as the Internet, distributed computers, and mobile communications. The rapid development of electronic hardware products and the widespread promotion of various mobile phone products have enabled the rapid application of computer network technology. The application of computer network technology can provide all users with accurate and timely digital information resources, regardless of time and region [10]. The widespread popularity of various mobile phones and the enhancement of mobile phone information processing functions have laid the foundation for the application of computer network technology, which makes people pay more attention to computer network technology. The growth of mobile Internet users has led to a continuous increase in the number of mobile readers. The library community quickly realized the need to change the concept of library services and develop new service elements to provide services for mobile users [11]. Mobile Internet users can receive the reminder service of borrowing documents provided by the library through their mobile phones, the revocation service of borrowing documents and the expiration of the borrowing document reservation service, as well as the book reservation and Internet library data research organized by the library. The portable library provides all-weather services and can perform real-time online reading of data and information resources, thus proving the powerful advantages of computer network technology.
2.3 Problems Existing in the Application of Computer Network Technology in University Library On the issue of whether to introduce new technologies, one should consider choosing a technological solution with development potential, and more should consider the trend of technological development in order to be able to make a correct judgment. More importantly, a strategic development perspective is required, and one must not choose without a development prospect. The solution or the technical solution that is outdated or about to be outdated may soon be faced with the problem of re-selecting again. This result will cause a large amount of repeated investment of resources and cause a lot of waste of funds. Some academic libraries pursue high-end technology and equipment selection [12]. The implementation of technical solutions is inseparable from the support of hardware equipment, which is the material basis, so comprehensive, system configuration, and reasonable planning must be considered in the selection of hardware facilities. Due to the limited resource allocation, university libraries do not need to blindly pursue high-end new equipment. The use of relatively mature and mainstream settings not only saves resources, but can also be used as a complete set. However, it is necessary to consider the compatibility and scalability of the future development of the equipment, leaving room for capacity expansion
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to facilitate future equipment upgrades. In the selection of service providers, some academic libraries lack a strategic vision. If university libraries want to use cloud services to support their library business, the library actually voluntarily gave up the right to save digital resources and gave the right to cloud computing service providers. Therefore, careful consideration should be given to the selection of service providers. If there is a lack of strategic vision, it may lead to unpredictable consequences. For example, it is impossible to predict how much the interruption of cloud services will affect the business.
3 Research Methods of Computer Network and the Development Trend of University Library 3.1 Multi-platform Resource Integration The foundation of cloud computing is “integration”. Only after resources are integrated can the diversity of information be shielded and the compatibility of various management systems can be realized, truly realizing the union and sharing of information around the world, and improving the efficiency of information resource sharing. The so-called data resource integration refers to the application of multiple technologies to fully integrate data sources from different sources, and use different communication protocols according to specific needs, so that different types and forms of data types can be seamlessly connected. Taking the cloud data resource library as the node, it can convert between platforms and integrate multiple databases to store resources in multiple formats to read and use multiple platforms. Embedded information resource is an information resource platform that can perform complete recovery among platforms, multiple databases, and multiple contents. In addition, by integrating the existing university library itself and external resources, an integrated external subscription, retrieval and collection platform is realized. At the same time, it explores the learning needs of teachers and students in depth, so that most teachers and students can summarize and sort resources twice. Integrate related resources into effective courses in a specific field, and all readers can also upload their own resources from their own resource library. Create a comprehensive resource and learning service platform, everyone will benefit from it, everyone is building, everyone is participating.
3.2 Establish a New Technology Application Alliance in University Libraries The creation of a new technology application alliance for university libraries can complement the advantages of information resources and form a comprehensive
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advantage. The alliance strategy is under the unified management of the cloud computing information resource service platform, the service functions of each university library, according to the information resource advantages of their respective libraries, form an interconnected cluster effect, so as to better provide for the majority of teachers and students. The resources of different university libraries have their own characteristics, which are caused by multiple reasons such as the different academic levels of each university, the characteristics of each discipline, the different geographical distribution and the development direction of the discipline. In the past, large university libraries were basically independent libraries. Except for a few academic exchanges, technical exchanges and resource exchanges were almost nonexistent, let alone exchanges with external libraries. The rapid development of new technologies makes it possible to use cloud resource platforms and remote interconnection functions between university libraries and public libraries and between university libraries to enhance the exchange of data and information resources, and to diversify and integrate them. The direction of globalization realizes differentiated integration. Make full use of existing information resources, expand the collection of digital resources in the main library of the university, and try to avoid repetitive construction of digital resources to save limited funds. In order to make full use of the complementary advantages of literature and information resources between different universities, strengthen the self-construction capacity of university libraries, and further improve the overall service level of university libraries, some university library alliances have been established to try to gradually realize the membership of the library.
3.3 Take the Compound Library as the Basic Form of the Library Hybrid library is an organic integration of traditional library and digital library. It is a library that provides printed resources and seamless access to electronic resources. It is a combination of local and remote, printing and digital information resources. The built-in library can supplement the advantages of traditional and digital libraries, and allows information users to search for required information in a complex environment where electronic and print resources coexist. The development of a hybrid library is also inseparable from the support of computer network technology. The use of cloud computing technology can replace expensive professional servers with inexpensive server groups, and can realize large-scale high-speed computing cloud computing services. New information technology is an important foundation for realizing the goals of future hybrid libraries. University libraries should regard hybrid libraries as an important development strategy. In the complex environment of future digital information resources, the future library must also be a combination of digital collection resources and printed matter collection resources, a combination of libraries and network services, and a combination of physical buildings and virtual spaces.
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Although digital libraries are very advanced, they cannot replace the role of physical libraries. Similarly, traditional physical libraries can only be expanded and expanded through digital libraries. It is precisely because of the differences in user needs that the two can complement each other.
4 Analysis of the Results of Applying Computer Network to University Library This article selects a domestic university as the research object, applies computer network technology to the library of the university, and selects 200 students from the library of the university to do a questionnaire survey. The formulas applied to the data processing of the survey results are: x=
−b ±
(1 + x)n = 1 +
√ b2 − 4ac 2a
(1)
nx n(n − 1)x 2 + + ··· 1! 2!
(2)
4.1 Analysis on the Efficiency of University Students Searching for Information According to Table 1 and Fig. 1, after using the library combined with computer network technology, 267 students out of 200 students said that they were more efficient when searching for materials, and 27 students said that they were more efficient than before. When searching for materials, the efficiency has not changed much compared with before. Only 6 students said that they were less efficient when searching for materials. The reason for the lower efficiency of the 6 students may be related to the students’ ability to accept new things, or the reason why the materials searched by these 6 students are not common. Generally speaking, after the application of computer network technology in university libraries, the efficiency of searching materials for university students has been improved. Table 1 Analysis of changes in the efficiency of students searching for data
Number of people Higher efficiency
167
No change in efficiency
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Less efficient
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number of people
changes in the efficiency of students finding information 167
HIGHER EFFICIENCY
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6
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Fig. 1 Analysis of changes in student search efficiency
Table 2 Students’ evaluation of the combination of computer and network technology in the library
Number of people
Percentage (%)
Very meaningful
87
43.5
More meaningful
84
42
General meaning
25
14.5
Meaningless
4
2
4.2 Evaluation and Analysis of University Students’ Evaluation of the Combination of Computer Network Technology in the Library According to Table 2 and Fig. 2, after using the library combined with computer network technology, 87 students out of 200 students said it was very meaningful, accounting for 43.5%, and 84 students said it was more meaningful, accounting for the ratio was 41%, 25 students said it was meaningless, accounting for 14%, and 4 students said it was meaningless, accounting for 2%. Four students said that the reason for the meaningless may be that they encountered difficulties or other special factors in the process of use. Generally speaking, most students think that the combination of computer and network technology in college libraries is of greater significance.
5 Conclusions After the reform and opening up, my country’s higher education has developed vigorously, which has made important contributions to my country’s economic take-off. However, there are also some problems and defects that must be resolved in the
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Fig. 2 Students’ evaluation of the combination of computer and network technology in the library
development process. University library is the basic platform of higher education and the most important educational resource. Its overall level directly affects the improvement of university teaching quality and scientific research level. This paper conducts a preliminary investigation on the application of computer network technology in university libraries, analyzes the problems in the application, formulates the development countermeasures for the application of computer network technology in university libraries, and conducts a questionnaire survey on 200 students. The results of the survey are It shows that computer network technology is beneficial to improving the efficiency of students searching for materials in the library. Most college students believe that the application of computer network technology in college libraries is of great significance.
References 1. Donghong L, Ying H, Wenhui Y (2019) The superimposed effect of “policy promotion + scientific and technological revolution” on the development of university libraries. Libr Inf Serv 63, 631(18):15–22 2. Heng Z, Tiantian C, Han Z (2019) Development trends of university libraries at home and abroad: a comparative analysis based on conference themes. J Acad Libr 037(005):12–18 3. Hong Z (2020) Analysis on the development countermeasures of private university libraries— take Tianhua College of Shanghai Normal University as an example. Educ Observ 9, 231(05):123–125 4. Yun H (2019) Analysis on the factors restricting the development of university libraries. Chinese Foreign Entrepreneurs 634(08):119 5. Zelian R (2019) New ideas and countermeasures of university libraries under the network environment. West China Radio Television 442(02):56–57 6. Aying L (2019) Considerations on the development strategies of university libraries in the internet+ context. J Commun 369(24):12–13
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7. Yan W (2020) Achievements and experience in the development of my country’s academic libraries in the past 70 years since the founding of the People’s Republic of China. Sci Educ Wenhui (Mid-Sate J) 491(04):25–27 8. Liqiong Z, Ying L (2020) Review and prospect of the development of local university libraries in my country. Guide J Sci Technol Econ 28, 699(01):143–144+153 9. Ping S, Dongchu S ((2020) The impact of new infrastructure on the development and construction of university libraries. Inner Mongolia Sci Technol Econ 458(16):151–153 10. Yongfeng M (2020) Research on the dilemma and countermeasures of the development of local university libraries. J Chifeng Univ (Nat Sci Edn) 036(005):86–88 11. Fei Y (2020) Research on the development trend of university libraries. Fireworks Technol Market 103(02):247+251 12. Luying Y (2019) SWOT analysis and countermeasures of the development environment of academic libraries in the digital age. Publ Investment Guide 336(16):287–288
Self-service Fetching of Image ROI Based on Computer-Aided Detection Yuan Tian, Yaming Mu, Ze He, Zuyuan Huang, and Yudou Gao
Abstract With the rapid development of computer technology, computer-aided detection, as a product of the combination of computer science, cognitive science, and engineering mathematics, facilitates image extraction to provide more and more accurate and relevant information. Fast and accurate extraction and region segmentation of the region of interest is a necessary prerequisite for image ROI self-fetching, which can greatly improve the efficiency and accuracy of image processing and analysis. The purpose of this paper is to study the self-service fetching of image ROI for computer-aided detection. Aiming at the problems of low accuracy and weak anti-interference ability in the existing target ROI extraction and target detection and recognition technology, according to the characteristics of the actual collected images, this paper studies a method suitable for multi-target ROI extraction and target detection and recognition in typical scenarios. The method improves the accuracy of ROI extraction and target detection and recognition. This article aims to develop practical image ROI automatic acquisition technology, with the support of computer-aided detection, using theoretical analysis and experimental verification research methods, statistical modeling of image ROI clutter, image ROI automatic target detection, and image ROI automatic Key technologies such as target identification have been systematically and deeply studied. The experimental results show that the segmentation method proposed in this paper based on self-service image ROI acquisition under computer-aided testing has an accuracy of 99.59% for the overlap percentage threshold, which can effectively achieve the segmentation of key regions, and the extraction of ROI has high accuracy and is stable and practical. Keywords Computer-aided detection · Image ROI · Self-service data retrieval · Segmentation method
Y. Tian · Y. Mu · Z. He · Z. Huang · Y. Gao (B) Information Center of Yunnan Power Grid Co., Ltd. No., 105 Yunda West Road, Guandu District, Kunming 650214, Yunnan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_99
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1 Introduction With the development of computer and artificial intelligence technology, image data can be digitized, which is very suitable for computer processing, so as to realize computer-aided detection [1, 2]. Using a computer to perform image processing and image analysis on information can effectively improve the accuracy of data extraction, reduce the error value, and shorten the time of manual operation [3, 4]. On the basis of accurate quantitative calculation, a more comprehensive, comprehensive and objective analysis of image data is of great significance to the development of automatic control, computer vision and pattern recognition [5, 6]. In the research of computer-aided detection of image ROI self-fetching, many scholars have studied it and achieved good results. For example, Salehi L. et al. proposed an ROI extraction method based on a saliency graph model [7, 8]. Polakowski W E proposed a multi-layer terrain area growth algorithm, that is, three incremental growth end thresholds to obtain a three-layer area whose area expands layer by layer [9, 10]. In the research process of this paper, threshold-based segmentation method is used to conduct in-depth discussion on computer-aided detection of image ROI selfservice access, using statistical methods, structural methods, model methods and spectral methods, combined with multidisciplinary knowledge such as mathematical morphology and computers. Aiming at the problem that ROI can not be extracted correctly due to the susceptibility to the interference of the application environment and the difficulty of rapid and effective detection and recognition of various targets, an ROI extraction method based on the background probability statistical model is proposed.
2 Self-service Fetching of Image ROI Under Computer-Aided Detection 2.1 Image Segmentation and ROI Extraction Target Feature Research Shape feature. The shape feature reflects the contour feature of the target on the image. Because the target is generally presented in the shape of a "rice grain", and most of the shape features are irregular, the shape feature has a certain degree of discrimination. Since the image data to be detected is often obtained by optical sensors with different resolutions, the selected shape features must have characteristics such as rotation, translation, and scale scaling. Gray features. When human vision observes things, the first thing it notices is the shape of the object, but because part of the information is very similar to the shape of
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the target, it is impossible to distinguish the target only by the shape feature. Therefore, other feature information must be considered, and based on the characteristics of the ship target, grayscale features are further introduced for target identification. The gray-scale statistical feature refers to the gray-scale feature of the target area, which is usually described by the second-order moment information, the first-order moment of the image histogram and the average information entropy. Texture characteristics. The texture information reflects the spatial information of the image. It is a pattern composed of grayscales in space in a certain form. The texture information is composed of mutually compiled elements, which reflects the mutual spatial relationship and the grayscale nature of the image. The texture description reflects the characteristics of smoothness, sparseness, and regularity of the region, and the extracted texture features are included in the ROI slice of the target region, taking into account the overall Information.
2.2 The Steps of ROI Automatic Detection Algorithm Perform ROI search. Without losing useful information, computers search for the area of interest in the image as much as possible. Common search methods include Laplacian Gaussian filtering, Gaussian differential filtering, pattern matching, etc. The Gaussian difference filtering method is based on the characteristics of an image that is flat and approximate to a circle, and uses a pattern with a circle and the same inner gray to match a circular ROI that looks like a search. The gray area of the partial image is similar to the two-dimensional sech curve, and the correlation is used as a function of the similarity between the measurement area and the standard. The ROI is searched based on this feature, and the sensitivity is high. Perform ROI segmentation. The goal of ROI is to separate the area of interest from the background area. Edge-based methods and region-based methods are two important ROI segmentation algorithms. Edge-based ROI segmentation usually uses edge detection operators to obtain some discontinuous edge line segments, then link the edges to obtain the boundary area of interest, and finally fill the area to obtain the area of interest. Edge-based ROI segmentation is simple and easy to use. However, it has a good effect on target areas with normal shapes and clear boundaries, but has a lower segmentation effect on target areas with low density and low contrast. For regions of interest with blurred boundaries, region-based ROI segmentation is more suitable. Regional development is a common method of such segmentation. According to the characteristics of the image format, the weighted distance development criterion is adopted to give priority to nearby pixels and suppress remote pixels. Although merging with the background is avoided, the position of the seed point will also affect the segmentation effect of the region of interest. Perform ROI classification. ROI classification is to classify the searched regions of interest. The main purpose of this step is to reduce the large number of ROIs
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with different characteristics in the search results of the first step. Use a variety of classifiers to select the most effective features to classify the ROI.
2.3 Application of Image Segmentation in Automatic ROI Extraction Image segmentation has played an important role in computer-aided diagnosis and other aspects, and automatic segmentation methods have been an important part of image processing. Image segmentation can determine the structure, distribution, shape, location area, etc. of the segmented image, and locate the target. Use image segmentation in the analysis of biological images. It is suitable for recording, fusing and measuring the anatomical structure of biological images, as well as image reconstruction and biological motion monitoring as prior knowledge. Use image segmentation in the process of measuring the volume of human organs, tissues or lesions. Pathological studies can be carried out to evaluate the efficacy of drugs. For example, quantitative measurement and analysis of lesion volume before and after treatment can assist doctors in making judgments, predictions, and formulating or modifying treatment plans for patients. Use image segmentation in the 3D reconstruction of the image. Obtain anatomical atlas information, provide original data for the three-dimensional reconstruction, three-dimensional display, registration, fusion, visualization of the image, and for the virtual realization of the human body and the roaming system. Use image segmentation in the effective management of image information. After extracting the region of interest from the image segmentation, focus on the target region, then each region is independent and easy to select. Compressed image data is convenient for archiving, retrieval and transmission, and useful information will not be lost. Because the number of features such as regions, borders, and textures in the image is much smaller than the number of pixels in the image itself. This plays an important role in improving the image transmission speed.
2.4 Iterative Threshold Method to Obtain Segmentation Threshold Using iterative threshold method to segment images is one of the basic methods of image segmentation. The iterative variable selected in this paper is the global threshold T_i of the image gray. The global threshold needs to have an initial value to be able to iterate. Here, we take the gray average of the entire image as the initial value. Next, you can start iterating. Before each iteration, you can use the global
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threshold to divide the image into two categories: bright pixels and dark pixels, and then calculate the respective gray-scale averages of these two types of pixels, which are the average bright pixel (Mbp ) and the average dark pixel (Mdp ). Its calculation formula is as follows: 1 xl,s vl,s , vl,s ≥ Ti nl s
(1)
1 ym,t vm,t , vm,t < Ti nm t
(2)
Mbp = Mdp =
Among them, v is the grayscale of the pixel, l corresponds to the bright pixel, and m corresponds to the dark pixel; vl,s is the grayscale value of the bright pixel with the serial number s, and xl,s has this grayscale value. The number of all pixels in vm,t , ym,t is the same; n is the total number of each of the two categories of pixels. Suppose k is the number of iterations, then the condition of iteration convergence is: Ti,k =
1 Mbp,k+1 + Mdp,k+1 2
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That is, if the global threshold of this iteration is exactly equal to the average of the two types of pixel averages before the next iteration, the iteration stops; otherwise, the new global threshold is equal to the average of the two types of pixel averages before the next iteration, and the iteration continues until it stops. The global threshold (let T f 1 ) when the iteration converges is the final estimated threshold.
3 Experiments Research on Self-service Image ROI Retrieval Under Computer-Aided Detection 3.1 Research Objects In this paper, 240 images of the same batch taken by the same technician are selected as the research object, 120 are taken as training samples, and the remaining 120 are used as experimental samples.
3.2 Algorithm Evaluation Use the test picture set to verify and evaluate the algorithm. During the test, all pictures are run under the same set of parameters without manual intervention. Among all 240 images, there are 17 that the algorithm in this paper cannot converge, that is,
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the zero-crossing point of the centrifugal gradient is not found. Part of the reason for the failure to converge is that the area is too large due to the intersection, and the high brightness of the background area itself makes the algorithm unable to perform accurate segmentation.
3.3 Judgment of Optimal Segmentation and Selection of Data The segmentation of the ROI region is a process of finding the optimal segmentation, that is, detecting the zero-crossing point of the centrifugal gradient, that is, defining the boundary as the zero-crossing point of the centrifugal gradient. If the seed point is used as the origin of the coordinates, the centrifugal gradient of each other point in the figure relative to the seed point can be calculated. If the weighted region expansion algorithm is ideal enough, then a certain iteration can be found, and the centrifugal gradients of all pixels on the boundary of the iteration region are all zero. At this time, this iterative area can be considered as the optimal segmentation. However, it is obvious that the weighted region iterative algorithm is almost impossible to guarantee the existence of such an optimal segmentation. Therefore, it is necessary to weaken the judgment condition of optimal segmentation, that is, at the end of the iteration, it is necessary to know that an iteration area is found to satisfy: the mean value of the centrifugal gradient of all pixels on the boundary is zero. In practical applications, when the mean value of the centrifugal gradient changes from a negative number to a positive number for the first time, we think that the optimal segmentation is found, the iteration stops, and the result of this segmentation is selected as the final result.
4 Experimental Research and Analysis of Self-service Image ROI Retrieval Under Computer-Aided Detection 4.1 Experimental Analysis of the Segmentation Effect of Image ROI Self-service on the Overlap Percentage Threshold Under Manual and Computer-Aided Testing A threshold of 0.5 was set for the 240 images tested, and then the percentage of correct segmentation of the same image was counted. The results are shown in Table 1. It can be found that the curve obtained under computer-aided detection is more stable than the curve obtained by manual operation on the same batch of images, and both are 99.59%. As shown in Fig. 1, the correct percentages of the manually operated region growing method, radial gradient segmentation and probability model segmentation methods are 87.4%, 92%, and 96.54%, respectively, which are lower than the segmentation method in this paper. Using the overlap percentage threshold-the correct
Self-service Fetching of Image ROI … Table 1 Overlap percentage threshold data
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96.54% 92% 87.40%
Area growth method
Radial gradient segmentation
Probabilistic model segmentation
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segmentation percentage curve, it verifies that the segmentation algorithm proposed in this paper is more effective and accurate.
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62
81
0.6
76
85
0.4
79
87
0.2
73
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4.2 Experimental Analysis of the Effect of Image ROI Self-fetching on Intensity Percentage Threshold Segmentation Under Manual and Computer-Aided Testing From the 240 images tested, 100 were selected as the image set for the evaluation of the seed lattice. In this way, 100 intensity map threshold-overlap percentage curves can be obtained, and the average value of the overlap percentage of all curves under the same intensity map threshold can be obtained, and then an intensity map thresholdaverage overlap percentage curve can be obtained, as shown in Table 2, the artificial average overlap percentage when the intensity map threshold is 0.8 is 62%. If it is considered accurate segmentation when the overlap percentage exceeds 60%, then the accurate segmentation of image ROI self-fetching under computer-aided testing is more effective. It can be seen from Fig. 2 that the intensity map threshold-average overlap percentage curve obtains the maximum value in the middle of the curve, and there is a higher overlap percentage around it. Under the computer-aided test, the image ROI self-fetching accurate segmentation data are all above 0.75, which shows that when the seed points are disturbed within a certain range, the segmentation algorithm in this paper can still achieve better segmentation results. It is proved that the segmentation algorithm proposed in this paper has great adaptability to the location of the seed point, that is, the seed point is allowed to fall within a certain range of the image without affecting the performance of the algorithm.
5 Conclusions The automatic detection method based on image ROI is a hot research problem in computer-aided diagnosis methods. However, many current detection algorithms have their limitations, or they cannot completely extract the image area data or the detection efficiency is not high. At the same time, the rapid development of imaging technology has made technicians more and more burdens to process images, and the quality of image data extraction is unavoidable. In this context, the advantages of computer-aided detection are becoming more and more obvious. It is suitable
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Computer-aided Detection
Manual Operation 79%
0.2 73%
87%
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0.4 79%
85% 0.6 76%
81% 0.8 62% 0%
20%
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60%
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Percentage Fig. 2 Intensity map threshold-average overlap percentage
for long-term work, can effectively save manpower, is not subject to the subjective influence of technicians, and is efficient and stable in image processing and analysis. The target detection algorithm proposed in this paper can implement correct target detection with high detection accuracy, can effectively improve the accuracy of image data extraction, and avoid data errors caused by technical personnel’s visual fatigue, reduced attention and other factors, with certain application value.
References 1. Salehi L, Azmi R (2014) A novel method based on learning automata for automatic lesion detection in breast magnetic resonance imaging. J Med Signals Sens 4(3):202 2. Polakowski WE, Cournoyer DA, Rogers SK et al (1997) Computer-aided breast cancer detection and diagnosis of massesusing difference of Gaussians and derivative-based feature saliency. IEEE Trans Med Imaging 16(6):811–819 3. Haisan A, Rogojanu R, Croitoru C et al (2013) Digital microscopy assessment of angiogenesis in different breast cancer compartments. BioMed Res Int 2013:286902 4. Umehara K, Nppi J, Hironaka T et al (2017) Deep super-learning of polyp images for computeraided detection in CT colonography. Int J Comput Assist Radiol Surg 12(Supplement 1):S278– S279
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5. Gao Y, Wang J, Zhang L (2020) Robust ROI localization based on image segmentation and outlier detection in finger vein recognition. Multimedia Tools Appl 79(27):20039–20059 6. Baker JA, Rosen EL, Lo JY et al (2003) Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. AJR Am J Roentgenol 181(4):1083 7. Karthiga R, Narashimhan K (2021) Deep convolutional neural network for computer-aided detection of breast cancer using histopathology images. J Phys Conf Ser 1767(1):012042 8. Kim DH, Heo CH, Cho HC (2018) A comparison of active contour algorithms in computeraided detection system for dental cavity using x-ray image. Trans Korean Inst Electr Eng 67(12):1678–1684 9. Bi L, Feng D, Kim J (2018) Dual-path adversarial learning for fully convolutional network (FCN)-based medical image segmentation. Vis Comput 34(6–8):1–10 10. Jalal DK, Ganesan R, Merline A (2017) Fuzzy-C-means clustering based segmentation and CNN-classification for accurate segmentation of lung nodules. Asian Pacific J Cancer Prevention APJCP 18(7):1869–1874
Three Dimensions of Campus Network Platform Construction in the Internet Era Di Gao
Abstract In the Internet era, to explore the profound connotation and deep foundation of the innovation mechanism of co construction and sharing of campus network platform, we should try to understand it from three dimensions: first, we should adhere to the principle of both management and service, and follow the theoretical dimension of legal and reasonable mechanism construction; The second is to establish a sense of standardization, improve the leadership system, formulate management rules, and increase the system dimension of supervision system; The third is to strengthen infrastructure construction, deepen innovation driven, attract technical talents and increase capital investment. Keywords Campus network platform · Community with a shared future · Innovation driven Those who master the Internet will grasp the initiative of the times; those who despise the Internet will be abandoned by the times. In today’s era, the network has increasingly become an important factor affecting the world, but also continues to develop rapidly, becoming an indispensable force in all walks of life. With the increasingly advanced mobile terminals, the number of mobile users around the world has increased dramatically (as shown in Fig. 1), and university campuses have become the main position of mobile users. The Internet plus mode is gradually popular. And good results have been achieved. For the campus network platform of colleges and universities, it is a new direction for the comprehensive development of the campus network platform to build and share the innovation mechanism and refine the specific practice of the campus network construction. Accurately grasping the three dimensions of theory, system and technology can provide a solid foundation for the mechanism innovation and development of campus network platform.
D. Gao (B) School of Marxism, Hohai University, Nanjing, Jiangsu, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_100
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Fig. 1 Global mobile subscriber growth by region (as at end-June 2016)
1 Theoretical Dimension: Guiding the Direction of Campus Network Platform Mechanism with Comprehensive Theoretical Basis As the forefront of personnel training, scientific research innovation and cultural heritage, colleges and universities shoulder the important responsibility of network information security education and construction, facing increasingly serious network security problems and situations such as hacker attacks, information leakage, network viruses and so on [1, 2]. Campus network platform is a virtual platform based on network technology, which is convenient for teachers and students to study, work and live in the whole campus. The mechanism of campus network platform is to set up rules and regulations for the virtual platform, and establish organizations for effective management.
1.1 Adhere to the Main Position of Harmonious Development of Network Platform The rapid development of the network, not only brings convenience, but also brings threat. In the campus, with the rise of network technology, network security problems become increasingly prominent. Criminals use the network more conveniently and quickly to destroy the security of the campus network platform. The primary meaning of the development of campus network platform mechanism is to adhere to the main position of harmonious development of network platform. All kinds of network traps make the security of university ideology in crisis, and also make the mechanism security of campus network platform face challenges. The guarantee of security is the most important for the innovation and development of campus network platform mechanism. Without the guidance of mainstream ideology,
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the campus network platform is easy to breed the evil forces, and even become the “Habitat” of these evil forces. The campus network platform mechanism is also easy to become the “empty paper” of the evil forces. To ensure the security of campus network is to be responsible for the education of students, for the campus network platform, and for the development of campus network platform.
1.2 Adhere to the Principle of Simultaneous Development of Management and Service Network is “the sum total of behavior, thinking, life style, values and social mentality formed by people’s information transmission, resource sharing, communication, leisure and entertainment activities through network” [3]. On the one hand, the campus network platform is carrying the continuous and rapid development of network technology, and has become a new way for college teachers and students to work, study and live on campus; On the other hand, trojan virus, rogue software, network violence, network pornography and other hazards also follow, and in the weak willed college students based campus penetration and spread, seriously affecting the atmosphere of campus network space, seriously threatening the stability of campus network platform, seriously eroding the establishment of campus network platform mechanism. Based on this reality, the campus network platform mechanism needs to adhere to the two purposes of management and service. Only strictly managing the use of campus network platform without providing complete services will make the function of campus network platform single and lack of certain attraction; Only providing complete services without strengthening management will make the campus network platform breed adverse factors, and then endanger the security and stability of the campus. Therefore, the campus network platform not only needs to strengthen the management, but also needs to serve the teachers and students. The campus network platform mechanism needs to improve the management efficiency and service function. To strengthen the effective management of campus network platform, we need to be interconnected in management, actively respond to security, and centralize the platform management. The main point of establishing the management mechanism of campus network platform is to realize the centralized management of network construction, network public opinion, network comment, network management, network research, etc. [4]. To improve the service function of the campus network platform, we need to be people-oriented, teacher-student-centered, actively solve the demands, and effectively solve the problems. Establish the standardized rules of network platform service mechanism to ensure the daily problems, special problems, problem solving, problem feedback and other service functions.
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1.3 Follow the Legal and Reasonable Mechanism Construction Only when the platform and mechanism of campus network are co constructed, can the achievements brought by campus network be shared, can the builders and users benefit from the platform, and can the security, stability and development of campus network be effectively maintained [5]. In recent years, there are many network security crises in Colleges and universities, such as many campus network loans, telecom fraud, wechat QQ account theft, which are related to students’ personal information and even major economic losses, and have seriously threatened the personal safety of college students. As we all know, the rapid development of network technology and the rapid expansion of cyberspace have resulted in revolutionary changes in the field of network security. Network security has become the “invisible territory” of maintaining security, which constitutes an important content and key factor of maintaining security, and has rapidly penetrated into various related fields, becoming the basic security of the country and society. Therefore, from the moment when the network is connected to the campus, the relevant departments and leaders of each campus pay close attention to the maintenance of network security. The mechanism of campus network platform should be co built, shared and innovated, but it can’t go beyond a “red line”, that is, to maintain campus security and campus network security [6]. Following the legal and reasonable mechanism construction provides an institutional guarantee for the maintenance of campus network security, which is the internal requirement of the innovation mechanism of campus network platform co construction and sharing. To build a network platform mechanism, teachers and students can form a community and jointly establish and abide by the rules of the mechanism; Teachers and students and organizations can form a community, negotiate and maintain each provision of the mechanism. For the outside of school, the campus and enterprise can form a community, and truly achieve the common development of industry, education and research; The campus and government can form a community and effectively implement the implementation of policies. Sharing campus network platform, whether it is the organizer or participant, or users, all exist in the campus network platform, which is the common body, and enjoy the convenience brought by the campus network platform and the effect of the campus network platform mechanism. Only when the campus network platform and mechanism are jointly constructed can the achievements brought by campus network be shared, and the builders and users can benefit from the campus network platform, and can the security, stability and development of campus network be effectively maintained. In recent years, many network security crisis events have occurred frequently in Colleges and universities, such as many campus network loans, telecommunication fraud, wechat QQ and other account stolen, which are related to students’ personal information and even significant economic losses, which have seriously threatened the personal security of college students. Most college students are just adults leaving their families, and are in the stage of the construction and improvement of their independent ideology. For
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many negative and implicit bad content, they have not fully recognized and understood the ability, some of them will even be affected by these bad content, forming wrong values, and even threatening the safety of the campus. The mechanism of building and sharing campus network platform is to bind the leaders, teachers and students of the school together, so that the administrators of the campus really understand the needs of the managed, make the managers really understand the difficult of the managers, negotiate, agree, agree and abide by them, so as to make everyone enjoy the benefits of the innovation mechanism of campus network platform.
1.4 Promote the Integration of Tradition and Advanced Culture College campus is a condensed version of the small society. Each college has its own long-standing motto and purpose, which is the spiritual soul of a college and the traditional culture of each campus. It needs every generation of teachers and students to actively study, abide by and spread. With the advent of the Internet age, network culture also follows. Network culture is a new form of human culture. It is the sum of behavior, thinking, life style, values and social mentality formed by people’s information transmission, resource sharing, communication, leisure and entertainment activities through the network. Network culture has a variety of forms and unique characteristics, and because of the rapid development of network technology, it will be updated from time to time. When the network culture enters the campus, it will more or less collide with the traditional culture of the campus. After a series of fusion, the traditional campus culture and the advanced network culture can blend with each other to form the advanced campus network culture, which is the cultural basis and theoretical basis for the construction of the innovation mechanism of the current campus network platform, It is the spirit of leading the mechanism innovation of campus network platform. It can be said that the advanced culture of campus network breeds the innovation mechanism of campus network platform, which promotes the sustainable development of advanced culture of campus network.
2 System Dimension: Maintaining the Establishment of Campus Network Platform Mechanism with Advanced System Standards The innovation of mechanism first needs the improvement of the system. Without rules, it can not be square. “The development of the Internet and the deep integration with traditional industries have changed all aspects of society, economy and life, and will inevitably challenge the original system.” [7]. The mechanism innovation of campus network platform must adhere to the combination of keeping pace
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with the times and overall consideration. By establishing a sense of standardization, improving the leadership system, formulating management rules, and increasing the supervision system, we can continuously improve the advanced system standards, so as to truly establish and long-term maintain the mechanism of campus network platform.
2.1 Establishing the Standard Consciousness of Campus Network Platform Whether the system of campus network platform mechanism is established or not depends on the effective implementation of campus network platform mechanism, whether the mechanism of campus network platform is standardized or not, and establishing the consciousness of standardization of campus network platform has become the primary task. The standard consciousness of campus network platform is fully reflected in the construction, use, management and other aspects of campus network platform. To establish the standard consciousness of campus network platform requires the joint efforts of all aspects, and the most critical point is to adhere to the consciousness of “law must be obeyed, illegal must be investigated”, and deepen the education and guidance of the standard consciousness with a firm legal concept. In the campus network platform, it is full of both mainstream ideology and non mainstream ideology, which brings some challenges to the implementation of the guidance of establishing normative consciousness. Therefore, we should always adhere to the correct position and direction in the consciousness of campus network platform standardization, and maintain the guiding position of the concept of network legal system in the campus from the grass-roots level, especially when it comes to some major and principled issues. We should strive to strengthen the leading position of the consciousness of campus network platform standardization, and ensure the correct direction in the campus network platform [8]. Only to establish the campus network platform standard consciousness as the first inevitable requirement, can we resist the influx of bad ideas in the campus network platform, firmly establish an effective campus network platform standard consciousness, and ensure that the campus network platform mechanism has a perfect system guarantee.
2.2 Improve the Leadership System of Campus Network Platform The multiple campus network field and diversified network participation behavior inevitably lead to the diversification of interactive relationship between managers and users. The effectiveness of campus network platform innovation mechanism
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can not be realized only by relying on the traditional teacher authoritative management system. Therefore, it is particularly important to build a management team of campus network platform who is familiar with network technology, has the leadership management concept and comprehensive working ability in the network era for strengthening and improving the leadership mechanism of campus network platform. Improving the leadership mechanism of campus network platform can be summarized as three core contents: “based on two types of positions, coordinating two organizations, and completing two major tasks”. Two types of positions refer to: one is the official network platform created by the school, including comprehensive information portal, news propaganda website, life service website, etc.; The second is a network platform based on campus communication spontaneously established by students, including BBS sites, FTP, P2P services, QQ group, wechat group, etc. Managers should take these two kinds of platforms as working positions and give full play to the functions of education management and guidance. Coordinate the two organizations: the leadership mechanism of campus network platform needs to rely on the two basic forces of campus network platform management organization and network technology team, need to establish “leadership responsibility system”, divide the management work to each organization, usually perform their own duties, encounter unified coordination, cross the division of labor and cooperation, form an effective cooperation mechanism. We should carry out two major tasks, namely, management and service.
2.3 Formulate the Management Rules of Campus Network Platform At present, our country has the legal supervision mechanism of cyberspace, which is “the supervision of the whole process of cyberspace legal operation” [9]. Managing the network according to law and managing the network according to law are the fundamental policy and long-term plan for the management of the campus network platform, and are the important guarantee for the mechanism innovation of the campus network platform. The campus network platform is the same as the real society we live in. We should not only advocate freedom, but also maintain order. Therefore, the campus network platform mechanism needs to develop a set of practical campus network platform management rules within the scope of the law, on the basis of the campus discipline, and guided by the management of the campus network platform. The campus network platform is in the network space of our country. The mechanism of the campus network platform should first fulfill the constraints of the “network security law”. On this basis, it should be refined to the campus network and formulate the relevant management regulations. Adhering to the principles of scientific management, democratic management and respecting the law of network development, we should first do a good job in the top-level design, speed up the formulation of the medium and long-term development plan of the campus network
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platform mechanism, and truly build up the management rules of the campus network platform; Secondly, it is necessary to make a comprehensive plan and carry out the research on the basic provisions of the management rules of campus network platform, so as to enhance the scientificity, authority and predictability of the management rules of campus network platform; Thirdly, it is necessary to grasp the management of key problems and unexpected problems, formulate emergency measures, and treat special cases according to the actual situation and past experience and lessons, so as to cope with the rapidly changing campus network platform.
2.4 Construction of Supervision System of Campus Network Platform At present, our country has the legal supervision mechanism of cyberspace, which is “the supervision of the whole process of cyberspace legal operation” [10]. The innovation of the campus network platform mechanism also needs to build the supervision system, which is an important part of improving the campus network platform mechanism. It has a very important position and role in establishing the system guarantee of the campus network platform mechanism and realizing the co construction and sharing of the campus network platform mechanism. On the one hand, it is necessary to establish a top-down supervision and inspection organization and system, which requires a certain degree of management power, authority and strength; On the other hand, we need to absorb a certain amount of bottom-up supervision and reporting from the masses. At this level, the majority of campus teachers and students, that is, the majority of the people, can supervise all the people and all kinds of things on the campus network platform, and feed back to the managers or supervision departments at the first time. Therefore, the construction of campus network platform supervision system also needs to establish a complete information feedback mechanism, which can achieve real-time information transmission. The report of the 18th National Congress of the Communist Party of China emphasizes the need to strengthen “inner-party supervision, democratic supervision, legal supervision and public opinion supervision, so that the people can supervise the power and let the power run in the sunshine.” With the rapid development of network technology, the campus network platform is more and more used by the majority of teachers and students, and has become an indispensable part of the masses. Under this background, the construction of the campus network platform supervision system can fully mobilize the masses, and play a role in promoting the campus network platform, It can guarantee the effective implementation of campus network platform mechanism.
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3 Technical Dimension: Ensure the Renewal of Campus Network Platform Mechanism with Excellent Technical Support The research and mastery of network technology is the technical support of campus network platform and the key to establish the mechanism of campus network platform. In the network era, the expansion of campus network platform has made the campus deeply expand into the network space. To ensure the perfect technology is the standard to support the campus network platform and ensure the mechanism of the campus network platform. Only by strengthening the infrastructure construction, deepening the innovation drive, attracting technical talents and increasing capital investment, can we ensure that the technology is perfect, and can we achieve the high standard of ensuring the timely update of the campus network platform mechanism.
3.1 Strengthen Infrastructure Construction, Build and Share the Achievements of Campus Network Platform Mechanism As an emerging industry, network has the characteristics of high technology, such as fast upgrading, instability or not easy to master, which makes the requirements of infrastructure construction very high. Strengthening the construction of campus network infrastructure can greatly promote the timely updating of campus network platform and the timely improvement of campus network platform mechanism. The network infrastructure here includes not only the infrastructure construction of hardware, but also the technology construction of software environment, which need to be strengthened. In the campus, whether users or managers, teachers or students, are a group with strong ability to use network technology. They have the ability to master high-tech knowledge and high-tech quickly and have strong understanding ability. They have the ability to co construct the campus network platform and mechanism, and can work together to develop a platform that meets their own needs Therefore, the establishment of campus network platform and campus network platform mechanism will become the inevitable result of the majority of teachers and students to share the results. For example, the wechat work platform established by a university includes a series of mobile terminal servers integrating campus work, learning, inquiry, life and other aspects, including workflow, meeting notice, online service hall, campus push to talk, all-in-one card, etc. at the same time, it establishes a demand response feedback mechanism, which is constantly improved and developed in the user’s proposal, It has become a typical campus network platform and mechanism of co construction and sharing.
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3.2 Deepen Innovation Drive, Enhance the Supply of Core Technology of Campus Network Platform Technology is the first driving force for the emergence and development of network, and an important part of network operation, use and development. Therefore, the strength of the core technology of the network is the most important for the construction of the campus network platform, and it is the key support to ensure the innovation of the campus network platform mechanism. The core technology will not be innovative, not ahead of others, the campus network platform mechanism innovation is just like a piece of empty talk. The core technology of the Internet is our biggest “gate of life”, and being controlled by others is our biggest hidden danger. Therefore, to build the innovation mechanism of campus network platform, we should not only have the system guarantee suitable for ourselves, but also the technology with independent intellectual property rights, and the scientific research ability to research and develop innovative technology. First of all, the core technology should adhere to the dialectical unity of learning advanced technology and self innovation research. On the one hand, the core technology needs to constantly strengthen its own innovation drive, and it is impossible and should not rely on others; On the other hand, self innovation research is not self closed, behind closed doors, or to learn from other people’s advanced technology. Secondly, we should persist in working hand in hand, tackling key problems cooperatively, carrying out the core idea of co construction and sharing to the end, enhancing the supply of core technology of campus network platform, striving to build the best campus network platform, and benefiting all the innovative achievements of campus network platform mechanism.
3.3 Attract Technical Talents and Optimize the Work Organization of Campus Network Platform The guarantee of technology needs the support of talents, especially the high-tech talents are the most important in the innovation and development of network technology. The development and competition of cyberspace, in the final analysis, is the competition of talents. Therefore, only by attracting technical talents can we really optimize the work organization of campus network platform and achieve the basic standard of ensuring the mechanism innovation of campus network platform. The campus should try its best to coordinate the relevant personnel and institutions, cultivate the corresponding technical backbone, and promote the optimization of the work organization of the campus network platform [10]. To ensure the high efficiency of talents in the organization, one is to ensure the high efficiency of talent recruitment and in place, and the other is to ensure the high efficiency of talent corresponding work handover. Only in this way, the campus network platform can continue to remain in the forefront of high and new technology, and the campus network platform mechanism can maintain its vitality forever. The campus network
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platform organization with a large number of technical talents has been established, which can make the campus network platform mechanism get continuous innovation ideas, innovative ways and innovation strategies in the fresh blood injection, and to the greatest extent, it has really optimized the organization of the campus network platform mechanism.
3.4 Increase Capital Investment, Always Maintain the Campus Network Platform, and Continue to Research and Develop Building the innovation mechanism of co construction and sharing of campus network platform is the cornerstone of continuous research and development of campus network platform through the continuous development of network technology. It is also a tool and umbrella to improve the management and service of campus network platform. Increasing the capital investment of technology R & D conforming to the campus network platform is the basis of strengthening infrastructure construction, deepening innovation drive and attracting technical talents. Without increasing capital investment, nothing else can be done. Of course, the investment of capital is not blind investment. First of all, we should build a campus network platform with first-class technology, distinct level and perfect function, and speed up the construction of campus network platform. Secondly, it is necessary to increase the investment in hardware and software of the campus network platform, and establish a safe campus network space with consistent standards, which integrates the functions of campus information release, online consultation and exchange, and network office. Only by constantly strengthening effective investment can we ensure the scientificity and technology of campus network platform, and ensure the innovation and advanced nature of the campus network platform mechanism.
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Large Data Recognition System for Speech Outliers Based on Deep Learning Mei Zhang, Yong-ji Pei, Gang Wang, and Xing-xing Ma
Abstract In order to effectively improve the recognition efficiency of large data speech outliers, a large data recognition system for speech outliers is designed based on deep learning theory. With the recognition hardware developed by Stratix IV GX series FPGA as the core, a speech recognition device is designed to parse large data network voice data. Based on the characteristics of traditional classifier and voice data, the traditional deep trust network is improved. The output layer of BP neural network is changed to KNN nearest neighbor classifier. The voice classification network is designed. The input vector and corresponding input link of voice data are set up. The data in the voice network are read into the system, and the deep learning recognition is established with the restriction of Boltzmann machine unit as the core. The system realizes the recognition of large data of speech outliers through the training of nerve layer. The experimental data show that, compared with the traditional speech big data recognition system, the system has higher recognition efficiency, the rate of increase is more than 35%, which meets the requirements of the current environment of large data explosion, and is feasible. Keywords Deep learning · Speech outlier · Input link · Recognition efficiency
M. Zhang (B) · Y. Pei · G. Wang · X. Ma State Grid Xinjiang Electric Power Research Institute CO., LTD., Urumqi 830000, China e-mail: [email protected] Y. Pei e-mail: [email protected] G. Wang e-mail: [email protected] X. Ma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_101
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1 Introduction After more than 20 years of rapid development, information technology has played a more and more important role in our production and life. Nobody can deny that information technology has brought great convenience to human life. Whether from daily life entertainment or daily production and operation of enterprises, human beings have become increasingly inseparable from the support of information technology [1]. In such an environment, the concept of big data emerges as the times require. Especially in recent years, with the continuous expansion of the scale of the Internet network, network data interaction is also more extensive. With the development of a series of new network applications, such as social networking, search sites and business representatives, a large number of Internet data integration and data aggregation phenomena, data processing applications and demand have blowout, and the amount of business that the Internet needs to analyze or process has become increasingly heavy. For example, the central processing system of Face book, a foreign social software, receives more than 3 billion messages and nearly 1000 T network data on average every day, while Taobao, an Alibaba-owned network in China, processes more than 900 T data every day. The generation and application of a large amount of data has speeded up the arrival of the “big data era”. For massive data services, how to allocate these data effectively and equally to provide convenient and fast network search services for network users has become an important issue facing the Internet at present. Unlike the traditional text and graphic data, voice data is mainly composed of multiple entries, which are collocated according to trajectory and coverage. It has distinct isolated characteristics and is more difficult to identify them in network data. Especially in the traditional information recognition mode, most of the speech data recognition work can only rely on word extraction or voice text conversion. The overall efficiency is low, and it has been difficult to meet the current large-scale burst of data application environment. In view of this situation, the deep learning idea is introduced in the design, and a speech outlier big data recognition system is proposed. The speech data recognition is carried out by matching data points [2].
2 Large Data Recognition System for Speech Outliers Based on Deep Learning 2.1 Speech Recognizer Design The speech recognition device used in the system is the recognition hardware developed by Altera’s Stratex IV GX series of FPGAs. This series of hardware provides perfect core devices and peripheral circuits for the design of the system. The speech recognition device is based on the FPGA on the network recognition platform, Marvell 88E1111 on the network physical layer chip, MT41J64M16LA-15E on the
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dynamic memory, and the clock source of the system. Its hardware structure and the data relationship between each module belong to the successive relationship [3]. The identification center module is the hardware resource on the development board. The speech recognizer obtains network data from RJ45 port, and the network data is parsed by network chip 88E1111 and Gigabit Ethernet IP core. The selfdefined receiving module obtains the parsed network data through Avalon-ST interface, extracts the complete IP data message from it, and temporarily stores the dualport RAM (temporary message storage area) through Avalon-MM bus [4]. At the same time, the receiving module also extracts the ID from the packet header of the message, and generates the ID identification number of the packet and sends it to the downstream search module. The big stream lookup module queries the big stream data table to determine whether the message belongs to the big stream. The large stream data list is stored in a 512 M DR3. If the ID of the message is stored in the large stream list, the data message is sent directly to the load balancing module of the later stage for shunting. Otherwise, it is fed into the statistical and decision module. Statistical and decision module first carries out probability sampling of the message. According to the data sampling results, it decides the current ID decision algorithm, updates the counter and discriminates the appendix value. The counter is stored in a 128 M DR3. If the counter exceeds the appendix value, it is identified as a large flow. The large flow update module is notified to update the large flow table, and the load balancing module is notified to allocate the corresponding channels. Otherwise, it will be sent directly to the load balancing module as an unknown flow for channel allocation. Load balancing module mainly distributes the load balancing channel according to the result of decision, so as to achieve the purpose of congestion control. The data message with the channel allocated will be sent to the sending module. According to the channel allocation result, the sending module retrieves the network data message from dual-port RAM (temporary message storage area) and sends it to the network through Gigabit Ethernet IP core. Because the development platform does not have the feedback mechanism of export congestion and multiple network exits, the system intends to use the Virtual Multi-channel exit method to achieve load balancing function, that is, by recording the number of the exit channel at the beginning of the message, to observe the distribution results of the exit channel [5].
2.2 Voice Classification Network Design Classification is an important method of data mining. Classifier is a classification model based on the existing sample data and a current classification function. At present, the commonly used classifiers include: artificial neural network, K-Nearest Neighbor classifier, decision tree, support vector machine (SVM) and so on [6]. The recognition effect of a classifier is related to some features of its input data. The speech data in the speech data feature library used by the system design classifier are not processed at all. A speech data can be regarded as a vector of 2000 columns in
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Fig. 1 Diagram of speech classification
a row. The whole database has two major categories, normal voice data and abnormal voice data, but because abnormal voice data is composed of a variety of non-long term voice data [7], so the voice data in this database is not strictly two simple categories, the second is different bands, as shown in Fig. 1. Figure 1 is a schematic diagram of different types of voice data. According to the basic idea of traditional KNN nearest neighbor algorithm, the performance of KNN nearest neighbor algorithm is related to the distribution characteristics of each kind in subspace [8]. Therefore, the performance of nearest neighbor classifier can also be improved if the distribution characteristics of each sample type in subspace can be enhanced. However, for large data voice data signals, the data gap is very small, so in theory, voice data without appropriate feature extraction is not suitable for KNN nearest neighbor algorithm. Traditional SVM support vector machine classification is more suitable for binary classification problems with less data dimension. But the dimension of speech data in large data is high, and although there are only two kinds on the surface, there are many kinds of abnormal speech data, which is not suitable for SVM to fit and classify. So the speech data without any pretreatment is not suitable for SVM classifier [9]. Based on the characteristics of various classifiers and voice data, the system improves the traditional deep trust network, and changes the output layer of BP neural network to KNN nearest neighbor classifier, so that the data processed by BP neural network can be classified by KNN nearest neighbor classifier. After improvement, the speech deep learning network is equivalent to a preprocessing of speech data. It can learn speech adaptively and extract features. Through the combination of these two classifiers, KNN nearest neighbor algorithm can be improved, and the similar distribution and heterogeneous distribution of data needed in the process of implementation are more centralized and more dispersed. At the same time, BP neural network is also improved, and the recognition rate of trained data can not reach 100%.
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2.3 Speech Data Reading The large data voice data acquired through speech recognition and speech classification network, although pretreated, is still only the initial data and only cached in the processor. It needs to be read into the system for subsequent processing. The flow chart of the original data transcription and reading module is shown in Fig. 2. Data input is mainly to read the voice data extracted and transcribed above into the system for subsequent outlier recognition. Because the original voice data is usually stored in accordance with the month, according to the characteristics of voice data, a self-organizing competitive link is designed for data entry and processing [10]. Firstly, the RBF neural network is used to train the initial data sample and obtain the sample data A. The singular value of the original data is processed by the starting point average method, and the sample data B is obtained. Through graphical display, sample A is analyzed. If there is no singular value of the data, it can be determined that the data meets the arrangement requirements and can be used as the basic data. The singular value analysis of sample B is performed by graphical display, and the above steps are repeated. Comparing the values of sample A and sample B, we can
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Fig. 2 The original voice data reading process
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find the data automatically deleted in sample A because RBF neural network thinks it is wrong, determine its location and corresponding data values, and input the data. The RBF network is set as follows: the relative voice point and voice signal noise of voice data are used as input layer of the input network respectively, and the competition layer is set as six link nodes (representing data input vectors); the distance between two-dimensional link nodes of the network is calculated by Mandist Euclidean distance weight function, and its operation principle formula is as follows: D = sqr t sum(x − y)2
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In formula (1), x and y are input column vectors of data data vectors,D are data distance matrix, 0.1 is learning rate orientation, 1000 training iterations are set for data input, 10–5 is training error, and other parameters are training network data using default data values. After data training, data need to be normalized. In order to facilitate the calculation, the parameters of different speech data indicators are processed in the same data according to the normalized formula. The formula is as follows: Y (i) =
X (i) max[X (i)]
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In formula (2), Y is normalized speech data, X is sample data, and max[X (i)] is maximum sample data. Data normalization can ensure that the cached voice data acquired by the system become uniform system data. After data validation, the RBF neural network can ensure the training accuracy of the training data, as well as the training accuracy of the voice signal and noise, and eliminate the wrong data in the original data, so as to complete the data reading.
2.4 Implementation of Speech Isolation Recognition Deep learning network originated from BP neural network. The basic components of the deep learning network are: Restricted Boltzmann Machines (RBM). The establishment of the deep learning network is accomplished through the layer-by-layer superposition of RBM. Through the establishment of deep learning network and the training of nerve layer, the system finally realizes the recognition of large data of speech outliers. In practical recognition, each RBM is a probabilistic generation model, through which the input and output voice data are limited, and the weights of the deep learning network are adjusted. The probabilistic model of any RBM is: P(x, h) =
e−E (x, k) z
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When multiple RBMs are superimposed, the lower voice data is called the visible layer, and the higher voice data is called the hidden layer. For a single RMB, if voice input is defined as x and output is defined as h, then x and h are binary data, and their values are only 0 and 1. When a link node is in an active state, it means that its current state is 0. E is the energy function of RBM and Z is the normalization factor of RBM. The connection weight between visible layer and hidden layer is expressed by w, and the offset between visible layer and hidden layer is expressed by b and c. The expressions of E and Z are as follows: E(x, h) = −bx − ch − hwx e−E(x,h) Z (x, h) =
(4)
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In order to avoid the long sampling recognition process, the speech training data 1 is input into the visible layer as a random value, and the state of the hidden layer unit is calculated. Then the state of the visible layer is calculated by using the calculated state of the hidden layer unit in reverse, which results in the reconstruction R of the input speech data. If R is the same as I, then the data output from the visible layer can be regarded as another expression of the original data, that is, the hidden layer data can be used as a feature representation of the visible layer data to declare matching. If they are different, the parameters in RBM can be adjusted by reconstructing errors. The specific weight adjustment process can be obtained by programming matrix, which is as follows: ⎤ ⎡ 2 σ1 0 ⎥ ⎢ σ12 ⎥ ⎢ ⎥ ⎢ ... ⎥ ⎢ ⎥ ⎢ 2 (5) s=⎢ σ3 ⎥ ⎥ ⎢ 2 ⎥ ⎢ σ3 ⎥ ⎢ ⎦ ⎣ σa2 2 0 σa Figure 3 is a flow chart of identification sampling. In sampling matching, the training data can be divided into n parts, and one data can be assigned to X at a time until the end of all the training. In this way, not only can we avoid the problem of too much data in one-time calculation, but also can we reduce the number of iterations and make the model balance as soon as possible. In general, a better set of network weights can be obtained by using CD algorithm to iterate once. Above is the training method of single RBM, and the whole deep learning network trains and outputs data from the underlying RBM. Output data is transmitted layer by layer as input of the higher level, that is, the hidden layer of any RBM is the visible layer of the upper RBM. The output state of any RBM is only affected by the
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Fig. 3 Training flow chart Training data
Calculate the hidden layer according to the current weight
Calculate the visible layer by weight
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current input state, and the whole propagation is a Markov process. The structure of the high level RBM will not affect the bottom RBM. The overall recognition method for large data recognition of speech outliers using deep trust network is as follows: Step 1: Initialize a multi-layer deep learning network, use CD algorithm to train the training data, and get the ownership value of the network. Step 2: Construct an artificial neural network with the same number of hidden layers and nodes as the deep learning network, and assign the weights of the deep learning network to the hidden layers of the artificial neural network. Step 3: The BP algorithm is used to fine-tune the neural network, and finally a usable deep trust network is obtained. The network is used to train and recognize voice outliers. Although the deep trust network has the self-organization of BP neural network, there is no requirement for the input data. After adding the hidden layer, it can theoretically better learn the deep characteristics of data and improve the overall performance of the system. Moreover, the deep trust network overcomes the slow convergence speed of BP neural network, which greatly improves the efficiency of the whole network. However, the deep trust network has not overcome the problem
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that BP neural network is easy to fall into local optimal solution. As the number of hidden layers increases, the training errors are transmitted layer by layer by layer by comparing the data between the use layer and the layer. Therefore, the problem of determining the number of hidden layers and the number of hidden layer nodes has become the key to improve the performance of deep trust network. According to the above method, the recognition speed of the whole system for large data of speech outliers can be greatly improved.
3 Simulation Experiment The speech outlier big data recognition system based on deep learning is mainly used to identify outliers of speech data in the current large database and optimize the network state. In order to verify the performance of the system, the testing link is carried out, and the overall performance of the current speech outlier large data recognition system is tested by building a test platform. Designing the test platform of the system is an important means to verify the function and performance of the whole system. The traditional voice isolated point data recognition and decision system test platform has a network topology. In the experiment, voice data messages are selected. First, they are collected by switches, and then forwarded by switches to the large stream identification system for data stream identification and streaming. The processed voice data is sent back to the switch through the data outlet of the large stream recognition system for data voice analysis and forwarding, so as to simulate the large voice data environment for the operation of the subsequent data recognition system. In addition to the test platform, some special testing tools are also needed to test the functions and technical indicators of the system. The test tool is the software running on the test platform. In order to test the function of the system, three sets of auxiliary testing tools are developed based on VC6.0, namely PktCapture, NetSimulator and IPCatchClient. PktCapture is responsible for monitoring messages and storing them locally in the experimental voice data platform; NetSimulator is the outsourcing tool, responsible for sending voice data sources stored locally to the test speech recognition system; IPCatchClient is the packet capture tool, responsible for monitoring voice data messages and statistical processing of the monitored data. The experimental objects are: the traditional TCT speech outlier recognition system and the proposed speech outlier big data recognition system (RBM). In order to simulate the transmission and reception of voice data streams in real large data networks, the monitoring software needs to access the LAN first, turn on the listening function, capture voice data messages in the network, and save them locally as the data source of the test system. When the test system starts to work, the test platform should open the local data message record, read out the data message record one by one, input data to TCT and RBM systems, and record their recognition rate. After calculation, the experimental results are shown in Fig. 4.
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Fig. 4 Recognition rate contrast diagram
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According to the data in Fig. 4, it can be clearly seen that with the continuous improvement of data release time, the recognition rate of the two systems also tends to be stable. The recognition efficiency of the design system (TCT) is obviously higher than that of the comparative RBM system, and the recognition rate has been increased by more than 35% through the actual quantitative calculation.
4 Conclusion Based on the basic principle of deep learning network, a new recognition system is proposed to identify outliers of speech data in large data. Through speech recognition hardware and recognition network, the system completes data preprocessing, reads data into the system, and finally completes the recognition of outliers. Experiments show that this method can effectively improve the recognition rate.
References 1. Niu X, Dou Y, Zhang P et al (2016) Optical remote sensing of airport and aircraft target recognition technology based on deep learning. Big Data 2(5):54–67 (in Chinese) 2. Hu, Wei, Le Z, Yong M et al (2017) Deep learning based transient stability evaluation of power system after failure. Power Grid Technol 5(10):51–57 3. Yimin H, Huiqiong Z, Zhengyi W (2017) Review on the research progress of deep learning in speech recognition. Comput Appl Res 34(8):2241–2246
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4. Xie Y, Jizhong C, Shuting Z et al (2018) A new algorithm study for TCM “syndrome” to “formula” based on big data deep learning. Asia-Pacific Tradit Med 8(1):51–53 5. Haijian Z (2017) Deep learning based Andriod malicious application detection system. Electron Technol Softw Eng 4(3):227–227 6. Suhua W (2017) Network teaching interactive platform based on big data deep learning. ICT 4(8):277–278 7. Lu Y, Hao S (2018) Film poster recommendation system based on deep learning. Modern Film Technol 478(5):44–47 8. Xu P, Li Y, Rui C (2016) Design and implementation of smart campus mobile client under Android platform. Electron Design Eng 24(22):80–82 9. Computer Simulation (2016), 33(11):379–383 (in Chinese) 10. Li W (2017) Application exploration of outlier analysis in big data era in marketing audit of power supply industry. Modern Mark (next month) 2(1):76–77
Artificial Intelligence for General Layout and Transportation Engineering with GIS Technology Lansheng Xu
Abstract From the current situation, artificial intelligence has obtained a very good development, but also for the geographic information system technology in the general plan transportation engineering provides greater help, general plan transportation design and management in industrial enterprise design and operation of each stage has an important role. With the progress of science and technology, industrial enterprises need to study the application of advanced science and technology and management means and methods in the design and management of industrial enterprises in order to realize the modernization of design and management. Geographic Information System (GIS) is a new geographic technology developed in the 1960s. Among them, the graphic attribute information function of GIS technology can provide quantitative analysis basis for general layout design and management, and the graphic data analysis and evaluation ability of GIS system can also be used for the evaluation and analysis of general layout related problems. Keywords Geographic information system · Design and management of general layout and transportation · General layout management system · Artificial intelligence
1 Introduction Artificial intelligence should become one of the important contents in the social development at present. In the process of continuous development of this technology, it provides a very big change for people’s production mode and life style. The application of artificial intelligence in public relations management also makes it play a very good effect. The development of this technology has more important significance to enhance the national competitiveness. Geographic Information System (GIS) is a comprehensive subject integrating computer science, surveying, geography, informatics and other disciplines. It is called L. Xu (B) West Yunan University, Lincang 677000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_102
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3S technology together with global positioning system (GPS) and remote sensing system (RS), which creates the era of digital earth [1]. Geographic Information System (GIS) has been widely concerned and developed rapidly in recent years. It has been applied to nearly 100 fields, including agriculture, forestry, water conservancy, land, resources and environment. Its specific applications include urban planning and management, transportation, surveying and mapping, environmental protection, mapping and other fields. Geographic Information System (GIS) is a highly applied system, which integrates the functions of collection, storage, management and analysis of information. Therefore, GIS has the ability of graphics, images, sound, text and other expression. In the twenty-first century, with the digital earth strategic plan put forward and gradually implemented, geographic information system with its own advantages, its application field is bound to expand, usher in a new round of development boom. General layout and transportation engineering is an important part of industrial enterprise design. With the development of industrial production, the increasing scale of enterprises, the increasing variety of products, the extensive use of mechanization and automation of production operation, and the requirements of modern enterprise management, economic and reasonable general layout design is more important. But the general layout design and management has been in the qualitative development stage for a long time, especially in today’s digital era. In the general layout management and design, only the digital method is used in the site leveling, and in other design and management processes, the qualitative analysis of experience is the main method. The application of GIS technology in general layout engineering can digitize the original quantitative design and management, so as to facilitate data screening, buffering, classification, and production status simulation and monitoring. GIS can be used as a digital platform for the design and management of general layout to realize the digitization of general layout engineering.
2 Overview of Geographic Information System (GIS) 2.1 Overview of GIS Geographic information is the general term of numbers, characters, images and graphics that represent the quantity, quality, distribution characteristics, mutual relationship and change law of various elements of geographic system. One of the most important tasks of geoscience is to collect the geometric, physical and cognitive information of geospatial space, and to transform, store, transmit, regenerate, display, control and apply the information in real time. Geographic information has the following characteristics [2]. (1)
Regional. Geographic information belongs to spatial information, location identification is related to data, and its positioning characteristics are reflected
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through the public geographic basis, which is the most significant symbol of geographic information different from other types of information. Multi dimensional structure. Based on the coding of two-dimensional space, the combination of the third-dimensional information structure is realized, which not only provides the possibility for the comprehensive research among various circles of geographic information, but also provides convenience for the multi-level analysis and information transmission of geographic system. Temporal characteristics. The dynamic change of time and space causes the change of attribute data or spatial data of geographic information. Therefore, real-time GIS requires timely collection and update of geographic information, which makes geographic information timely and real-time.
2.2 Development History of GIS Software is one of the core contents of GIS technology. GIS software technology is the organization mode of GIS software, which depends on a certain software technology foundation, determines the application mode and integration efficiency of GIS software. From the development process, GIS software technology has gone through five stages (Fig. 1), namely integrated GIS, modular GIS, core GIS, component GIS and Web GIS. Fig. 1 Development of GIS software technology
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3 Discussion on the Application of GIS in General Layout and Transportation Engineering 3.1 Contents of General Layout and Transportation Engineering General layout and transportation engineering is a discipline with strong policy and wide knowledge, which can comprehensively reflect the comprehensive technical level of industrial enterprise construction and production. Its research objects include site selection; The space configuration of various structures and transportation facilities in the enterprise construction site; Site greening; Internal and external transportation of enterprises; Enterprise general layout management, etc. The so-called general layout and transportation engineering refers to that according to the needs of industrial enterprises, combined with natural conditions, through the creative thinking of designers, through repeated judgment and research, specific decisions are made and expressed in standard design language. Through this process, the main research object of general layout and transportation is transformed into the functional requirements of human and society, It can make the general layout of industrial enterprises obtain better social and economic benefits.
3.2 System Structure and Technical Route of Establishing Digital General Layout Management System by Using GIS Technology After the introduction of the general layout management content and the feasibility analysis of the construction system, the GIS digital general layout management system includes the following modules: digital editing module, map file management module, data query and statistics module and data output module. The main functions of these modules are as follows [3]: (1)
Data editing module
The main function of this module is to input, modify and add graphics and attribute information. The data of DXF, DWG format and DOC, xls format used in industrial enterprise design are transformed into the corresponding GIS digital spatial graphics information, and the attribute data of spatial graphics information is connected with the graphics through the unique identifier. In this way, the industrial enterprises in the expansion, construction of the plane or vertical changes, industrial enterprise land changes, changes in the stacking position of various equipment, materials, raw materials, fuel in the enterprise can be timely reflected in the general layout management system. (2)
Drawing management module
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The map file management module corresponds to the data editing module. Through the map file management in the GIS software, it can access, splice and cut the GIS digital spatial graphics information converted by the data editing module, and manage the drawing time and drawing people of the map file. (3)
Data query and statistics module
The main function of the data query and statistics module is to query the records in the database according to the requirements of attribute information or graphic information, and to classify and count the query records. (4)
Data output module
The main function of the data output module is to display and output the spatial information or attribute information according to the user’s requirements, and print out the query results. It can also realize the intercommunication with various mainstream GIS software data formats and output various common data formats. Figure 2 shows the structure of the digital general layout management system based on GIS technology. Fig. 2 Structure diagram of digital general layout management system
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4 Application of GIS in General Layout and Transportation Engineering Under Artificial Intelligence 4.1 Overview of GIS Application Based on Artificial Intelligence In terms of the development of artificial intelligence, it will bring great changes to people’s lives, help people complete more public work, and make people enter the era of intelligent society. Because artificial intelligence can replace people to complete some mental work, it will change the working mode of some staff members, which may cause them to have a sense of crisis. However, relevant personnel need to pay attention to that artificial intelligence is an auxiliary tool, not a competitor who does not have a job, so people need to correctly recognize this situation, In order to better play the practical effect of artificial intelligence in the application of GIS in the general layout and transportation engineering. GIS has powerful data processing and spatial analysis capabilities, and its new advantages combined with planning model, visualization and Internet make it can be applied in all aspects of general layout design and optimization. In general layout specialty, GIS technology and methods are used to study site selection, general layout planning, vertical design, pipeline design and related problems. Some of them are still in the preliminary stage, but their advantages are very obvious.
4.2 Application in Site Selection Reasonable regional planning and industrial layout are important factors for the high-speed and proportional development of national economy, and the rationality of industrial enterprise site directly affects the specific implementation and economic effect of industrial layout. Site selection is a political, economic and highly technical comprehensive work, which should be comprehensively considered according to the national economic construction plan, industrial layout requirements, regional planning, natural resources, construction conditions and other factors. Because GIS system has the characteristics of graphic attributes, and can carry out various graphic calculations. Therefore, the site selection system based on GIS technology is different from the traditional site selection method. In the traditional method of site selection, several alternative sites are initially selected, and then evaluated and analyzed. The commonly used methods include comparison matrix method, analytic hierarchy process, priority determination method, gravity method and mathematical programming method, mathematical programming method, evaluation and optimization method, etc. The use of GIS technology for site selection is based on the powerful computing power of GIS technology. All points in the selected area are
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selected as alternative sites, and all points are evaluated and analyzed for site selection. Because GIS technology fully adopts digital analysis technology, it has strong ability to analyze and evaluate the factors with very clear quantitative indicators such as location, freight, distance and so on. On the other hand, it has a strong ability for economic, cultural and overall industrial layout. On the other hand, when it comes to economic, cultural and overall industrial layout factors, it seems to be a little inadequate. The application of GIS technology in site selection is mainly based on the data and information collected in the target area, the basic principles and specifications of site selection as the benchmark, the spatial and geographical analysis through GIS technology, and finally get the alternative site that meets the requirements in the region, and then further demonstration and Analysis on this basis. The application of GIS technology in site selection provides powerful support for the informatization, quantification and visualization of site selection, and the assistant decision-making of management department [4]. The application of GIS technology in general layout design is still in the initial stage, and can only be used as an auxiliary tool for site selection at present, because there are many non spatial factors in site selection constraints. However, in terms of spatial factors, GIS technology provides a powerful basis for site selection. Therefore, the application of GIS technology in site selection is worthy of further study.
5 Conclusion Today, when digital analysis is advocated, the qualitative analysis in general layout design in the past can not keep up with the pace of the times. It is an inevitable trend for general layout development to use various new technologies. GIS technology as a new technology developed in recent years, CIS technology perfectly combines computer-aided design (CAD) with database technology, adding attribute information for graphics. In this way, the basis of quantitative analysis is added to the simple graphic information which can not be analyzed quantitatively. These are the core of CIS technology, but also the unsolved problems of general layout and transportation design and management for many years: unable to analyze the problems quantitatively, lack of persuasion. GIS technology has been gradually applied to the design and management of general layout and transportation, and some have achieved good economic benefits. In the future work, through the application of GIS technology, every step of the general layout specialty will be from qualitative analysis to quantitative analysis.
References 1. Huang X, Tang Q (1989) Introduction to geographic information system. Higher Education Press, Beijing
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2. Xu G et al (1996) Progress and prospect of remote sensing information science. Acta Geographica Sinica 5(5) 3. Jun C (1995) Basic problems and academic frontiers of GIS spatial data model. Acta Geographica Sinica 50(Supplement) 4. Yao Y, Zhang B, Luo Y, et al (2006) Application of grid computing method in spatial pattern analysis. Earth Inform Sci 8(1)
Research on BP Neural Network Image Restoration Algorithm Based on Genetic Algorithm Chen Chen and Hongbo Zhou
Abstract On the basis of applying BP neural network algorithm to image restoration, this paper introduces genetic algorithm to train and adjust BP network, gives the specific design and implementation steps of GA-BP algorithm, and applies the improved algorithm to image restoration simulation, which verifies the superior performance of hybrid algorithm in convergence speed and mean square error compared with traditional BP network. Keywords BP neural network · Image restoration · Genetic algorithm · GA-BP algorithm
1 Introduction BP neural network is a feedforward network structure composed of neurons with nonlinear mapping ability. The network information is stored in the weighted value of neurons. The network has high utilization possibility and soundness. BP neural network realizes special nonlinear change in silver layer. Since the hyperplane reconstruction of existing finite data points in multi-dimensional space is possible, BP neural network can apply the nonlinear mapping relationship between degraded image and original image to [1] BP neural network without knowing the point spread function. Not only accurately project samples, but also similar to pure samples, BP network has higher universality and higher potential performance than other neural networks in image restoration [2]. In the field of underlying vision algorithms, convolutional neural network (CNN) has made great progress in recent years, and has achieved excellent performance in image restoration tasks such as deblurring, denoising, JPEG distortion removal, super-resolution and so on. However, the distortion in real images is often more C. Chen · H. Zhou (B) Ningxia Normal University, Ningxia 756000, China C. Chen e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_103
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complex. For example, after multiple image degradation processes, the image may contain mixed distortion. This kind of mixed distortion image is still challenging to the current image restoration algorithm. Recent image restoration work (such as VDSR, dncnn, etc.) has proved that a CNN network can deal with degraded images with various distortion types or degrees, mixed distortion. However, this kind of algorithm selects the network model with high complexity, which brings large computational overhead. In addition, the networks of these algorithms use the same structure to process all images. Some images with low degradation degree are not considered, and smaller networks can be used for restoration.
2 Genetic Algorithm Genetic algorithm (GA) is a direct retrieval optimization method based on evolution theory and genetic mechanism. It has the characteristics of self-organization, self adaptation, parallel computing, logic and overall optimization. The selection strategy from the initial military to fitness is now in the military. Select an individual from the list. The next generation of population is generated by using replication, hybridization and mutation methods. Such a generation evolves until the expected termination conditions are met [3–5]. Genetic algorithm contains five basic elements. Parameter code, initial military setting. Fitness function design the operation design of oil field and the setting of control parameters (mainly referring to the military scale and the probability of genetic operation) constitute the central content of genetic algorithm. Parameter coding is to map the variables of optimization problems to chromosome genes through a certain transformation. The initial group setting should be sufficient in scale and randomness. The genetic algorithm calculates the chromosome fitness according to the chromosome gene value, and determines the mating probability of the chromosome according to the fitness value, and the chromosome mating probability with large fitness is higher. Chromosome mutation should be carried out after chromosome mating, which can avoid premature convergence of the algorithm. The population after mutation is the offspring, which will be the father of the next generation, and carry out the same genetic operation, so as to cycle.
3 GA-BP Algorithm The basic idea of GA-BP algorithm is to use genetic algorithm to search a certain range of weights, and solve the shortcomings of BP algorithm, such as small local area, slow convergence speed and easy to produce vibration effect. Among them, the weight is used as the initial weight of BP algorithm. yes.
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GA-BP algorithm uses genetic algorithm to optimize the connection weight and neuron threshold of neural network, and produces many individuals in the initial stage. According to personal fitness, some higher individuals were selected as parents to form the early army, while military individuals were optimized by bpne for work weight and threshold. The dimension of the population is also the weight and the total number of thresholds. Then, according to the roulette method, it is generated from two individuals by cross operation Two offspring, or select an individual from them for mutation operation to produce one offspring. If the fitness of the offspring is higher than that of the parent, the individual in the parent is removed to form a new parent, and the number of individuals in the parent is kept unchanged. When the accuracy is satisfied, that is, the sum of squares of GA error is less than the target error EGA, the BP network is transferred to continue training. If the network training is finished, the sum of squares of errors still cannot meet the requirements of EBP. At this time, it is possible to fall into a local minimum point, so turn back to the GA method and repeat the above process until the network error square sum meets the accuracy requirements. In view of the macro optimization characteristics of GA method and the fine tuning characteristics of BP network, the combination of GA and BP network can complement each other [6].
4 Design of GA-BP Algorithm for Image Restoration The topology of BP Newell network designed in this paper is 9201. Image is closely related to the gray value of the surrounding points. The smaller the distance, the greater the influence. Therefore, if the neighborhood of the pixel with the same gray value is different, the gray value of the degraded image will be greatly different. It can be considered that the pixel of the clear image is highly related to the neighborhood of the corresponding pixel point of the corresponding fuzzy image. Therefore, taking the gray value of a certain point and its surrounding eight points of the degraded image as an input sample, and the corresponding gray value of the original image as the target output. According to the characteristics of image restoration, the GA-BP algorithm is designed as follows: (1)
(2)
Coding method: the weights and thresholds of the above BP network are sequentially converted into strings, and real number coding is used instead of binary coding. This is to prevent the coding string from being too long, the complexity is too high, and the efficiency of genetic algorithm is reduced. The matching degree evaluation function genetic algorithm generally determines the probability of each individual in the current group inheriting to the next generation group based on the probability proportional to the individual fitness. In this paper, the expected reciprocal of the sum of squares of the actual output error is taken as the adaptive function.
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1 2 i=1 (A − T )
F = n
(1)
where n is the number of input samples, a is the actual output, and t is the predicted output. Basic genetic operators include the selection of operators, crossover operators and variable operators. Select the operator and use the sequence selection function norm select based on normal distribution. The crossover operator adopts the arithmetic crossover operator Alice XOver, which is basically a linear compound crossover, and generates two individuals according to the following equation. X = α · x + (1 − α) · y
(2)
Y = (1 − α) · x + α · y
(3)
where a is a given or randomly generated real number between [0, l], X and y are hybridized to produce X and Y . Mutation operator: non-uniform mutation function is used based on non-uniform probability distribution. Super resolution task is to convert the input low resolution image into high resolution image, which is consistent with image noise removal and blur removal. Super resolution focuses on how to fill new pixels from small images into large images. The key point of image noise removal is to replace the contaminated pixels with the correct pixels when the image size is unchanged. Srcenn is the first super-resolution algorithm from edge to edge using CNN structure (i.e. based on deep learning). What is this equal to? As in fast r-cnn target detection, the whole algorithm is realized by deep learning method, and the effect is better than the traditional multi module integration method. The flow of srcnn is as follows: first, input preprocessing. The low resolution LR image is enlarged to the target size by bicubic algorithm. Then the next goal of the algorithm is to get the super-resolution SR image from the input blurred LR image through the convolution network, so that it is similar to the high-resolution HR image of the original image as much as possible.
5 Algorithm Application and Experimental Results The GA-BP neural network algorithm designed above is applied to image restoration. In order to reduce the amount of calculation, the original gray value of the image is preprocessed, that is, each original gray value is divided by 255, and the gray value range of the image is converted from [0,255] to [0,1]. In training, the original image and the blurred image are divided into 3 × 3 sub blocks. Each pixel in a sub block
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of the fuzzy image and eight points around it are selected with a total of 9 Gy values as the input samples of the pixel points, so that the sub block has a total of 9 × (3 × ) 3 = 81 Gy values are used as the input vector p of the whole network, and 9 Gy values of the corresponding sub blocks of the original image are used as the expected output vector t of the network. In this way, the GA-BP network mentioned above can be used to train each 3 × 3 sub block, so that the whole image can be trained. When the image is restored, the trained network molecular block can be used to restore the corresponding gray image, and then the gray value of each pixel is multiplied by 255 to restore the gray level to [0,255]. Compared with complex high-cost technology, using image processing and computer vision technology to explore, develop and protect the ocean has the advantages of low cost and easy operation. Its specific applications include the following aspects: (1) marine military, which can monitor and track underwater suspicious targets, search and detect submarine sunken ships and aircraft wrecks; (2) In terms of marine environmental protection, it can monitor the marine ecological environment; (3) In terms of marine engineering, it can monitor and detect the engineering construction of the seabed, the docking of deep-sea workstations and the process of emergency escape, and also provide topographic maps for the exploration and development of seabed resources [7, 8]. which is basically based on gray mapping transformation, such as increasing image contrast, improving image gray level and so on. Transform domain method is to use transform technology, such as Fourier transform and wavelet transform, to adjust the definition of image by digital filtering.
6 Conclusion We propose a BP neural network hybrid algorithm to optimize genetic algorithm. After repeatedly using the weight threshold of genetic optimization BP network, better BP network parameters are further trained to decode, and the network parameters are dynamically adjusted to speed up the training convergence speed of the network and reduce the square error and average value. Algorithm analysis and experimental results show that the hybrid GA-BP algorithm has better image restoration effect than the conventional BP network. The main purpose of image restoration is to generate the complete structure and details of visual effect for the effective area and the defaced area in the image. Users can not only use this technology to repair the image defects, but also can use it for image editing and object removal tasks. The most difficult problem of image restoration is to generate the correct and complete structure and the more realistic detail texture with visual effect. The method based on diffusion mainly realizes texture synthesis by spreading the domain information to the missing position. However, this method can only deal with some small holes, but it cannot be effectively handled for the large-scale structural defects.
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The image slice based method not only uses the pixel points near the missing position, but also can effectively use the information of the further position to recover the missing area. The image slice based method can search the target area similar to the structure of the polluted area and copy the image slice to reconstruct the missing area. This method can generate the real texture of visual effect for the larger missing area. This method is to search for the appropriate image slice based on bilinear similarity. However, the method based on slice element mainly assumes that the non defaced region contains the same semantic content as the defaced region, but it is not true in tasks such as face detection. This method is good in some images with repetitive structure, but it can not be effectively processed for the images with special structure. many methods treat image restoration as a condition generation problem, and use the image as network input to get the repaired image. The advantage of deep learning method is that the effective semantic information in the image can be extracted and new images can be generated. Acknowledgements Guyuan Science and Technology Project Development Program, No. 2019GKGY038
References 1. Wang X, Cao L (2002) Genetic algorithm: theory, application and software implementation. Jiaotong University Press, Xi’an 2. Shi Z (2000) Neural network control theory. Northwest Polytechnic University Press, Shaanxi 3. Jiao L (1990) Neural network system theory. Xi’an University of Electronic Science and Technology Press, Xi’an 4. Hu S (1993) Introduction to neural networks. National Defense University of Science and Technology Press, Changsha 5. Luo S (2019) Application of neural network in traffic image compression. Shanxi Electron Technol 6 6. Yan C, Li M, Zhou X (2019) Auto insurance fraud identification model based on improved genetic algorithm and BP neural network. J Shandong Univ Sci Technol (Bairan science edition) 5 7. Chen C, Ryad C, Xing Y (2019) Speech emotion recognition based on BP neural network optimized by improved genetic algorithm. Comput Appl Res 2 8. Yang C, Qian Q, Wang F, Sun M (2018) Application of improved adaptive genetic algorithm in function optimization. Comput Appl Res 4
IOts Technology and Deep Learning for Process Parameters with Laser Welding Crack of Alloy Hai-yun Gao, Run He, Dong-yun Zhang, and Kun Lu
Abstract In this paper, the influence of laser parameters on the thermal cracks generated by welding MX246A material is analyzed, and it is believed that the influence trend of laser power and speed on the shape of weld is the same, that is, with the increase of laser power and the decrease of scanning speed, the weld fusion width becomes wider and the weld fusion depth becomes deeper. With the increase of scanning speed, the proportion of dendrite dry phase in the weld microstructure of MX246A material increased, while the intergranular phase decreased. In the experimental results, it is also found that the crack tendency decreases with the increase of laser scanning speed, but the crack cannot be completely eliminated. The MX246A material should be welded with focus welding and appropriate shielding gas, which is conducive to the reduction of cracks. Keywords Laser welding · Ni3 Al-based alloy · Laser parameters · The crack
1 The Introduction Ni3 Al intermetallic compound base alloy has the main advantages of high melting point, good high-temperature oxidation resistance, corrosion resistance, high temperature strength and creep resistance, and has the positive temperature effect of yield strength below peak temperature [1–4]. However, the thermoplasticity of the alloy at high temperature is poor [5, 6], which determines that the intermetallic compound Ni3 Al-based alloy castings are prone to solidification cracks [7, 8] and have poor H. Gao (B) Department of Mechanical and Electronic Engineering, West Anhui University, Lu’an 237012, Anhui, China e-mail: [email protected] R. He Department of Material and Chemical Engineering, West Anhui University, Lu’an 237012, Anhui, China H. Gao · D. Zhang · K. Lu Beijing University of Technology, Laser Research Institute, Beijing 100000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_104
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Fig. 1 AS-casting microstructure of MX246A
weldability during the welding solidification process. A large number of researchers at home and abroad have done a lot of research on Ni3 Al-based alloys [9, 10]. In this paper, laser welding MX246A alloy was studied, and the influencing factors of process parameters on welding crack were analyzed.
2 Materials and Methods The test material is MX246A superalloy based on Ni3 Al.
2.1 Microstructure of Alloy Base Material Figure 1 shows the MX246A alloy structure with hypereutectic alloy structure, which is mainly composed of dendrite stem and interdendrite. The dendrite matrix is γ, and the reticular phase is γ uniformly distributed above the dendrite, and the interdendrite is (γ + γ) eutectic. In addition, there are a few carbides and borides in the grain boundary, and the grain size is small.
2.2 Sample Preparation In this experiment, Nd: YAG solid state laser and slab CO2 laser were used respectively. Ar was used as protective gas with a flow of 15 L/min. The laser spot focus is located on the surface of the workpiece and defocusing welding. Samples with thickness of 0.8, 1.2 and 1.5 mm are selected for the test. The tissue and energy
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spectrum components obtained are compared and analyzed by laser scanning. Clean the specimen with acetone before welding.
3 Effect of Process Parameters on Laser Welding Crack of NI3 Al-Based MX246A Alloy When laser welding MX246A superalloy, the casting alloy has a great tendency of hot cracking, and the influence of laser parameters on it is analyzed.
3.1 Analysis of Laser Power and Welding Speed It can be seen from the experimental results that deep fusion welding can be realized under the parameters used in the laser scanning welding of experimental materials. With the increase of laser power and the decrease of scanning speed, the weld penetration width becomes wider and the weld penetration deepens, and the material penetration gradually changes, but slightly fluctuates due to the influence of material thickness and other reasons. The shapes and sizes of welds at different power and scanning speeds are shown in Fig. 2. It can be concluded from the experimental results that when the welding speed is 0.1 m/min, the material power below 1100 W is thermal conductivity welding, which cannot realize deep fusion welding. At 1200 W power and 0.1 m/min speed, the material is cut off. When the power is below 1100 W, with the increase of power, the dimensions of weld depth and weld width increase slightly, but have little change, as shown in Fig. 3.
Fig. 2 Influence of scanning speed on weld size
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Fig. 3 Influence of power on weld penetration depth and width (V = 0.1 m/min)
In addition, the scanning speed has an impact on the metallographic structure. As shown in Fig. 4, under high-speed scanning, the cooling speed is faster, and the microstructure of the weld is dominated by dendrite phase, with little intercrystalline phase content. At low speed scanning, the intercrystalline phase content is higher. This is due to the unbalanced solidification caused by the excessive cooling speed of the alloy during laser high-speed scanning. By Fig. 5 micrograph analysis can be seen, thickness is 1.2 mm, from coke— 1 mm welding hot crack mostly liquefied crack, there are some solidification crack, generally liquefied crack produced in nearly the heat affected zone of seam area, low melting eutectic (gamma + gamma ‘eutectic and some carbide) at the time of welding heat melt, under the action of tensile stress along the austenite grain boundary cracking occurs, wide source of crack width compared commonly, to the end of the narrow width crack, crack direction vertical stress direction in general. However, due to composition segregation, solute and impurities are enriched at the grain boundary at the central part of the intersection of columnar crystals, forming low-fusion eutectic. Under the action of tensile stress, solidification cracks are formed, which generally
Fig. 4 Microstructure of welding seams at 5 and 1 m/min
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Fig. 5 Liquefaction crack and solidification crack
occur in the weld seams and sometimes extend to the heat-affected zone. However, in the weld seams, some of them are near the fusion line. The thickness of the plate is 1.5 mm, the welding speed is 0.2 m/min, there are no cracks in the focus welding, the surface quality is good, and the welding is not thorough, and the microstructure analysis shows that there are no cracks in the cross section and the longitudinal section, which need to be further studied.
3.2 Influence of Protective Gas on Penetration Depth When the laser power is greater than 1000 W, the weld is deep-melting, and the shielding gas increases from 5 to 15 L/min, the shape of the weld does not change much. When the laser power is lower than 1000 W, the welding depth and width of welding seam vary greatly with the flow rate of protective gas, which increases the flow rate of protective gas and makes the welding seam become thin and shallow, which may be caused by the increased flow rate of gas and the heat taken away by it.
4 Conclusion This paper mainly studies the influence of welding thermal crack on MX246A alloy from the aspect of welding parameters. 1.
2.
Low-speed welding of MX246A alloy is prone to cut off, which is caused by excessive heat input. The influence trend of laser power and speed on the shape of weld is the same, that is, with the increase of laser power and the decrease of scanning speed, the weld fusion width becomes wider and the weld fusion depth becomes deeper. The scanning velocity has an influence on the microstructure of the weld. With the increase of scanning velocity, the dendrite dry phase proportion in the weld
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microstructure of MX246A material increases, while the intercrystalline phase decreases. The experimental results show that the crack tendency decreases with the increase of laser scanning speed, but the crack cannot be completely eliminated. The MX246A material is welded with focus welding and suitable shielding gas, which is conducive to the reduction of cracks.
Acknowledgements College level Natural Science Research Project of West Anhui University of Lu’an City (0041015014), Anhui quality Engineering-Photoelectric Information Technology experiment (2018 mocc364) (006020119090)
References 1. Westbrook JH (1957) Trans AIME J Met 6:895 2. Aoki K, Izumi O (1979) J Japan Inst Metals 43:38 3. Chen J, Zhu D, Lin D (2006) Research and application progress of Ni3 Al-based alloys. Mater Rev 1:1–4 4. Kayacana R, Varola R, Kimillib O (2004) The effects of pre- and post-weld heat treatment variables on the strain-age cracking in welded Rene 41 components. Mater Res Bull 39:2171– 2186 5. Luo H, Ye W, Feng D, Chen B, Zhang C (1999). Research on structure welder of intermetallic compound Ni3 Al-based alloy. High Technol Commun 5 6. Feng D, Li S, Luo H, Zhang C, Chen B (2002) Microstructure and properties of modified cast Ni3 Al-based Alloy MX246. J Metals 11 7. Zhang Y, Han Y, Chen G et al (2001) Intermetallic compound structural materials. National Defense Industry Press, Beijing, pp 546–631 8. Li YJ (2002) microstructure and compressive property of ni3ai alloys. Xi’an University of Technology 6 9. Wei YS, Qian YC, Li ZK et al (1999) Study on the mechanism of welding hot crack in Ni3 Al alloy containing Zr. Mater Sci Eng 17(2):52–57 10. Xiao X, Xu H, Qing XZ et al (2011) Acta Metallurgica Sinica 47(9):1129–1134
Secure Vertical Handoff Algorithm for Wireless Mobile Networks Supporting Cloud Computing Xi Cheng
Abstract In this paper, considering the different available wireless networks, application requirements, user preferences and other factors, according to the artificial ant colony algorithm (ABC) and particle swarm optimization (PSO), an intelligent vertical handoff hybrid algorithm (abc-pso) is proposed. Compared with the related work, the simulation results show that abc-psc algorithm has lower log and communication delay, higher available bandwidth and less handoff times. Keywords Mobile cloud computing · Mobile terminal · Optimal network · Abc-pso algorithm
1 Introduction Cloud computing, as a distributed computing model, has made an important breakthrough in overcoming these limitations of Mt. recently, new research areas have emerged in using cloud resources to improve MT performance, It is called. MCC is a new technology that. The main goal of MCC is to apply the advantages of cloud computing, storage and other resources, break through the resource constraints of MT, and provide richer applications and better user experience for mobile users. The mode of using and delivering the [1].
X. Cheng (B) School of Health Caring Industry, Sichuan University of Arts and Science, Dazhou 635000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_105
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2 Network Handoff Management of Mobile Cloud Computing 2.1 Related Research In MCC environment, it is necessary not only to provide continuous network connection service for MT, but also to select the appropriate cloud service provider for MT according to the performance, terminal capability and user preference of different network access technologies [2–5]. This limitation of mobile terminals urges us to create intelligent algorithms to provide close to real-time solutions as far as possible and reduce the computing time, that is, to mainly solve the communication continuity problem when MT roams between different base stations, to achieve reliable and fast service handover between different wireless access points (APS), and the wireless AP in MCC can be assigned to cloud clone, It is used for data transmission between cloud clone and local Mt. the movement of MT in various network environments will aggravate communication defects and lead to long handover delay. If the designed service handover scheme is not good, it may lead to disproportionate delay and high packet loss rate between MT, resulting in cloud resource unavailability and service interruption, and seriously affect the performance of MCC [6–8]. It is very difficult for active handoff method to design accurate and scalable prediction MT movement. In the handoff process, there are two kinds of inappropriate decisions: (1) improper selection of candidate AP; (2) transmission of communication link at wrong time increases handoff delay and jitter of mobile cloud QoS, especially for content and service sensitive to delay, We need to improve the communication handoff mechanism in MCC to solve the above problems. The selection of effective intelligent vertical handoff decision (VHD) algorithm can be effectively used for seamless handoff of mobile terminals in different networks, with high precision, stability and faster convergence, and robustness to adapt to the dynamic conditions [9].
2.2 Switch Management Framework Handoff management is the core of mobility management in MCC environment. The framework of vertical handoff management in mobile communication is presented. As shown in Fig. 1.The framework mainly includes information collection (1) information collection stage: collecting task status information, mobile terminal status, resource status of unloading task and other available agent resource status information, (2) handover decision-making stage: according to the collected state information, determine whether to switch, when to switch, switch destination, how to switch and other related decision-making issues [10].
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Fig. 1 Switch management framework model
3 The Basic Principle of Vertical Handoff Algorithm The literature on vertical handoff decision algorithm is mainly divided into four categories. The first one takes the signal strength as the reference object and makes the switch decision when the signal strength meets certain conditions; the second one uses artificial intelligence methods such as neural network and fuzzy control to make the switch decision by integrating various factors such as signal strength, network bandwidth and node speed; the third one is the cost function method, That is to say, the cost of network is expressed as the function of bandwidth, delay, cost and other factors [11, 12]. Nodes compare the cost from each network and select the network with the lowest cost. The first three kinds of algorithms are from the point of view of the node to improve the performance of the node; the fourth kind is from the point of view of the system to get a handoff threshold in the case of maximizing the system performance.
3.1 Vertical Switching Process Vertical handoff generally includes three steps: Decision of handoff trigger event, initialization of handoff and execution of handoff [13, 14]. The most important thing is to decide whether to switch according to the given decision strategy after the trigger event. In heterogeneous networks, the events that may trigger handoff generally include.
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Fig. 2 Vertical switching process
(1) (2) (3) (4)
At present, the signal strength of the network in use decreases obviously; As shown in Fig. 2. The network in use is congested seriously; The new network has better service quality; Users put forward higher service requirements.
3.2 Vertical Handover Decision Various physical layer technologies in heterogeneous wireless networks will produce different received signal strength. In a homogeneous network, MT can usually decide the start time of handoff according to the received RSS [15, 16]. However, RSS, as the network detection and decision mechanism of VII in heterogeneous networks, will cause networks. Standard f function expression is as follows: f n = wb × N (
1 ) + w p × N (Pn ) + wc × N (Cn ) Bn
(1)
Time interval between the old receiving the last data packet and the network receiving. It refers to the total time of compensating packet loss during handover delay. The formula of stable period is as follows: Ts = lhando f f + Tmakeno
(2)
Based on the policy enabled handoff decision mechanism, the future stable state can be predicted by the latest statistics. The fast Rayleigh fading can be compensated by the average of RSS. We can get the channel models of Wan and GPRS related to RSS: RSSw = P0 − 10n log(d) + F(d)
(3)
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RSSG = K 1 − K 2 log(D − d) + v(d)
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(4)
d and D-d are the distance from mobile station to wn hotspot and GPRS base station respectively.
4 Research Content of Mew Algorithm 4.1 Handover Decision Scheme The handoff decision scheme, that is distributed vertical handoff decision (DVhd), is to put the handoff calculation to the access network, rather than carry out the handoff calculation on the mobile terminal as other schemes in the literature. Distributed vertical handoff decision (DVhd) should consider bandwidth, packet loss rate and cost (money) as the evaluation index to select an appropriate access network. These indicators are collected in the access selection function of MADM method (TOPSIS). The comparison among simple weighting method (SAW), TOPSIS, GRA and VII EW is given. In this paper, the switching decision mechanism is described as an optimization problem. Each candidate network is associated with a cost function, which depends on many criteria, including bandwidth, delay and. The distributed vertical handoff decision (dwhd) scheme includes the following steps: 1. 2.
Firstly, it transfers the calculation of handover index to the destination network. Secondly, it includes the index of call loss rate as a parameter to calculate the handover index. Therefore, no terminal will switch into any access terminal network with high call loss rate. In addition, this paper also assumes that access network can provide these indicators.
5 Conclusion With the rapid development of mobile communication technology and the concept of next generation wireless network, NGWN integrates a variety of wireless access technologies. Seamless handoff between different network structures has become the main topic of research, so the Vertical Handoff Technology of heterogeneous wireless network arises at the historic moment.
References 1. Lin L (2005) Why 4G stands for Wmax and is willing to play a supporting role in TD, but it is difficult to be the 3G terminator. It Times 23:46–47
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2. Zhu C, Jiang H, Wu W (2002) End to end QoS architecture and guarantee mechanism of UMIS. J Chongqing Univ Posts Telecommun 14(4):10–14 3. Jian G (2010) Ontology construction method based on knowledge engineering methodology. Inform Explor 149(3):26–28 4. Li Y, Zhao B, Gan J (2015) MOOC curriculum development based on knowledge map. Modern Educ Technol 25(5):85–90 5. Hui Y (2013) Research on vertical handoff algorithm based on fuzzy logic. Nanjing University of Posts and Telecommunications 6. Dan T (2015) Research on vertical handoff algorithm in heterogeneous networks. Xinjiang University 7. Tao J (2015) Research on vertical switching algorithm based on multiple attribute decision making or fuzzy logic control. Shanghai Normal University 8. Xu D (2016) Research on Handoff Algorithm based on heterogeneous wireless communication network. Chongqing University of Posts and Telecommunications 9. Wang D (2018) Research on vertical handoff algorithm for enhancing performance and user experience in heterogeneous wireless networks. Chongqing University of Posts and Telecommunications 10. Su J (2019) Optimization of handoff algorithm in frmcs. Lanzhou Jiaotong University 11. Zhang S (2019) Channel modeling and handoff algorithm in LTE-R system. Beijing University of Posts and Telecommunications 12. Yu X (2019) Research on vertical handoff algorithm based on high speed mobility of users in ultra dense networks. Jilin University 13. Yang B (2019) Research on vertical Handoff Algorithm in heterogeneous wireless networks. Jilin University 14. Ren B (2018) Research on vertical handoff algorithm based on hidden Bayesian classification method in Internet of vehicles. Jilin University 15. Ren Z, Li Q, Chen Q (2009) Modeling and Simulation of wireless network fading and loss. Systems Eng Electron Technol 12 16. Kang Y, Xu K, Shen Y, Ma J (2009) Handoff prediction algorithm in wireless heterogeneous networks. Acta Communication Sinica S1
Curriculum Reform and Research of Airport Safety Inspection Based on Cloud Computing Yuehong Shi
Abstract With the rapid development of science and technology, the continuous innovation and progress of computer network, the use of big data in society is more and more widely, especially in recent years, cloud computing has a significant impact on the reform of college teaching. This paper mainly explores the curriculum reform of airport safety inspection major under cloud computing, and puts forward corresponding solutions. Combined with the actual work of security inspection, this paper puts forward the curriculum system of airport safety inspection major. Keywords Cloud computing · Security check · Curriculum reform
1 Introduction Compared with the traditional teaching mode, cloud computing has its advantages in college teaching, but its problems in teaching cannot be ignored. Only by scientifically and reasonably improving the problems of cloud computing in the teaching application of colleges and universities, can it give full play to the prominent role of cloud computing application in the teaching of the new era, and improve the teaching efficiency and quality of colleges and universities. Airport safety inspection is a new specialty in Higher Vocational and technical education. In all aspects of civil aviation system, airport safety inspection department is in a very important position. It is one of the window units of civil aviation, and also the first step to ensure air defense safety. Security inspection is very important, not only related to the safety of people’s lives and property and the reputation of civil aviation, but also related to the reputation and safety of the country. Safety inspection is the most critical and important part of the ground control measures to prevent hijacking and bombing. The quality of safety inspection directly affects the air defense safety and the healthy development of civil aviation. In some developed countries, aviation safety has been promoted to the height of national security, Y. Shi (B) Sanya Aviation & Tourism College, Sanya, Hainan 572000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_106
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which shows that the airport safety inspection is not only important, but also urgently needed. Airport safety inspection is a major to fill the gap in China. There is no experience and Practice for reference before. Therefore, based on cloud computing, this paper studies and discusses the curriculum reform of airport safety inspection major, aiming to cultivate students who master laws and regulations, civil aviation service and safety management knowledge, master basic skills of security inspection, have higher English application ability, strong computer application ability, can engage in safety inspection in civil aviation units, and have good professional ethics, High end skilled talents with high humanistic quality [1].
2 Cloud Computing 2.1 Definition of Cloud Computing So far, there is no clear definition of cloud computing, according to the definition of cloud computing on Wikipedia. Cloud computing is a computing method based on Internet. In this way, shared hardware and software resources and information can be provided to computers and other devices on demand. The whole operation mode is very similar to the power grid. That is, cloud computing is a service. Generally speaking, cloud computing is not a technology, but an idea. The common way to realize this idea is to use virtualization technology. Cloud computing is another great change after the big change from mainframe to client server in 1980s. In the past, cloud was often used to represent telecom network, and later it was also used to represent the abstraction of Internet and underlying infrastructure. Cloud computing describes a new mode of IT service increase, use and delivery based on the Internet, which usually involves providing dynamic, scalable and often virtualized resources through the Internet. Through the Internet, the IT infrastructure of data center, such as computing, storage and network, and its development platform, software and application, are provided to users in the form of services. The core idea is to unify the management and scheduling of a large number of computing resources connected by the network to form a pool of computing resources to serve users on demand. The architecture of cloud computing is shown in Fig. 1.
2.2 Characteristics of Cloud Computing Cloud computing is the development of parallel computing, distributed computing and grid computing, or the commercial implementation of these computer science concepts. Users can use it infrastructure like water and electricity, which means that computing power can also be circulated as a commodity. Just like gas and water and
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Fig. 1 Cloud computing architecture
electricity, it is easy to use and low-cost. The biggest difference is that it is transmitted through the Internet. The system characteristics of cloud computing are shown in Fig. 2. Cloud computing is the result of the hybrid evolution of virtualization, JAAS (infrastructure as a service), PAAS (platform as a service)/SaaS (software as a service) and other concepts. Its characteristics are as follows: (1) (2) (3) (4) (5)
Large scale and transparent, it is an inexhaustible resource pool for users; It can be measured and charged on demand, such as gas, water, electricity and telecommunication services; With ubiquitous computing, all kinds of end devices can easily access the cloud and obtain cloud services; Continuous, efficient and professional services to simplify and optimize it; Reduce investment and it spending.
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Fig. 2 Characteristics of cloud computing system
2.3 Advantages of Cloud Computing in College Teaching Application (1)
(2)
(3)
More efficient and convenient. The advantages of cloud computing are more efficient and convenient, so that the teaching data needed by teachers or relevant staff can be accessed to the cloud computing center at any time without the limitation of time and place. In case of server failure, cloud computing can transfer the virtual service platform to other servers to ensure that the teaching arrangement will not be affected in the transmission process. More abundant and safer. Traditional teaching applications often have many restrictions on data and content transmission. The teaching data that needs to be used is only stored in the personal computer or hard disk. In this process, there will be the situation that the data transmission is missing or the virus cannot be used. The data center of cloud computing adopts the method of computing node isomorphism, interchangeability and multi copy fault tolerance, which is richer, larger capacity and more secure and reliable data, and will not cause data damage or loss due to hardware damage or virus intrusion. The cost of resources is lower. The storage network devices and servers used in the data center of cloud computing are virtual, and the resource database of cloud computing can allocate and share data. In the data resource center, the regional data can be managed uniformly. Compared with the traditional teaching application, cloud computing greatly reduces the cost of storage network equipment, servers and other hardware, and can reduce the cost of managers, making it lower in the teaching application cost [2].
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3 Basic Structure of Curriculum According to the characteristics and tasks of airport security inspection, combined with the reality of China’s civil aviation security inspection, referring to other countries in the world, especially Israel and other countries’ airport security inspection practices, and on the basis of carefully studying the impact of the “9/11” terrorist attacks on air defense security in the United States, The training objectives of airport safety inspection major are as follows: airport safety inspectors are administrative law enforcement teams authorized by relevant state organs to engage in airport safety inspection and security work and ensure aviation safety. They are an important force to ensure air defense safety, the main force to counter hijacking and bombing, and an important defense line to ensure air defense safety. This major aims to cultivate students who can adapt to the needs of socialist modernization, especially civil aviation safety, develop morally, intellectually and physically in an all-round way, have high political quality, master air defense safety, civil aviation safety inspection policies and regulations and civil aviation knowledge, have the basic police professional skills required by security personnel, and skillfully use all kinds of security equipment and equipment, They have good psychological quality and ability to deal with the emergencies encountered by civil aviation passengers in the process of safety inspection, and can serve the national civil aviation air defense safety cause [3]. The specific training requirements for airport safety inspectors are as follows: First of all, in terms of ideological quality, we should love the socialist motherland, love civil aviation, and have a firm and correct political direction and political belief. He supports the party’s line, principles and policies, has high political quality, adheres to the four basic principles, masters Marxism Leninism, Mao Zedong Thought and Deng Xiaoping theory, is loyal to the motherland, is not afraid of sacrifice, is dedicated to his job, is united and cooperative, has a strong sense of organization and discipline, has a good professional ethics, is clean and honest, selfless dedication, is not afraid of sacrifice, is not afraid of coercion and inducement. Secondly, in terms of cultural quality, he has a relatively solid cultural foundation, certain cultural accomplishment, and can face Chinese and foreign tourists appropriately. Master certain foreign affairs knowledge, understand foreign customs, religious beliefs and religious taboos, have the foundation of Chinese traditional culture, have relatively solid aviation security knowledge, be familiar with the relevant laws and regulations of safety inspection and law enforcement, understand the legal regulation degree of air defense safety, understand the operation rules of civil aviation, and be familiar with the basic knowledge of civil aviation passenger and freight transportation, Understand the basic functions of civil aviation joint inspection unit and be proficient in using a foreign language. In terms of ability and quality, they should have the ability to face the increasingly complex service groups with different cultural backgrounds, the ability to deal with various situations, the ability to determine the harm degree of various kinds of prohibited dangerous goods, the ability to identify the authenticity of various kinds of dangerous goods, the ability to skillfully use various kinds of safety inspection equipment and instruments, and the strong ability of logical analysis and
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judgment, Basic pre-trial and reconnaissance capabilities. Physical and psychological quality, with good appearance, good facial features, can have a relatively strong physique, master the skills of catching and fighting, have a good psychological quality to deal with emergencies. It is a complex and continuous process to train a qualified airport safety inspection personnel. According to the law of education development, high-quality airport safety inspection personnel must be trained through systematic higher education. On the basis of a large number of research on the national airport safety inspection field, absorbing the experience of other related majors such as criminal investigation and border defense, the research group puts forward the curriculum system plan of airport safety inspection. Based on the professional curriculum reform of cloud computing, this paper adapts to the needs of civil aviation air defense safety, combines with the reality of China’s air defense safety, carries out modular design, and gives full play to the advantages of the school, so that the students receiving this professional education can realize the comprehensive and coordinated development of knowledge, ability and quality [4].
4 The Reform of Aviation Port Safety Inspection Course Based on Cloud Computing With the advent of the “cloud” era, information technology has had a profound impact on human work, study and life. The development of school-based curriculum with localization, individuality and characteristics as the main characteristics is necessary to be deeply transformed. After the teaching purpose of higher vocational education is changed to development, application and application, it is necessary to aim at the forefront of aviation port safety inspection technology development, update, expand and reorganize the intelligent structure of students in teaching and research practice to improve their ability to master and apply new technology and new knowledge.
4.1 Application of Cloud Computing in Teaching Management System The application of teaching course management mainly shows that the relevant information of curriculum arrangement in Colleges and universities is collected, summarized and classified through cloud computing. The overall arrangement of courses, the time of corresponding courses, classes of courses, teachers of courses and the management of teaching quality of relevant teaching contents are mainly managed through the platform of cloud computer, The management application of application teaching achievement, which is based on the evaluation of teachers’ teaching quality and the integration of cloud computing data, is mainly used by cloud computing
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to input and collect students’ learning achievements, analyze and deal with the comprehensive results.
4.2 Application of Cloud Computing in Teaching Resource Sharing The application of cloud computing in teaching resource sharing depends on the advantages of cloud computing having the advantages of unified collection and management of data information. Users can find the teaching equipment, teaching plans, digital textbooks or network courses that they want to use in the cloud computing data center. On the one hand, the application of cloud computing in teaching resource sharing enables the teachers to obtain data and platform operation students can also find their own learning materials and data according to their own learning needs. On the other hand, the application of teaching resource sharing also includes the resource sharing of teaching management documents resource sharing of computer room management and data resource sharing of digital library.
5 Conclusion In a word, cloud computing in the teaching reform and application in Colleges and universities, than the traditional teaching application is more convenient, fast, safe and reduce the cost of teaching expenditure, teaching efficiency and quality have significantly improved. The curriculum system of safety inspection specialty is a very complex system project, just as all the teaching plans of higher vocational education are made. Through the research of the reform of the aviation port safety inspection course of cloud computing, it is helpful to improve the teaching level of security inspection specialty and improve the quality of the students in the security inspection specialty. Acknowledgements Research Funding Project of Education and Teaching Reform in Hainan Higher Education (Project No. Hnjg2018-104), Research on the Curriculum System and Teaching Content Optimization of Safety Inspection of Higher Vocational Airport.
References 1. Shen Q (1995) Curriculum reform of technical education for high tech development. Shanghai: J East China Normal Univ (Education Science Edition) 44 2. Zhang R (2016) Teaching reform and application in Colleges and Universities based on cloud computing. J High Educ 11
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3. Huang K, Yan X (1997) Research summary on some problems of curriculum system reform of Vocational and technical education in China. Science Popularization Press, Beijing 4. Jiang G (1998) Research and exploration of Higher Vocational Education. Xiamen University Press, Xiamen
Bionic Design of Acquisition System of Underwater Sea Cucumber Search and Capture Robot Lu Chen, Zheyu Fan, and Peng Wang
Abstract This paper analyzes the relevant technical parameters of the fishing robot working underwater, designs the grasping mode, and innovatively uses the turbine siphon and reverse pressure reducing device; Innovative design of bionic human lower extremity venous valve surging device; Combined with efficient collection device; Combined design of sea cucumber flexible grasping system to solve the technical requirements of accurate, efficient and flexible sea cucumber collection; The design of motion balance, bionic vertebrate reptile combined with breakwater structure, improve the underwater compression, anti-overturning, anti-disturbance ability, enhance the stability of the body movement posture; Scientific and technological elements, bionic design and complex functional structure form a cyberpunk style high-performance, high-efficiency, high-precision underwater sea cucumber search robot, which provides a solution and idea for the future marine ranch intelligence. Keywords Sea cucumber fishing robot · Exoskeleton · Disturbance rejection · Bionic design · Structural design
1 Introduction Under the strategic background of the fourteenth five year plan, coastal provinces make full use of marine resources, promote the construction of marine ranch, and the deep-water aquaculture industry develops rapidly. Sea cucumber, known as “soft gold”, plays a very important role in the development of national economy and the improvement of life quality. Take the sea cucumber breeding industry in Zhangzi Island of Liaoning Province as an example [1]. Zhangzi Island started the construction of marine ranch a long time ago, putting in artificial reefs, seafloor greening, and developing ecological and sustainable marine aquaculture industry. After more than 20 years of practice, Zhangzidao group has basically established a large-scale, worldclass and standard marine ranch. At present, its marine ranch area has reached 2000 L. Chen (B) · Z. Fan · P. Wang Institute of Art and Design, Nanjing Institute of Technology, Nanjing, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_107
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Fig. 1 The description of the marine ranch area
square kilometers. However, behind the rapid development of sea cucumber breeding, the most traditional artificial fishing method is still used (as shown in Fig. 1), and the lag of this fishing method is very serious. It is understood that the time of sea cucumber fishing is usually in April and may in spring and late October to mid-December in autumn. At this time, the sea water is very cold and the fishing efficiency is very low. According to the data, the pressure at 10 m underwater is equivalent to two atmospheres. Fishing workers need to work in such a low temperature and high pressure environment for eight hours, which has a serious load on the body of the fishermen. Almost all the fishermen suffer from serious joint disease and decompression sickness. At the same time, due to the complexity and danger of the seabed environment, we often encounter underwater undercurrent in the process of fishing, which always threatens the safety of the fishermen, and fishing accidents occur frequently. The government needs to pay high subsidies to the fishermen, which aggravates the social burden. The highrisk and high harmful fishing operations lead to less and less young people willing to engage in this industry, and the fishing industry is facing the situation of serious aging and no successor. Therefore, it is an important means to promote the further development of sea cucumber aquaculture that the artificial diving fishing mode is replaced by intelligent fishing robot. From the existing data and information, there are very few researches related to the sea cucumber fishing machinery and equipment at home and abroad. Although there are several sea cucumber fishing robots on the market, there are big and small problems. Therefore, in this paper, the relevant technical requirements of sea cucumber fishing, the special habits of sea cucumber itself and the complex environment of the seabed are considered, and the existing fishing equipment is investigated, aiming at its imperfect place, a set of underwater sea cucumber fishing robot equipment integrating positioning, recognition and fishing is researched and designed [2]. Hereinafter referred to as the sea cucumber search and capture robot.
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2 Inserting Content Elements Sea cucumber breeding industry plays a very important role in China’s fisheries, and it is also the main part of promoting the development of China’s fisheries. Now, China has gradually shifted its development center from fishing industry to aquaculture industry. Under the trend of large-scale development of aquaculture industry, the sea cucumber aquaculture industry has also developed rapidly. The industry of sea cucumber breeding can be divided into two types: high-density and high-yield offshore shallow water breeding based on mass market and deep-water wild breeding based on high technology and high value. Sea cucumbers cultured in shallow water usually live in waters four or five meters deep. Here, the water surface is stable, the underwater environment is friendly, and the fishing operation is less difficult. The deep-water cultured sea cucumbers live in the complex environment of low temperature and high pressure, and there are often undercurrent and turbulence under the water, so it is very difficult to catch them. And the improvement of economic level brings the pursuit of quality of life. High quality sea cucumber is sought after by consumers. According to relevant data, in the 15 years from 2003 to 2017, the area of sea cucumber culture in China has increased by nearly 150,000 hectares (as shown in Fig. 2). In this paper, the deep-water high-quality sea cucumber as an example, sea cucumber breeding scale has been rapid development, at the same time, the fishing yield has become an important constraint in the industry.
2.1 Current Situation of Deep-Water Sea Cucumber Fishing Industry The annual import and export demand of China’s seafood can reach tens of billions of US dollars. Not only that, but also it is growing rapidly at the rate of 10% every year. As early as 2017, the annual output value of China’s seafood market has reached 140 billion. In the face of such a huge market scale, under the strategic background of the “fourteenth five year plan”, the deep-water sea cucumber fishing industry has Fig. 2 The description of the area of sea cucumber culture in China
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developed rapidly. However, the artificial fishing mode is seriously lagging behind (as shown in Fig. 3). Although artificial fishing has a low degree of damage to the underwater natural environment, it has high professional requirements. The workers have been in the low temperature, high pressure and closed underwater environment for a long time. The high intensity of fishing operation leads to the high incidence of occupational diseases, frequent fishing accidents and heavy social burden. High
Fig. 3 The description of the artificial fishing mode
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intensity, high risk, high technology and high harm lead to the serious aging of fishing industry.
2.2 Market and Current Situation of Search and Capture Robots Considering that the risk of deep-water aquaculture is greater than that of agriculture and fishery on land, and the level of automation in deep-water aquaculture is very low, in order to solve all the risk problems caused by divers fishing for sea cucumber, the demand for sea cucumber fishing robots in China’s sea cucumber market is very urgent. And due to the continuous growth of the domestic sea cucumber market scale, and considering that the research and development of this project can complete a variety of underwater operations, the future market scale of underwater fishing robot in the world will exceed 50 billion. It is understood that in 2016, the National Natural Science Foundation of China funded three projects to develop fishing robots, but failed to develop practical fishing robots. In 2018, in the “second underwater robot target capture competition” held in Dalian, Dalian Zhangzidao group once indicated that once the research and development of underwater fishing robot has made new progress, it will provide up to 10 million yuan as an innovation bonus. Therefore, the development of a practical new type of sea cucumber fishing robot is the common goal of market development. Domestic research on underwater vehicles started late, and the related industrial chain and product development are still facing many problems. Among them, the key is that the domestic core technology is limited by foreign countries, and the cost of later maintenance is generally high. Although there is a great demand for sea cucumber fishing robots in the deep-water aquaculture industry, the domestic research and development of sea cucumber fishing robots is very slow, leading to the difficulty of mass production, and the cost of popularizing related technologies is also very expensive. In the early days, the research related to underwater vehicles was mainly applied in the military field and scientific research projects. With the popularity of the Internet and the development of artificial intelligence, underwater robots are gradually applied in the professional market and mass consumption field. However, few domestic products enter the mass market, and the whole underwater robot market is still in its infancy. The reason is that the capital cost and technical threshold of research in this field are relatively high, In particular, the real underwater robot for fishing is not only complex in overall structure, but also long in research and development cycle and difficult in industrialization. Therefore, the field of underwater robot has not been fully developed, which has great market potential and high technical barriers. At present, many university teachers, students and graduate students have begun to try to do underwater robots, but their machines are only developing in the direction of military research. At present, there are still many disadvantages of the fishing
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underwater robot on the market. For example, there has been a fishing robot on the market that uses a mechanical arm to control the front grab to grab sea cucumbers on the seabed. Because of the soft and slippery characteristics of sea cucumbers, this kind of sea cucumbers robot is very easy to fall in the process of grasping. In addition, due to the high requirements of sea cucumbers for their own growth environment and their unique living habits, sea cucumbers are easily frightened when the fishing robot dives into the water on a large scale, Because of fishing errors, the dropped sea cucumber will stop eating and no longer grow, and the manipulator movement is not flexible, the operation efficiency is also very low. A pump suction fishing robot developed by Harbin Engineering University in 2017 is very large in size and poor in mobility. Moreover, the pump suction robot will cause great environmental disturbance due to excessive suction. The seabed gravel and marine plants will also be sucked into the collection box together, causing channel blockage. In addition, excessive suction often leads to sea cucumber being broken by negative pressure impact, resulting in serious economic losses and environmental damage. Therefore, the research and development of underwater sea cucumber fishing robot is very urgent.
3 Analysis of Technical Difficulties 3.1 Adaptability Analysis of Underwater Complex Environment The living environment of deep-water sea cucumber is not only less sunshine, low water temperature, but also often encounter turbulence, which makes the breeding environment very harsh. Combined with the above analysis, when designing the sea cucumber fishing robot, we need to consider that the pressure at the depth of 10– 20 m will not cause extrusion deformation to the machine, and the body needs to have a certain degree of rigidity and stability. When encountering turbulence and underwater undercurrent, the sea cucumber fishing robot also needs to keep balance underwater to avoid rollover. At the same time, the breeding environment of deep-water sea cucumbers simulates the growth environment of wild sea cucumbers, and there are often corals, reefs and other interferences at the bottom of the water (as shown in Fig. 4). Therefore, when the sea cucumber fishing robot is working underwater, it also needs to have a certain degree of flexibility to avoid seabed gravel and collision.
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Fig. 4 The breeding environment of deep-water sea cucumbers
3.2 Difficulty Analysis of Ontology Design There are many factors that need to be considered in the design of the sea cucumber fishing robot. Because the sea cucumber cultured in deep water grows at the bottom of 10–20 m, the selection of materials needs to ensure that the sea cucumber fishing robot can adapt to the underwater high pressure environment for a long time. Therefore, it is very important to use high pressure and high strength materials. The sea cucumber fishing robot is an intelligent device which integrates many key technologies, such as power control, intelligent recognition and so on. In the special environment such as deep-water fishing, in order to avoid the influence of high voltage and low temperature, the internal electric device of the sea cucumber fishing robot cannot be exposed directly, so it needs to be sealed. Therefore, the waterproof property of the sea cucumber fishing robot should be considered in the aspect of material to ensure the normal fishing work [3]. In structure, the frame structure of sea cucumber fishing robot mainly includes streamline structure and frame structure. Streamlined frame structure can greatly reduce the resistance caused by water flow, so as to reduce energy consumption. However, due to the streamlined external shape, the internal accommodation space may be affected, so it is necessary to increase the length or width, so as to expand the internal accommodation space, but this design will also lead to problems such as increasing the production cost. Although the frame structure of the body is small, economical and practical, the space is more open, but it is precisely because of the above characteristics, most of the frame structure of the sea cucumber fishing robots are only suitable for shallow water environment. Relevant data show that the traditional sea cucumber fishing device on the market is usually frame structure, as shown in Fig. 5. Although there is waterproof treatment, due to the small space and poor sealing, most of the actuators and sensors of the frame structure are exposed to the air. Therefore, not only the appearance does not meet the aesthetic standards of consumers, but also the service life of the fishing robot body will be greatly shortened.
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Fig. 5 The traditional sea cucumber fishing device
Deep water fishing operation environment is different from shallow water. Due to the unknown underwater environment, it often encounters undercurrent and turbulence. Therefore, in deep water fishing operation, the sea cucumber fishing robot needs to have higher mobility and flexibility. At the same time, it also needs to strengthen the rigidity of the body, so as to control the stability of the system and increase the safety factor of the parts, Only in this way can we adapt to different complex underwater environment and successfully complete the fishing work. Due to the different underwater environment and time in different sea areas, the size of current and undercurrent is also different. Therefore, it is also a technical difficulty to design a sea cucumber fishing robot with different functions in different situations. In the environment-friendly breeding environment, the requirements for the floating stability of the seabed fishing robot are not so high, so the design can be reduced appropriately to save the production cost. As for the deep-water aquaculture sites with complex environment, the sea cucumber fishing robot is required to overcome the interference of the environment and the influence of constantly changing load when carrying out towing, diving, floating and other motion modes and fishing operations, and remain stable, so it is necessary to carry out more sophisticated design in mechanical structure and appearance [4]. Although the cost is high, it can adapt to the complex underwater environment and improve the efficiency of fishing.
4 The Overall Structure Design of Sea Cucumber Fishing Robot 4.1 Design of Bionic Flexible Grasping Device The robot mainly adopts bionic flexible grasping design. During the fishing, the suction is provided by the turbine to make the sea cucumber out of the sea bottom. The petal shaped elastic baffle (as shown in Fig. 6) at the front end of the suction cup corrugated elastic expansion pipe can form an effective closure and adapt to different
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Fig. 6 The description of the petal shaped elastic baffle
shapes of the seabed surface. At the same time, the gap around the petal shaped elastic baffle can not only concentrate the water flow, but also ensure that the water flow gap produces the scouring water from the bottom up. Make the sea cucumber produce an initial velocity when it is off the ground. In addition, when absorbing sea cucumbers, a part of seabed sediment and seaweed are preliminarily filtered out, which solves the problems of sediment blockage and seaweed entanglement, and reduces the workload of artificial separation and the damage to the underwater environment. When the sea cucumber is separated from the water bottom by the suction generated by the hollow double-layer turbocharged turbine, an acceleration will be generated. At this time, the suction on the sea cucumber will be greater and greater (as shown in Fig. 7), which is very easy to cause the sea cucumber to be damaged due to excessive suction. In order to avoid this situation, the sea cucumber is allowed to produce a kind of flexible motion, so that the force received by the sea cucumber can change smoothly according to the need (as shown in Fig. 8). Therefore, when the sea cucumber passes through the suction cup, part of the water flow will be dispersed by the reverse flow reducing hole to slow down the vortex, reduce the suction received by the sea cucumber, and make the sea cucumber reach the rear device smoothly. Fig. 7 The suction on the sea cucumber
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Fig. 8 The force received by the sea cucumber
The design of bionic surging flow device for human venous valve is mainly based on the working principle of human venous valve of lower limbs: when the muscles on both sides of the vein contract, the valve opens, and the blood flows from the foot to the heart; When the muscles on both sides are in a relaxed state, the venous valves on both sides are closed to prevent blood from flowing from the heart to the foot to form backflow [5] (as shown in Fig. 9). Such a design can first produce continuous unidirectional circulating surge flow. When the sea cucumber passes through the bionic device, the bionic valve structure is opened, the sea cucumber is transported flexibly, and the larger sand is separated. With the tail collecting device, the sea cucumber can be put out to sea in batches. At Fig. 9 The description of the venous valves on both sides
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Fig. 10 The working principle of the body grabbing device
the end of fishing, the valve like structure is closed, and the sea cucumber cannot fall to the outside of the device, which greatly improves the collection efficiency of sea cucumber. The working principle of the body grabbing device is shown in Fig. 10. The specific work flow is as follows: Firstly, the specific position of the sea cucumber is identified by the camera device, and the petal shaped elastic choke at the front end of the suction cup is facing the target sea cucumber. Then, the suction is provided by the turbine device, and the sea cucumber is sucked off the sea floor and into the suction cup pipe. The reverse guide hole disperses part of the water flow, reduces the suction, and the sea cucumber slowly enters the bionic device. Then a single thrust is provided by the bionic device, and the sea cucumber is smoothly transported to the tail collection device. Finally, when the sea cucumber in the net box reaches a certain weight, the air bag in the net box bulges, the net box breaks away, rises to the water surface under the buoyancy, and is salvaged by the staff on the accompanying ship.
4.2 High Pressure Difference Exoskeleton Bionic Design Because the sea cucumber grows in the high pressure environment at a distance of 10–20 m from the sea level, the pressure difference between the water surface and the bottom of the sea cucumber fishing robot is very large, which will lead to the body deformation when it is serious. The design of sea cucumber fishing robot will imitate the structure of crab exoskeleton. In appearance, the crab’s body is flat as a whole, the upper part of the cephalothorax is uneven, symmetrical and slightly uplifted; The lower part of the carapace is arcshaped, and the important organs are located in the lower part. This shape can make it crawl under water, ensure the force of water flow on the crab body is lower, form the
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pressure from the top to the bottom, so that the crab with small volume and mass can maintain balance at the bottom, and will not easily cause the condition of rollover. The grabbing device and the lateral wave front of the upper part of the main control cabin, combined with the design of breakwater and crab bone armor, can effectively decompose the scouring force of the lateral undercurrent, reduce the thrust and prevent the machine from overturning. The cabin interface is designed with the shape of “upper slow and lower circle” (as shown in Fig. 11), which can transform part of the scouring force of the lateral undercurrent into downward pressure and increase the robot’s grasping ability, And the power device and other important equipment are set at the bottom of the body, and the overall center of gravity moves down to enhance the movement stability of the body. Fig. 11 The design of cabin interface
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5 Product Appearance Design The design of product appearance is mainly reflected in the comprehensive application of bionic design. This product is a functional bionic design based on the characteristics of vertebrate reptile bones, and a morphological bionic design combined with reptile crab bones. The design elements of appearance are reflected in the sense of science and technology of bionic design, the uneasiness of bionic motion balance device, the restlessness of multiple power devices, the confusion of lateral undercurrent scattered structure, and the style of cyberpunk is full of sense of science and technology and future.
6 Conclusion This paper takes the sea cucumber fishing robot as the research object, analyzes and studies the current situation of the sea cucumber fishing industry and the technical difficulties of the sea cucumber fishing robot. First, the overall imitation of reptile skeleton design exoskeleton. Second, one-way flexible conveying of venous valve of human lower limbs. Thirdly, the bionic design of exoskeleton with high pressure difference is carried out to improve the overall stability. The main research results are as follows. First of all, according to the particularity of seabed movement, combined with the structure of reptile exoskeleton and the characteristics of vertebrate, the bionic design is carried out, which imitates the lizard limbs and sets the active joint point on the central main frame. It always maintains a relatively stable motion state and provides a stable reference plane for the overall motion. Secondly, in the way of grasping, the turbine siphon is used to absorb the sea cucumbers at the bottom of the water, the rear decompression hole is used to disperse the water flow and reduce the impact of vortex on the sea cucumbers, the bionic surge water generating device of human lower limb venous valve generates the circulating surge one-way water flow, the flexible transportation of sea cucumbers and separation of large particles of impurities, and the tail collection device is used to realize the efficient collection and batch water outflow of sea cucumbers. Finally, combined with the exoskeleton structure of the vertebrate reptile, the shape skeleton of the fishing robot is designed by imitating the exoskeleton structure of the crab, and the shape of the robot is designed as “upper slow and lower circle”. It can transform the side scouring force into downward pressure to increase the grasping ability of the machine and the stability of the body movement. At present, due to the influence of practical factors, the product is still in the conceptual stage, without a lot of simulation testing and field verification of the research results. In addition, the project design is large, wide range of knowledge,
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strong professionalism, team ability is still insufficient, theoretical demonstration is still insufficient, the follow-up will be further strengthened.
References 1. Wang X, Li X, Wang W et al (2019) Discussion on the quality and safety control strategy of aquatic products breeding in Yantai. Qilu Fish 36(5):39–40 2. Pi Z (2017) Research on sea cucumber fishing robot technology. Harbin Engineering University; Finn C (2018) Learning to learn with gradients. PhD Thesis, EECS Department, University of Berkeley 3. Cong M, Liu Y, Li Y, Liu D, Du G (2016) Research status and development of underwater fishing robot. Dalian University of Technology, School of Mechanical Engineering 4. Tan J, Tian J, Wang M (2018) Current situation of underwater robot technology and its application prospect in water conservancy industry. China Water Conservancy 5. Tian Y (2012) Comparative study on postoperative complications of traditional and minimally invasive surgery for varicose veins of lower extremities. Ningxia Medical University
A Novel Sea Cucumber Search and Capture Robot Using Bionic Design Theory Lu Chen, Yun Gao, and Peng Wang
Abstract Deep sea cucumber fishing has the technical difficulties of high professional and technical difficulty, poor working environment and long working time. The long-term operation under the harsh environment of low temperature and high pressure results in heavy physical burden and high incidence of occupational diseases; The aging of professional fishermen leads to no successor; The high rate of disability leads to the aggravation of social burden. Therefore, the development of an underwater sea cucumber search and capture robot has become an urgent need for industry development and social progress. Aiming at the development of the industry, this paper analyzes the underwater pressure resistance, smooth motion, anti-undercurrent, and solves the aesthetic needs of users; The underwater bidirectional anti undercurrent structure improves the anti-overturning ability; Using cyberpunk style to design product appearance. By comprehensively analyzing and solving the above technical difficulties and problems, a high-performance, high-efficiency and high-precision underwater sea cucumber search and capture robot is designed, which provides a solution and idea for the conceptualization of marine ranching in the future. Keywords Marine ranch · Sea cucumber fishing robot · Bionic design · Cyberpunk
1 Introduction Under the background of marine war rate, deep water aquaculture industry has developed rapidly. Sea cucumber, known as “soft gold”, plays a very important role in the development of national economy and the improvement of life quality. The bad environment of sea cucumber fishing threatens the health and even life safety of the fishermen. Most of the sea cucumber fishermen quit the sea cucumber fishing industry. The scale of sea cucumber culture is growing rapidly, and the artificial fishing mode lags behind seriously; The workers are faced with the low temperature, high pressure and closed underwater environment for a long time,; Sea cucumber L. Chen (B) · Y. Gao · P. Wang Institute of Art and Design, Nanjing Institute of Technology, Nanjing, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_108
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fishing accidents occur frequently, which brings heavy social burden; High technology, high intensity, high risk and high harm lead to the serious aging of fishing industry. But at the same time, sea cucumber plays an important role in the marine ecosystem and in providing employment opportunities and increasing income for coastal areas [1]. The huge profits from sea cucumber farming force sea cucumber growers to find alternatives to artificial equipment. Nowadays, with the rapid development of robot technology, remarkable achievements have been made in all walks of life, and the development of robot technology has high expectations at home and abroad. Because of the rapid development of the robot industry, it has been widely used in the development of marine resources. The purpose of this paper is to study a kind of sea cucumber fishing robot which can replace artificial fishing and has consumer product modeling.
2 The Research Status In recent years, according to the relevant data, the consumption age of sea cucumbers has been declining. In the past, the average age of social consumers who bought sea cucumbers was 45–55 years old, because sea cucumbers have high nutritional value and medicinal value. But now the consumer population has gradually expanded to the age of 30, and there is a trend towards younger development. Young people pay more attention to health care, resulting in the growing demand of sea cucumber market. However, artificial fishing alone can not meet the growing demand of sea cucumber market, so expanding the number of sea cucumber fishing has been put on the agenda. Because the output of sea cucumber is limited by the fishing output, and with the continuous growth of the demand in the international market, especially in Asia and China, the price of sea cucumber has soared (see Fig. 1), and the output of sea cucumber fishing is in steady development [2]. Nowadays, the fishing work of deepwater sea cucumber culture at home and abroad is still relatively backward, mainly by artificial fishing, the fishing efficiency is very low, and divers need to dive in and catch sea cucumber for many times, resulting in the increase of labor cost. In addition, good quality sea cucumbers live in deeper waters and require more divers. Every one meter underwater will increase the pressure of a certain atmospheric pressure. Long term working in deep water will greatly increase the probability of staff suffering from illness. Artificial fishing will cause huge and irreversible damage to the fishermen (see Fig. 2), resulting in high fishing cost and no successor. According to statistics, the life span of underwater operators is significantly shorter than that of other types of work. This kind of fishing method, which can achieve the purpose of fishing by damaging people’s health, obviously does not meet the development requirements of modern society. Therefore, the development of sea cucumber fishing machine is of great significance to promote social and economic development.
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Fig. 1 Price fluctuation of sea cucumber in recent three years
Fig. 2 Heavy equipment for fishermen
2.1 Market and Current Situation of Search and Capture Robots Deep water underwater vehicle can be divided into ROV type robot, AUV type robot and HOV type robot (see Fig. 3). HOV type underwater fishing robot is an important transportation and operation equipment in the deep sea. It is used for seabed exploration, camera, seabed mineral sampling and biological fishing. The famous HOV type robots at home and abroad include Alvin of the United States, Nautilus of France, He ping I and He ping II of Russia, deep sea 6500 of Japan and Woolongong of China. These deep-sea HOV fishing robots are basically equipped with manipulators to facilitate deep-sea fishing, but its high cost makes it not widely used in commercial fishing, which is mainly used for national exploration and capture of geological biological samples. AUV type underwater fishing robot has the advantages of
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Fig. 3 Classification of underwater vehicles
convenient activities, low cost, and carrying power resources without relying on cable activities. It is a hot research topic at home and abroad. At present, it is only in the experimental stage. At present, the underwater robot with mature technology, wide application and the highest practicability is ROV type robot, which has the advantages of good economy, good environmental adaptability, high flexibility, high operation efficiency and strong endurance [6]. It is mainly used for high difficulty underwater operations, and has wide application in geological exploration, energy acquisition, and fishing [7]. Because the working efficiency of ROV is higher than other types of robots, it is more popular. For ROV robot, our country also launched some in-depth research, such as Shanghai Tongan University, Harbin Institute of engineering and other colleges and universities on the robot system structure, sonar system, underwater environment adaptation device for in-depth research, and achieved good results. Chen Weizmann developed an underwater control system [8], and Aid Guofeng designed a new guidance law based on bsav-1, which can accurately realize fixed-point tracking control [9]. At present, the research of underwater fishing robot is not mature. At present, the overall structure of the machine on the domestic and foreign markets is complex, and the research and development cycle is long, industrialization is difficult, mobility is poor, operation efficiency is low, and environmental disturbance is large. Based on the analysis of the existing underwater fishing robots, an underwater wild sea cucumber fishing robot is designed.
3 Analysis of Technical Difficulties 3.1 Analysis of Technical Difficulties in Materials The environment of natural underwater ranch is complex, and the route is not clear, so it is necessary to improve the adaptability and tolerance of the movement of moving
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objects. To catch sea cucumber, it is necessary to go deep into the seabed, and the water pressure is constantly increasing. The materials are required to have high water tightness, waterproof and compression resistance. At the same time, they should be systematic and can be operated remotely. As for the signal transmission of deepsea operation, due to the growth of cables, the transmission loss will also increase. Although the voltage and frequency can be increased, it will cause insulation and safety problems. Nowadays, underwater fishing robots are generally divided into ordinary frame machines and bionic machines. Bionic machines have become a trend with fast speed, low resistance, low energy consumption and beautiful appearance. They are small and delicate in appearance but complex in internal structure layout. It is very difficult to make full use of its internal space, optimize the configuration and reasonable planning, In terms of structure, we need to make overall plans and coordinate with each other. The streamlined underwater machine mainly uses the propeller boost mode to drive, which will disturb the underwater environment, make the sediment particles and other objects suspended, and the water quality will become turbid. With the accumulation of time, the binocular positioning image acquisition camera will gradually attach stains and debris at the bottom of the sea, which need to be cleaned and replaced regularly. There will be great visual obstacles in the acquisition of images. In order to obtain a clear image, we need to overcome the weak light of the deep sea and use different transmission media on the seabed [10]. The moving device at the bottom will produce a lot of noise and eddy interference when it works. It is inevitable that the machine will shake. It needs to resist external interference and maintain the balance and stability of the underwater fishing robot. The wheel carcass adopts a Pentagon honeycomb structure, which can reduce noise. Because there are a lot of sediment and other debris at the bottom of the water, it is necessary to separate the inhaled sediment, which will cause low work efficiency. Relatively speaking, the robot under the frame will face the problems of large resistance and slow action for the bionic streamline machine in the process of propulsion, but for the complex underwater environment, the frame machine can try to prevent collision. In the collision, the reef did not escape in time, and the internal structure of the machine was also protected because of the hardness of the shell. Using wheel walking mode to work underwater, underwater walking is more stable and reliable for image transmission, and there is no large bitter and other problems of suspension type. In the water inlet and outlet, the method of using bilateral rotors to assist floating to achieve floating and sinking is more stable and reliable as a whole. The advantages and disadvantages of the two are obvious. But for this topic, we choose the more difficult bionic robot, because its relatively beautiful shape is more in line with the increasing demand of the market.
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Fig. 4 Bionic design of underwater motion and attitude adjustment of sea cucumber fishing robot
3.2 Analysis of Technical Difficulties in Appearance The appearance design of the product adopts exoskeleton bionics, which needs to be more humanized. Apart from the pursuit of material, what’s more important is to be more detached in spirit, pursuing the harmonious coexistence between human and nature, so that technology and art can constantly create new ideas and new designs. Bionic design is the extraction of all natural things, in-depth analysis of sound, function, structure, form, color, picture and other aspects of the characteristics reflected. In the design, we need to combine these features with the results of bionics, which provides new ideas, new principles, new methods and new ways for the design (see Fig. 4).
4 Underwater Bidirectional Anti Undercurrent Design Irregular protrusions on the body (see Fig. 5). The grab device and the upper half of the main control cabin are designed to meet the wave side. Combined with the breakwater and crab bone armor, the side undercurrent and scouring force can be effectively decomposed, the thrust can be reduced and the machine can be prevented from overturning. The bionic crab bone shell on the cabin interface is designed as a shape of “slow up and round down”, which can transform part of the scouring force of the lateral undercurrent into downward pressure, increase the robot’s grip ability and enhance the stability of the body movement. The overall modeling color of the sea cucumber fishing robot is silver white and black (as shown in Fig. 5). The overall appearance design refers to the skeleton structure of lizard, which is stable as a whole. The body design is flat on the top and a circular arc on the bottom, just like the round belly of a crab. The auxiliary machine can walk steadily in the harsh underwater environment of sand, stone, waves and surging. Even if there is turbulence in the bottom, it can achieve balance according
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Fig. 5 Irregular protrusions on the body
to its own firmness, In order to facilitate the operation of efficient and accurate implementation. Products include propulsion device, positioning and identification device, grabbing device, floating device and collecting device. Six propellers and four tires are symmetrically arranged on both sides of the body. The thruster is turbine shaped and arc-shaped; The tire is not a conventional round tire. The side view is like a trapezoid, which can disperse the water flow and enhance the stability of the machine; The grabbing device is designed with petal structure and bell mouth; When the machine starts up, there is blue light on the side of the positioning and recognition device, which has a sense of science fiction. The design elements of the appearance are reflected in the sense of science and technology of bionic design, the uneasiness of bionic motion balance device, the restlessness of multiple power devices, the sense of chaos of the lateral undercurrent scattered structure, the style of cyberpunk, full of sense of science and technology and the sense of future.
5 Conclusion This paper takes the sea cucumber fishing robot as the research object, through the analysis of the current situation of the sea cucumber fishing industry and the technical difficulties of the sea cucumber robot, the design is carried out around the following points: First: underwater bidirectional anti undercurrent. The irregular protrusion on the body of the sea cucumber fishing robot can prevent the excessive waves on the seabed, disperse the force in the same direction on the body into forces in all directions, disperse the water flow, and transport the undercurrent in all directions. Second: the overall modeling color of sea cucumber fishing robot is silver white and black, full of sense of technology and future. The overall appearance design refers to the bone structure of the crab, which is stable as a whole. The body design
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is that the upper part is relatively flat, and the lower part is an arc, just like the round belly shape of the crab. The two sides of the body are symmetrical, with six propellers and four tires. The thruster is turbine shaped and arc-shaped; The tire is not a conventional round tire. The side view is like a trapezoid, which can disperse the water flow and enhance the stability of the machine; The grabbing device is designed with petal structure and bell mouth; When the machine starts, there is blue light on the side of the positioning and recognition device, which is more sci-fi; The collecting device adopts the shape of net bag. At present, due to the influence of practical factors, the product is still in the conceptual stage, without a lot of simulation testing and field verification on the research results. In addition, the project has a large amount of design, a wide range of knowledge, strong professionalism, insufficient team ability and theoretical demonstration, which will be further strengthened in the future. Due to the increasing market demand of sea cucumber, the new development of underwater robot shows a broad prospect for the application of underwater robot in fishery. Therefore, the inefficient and high-risk artificial fishing will be replaced by machines. The underwater sea cucumber fishing robot, which is unique to ROV, is expected to be able to capture sea cucumber stably and efficiently, which is more compatible with artificial intelligence, Make great contribution to marine product economy and production capacity.
References 1. Swansong C, Gui T (2013) Study on the change trend of global sea cucumber fishing and aquaculture output. China Fish Econ 3 2. Shostakovitch AM (1997) 10 years anniversary of deep manned submersibles MIR-1 and MIR-2. In: OCEANS’ 97. MTS. IEEE conference proceedings 3. Takeaway S, Tsitsihar K, Sane T et al (1989) 6500 m deep manned research submersible SHINKAL 6500 system. In: OCEAN S’89. Proceeding 4. Spanish T, Takeaway S, Shiatsu T et al (1986) Japanese 6,500 m deep manned research submersible project. Washington D.C., USA, OCEAN S’86 5. Bu J, Wang Y, Hou B et al (2011) Research status and development trend of ROV. J Sicilian Ordnan Indus 4:71–74 6. Cong M, Li B, Li Y et al (2016) Research status and development of underwater fishing robot. Ship Eng 6 7. Chen W, Sui X, Deng J et al (2014) Control system design and experiment of functionalism mode switching cable remote control underwater vehicle. J Jungian Univ Sci Technol (Nat Sci Edn) 28(5):466–472 8. Aid G, Ban X (2001) Simulation research on trajectory control of 6-DOF underwater vehicle. J Syst Simul 13(3):368–369 9. Li Y, Chen Q, Ca S (2019) Design of water quality sensor monitoring and self-cleaning device based on Internet of things. Fish Modern 46(4):42–48 10. Mia K, Thu M, Deng B et al (2021) Research progress of aquaculture underwater robot. J Songhua Agric Univ 3
The Application of Nosocomial Infection Monitoring System in the Management of Nosocomial Infection Control Hairui Zhang, Yancheng Feng, Yonghong Ma, and Ke Men
Abstract With the continuous improvement of the hospital management system and the introduction of information into the nosocomial infection monitoring, the accuracy of a large number of data was not only improved, and the workload of full-time infection control was also reduced. The monitoring content of nosocomial infection was very complex, which requires a lot of statistics and data analysis. The general manual processing was neither comprehensive nor perfect, and the efficiency was low. In particular, there were many potential risks in hospitals and the nosocomial infections often break out suddenly in some special cases, but the causes of the outbreaks were very complex and manual analysis was difficult to achieve the expected results. The aim of this study was to analysis the effect of nosocomial infection monitoring system implementation and through the system find out the causation of nosocomial infection timely. The threshold of nosocomial infection monitoring was moved forward to provide data support for the prevention of outbreaks of nosocomial infection through the real-time monitoring. Keywords Nosocomial infection · Monitoring system · Effect · Control
1 Introduction With the continuous development of modern medicine, the medical quality and safety of hospitals are deeply affected by nosocomial infection, which also brings great life and health threat to patients [1]. Therefore, the importance of effective control of nosocomial infection becomes increasingly prominent. In recent years, the public’s demand for medical services has been increasing, and caring for the safety of patients has become an issue of coordination concern for relevant government departments, relevant medical institutions, health managers, medical personnel and even all relevant aspects of the society [2]. With the continuous development of H. Zhang (B) · Y. Feng · Y. Ma · K. Men Institute for Research on Health Information and Technology, School of Public, Xi’an Medical University, Xi’an, Shaanxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_109
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modern science and invasive diagnosis and treatment technology widely used, the nosocomial infection work also become more and more challenges, especially in the prevention and control areas. The problem of nosocomial infection was accentuated by the susceptibility of inpatients and medical staff transmission of the newly discovered phenomenon appears unceasingly, antimicrobial drugs cause microbial resistance, susceptibility of hospitalized patients and medical staff in hospitals and so on has become more and more serious [3]. Nosocomial infection management is one of the main components and key links of medical quality and safety management. Its importance cannot be ignored, and it is also an important content indispensable in modern hospital management [4]. The task of real-time monitoring information system was to collect the nosocomial infection occurrence of hospitalized patients, distribute and various influence factors of materials, arrange and analysis the data on a regular basis systematically and continuously [5]. After that, the hospital infection control department carried out statistics, formulated and improved prevention measures, and evaluated the control measures of the relevant departments in order to achieve the goal of controlling the hospital infection. At present, the work of nosocomial infection monitoring has been carried out in all hospitals in the country [6]. The purpose of this study was to analysis the effect of nosocomial infection monitoring system implementation and through the system find out the causation of nosocomial infection timely. The real-time data were used to analyze the essential role of nosocomial infection monitoring system in nosocomial infection control and the threshold of nosocomial infection monitoring was moved forward to provide data support for the prevention of outbreaks of nosocomial infection through the real-time monitoring.
2 Materials and Methods 2.1 Data Source The data were obtained from the nosocomial infection monitoring system of hospital in Xi’an. The infection information of patients in each department was obtained from the nosocomial infection case report form.
2.2 Calculation Methods [7] Nosocomial infection rate = (Total number of all patients with nosocomial infection during a given period/Total number of Inpatients during the period) × 100%
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2.3 Introduction of Nosocomial Infection Monitoring System The nosocomial infection monitoring system was made up of B/S architectures. The main function of the data access layer is to complete the data interaction. The main function of the business logic layer is to analyze and process the data of the data access layer. The main function of the presentation layer is to communicate with users. The data analysis module includes the following aspects: Hospital Information System (HIS): Include basic information about patients, the attending physician, and doctor’s orders. Laboratory Information System (LIS): Responsible for daily data processing, including specimen collection, specimen data receiving, data processing, report review, report release, report query and other daily functions. Electronic Medical Record (EMR): Electronic management of personal health status and health care behavior, involving the collection, storage, transmission, processing and use of patient information all process information. Radiology information system (RIS): Including registration and appointment, medical treatment, image generation, film release, report, review, film release and other links. Operation Anesthesia Management System (OAMS): Includes the process control of patients from appointment application for surgery to preoperative, intraoperative and postoperative. The nosocomial infection monitoring system obtained data from HIS, LIS, EMR, RIS, OAMS and other data systems on a daily basis. According to the requirements of the hospital awareness department, the system sorted out and entered into the awareness database. After analysis, suspected cases were generated, and then confirmed by the awareness staff and clinicians in the form of early warning.
2.4 Quality Control Conduct a focused examination of the nosocomial infection case report form at the time of collection of baseline information. Ensure the accuracy and completeness of the information by means of self-examination and mutual examination. Before statistical analysis of the data, double-check the input data to ensure accuracy.
2.5 Statistical Analysis Descriptive analysis for continuous variables was performed with mean ± standard deviation for normally distributed data and interquartile range and median for nonnormally distributed data. Chi-square test was used to compare the rate and P < 0.05 was considered statistically significant.
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Table 1 Nosocomial infection rate before and after using the nosocomial infection monitoring system Variables
Infection or not
Total
Infection rate (%)
χ2
P-value
9.236
0.002
Yes
No
Before
469
46,427
46,896
1.00
After
422
51,253
51,675
0.82
Total
891
97,680
98,571
0.93
3 Results 3.1 The Change of Nosocomial Infection Rate Before and After Using the Nosocomial Infection Monitoring System There were 98,571 inpatients before and after the use of infection monitoring system, and 891 patients had nosocomial infection with an overall infection rate of 0.90%. Before the use of the system, the infection rate was high with infection rate of 1.00%, but after the use of the system, the infection rate showed a significant decrease with infection rate of 0.82%. The difference of infection rate was statistically significant before and after the use of infection monitoring system (Table 1).
3.2 Analysis of Ventilator-Associated Pneumonia in Intensive Care Unit (ICU) Specific monitoring was realized in ICU ward of hospital. In terms of the distribution of nosocomial infection monitoring ventilator associated pneumonia infection in different ICU departments, the nosocomial infection rate in the cardiac ICU department was the highest (Before with 5.40%, after with 2.10%), pediatric ICU (Before with 0.46%, after with 0.24%), neonatal ICU and obstetrics ICU had the lowest infection rates in all departments (Before with 0.35 and 0.00%, after with 0.10 and 0.00%) (Table 2).
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Table 2 Monitoring data of ventilator-associated pneumonia infection in ICU (n, %) Variables
ICU
inpatients
Number of ventilators
Ventilator utilization rate
number of infections
Infection rate
Before
Neonatal
1699
276
16.24
6
0.35
Obstetrics
1083
89
8.21
0
0.00
Pediatric
217
37
17.05
1
0.46
Cardiac
74
18
24.32
5
5.40
Neonatal
1964
316
16.08
2
0.10
Obstetrics
1374
114
8.29
0
0.00
Pediatric
415
106
25.54
1
0.24
Cardiac
139
84
60.43
3
2.10
After
3.3 The Effect of Real-Time Monitoring on the Use of Therapeutic Antimicrobial Drugs and the Submission of Microbial Specimens for Examination The system actively captures the patients’ use of therapeutic antibacterial drugs and the submission of microbial specimens. The quarterly inspection rates before use the monitoring system were 72.20, 83.50, 88.70 and 89.24%, respectively. The quarterly inspection rate after use the monitoring system were 88.21, 88.57, 91.23 and 92.12%, respectively.
3.4 Analysis of Staff Motivation After the Application of Infection Monitoring System Through frequent inspection and supervision by nosocomial infection management personnel in various clinical departments and nosocomial infection quality control personnel punish the omission of reporting, which gradually forming a positive working atmosphere, and hospital infection management personnel keep track of the nosocomial infection of inpatients in the hospital at any time. Discussion The nosocomial infection real-time monitoring system can enable hospitals to carry out nosocomial infection monitoring with high quality and high efficiency. Compared with the manual monitoring, the testing time, the work intensity of the management staff and pressure can be greatly reduced by the new system [1]. It can free clinicians from the tedious manual data source collection and devote more time and energy to supervising and guiding clinical front-line staff to implement infection control measures. The monitoring system can judge complex cases and a large amount of
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data quickly and accurately, which fully reflects the timeliness and accuracy of the real-time monitoring system for hospital infection [8]. The timely detection of nosocomial infection by nosocomial infection monitoring system is the key to prevent and control the outbreak and epidemic of nosocomial infection. It is an important link for physicians to find the epidemic of drug-resistant bacteria infection in the first line of clinical practice to control the outbreak and epidemic of drug-resistant bacteria [9]. The nosocomial infection real-time monitoring system is the basic step to prevent nosocomial infection that full-time personnel take the initiative to find problems such as disinfection, isolation and application of antibacterial drugs in the field, and solve them in time. Therefore, prospective case investigation should be adopted to improve the level of nosocomial infection case monitoring [10]. The nosocomial infection monitoring system not only facilitates the comprehensive development of all kinds of nosocomial infection monitoring work, but also provides the staffs with real-time details of hospital perception dynamics [11]. In order to facilitate the communication, cooperation and coordination of various departments, the nosocomial infection monitoring platform can be completed jointly. In order to improve the level of nosocomial infection control, we should optimize the medical procedure, improve the quality of data sources and implement the management system [12].
4 Conclusion The nosocomial infection real-time monitoring system provides whole-process monitoring with target monitoring function to improve intervention intensity, effectively reduce nosocomial infection rate and control the incidence of nosocomial infection. But the current shortcoming was that the system does not support automatic retrieval of complex and structured text information such as image report card and electronic medical record. In a word, the nosocomial infection monitoring system still needs to be separately inquired by hospital infected personnel, which all need to be further improved. Acknowledgements This work was financially supported by Education Department of Shaanxi Provincial Government (Grant No. 20JK0884), Xi’an Medical University (Grant No. 2020ZX01 and Grant No. 2020JG-34).
References 1. Fu C, Wang S (2016) Nosocomial infection control in healthcare settings: protection against emerging infectious diseases. Infect Dis Poverty 5:30
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2. Yang L, Yao T, Liu G et al (2019) Monitoring and control of medical air disinfection parameters of nosocomial infection system based on internet of things. J Med Syst 43(5):126 3. Mermel LA, Allon M, Bouza E et al (2009) Clinical practice guidelines for the diagnosis and management of intravascular catheter-related infection: 2009 update by the infectious diseases society of America. Clin Infect Dis 49(1):1–45 4. Masroor N, Doll M, Stevens M et al (2017) Approaches to hand hygiene monitoring: from low to high technology approaches. Int J Infect Dis 65:101–104 5. Hughes JM (1987) Nosocomial infection surveillance in the United States: historical perspective. Infect Control 8(11):450–453 6. Thomas A, Khader K, Redd A et al (2018) Extended models for nosocomial infection: parameter estimation and model selection. Math Med Biol 35(suppl_1):29–49 7. Jiang N, Wang Y, Wang Q et al (2014) Clinical analysis of nosocomial infection and risk factors of extremely premature infants. Zhong Hua Er Ke Za Zhi. 52(2):137–141 8. Gastmeier P (2004) Nosocomial infection surveillance and control policies. Curr Opin Infect Dis 17(4):295–301 9. Fu C, Xu R (2019) Challenges remain for nosocomial infection control in China. J Hosp Infect 103(2):233–234. https://doi.org/10.1016/j.jhin.2019.07.002 Epub 2019 Jul 4 10. Menegueti MG, Canini SR, Bellissimo-Rodrigues F et al (2015) Evaluation of nosocomial infection control programs in health services. Rev Lat Am Enfermagem 23(1):98–105 11. Gorse GJ, Messner RL, Stephens ND (1989) Association of malnutrition with nosocomial infection. Infect Control Hosp Epidemiol 10(5):194–203 12. Sun B (2016) Nosocomial infection in China: management status and solutions. Am J Infect Control 44(7):851–852
Analysis on Legal Issues of Cloud Computing Software-as-a-Service (SaaS) Model Fen Li
Abstract Cloud computing is a new business model based on the Internet. It uses the network as a carrier, supported by virtualization technology, and realizes information sharing and interoperability among users through business processes such as data mining and online analysis. In this environment, we can find that there are still many shortcomings in my country’s existing laws and regulations. This article first elaborates the related concepts, characteristics and development history of cloud computing, and conducts an in-depth analysis of its software as a service (SaaS) model, points out the legal issues involved in the current model, and then elaborates and studies cloud services from two aspects Infringement and related issues; On the one hand, it proposes perfect suggestions for the current legislative situation and prospects for future development; On the other hand, it discusses the network privacy protection system under the background of the Internet. Keywords Cloud computing · Software and Services (Saas) · Legal issues · Exploratory analysis
1 Introduction With the rapid development and popularization of the Internet, the emergence of the network distribution model has greatly changed the software distribution channels, and the customer service/server model has been transformed into a browser/server model, providing an Internet foundation for the production and development of cloud computing. In the context of the era of big data, the cloud computing model has won the support of software vendors with its extremely high energy efficiency and excellent cost performance. However, its business model and technical functions have had a disruptive impact on the legal system. For example, whether the traditional software legal protection model composed of copyright law, patent law and software license agreement can continue to protect software services under the cloud F. Li (B) Hainan Radio and Television University, Haikou 570000, Hainan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_110
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computing model. What should we do when we cannot protect it? With the rapid development of cloud computing technology, the nature of legal delay has become particularly noticeable. The research history of SaaS in our country has a long history, and many scholars have conducted in-depth research on it. Veiga E, Calnan N and others studied the current business regulatory expectations from the perspective of data integrity and data privacy, and proposed that when cloud computing is involved, the most powerful tools that organizations have to ensure data quality and security depend on Thirdparty supplier contract. Finally, it is pointed out that to ensure data integrity and data privacy in the cloud, it is necessary to have sufficient information. After the data is hosted on the cloud provider’s site, the supervised organization shall take measures to plan and maintain control of its data [1]. Vivekanand, Randhawa et al. proposed a model suitable for information security services, which does not require internal hardware, thereby avoiding substantial capital expenditures. These security services usually include authentication [2]. Cannas, Francesco, and others dealt with selected legal and practical issues related to the VAT processing of cloud computing technology. And pointed out that cloud computing is the main stage of development and expansion, and may face new tax challenges in the future. In particular, in the first part of this contribution, certain aspects of the relationship between cloud computing and the “value added tax concept” of fixed assets and real estate are discussed. In the second part, special attention was paid to the latest proposal, and some practical issues from customer localization and identification were raised [3]. Marcello C, Stefano R, Christian and others claim that the universality of cloud technology is due to the ability to easily share and obtain resources on a pay-per-use and flexible configuration model. Enterprises use the cloud as an inexpensive solution to achieve the computing and storage functions they need without incurring the costs associated with owning and maintaining data centers. This means that they rely on the correctness of the services provided by the cloud platform, and any possible interruption may result in a large loss of reputation and money [4]. This article first introduces the concept and principles of cloud technology, then focuses on the characteristics of software as a service and discusses the legal issues brought about by software as a service. Finally, it provides suggestions for improvement of current legislation and prospects for future development.
2 Method 2.1 Cloud Computing The National Institute of Standards and Technology (NIST) has given this definition of cloud computing: The payment model of cloud computing is pay-as-you-go. The emergence of this model provides people with easy-to-use and convenient ondemand network access services. Although there is little management work or little
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interaction with service providers, it is still possible to enter the computing resource sharing pool to enjoy configurable network, storage, servers, application software and services and other resources [5]. To put it simply, cloud computing no longer just relies on local computers or servers to store and calculate data, but publishes the data of local computers and private servers as computing tasks and connects to the Internet, and then all other users A large-scale computer integration center for sharing is completed. This will help realize the comprehensive sharing of computer software and hardware resources, including service resources such as installation, configuration and maintenance. Cloud computing not only allows users to install and use servers on demand, providing a virtualized software and hardware service for the entire cloud computing platform, but also allows users to access the cloud computing platform through network access devices. Users can connect to the cloud computing service center through any Internet-capable device without high-performance hardware or fully functional software, and enjoy all the resource services provided by the cloud computing service center on demand [6].
2.2 Cloud Computing Service Types (1)
Software as a Service (SaaS)
SaaS (Software-as-a-Service) is to provide users with software services through the network. A wealth of application software is deployed on the servers of the SaaS platform. Users only need to purchase the corresponding services from the merchants through the network according to their own needs. The emergence of this model saves users the cost of software updates and hardware maintenance, and the return on investment of software suppliers has also been improved, which is applicable to enterprises of all sizes [7]. (2)
Platform as a Service (PaaS)
PaaS (Platform-as-a-Service) is a business model that provides a server platform or development environment for software development as a service. PaaS is delivered in the SaaS model, which provides a software development platform and a complete deployment environment [8]. In other words, it provides a framework for software development, and developers can expand based on it. They don’t need to set up their own environment and only need to focus on their own business logic. (3)
Infrastructure as a Service (IaaS)
Iaas (Infrastructure-as-a-Service) provides information technology infrastructure as a service to users through the network, and users use infrastructure services such as servers, storage, and networks through leases [9]. The virtual data center and computing center created by it combine memory, input and output devices, storage and computing capabilities to form a virtual resource pool to provide services for the entire network.
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2.3 Software as a Service Features (1)
Network characteristics
SaaS relies on the mobile Internet to provide services to customers, through the direct transmission of information and data and the delivery of resources through the mobile Internet, which greatly shortens the distance between SaaS users and SaaS service providers, thus making the marketing methods and delivery methods of SaaS products There are many differences between it and other traditional software. (2)
Multi-tenancy
SaaS services are based on a set of standard software systems to provide services to a large number of different customers. This requires SaaS services to achieve data isolation and configuration isolation between different customers, thereby ensuring privacy and data security between customers, and meeting the individual needs of different customers for services. (3)
Service characteristics
SaaS uses the Internet as a carrier to provide software to customers in the form of services, so it must rely on the signing of service contracts and the collection of service fees, which are also not available in traditional software. (4)
Scalability
The SaaS platform can optimize the persistence of resource locks and handle stateless processes. At the same time, it can use resource pools to realize data sharing and database connections, which greatly improves the concurrency of the system and improves the effective use of resources.
2.4 Detailed Definitions and Formulas of Key Indicators of SaaS Darwin said: “If you can’t measure, you can’t improve.” Companies that use SaaS services use a completely different traditional business model, and its measurement indicators are also different from those in the traditional business model. The customer life cycle is a very important indicator. How to calculate the customer life time value (Life Time Value, referred to as LTV)? If all customers have roughly the same ARPA (Average monthly Recurring revenue Per Account), during the customer’s entire life cycle, there is no additional purchase revenue, we can use the following formula: LTV = ARPA ∗ Customer Life Cycle
(1)
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It can also be expressed as: ARPA Customer Chum Rate
LTV =
(2)
When there are large differences in ARPA between customer groups, a more accurate expression formula is needed: LTV =
ARPA Revenue Chum Rate
(3)
In order to understand LTV more accurately, gross profit margin needs to be taken into consideration. which is: LTV =
ARPA ∗ Gross Margin Revenue Chum Rate
(4)
2.5 Legal Issues Brought by Software as a Service In recent years, the domestic government has vigorously built cloud computingrelated platforms, and SaaS platforms have gradually been used in all walks of life. But it is undeniable that the SaaS platform is still in a stage of rapid development, and the relevant legal protection mechanisms are still incomplete. This is mainly reflected in two aspects. On the one hand, the data of the SaaS platform is stored in the cloud, and the processing process and the specific location of the storage are unknown. The lack of legal protection of the data leads to a low degree of trust in the supplier by users; on the other hand, the relevant legal system is imperfect, and lawbreakers take advantage of it. SaaS suppliers have to bear the risks and responsibilities of user data loss and leakage, and their production enthusiasm and innovation enthusiasm are greatly reduced [10, 11].
2.6 Breakthrough Direction of Cloud Computing SaaS Platform Development (1)
Establish a legal mechanism for data security protection Most users are willing to choose cloud computing products that can provide them with a high level of security, especially users who are greatly related to information security. Under the background that all countries are actively developing the cloud computing industry, our country should step up the formulation of the “Data Security Law” to promote the construction of a highly secure cloud computing industry development environment, so as to promote
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the development of my country’s cloud computing industry. For SaaS platform providers, the Data Security Law specifies their security responsibilities, including protecting consumers’ right to know, how to process consumer information, and how to establish an internal security protection mechanism for data contacts and data processing specifications. The data security law must also determine the responsibilities of the industry’s competent authorities, and promulgate industry norms and standards based on the current industry form and development, so as to promote the macro-control of the cloud computing industry. In addition, the data security law should also specify the regulatory content of the regulatory agency. For example, the regulatory agency should conduct comprehensive supervision of the cloud computing market in accordance with the relevant regulations and policies issued by the competent agency, and formulate corresponding penalties for violators. Patent protection of business model The business model is also the result of the intellectual labor of innovators, but it is also easy to be copied, which has caused a great obstacle to the development of cloud computing. Although the business model is different from traditional patents, it also has the three characteristics of novelty, creativity, and practicality that are in line with the granting of patents. In reality, the business model applies the business method to e-commerce through the data processing system, which is in line with novelty and creativity. This method has a huge promotion effect on the management and development of business, which proves its practicality and fully meets the patent requirements. The criteria for application consideration can become a new type of invention patent. Improve government supervision mechanism
At present, the domestic supervision of cloud computing still remains on the expost supervision. However, for many low consumers, it is difficult to supervise service providers. If there is a problem, the government will intervene in the investigation and impose penalties. Consumer rights Has long been damaged. If we can combine the control during the incident with the punishment after the incident, it will greatly promote the improvement of the punishment supervision mechanism.
3 Experiment 3.1 Experimental Content We found that in reality, many people still don’t know enough about SaaS, or even know nothing, so they don’t know much about the laws that SaaS software touches. In order to understand the application status of SaaS services in real life and improve
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people’s understanding of SaaS services, we used the research method of data collection, collected and integrated the SaaS software published on the Internet, and classified the collected software Statistics are mainly classified by business function and by industry. According to the statistical results, the data is displayed in the form of graphs, and then the graphs are analyzed and summarized.
3.2 Experimental Process At the beginning, we first collected relevant information about SaaS through major websites, then collected statistics on SaaS software that had been published on the Internet, and finally aggregated and classified the collected information and released the results to social networking platforms.
4 Discussion 4.1 SaaS Applications Are Classified by Business Table 1 is the statistics of SaaS applications by business classification. From Table 1 and Fig. 1, we can see that in vertical business categories such as retail, property, supply chain and other fields, SaaS is the most widely used, and its number of subcategories is 34, and the number of specific applications is 279 accounting for 30% of the total. The trading platform uses the least SaaS applications, with only two.
4.2 SaaS Applications Classified by Industry From Table 2 and Fig. 2, we can see that SaaS services are most widely used in the Internet industry and business. The Internet industry and business services should pay more attention to the legal issues involved in SaaS services. The SaaS field is supervised, and the application of SaaS will be more extensive.
5 Conclusion Regarding the legal issues involved in the cloud computing SaaS model, the country and the government should establish relevant laws and form industry norms to create a safe and reliable network environment for the development of cloud computing SaaS, and improve the enthusiasm of suppliers and customers’ trust in suppliers. The
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Table 1 SaaS applications are classified by business
Serial number
Business categories
Number of sub-categories
Number of applications
1
Vertical business
34
279
2
Marketing
9
78
3
Customer management
7
109
4
Human administration
11
85
5
Work together
6
85
6
Finance and Taxation
7
54
7
Project management
4
46
8
Program development
15
128
9
Life tools
13
31
10
Business management
1
11
11
Store management
1
9
12
Trading platform
1
2
13
Other
1
26
11, 1% 9, 1% 2, 0%26, 3% 31, 3%
128, 14%
279, 30%
46, 5% 54, 6% 85, 9%
78, 8%
109, 85, 9% 11%
Vertical business
marketing
Customer management
Human administration
work together
Finance and Taxation
project management
Program development
life tools
business management
store management
trading platform
other
Fig. 1 SaaS applications are classified by business
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Number of ApplicaƟon
Table 2 SaaS applications are classified by industry
140 120 100 80 60 40 20 0
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Serial number
Category
Number of applications
1
Pan Internet
98
2
Finance related
10
3
Real estate construction 46
4
Business service
125
5
Retail trade
56
6
Culture, education, sports
26
7
Pan-service
72
8
Entertainment media
28
9
manufacturing
23
10
Transportation logistics 13
11
Government research
3
12
Agriculture related
7
13
Pet industry
4
14
Other
2
125 98 46 10
72
56
28
26
23
13
3
7
4
2
Category Number of applicaƟons
Fig. 2 SaaS applications are classified by industry
cloud computing industry should also formulate corresponding industry guidelines to improve the ethical level of the cloud computing SaaS industry and form a good industry atmosphere. At the same time, it is necessary to strengthen the popularization of cloud computing SaaS model and related legal knowledge, and improve customers’ awareness of cloud computing SaaS model, so as to better protect their legitimate rights and interests.
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References 1. Veiga E, Calnan N (2018) Assuring data integrity and data privacy compliance when using Software-as-a-Service (SaaS) in the life science sector. J Validat Technol 24(6):1–17 2. Vivekanand A, Randhawa TS (2018) A case study: security as a service (SAAS) in cloud computing environment. Int J Sci Res Sci Technol 2018:105–111 3. Cannas F (2016) The VAT treatment of cloud computing: legal issues and practical difficulties. World J Vat/gst Law 5(2):92–110 4. Marcello C, Stefano R, Christian E et al (2018) Cloud reliability: possible sources of security and legal issues? IEEE Cloud Comput 5(3):31–38 5. Xu C, Lei J, Li W et al (2016) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Network 24(5):2795–2808 6. Hameed A, Khoshkbarforoushha A, Ranjan R et al (2016) A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7):751– 774 7. Saas P, Daguindau E, Perruche S (2016) Concise review: apoptotic cell-based therapiesrationale, preclinical results and future clinical developments. Stem Cells 34(6):1464–1473 8. Wang Y, Li C, Cui W et al (2016) Construction of PaaS platform based on Docker. Comput Syst Appl 25(03):72–77 9. Madni S, Abd Latiff MS, Coulibaly Y et al (2016) Resource scheduling for infrastructure as a service (IaaS) in cloud computing: challenges and opportunities. J Netw Comput Appl 68:173–200 10. Abbas H, Maennel O, Assar S (2017) Security and privacy issues in cloud computing. Ann Telecommun 72(5–6):233–235 11. Xia Z, Wang X, Zhang L et al (2017) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Secur 11(11):2594–2608
Work on Optimal Configuration of Internal Network of District Energy System Based on Ant Colony Algorithm Hao Li, Chang Liu, Wen Li, and Bo Miao
Abstract This article establishes the two-level planning model for the internal electric and heating network planning of the system: the upper level takes the lowest annual cost as the optimization goal, and the lower level takes the lowest annual operating cost as the goal. The results show that the actual operation of the system is considered in the internal path optimization planning of the district energy system, which can significantly reduce the operating cost of the system. Keywords District energy system · Network optimization · Multi-system coordination · Two-level planning
1 Introduction The application of the integrated energy system is an important link in China’s development of the energy Internet, returning non-renewable natural resources to the role of basic production capacity, and realizing the clean replacement of multiple energy sources. Through the research on the multi-energy efficiency perception and datadriven optimization and control methods based on the customer-side park Achieving energy conservation and efficiency enhancement of the whole society is conducive to promoting the construction of the energy Internet, and is of great significance to the further promotion and application of the integrated energy system. Many experts and scholars at home and abroad have conducted research on the optimal configuration of regional energy systems. Literature [1] discussed the optimization problem of ice storage air conditioning system from the two aspects of the total cost and efficiency of ice storage. The dynamic programming algorithm was used to optimize the energy efficiency model of the refrigeration device and auxiliary equipment and the heat transfer model of the cold storage device. Optimal cooling capacity and cold storage capacity. Literature [2–4] comprehensively analyzes the operation and coordinated scheduling problems H. Li (B) · C. Liu · W. Li · B. Miao China Electric Power Research Institute Co., Ltd, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_111
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of heat/electricity/gas systems. Literature [5] described the various links in the multienergy complementary system by modeling the energy conversion, distribution, and storage. Based on the characteristics of energy flow, the all-energy flow model and the regional multi-energy complementary system model are constructed. The current domestic and foreign research on the synergy optimization model of the integrated energy system has been relatively complete, but the research mainly uses the cloud platform to centralize all model algorithms and the terminal to execute the cloud platform commands based on the design of comprehensive energy efficiency improvement strategies. This paper takes the line transmission power, node voltage and temperature, and the radial shape of the electric heating network as constraints, and establishes a two-layer planning model for the internal electric heating network planning of the regional energy system: the upper layer takes the lowest annual cost as the optimization goal, taking into account the line investment and fuel. The decision variable is the path of the electricity and heat network; the lower level aims at the lowest annual operating cost, and the optimization variable is the output of each device on a typical day; for the built model, the ant based on the spanning tree strategy is adopted. The group algorithm solves the upper-level plan, and the improved ant colony algorithm is used to solve the lower-level plan.
2 Objective Function This paper takes the line transmission power, node voltage and temperature, and the radial shape of the electric heating network as constraints, and establishes a two-layer planning model for the internal electric heating network planning of the regional energy system. The group algorithm solves the upper-level plan, and the improved ant colony algorithm [6–10] is used to solve the lower-level plan.
2.1 Upper Target The goals of establishing an optimal configuration model for the internal network of the district energy system are ensure that all nodes formed by each power source and electric load are connected, and all nodes formed by each heat source and heat load are connected, so that the annual planning cost is minimized, including line investment f cap , Fuel costs f f uel , Environmental costs f env and grid interaction costs f grid , the specific objective function is min
Fup = f cap + f f uel + f env + f grid
Line investment fee f cap it can be calculated by the following formula:
(1)
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f cap =
d(u, v) f [ p(u, v)] +
(u,v)∈Te
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d(m, n)g[h(m, n)]
(2)
(m,n)∈Th
where: d(u, v) represents the length of the power line connecting node u to node v; f [ p(u, v)] represents the unit length investment cost of the power line connected to node u and v, and the line transmission power p(u, v) related; Te a collection of connection lines to connect all power nodes;d(m, n) represents the length of the thermal pipeline connecting node m to node n; g[ p(u, v)] represents the investment cost per unit length of the thermal pipeline connected to node m and n, and the pipeline transmission capacity h(m, n) related; Th a collection of connecting pipes that connect all thermal nodes. Fuel costs, environmental costs, and interaction costs with the grid can be calculated from the lower level planning.
2.2 Lower Target The lower-level planning aims to minimize the annual operating cost of the system, including fuel costs f f uel , The cost of interaction with the grid f grid And environmental costs f env , details as follows: min Fdown = f f uel + f grid + f env (a)
Fuel cost f f uel = ςF
(b)
(3)
1 f t,s σ t,s
(4)
where: f t,s is the natural gas consumption of the sth device in the district energy system at time t; ςF , σ . It is the price of natural gas and the low heating value produced by its combustion. Interaction costs with the grid f grid =
buy
buy
( pt ωt
− ptsel ωtsel )
(5)
t buy
pt , ptsel respectively represent the electricity purchased and sold by the buy district energy system at time t; ωt , ωtsel Indicates the time-of-use electricity price for buying electricity and the time-of-use electricity price for selling electricity.
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Environmental cost f env = ω
Ctax
Ef
t,s
f t,s
E grid buy + pt ηgrid t
(6)
loss means t moment energy i the amount of loss during transmission. where: Pt,i
(1) 1.
Constraints Power constraint of branch road pipeline
pt,k ≤ pkmax h t, j ≤ h max j
(7)
where: pi,k is the active power value of branch k at time t,Pkmax is the maximum allowable active power of branch k; h i, j is the thermal power transmitted by is the rated transmission capacity of pipe j. pipe j at time t, p max j 2.
Node voltage and temperature constraints
Uimin ≤ Ut,i ≤ Uimax Tmin ≤ Tt, j ≤ Tmax
(8)
buy
where: Pmax,i represents the i the purchase limit for this energy. 3.
Restrictions on charging and discharging of the storage tank of the battery pool
Excessive air pollutant emissions will cause many adverse effects on the regional environment, so it must be restricted. ⎧ e e ⎪ ⎨ S OCmin ≤ S OC(t) ≤ S OCmax pCh_ min ≤ pCh (t) ≤ pCh_ max ⎪ ⎩ p DCh_ min ≤ p DCh (t) ≤ p DCh_ max ⎧ (9) h h ⎪ ⎨ S OCmin ≤ S OC(t) ≤ S OCmax h Ch_ min ≤ h Ch (t) ≤ h Ch_ max ⎪ ⎩ h DCh_ min ≤ h DCh (t) ≤ h DCh_ max e e e where: S OCmin is the state of charge of the battery at time t;S OCmin , S OCmax they are the upper and lower limits of the state of charge of the battery; pCh (t) is the charging power of the battery at time t; pCh_ min , pCh_ max the minimum and maximum charging power of the battery respectively; p DCh (t) is the discharge power of the battery at time t; p DCh_ min , p DCh_ max they are the minimum and maximum h is the “charged” state of the storage tank at discharge power of the battery;S OCmin h h time t; S OCmin , S OCmax they are the upper and lower limits of the “charged” state
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of the storage tank; h Ch (t) is the charging power of the water storage tank at time t; h Ch_ min , h Ch_ max the minimum and maximum charging power of the water storage tank respectively;h DCh (t) Is the heat release power of the storage tank at time t; h DCh_ min , h DCh_ max they are the minimum and maximum heat release power of the storage tank. 4.
Equipment output constraints
All types of equipment in the district energy system have maximum and minimum load rates, and the actual output must meet the load rate requirements to play the maximum role. max Cs χsmin ≤ Q out t,s ≤ C s χs
(10)
min where: χ max j,s , χ j,s respectively represent the maximum load rate and minimum load rate of the sth device in the district energy system j.
5.
Node head pressure constraint vt,i ≥ Vmin
(11)
where: vt,i is the head pressure of node i at time t; Vmin is the minimum head pressure required for node. 6.
Variable non-negative constraints
All variables in the optimal allocation model of the district energy system are nonnegative numbers.
ge ∈ G e gh ∈ G h
(12)
where: ge the network topology structure optimized for the power network path; G e it is a collection of radial topology structure of power network; gh the network topology structure optimized for the heating network path; G h it is a collection of radial topological structures of the thermal network. 7.
Line installation capacity constraints
≤ p(u, v) ≤ C max C min p p min C h ≤ h(m, n) ≤ C hmax
(13)
3 Case Analysis Scenario 1: Regardless of the operation of the system, the district energy system has only gas turbines that both generate electricity and heat, and absorption chillers
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provide cooling. After the route planning in the area is completed, the corresponding fuel, electricity, and environmental costs will be calculated; Scenario 2: Considering the operation of the system, the configuration of the system is the same as in Case 1, that is, there are fewer coupling components in the district energy system, and no electricity-heating, electricity-cooling and energy storage equipment; Scenario 3: Considering the operation of the system, in addition to gas turbines and absorption chillers, the district energy system also includes conversion equipment such as electricity-heating, electricity-cooling, and energy storage equipment such as batteries and water storage tanks (Table 1). The path planning results of Scenarios 1 and Scenarios 2 are shown in Figs. 1 and 2. The dotted line in the figure represents the thermal pipe, and the value is the inner diameter (such as “80”, which means the inner diameter of the thermal pipe is 80 mm); the solid line represents the power line, The value is the cross-sectional area (for example, “120” means that the cross-sectional area of the power line is 120 mm2 ). Comparing Scenarios 2 and Scenarios 1, the equipment configuration in the two energy systems is the same, and the investment cost of power lines and heating pipelines has increased by about 40,000 yuan, about 12%. It can be seen that the system is considered in the optimization of the route in the area. Actual operation can significantly reduce the planned annual cost of the system and improve economy. Table 1 Comparison of various expenses in three scenarios Investment
Fuel
Distribution network
Surroundings
Total
1
34
290
96
90
510
2
38
255
90
80
463
3
38
233
80
73
425
Fig. 1 Optimal configuration results without considering the optimal operation
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Fig. 2 Optimal configuration results considering optimized operation
Comparing Scenario 3 and Scenario 2, the line and pipeline investment costs of the two are the same, about 380,000. This is because when selecting the capacity of the line and pipeline, the capacity is calculated based on the annual maximum load of each load point, Without considering the timing load characteristics of the load point, and ignoring that the maximum value of each load may not appear at the same time, so the line selection results are relatively similar. In case 3, the costs of fuel, environment, and distribution network interaction are all compared with case 2. Low, which is about 7%, 10% and 8% lower, respectively, because electric-heating, electric-cooling, and energy storage equipment make energy transmission more free, and the coordinated dispatch of multi-energy flows is more convenient and flexible, which promotes more economical satisfaction of the system The user’s multiple energy needs have reduced annual costs by 380,000, or about 7%. It can be seen that the impact of the richness of coupling components on the planning economy is mainly reflected in the reduction of operation-related costs. Acknowledgements This work was financially supported by the Basic Forward-looking Project of State Grid Corporation (Research on Multiple Energy Efficiency Perception and Data-Driven Optimal Regulation Methods for industrial parks in Customer-side).
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References 1. Chen HJ, Wang DWP, Chen SL (2005) Optimization of an ice-storage air conditioning system using dynamic programming method. Appl Therm Eng 25(2–3):461–472 2. Liu X, Jenkins N, Wu J et al (2014) Combined analysis of electricity and heat networks. Energy Procedia 61:155–159 3. Xu X, Jia H, Chiang HD et al (2015) Dynamic modeling and interaction of hybrid natural gas and electricity supply system in microgrid. IEEE Trans Power Syst 30(3):1212–1221 4. Zhang X, Shahidehpour M, Alabdulwahab A et al (2015) Optimal expansion planning of energy hub with multiple energy infrastructures. IEEE Trans Smart Grid 6:1 5. Li Y, Wu M, Zhou H, Wang W, Wang D, Ge L (2015) Discussion on several issues of regional multi-energy system based on all-energy flow model. Power Syst Technol 39(08):2230–2237 6. Wu JS, Song MX, Min W et al (2020) Joint adaptive manifold and embedding learning for unsupervised feature selection pattern recognition (prepublish) 7. Wu J-S, Song M-X, Min W et al (2021) Joint adaptive manifold and embedding learning for unsupervised feature selection. Pattern Recognit 112 8. Liu Y, Cao B, Li H (2020) Improving ant colony optimization algorithm with epsilon greedy and Levy flight complex & intelligent systems, 2020 (prepublish) 9. Jha K, Saha S (2021) Incorporation of multimodal multiobjective optimization in designing a filter based feature selection technique. Appl Soft Comput J 98 10. Han Z, Wang Y, Tian D (2021) Ant colony optimization for assembly sequence planning based on parameters optimization. Front Mech Eng 2021(prepublish)
Marketing Data Refined Push Algorithm Analysis Under the Background of Artificial Intelligence Qin Xiao and Wei Li
Abstract In order to push marketing data fine, this paper will carry out related research, mainly discussing the demand for marketing data fine push, introducing the algorithm, and finally carrying out the system design and simulation analysis. The simulation results show that the push algorithm of artificial intelligence has higher precision, can accurately push marketing data and improve marketing quality. Keywords Artificial intelligence · Marketing data · Fine push
1 Introduction At present, intelligent technology has gradually penetrated into many fields, giving birth to the background of artificial intelligence. Under this background, the ecommerce field has developed rapidly, and various online marketing methods have emerged, which has improved the economic output capacity of this field to some extent. In depth, various marketing means in the current e-commerce field have a defect, that is, the lack of marketing target, which leads to the push or release of marketing information is blind, this defect is the direction of the current e-commerce field efforts, how to improve the precision of marketing push is the field need to focus on the problem. Around this problem, with the support of artificial intelligence researchers put forward the marketing data refinement push algorithm, this algorithm can accurately calculate the user demand, and classifying the user, and then push, so the precision marketing push, so in order to implement this algorithm, eliminate marketing defects, it is necessary to expand related research.
Q. Xiao (B) · W. Li Jiangxi Teachers College, Yingtan 335000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_112
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2 Refined Marketing Data Push Demand Electric dealer market network marketing means the dependence of the network, can use the network characteristics of openness, popularity, convenience to send information, so as long as the relevant marketing information, to push through the network directly, can let the number of Internet users to see information, and attracted to information, to achieve marketing purposes, this way is more efficient than traditional marketing means, The range is also more extensive, so that a large number of people to participate in the e-commerce market to become a member of the network marketing, and the huge human base makes the network marketing frequency rapidly increased, but also created countless marketing means, so the network marketing related information can be seen everywhere in the network environment. And network marketing emerge in endlessly, but the essence of a variety of means constant, practitioners from electric business perspective, only for their marketing purpose will information as soon as possible, as much as possible into the network, does not consider whether the user can accept marketing information, thus formed the “wide net, many fishing” situation, the blindness of the use of with distinct characteristics. The widespread use of this way of marketing at the same time also caused some negative effects, namely the frequent use of the network marketing means, make the marketing information with the network environment, and all of the information cannot be accepted, at least for the marketing information did not understand the desire of the user is not willing to accept this kind of information, but information pervasive, caused trouble to the user, Therefore, many users define this kind of information as “junk information” and have a certain resistance to it. This kind of sentiment plays up in a large area in the market and has become a normal state. Its influence involves the foundation of the e-commerce market, so in order to maintain the market, the network marketing frequency of modern e-commerce enterprises has been somewhat restrained [1]. Look at this, electricity enterprises because of reduced the frequency of the network marketing, so economic yields are low, this is the enterprise can not accept, but is limited by market sentiment, so the enterprises begin to realize that the previous network marketing, think to avoid negative emotions, to which the market network marketing work and be able to let go of the hands and feet, you must change the network marketing means, By accurately delivering network marketing information to people in need, the so-called “junk information” will not be generated, and negative emotions will not be generated naturally. This shows that enterprises have no worries and can freely carry out network influence. Under this idea, the e-commerce field puts forward the demand for refined push of marketing data, hoping to understand the preferences and behavioral characteristics of different users through technical means, and then compile targeted network marketing information, and deliver it accurately through the network. But the demand in the past is hard to realize, the reason is that the number of users, huge information data to be analyzed is complex, but also for both information collection and so on related work, so the demand of human can’t accurate analysis, elaborating push is not set up, and smart technology to solve this difficult problem, its powerful information processing ability to deal with huge data,
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Fig. 1 The basic framework of marketing data refinement push
to replace artificial to make analysis, It can also automatically complete information collection, which makes it feasible to push refined marketing data, so it is necessary to study the push method centering on artificial intelligence [2]. In addition, the basic framework of refined push of marketing data has been proposed in relevant studies, as shown in Fig. 1.
3 Refined Push Algorithm of Marketing Data 3.1 Algorithm Introduction In fact, there are many algorithms that can realize the refined push of marketing data, but there are not many truly appropriate algorithms around the demand of ecommerce network marketing. Collaborative filtering algorithm is one of the few algorithms that meet the demand. Collaborative filtering algorithm’s main function is to classify the user and the product information, according to the classification results indicate whether the user demand for goods, so can do fine push, the algorithm is mainly composed of UserCF, ItemCF two parts, of which the former is to collaborative filtering of user information, can be applied to slow speed of change in properties such as the number of users, be fond of, But commodity information update speed of the marketing environment, while the latter is to collaborative filtering of goods, the applicable scope UserCF exactly the opposite, because it exists,
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therefore, UserCF ItemCF respective algorithm is consistent, the only difference is that the switch according to the target information is needed in calculation, so the information difference does not affect the calculation process and the result of [3].
3.2 Algorithm Analysis Taking ITEMCF as an example, the use of collaborative filtering algorithm can be generally divided into two steps, namely, classification of commodity similarity and establishment of recommendation list. The specific contents of each step are as follows. Classification of commodity similarity Simulate the commodity environment of network marketing, preset N commodities, and set the number of buyers of the i-th commodity as N(i), and then list the number of buyers of the i-th commodity and the j commodity simultaneously N (i ∩ j), List the total number of people who purchased the I or j goods N (i j),then formula 1 can be used to calculate the similarity between the i-th and the j-th goods. N (i ∩ j) Si j = √ N (i j)
(1)
Establishment of recommendation list The similarity of commodity I and j can be obtained through the calculation of Formula 1. According to the result and combined with users’ shopping habits, users’ preferences for different commodities can be known. Goods can be arranged according to their preferences to form a recommendation list, and enterprises can carry out marketing promotion according to the list. For example, for the user u, the goods I and j, the evaluation score of u on I is defined as PUI, and the set of k goods close to the commodity j is listed A( j, k), list the score set of goods by U N (u), Then, according to the evaluation score of u on I, Formula 2 can be used to calculate to establish the recommendation list. L ui =
i∈N (u)∩A( j,k)
Si j Pui
(2)
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4 Marketing Data Refined Push System Design Collaborative filtering algorithm can be used as the logic of marketing data refinement push, but the logic operation needs the support of carrier. Therefore, in order to achieve marketing data refinement push, relevant system design needs to be carried out, and the system is not only the carrier of algorithm logic operation.
4.1 Basic Ideas Center on the demand of refined push of marketing data, refined push only needs to meet three conditions, namely information collection, information storage and information analysis. Therefore, the basic idea of system design in this paper is as follows: Firstly, the browsing and consumption records of users are collected by technical means. On the one hand, users’ preferences and consumption needs can be known. On the other hand, the similarity of goods can be indirectly calculated to classify goods from the perspective of users. Secondly, because the collected information is intricate and of a large magnitude, it must be stored before analysis so that the system can access and analyze it. In the face of a large amount of information, the capacity of information storage space must meet the requirements. Therefore, this paper will focus on the capacity when selecting storage means. Third, information analysis mainly relies on collaborative filtering algorithm, so in order to support the operation of the algorithm, this paper establishes a computing framework. The three steps enable the system to meet the three conditions of marketing data refinement push, so the system design scheme is feasible.
4.2 Detailed System Design Around the basic idea, the following will analyze the three steps of system design, as follows. Network tool application In fact, in the network marketing information collection difficulty is small, the reason: unless deliberately, or any Internet activity will leave a mark in the network, such as a user in a web page click on a product, even if this behavior is not intentional of, also can form a “hits”, traces the type information collection difficult many. It is worth noting that, although the difficulty of trace information collection is low, relevant technical means are also needed to achieve the purpose. Therefore, the system mainly chooses the network information collector as the collection tool, which has fast collection efficiency and can be controlled by artificial intelligence to achieve accurate collection. The principle is: As a terminal, artificial intelligence will pre-set
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the information collection items and list the keywords and other characteristics of each item, so that network tools can collect relevant information for the project, and the information that does not belong to any project will be excluded. Data storage system Network marketing data storage in addition to the capacity to meet the data magnitude, but also with high fault tolerance, low cost, high throughput and other advantages, and the current data storage system to meet these requirements for the distributed storage system, such as HDFS distributed repository. HDFS distributed repository can achieve large file storage, that is, large files can be cut into several small databases, and the relevant databases are listed as a group, and then stored in blocks, and HDFS distributed repository will also carry out three backups of each database, so the fault tolerance rate is very high. Once the data error is found, the original error or missing data will be replaced with a backup in time, the data repair will be completed, and a new backup will be generated. At the same time, HDFS distributed repository model consistency is good, can fully achieve read and write separation, so the system only need to write data once, can be read many times, indicating a high data throughput. In addition, the environment of HDFS distributed repositories is less demanding, so the deployment cost is low. As for the technical architecture of HDFS distributed repository, this paper adopts the master–slave relational architecture. The hardware devices in the architecture are shown in Table 1, and the architecture is shown in Fig. 2. Calculation framework Combined with the operation needs of collaborative filtering algorithm, this paper mainly chooses MapReduce as the computing framework in the system design. This framework can calculate the process of marketing information push. It is a typical Table 1 HDFS distributed repository architecture hardware devices
Fig. 2 HDFS distributed repository architecture
Equipment
The selection
The server
Master
The server group
Slave
Marketing Data Refined Push Algorithm Analysis … Table 2 Simulation analysis results
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Methods
Push the number
Compatibility (%)
Collaborative filtering algorithm
8000
89
The traditional method 8000
61
distributed computing framework with the following principles: Map and Reduce are regarded as two modules, distributed to multiple computers, and the final result can be obtained through parallel computing of devices. The reason why MapReduce is chosen as the computing framework is that, on the one hand, this framework has a high fit with collaborative filtering algorithm; on the other hand, this framework has excellent scalability, fault tolerance and scheduling capability.
4.3 Simulation Analysis Respectively by using the above method and traditional method 8000 marketing push, push the target for a computer equipment, the equipment in advance to browse and purchase a large amount of goods, with the data source, weights, the type of equipment needs and demand, so the contrast of the two methods to push the result, known each method and the matching degree between push the target demand, The higher the matching degree, the better the method. As can be seen from Table 2, the collaborative filtering algorithm works smoothly under the support of the system and can carry out marketing push. The matching degree between push information and demand is 89%, which is higher than the traditional method’s 61%. Therefore, the method in this paper is more excellent. However, the main problem of the traditional method is that it cannot distinguish the demand weight of the push target, but can only judge the possible demands of the target, which leads to poor matching degree. This defect does not exist in the collaborative filtering algorithm.
5 Conclusion To sum up, because the traditional marketing push mode has defects, and has caused a great impact, and even threatened the foundation of the e-commerce market, so the push mode needs to be updated, and the direction of the update is refinement. Look at this, electricity enterprises should focus on selection algorithm logic, using the algorithm implementation of fine push, at the same time in order to support the algorithm logic operations, to build corresponding system platform, the platform should meet the needs of the algorithm logic operation and data processing, storage and other aspects of demand, this platform as the carrier, operation security algorithm,
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can be done through the algorithm of fine push, Push information is likely to fit users’ needs and distinguish the weight of users’ needs, which solves the problems caused by the traditional marketing push method and improves the economic capacity of the e-commerce market.
References 1. Ji Q (2020) Analysis on E-commerce course reform under the background of artificial intelligence. J Phys Conf Ser 1533:032088 (2020) 2. Lorenzo E, Piscopo G, Sibillo M et al (2021) Reverse mortgages through artificial intelligence: new opportunities for the actuaries. Decis Econ Finan 44(1):23–35 3. Terry J, Lau ML, Sun J et al (2021) Analysis of extended X-ray absorption fine structure (EXAFS) data using artificial intelligence techniques. Appl Surf Sci 547:149059
Research on Enterprise Precision Marketing Based on Data Mining Technology Yi Hu
Abstract In today’s society, the development of network information technology make information, big data era provide powerful data support for the enterprise marketing at the same time, also let the pressure of competition between enterprises is becoming more and more big, can from the vast data to obtain valuable information for the enterprise development, enterprises will be able to gain a foothold in the increasingly fierce market competition, On the contrary, enterprises will gradually be eliminated by the market. Therefore, for modern enterprises, it is particularly important to have a better insight into consumer behavior, obtain more useful information and help enterprises achieve precision marketing in the era of big data and the ever-changing marketing environment. Based on this background, combined with data mining technology, this paper focuses on the analysis of enterprise precision marketing strategy. Keywords Ddata mining · Precision marketing · Big data In today’s society, with the continuous development of network technology, data information, big data has become the main trends of development of this era, the era of big data to bring to the modern enterprise market marketing business development provides a great support, of course, the enterprise the traditional marketing model has been unable to meet modern the huge needs of customers, Facing the development opportunity of the big data and impact, enterprise based on the perspective of benefit maximization and marketing prospects must actively changing the concept of development, make good use of the advantages of the era of large data, based on my age development, make good use of the technical characteristics of the era of large data, help enterprise at a deeper level to carry out the market data analysis, from the data mining is more useful in the analysis customer demand, Continuously improve the quality of enterprise products or services, so that enterprises in the increasingly fierce market competition to obtain competitive advantages [1, 2]. Y. Hu (B) School of Business Management, Jiangxi University of Engineering, Xinyu 338000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_113
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1 The Impact of the Era of Big Data on Enterprise Precision Marketing 1.1 Opportunities for Precision Marketing in the Era of Big Data With the advent of the era of big data, data and information are omnipresent. The omnipresent information and data can provide necessary conditions for enterprises to carry out precision marketing and further enrich the online and offline marketing channels of enterprises. For enterprises, they can use data mining technology to carry out in-depth mining and analysis of data information, and then improve customer satisfaction with the improvement of product or service quality, so as to open up a broader market and provide high-quality customer groups for enterprises [3].
1.2 Constantly Enrich the Marketing Channels of the Enterprise In the era of less developed data and information, the channels and methods for enterprises to carry out precision marketing are relatively traditional and fixed, such as offline physical stores, intermediate dealers, exhibition activities, e-commerce platforms, etc. Enterprises are generally divided into direct marketing channels and indirect marketing channels according to the existence of intermediate links. But with the advent of the era of big data, the enterprise in market competition will be more choice, through continuous market research to get more marketing data, thus to provide support for the enterprise market marketing strategy, such as helping enterprises to choose a more suitable offline store set position, and build more conducive to their own way of cooperation between dealers. Especially for the development of online platforms, it can help enterprises to open e-commerce online stores, instead of the traditional single e-commerce model. At the same time, with the help of data mining technology, enterprises can choose more suitable marketing cooperation mode, further expand marketing channels, and help enterprises to occupy more market share.
1.3 Further Improve Enterprise Data Mining and Analysis Capabilities Information advantages in the era of big data can help enterprises to provide consumers with more accurate marketing services, of course, such results must be based on data analysis. With the help of a professional analysis of the data
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mining technology, software, can help enterprises to a more comprehensive control of industry development trends, from the huge data in the cluster to obtain more valuable data information, and then through the analysis of business professionals, clear enterprise’s current market position, at the same time with the help of data mining technology to adjust the marketing strategy of the enterprise [4].
1.4 Further Improve Customer Satisfaction Traditional marketing mode of enterprises, to provide customers with products and services is mainly using online channel way of choose and buy, so the communication channels between the two parties are relatively limited, are basically around customer purchasing behavior, namely the consumer through the enterprise sales personnel consulting information about a product or service, after buying the product or service will have corresponding after-sales service, There was no other connection. However, in the era of big data, enterprises can timely conduct in-depth market data research with the help of data mining, and then provide personalized marketing plans for consumers, so as to more accurately meet the actual needs of market customers. Meanwhile, enterprises can also strengthen communication with customers with the help of new social networking platforms. For example, with the help of WeChat official account, WeChat group and other activities to provide customers with daily promotion, brand publicity and other information, so that customers can timely understand the marketing strategy of the enterprise, but also let the enterprise know more about the change of customer needs, and further improve customer satisfaction [5].
2 Precision Marketing Strategy Design Based on Data Mining Technology 2.1 Design Objectives and Requirements of Precision Marketing This paper for the application of data mining technology in precision marketing strategy analysis in A regional mobile company, for example (hereinafter generally referred to as A company), hope that through the data mining technology, better improve the precision of the A company marketing strategy, help at the right occasion, A company through the appropriate channels to provide suitable products or services to consumers. For example, in the 5G era, how to push the latest 5G business to consumers and make consumers accept the 5G business? On this basis, the overall planning requirement of precision marketing is to gradually expand the service audience of enterprises by virtue of the operation system of data mining technology, highlight the value of big data, gradually transform the operation system into
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Fig. 1 “Customer-centered” precision marketing operation support system
data support mode, and build a “customer-centered” precision marketing operation support system, as shown in Fig. 1. The above figure data marketing support system as the foundation, with the help of A company is the focus of the data mining technology of precision marketing from customer perception and customer demand control as the breakthrough point, combined with the company’s market transformation needs (mainly the advent of the era of 5 g the impact on the market development direction), with the help of big data analysis technology, in close connection with the development of market demand, Looking for enterprise data-driven operation breakthrough, to help enterprises research and develop new marketing mode.
2.2 Precision Marketing Strategy Based on Data Mining Overall framework of data mining system A company can be based on the product or service, from the direction of large data mining technology, from the pursuit of the direction of the precise marketing strategy as the breakthrough point, build scientific and perfect data mining technology, A company design of precision marketing strategy based on data mining is mainly according to the data aggregation, data mining, data operation, data to assess the four process implementation. Specific content of precision marketing strategy The first part: The first part is the subdivision research of enterprise customer value. Big data era for mobile Internet enterprises, especially for mobile company, such enterprises can have into the traffic operation era, in such a market environment, the market is particularly obvious difference customers personalized, the traditional
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Fig. 2 Precision marketing customer analysis under customer value segmentation
“one-size-fits-all” marketing model and market segmentation has been unable to meet the requirement of precision marketing, Therefore, it is necessary to comprehensively consider the customer group’s consumption, flow, region, social attributes and other factors and characteristics, and then conduct value segmentation of customers, further optimize the hierarchical level, and carry out refined customer management, as shown in Fig. 2. The second part: Based on the value segmentation of customers, we need to study the location trajectory of customers. For different areas of the customer, if the lack of scientific and orderly organization and management, will inevitably lead to imbalance of customer relationship management (CRM), enterprise marketing resources also can appear repetitive or market space, can appear even customer management development imbalance between regions, so you have to use data mining analysis technology, path analysis was carried out on the platform of the customer, Timely locate the area where the customer is, and carry out the process for offline customers. The third part: the enterprise customer life cycle on the depth of research. Customer life cycle research is mainly targeted at customers in different periods of management, the first is for the new network market customers, the second is for the business growth stage of customer groups; Secondly, it is the consumer group in the mature stage of the business, and finally the customer group in the decline period. Develop reasonable marketing strategies according to the characteristics of customers in different periods. Part IV: Study on customer preference The preferences of enterprise customer groups are studied, and the preferences of enterprise customer groups for mobile terminals, package services, Internet communication and other customer groups are comprehensively sorted out and studied, so as to serve as the reference conditions for recommending follow-up marketing strategies of different customers. Analyze the integrity of customers’ behavior, customers’ space–time trajectory and marketing strategies of market competitors, so as to provide
1022 Table 1 Segmentation of market customer preferences
Y. Hu Customer preferences category Customer preference type description Terminal preference
Brand preference, preference for active users
Set preferences
Single package, overlay package, package saturation
Network access mode
CMNET users, CMWAP users
The network system
3G user base, 4G user base, 5G user base
The net demand
Life needs, work needs, entertainment needs
Internet access preferences
Video, news, entertainment, etc.
Content preferences
Use of WeChat, Weibo, etc.
Social identity
Entertainment class, fantasy class, through class and so on
Internet time
Office hours, after hours, lunch break, midnight, etc.
enterprises with more accurate marketing strategies and advertising strategies, further analyze the overall competitive situation in the market competition circle, and formulate targeted marketing strategies in response to changes in the competitive environment. For the enterprise to stimulate new users, sales and storage strategies and other market strategies. The details are shown in Table 1. Precision marketing design based on data mining technology First: Aggregate the user base information database. According to the data and information sources collected by the data mining technology, A company can divide the data into three areas according to different needs, namely, BOSS side, business side and network side. The data in these different areas form the user group information database. On this basis, different information sources and internal corresponding storage layers are also different, specifically divided into available information data, data information in the storage layer and data sources. Second: Analyze and mine huge data information. Is mainly based on the enterprise customer groups consumption, product or service package business order relations, terminal information, location information and Internet behavior data in-depth digging, finally form the different types of customer labels and user, and then combined with aggregate mining depth information database for customer information of the user, Analyze the data information that can provide support for enterprise precision marketing. Specifically speaking, it mainly carries out data information mining from three aspects: firstly, data information mining based on network port; The second is data information mining based on GN/GB port. The second is information data mining and analysis based on terminal data.
Research on Enterprise Precision Marketing … Table 2 Perform user category analysis on customer information groups
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Customer group
Customer representative
Area number
Identification number
Industrial park users
A technology park
70
600,000
School users
A university
50
70,000
Village users
Some villages 10
450,000
Business users
A city square
1
130,000
Cinema user
A cinema
8
20,000
For example, social attributes of users can be identified with the help of data information mining in Port A, as shown in Table 2. Then, combined with data analysis, user preferences can be analyzed and specific types of preferences of enterprise user groups can be analyzed, such as content preferences (such as users often listen to music, often play games, etc.), or time preferences (use them at work time, after work time, or at midnight, etc.). Or habitual preference (used in business circles, at work, etc.). On this basis, with the help of data mining analysis and IEMI and IMSI identification technology, enterprises can timely grasp the mobile phone operating system and network mode and other data information used by customer groups, so as to provide basic data information reference for the development of enterprise digital content service, mobile Internet client business and other business. Convenient enterprises to further improve the precision marketing strategy.
3 Conclusion To sum up, the advent of the era of big data for the development of the modern enterprise provides powerful data information support, of course, for the long-term development of the enterprise, the enterprise must according to the characteristics of the era of big data, is good at using data mining technology to further control the market customer base information, using data mining and analysis techniques, on the basis of data driven, Strengthen the operation efficiency of enterprise data information, further improve the effectiveness of enterprise precision marketing strategy, and help enterprises stand firm in the increasingly fierce market competition.
References 1. Xie HM (2019) Research on e-commerce enterprise marketing management based on data mining. Brand Res 3(11):35–36 2. Yang Y, Qin R (2019) Research on the predictions and countermeasures of precision marketing of exhibition enterprises based on data mining—a case study of Chongqing region. Rural Family
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Staff 19(12):216+218 3. Ling Z (2019) Research on the status quo of enterprise precision marketing based on big data application. Mod Market (Bus Ed) 26(07):116 4. Ran C (2019) Challenges and solutions of enterprise precision marketing development in the era of big data. China Econ Trade Guide (in Chinese) 34(05):95–96 5. Yue PF (2019) Accurate marketing of telecommunications business based on data mining technology. China Inf Ind 25(02):87–89
Analytics and Machine Learning Applications to Smart City
Intelligent Information Construction of Enterprise Management Based on Big Data Processing Technology Yun Jiang
Abstract With the rapid development of computers and networks, the emergence of big data concepts and big data technologies have had a huge impact on the development of modern society and business. The powerful analysis and insight capabilities of big data. The analysis of massive target data can provide “insight” into the real laws hidden behind the data, so as to guide us to make faster and more accurate decisions. This article aims to study the application of big data technology in enterprise management innovation. On the basis of analyzing the types and consideration factors of management mode and the necessity of enterprise management mode innovation, a questionnaire survey is conducted on a company’s management mode. Investigation, and then put forward the innovation path of enterprise management model. The survey results show that employees do not have high requirements for attitude and discipline. They pay more attention to the ability of assessment and the quality of products. The company should refine the assessment indicators and formulate the assessment content more accurately. Keywords Big data · Enterprise management · Innovation path · Management model
1 Introduction Big data technology leads the development trend of the current era, integrates into all aspects of our lives, changes people’s lifestyles, but also affects people’s attitudes towards life [1, 2]. Under the background of the Internet revolution, whether it is the daily work, study and life of ordinary people, the operation and management of modern enterprises, and even the government’s decision-making and services have been affected by it, and the social economy has undergone earth-shaking changes [3, 4]. Y. Jiang (B) Shengyang Institute of Technology, Fushun, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_114
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With the advent of the Internet age, changes in communication and communication methods have led to subversive changes in consumption patterns and production patterns, which have comprehensively affected the world’s economic lifestyle and social development patterns [5, 6]. Competition at the speed of life and death and opportunities for a wider space are simultaneously presented to Chinese enterprises. In the information age, how to keenly discover laws, quickly change mechanisms, grasp the pulse of the times, redefine the relationship between people and enterprises, conform to new business rules, and establish a new management model has become an urgent need for every enterprise [7, 8]. This paper conducts a survey on a company’s management model by means of questionnaire surveys, and then proposes a path to the innovation of corporate management model. The survey results show that employees do not have high requirements for attitude and discipline. They pay more attention to the ability of assessment and the quality of products. The company should refine the assessment indicators and formulate the assessment content more accurately.
2 Research on the Application of Big Data Technology in Enterprise Management Innovation 2.1 Types of Management Models and Consideration Factors (1)
Type 1.
2.
(2)
People-oriented management model, taking employees as the most important resource of the organization, taking into account the interests of the organization, employees and stakeholders, rationally deploying human resources, and digging out people’s potential through various means such as incentives and training, and maximizing. To mobilize people’s enthusiasm, and finally let people agree with the organization’s goals from the heart, and realize the development of the enterprise [9, 10]. The intellectual capital management model believes that intellectual capital is the relevant knowledge that can be transformed into profit, which can be mastered and controlled by the enterprise, and to some extent, it can create greater value and capital advantages for the enterprise. It includes human resources, capital, structural capital and customer capital, among which human capital is the most critical element.
Factors to be considered The management model needs to consider many and complex elements. It is not only necessary to analyze the specific internal control, leadership style, and organizational structure of the enterprise, but also to analyze the opportunities and risks of the external environment, social values, resource conversion and
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other factors, and manage management from the perspective of management elements. Analysis of the pattern can be close to its essence and law [11, 12]. The basic elements considered by the management model are the environment, human resources, property capital, technology, and information knowledge. The main task of management is to find the decisive influence factors among many elements, analyze the profitability of each main factor, allocate resources to high-yield elements, and establish corresponding models to ensure maximum returns. New management models are all produced in crises. The changes of the times will definitely hit the traditional management models. When the importance of management elements changes, the company’s management model cannot adapt to the current external environment. The new management theory and practice promote each other, and continue to improve to cater to the new environmental development trend. Therefore, the constant change in the importance of management elements in various periods is an important reason for the continuous innovation and change of management models.
2.2 The Necessity of Enterprise Management Model Innovation (1)
The existing financial management model has drawbacks
With the progress and development of the Internet society, network technology has become more and more mature. With the rise and prevalence of e-commerce in China, the future development direction and goals of financial management are constantly changing. The emergence of three payment transaction modes, Alipay and WeChat. As a result, many transactions and payments of Chinese enterprises have begun to completely rely on the Internet. Internet-based transactions and payment methods have greatly improved the comprehensive competitiveness and work efficiency of Chinese enterprises. However, because Chinese enterprises have slow response to the needs of traditional financial management models other shortcomings, the liquidity of funds is more suitable for other traditional models in our country, and the rise of this networked corporate financial management model has delayed the feedback time of traditional financial management, and its work efficiency will also be improved. And in many cases, the traditional corporate financial management model is often only focusing on some basic technology and basic work, but in the context of mobile Internet, companies urgently need to master complete financial data, in the era of mobile Internet for corporate finance data analysis can help companies more accurately analyze the operation of funds and their tax planning, and can also make better and accurate predictions about the direction of investment. Therefore, the backwardness of the financial management model under the background of the Internet has become a major drawback that hinders the development of the company.
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Adapt to the needs of the market and consumer demand changes
In the context of the Internet age, the widespread use of the Internet has promoted the growth and acceleration of the speed of information dissemination, and the exchange relationship between product and service providers and consumers has also undergone drastic changes, which has greatly weakened the market’s problem of information asymmetry, and compared to the same product and service, consumers have a larger choice in the market, and they gradually control their own initiative, and the influence of time and space on consumer behavior is also increasing. Therefore, in the development of enterprises, they are faced with the expansion of the scale of consumer groups and the multiplicity of choices, which has increased the fierce competition between the two enterprises. Third, if companies want to be able to cater to the ever-changing market environment, seize opportunities, and win over competitors in the future, they will inevitably need us to better use and learn Internet marketing techniques, and adopt traditional online or offline marketing that effectively combines them. Model, accurately grasp the current consumer’s psychology and needs, and continuously innovate its marketing model. (3)
Lack of innovative management concepts
For most of the current enterprises, they have not yet established a business management concept with mobile and online link thinking. Although the enthusiasm of enterprises in developing Internet information technology activities is constantly increasing, they have not perfectly integrated the application of Internet technology with traditional business management models, but simply applied mobile Internet technology to the operations of other traditional enterprises. Therefore, it also greatly limits its full use of its functions in the mobile Internet; again, in our traditional corporate management thinking and business model, the self-centered personality identity awareness in production and business activities relatively strong, and under the background of the mobile Internet, a requirement for the highest customer needs is proposed, and customer needs must be regarded as the core of company management. Therefore, if an enterprise lacks innovative management ideas, then their development will also face many bottlenecks.
2.3 Innovation Path of Enterprise Management Model (1)
Innovative marketing model, consumer-oriented
The achievement and realization of a company’s profits are inseparable from its demand for consumers. In the context of the mobile Internet, the needs of consumers are becoming more complex and diversified, and the ways and ways to meet their needs and needs are becoming more and more diversified. If companies want to maximize profits, we must always understand and analyze the situation and needs of each consumer, with the core of understanding the consumer’s intentions and
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needs, through the mobile Internet, the data resources of consumers have been excavated, and the in-depth research and analysis of data analysis technology should be strengthened. In the application, the information resources of each consumer are also carefully sorted and classified, and different marketing strategies and marketing strategies are formulated to make their products and services more pertinent. The continuous adjustment, transformation and upgrading of marketing strategies can better improve and enhance the company’s satisfaction with consumers. While retaining new and old customers, it also taps out potential customers and population groups to ensure the company’s social and economic development. Reached the maximum; in addition, the extensive use of the Internet and other new-generation technologies allows our companies to seize the market more and develop more customers, enhance our company’s international market competitiveness, and benefit our company Long-term development. (2)
Innovating enterprise human resource management model
At present, many of the recruitment of Chinese companies have been carried out through the Internet, but our companies are often only because HR releases the relevant information they recruit, and has no further knowledge and understanding of the talents in the department, so all departments of the enterprise will compile the relevant talent specifications and standards they need, and then pass them to the hands of HR, which promotes the recruitment of relevant information to become more professional. (3)
Establish an effective performance evaluation system
The performance evaluation system is an extremely important employee work inspection strategy for the company. It mainly uses the comparison between the manager’s expected work goals for the employees and the actual work status of the employees, and then obtains the actual work efficiency of each department, including the manager’s set the ratio of performance to completion of the staff, as well as the difficulties encountered in the work. Efficient performance on the one hand can measure staff salary and treatment, on the other hand it will improve staff performance. The managers quickly check the evaluation results and inform the staff. When problems arise, the two parties will communicate to make up for the problems in the work. The managers can reasonably set the next stage of work goals in order to use the staff to complete the efficiency. The two parties are communicating at the same time. The results of performance can also be used as the basis for evaluation of staff bonuses. Managers can use the results to develop employee issues, and they can set up supervision departments to maximize the quality of their work. At the same time, the results obtained should be distributed to the corresponding department, and the corresponding department can adjust the work target for this. (4)
Improve the corporate salary system
Performance should be positively correlated with staff salaries. The higher the staff’s performance, the higher the salary they will receive. If their performance is too low, their income will decrease at the same time. This method improves the
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enthusiasm of the staff and makes continuous efforts to obtain higher salaries. In fact, this phenomenon also happens to be the ultimate goal of the positive theory of compensation. The main goal of creating a reasonable performance system is to quickly improve the performance of the staff. While the staff performance is improved, their income also increases, and the company’s income also increases at the same time. Can be said to be a win–win situation for managers and staff. Performance evaluation is actually a tool for assessing the work quality of staff, in which all the work conditions of staff are reflected. For this reason, the system urgently needs to be fair and just. Because the performance level will affect the staff’s income, the company will need to be fair and just, and the performance evaluation results need to be fed back to the staff in a timely manner, so that the staff can not only understand their own working conditions for a period of time, but also understand the work status of others and the fairness of the system.
3 Experiment 3.1 Questionnaire Survey Design This paper selects a certain company as the research object and develops 150 questionnaires. The human resources department agrees to review the questionnaires during working days and distributes them to each department. The person in charge of each department arranges a uniform time to distribute to department employees one by one, and conduct a centralized questionnaire survey. Collect uniformly and record the recovery rate. Among the 150 questionnaires sent out, 128 valid questionnaires were retrieved, with a recovery rate of 85.3%.
3.2 Reliability Test of the Questionnaire The so-called half-reliability is to divide the questionnaire into two halves, and then calculate their reliability coefficients separately. When the reliability coefficients of the two halves are the same, the Spearman-Brown formula is often used to obtain the reliability coefficient of the entire questionnaire. r S B = 2r S H /(1 + r S H )
(1)
When the coefficients of the two halves are not the same, the Lulun formula should be used for calculation. rRulon = 1 −
S2a−b S2t
(2)
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4 Discussion 4.1 Satisfaction with Salary and Benefits It can be seen from Table 1 and Fig. 1 that the company’s employees’ overall satisfaction with remuneration has only reached a general level, and the company should pay more attention to remuneration and benefits. On the whole, this is in line with the transformation of our country’s economy and society. The society is in a period of great development. With the changes in the social environment, people’s perceptions of employees and their treatment are also changing. In the current economic society, people’s lives with the gradual improvement, the evaluation standards of various professions in the society have also changed. Especially since the reform and opening up, with the tremendous development and progress of society, people’s evaluation of corporate employees has begun to become more secular and utilitarian. At the forefront of reform and opening up, all the thoughts of looking at money still exist in the hearts of some people. As a result, in the survey, we can see that more than half of the people think that their level is at the general level of society, and they just maintain a living. “Improved treatment” ranks first, all of which also show that the most prominent problem of current human resource management improvement Table 1 Survey of employee satisfaction with salary and benefits
Salary (%) 15.1
5.1
satisfaction
36.7
6.3
general
38.5
83.7
9.7
4.9
Percentage
Dissatisfied
90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00%
Welfare allowance (%)
Very satisfied
Very satisfied
satisfaction Salary
general Level Welfare allowance
Fig. 1 Survey of employee satisfaction with salary and benefits
Dissatisfied
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is to change one’s own income. These are also the most prominent issues with the strongest response from current employees.
4.2 Performance Appraisal Situation In the current society, the problem of difficult employment is very prominent. With the emergence of the popularization of enterprises, the scope of employment for graduate students in the current society is gradually deepening, and even most corporate recruitment announcements directly mention the recruitment of graduate students. Under such circumstances, in order to improve the level and quality of employees, some companies have to adopt the method of eliminating existing employees to introduce new work forces. Therefore, when evaluating existing employees, companies will issue very strict evaluations. Under the guidance of this policy and evaluation standards, some employees began to show their pressure on work and the mental burden of losing their career in order to be able to work in the enterprise for a long time. Figure 2 shows that employees do not have high requirements for attitude and discipline. They pay more attention to the ability of assessment and the quality of products. The company should refine the assessment indicators and formulate the assessment content more accurately. 45.00% 40.00% 35.00%
Percentage
30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00%
product quality
Work attitude
The proportion
work ability
discipline
Content Percentage of employees' opinion
Fig. 2 Investigation on the proportion of enterprise performance appraisal content
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5 Conclusions The advent of the Internet era has brought unprecedented challenges to enterprises, and it has also brought a once-in-a-lifetime opportunity. Information technology has brought new ideas, new methods and new conditions to management model innovation. Those who miss the opportunity and stand still will be eliminated: seize the opportunity and those who are determined to break through will win the first opportunity. As the economic and social environment has changed, management will also change. With a deep understanding of the characteristics of the new media environment, jumping out of the limitations of existing management thinking models, and broadening your horizons, you will discover and create a new management model that conforms to the new era.
References 1. Soliman A, Adam M (2017) Enterprise risk management and firm performance: An integrated model for the banking sector. Banks Bank Syst 12(2):116–123 2. Mamonov K, Grytskov E, Prunenko D et al (2020) Staikholder-oriented enterprise management model enterprises. Finan Credit Act Probl Theory Pract 4(35):396–410 3. Wang W, Srivastava G (2020) Enterprise human resource quality management model based on grey relational analysis. Int J Perform Eng 16(3):419 4. Ivanov AO (2020) Labour productivity management model taking into account organizational culture of enterprise. Upravlenie 8(3):33–41 5. Cheng Z, Ye Y, Huang W et al (2021) Research on power enterprise data model online management decision system based on big data. J Phys Conf Ser 1802(4):042096 (7pp) 6. Gellweiler C (2020) Connecting enterprise architecture and project portfolio management: a review and a model for IT project alignment. Int J Inf Technol Manage 11(1):1–16 7. Dovgiy SO, Kopiika OV (2021) Changing the business model of IT management at environmental enterprise in connection with the development of service-oriented information technologies. Environ Saf Nat Resour 37(1):5–19 8. Skrynkovskyy RM, Sopilnyk LI, Tsyuh SI (2020) Improving the enterprise development model: new solutions based on the principles of management, marketing and economic diagnosis. Bus Inform 4(507):191–199 9. Morawski M, Piepiora Z, Rogala P (2017) Model of enterprise management in the creative industries on the basis of empirical research. Rev Bus Res 17(1):61–66 10. Angelova J (2019) Market model for management of profit in industrial enterprise. Trakia J Sci 17(Suppl.1):606–610 11. Nikitina AV, Novikova TV, Khrystoforova OM (2019) Structural and functional model of enterprise economic safety management system in the global financial space. Financ Credit Activ Probl Theory Pract 3(30):136–146 12. Chen C, Tang X, Li Y (2019) The application research of application decision model based on internet of things in enterprise supply chain management. J Intell Fuzzy Syst 37(5):1–9
Recognition Method of River Sewage Outlets in UAV Aerial Images Based on Deep Learning Shuang Wu, Qifan Yang, Jiasheng Ye, Xiaocong Wang, Shuyu Huang, Tao Gu, Shiting Cai, Peijia Yan, and Kunrong Zhao
Abstract The traditional way of river sewage outlets management highly relies on artificial inspection, which is time-consuming and labor-consuming, with limited covered areas. The drawbacks still exist when UAV inspection is now using for sewage outlets management, due to the lack of ability that automatically recognizes the river sewage outlets on UAV aerial images. To solve this problem, this research studies the sewage outlet recognition method based on deep learning. After the image preparation work such as image preprocessing, registration, and mosaic, a YOLOv3 model is selected among various common models, and adjusted to improve model performance. The research help management departments improve their working efficiency on river sewage outlets management, and also make a solid foundation for a long-term mechanism for the management. Keywords Deep learning · Convolutional neural networks · Target recognition · Sewage outlets management
1 Introduction The management of river sewage outlets becomes one of the most important tasks in water pollution control, since they are the final outlets that water pollutants enter the river. Therefore, proper management of sewage outlets is necessary for improving water quality in the river basin. The traditional supervision and management of sewage outlets mainly rely on artificial inspection. However, not only the scope of the investigation is limited, but also the job is time-consuming and labor-consuming. Also, it is difficult to obtain the comprehensive and true situation of sewage outfalls in time. Therefore, new technologies and methods must be used to improve the S. Wu · J. Ye · S. Huang · T. Gu · S. Cai Guangzhou Huake Environmental Protection Engineering CO. LTD, Guangzhou, Guangdong, China Q. Yang · X. Wang · P. Yan (B) · K. Zhao Ministry of Ecology and Environment, South China Institute of Environmental Sciences, Guangzhou, Guangdong, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_115
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efficiency of the management work. With the widespread application of UAV in coastal monitoring [1, 2], land use survey [3, 4], and power equipment inspections [5], environmental management departments began to use drone aerial surveys to carry out inspections of river sewage outlets [6, 7] and take pictures. Technicians visually interpret the status of the sewage outlets based on the pictures, but it is still failed to solve the problem of working efficiency. The computer automatic identification technology of the sewage outfall into the river is still in the research stage and has not been widely promoted. Object detection is an important subject in the field of computer vision. The main task is to locate the target of interest from the image. It is necessary to accurately determine the specific category of each target and identify the bounding of each target. In recent years, object detection has been widely used in fields such as intelligent video surveillance, automatic driving of vehicles, and robot environment perception. Deep learning is a branch of the field of machine learning research. Deep neural networks can be used to automatically learn high-level features from a large amount of data. Compared with other machine learning methods, deep learning has richer features and stronger expressive capabilities. With the continuous development of deep learning, researchers have found that the accuracy of target detection using convolutional neural networks can be greatly improved. Target detection based on convolutional neural networks has received extensive attention and has become one of the hot spots in the field of computer vision research. This study will use the convolutional neural network to study the automatic identification technology of the sewage outlets, by fully considering the work scene of the sewage outlets inspection and help to develop a method for automatic recognition of sewage outlets.
2 Research Objects and Methods 2.1 Research Objects The research objects for this study is to develop and optimize the target recognition method of the river sewage outlets in aerial images taken during UAV inspection. The optimal model can be found by comparing the recognition results for all models, including performance indicators of recall and rate of accuracy. The research results of this study apply the intelligent image target recognition technology to the state of the sewage outlet, to realize the changes in the color of the water area near the sewage outlet, floating objects, and other aerial image data, so as to evaluate whether the sewage outlet has abnormal conditions such as direct discharge and overflow. This study will help the management department to conduct autonomous, intelligent, and refined inspections of illegal sewage outlets across the river by UAV, which effectively improve the scope and efficiency of inspections of sewage outlets.
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2.2 Research Methods In the field of deep learning, the development of target recognition has mainly focused on two directions: two-stage detectors such as the R-CNN series and one-stage detectors such as YOLO, SSD, etc. The main difference between them is that two-stage detectors needs to generate a proposal before perform fine-grained target recognition, while one-stage detectors will directly extract features from the network to predict the classification and location for targets. In this study, a one-stage detector YOLOv3 model is used to detect the target of the sewage outlets. YOLOv3 has been improved from YOLO9000, with higher detection accuracy and slightly slower speed [8].
3 Image Preparation 3.1 Image Preprocessing The purpose of image preprocessing is to improve the performance of registration and ensure the accuracy of registration. Because there is a certain amount of noise and geometric distortion in the aerial image of the drone, it is necessary to choose an appropriate preprocessing method based on the actual situation. In this research, the original images for image stitching are the digital images of aerial photography, which are captured by the digital camera mounted on the drone. During the stages of acquisition, transmission, and storage of aerial images, there may be a certain amount of noise points, geometric distortion, and gray-scale distortion. If these noises cannot be effectively eliminated, it will bring great interference to the extraction of image feature points in the later stage, and eventually result in poor image stitching quality. Image preprocessing is to remove or correct noise and distortion. One purpose of this is to ensure the quality of image stitching; the other is to improve the efficiency of stitching processing. The process of reducing, suppressing or eliminating such noises to improve image quality is called image smoothing, which can be achieved in the image space domain or in the image transformation domain. The former is to directly perform operations on the data on the original image, that is, to calculate and process the pixel gray-value. The more commonly used methods are multi-image averaging, neighborhood averaging, and median filtering, etc.; while the latter is It is processed in the frequency domain or wavelet domain, and noise is generally corresponding to higher frequency components in the frequency domain, and a low-pass filter can be used to achieve a smoothing effect.
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3.2 Image Registration Image registration is a key step in the stitching of the entire image. The goal of image registration is to find a suitable technical method to retrieve the same points of multiple images with higher overlapping areas, and to match these images according to these same points, that is, to establish these images in the form of mathematical functions. The relationship between. Objects in different viewing angles can be obtained by transforming the perspective matrix. Principle of Image Registration The basic principle of image registration is to use certain technical strategies for two pictures with the same partial area, using one of them as the original reference and the other as the target image. The target image can be regarded as a mathematical mapping of the original reference image on the grayscale. The task of image registration is to find the correspondence between two images so that the pixels on the two images are spatially consistent. Define two two-dimensional arrays with Ml(x, y) and M2(x, y) to represent the original image and the target image, the gray value of each pixel is the value of the corresponding array element, and the mapping relationship between the two Can be expressed as: 2−1 M2(x, y) = t Ml f(x, y)
(1)
Among which t() is a two-dimensional transformation function of geometric space, and its function is to transform the pixel coordinates of the target image into the coordinate system of the original reference image. The function of t is to convert the gray-value. After the operation of the above-mentioned unified coordinate system, the transformed target image can be registered with the original reference image. Find the optimal transformation function plant and f, and further optimize the registration transformation parameters to obtain the connection between the common areas of the two images. Selection of Feature Matching Algorithm Feature-based image-matching technology cannot be affected by unfavorable factors such as illumination changes, scale changes, and affine deformations that exist between pictures. But relatively speaking, the algorithm is more complicated and the stability is poor. Feature detection based on scale-space can stably perform more accurate feature detection and feature matching on multiple images. The image splicing in this study needs to realize seamless splicing of UAV aerial photography with a certain overlap rate and large geometric deformation. Since it is necessary to complete the feature point retrieval, the calculation of feature vectors and the matching of feature vectors in a relatively short period of time, this study compares the characteristics of the SIFT (Scale Invariant Feature Transform) algorithm and the SURF (Speeded Up Robust Features) algorithm, as well as certain real-time
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Fig. 1 Image matching result based on SURF algorithm
Fig. 2 Mosaic of panoramic images based on SURF algorithm
requirements, and chooses to use the SURF algorithm for image stitching (Figs. 1 and 2).
4 Target Recognition Method of Sewage Outlets By comparing the characteristics of common one-stage and two-stage detectors, a one-stage detector, the YOLOv3 model is chosen for further optimization. YOLOv3 uses a 53-layer convolutional network structure, which is known as darknet-53. The network design to only uses 3 × 3, 1 × 1 convolutional layers, which is referred from ResNet’s residual network.
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Fig. 3 The curve of loss and curve of accuracy after data enhancement
4.1 Image Data Enhancement To improve its generalization ability, image data enhancement technology is used before model training. It is mainly to add small disturbances or changes to the training data. On the one hand, it can increase the training data to improve the generalization ability of the model. On the other hand, it can increase the noise data to enhance the robustness of the model. The commonly used data enhancement methods include flipping, random cropping, color jittering, shifting, scaling, contrast transformation, noise disturbance, rotation or reflection. In view of the identification scene of the sewage outfall of its river, the main data enhancement operations in this study are image cutting, image flipping, and image whitening. To conduct comparative experiments to observe the performance of different data enhancement methods, experiment 1 only performs image cutting, experiment 2 only performs image flipping, experiment 3 only performs image whitening, and experiment 4 performs these three data enhancement methods at the same time, and the same training is 5000 rounds. The curve of loss and curve of accuracy is shown in the Fig. 3, which indicates the combination of these 3 data enhancement methods make the model more stable, and the accuracy of the verification set can be increased to about 82%.
4.2 Model Regularization To further prevent overfitting, model regularization methods including batch normalization, weight decay, LRN (local response normalization), and dropout are used to study their contribution to enhance model accuracy. In the comparative experiments,
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Fig. 4 The curve of loss and curve of accuracy after model regularization
experiment 1 uses the weight decay method only, while dropout is added in experiment 2. Experiment 3 adds batch regularization on the basis of experiment 2, and experiment 4 uses all 4 methods. By comparing the results of these experiments, it is found that improvement brought by batch regularization and dropout methods are very obvious, and the use of both methods increases the accuracy of the verification set from about 82% to about 88%. Especially, the loss curve keeps falling after the batch normalization method is used, which shows that the batch normalization method can strengthen the stability of model training and can greatly improve the generalization ability of the model (Fig. 4).
4.3 Network Structure Optimization Another experiment is conducted to investigate whether the adjustment of the number of network layers is helpful to increase model performance, in which 4 comparative experiments are planned. In the experiment, the number of network layers in each group was set to 8, 14, 20, and 32 respectively, and all of the models are trained for 5000 rounds. By comparing the curves below, it is found that the accuracy rate increases when the number of network layers was increased from 8 to 14, but becomes decreased when the number of network layers further increases. This shows that the network performance will decline due to gradient attenuation if the number of network layers is too large. Therefore, other methods need to be used to solve the gradient attenuation problem (Fig. 5). To solve the above-mentioned gradient attenuation problem, this research uses the residual network structure adopted in YOLOv3 to make the deep neural network work
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Fig. 5 The curve of loss and curve of accuracy after adjustment of the number of network layers
efficiently. As the number of network layers is increased, the gradient will be continuously attenuated during the error backpropagation process, and the cross-layer direct connection edge can reduce the attenuation of the error during the backpropagation process so that the deep network can be successfully trained. Four comparative experiments with different network layers are conducted, and the results of which showing that when the number of network layers is increased from 20 to 56 layers, the training loss is steadily reduced, and the accuracy of the verification set is steadily improved. When the number of network layers is 56, it can achieve an accuracy of 91.55% on the verification set. This shows that the use of residual network technology can solve the problem of gradient attenuation, give full play to the feature extraction capabilities of deep networks, and enable the model to obtain strong fitting and generalization capabilities (Fig. 6).
5 Conclusions In this study, digital image processing technology is used to splice and integrate the panoramic pictures taken by the drone, while YOLOv3 deep learning technology is used to build a training model which can automatically identify illegal sewage outlets in panoramic pictures. It also found that a proper combination of image data enhancement methods, model regularization methods, and network structure optimization methods will help to stabilize the model and provide higher accuracy in the recognition work. This research not only greatly reduces the labor cost for artificial inspections and improves sewage outlets inspection efficiency based on the automatic and intelligent recognition of river sewage outlets from UAV aerial images, but also helps management departments to make precise and scientific decisions and build up a long-term supervision mechanism of river sewage outlets management.
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Fig. 6 The curve of loss and curve of accuracy after adjustment of the number of network layers
References 1. Cui DD, Lu L, Fa Ng WD (2013) Application of no man machine remote sensing technology in the Jiangsu sea area and island dynamic monitoring. Mod Surv Mapp 2. Saito G, Seki H, Uto K, Kosugi Y (2014) Development of a hyperspectral sensor on UAV for biomass estimation at costal zone. J Integr Field Sci 11(4):180–182 3. Shaolin HE, Jinghua XU, Zhang S (2013) Land use classification of object-oriented multi-scale by UAV image. Remote Sens Land Resour 25(2):107–112 4. Natesan S, Armenakis C, Benari G et al (2018) Use of UAV-borne spectrometer for land cover classification. Drones 5. Zhang Y, Yuan X, Yi F, Chen S (2017) UAV low altitude photogrammetry for power line inspection. ISPRS Int J Geo Inf 6(1):14 6. Shang G, Qu M, Hu F, Yang W, Li W (2019) Application research of UAV monitoring technology in comprehensive monitoring of emissions and pollution sources in Qiujiang river, shanghai. Guangdong Chem Ind 7. Feng L, Cui S (2019) Application of UAV remote sensing technology in monitoring land source sewage in sea area. Geomat Spat Inf Technol 8. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv e-prints
Intelligent Value-Added System Service of Automobile Manufacturing Enterprise Based on Forecast Demand Algorithm Analysis Zhao Wang
Abstract With the rapid development and popularization of the new generation of information technology and the Internet, service businesses in various application fields continue to cross the network, cross the domain and cross the boundary, thus forming a complex service ecosystem. This paper mainly studies the intelligent value-added system service of automobile manufacturing enterprises based on the analysis of predictive demand algorithm. Firstly, this paper studies the interaction between value-added service and traditional business model of automobile manufacturing from the perspective of automobile manufacturing enterprises. In this paper, the Kalman-ARMA combination prediction model is used to forecast the parts demand. In this paper, the data are processed by Kalman filtering, and then the iterative prediction model of ARMA is established. The parameters of ARMA are adjusted by the adaptive LMS algorithm. Each management module of the system is realized according to business requirements, including statistical analysis management, data extraction management, demand forecasting management, basic information management and system management. In addition to complete the system overall test, including functional test and performance test, to verify the stability and practicability of the system. Keywords Demand forecasting · Automotive manufacturing · Intelligent value-added · Application systems
1 Introduction Since the beginning of the twenty-first century, China’s auto market demand and ownership have shown a trend of rapid growth. After experiencing continuous explosive growth, China’s auto industry has gradually slowed down its pace of progress and entered a period of stable development. With the continuous maturity of the automobile market, the factors that determine the customers’ car purchase are no longer Z. Wang (B) Department of Automotive Engineering, Sichuan Aerospace Vocational College, Guanghan, Sichuan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_116
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the traditional price and product performance, but the service quality. The more satisfied the customers are with the service quality, the more likely they are to buy a car and the higher their loyalty will be. Under the condition of continuous demand for automobiles and market fatigue, the traditional price war and the marketing mode with the concept of small profits and quick turnover have been gradually eliminated, and the core competitiveness of the automobile industry has been transferred from product to service, which is a sign that the automobile market is gradually maturing [1]. At the same time, the key factor affecting the development of enterprises has also shifted from product value to customer demand. Whether the production mode and service mode of enterprises can be developed according to customer demand has become the key factor affecting the development of enterprises. Among them, the value-added service business model of automobile manufacturing enterprises is a model in which special purpose vehicle manufacturing enterprises integrate production-based product economy with consumption-based service economy in order to achieve value increment, so as to obtain core competitiveness, achieve efficient innovation and realize “manufacturing + service” of enterprises [2]. Therefore, from the perspective of automobile manufacturing enterprises, it is valuable and necessary to systematically study the construction of value-added service business model of automobile manufacturing enterprises, its operation mechanism and incentive mechanism, and develop intelligent value-added service system based on the prediction demand algorithm. Demand forecasting is a very important project, and its influencing factors are complicated. When we make demand forecasting, we should not only take into account the scientific and rigorous historical data, but also study a demand forecasting model that conforms to the actual situation [3]. In recent years, there are many researches on demand forecasting abroad. Some scholars adopted the time series prediction method Bootstrap method to predict the cumulative distribution, and carried out probability integral transformation on the predicted results to predict the demand of accessories [4]. Some scholars used feed forward and back propagation artificial neural network algorithm to predict the inventory, which played a very good role in inventory optimization [5]. Although there are a lot of researches on the model algorithm of demand forecasting, few researches focus on the demand forecasting of the automotive intelligent value-added service industry. High-quality value-added services include keeping pace with the times of the service concept, comprehensive service quality, comprehensive accessories supply services. In order to make the comprehensive parts supply service to meet the requirements of users’ parts supply, it is essential for enterprises to control the parts inventory scientifically, and the parts demand prediction can solve this problem well. In general, the demand forecasting system has a very good strategic significance for enterprises, but also provides a planning decision basis for the enterprise’s after-sales service.
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2 Intelligent Value-Added System Based on Forecasting Demand Algorithm 2.1 Interaction of the Value-Added Service and the Traditional Automobile Business Model It is an obvious trend that China’s manufacturing enterprises industrialization and service-oriented develop simultaneously from simple tangible product manufacturing to product-based value-added service provision. Value-added services business model in the process of the development of the service innovation and development, pay more attention to the enterprise itself gradually turn the traditional manufacturing business, toward a service-oriented development direction transformation, it is also the key to the development of value-added services business model, automobile manufacturing enterprises to develop value-added services business model need to focus on business model to build, run and incentive problems, business model is an important medium for value-added services and economic benefits. At present, the high value link of automobile manufacturing industry is changing from manufacturing link to service link. Due to the gradual standardization and automation of automobile manufacturing, the profit of the manufacturing link is constantly declining. Product differences are mainly determined by the research and development and design of the upstream of the value chain and the brand and aftersales service of the downstream. Therefore, automobile manufacturing enterprises have to actively extend to the downstream of the value chain, develop value-added service strategy, and by building value-added service business model, reshape the value creation logic of automobile manufacturing enterprises, form differentiation strategy, and finally achieve considerable economic benefits.
2.2 Predictive Demand Algorithm ARMA prediction method is a combination of autoregressive (AR) model and moving average (MA) model. The model is a representative stationary time series model, which can be used to predict the stationary linear minimum mean square error and has a very good prediction effect. However, the prediction effect of ARMA is poor for time series with many fluctuation factors or abrupt values [6, 7]. Kalman filter is used to preprocess the accessory data, and then the ARMA model is established. At the same time, LMS adaptive parameter adjustment method is adopted to carry out iterative training and prediction, making the whole prediction process more standardized and scientific, and the prediction results more accurate [8].
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ARMA model ARMA (Autoregressive Moving Average) model is composed of AR (Autoregressive) model and MA (Moving Average) model [9]. AR model is a linear combination prediction model of observed values and disturbance values. The expression is as follows: αt =
n
β p αt− p + εt
(1)
p=0
In the above equation, αt represents the stationary sequence of observed values, βp represents the model coefficient of AE, p represents the order of Ar, and εt represents the perturbation value, which satisfy the equations E(εt) = 0, Var(εt) = > 0. In the AR model, the value of the current sequence {αt} is determined by the previous sequence value {αt − p} of length p of αt and the sequence {αt}. The prediction variance of AR is: Var[et ( p)] = 1 + G 21 + · · · + G 2p−1 δz2
(2)
In the above formula, G0 = 1, G1 = β1 G0 , G2 = β1 G1 + β2 G0 … The MA model predicts the current value by combining the previous and current perturbation values, which is expressed as αt =
n
ωq εt−q = εt − ω1 εt−1 − . . . − ωq εt−q
(3)
q=0
In the above equation, αt is the stationary sequence of observed values, ωq is the coefficient of the MA model, q is the order of the MA model, and εt is the perturbation value, satisfying the formula E(εt) = 0. ARMA is a combination of AR and MA models. The expression is as follows: αt =
n p=0
(2)
β p αt− p + εt +
n
ωq εt−q
(4)
q=0
In the ARMA model, the value of the sequence {αt} is determined by the value of the sequence in the window of length p before {αt} and the sequence {εt} in the window of length q. Establishment of prediction demand algorithm model Random fluctuations weaken the regularity of the raw data that is needed to make predictions more accurate. Therefore, we need to carry out Kalman filtering on the original sales data to eliminate the random fluctuations of the original data, so as to obtain the regularity of the original data more accurately [10].
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After eliminating the random fluctuation of the original data, the ARMA model is used to approximate the regularity of the original data. The parameters of the ARMA model are learned by LMS adaptive algorithm. Once the ARMA model parameters are obtained, future demand data can be predicted using the ARMA model parameters and raw component demand data. The establishment of the model mainly includes two parts: Kalman filter processing process and adaptive ARMA process. The state transition matrix of Kalman filter is selected as the parameter model estimated by ARMA model. It can be found that after Kalman filtering, the random jitter of Gaussian distribution affecting the estimation can be effectively eliminated, and the regular curve of accessory demand data can be presented to a greater extent [11, 12]. Comparing the performance of the prediction model, we compared the three indexes of MAPE, MAD and MSE. By comparing the three indexes of MAPE, MAD and MSE, we found that after Kalman pretreatment, better performance would be obtained when predicting the data with random interference in reality. Therefore, when the system is forecasting the demand of parts, the Kalman-ARMA combined forecasting model is used to forecast the parts demand.
2.3 Value-Added System Architecture Design (1)
(2)
1.
Physical architecture design The intelligent value-added system based on predictive demand algorithm established in this paper is deployed on the industrial chain collaborative service platform in B/S mode, and all the roles of manufacturers, distributors, service providers and customers can access and use the system through the Internet. The access process is: the user makes a request to the server, the server processes the request submitted by the client browser, and then returns the processing results to the client browser in the form of web pages for the user to view. In this system, manufacturers, distributors, service providers, customers and other system roles access the industrial chain collaboration platform through the Internet, according to the different access rights, different operating functions, jointly achieve business collaboration. Logical architecture design The whole system is logically divided into three layers: data access layer, business logic layer and user representation layer. User presentation layer It is located in the top layer of the three-tier architecture, directly contacting the user, the user can enter data or commands in this interface, carry out information interaction with the server, receive the server response data, the specific data processing is completed by the lower layer. In this system development, using the Web page with the suffix.aspx to achieve.
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Business logic layer The business logic layer is in the middle layer of the three-tier architecture, which is related to the formulation of business rules and the implementation of business processes. It receives the data from the user layer, calls the methods of the data access layer to realize the data processing and sends the results back to the user layer, and plays a connecting role in the process of data exchange. Data access layer The data access layer is at the bottom of the three-tier architecture. It provides various methods and interfaces for adding, deleting, querying and modifying data, and processes the data in the way of stored procedures or SQL statements. On the one hand, it transmits the data obtained from the database to the business logic layer to meet the business needs; on the other hand, it transmits the instructions of the business logic layer to the database to process the data. The configuration items for the database are set in the web.config file.
The three-layer architecture designed with the idea of “high cohesion, low coupling” makes the layers separate from each other, which greatly reduces the coupling between modules and the interdependence between layers. For developers, they can only focus on one layer of the whole architecture, and replace the implementation of the original layer with a new implementation when the business needs change. This is conducive to the realization of standardization and logic reuse of each layer, improve development efficiency, and facilitate maintenance and expansion in the later stage.
3 Simulation Test of Intelligent Value-Added System 3.1 Test Requirements This paper will explain whether the system meets the requirement analysis content and the overall design requirements through the functional test; through the performance test, it shows whether the interface execution efficiency and system response time of the system conform to the performance index. The function realization and performance operation of the system are explained through the function test and performance test of the system. The system test should include the following aspects: (1) (2)
Establish the server side of the accessories demand prediction system and configure the corresponding system operation environment. Conduct functional tests of each module of the system, including statistical analysis management module, data extraction management module, demand prediction management module, basic information management module and system management module, etc., respectively to test whether the operation of adding, deleting, querying and modifying data is normal.
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Test the performance of the system, set the number of concurrency and test time according to the actual situation, and analyze the performance indicators, especially the system response time index, to analyze whether the indicators meet the actual use requirements of the system.
3.2 Testing Environment The test environment of the system mainly includes software environment and hardware environment. The environment configuration is as follows: Hardware environment: Win64 processor and operating system, port number 8080. Background server configuration: CPU: IntelCore i5-4200h 2.8 GHz; Memory: DDR4 8G 2400 MHz; Hard disk: Toshiba 500G (5400 RPM). Software environment: Windows10 operating system, MySQL database system, development tools Eclipse 4.5, JDK1.7.0_79, Tomcat 7.0, LoadRunner 12 performance test tools.
4 System Test Results Analysis 4.1 System Function Test As shown in Fig. 1, a total of 17 items have been tested in the function of adding test data in this paper, among which 3 items have problems. In the data deletion function, 28 items were tested and 7 of them were defective. In the data query, 25 modules were tested and 6 of them were defective. Data modification has been tested for 23 items, of which 4 items have problems. The above problems mainly focus on the database connection is not on, the information appears garbled, the length of the extended Number of tests
28
30
25
Quantity
25 20
Number of defects 23
17
15 10 5 0
7
6
3 Addition
Delete
Query
Testing capabilities Fig. 1 System function test results statistics
4 Modify
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Table 1 System performance test User number
50
100
150
200
300
20
40
60
100
200
Response time (ms)
23
35
41
51
63
Transaction number
11
13
14
17
20
250
User number
Response time(ms)
Transaction number
Value
200 150 100 50 0
50
100
150
200
300
Time(s) Fig. 2 System performance test
field is not reserved enough, the old and new code is not completely corresponding to the individual. In view of the above defects, this paper modified the system code, until the system can be used normally.
4.2 System Performance Test As shown in Table 1 and Fig. 2, it can be seen that with the increase of test time, the number of concurrent users also increases, and correspondingly, the system response time also increases. When the test time reached 300 s, the number of concurrent users of the system reached 200, and the system response time was 63 ms, indicating that the response time of the system fully met the daily use requirements.
5 Conclusions This paper takes the intelligent value-added service system of automobile manufacturing enterprises as the research object. In this paper, a value-added service business model suitable for automobile manufacturing enterprises is proposed. By comparing the existing forecasting algorithms and combining the data characteristics and actual demand of project parts, Kalman filter and ARMA model are selected to forecast the
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demand. The system is designed around Kalman filter and ARMA model, and the predicted results are analyzed to verify the feasibility of the model. The design and implementation of each module of the system, including statistical analysis management, data extraction management, demand prediction management, basic information management and system management modules, at the same time to achieve the design and connection of the background database, the final test analysis, the formation of a test report.
References 1. Bagci KT, Tekalp AM (2018) Dynamic resource allocation by batch optimization for valueadded video services over SDN. Multimedia IEEE Trans 20(11):3084–3096 2. Fine MB, Clark MN, Scheuer CL (2016) Value-added university services: the importance of on-campus recreational facilities. Serv Mark Q 37(1):24–35 3. Pinto Ferreira R, Martiniano A, Ferreira A et al (2016) Study on daily demand forecasting orders using artificial neural network. IEEE Lat Am Trans 14(3):1519–1525 4. Ghalehkhondabi I, Ardjmand E, Weckman GR et al (2016) An overview of energy demand forecasting methods published in 2005–2015. Energy Syst 8(2):1–37 5. Hofmann E, Rutschmann E (2018) Big data analytics and demand forecasting in supply chains: a conceptual analysis. Int J Logist Manag 29(2):739–766 6. Boroojeni KG, Amini MH, Bahrami S et al (2017) A novel multi-time-scale modeling for electric power demand forecasting: from short-term to medium-term horizon. Electr Power Syst Res 142:58–73 7. Saito H (2017) A demand forecasting method for new telecommunication services. J Oper Res Soc Jpn 30(2):248–262 8. Pan XJ, Zhang W, Zhao T et al (2017) Fractional Order Discrete Grey Model and Its Application in Spare Parts Demand Forecasting. Binggong Xuebao/Acta Armamentarii 38(4):785–792 9. Baecke P, Baets SD, Vanderheyden K (2017) Investigating the added value of integrating human judgement into statistical demand forecasting systems. Int J Prod Econ 191:85–96 10. Tratar LF, Mojskerc B, Toman A (2016) Demand forecasting with four-parameter exponential smoothing. Int J Prod Econ 181(pt.A):162–173 11. Kourentzes N, Rostami-Tabar B, Barrow DK (2017) Demand forecasting by temporal aggregation: using optimal or multiple aggregation levels? J Bus Re 78:1–9 12. Veiga C, Veiga C, Puchalski W et al (2016) Demand forecasting based on natural computing approaches applied to the foodstuff retail segment. J Retail Consum Serv 31:174–181
Fast Retrieval Algorithm of English Sentences Based on Artificial Intelligence Machine Translation Chuncai Lai
Abstract With the continuous development of artificial intelligence technology, people all over the world are no longer bound by distance, accompanied by obstacles to group communication between different populations. In order to allow people to better communicate and talk, machine translation. It came into being, and this brand-new technology has become a hot research topic at home and abroad under the artificial intelligence model. This article first summarizes the basic theory of artificial intelligence technology, and then extends the core technology of artificial intelligence. Based on the current status of machine translation English sentences on the Internet, artificial intelligence technology is used to quickly retrieve machine translated English sentences. This research systematically expounds the rules and corpus of the machine translation system, as well as the principle, calculation process and model building of related algorithms. Through experimental analysis, the effect of rapid retrieval of English sentences based on machine translation under artificial intelligence is studied. This experiment carried out research on the theme of this article by the express questionnaire survey method and the analytic hierarchy process. The experimental research shows that the Simhash algorithm reduces the impact of the synonym processing stage on the overall performance, while retaining the advantages of high precision and high accuracy of the calculation results. Keywords Artificial intelligence · Machine translation · Fast retrieval · Algorithm research
1 Introduction With the development of corpus linguistics and the improvement of computer performance, the effect of machine translation is getting better and better, and it is widely used [1, 2]. Researchers continue to explore new methods of machine translation, and the performance of machine translation is constantly improving [3, 4]. In today’s C. Lai (B) Guangdong Peizheng University, Guangzhou 510830, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_117
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world, with the rapid development of big data and artificial intelligence technologies, people of different groups communicate more and more frequently, and communication barriers are getting more and more attention. In order to enable people to communicate more smoothly, machine translation will be produced and integrated globally. It plays an active role in promoting the process [5, 6]. In the research on machine translation, many scholars have achieved good results. For example, Och et al. replaced the translation model in the basic equations of statistical machine translation with a reverse translation model, and then conducted machine translation experiments. The experimental results show that the overall translation accuracy rate has not decreased. This phenomenon cannot be explained by the theory of the source channel[7]. With the rapid popularization and development of computers, it has become possible to calculate high-dimensional vectors quickly and accurately, and the vector space model method has also been widely used [8, 9]. Brown et al. proposed several translation models in 1993, which laid the foundation for word-based statistical machine translation [10]. The machine translation research team followed up a series of studies and implemented a statistical machine translation system [11, 12]. The purpose of this paper is to improve the efficiency of machine translation, and to study the English fast retrieval algorithm based on artificial intelligence machine translation, combined with our country’s modern network translation status quo, using artificial intelligence technology to study and discuss the fast retrieval algorithm of English sentences.
2 Application Research of English Sentence Fast Retrieval Algorithm Based on Artificial Intelligence Machine Translation 2.1 Analysis oF Existing Problems in Machine Translation The more common method based on edit distance is currently used on the Internet in our country. Its basic operation is simple, but it is not flexible enough, and the accuracy of the calculation result is not high. The other is a method based on the vector space model. Compared with the former, the method based on the vector space model has higher accuracy, but its calculation speed becomes slower and is not suitable for separate retrieval. The third method is based on the same vocabulary, and its accuracy is very poor, and it is hardly used. It is also not suitable for searching similar sentences.
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2.2 Language Translation Model (1)
n-gram language model The mainstream method in the language model is the n-gram language model. The n-gram language model is based on the possibility of words appearing next to each other. For example, given a sentence W = w1 , w2 , w3 … wn , the most intuitive way to determine the fluency or rationality P(W) of this sentence is to find a part of the corpus and count the probability of the sentence appearing in this corpus. But the corpus is not enough to contain all sentences, and the phenomenon of data sparseness will appear. Therefore, how to calculate P(W) and deal with the problem of data sparseness is the key, and it is also the basic problem of language modeling. In the n-gram language model, the process of predicting a word sequence w is decomposed into predicting one word at a time. First use the chain rule to decompose the probability: P(w1 , w2 , . . . , wn ) = p(w1 )p(w2|w1) . . . p(wn |w1 , w2 , . . . , wn−1 )
(1)
In order to be able to estimate the probability distribution of these words, restrict the current word to be only related to the m preceding words: p(wn |w1 , w2 , . . . , wn−1 ) ≈ p(wn |wn−m , . . . , wn−2 , wn−1 )
(2)
(2)
This kind of model that processes a vocabulary sequence step by step and only considers the finite number of words in front of each step is called a Markov chain. The number of preceding words is the order of the model. Data smoothing One of the simplest smoothing techniques is additive smoothing. It is assumed that the number of occurrences of each metagram is more than the actual counted number of times, so that the metagrams of all occurrences are no longer second. The formula is as follows. i = p wi wi−n+1
i β + c wi−n+1 i β|V | + wi c wi−n+1
(3)
i i and |V| is the number of is the number of occurrences of wi−n+1 where c wi−n+1 all words considered. Good-Turing estimation method is the core of many smoothing methods. The basic idea is: For any n-gram where an r word occurs, it is assumed to occur r* times: r ∗ = (r + 1)
n γ +1 nr
(4)
where nr is the number of n-grams that occur exactly r times in the training corpus.
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For an n-gram with a statistical number of r, the probability is: pr = And, N =
∞ r=0
r∗ N
(5)
nr r∗ .
2.3 Simhash Algorithm Analysis 1.
2.
Overview The Simhash algorithm filters out sentence examples with a high degree of similarity and forms a set, then uses the thesaurus to synonymize all sentences in the set, and finally uses the improved TF-IDF method to calculate the difference between each sentence in the set and the sentence to be translated sentence similarity, so as to find the example with the greatest similarity, which is the most similar instance. Principle
Step1: Generate fingerprint library. The source language sentences in the corpus are preprocessed and processed by the Simhash algorithm to obtain the fingerprint corresponding to each sentence to form a fingerprint database P. Simhash algorithm’s processing of source language sentences. Step2: Process the input sentence S to be translated. Perform the same preprocessing operation on S, and use the Simhash algorithm to generate a sentence fingerprint F’based on the same hash function as the fingerprint library P. Step3: Send fingerprint F’to fingerprint database P for retrieval, find fingerprints whose Hamming distance from fingerprint F’is less than or equal to k, and extract the original sentences corresponding to these fingerprints, which is a set of examples of reduced scope, denoted as E. Step4: Use the synonym processing tool to query and replace the synonyms of sentences, and calculate the replacement cost. Step5: Construct feature vector. According to the improved TF-IDF algorithm proposed in this paper, each of the examples in the library E is constructed. The feature vector of the source example sentence and the sentence S to be translated. Step6: Calculate the similarity between each source example sentence in E and S, so as to find the most similar instance S . Among them, the generation of the fingerprint database belongs to the processing stage of the corpus and should be completed separately. An example-based machine translation system can generate the fingerprint database once, and only need to add the newly added corpus fingerprints to the fingerprint database in the later stage.
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2.4 Determination of Threshold The threshold value in the Simhash algorithm is an empirical value, and its determination needs to be determined according to the specific use environment. When Google uses the Simhash algorithm for web page deduplication, it uses 64-bit fingerprints to represent billions of web pages, and sets the threshold to 3. According to empirical judgment, for a 64-bit fingerprint, when the Hamming distance is within 3, the two texts are almost identical. However, when applying the Simhash algorithm to similar instance retrieval in a corpus, the current corpus size can only reach millions of levels, and the function of Simhash in this algorithm is to reduce the existence of similar instances, rather than directly determining the result. Therefore, in the algorithm proposed in this paper, the number of fingerprints and the threshold are adjusted appropriately. For search results, people always hope that the accuracy and recall rates are as high as possible at the same time, but in fact the two are usually contradictory. The threshold is not a fixed value, it is related to the corpus, accuracy and recall, the number of fingerprints, and the environment of use. With the gradual increase in the size of the corpus, the threshold in this algorithm may gradually shrink.
2.5 Translation Model The translation model is an important part of the statistical-based machine translation method. It is used to describe the correspondence between the source language segment and the target language segment. The translation model ensures that the source language segment can be translated into the optimal target language segment. To characterize the translation model P(S|T), a very critical issue is how to define the correspondence between the words in the source language sentence and the words in the target language sentence. For example, in the translation of a simple Chinese sentence to English sentence, in the translation sentence pair (I am Chinese|I am Chinese), the components of the sentence are aligned. Alignment is an important concept in statistical machine translation. With the alignment model, you can use P(S, A|T) to obtain: P(S|T) =
P(S, A|T )
(6)
A
With the alignment model A, we can get: P(S, A|T) = P(m|T)
m j=1
j−1
P(a j |ai
j−1
, si
j
j−1
, m, T )P(s j |ai , si
, m, T )
(7)
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From formula, we can see that to generate source language sentence S, the alignment model of T and S is A. The generation process is: generating the first word of the source language sentence under the condition that the target language sentence T, the source language sentence length m, and the first alignment position a1 are known. And so on, until the entire source language sentence is generated.
3 Experimental Research on the Fast Retrieval Algorithm of English Sentences Based on Machine Translation Under Artificial Intelligence 3.1 Subjects (1)
(2)
In order to make this experiment more scientific and effective, this experiment will test the translation of English sentences in the test set. This experiment will take time to translate sentences and calculate the algorithm, and the scale of the selected data set this time is 9,948 pairs of English-Chinese parallel corpora. In order to further research and analyze the algorithm proposed in this article, this experiment will also compare it with the most commonly used TF-IDF algorithm to judge the feasibility of the Simhash algorithm.
3.2 Test Method 1.
2. 3.
Logic analysis method Based on the analysis of the current situation of the fast retrieval algorithm for English translated sentences, this paper proposes the Simhash algorithm based on big data, and analyzes its basic theory, principle and operation process. This makes the research conclusions of this article more scientific and effective. Mathematical Statistics Use relevant computer software to process the obtained data. Contrast method Compare the fast retrieval algorithm studied in this paper with the currently used English sentence retrieval algorithm to judge the feasibility of this research.
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4 Experimental Analysis of a Fast Retrieval Algorithm for English Sentences Based on Machine Translation Under Artificial Intelligence 4.1 Comparison of Translation Time The 50 English sentences in the test set were compared with the algorithm in this paper, the algorithm based on edit distance, the algorithm based on the same vocabulary, and the TF-IDF algorithm to calculate the running time of each sentence. The results are shown in Table 1. It can be seen from Fig. 1 that the time consumption of the separate TF-IDF algorithm is much higher than the other three algorithms due to the need to construct the high-dimensional vector of each instance; the method based on edit distance and the method based on the same vocabulary has little difference in running time; the algorithm proposed in this paper is superior to the other three algorithms in terms of time performance. In the algorithm used in this article, because the scope of existence of similar instances is reduced first, the number of times of synonym query and word meaning similarity calculation is reduced, so the impact of synonym processing stage on the overall performance is reduced, while the high precision and accuracy of the calculation results are retained. Table 1 Translation time comparison Algorithm
Edit distance
Same vocabulary
TF-IDF
Have synonyms
1.8417
6.3556
6.1740
9.7120
Not have synonyms
0.7277
1.4079
1.3445
7.0821
Have synonyms
Second
15
Not have synonyms 9.712
10 6.3556 5 0
1.8417 0.7277 Algorithm
6.174
1.4079 Edit distance
1.3445 Same vocabulary
Categorys Fig. 1 Translation time comparison
7.0821
TF-IDF
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Table 2 Comparison of results when replaced correctly Synonym 1
Synonym 2
Synonym 3
Synonym 4
TF-IDF
0.7095
0.8477
0.5042
0.2415
Simhash
0.7715
0.9205
0.8285
0.3340
0
0.2
0.4
0.6
Similarity
0.8
1
Synonym 1 Synonym 2 Synonym 3 Synonym 4
TF-IDF
Simhash
Fig. 2 Comparison of results when replaced correctly
4.2 Comparison of Similarity Optimization Results Compare the algorithm proposed in this paper with the similarity results calculated by using the TF-IDF method alone. When the synonyms are correctly replaced, some test results are shown in Table 2. It can be seen from Fig. 2 that if the synonyms in the sentence are replaced correctly, the similarity result calculated by the algorithm proposed in this paper will be higher than the traditional TF-IDF method, and the calculated result will be closer to the true value.
5 Conclusion In recent years, with the widespread application of machine translation and the rapid development and popularization of Internet technology, humans have become increasingly demanding on the intelligence of machine translation. It is hoped that computers can think like humans. Artificial intelligence has received more and more attention in the field of machine translation. As a branch of artificial intelligence, natural language processing research has also received unprecedented attention and has gradually developed into a relatively independent subject. Natural language processing is a technology that uses computers as tools to process and process various types of natural language information in written and oral forms specific to humans. As an implementation of deep semantic analysis, it has been widely used in natural
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language processing. Related tasks, such as question answering systems, language generation, and machine translation.
References 1. Marco T et al (2017) Continuous learning from human post-edits for neural machine translation. Prague Bull Math Linguist 108(1):233–244 2. Cao R, Freitas C, Chan L et al (2017) ProLanGO: protein function prediction using neural~machine translation based on a recurrent neural network. Molecules 22(10):1732 3. Wu S, Zhang D, Zhang Z et al (2018) Dependency-to-dependency neural machine translation. IEEE/ACM Trans Audio Speech Lang Process 26(11):2132–2141 4. Avramidis E (2017) Comparative quality estimation for machine translation observations on machine learning and features. Prague Bull Math Linguist 108(1):307–318 5. Miura A, Neubig G, Paul M et al (2017) Selecting syntactic, non-redundant segments in active learning for machine translation. J Nat Lang Process 24(3):463–489 6. Antony PJ (2017) Machine translation approaches and survey for indian languages. Chin J Comput Linguist 18(1):47–78 7. Och (2016) Two tier search scheme using micro UAV swarm. Wireless Personal Commun 93(2):1–15 8. Daszkiewicz P, Daszkiewicz Z, Daszkiewicz P (2018) Custom-made database enabling a quick search for patients fulfilling specific criteria. A simple and effective tool for clinical data collection forscientific purposes. Aktualnosci Neurologiczne 18(3):113–116 9. Rodgers JR, Harrington C (2017) What we learned about Quicksearch (and didn’t) from the “Top Search Terms” report: by James R. Rodgers and Caitlin Harrington, University of Memphis. J Electron Resourc Librarianship 29(4):269–274 10. Brown S, Barnouti NH, Naser M et al (2016) Parallel quick search algorithm for the exact string matching problem using OpenMP. J Comput Commun 04(13):1–11 11. Maulana YI, Salim A (2021) Evaluasi Penggunaan supporting applications for quick data search (SuApQuDaS) Dengan Metode PIECES framework. Jurnal Ilmiah Teknologi Informasi Asia 15(1):13–18 12. Meddage NR, Pradeepika V (2020) Avquick meta search engine for quick audio visual searching. Int J Sci Technol Res 3(11):176
VR Product Quality Evaluation Based on Analytic Hierarchy Process Longtian Fu and Qi Zhang
Abstract The article designs VR product quality evaluation indicators on the basis of predecessors, and uses analytic hierarchy process and fuzzy comprehensive evaluation method to conduct empirical research. The research results show that the quality of VR products is in a “pass” state; the article finally analyzes the existence of VR products Problems, and proposed targeted countermeasures. Keywords Product quality · VR products · Quality evaluation
1 Introduction Virtual Reality (VR) technology crosses computers, electronic information, and simulation technologies, and uses computers to simulate virtual environments to give users a sense of environmental immersion [1]. Since Jaylen Rani officially proposed the concept of virtual reality in 1981 [2], a large number of VR products have appeared on the market until now. These VR products provide users with a good virtual reality experience. However, the uneven product quality is an indisputable fact. The quality of VR products obviously has a great impact on users’ VR experience. Many scholars have conducted a lot of research on the quality of VR products. Tao and Weiwei [3] used PFMEA to improve the quality of international subcontracted products; Yafeng et al. [4] analyzed the quality and safety of tea products in Hanzhong City, Shaanxi Province, and put forward relevant Countermeasures and suggestions; Yifei [5] studied the quality of spectacle lens products and believed that the overall quality of spectacle lenses on the market was excellent; Yan and Yuxuan [6] studied the relationship between product quality and pricing order, and believed that product quality and pricing have a strong correlation Shunbo et al. [7] studied product quality control issues based on dimensional engineering, and believed that the quality of automobiles comes from process control; Niangzhao [8] studied product quality L. Fu (B) · Q. Zhang Fuzhou University of International Studies and Trade, Fuzhou 350202, Fujian, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_118
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Table 1 VR product quality evaluation index system Target layer
Criterion layer
Sub-criteria layer
Weights
CR
VR product quality evaluation
Product properties B1
Appearance B11
0.7429
0.0685
Product ease of use B2
Product price B12
0.1939
Product reputation B13
0.0633
Ease of disassembly B21 0.6554 Ease of wearing B22
VR effect B3
0.0772
0.2897
Ease of operation B23
0.0549
Screen clarity B31
0.7306
0.0624
Screen virtual effect B32 0.1884 After-sales service B4
Screen delay B33
0.0810
Customer service attitude B41
0.5736
Customer service operation guidance B42
0.3614
Ease of return and exchange B43
0.0650
0.0516
control issues based on supply chain management and believed that the supply chain is a product The key link of quality assurance. However, few scholars have conducted research on the quality of VR products. This article uses analytic hierarchy process and fuzzy comprehensive evaluation method to evaluate the quality of VR products.
2 Construction of VR Product Quality Evaluation Index System This article turns the pages of a large number of literature materials, combined with the research results of senior scholars such as Yan [9], Bowei [10], Mi [11], Xiaogang [12], Shanwu [13], and designed an index system, Mainly including product attributes, product ease of use, VR effects, after-sales service and other four aspects, as shown in Table 1.
3 VR Product Quality Evaluation (1)
Index weight calculation based on analytic hierarchy process American operations researcher Saaty proposed a multi-criteria decision-making method in the 1980s, namely the analytic hierarchy process, which has been widely used
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in weight calculations in various fields [14]. This article uses this method to calculate the weight of each indicator. (i)
Construct a judgment matrix. In this paper, 10 experts in the industry are invited to score two indicators, and the average value is taken to obtain the judgment matrix. Taking the criterion layer as an example, the judgment matrix is shown below. ⎤ 1595 ⎢1 15 1⎥ 5 2⎥ A=⎢ ⎣1 1 1 1⎦ 9 5 5 1 251 5 ⎡
(ii)
Calculate the weight, this paper uses the geometric average method to calculate, the formula is as follows. Gi Wi = n i=1
Gi =
n n
j=1
n
i=1
λmax =
(1)
Gi Ai j AWi Wi
n
(2) (3)
where Aij represents the judgment matrix, Gi represents the geometric mean of the i-th index, W i represents the weight of the i-th index, λmax represents the maximum eigenvalue, and n represents the number of indicators. After calculation, the weight value of the criterion layer is as follows. ⎡
⎤ 0.6286 ⎢ 0.1365 ⎥ ⎥ W =⎢ ⎣ 0.0419 ⎦ 0.1930 (2)
Consistency test, in order to verify whether the weight value calculated by the judgment matrix is reasonable, the consistency test is carried out, and the formula is as follows. CI =
λmax − n n−1
(4)
CI Ri
(5)
CR =
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where CI represents the consistency check value and λmax represents the maximum characteristic value and the calculation is as shown in formula (3). n represents the number of indicators. The Ri value can be queried through the “Analytic Hierarchy Process Consistency Check Value Table”. When n = 4, Ri = 0.89, CR is the ratio of CI and Ri . When the CR value is less than 0.1, it means passing the test. According to the calculation of Formulas (4) and (5), the CR of the criterion layer is 0.0725, which is less than 0.1, so it passes the test. The calculation steps of the weights of the sub-criteria layer are exactly the same as those of the criterion layer. The four judgment matrices are shown below. ⎡ ⎡ ⎤ ⎤ 159 139 B1 = ⎣ 15 1 4 ⎦ B2 = ⎣ 13 1 7 ⎦ 1 1 1 1 1 1 9 4 9 7 ⎤ ⎤ ⎡ ⎡ 157 127 B3 = ⎣ 15 1 3 ⎦ B4 = ⎣ 21 1 7 ⎦ 1 1 1 1 1 1 7 3 7 7
(3)
The weight value of each indicator is obtained through calculation, and the weight value and CR value are shown in the last two columns of Table 1. It can be seen from Table 1 that all CR values are less than 0.1, indicating that they have passed the consistency test. Fuzzy comprehensive evaluation
In 1965, the American scholar Zadeh proposed a method of business evaluation with the help of fuzzy mathematics, namely the fuzzy comprehensive evaluation method. This method uses the principle of fuzzy relationship synthesis to quantify each index and comprehensively integrate the membership between multiple indexes and the evaluation object. Evaluation [15]. ⎡
0.25 ⎢ 0.35 ⎢ ⎢ ⎢ 0.35 ⎢ ⎢ 0.1 ⎢ ⎢ 0.25 ⎢ ⎢ 0.3 M =⎢ ⎢ 0.1 ⎢ ⎢ 0.2 ⎢ ⎢ ⎢ 0.1 ⎢ ⎢ 0.3 ⎢ ⎣ 0.35 0.3
0.2 0.2 0.35 0.2 0.5 0.1 0.35 0.25 0.2 0.4 0.35 0.2 0.2 0.25 0.25 0.3 0.1 0.3 0.35 0.25 0.3 0.2 0.25 0.2
⎤ 0.35 0 0.1 0 ⎥ ⎥ ⎥ 0.05 0 ⎥ ⎥ 0.3 0 ⎥ ⎥ 0.15 0 ⎥ ⎥ 0.15 0 ⎥ ⎥ 0.45 0 ⎥ ⎥ 0.25 0 ⎥ ⎥ ⎥ 0.5 0 ⎥ ⎥ 0.1 0 ⎥ ⎥ 0.15 0 ⎦ 0.25 0
VR Product Quality Evaluation Based on Analytic Hierarchy Process
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Suppose the evaluation result set R = {excellent, good, medium, passing, poor}; factor set F = (B11, B12, B13, B21, B22, B23, B31, B32, B33, B41, B42, B43), which is Table 1 All indicators in the neutron criterion layer; weight set W = (0.7429,0.1939,0.0633,0.6554,0.2897,0.0549,0.7306,0.1884,0.0810,0.5736, 0.3614,0.0650), that is, each indicator in the subcriterion layer corresponds to Weights. The fuzzy matrix is obtained by scoring by 10 experts in the industry and taking the average value, as shown above. Using fuzzy composition operator to calculate Rs = W * M = {0.35, 0.35, 0.29, 0.45, 0}, after normalization, Rs = {0.243, 0.243, 0.201, 0.313, 0}, 0.313 is the largest, Compared with the result set R, it can be seen that the evaluation result is “pass”.
4 Problems and Suggestions From the results of the empirical analysis, the quality of my country’s VR products is “pass”, so there is still a lot of room for improvement. This article proposes countermeasures from the following aspects. (1)
(2)
(3)
(4)
Product attributes. In the course of the investigation, this article found that many VR products are not affordable enough or even high and the prices of many VR products are more than 1000 Yuan; there is still a certain distance between the aesthetics of the products and the needs of customers; the word of mouth is average. These all reflect that VR products are not yet mature enough. It is recommended that VR product manufacturers should pay attention to product design, and avoid making temporary gains and inflating product prices, which will damage long-term interests. In terms of word-of-mouth, this article believes that corporate image and brand building should be emphasized. There should be a long-term brand building plan. Product eases of use. The survey found that the ease of use of most VR products is not good enough. Only a few products, such as Huawei VR glasses, are easy to disassemble and operate. It is recommended that the manufacturer optimize the product design and reduce the disassembly and assembly steps as much as possible. VR effects. Most VR products currently on the market have screen clarity, poor screen virtual effects, and delays. It is generally believed that the current VR products on the market are not mature enough. Therefore, it is recommended that manufacturers increase research and development efforts, continuously improve VR products, and solve the problem of VR effects from the source. After-sales service. According to the survey results, most of the VR products have good after-sales service. Only a small part of the after-sales staff is not familiar with VR products and lead to ineffective guidance on the operation of VR products. It is recommended that manufacturers strengthen after-sales services. Patent skills training.
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Acknowledgements This article is supported by the Fujian Social Science Planning Project: Research on Value Added Accounting of Sharing Economy in the Big Data Era (FJ2018B064)
References 1. Qing Y, Shuhua Z (2021) A review of foreign research on the development and evolution of virtual reality technology. J Dialect Nat 43(03):97–106 2. Tong L (2021) Literature review of the role of VR technology in physical education. Contemp Sports Sci Technol 11(09):4–6+22 3. Tao Z, Weiwei Y (2021) Using PFMEA to improve the quality of international subcontracted products. Manag Technol Small Med-sized Enterpr (Late Edition) 06:154–155 4. Yafeng D, Ting L, Yifan M, Chongyong L, Shuang L (2021) Analysis on the quality and safety of tea products in Hanzhong city, Shaanxi Province from 2018 to 2020. Food Saf Guide 16:28–31 5. Yifei C (2021) Analysis of the quality supervision and spot check of spectacle lens products. Glass Enamel Spect 49(05):23–26 6. Yan S, Yuxuan Z (2021) Product quality and pricing order in bilateral moral hazard. Math Pract Underst 51(10):22–32 7. Shunbo H, Yanyan M, Haiju W (2021) Product quality control based on dimensional engineering. Times Auto 10:122–124+129 8. Niangzhao L (2021) Research on product quality control based on supply chain management. Mech Electr Technol 02:106–108 9. Yan L, Zhenxing H, Qinjian Y (2016) Research on the virtual product service quality evaluation system in the mobile business environment. Mod Inf 36(02):63–69 10. Bowei Z (2020) Epac’s product quality evaluation boosts the upgrade of the cable industry. Qual Certif 04:86–87 11. Mi Z, Yan L (2020) Customer-perceived product quality evaluation based on comment big data. Technol Dev 16(07):804–810 12. Xiaogang H (2020) Analysis and application of valve product quality evaluation on e-commerce platform. Valve 04:51–53 13. Shanwu W, Yimin X, Fenghua Q (2021) Research on the quality evaluation index system of biomass boiler products. Ind Boiler 01:34–40 14. Yan X, Jinhang X, Yusui Z, Qingqing W, Shuiyang X (2021) Application of analytic hierarchy process in establishing the weights of network health information evaluation index system. China Health Educ 3(03):213–216 15. Dongming L, Hao G (2020) Construction and verification of fuzzy comprehensive evaluation model for rice information system based on analytic hierarchy process. J Chongqing Univ Technol (Nat Sci) 34(07):212–219
The Cutting-Edge Applications and Trends of Big Data and AI Technology in the Digitalization of the Fashion Industry Youyang Lyu and Xiaojing Lv(u)
Abstract The rise of Internet technology and artificial intelligence has promoted the rapid development of e-commerce and social networks, making the amount of fashion-related data reach an unprecedented height, and the decision-making of the clothing industry has begun to move towards a data-driven model. Using big data (BD) technology to analyze massive amounts of clothing fashion data is of great value to scientific research and the development of the clothing industry. The purpose of this article is to apply research on the cutting-edge applications and trends of digitalization in the fashion industry based on BD and AI technology. This article first summarizes the basic theory of BD and AI technology, and then extends its core technology. Combining with the current digital status of my country’s fashion industry, analyze the existing problems and shortcomings. On this basis, it uses BD and AI technology to supplement and improve it, and to do research on the cutting-edge applications of the digitalization of the fashion industry, and to discuss the development trend. This article systematically expounds the application of BD to the digitalization of the fashion industry, such as pricing and color selection, targeted marketing and forecasting trends. And through questionnaire surveys, field surveys and other forms to carry out experimental research on the theme of this article. Research shows that the digitalization of the fashion industry based on BD and AI technology is superior in many aspects. Especially in terms of pricing choices, targeted marketing and forecasting trends, they are all about 15% higher, fully demonstrating the feasibility of applying BD and AI to the digitalization of the fashion industry. Keywords Big data and AI technology · Digitalization of market industry · Applied research · Analysis and discussion
Y. Lyu Johnston Heights Secondary, Surrey, BC V4N 49, Canada X. Lv(u) (B) Graduate School of Management of Technology, Pukyong National University, Busan 48547, Korea © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_119
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1 Introduction With the advent of the 5G era, the application of 3D digitization, virtual reality, artificial intelligence, BD and other technologies on the production side and the construction of the industrial Internet will become the focus of the fashionable digital economy, and multiple business models coexist [1, 2]. The revolutionary changes in the fashion industry have put forward requirements for technological innovation and the transformation of business models, which are reflected in many aspects such as supply chain integration, marketing links, production design, and digital technology applications [3, 4]. Overseas GOHEADLINE uses Internet thinking and technological means to provide timely and smooth sample lending solutions for the fashion industry, and build a shared sample lending platform, thereby subverting the traditional sample management mode, using WeChat public accounts and mini programs to update the latest brands in real time News information, notification of the arrival of the latest samples, media lending management and other functions are integrated, opening a new era of fashion public relations [5, 6]. Grobelink stated in a column in the New York Times in February 2012 that the “BD era” has come. In business, economics and other fields, managers rely more and more on data analysis instead of experience in decision-making. And intuition [7]. With the purpose of fashion digitization, this article aims to study the cutting-edge applications and trends of BD and AI technology in the digitalization of the fashion industry. It compares and analyzes the traditional fashion industry with fashion digitization based on BD and AI technology. Judge the feasibility of the content studied in this article.
2 The Frontier Application Research and Trend Analysis of Big Data and AI Technology in the Digitalization of the Fashion Industry 2.1 Definition of BD BD refers to a collection of data that cannot be captured, managed, and processed with conventional software tools within a certain time frame. It is a massive amount of data that requires a new processing model to have stronger decision-making power, insight and discovery, and process optimization capabilities. High growth rate and diversified information assets [8, 9]. In fact, in general, there are too many potential data for social development now, so we have entered the “BD era”. BD includes structured data and unstructured data. Structured data is easy to understand, that is, common sales data, order volume, etc., have a clear structure and easy to obtain;
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unstructured data can be a variety of traffic, which includes video, voice, text, social media dynamics, etc. That is “everything”.
2.2 The Status Quo of the Fashion Industry (1)
(2)
(3)
(4)
Data source Both from the business system of the enterprise (such as ERP, CRM, supply chain, commodity management, POS, e-commerce platform, etc.), but also from external data, such as industry data, weather data, competitor data, geographic data, etc., and Social media dynamics, fashion dynamics, etc. on the Internet [10]. It should be noted that whether it is internal data or external data, the quality of the data itself has a direct impact on the effect of the analysis [11, 12]. Data form Business transaction data is mostly structured data, while external data, especially Internet data, free text, and even pictures, videos, etc., are mostly unstructured data. Analysis efficiency The extraction and analysis efficiency of traditional enterprise data warehouses is low. Today, with the development of data management and analysis technology, BD management, unstructured data analysis, and real-time data analysis have become easier. Wisdom Through the collection and sorting of data, a data lake is formed; according to business needs, extraction, cleaning and analysis, using algorithm design, as shown in the figure below, from “descriptive analysis” to “guided analysis”, the more complex and intelligent the analysis Gao, gradually entered the category of “artificial intelligence”.
2.3 BD and AI Technology Cited in Fashion Industry Analysis (1)
(2)
Pricing and color selection The most important application of BD is to choose the right color. Designers can collect market data on previous fashion lines. Using BD, fashion designers can view the most popular colors and make changes to their designs to meet the needs of their customer base. The data can also be used to help determine the price of clothes.. BD can help designers find the average price that attracts more buyers. Choice between men’s and women’s clothing
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BD can help designers understand which customers are buying goods, how many items have been sold, and the quantity purchased. Using this information, designers can determine whether it makes more sense to focus on men’s or women’s clothing. Niche markets may be more profitable.
2.4 The Establishment of BD and AI Analysis Framework for Fashion Companies (1)
(2)
(3)
Commodity and supply chain analysis Most clothing companies use “season” as a unit for product management. The end-to-end process of a season’s products includes product planning, design/development, order production, store goods and other processes. In product development, it is very important to predict and analyze fashion trends, and directly guide the range planning and purchase plan of a season’s products. The use of computer image recognition technology to predict fashion trends in clothing has been discussed for a long time in academia and technology circles. The data of some market research institutions and fashion information institutions can also be used as data sources for fashion forecasts. Location analysis The choice of retail location has a huge impact on sales performance. There are many types of fashion retail locations, such as independent stores in large cities, downtown boutique shopping centers, suburban shopping centers, large shopping centers, street shops in small and medium-sized cities, department stores, and so on. The property conditions of shops will also have an impact on sales. For example, Anchor Store is a good choice. Today, various shopping malls have formed their own characteristic passenger flow paradigm. For young people’s trendy brands, the influence of the combination of e-commerce and shopping centers on sales has made the evaluation of the store location model a new way. In the new retail era, the sales that need to be considered when selecting a location will not only take place in the store, but will even consider the comprehensive online and offline sales situation in the area around the store. Customer analysis Fashion retailers can accurately locate customer groups and analyze the dynamic changes of customer groups through data analysis. Establishing a framework for shopper types can not only be subdivided from the data of consumers’ demographic characteristics (age, occupation, income, living area, etc.), but also from other external attributes of shoppers (such as cultural influence, group type, etc.) And the shopper’s personality traits.
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2.5 Online Clothing Model Design Based on BD and AI Technology (1)
Bayesian network Bayesian Networks is a probabilistic network consisting of a directed acyclic graph and conditional probability. The Bayesian network can be defined mathematically, so that the concept of Bayesian network can be better understood. For example, let G = (I, E) represent a directed acyclic graph (DAG), where I represents the set of all nodes in the graph, and E represents the set of directed connected line segments, and the parameter set θ = {θ1 , θ2 , …, θn }. The set of conditional probability distributions of variables, θ1 = P(X i |Pa (X i ))
(1)
Represents the probability distribution of node Xi , Pa (Xi ) represents the set of parent nodes of node Xi, where the parent node refers to the node starting from the arrow, and the child node refers to the node pointed to by the arrow. The joint probability of Bayesian network is: P(V ) =
n
P(X 1 , X 2 , ..., X i−1 )
(2)
i−1
The Bayesian network structure is based on a set of conditional independence assumptions, so there are: P(X i |X 1 , .., X i−1 ) = P(X i |Pa(X i )), (i = 1, ..., n) (2)
(3)
Causal Bayesian network structure learning based on constraint learning The existing causal network discovery algorithms based on constraint learning are all based on the conditional independence test. The conditional independence means that in the set of random variables U = {X1 , X2 , …, Xn }, A, B, and C are three disjoint subsets of U, if for ∀X i ∈ A, ∀X j ∈ B, ∀X k ∈ C, all have: P(X i |X k , X j ) = P(X i |X k )
(4)
Then it is said that given Xk , Xi and Xj are conditionally independent, denoted as Ind(Xi , Xj , |Xk ); given that Xk , xi and xj are dependent, denoted as: Dep(X i , X j |X k ) (3)
(5)
Network clothing recommendation model based on BD and AI technology In order to obtain a graphical model of online clothing shopping, all variables need to be tested for correlation. If there are any two variables that pass the
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correlation test, you can add an undirected edge between these two variables, so that an undirected graph can be drawn in the three-layer network structure model to form a Bayesian network diagram of online clothing shopping model.
3 Experimental Research on the Frontier Application of Big Data and AI Technology in the Digitalization of the Fashion Industry 3.1 Experimental Protocol 1.
2.
In order to make this experiment more scientific and effective, this experiment conducted a questionnaire survey of relevant staff by a clothing design company in a certain place. This time, the traditional fashion industry digital system and the application of BD and After the AI technology, the digitalization of the fashion industry is compared and analyzed, and the results obtained are analyzed and researched using the analytic hierarchy process. In order to further research and analyze the experiments in this article, this experiment conducted face-to-face interviews with professors of related majors on the application of BD and AI technology and the performance of the fashion industry by going to a fashion design college in a certain place. The sex ratios of the interviewed subjects are equal to ensure the validity of the experimental data.
3.2 Research Methods 1.
2.
3.
Questionnaire survey method This article sets up targeted questionnaires by asking relevant experts on relevant fashion designers, and conducts the questionnaire survey in a semiclosed manner, the purpose of which is to promote the correct filling of relevant personnel. Field research method This article goes deep into a first-line clothing design company in a certain place, and conducts field research on the digitalization of the fashion industry and collects data. These data provide a reliable reference for the final research results of this article. Interview method This paper conducts face-to-face interviews with a fashion design professor and records the data, and organizes and analyzes the recorded data. These data not only provide theoretical support for the topic selection of this article, but also provide reliable results for the final research results of this article. stand by.
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Mathematical Statistics Use related software to make statistics and analysis on the research results of this article.
4 Experimental Analysis of the Frontier Application of Big Data and AI Technology in the Digitalization of the Fashion Industry 4.1 Comparative Research on Digitalization of Fashion Industry In order to make this experiment more scientific and effective, this experiment compares and analyzes the traditional fashion industry digitization model and the BD and AI technology studied in this article in the fashion industry digitization. The data obtained is shown in Table 1. It can be seen from Fig. 1 that compared with the traditional fashion industry digitization model, the digitization of the fashion industry based on BD and AI technology is superior in many aspects. Especially in terms of pricing choices, targeted Table 1 Comparative study on digitalization of fashion industry Pricing options (%)
Targeted marketing (%)
Forecast trend (%)
Others (%)
BD and AI
71.2
70.9
73.6
68.7
Traditional
55.7
54.9
58.1
62.4
Traditional
Percentages
Big data and AI
71.20%
55.70%
Pricing options
54.90% 70.90% Targeted marketing
58.10% 73.60% Forecast trend
Categorys
Fig. 1 Comparative study on digitalization of fashion industry
62.40% 68.70% Others
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marketing and forecasting trends, they are all about 15% higher, fully demonstrating the feasibility of applying BD and AI to the digitalization of the fashion industry.
4.2 Performance Analysis of BD and AI Technology Applied to Fashion Industry In order to further research and analyze the experiments in this article, this experiment conducted face-to-face interviews with professors from a fashion design college in a certain place. This experiment uses a ten-point scoring system. The recorded data is shown in Table 2. It can be seen from Fig. 2 that most professors agree that BD and AI technology are applied to the digitalization of the fashion industry. Among them, the data accuracy of their applications is the highest, which fully reflects the excellent performance of BD and AI technology applied to the digitalization of the fashion industry, as well as traditional the problems in the fashion industry in China need to be solved urgently. Table 2 BD and AI technology applied to fashion industry performance analysis Data accuracy
Method feasibility
Customer satisfaction
Others
Man
7.62
7.12
6.87
5.49
Woman
8.66
7.48
6.78
4.87
Categorys
Others
4.87 5.49
Customersatisfaction
6.78 6.87
Methodfeasibility
Woman
7.48 7.12
Dataaccuracy
Man
8.66 7.62 0
2
4
6
8
10
Satisfactions
Fig. 2 BD and AI technology applied to fashion industry performance analysis
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5 Conclusion Nowadays, the fashion industry has entered the routine stage of BD-driven marketing and public relations decision-making. The emergence of BD will provide digital collaborative assistance for the fashion industry, establish continuous partnerships with major fashion brands, and solve the problems of resource docking, work efficiency, and tracking of actual effects after decision-making in public relations and marketing business. The hard cornerstone to promote the development of the industry. In the future, people will directly predict the ROI of public relations and marketing decisions through data, to assist brands in making the best public relations and marketing decisions. This is an inevitable change in the new era of the fashion industry.
References 1. Jänecke M (2019) Techtextil draws together threads from trends in digitalization, cities of the future and sustainability. Technische Textilien 62(2):51–52 2. Gaur A, Tiwari S, Kumar C et al (2020) Flexible, lead-free nanogenerators using poly(vinylidene fluoride) nanocomposites. Energy Fuels 34(5):6239–6244 3. Sebald AK, Jacob F (2020) What help do you need for your fashion shopping? A typology of curated fashion shoppers based on shopping motivations. Eur Manag J 38(2):319–334 4. Keramatian A, Gulisano V, Papatriantafilou M et al (2021) MAD-C: multi-stage approximate distributed cluster-combining for obstacle detection and localization. J Parallel Distrib Comput 147(9):248–267 5. Ying Z (2020) “Digital Survival” and the diversification of stage art. Popul Color 000(003):112– 113 6. Xiaohong Y, Dan Y (2019) Digital innovation to solve the regional imbalance in the development of cultural heritage—thinking about the investigation of dinosaur fossil sites in western Liaoning. Popul Color 07:165–166 7. Grobelink M (2019) Assessing the comfort of functional fabrics for smart clothing using subjective evaluation. J Ind Text 48(8):1310–1326 8. Miao Y, Wu G, Liu C et al (2019) Green cognitive body sensor network: architecture, energy harvesting, and smart clothing-based applications. IEEE Sens J 19(9):8371–8378 9. Jeong EG, Jeon Y, Cho SH et al (2019) Textile-based washable polymer solar cells for optoelectronic modules: toward self-powered smart clothing. Energy Environ Sci 12(6):1878–1889 10. Lepak-Kuc S, Podsiady B, Skalski A et al (2019) Highly conductive carbon nanotubethermoplastic polyurethane nanocomposite for smart clothing applications and beyond. Nanomaterials 9(9):1287 11. Hong Y, Cao X, Chen Y et al (2019) A conceptual wearable monitoring system for physiological indices and clothing microclimate measurement. Int J Cloth Sci Technol 31(3):318–325 12. Zhang P, Zhang W, Zhang H (2021) A high-performance textile-based triboelectric nanogenerator manufactured by a novel brush method for self-powered human motion pattern detector. Sustain Energy Technol Assess 46(7):101290
Remote Sensing Image Target Recognition System Based on Heapsort Sidong Cui, Zerong Jiang, and Ping Li
Abstract Remote sensing image refers to the digital ground image formed by loading different types of cameras on the ground platform such as satellite. In many fields of remote sensing, satellite image is favored by people because of its wide coverage, high quality, low cost and so on. Remote sensing image technology is also widely used in football matches. This paper designs a remote sensing image target recognition system based on HEAPSORT algorithm, expounds the related theories of remote sensing image, introduces the algorithm used in remote sensing image, designs each module of the system, and tests the system. Finally, the experimental results show that the algorithm is feasible. Keywords Heapsort algorithm · Football match · Image target · Recognition system
1 Introduction Human beings are entering the information age, with the rapid development of computer technology, the amount of remote sensing data is huge, the amount of information is large, and the updating speed is fast [1, 2]. Extracting useful information from massive remote sensing data is the key problem of remote sensing technology. Remote sensing game is a part of the field of target recognition, and target recognition is an important tool in football match [3, 4].
S. Cui Yunnan Normal University, Kunming, Yunnan, China Z. Jiang (B) Kunming University, Kunming, Yunnan, China e-mail: [email protected] P. Li Shangri La Municipal People’s Government, Diqing Tibetan Autonomous Prefecture, Yunnan, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_120
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In the field of remote sensing image target recognition, a lot of research has been carried out by relevant scholars. Some scholars believe that with the development of science and technology, remote sensing technology is developing in the direction of high resolution and multi-dimensional, and the rapid development and wide application in the field of image recognition. Some scholars believe that with the improvement of remote sensing image resolution, countries all over the world are studying high-resolution remote sensing products, aircraft, cars, buildings and even people can be clearly seen on the image, it is very convenient to use remote sensing data to analyze the specific greening of different targets [5, 6]. The purpose of this paper is to recognize football targets in remote sensing images. The background information of remote sensing image is rich, and different geographical locations will also produce different background information [7, 8]. Only by selecting the appropriate algorithm can the football target be identified quickly and accurately. This article uses the Heaport algorithm to locate and identify the goals in the football game, but it also contains a small number of non-football related goals. In the recognition stage, the target model of football match is correctly identified from the candidate targets in the region of interest, and the non-target is eliminated, which is the process of object type recognition in football match [9, 10].
2 Based on Heapsort Research on the Theory of Remote Sensing Image Target Recognition System for Football Match Based on the Algorithm 2.1 Remote Sensing Image Related Theory 2.1.1
The Process of Obtaining Remote Sensing Image of Football Match
The image sensor collects the spectral information of football match from the satellite, and the sensor measures the reflected energy of the surface material and the energy intensity of the spectrum in different parts, mainly sunlight (the most commonly used light mode in visible light and near infrared). This information is processed into a set of remote sensing data, and the remote sensor provides image data, including spatial and spectral information. The remote sensor measures the radiation of each pixel to form a different number of bands. The amount of radiation reflected by an object is very small for a given substance [11, 12]. Remote sensing system is composed of four basic components: radiation source, atmospheric path, image plane and sensor. Solar energy propagates in the atmosphere, its intensity and spectral distribution are determined by spectrum, and then interact with surface matter that needs to be described and reflected. Reflected or emitted energy returns to the atmosphere and experiences additional changes in intensity
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and spectrum in the atmosphere. Finally, the energy reaches the sensor and is then measured and converted into digital form for further processing.
2.1.2
Information Features of Remote Sensing Images
In the field of remote sensing image acquisition, the most basic problem is how to identify pixels with specific spectral shapes. Many scholars have carried out in-depth research on remote sensing image acquisition. Remote sensing images have spectral features, as well as rich spatial features: spatial relational functions, texture features and geometric features. Pixels have spectral similarity in a specific region, and texture features reflect the arrangement and combination of objects in space and represent a variety of complex object information. The geometric features of remote sensing images reflect the shape of graphics and play an important role in the field of graphic recognition.
2.2 Remote Sensing Image Target Detection Method 2.2.1
Heapsort Algorithm
In the field of remote sensing image processing, scrting takes up 25–50% of the machine time. At present, there are various methods of sorting, which have reached the point of dazzling. However, to explore its essence is only to compare the key words (except the distributed sorting method). Complete binary tree can vividly describe the comparison process. Generally speaking, the time taken to sort a file with n records is approximately proportional to nlog1n. Because you need to scan the data log:N “Trend”, that is to say, it will take so much time at least. In practical application, the specific method should be selected according to the number of days to be sorted and the initial arrangement of records. In fact, no one method is the best in any situation. Among many row counting methods, HEAPSORT (HEAPSORT), which was discovered by J. W. Williams in 1964 and named for it, is unique in both efficiency and gracefulness. This is not only an important sorting method, but also a novel one. In addition to the excellent time/space ratio of the algorithm, it also reflects the beautiful programming style because of its ingenious conception and implementation skills. Heap sorting logically regards the records to be sorted (with one-dimensional array as the storage structure) as a binary tree. Each node of the binary tree represents the keyword of a record to the nth record {65, 109, 32, 87, 98, 10, 54, 21, 43, 76}. In the binary tree, for any node R[i], its child nodes are R[2I] and R[2I + 1], and its parent nodes are R[[I/2]]. Heap sorting takes the binary tree as the medium, and achieves the sorting of the records to be sorted in the array through the operation of the binary tree. When every node of the binary tree satisfies:
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R[[I /2]J > RC I ](1 < [I /2] < I < N )
(1)
Then the binary tree in this form is called heap. The top of the heap, that is, the root node of the binary tree, must be the largest Guan Jian character. By using the properties of the heap, it is easy to sort. Therefore, the sorting problem of array elements is transformed into the stacking problem of binary trees.
2.2.2
Adaptive Matched Filtering Algorithm
Adaptive matching filtering algorithm is a typical target detection algorithm. In this algorithm, the pixels to be detected are represented as a linear combination of target spectrum and background scrambling noise: H = bq + k
(2)
where H represents the spectral sample with n channels to be detected, Q represents the spectral information of the target of interest, b is the attenuation constant and k is the background noise. When b = 0 Indicates that there is no target, when b > 0 Indicates that there is a target.
2.3 The Main Framework of Target Image Recognition The main framework of target image recognition is divided into four stages: 1.
2. 3. 4. 5.
The original telemetry image is processed by target image preprocessing, denoising, edge processing, wavelet transform, denoising and so on, in order to assist the subsequent target processing; Image segmentation: using Image Segmentation Technology to extract target image from background image as template; Target matching, that is, extracting the selected target from the image to be edited; Clustering algorithm classifies and corrects the extracted targets, eliminates false alarm and other data collection work; Based on Heapsort testing of remote sensing image target recognition system for football match based on DSP.
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3 Test of Remote Sensing Image Target Recognition System Based on Heapsort Algorithm 3.1 Background of System Research and Development In order to solve the practical remote sensing problem, this paper is based on the Heaport. The algorithm establishes a software development platform suitable for multilingual programming and experimental environment, which can use a variety of programming tools and languages to achieve a large number of remote sensing algorithms and different performance image processing. At present, it is urgent to integrate and make use of the existing image processing algorithms in different programming languages, in which the scheduling habits of different algorithm hunters are satisfied, and the efficient SAR image processing and SAR visualization of the new algorithm are the problems to be solved in the process of remote sensing image application and development.
3.1.1
Characteristics of Remote Sensing Image Software Processing
The task of remote sensing image processing can be divided into pre-processing and post-processing. The purpose of processing is to correct, transform, interpret, extract and obtain the attribute information of the target. Although remote sensing image cannot provide accurate and complete conditions for geometric and physical attributes, remote sensing software is different from the general image processing system in six aspects. (1) remote sensing imaging emphasizes data function mechanism, and applies imaging mechanism to data analysis and conversion; (2) there are a large number of data in remote sensing image processing, especially high resolution hyperspectral remote sensing image, which is part of sparse large format data processing algorithm; (3) remote sensing image format is complex, which makes remote sensing image format complex. I/OThe processing is very complex, the remote sensing data loss is small, the precision is high; (4) the calculation of remote sensing imaging algorithm is mainly a part of data-intensive matrix calculation, most remote sensing image processing algorithms are poor in generality and strong correlation, especially the advanced remote sensing post-processing algorithm, target recognition and classification and recognition are very complex; (5) remote sensing algorithm has obvious modularity and the timing of typical workflow, which is an important prerequisite for using component-based software development model.
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Research and Development Status of Related Systems
Using appropriate algorithms to analyze and process potentially useful information in remote sensing data is an important task in the development of remote sensing imaging software. The main software of remote sensing image in China is RSIES,
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IRSA, SARNFORS, CASM LNIMAGEFO. The 3D visualization of remote sensing image is an important platform for remote sensing image application. A number of scientific research institutes and commercial enterprises at home and abroad have carried out Google Earth, Virtual Earth, World Wind and other successful studies EV Globe, Geo Globe, LTEarth and other local software has also achieved fruitful results. However, in an open environment, the programming of multilingual algorithms and the digital terrestrial application platform for SAR image processing are not publicly reported. Considering the modularization of remote sensing imaging algorithm and workflow report, according to the different reusability and granularity, the components can take the form of object class, class library, function module, interface specification and so on. Software component is the focus of software reuse technology. By compiling the existing remote sensing image processing components, a new application system can be developed, which greatly accelerates the research progress of related algorithms and reduces the workload of the system.
3.2 System Function and Architecture Design 3.2.1
Overview of System Functions
The goal of rsii PDP is to support and improve the use of general format SAR image algorithm and improve the display effect of SAR image. SAR Digital Earth system consists of four subsystems: SAR image preprocessing, SAR image post-processing, SAR Digital Earth and system management and service. The purpose of SAR image preprocessing is to eliminate the error of original satellite image, improve image quality and geocoding. Its basic functions are image location, geometric correction, radiometric correction, filtering, enhancement, registration, fusion and mosaic. The purpose is to obtain the relevant information of the ground target, recognize, classify and recognize the football target, and recognize and extract the complex target such as high-speed athletes. SAR Digital Earth realizes the comprehensive processing, representation and management of satellite images. Its basic functions include camera control, position management, image reproduction, image superposition, roaming and navigation. The enhanced functions include historical images, satellite orbit simulation, satellite navigation, etc. System management and service are the guarantee of system integration, including download service, data processing service, advanced communication service, task monitoring, system configuration, data processing service, multi computer parallel computer control, automatic batch processing, etc. SAR image database is an image processing database, which includes the control functions of original SAR image, pre-processing SAR image, post-processing SAR image, visible light image, DEM image and model data.
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According to the requirements and functions of SAR image information platform, the overall structure of four subsystems of the system platform and five parts of SAR image database are proposed. SAR digital system is the operation and operation interface of remote sensing imaging processing. The management and service subsystem of the system is the core of the whole system, and the pre-processing and post-processing are the core objects of the algorithm. Due to the high complexity of remote sensing algorithm and large amount of data, the processing speed often becomes the bottleneck of system application. In order to improve the speed of image processing and display as well as the efficiency of the system, the system architecture is extended based on the realization of basic functions. The hybrid parallel processing of high-resolution remote sensing images is realized by using Hadoop + OpenMP + CUDA scalable parallel processor architecture and GPU accelerated parallel computing technology.
4 Application Effect Analysis of Football Competition Remote Sensing Image Target Recognition System Based on Heapsort Algorithm 4.1 Heapsort Computing Experimental Software and Hardware Environment The experiment adopts Zhongke dawning (Sugon) high performance cluster system. The hardware and software environment of the system is shown in Table 1. MNIST data set, hstcr ooms-tda and MSTAR Alex amplification data set are still used for the experimental data, and the specific parameter settings are shown in Fig. 1. Table 1 Experimental parameters Hardware name
Allocation
Master control node
Xcon E5-2620v2 × 2,12,2.1 GHz, 64 GB DDR3
Calculation node 1–9 Storage node Cluster communication switch
Xeon E5-2680v2 × 2,20,2.8 GHz, 128 GB DDR3,Nvida Tesla K20 × 2, 5 GB ×2, Cuda2496 × 2 20 TB, 7200 Rpm, 32 GB
Disk array storage switch
Infiniband FDR, 56G,4.032 Tb/s
Network communication switch SAN 8GbFC Operating system
Ethemet1 000 M
Hardware Name
Red Hat Enterprise Linux Server release 6.5 (Santiago)
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Maximum number of iterations of intensive training
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The maximum number of incremental iterations is Epochs
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Increase the number of samples each time
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BatchSize of batch samples in HSTCR dataset Value
200 0
50
100 150 200 250 300 350
Quantitative value
Fig. 1 Parameter setting
4.2 The Influence of Heapsort Algorithm on the Target Recognition Rate of Football Competition In order to verify the influence of parallel HEAPSORT on the average recognition rate of the algorithm, the HEAPSORT algorithm is used to carry out experiments on data sets. Table 2 shows the experimental results of HEAPSORT’s iterative times and average recognition rate with epochs = 1–10. Figure 2 shows the relationship between the number of parallel HEAPSORT iterations and the average recognition rate. The experiment shows that the parallel HEAPSORT algorithm has little influence on the average recognition of football match data set, but has great influence on the hsheapsort data set. After 10 iterations, the average recognition rate of series parallel HEAPSORT algorithm is almost the same. The HEAPSORT data set shows an upward trend with the increase of iterations. This is because the number of samples in the data set, the complexity of the object to be identified, and the size of the object resolution have a great impact on the system identification. Generally Table 2 Average recognition data Epochs
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MSTAR paralle1 AP(%)
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MSTAR Paralle1 AP(%) 57.52 58.75 73.55 53.85 47.53
Fig. 2 Average recognition rate
speaking, parallel HEAPSORT algorithm can effectively improve the efficiency of the algorithm.
5 Conclusion With the continuous improvement of image processing technology, the requirements of target detection are also constantly improving. As the sport with the largest number of spectators, the video image processing of football match must also meet the needs of the audience and professionals. In the football game video, the football can be detected and tracked accurately and quickly. This paper proposes a kind of object recognition system based on HEAPSORT algorithm, which is applied to image processing technology. The experimental results show that the algorithm is feasible.
References 1. Zheng C, Wang C, Lin Z et al (2005) Target recognition algorithm research based on combined feature selection. Shanghai Space Space 034(005):59–64 2. Zhao W, Ma X, Liang L et al (2019) Remote sensing image registration based on dynamic threshold calculation strategy and multiple-feature distance fusion. IEEE J Sel Top Appl Earth Observ Remote Sens 12(10):4049–4061 3. Kang F, Wang C, Jia L et al (2018) A Multiobjective Piglet image segmentation method based on an improved noninteractive GrabCut algorithm. Adv Multimed 2018:1–9 4. Haiqing Z, Jun H (2019) Research on infrared multi-target intelligent tracking method based on laser visual recognition. Laser Mag 040(007):19–23 5. Chen S, Zhao F (2018) The adaptive fractional order differential model for image enhancement based on segmentation. Int J Pattern Recogn Artif Intell 32(3):1854005.1–1854005.15
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6. Wang Z, Liu P, Cui T (2017) Research on forest flame recognition algorithm based on image feature. ISPRS-Int Arch Photogram Rem Sens Spat Inf Sci XLII-2/W7:925–928 7. Zhang QY, Guan S, Xie H et al (2004) Image adaptive target recognition algorithm based on deep feature learning. J Taiyuan Univ Technol 049(004):592–598 8. Yang W, Gao X, Zhang C et al (2021) Bridge extraction algorithm based on deep learning and high-resolution satellite image. Sci Program 2021:1–8 9. Wang J, Zhai Y (2021) Target recognition of synthetic aperture radar images using multi-criteria SRC. Remote Sens Lett 12(8):739–749 10. Luo X, Feng Y, Zhang M (2021) An underwater acoustic target recognition method based on combined feature with automatic coding and reconstruction. IEEE Access 9/63841–63854 11. Kazaryan M (2021) Mathematic-systemic researching with the involvement of fractals when processing of space surveillance systems on recognition of waste disposal objects. Aerosp Res Bulgaria 33:124–139 12. Xu J, Bi P, Du X et al (2019) Target recognition based on dynamic (2D) 2 PCA for UUV optical vision system. Optik 179:154
The Application of Artificial Intelligence in AI News Anchor Xuya Wang and Feng Zhu
Abstract The essay critically analysed the application of AI in broadcasting and media sector, with specific focus on the critical case study of AI news anchor. The relevant concepts, theories and frameworks of AI and its application in media were reviewed. Then, these concepts were used to critically analyse the case of AI news anchor. From the literature, the foundation framework of AI is machine learning, deep learning, neural networks and big data, which make machine as smart as human. To conclude AI’s application in the AI news anchor, current AI technologies can make AI anchor have synthesised voice and digitally manipulated face though the input content from developers. However, the technology of AI is not mature, and there are still many limitations for AI’s adoption in media. The future trend of AI news anchor will be interactive broadcasting and emotional AI anchor. Keywords Artificial intelligence · AI news anchor · AI application
1 Introduction Artificial Intelligence (AI) has been developed rapidly in recent years, which is with the purpose of making machine as smart as human [1]. In the media sector, AI can perform a wide range of tasks to improve the productivity and efficiency. The purpose of the essay is to critically analyse the case AI news anchor to illustrate the application of AI in media and broadcasting sector. Firstly, the concepts of AI will be reviewed, containing the definition of AI, machine learning, categories of AI, deep learning, neural networks and big data. Then, some concepts that are relevant to AI and media will be introduced. The case of AI’s application in AI news anchor will be critically analysed and discussed. Finally, a conclusion can be reached to summarize the content of the essay. X. Wang (B) School of Media and Communications, Mianyang Teacher’s College, Mianyang, Sichuan, China F. Zhu Wuqing Integration Media Center, Tianjin, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_121
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Fig. 1 AI, machine learning and deep learning (New European media [11])
2 Theoretical Concept 2.1 Definition of AI AI, of course is not new, it has theoretical roots in the 1940s with Warren Ac Culloch and Walter Pitts. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine. Minsky (2016) said that AI makes machine to think, learn and crate. Artificial Intelligence (AI) is a frequently discussed item, which is a wide concept that encompasses some fields, algorithms and techniques [2]. It can be simply recognized as to make a machine like a human [1]. From Fig. 1, AI contains machine learning and deep learning. Academically, AI is employed to describe machines which mimic some functions that are cognitive [3]. These functions are typically associated with human, for instance, “problem solving” and “learning”.
2.2 Categories of AI Typically, there are three different categories for AI: humanized, human-inspired and analytical artificial intelligence. All kinds of competencies including social, emotional and cognitive intelligence are presented in Humanized AI, which can be self-aware and self-conscious in interactions with others [4]. The second category, human-inspired AI, contains two elements from emotional and cognitive intelligence. In detail, the emotional intelligence can help it to understand emotions of human [5]. On the other hand, the cognitive elements help it to think as human for decision making. The third category, analytical AI, only has the ability of cognitive intelligence in order to use learning from previous experience for the decisions in the future and to generate a world’s cognitive representation [5].
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Fig. 2 Machine learning technology (Gartner 2017)
2.3 Machine Learning Machine learning is the foundation of AI which offers systems with the capacity to study automatically through using information and data to modify and improve from past experience, which is without explicitly programmed [4]. Some real cases can be found, such as automated advanced transcribing, audio descriptions, speech recognition and programme production. The idea of AI is gendered to make system have the capacity to learn without being explicitly programmed. Figure 2 presents the basics of machine learning. Through the machine learning system, the input data is processed by the machine learning system, and the output data is then presented [6]. Machine learning can be understood as that machines are provided with a huge amount trial cases for a specific goal. Then, machines would go through these cases, learn, adapt and improve the strategy to reach the goals [6].
2.4 Deep Learning and Neural Networks Artificial neural networks make deep learning possible, which imitates brain cells or neurons. Artificial neural are inspired by biology. The principles from computer science and math are used by the neural net models to mimic the processes of the brain of human, to allow for more learnings [7]. For an artificial neural network, it attempts to simulate the processes of human’s brain of densely interconnected brain cells. In the artificial neural network, the nodes or neurons are based upon code. In addition, there are three layers of the neural networks, which are output layer, hidden layer and input layer [7]. Millions of nodes are included in these layers. In the process, information is put into the input layer. The input information is provided with a specific weight. Then,
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Fig. 3 Neural networks model
the weight of the connection is multiplied by the interconnected nodes as the inputs travel. If a specific threshold is achieved by the unit of information, it can pass to the next hidden layer [8]. To learn from past experience, outputs are compared by machines from a neural network. Then, connections, thresholds and weights are modified by machines, based upon the differences among them [9] (Fig. 3).
2.5 Big Data In some ways, AI presents the ability of human intelligence [10]. Big data can systematically extract and analyse data sets which are too complex or large to be processed by traditional statistical applications. It promotes AI to learn more information and process huge amount of data and information [10]. In fact, the technological developments of big data have kept pushing the intelligent systems’ boundaries in in media sector. For instance, the film, “Surprising”, released in 2016, was written by AI. “Hello World”, the first music album produced by AI, has been released [11].
2.6 AI in Broadcast and Media Sector From the concepts of AI, it can be regarded as an item with the purpose of creating intelligent technology to solve problems and replicate the intelligent capacity of human. Machine learning, deep learning, neural networks and big data can be regarded as the sub-sets of AI. However, the application of AI in media and broadcast is at an early stage [12]. For the media and broadcasting sector, the development and adoption of AI technology open a new door for monetization, utilization and media cataloguing possibilities. Some big companies including IBM, Google and Microsoft Azure have developed AI systems to make the automation of repetitive processes and tasks, which may need the allocation of more resources and time, especially in sectors of media management and production [13]. The requirements for AI systems to the development of media management and editing have increasingly jumped. Especially for
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some repetitive tasks, the automated AI system can increase efficiency and reduce the human capital [1]. In addition, AI can be helpful in content management. For human, it is hard to classify the unstructured texts, images, audios and videos. Some advanced AI techniques including speech, emotion and image recognition make it easy for content management and make people rely on AI technologies to search and organise the content archives [12]. The adoption of AI for content management is cheaper and more effective.
3 Critical Case Study 3.1 The Application of AI in News Anchor In this sector, the case of Xinhua AI News anchor will be critically analysed by using previous concepts and theories to discuss the application and AI in broadcasting sector. In November 2018, Xinhua, which is a state-run broadcaster in China, unveiled an AI anchor who has synthesised voice and digitally manipulated face [14]. Generally, machine learning was used to make the AI anchor synthesise facial expressions, lip movements and realistic speech so that it can deliver the news the same as a real human anchor [14]. From theoretical aspect, the AI anchor can learn from previous news broadcasting videos to improve its function and is able to read texts as real news anchor. However, there are some opponents who do not agree the AI anchor is natural. Compared with real human news anchor, the AI anchors are more efficient and are almost tirelessly. It can broadcast anytime and anywhere without a break. For a real human news anchor, it is not possible for him/her to report 24 h a day and 365 days [10]. The AI anchors can endlessly classify the unstructured texts, images, audios and videos, and bring the audience the news anytime. More importantly, its facial expressions, lip movements and speech are like real professional human anchors. Therefore, it can mimic human mannerisms and expressions when it reads texts. Currently, it still requires humans to feed the Chinese and English texts into in AI anchors’ receiver for it to read the content like a real news anchor. It is believed that with the development of machine learning and big data, the AI anchor can create the content of news by itself though learning texts and videos.
3.2 The Analysis of AI News Anchor In previous section, the concepts of AI in media has been introduced. The foundation and most important parts of AI are machine learning, deep learning, neural networks and big data. Through machine learning, it can be with the capacity to study
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automatically through using information and data to modify and improve from past broadcasting news videos, which is without explicitly programmed [6]. Firstly, by using big data, the search engine, Sogou, works to collect information to prepare contents for news. Big data can systematically extract and analyse data sets which are too complex or large to be processed by traditional statistical applications. It promotes AI to learn more information and process huge amount of data and information [10]. To learn from past experience, outputs are compared by machines from a neural network. Then, connections, thresholds and weights are modified by machines, based upon the differences among them [9]. Currently, due to the limitation of AI technology, this work is still done by human employees. They gather the latest information through search engine and organize the content as texts for AI anchors.
3.3 Future Development of AI in News Broadcasting 3.3.1
Emotions
Emotions are complex human behaviours, which are hard for AI robots/anchors to imitate. If the audience expects an AI news anchor to express emotions, the processor of the anchor should evaluate whether the context it reads has a good or bad meaning when it pronounces a word. If this can be realized, the electric motors of the AI news anchor can be programmed and designed to respond to the context though its configurations and create the corresponded emotions on its face. Emotion plays a vital role in human behaviours [15]. Therefore, emotions are important for the rational thinking of human, when AI Chatbots attempt to simulate human responses, it should not only think, but also should have the capacity to present emotions, which is vital in the broadcasting sector. The gap between AI and human can be closer by this emulation because emotion is currently regarded as the replicable item by AI through artificial neural networks and emotion is no longer separated from reason [16].
3.3.2
Interactive Broadcasting
Another development should be interactive broadcasting so that AI news anchor can respond to the requirement of its audience in time [17]. From the perspectives of users, it is vital to make sure that the products can meet the requirements of users. For instance, if the user expects to watch the entertainment news in the UK, the AI news anchor can quickly search, collect content and generate the required news, and then present the news to the user in real time. The user can speak to the AI news anchor, and the anchor can respond to the user, which is like the scenario in Iron Man, the communication between Iron Man and Jarvis. There are wide range of applications of AI in the broadcasting sector, especially the AI news anchor. The
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adoption of AI in this sector can improve the productivity and efficiency. AI can be used to develop the value chain of broadcasting.
3.4 Ethics Issues of AI Like other things, AI also have both sides. Among the negative sides, the top concerns are about the ethics issues. Especially when the creating thinking machines become possible in the near future. Therefore, the most important rules are to make sure that the AI machines/robots do not harm the morally relevant beings and humans [18]. For example, AI technology may make users’ sensitive personal data exposed to others. When users share ideas with the “AI virtual friends”, the data may be exposed. Otherwise, especially for the AI news anchor, the content presented to users should be strictly controlled. For instance, violence, terrorist and criminal content cannot be presented.
4 Conclusion In its application, AI can improve the process of delivering and enhancing audience experience by using machine learning, deep learning, neural networks and big data. In the media sector, AI can perform a wide range of tasks to improve the productivity and efficiency. The essay critically analysed the application of AI in broadcasting and media sector, with specific focus on the case of AI news anchor. The relevant concepts, theories and framework of AI and its application in media were discussed, such as machine learning, deep learning, neural networks and big data. Then, these concepts were used to critically analyse the case of AI’s adoption in AI news anchor. To conclude AI’s application in broadcasting, especially the AI news anchor, current machine learning, deep learning, neural networks, big data and other related technologies can make AI anchor have synthesised voice and digitally manipulated face with the input content from developers. However, the technology of AI is not mature, and there are still many limitations. The future trend can be in interactive broadcasting and emotional intelligence. It is believed that AI will affect and change the media sector, enhancing content users’ experience, making editors more productive and making content creators more creative. Acknowledgements This work was supported by the Teaching Quality and Teaching Reform Research Project of 2020 School-level Undergraduate-Research on the Training Mode of Broadcasting and Hosting Talents under the Background of AI Anchor (NO. Mnu-JY20097).
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References 1. Jarrahi MH (2018) Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus Horiz 61:577–586 2. Moravec H (1999) Robot: mere machine to transcendent mind, 1st edn. Oxford University Press, New York 3. Chrisley R (2004) Embodied artificial intelligence. Artif Intell 149:131–150 4. Alan G, Claire C (2019) Artificial intelligence, chatbots, and the future of medicine. Lancet Oncol 20:481–482 5. Oord A, Dieleman S, Zen H, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) WaveNet: a generative model for raw audio. In SSW, 3:125 6. Sapp CE (2017) Preparing and architecting for machine learning, Gartner. Available from: https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/ preparing-and-architecting-for-machine-learning.pdf. Accessed 23th April 2019 7. Lu Z, Li H (2013) A deep architecture for matching short texts. In: Advances in neural information processing systems 8. Spector L (2006) Evolution of artificial intelligence. Artif Intell 170:1251–1253 9. Shoham Y (1999) What we talk about when we talk about software agents. IEEE Intell Syst Appl 14:28–31 10. Jadhav L (2018) A survey of chatbot in artificial intelligence and comparing chatbots used earlier with chatbots used now. Int J Res Appl Sci Eng Technol 6:448–451 11. New European media (2018) Artificial intelligence in the media and creative industries. Available from: https://nem-initiative.org/wp-content/uploads/2018/10/nem-positionpaper-aiinceati veindustry.pdf. Accessed 25th April 2019 12. Lorenzo Z (2017) The future is artificial: AI adoption in broadcast and media, Research Analyst IABM. Available from: https://www.ibc.org/tech-advances/the-future-is-artificial-ai-adoptionin-broadcast-and-media/2549.article. Accessed 18th April 2019 13. Qian L, Zhu J, Zhang S (2017) Survey of wireless big data. J Commun Inf Netw 2:1–18 14. Lily K (2018) World’s first AI news anchor unveiled in China, The Guardian. Available from: https://www.theguardian.com/world/2018/nov/09/worlds-first-ai-news-anchor-unv eiled-in-china. [19th April 2019] 15. Martínez-Miranda J, Aldea A (2005) Emotions in human and artificial intelligence. Comput Hum Behav 21:323–341 16. Goya-Martinez M (2016) The emulation of emotions in artificial intelligence: another step into anthropomorphism. In: Emotions, technology, and design, 171–186 17. Turing A (1950) Computing machinery and intelligence. Mind 59:433–460 18. Bostrom N (2010) Analyzing human extinction scenarios and related hazards. J Evol Technol 9:110–119
Oil Painting Art Communication System Based on Artificial Intelligence Optimization Algorithm Nan Gao and Liya Fu
Abstract Since the twentieth century, with the rapid development of science, technology, and media, the combination of new media and art represented by digital media such as the Internet, digital television, and mobile phones has gradually separated traditional oil painting from newspapers, magazines, and television new media integration. The new media has carried out a comprehensive and in-depth change in the way of spreading oil paintings. This paper conducts research on the oil painting art communication system based on artificial intelligence optimization algorithm. First, analyzes the oil painting art communication appeal on the basis of relevant literature data, proposes a communication algorithm based on artificial intelligence optimization, and then designs the oil painting art communication system according to its appeal. Finally, the system designed in this paper is tested, and the test results show that although the oil painting art communication system based on artificial intelligence optimization algorithm designed in this paper still has shortcomings, overall, the performance evaluation of the system is good the number of people accounted for more than 32%, generally more than 30%. Keywords Artificial intelligence · Optimization algorithm · Oil painting art · Communication system
1 Introductions With the advent of the information age, traditional oil painting art has also ushered in a new growth opportunity [1, 2]. Under the new background of modern society, the intellectual comfort brought by painting art has attracted more and more attention [3, 4]. As a brand-new art carrier, new media integrates with traditional media channels by virtue of its own free and convenient communication methods to realize the diversification of oil painting art communication [5, 6]. N. Gao (B) · L. Fu School of Digital Art and Design, Dalian Neusoft University of Information, Dalian 116000, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_122
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Aiming at the research of oil painting art communication based on artificial intelligence optimization algorithm, first of all, for the research of artificial intelligencebased optimization algorithm, some researchers use damping factors and bias parameters to solve the problem of creating bird nests to improve the clustering of the communication algorithm itself performance [7, 8]. At the same time, the cuckoo optimization algorithm is applied to the propagation algorithm to find the best in the world in the bird’s nest and find the best solution position faster. In order to improve the local optimization ability of the algorithm, the algorithm uses internal scoring indicators and Silhouette values to determine the step length of the cuckoo algorithm, so as to scientifically locate the search direction, and find the optimal parameter value space reasonable grouping result through the search parameter [9, 10]. Regarding the study of oil painting communication, some researchers pointed out that the new communication medium of traditional painting art is a dynamic process of transmitting painting information from creators to consumers through new media such as the Internet and digital television. In this process, there must be some thoughts on how to disseminate artistic information and how the dissemination effect is [11]. Therefore, the study of communication methods and results has also become the focus of the subject. In the study of the communication methods and effects of new painting media, most of the ideas are realized in the network media [12]. This article explores and researches oil painting art communication system based on artificial intelligence optimization algorithm, summarizes the basic demands of oil painting art communication on the basis of relevant materials, and then proposes a communication algorithm optimized based on artificial intelligence algorithms based on literature data, and then oil paintings according to the demands. The communication system of the artwork is designed, and finally the designed system is tested, and the rationality of the system design is obtained according to the results.
2 Research on the Spread of Oil Painting Art 2.1 The Appeal of Oil Painting Art Dissemination (1)
Recipient orientation in oil painting art creation activities The creation of oil painting art is a process of aesthetic objectification. The artist meets the subjective emotional needs of the public and realizes social functions through subjective creation with specific aesthetic and creative methods. From the perspective of the motivation and purpose of the artist’s creation, artistic creation is the act of informing the recipient. The artist conveys his personal artistic achievements and cultural heritage to the public through his works of art, reflects his inner thoughts and feelings, talks with others in the name of art, and expresses the feelings of others consciously or unconsciously in the creative process. In traditional Chinese painting, we can clearly see a large number of sources of information, not only reflecting the author’s personality
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and habits through the image style and creation method, and feeling the author’s inner thoughts, but also receiving information from the content of the image related history, culture, customs, etiquette and other information. For the human consciousness in the past, it is a continuation, it is an expression of time. For the later recipients, it is a kind of cultural heritage, which has the dual functions of continuity and teaching. Aesthetic call in oil painting works The aesthetic value of oil painting is because the aesthetic value of oil painting does not arise naturally, but appears and perceives in the dialogue between the artist and the art recipient, and is presented in the process of appreciating the art of the recipient. Therefore, the existence of inviting others to appreciate works of art is an objective internal communication orientation. Both the display of artistic symbols and the aesthetic value of artistic works show the text interpretation activities of art recipients and the practical behavior of appreciating art. This kind of orientation makes oil painting the center of art activities, and makes artists and recipients relatively independent, equal and interactive in art communication activities. Active Recreation in Appreciation of Painting Art As a symbolic interpretation, oil painting appreciation is a process for the public to read and understand the artist’s thoughts and feelings, as well as an aesthetic activity for the public to dialogue with the artist. There are two kinds of symbols in art appreciation, one is the image symbol faced by the audience, and the other is the thought symbol formed by the audience through appreciation. The dissemination of works of art is a process of symbol transformation. Through the aesthetic dialogue with the image symbols, the audience integrates the aesthetic emotion embodied in the work with the artist’s creative background and purpose, and transforms the graphic symbols into ideological symbols.
2.2 Propagation Algorithm Based on Artificial Intelligence Optimization (1)
Algorithm description The nearest neighbor propagation algorithm is mainly that the algorithm does not need to select the class center in advance. The algorithm transfers information between different data points through performance and attractiveness, and uses the similarity between data points as a function reference point. After repeated repetition, the similarity of the data is high, and the similarity of the non-identical data is low. Let X = {×1, × 2, …, Xn } be a finite set of pattern space Rn , where xi (i = 1, 2,…, N) is a point in the vector space composed of dimensional features. The similarity s(i, k) between any two samples is measured by negative Euclidean distance, as shown in Eq. (1).
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s(i, k) = −dik = −xi − xk
(1)
The AP algorithm defines the polarization parameter s (i, i) for each sample xi . The algorithm initially assumes that each sample has the same probability of becoming the cluster center, that is, all s(i, i) are set to the same value P. Generally, let P be the average similarity table of all elements in the cluster, as shown in Formula (2). p = median(s(:))
(2)
AP algorithm has two important information update tables, namely attractiveness table r(i, k) and performance table a(i, k). The attractiveness r(i, k) is the information transferred from the sample point xi to the sample point x, which represents x; xx is selected as the support degree of the order point; the rate of return a(i, k) is the sample point xk sent to the sample point the amount of information of xi , namely x, is appropriate to choose x as the representative point of the class; it is calculated by the decision table E. E = arg max(a(i, k) + r (i, k)
(2)
(3)
In order to avoid unstable grouping results during the algorithm iteration process, a damping factor is used for adjustment. The value range of the damping coefficient is λ [0, 1]. The new r (i, k) and r (i, k) are obtained by summing the weights. (i, k), this paper chooses λ as 0.5. When the representative point of the class does not change for 10 consecutive iterations or reaches the maximum number of iterations, the nearest neighbor propagation algorithm ends. Optimization based on artificial bee colony In the nearest neighbor propagation clustering algorithm, if the number of clusters is between [1, m], the range of the corresponding P value is (−∞o, 0], where m is the number of samples. Aiming at how to improve the clustering efficiency problem, the algorithm first changes the value range of P and restricts its range to [pmin , pmid ], that is, the initial search space of the lead bee is also within this range. Experiments verify that reasonable clustering can be obtained within this range as a result, the waste of computing time can also be avoided, where pmin is the minimum value of S (i, j), and pmid is the median of S (i, j). The bias parameter P in the AP algorithm is the main diagonal element of the similarity matrix. Because the P value has a certain trend relationship with the clustering result, that is, the increase in the P value, the increase in the number of clusters, the decrease in the P value, and the decrease in the number of clusters and it is difficult to find the optimal result by manually setting the P value. Artificial bee colony algorithm makes up for the shortcomings of AP algorithm with its characteristics of self-organization, good convergence performance, few control parameters, and strong global optimization ability.
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3 Design of Oil Painting Art Communication System Based on Artificial Intelligence Optimization Algorithm 3.1 Development Mode The system adopts B/S architecture for design and development. The B/S (browser/server) structure is called the browser/server structure, which is based on a three-tier structure, namely, the browser, the Web server, and the database server. The browser is the interface between the system and the user, and is responsible for transmitting user requests and displaying the content that the user wants. The web server is responsible for retrieving the data that the user wants from the database, and accepting the data returned by the database for sorting. The database server provides data for the application.
3.2 Development Tools Java is very suitable for the Internet or corporate network environment, so it has become one of the most popular and important programming languages on the Internet. Compared with C ++ , Java has deleted many unused features, including those whose advantages outweigh the disadvantages: simple, object-oriented, distributed, structure-neutral, portability, high performance, interpretability, reliability, security, multithreading, dynamic and other advantages. Clients of any processor are allowed to run and stream on the Internet, so this article chooses Java as the development tool.
3.3 The Functional Structure of the Oil Painting Art Communication System Based on the analysis of reference related materials, the overall functional structure of the oil painting art communication system is proposed as shown in Fig. 1.
3.4 Register and Log in (1)
Identity authentication In order to perform identity verification, the system in this document has a two-factor authentication function with a password and an IP address. Before running the system, the system administrator will create users that allow users
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Art online display system
register log in
My workbench
Exhibit management
Exhibition management
Exhibits Statistics
Fig. 1 System function overall architecture diagram
(2)
to access the system one by one, and set up initial passwords and corresponding IP access rights for them. After the setting is completed, the legal username, password and corresponding IP address will be stored on the system. When a user logs in, first check whether the user name exists, if the user name does not exist, the user is denied access to the system. When the user is on the system, please check the current IP address and the corresponding IP address of the user stored on the system. If they are the same, they do not match and access will be denied. If they match, continue to check whether the entered password matches the password stored on the system; if it does not match, the user will only be denied access if the user name you entered exists. The user is allowed to access the system only if the password you entered and the IP address you have access to match the password and IP address stored on the system. Role management The concept of role capacity has also been added to the system applied to this document to improve system security. Role capacity is defined as the number of Internet users with a specific role in the system within a specific period of time, and the role capacity is determined when the role is created. For example, in this system, the roles of file manager, room manager and system administrator are all 1. That is to say, among the online users of the system, there is only one user as file manager, room manager and administrator log in. In the current system, the file manager user has logged in. When another user wants to log in as the file manager, he/she will refuse to log in to ensure system security.
3.5 My Workbench The personal workbench of this system mainly includes the collection of oil painting works, the display of various types and forms of oil painting statistical information, and the uploaded oil painting information. My desktop user information includes
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modified pages, my favorites, statistical analysis and my forms. My workbench is the personal information management platform of this system. It can manage its own user information and oil painting information, and can also perform routine maintenance and management of its own reports, view and analyze historical data. Therefore, the management form and concept of my workbench should be unified in principle, not only to facilitate and display the personal information of users, but also to facilitate the operation and maintenance of system management and improve user value.
3.6 Oil Painting Management Exhibit management includes scoring, query, uploading, downloading, commenting, voting, favorites, offline reporting, deleting, creating paintings and other functions. Users must register before logging in to perform related functions.
3.7 Exhibition Management The exhibition information mainly includes three parts: current exhibition, exhibition preview and exhibition view. The current exhibition mainly displays exhibition information that is still within the validity period of the exhibition. The exhibition preview displays information about upcoming or future exhibitions. The report view mainly displays past exhibits and offline exposure information. The exhibition information includes exhibition time, exhibition theme, content introduction, etc.
3.8 Oil Painting Statistics Oil painting statistics collects the oil paintings of the online art display system according to the data types set by the user, forms a report form, and can be displayed intuitively. Through statistics, it is convenient for the manager to analyze the oil painting statistics.
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Table 1 Detection results of oil painting art communication system based on artificial intelligence optimization algorithm Experimental interface effects (%)
System response time (%)
Facilitate project management (%)
Content design (%)
Good
46
34
48
32
General
32
45
30
47
Not good
10
9
13
12
Do not know
12
12
9
9
4 Detection of Oil Painting Art Communication System Based on Artificial Intelligence Optimization Algorithm 4.1 Experimental Design The subjects selected for trial are students from a university randomly selected, a total of 82 people, after a semester of use of the oil painting art communication system based on artificial intelligence optimization algorithms. After the students have tried it out, a questionnaire will be issued for students to fill out. A total of 82 copies were issued and 82 copies were recovered, of which 80 were valid questionnaires.
4.2 Result Analysis Through the questionnaire survey, the oil painting art communication system based on artificial intelligence optimization algorithm designed in this paper is tested, and the rationality of the oil painting art communication system based on artificial intelligence optimization algorithm is tested. The survey results are shown in Table 1. It can be seen from Fig. 2 that although the oil painting art communication system based on artificial intelligence optimization algorithm designed in this paper still has its shortcomings, on the whole, the number of people with good evaluation of the system’s performance accounted for more than 32%, of more than 30%.
5 Conclusions This paper designs the oil painting art communication system based on artificial intelligence optimization algorithm, and the system adopts B/S architecture for design and development. The B/S (browser/server) structure is called the browser/server
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percentage
46%
Experimental interface effects.
System response time
Facilitate project management
Content design
48%
34%
45%
32%
30%
13% 10%9%
Good
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General
Not good
12%12% 9%
Do not know
evaluation
Fig. 2 Detection results of oil painting art communication system based on artificial intelligence optimization algorithm
structure. Java is selected as the development tool, and then the overall function architecture diagram of the system is proposed, and the subdivision design is carried out according to the overall function architecture diagram. Acknowledgements This work was supported by Humanities and Social Sciences Research of the Education Department of Liaoning Province Project “Chinese Contemporary Oil Painting Based on the Mission of Cultural Communication and Research on Innovation and Convergence of Digital Media” (SYDR202010).
References 1. Dutta T, Gupta HP (2017) Leveraging smart devices for automatic mood-transferring in realtime oil painting. IEEE Trans Industr Electron 64(2):1581–1588 2. Wang H (2019) The art of communication. Beijing Rev 62(50):22–22 3. Musso CG, Enz PA (2016) Art as an instrument to develop communicational skills. Arch Argent Pediatr 114(1):4–5 4. Duh M (2016) Art appreciation for developing communication skills among preschool children. Ceps J Center Educ Policy Stud J 6(1):71–94 5. Yaghy A, Shields JA, Shields CL (2019) ART OF MEDICINE representing communication, compassion, and competence in the Era of AI. J Ethics 21(11):1009–1013
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6. Ma X, Kim J, Paik J (2017) The digital application of ink painting techniques: interactive media art using interactive devices. TECHART J Arts Imaging Sci 4(4):18–20 7. Badjie G (2021) Leadership should or should not delegate communication? (a systematic literature review: the art of delegation). Eksis Jurnal Riset Ekonomi dan Bisnis 15(2):67–78 8. Nacak A, Yücesoy Y (2020) Art education for communication in the society: a content analysis. Revista Tempos e Espaços em Educação 13(32):1–15 9. Duh M (2016) Matja duh: art appreciation for developing communication skills among preschool children. Center Educ Policy Stud J 6(1):71–94 10. Cano C, Pittolo A, Malone D et al (2016) State of the art in power line communications: from the applications to the medium. IEEE J Sel Areas Commun 34(7):1935–1952 11. Luo J, Fan L, Li H (2017) Indoor positioning systems based on visible light communication: state of the art. IEEE Commun Surv Tutorials 19(4):1–1 12. Zhang Y (2020) Simulation of public art communication in colleges based on smart cloud platform and artificial intelligence algorithm. J Intell Fuzzy Syst 11:1–11
Application of AI in Computer Network Technology in the Big Data Era Yanli Zou
Abstract Emerging technologies have always been a strong driving force for the continuous development of Computer Network Technology (CNT). A variety of emerging technologies, including big data (BD), cloud computing, VR, AR, and AI, are widely used in the field of computer networks, bringing network users Here comes a rich new experience. CNT is a product of the era of BD. People can use CNT to complete ultra-long-distance information transmission in a very short time. Combining Artificial Intelligence (AI) technology with CNT can increase data security while improving data processing efficiency. Data cleansing, also called data cleansing, detects and removes errors and inconsistent data parts from the data to improve data quality. This article takes the application of AI in data cleaning as an example to study the application of AI in CNT in the era of BD. Experiments show that the data cleaning program described in this article has the characteristics of high degree of automation, less human management, clear structure, easy maintenance and high operating efficiency. Keywords Big data · Artificial intelligence · Computer network · Data mining
1 Introduction At present, when people use computers to process relevant data, they will rely on AI technology to assist in completion [1, 2]. The so-called AI technology is the application of human logical thinking, learning ability, etc. to machine programming, so that the machine can better simulate human performance, thereby showing intelligent actions and behaviors [1, 3]. The advantages of AI are mainly reflected in the three aspects of computing power, data, and algorithms. By applying it to CNT, it can effectively improve the efficiency of computer data processing, as well as improve the Y. Zou (B) Kunming University of Science and Technology Oxbridge College, Kunming 650200, Yunnan Province, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_123
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stability and intelligence of system operation, and serve the long-term development of society [4, 5]. The research on data cleaning started in the United States in the 1950s and 1960s, and it mainly originated from the cleaning and error correction of the National Social Security Number [6, 7]. However, in recent years, due to the rapid development of the Internet and technology, the era of global informatization and BD has been promoted, making data cleaning a hot issue for scholars. At present, the research objects of data cleaning abroad are mainly data integration and data redundancy, data missing, data abnormality and other issues [8, 9]. For the problem of data integration, the data warehouse field has been studied more, and many data integration platforms have been developed. For example, the Data Works platform, which is a platform dedicated to data integration and analysis developed and released by IBM. The Data Works cloud service can effectively help workers improve data by loading, analyzing, and processing data [10]. Based on the background of the era of BD, this article analyzes the relationship between BD and AI, and discusses the specific application of AI in CNT from the perspective of technical application, including network security management technology, network system management and evaluation technology. At the same time, it enumerates the application of AI in CNT [11].
2 Application of AI in CNT in the Era of BD 2.1 Advantages of Integrating AI Technology into CNT (1)
(2)
Improve the stability of network operation AI can improve the operational stability of computer networks. AI can not only make data dynamic during network transmission, but also make data transmission more flexible through powerful computing and information exchange capabilities. When processing data, it is also possible to solve problems in the network by simulating human logical thinking. Improve the processing effect of BD information The main feature of BD is that the data information is huge and messy, and a large amount of data information is produced every day. Because AI technology has good stability and security in data processing, it can avoid many problems caused by unpredictable factors in the process of data processing. Therefore, the integration of AI and computer technology can significantly improve the processing quality of BD.
2.2 Application of AI Technology in CNT (1)
Application of AI in data security management
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Under the influence of BD, AI technology can be mainly used in the following points in data security management. First, the use of AI in intrusion detection. Intrusion detection refers to a detection activity for computer network intrusion. Through the analysis and processing of key data information such as network activities and network security diaries, it can find out some of the current network system behaviors that affect data security, and found traces of intrusion left in the network system. As an important part of network security protection, intrusion detection technology must ensure that traces of network attacks can be found in time, and the difference between external attacks and misoperations can be distinguished technically and accurately, so as to complete real-time protection of the network. Intrusion detection technology, as the main technology to protect the security of network data information, can classify network data information according to different types in the actual application process, and timely feedback data security detection reports to users, so that users can learn about the computer in the first time. With the current network security situation of the system, targeted problem handling can be carried out in time to increase network data security. Second, the use of spam processing. Many people receive a large amount of spam in their e-mails, and people can only find useful messages by constantly filtering through spam. Spam has brought people a very bad mailbox experience, and both life and work will be affected by it. The use of AI technology can classify spam based on human logical thinking, making the email information received by users more concise and clearer, and improving the security of mailboxes while improving the efficiency of user email processing. Third, the application in the firewall. As an indispensable link in the computer system, the firewall system can protect the data information in the computer system when people’s abnormal data invades the network system. Traditional firewall technology often does not have enough flexibility in the process of information interception and system protection, so it is difficult to fully exert the protection effect. The intervention of AI technology can greatly improve the ability of the firewall system to judge harmful information. By improving the judgment of spam, it can make computer information classification easier and increase the processing efficiency of data information. Application of artificial neural network AI can complete the analysis and simulation of the human brain and produce artificial neural networks. That is, the CNT is used to complete the simulation of the way the human brain sees things and problems, so that the AI technology shows the effect of simulating human thinking in the actual application process. Artificial neural networks have very strong compatibility, so in many industries, AI technology can be used to optimize the industry. Moreover, the artificial neural network can also improve the management efficiency of the computer system, and increase the security of the system by monitoring the intrusion path of abnormal data in the computer system. In addition, the use of AI to simulate human thinking can also increase the fault tolerance rate of the system. Our country’s artificial neural network technology
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has been widely used in noise and distortion recognition, and artificial neural network technology has also received attention from all walks of life in the era of BD. Data mining technology Data mining technology is one of the most important technologies in the field of AI technology in the new era. During actual application, data mining technology can complete the extraction of network connections and host sessions. In order to ensure the reasonable application of AI technology, the improvement of data mining capabilities is very important. The relationship between data mining capabilities and data processing capabilities is very close. During practical applications, AI can dig deeper into data and combine with powerful Data analysis capabilities to increase the quality of data information processing, so that the computer system becomes more secure. Regarding the operation mechanism of the computer system, by mining keywords, it is possible to deeply understand the intrusion of abnormal data, which facilitates subsequent analysis and judgment of abnormal data, and avoids the normal operation of the computer system due to the intrusion of abnormal data. For example, in the face of abnormal data intrusion from the outside world, AI technology can further increase the data accuracy of abnormal data in the judgment process, so that the computer’s anti-risk ability can be greatly strengthened. In addition, a data alarm system can also be set up through data mining technology, and when abnormal data invades the computer system, an alarm can be issued in time, and then the abnormal data can be processed through the intelligent mode. AGENT technology The application of AGENT technology can also use the host as an intrusion detection system to assist the computer to complete the analysis of abnormal intrusion data. In practical applications, the AGENT technology also has relatively strong learning ability and adaptability. It can process the external information by itself after entering the computer system. Therefore, the AGENT technology is not highly dependent on the network environment and can assist the development of the computer system. In addition, because AGENT has independent computing resources and behavior control mechanisms, it can also control behavior patterns based on the internal conditions of the system and the information detected around it without external instructions, and independently carry out autonomous processes. Application of AI technology in system management and evaluation In the problem-solving technology, AI technology can solve some problems through human thinking mode under given conditions. In the process of solving problems, the search and reasoning capabilities of AI often play a very important role. During the functional evaluation period, AI can complete the calculation of the optimal solution to the functional situation, thereby improving the final operating effect of the computer network. Expert knowledge base, as an important technology of AI, can influence the expert system in the application process. The construction of the expert knowledge base is formed by the accumulation of a large amount of data and information. When dealing with
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network coding, the expert system can increase the management and evaluation efficiency of the system.
2.3 Application of AI Methods Bayes’ theorem: P(Yi |x) =
P(x|Yi )P(Yi ) P(x)
(1)
P(x|Yi )P(Yi ) = P(a1 |Yi )P(a2 |Yi ) . . . P(an |Yi )P(Yi ) = P(Yi )
n
P(a j |Yi )
(2)
j=1
3 Application Experiment of AI in Data Cleaning in the Era of BD 3.1 Experimental Method Retrieve any day’s historical file from the CATT historical data repository as the data source file and save it in the test data pool to prepare for testing. By running the data cleaning process of this program and comparing the records of applicants and recruitment information stored in the personnel system, the effect of data cleaning can be judged.
3.2 Experimental Environment Experimental database platform: • • • •
CPU: PII Zhiqiang 1 GHz Memory: 1 GB Operating system: Windows 2016 Server Database system: MS SQL Server 2000 Development Edition Experimental Linux platform:
• CPU: P4 3.0 GHz • Memory: 1 GB
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• Operating system: SUSE Linux 10 • Compiler: GCC4.0.2 • Perl environment version: v5.87
4 Experimental Analysis This article mainly takes the data set of the fluid supply monitoring and control unit in the corporate coal mining system as an experimental object, including the oil temperature sensor, oil level sensor and oil pressure sensor. We divide this data into three groups for testing and the specific distribution is shown in Table 1.
4.1 Evaluation of Correctness From this results comparison graph, we can see that as the feature sample data set increases, the accuracy of the improved algorithm is significantly higher than the accuracy before the improvement. Although the accuracy rate at the beginning of the test is slightly lower than before the improvement, this is because the improved algorithm is based on the cost function and the improved algorithm is intended for feature selection of huge data, so this phenomenon only occurs when the data set is small. When the amount of data reaches a certain level, the advantages of the improved algorithm will gradually appear, especially the larger the amount of data, the more obvious its advantages are (Fig. 1).
Accuracy%
Table 1 Distribution of experimental data sets
95 90 85 80 75 70 65
data set
Data file size/MB
Number of samples/piece
Training set
100
100,000
Test set 1
400
400,000
Test set 2
800
800,000
Improved data cleaning
1
2
3
4
Unimproved data cleaning
5
Number of samples Fig. 1 Comparison of accuracy analysis results
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4.2 Speedup Analysis As shown in the data set distribution in Table 1, we divided the initial data set into three sets of experimental data to check the algorithm speed-up ratio before and after the upgrade, and to verify the performance and feasibility of the improved algorithm. The three sets of experimental data sets have 100,000 training sample data, 400,000 test data, and 800,000 test data. The specific experimental results are shown in Figs. 2, 3 and 4. From the experimental results of the above three different data sets, it can be seen that the speedup test of AI in the parallel design of the data cleaning algorithm in this article basically shows a linear increase. Although the initial speedup ratio in the experimental results of the training set and test set 1 is less than 1, this is due to the small data size and the inevitable job delay when using the Hadoop platform under BD. With the expansion of the data scale, the phenomenon of this kind of job delay can be ignored. This phenomenon becomes more obvious with the expansion of the data scale, which fully illustrates the effectiveness of the improved algorithm.
Speedup ratio
Improved data cleaning 3.5 3 2.5 2 1.5 1 0.5 0
1
2
Unimproved data cleaning
3
4
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Number of nodes/piece Fig. 2 Speedup of the training set
Improved data cleaning
Unimproved data cleaning
Speedup ratio
5 4 3 2 1 0
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Number of nodes/piece Fig. 3 Speedup for test set 1
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Improved data cleaning 6 5 4 3 2 1 0
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Number of nodes/piece Fig. 4 Speedup for test set 2
5 Conclusions In the context of the era of BD, the development of AI technology and CNT is very fast, and the combination between the two has also become a development trend in the progress of the times. Through the application of AI technology, the storage security and transmission stability of computer network data can be greatly improved. I believe that as more people learn about the advantages of AI technology, its application in CNT will definitely become more complete.
References 1. Wang Q, Lu P (2019) Research on application of AI in CNT. Int J Patt Recogn AI 33(5):1959015.1–1959015.12 2. Yao J, Liu J (2021) Research on CNT system based on AI technology. J Phys: Conf Ser 1802(4):042028 (6pp) 3. Jiang Y, Han DK, Ko H (2019) Relay dueling network for visual tracking with broad field-ofview. IET Comput Vision 13(7):615–622 4. Chen C (2020) Analysis of the application of AI in computer networks technology. IOP Conf Ser: Mater Sci Eng 750(1):012097 (11pp) 5. Huang Y (2020) Research on computer network service quality optimization method based on integration of multiple AI technologies. IOP Conf Ser: Mater Sci Eng 740(1):012113 (5pp) 6. Kannaiyan GN, Pappula B, Veerubommu R (2021) A review on graph theory in network and AI. J Phys: Conf Ser 1831(1):012002 (8pp) 7. Zhang L, Chen Z, Yang S (2021) Application of AI in computer network security. J Phys: Conf Ser 1865(4):042039 (3pp) 8. Yong QL (2021) Application analysis of AI in library network security. J Phys: Conf Ser 1744(3):032024 (7pp) 9. Dong Z, Wei J, Chen X et al (2020) Face detection in security monitoring based on AI video retrieval technology. IEEE Access 8(99):63421–63433 10. Oikonomou VP, Blekas K, Astrakas L (2020) Identification of brain functional networks using a model-based approach. Int J Patt Recogn AI 34(08):1–21 11. Zhang RS, Quan WZ, Fan LB et al (2020) Distinguishing computer-generated images from natural images using channel and pixel correlation. J Comput Sci Technol 35(3):592–602
Business English Online Classroom Teaching Based on ESP Demand Analysis Technology Weihua Kuang
Abstract With the deepening of international trade, people’s demand for the application ability of business English (BE) is increasing, and English for special purpose (ESP) comes into being. ESP is based on the special needs of learners, and one of its distinctive features is to meet the needs of learners. The purpose of this article is to conduct research on BE online classroom teaching based on ESP demand analysis technology. Based on demand analysis, this article analyzes the problems and causes of BE online classroom teaching with questionnaire survey and interview methods, and then proposes optimization of BE online classroom teaching in terms of course objectives, teaching system settings, and teacher team construction. Suggestions, so as to provide reference for the online classroom teaching of “BE” in colleges and universities. According to the survey data of student demand on teaching content, 71.13% of students believe that the classroom teaching content of ESP “BE” should be based on comprehensive oral learning applied to business scenarios, and 54.12% of students believe that business correspondence should be written as the Lord. Therefore, teachers’ teaching goals and teaching content should be combined with the latest industry trends, to transfer knowledge to the target application field, so that students can apply what they have learned in the future. Keywords Needs analysis · Business English · Online classroom · Course optimization
1 Introduction The emergence and development of ESP conforms to the requirements of the development of the times and is the inevitable trend of English language development [1, 2]. With the continuous development of international trade, there is an increasing demand for the practical application ability of BE [3, 4]. Analyzing the current situation of BE online classroom teaching based on the needs of ESP, and formulating a W. Kuang (B) Jiangxi University of Engineering, Xinyu 338000, Jiangxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_124
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classroom teaching design that meets the needs of students, is of great significance to students, teachers, and professional education [5, 6]. Research on needs analysis is in full swing. For example, Yu and Xu believes that the focus of needs analysis is to grasp what learners already know and what they need to know, and then to bridge the gap between these two aspects [7]; Si believes that attention should be paid The learning needs of learners in a learning environment, which includes language use environment and language learning environment[8]; Muoz through research on tourism English, finds problems in tourism English curriculum design and teaching, and gives improvements Countermeasures [9]. The purpose of this article is to conduct research on BE online classroom teaching based on ESP demand analysis technology. Based on demand analysis, this paper analyzes the problems and causes of BE online classroom teaching through questionnaire surveys and interviews, and then proposes optimization of BE online classroom teaching in terms of course objectives, teaching system settings, and teacher team construction. Suggestions, so as to provide reference for the online classroom teaching of “BE” in colleges and universities.
2 BE Online Classroom Teaching Based on ESP Demand Analysis Technology 2.1 Problems in BE Online Classroom Teaching Based on ESP Demand Analysis Technology (1)
(2)
The professional training objectives are not clear A series of documents formulated by the Ministry of Education on the training objectives of BE majors have pointed out the direction for the construction and development of BE majors in various schools [10, 11]. These policies have legal significance. Normally, the Ministry of Education also uses this as an evaluation basis for the professional evaluation of schools. If the evaluation fails, it will be ordered to rectify or cancel the major. Therefore, all colleges and universities will all follow the regulations of the Ministry of Education when formulating talent training plans. However, the history, characteristics, orientation, and level of teachers of BE in each school are different, but the professional training goals are roughly the same. It is unscientific and unreasonable to formulate a unified training goal if you ignore the actual situation of the school and the needs of local economic development. The course structure is unreasonable Most of the BE majors in schools are in the foreign language department. The teaching staff is mainly based on teachers with English majors, and the curriculum is mainly based on English language courses [12]. Students are mainly taught in English and cultivated. There is no big difference between the
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(3)
(4)
(5)
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students who come out and ordinary English majors, and they cannot understand and communicate well with business knowledge and English knowledge. Such talent standards do not meet the needs and expectations of society. Internship training is just a formality Most schools cannot provide good training venues or internship opportunities, and even some schools have not opened any professional training courses. The difficulty of internship training has become a major problem for the development of BE in higher vocational colleges. Schools do not pay attention to students’ practice. Most schools only arrange students to support teaching in mountainous areas or to investigate folk customs in the countryside during the summer vacation. In order to attract the attention of the society and the government and create a news effect, schools pay more attention to the external effects of practical activities. It’s not a practical effect on students. Practical activities lack professional pertinence, and the content is broad and it is difficult for students to apply what they have learned, and it is difficult for students to play an educational role in their study and future work. Unreasonable teaching staff According to related research findings, most BE teachers are from English majors and master business knowledge through textual research and post-study. Since they have no business experience, they only follow the textbooks when giving lectures. Knowledge-based lectures; another small group of teachers have business backgrounds and have little knowledge of English, so they cannot teach in English and are only responsible for teaching business professional courses; there are very few dual-professional teachers who understand English and have business work experience. The school is almost zero. A single faculty structure is easy for students to split business knowledge and English knowledge, which is not conducive to students’ grasp of the integrity of BE knowledge, and it is impossible to integrate the two to understand and use them flexibly in practical work. In addition, most of the teachers are college graduates majoring in English, and they mainly focus on knowledge of English in the course setting and teaching process. This is not in line with the composite training goal of “business” and “English” for BE talents. The course evaluation system is unreasonable In terms of evaluation subjects, the current evaluation subjects of higher vocational colleges mainly include class teachers and school experts. For various reasons, the evaluation of the students’ learning level by school experts is almost negligible, and the teacher in charge has become the main body of the evaluation. However, higher vocational education is an education that focuses on the training of operational skills and practical ability. The content of learning includes various business abilities and skills. Therefore, only letting teachers who teach theoretical knowledge as the main body of evaluation will highlight many drawbacks.
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2.2 Optimization Strategy of BE Online Classroom Teaching Based on ESP Demand Analysis Technology (1)
(2)
(3)
Improve the curriculum The formulation of the course plan must be based on relevant actual research. The first is the investigation of students’ learning needs. In the early stage of formulating the training plan for the BE major in the transitional period, the individual colleges must classify the graduated students according to the types of jobs, and then randomly select all the types of jobs that are divided One person acts as a representative of the survey or conference. This is to increase the breadth and representativeness of the sample of the survey or conference. The second is the survey of employers’ ability requirements for graduates. Employers’ ability requirements for BE graduates are constantly evolving and improving with the development of the times. Therefore, it is said that the participation of graduates and graduated students is Apart from entering, it is necessary to invite senior engineers or senior management personnel of different levels of employers to participate, and ask them to tell what kind of talents they need most, so as to improve the professional curriculum setting, and to cultivate suitable talents. Curriculum program for the employing unit’s compound talents. Develop teaching goals based on the characteristics of the colleges and universities Colleges and universities should combine local characteristics and their own development characteristics, and introduce relevant systems suitable for school development, so as to better improve the ‘strong’ posture of the Academic Affairs Office. The first is to delegate the rights of the professional talent training program to each secondary college, regarding the specific division of theoretical courses and practical courses in the curriculum of the training program; the second is to reserve the rights of related supervision and guidance to the Academic Affairs Office, that is, the Academic Affairs Office is to convey. The concept of the school’s top-level design is applied to each secondary college, and the professional talent training plan designated by the secondary college based on the research is fully grasped, as long as it conforms to the school’s top-level design philosophy and can promote better development of the school. Increase government support to build a team of ‘dual teacher’ teachers If colleges and universities are able to formulate curriculum plans that meet the local economic development, the relevant ability requirements of the employer, and the learning requirements of the students of the major they are studying, they must have sufficient support from a team of “composite” and “dual-professional” teachers. The construction of a sufficient and qualified teaching team requires strong support from school-level and local government-level education funds.
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The first is in terms of teachers. At the school level, the first is to strengthen the training and education of teachers after they are employed. For BE teachers with English majors, the school can invite employees with rich experience in foreign trade companies or outstanding salesmen, orderers, etc., to provide relevant training for teachers on the school during holidays or winter and summer vacations. The second is to encourage full-time teachers to participate in various BE competitions. Schools and companies regularly organize various business skills competitions to encourage teachers to consolidate their business theoretical knowledge and improve business practice capabilities during the competition. Secondly, in terms of capital investment, the local government where the local colleges and universities are located must actively connect with the national vehicle, respond to the country’s major policies, proactively introduce relevant local-oriented policies, and clearly indicate that it will vigorously give the transformation colleges and universities technically and financially. In addition, certain guidance must be given while supporting, and technical resources and funds must be used where necessary and with the greatest value. Don’t let the transformation colleges and universities do superficial efforts, but down-to-earth and truly shift their majors to drive the localities. Economic development has also improved the employment rate and employment quality of students with applied courses.
2.3 SVM Classification Algorithm for Classroom Teaching Evaluation Classroom teaching evaluation should determine the scientific evaluation standard and content framework according to the educational goals and standards, and establish a scientific and correct evaluation system and evaluation method. The SVM classification algorithm for classroom teaching evaluation can give a comprehensive evaluation of the combination of quantity and sex to each link of the classroom, teachers and teaching according to the changes of the evaluation information. Suppose F(x, y) is the joint probability distribution of variables x and y (that is, the dependence relationship between x and y), (xi , yi ) is an independent and identically distributed observation sample, and machine learning is based on (xi , yi ) from the prediction function set f (x, w) to find an optimal function f (x, wo ). R(w) =
L(y, f (x, w))d F(x, y)
(1)
Obtain the minimum value, that is, the expected risk R(w) is the smallest, where and L(y, f (x, w)) w respectively represent the loss function and the generalized parameter.
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However, the hope risk of Formula (1) cannot be obtained by a small amount of observation samples. Traditional learning methods can solve this problem, as shown in Formula (2): Remp (w) =
n 1 L(yi , f (xi , w) n i=1
(2)
3 Investigation and Research on BE Online Classroom Teaching Based on ESP Demand Analysis Technology 3.1 Research Methods (1)
(2)
Questionnaire survey method Collect and analyze relevant students’ data through questionnaire survey. The questionnaire is designed based on the needs analysis model of DudleyEvans and St. John and combined with the purpose of this research. The questions appear in the form of multiple-choice, multiple-choice and subjective essay questions. Taking into account the English proficiency of the students in this school, in order to ensure for the accuracy of question understanding, the language of the questionnaire is Chinese. The content of the questionnaire is mainly divided into three parts: personal information, the status quo of “BE” online classroom teaching design and students’ needs for “BE” online classroom teaching design. Interview method In this study, information was collected from graduate representatives and employers through telephone and face-to-face interviews.
3.2 Survey Design (1)
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Research object The research objects of this article are mainly divided into two categories: the first category is based on the learning needs of students, mainly the representatives of the 2015 international trade major of A school and 3 graduates engaged in cross-border e-commerce; the second category is From the purpose of the target demand analysis, the main employer information. Data collection A total of 200 questionnaires were issued, and 194 were effectively recovered, with an effective recovery rate of 97%.
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4 Data Analysis of BE Online Classroom Teaching Based on ESP Demand Analysis Technology 4.1 Analysis of BE Online Classroom Teaching Content The survey on the difficulty of the current “BE” online classroom teaching materials is shown in Table 1: 45.88% of the candidates are “difficult”, 32.99% of the candidates are “harder”, and 11.86% are candidates. “Easier”, 9.28% of the candidates are “Very easy”. When asked about the reasons, 54.64% thought that “the vocabulary was not enough” and 51.80% thought that they were “not strong in listening and speaking skills”. It can be seen from Fig. 1 that among the students majoring in International Trade, students with a good foundation in English account for a small proportion. Most students in this major think that the subject of “BE” is still relatively difficult, and the overall level of BE still needs to be improved. Therefore, when BE teachers choose teaching content and teaching methods, they should start from the students’ actual ability level and choose “applicable” teaching content and teaching methods. According to the analysis of students’ needs, we should pay attention to the cultivation of vocabulary, listening and speaking, and grammar skills in the teaching process. In terms of teaching strategies, we can cultivate students’ comprehensive ability by creating real BE communication scenarios. Table 1 Analysis of BE online classroom teaching content Difficulty of the textbook
The biggest difficulty in learning “BE” (multiple choices)
Options
Number
Proportion (%)
Difficult
89
45.88
Harder
64
32.99
Easier
23
11.86
Very easy
18
9.28
A Lack of vocabulary
106
54.64
B Listening and speaking are bad
72
51.80
C Grammar structure is weak
87
44.85
D Lack of real language chat situation
52
26.80
E Less time to learn English
52
26.80
F Other
10
5.16
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proportion 5.16 10
F E
26.8
D
26.8
52 52 44.85
C
Options
Number
87
51.8
B
72
54.64
A
106
9.28
Very easy
18 11.86
Easier Harder
23
32.99
64
45.88
Difficult 0
20
40
89 60
80
100
120
Proportion: % Fig. 1 Analysis of BE online classroom teaching content
4.2 Teaching Content Student Needs Regarding the survey on “Which aspect do you think the ideal ESP “BE” online classroom teaching content should be? (multiple choices are available)”, the results are shown in Table 2: 71.13% of the students think ESP “BE” teaching content should be based on comprehensive oral learning applied to business scenarios. 54.12% of students think that the writing of business correspondence should be the main focus, and 10.30% of students think that BE and e-commerce professional knowledge should be used for learning. Mainly, 34.02% of students think that intensive reading of BE articles should be the main focus. It can be seen from Fig. 2 that students majoring in International Trade most hope to improve their comprehensive oral and business writing skills in business scenarios through the online course “BE”, which meets the industry requirements of international trade. Secondly, intensive reading of BE articles, BE and e-commerce Table 2 Teaching content student needs Ideal ESP “BE online classroom Options teaching content” (multiple A BE article accuracy choices) B Comprehensive oral learning applied to business scenarios C Business correspondence writing
Number Proportion (%) 66
34.02
138
71.13
105
54.12
D BE and e-commerce 20 professional knowledge docking learning
10.30
Business English Online Classroom Teaching Based on ESP …
Number
160
proportion
138
140 120
Options
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105
100 80
71.13
66
54.12
60 40
34.02 20
10.3
20 0 A
B
Proportion: %
C
D
Fig. 2 Teaching content student needs
professional knowledge are also preferred by students. Teachers should choose suitable teaching materials according to students’ needs and combine the latest industry trends to transfer knowledge to the target application field, which is convenient Students will apply what they have learned later.
5 Conclusion The purpose of this article is to study the online course teaching of “BE” based on ESP demand analysis technology. Based on the analysis of the survey results of current students, graduates and employers, this paper puts forward suggestions for optimizing the classroom teaching of “BE” online courses based on the perspective of ESP demand analysis, such as perfecting the curriculum, combining the characteristics of the colleges and universities, and formulating teaching goals, etc., Aims to improve the effectiveness of the classroom teaching of “BE” in this school, and at the same time provide suggestions for the teaching of this course in similar colleges.
References 1. Price JM, Whitlatch J, Maier CJ et al (2016) Improving online teaching by using established best classroom teaching practices. J Contin Educ Nurs 47(5):222–227 2. Branzila CI (2020) Online teaching English for business and economics in the time of pandemics. Virgil Madgearu Rev Econ Stud Res 13(2):27–36
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3. Glatz MK (2018) Conceptualising English as a business lingua franca. Eur J Int Manage 12(1/2):46 4. Raisanen T (2020) The use of multimodal resources by technical managers and their peers in meetings using English as the business lingua franca. IEEE Trans Prof Commun 63(2):172–187 5. Komori-Glatz M (2018) Conceptualising English as a business lingua franca. Eur J Int Manage 12(1–2):46–61 6. Vila-Cabrera JJ, Esteban AC (2021) The project SubESPSKills: subtitling tasks for students of BE to improve written production skills. Engl Specif Purp 63(4):33–44 7. Yu Q, Xu S (2019) Compiling BE course books guided by national criterion and test syllabus. Linguistics 1(1):1–11 8. Si J (2019) An analysis of BE coursebooks from an ELF perspective. ELT J 2:2 9. Muoz A (2019) Beyond language: a multimodal analysis of success in non-native business English pitches. Iberica 37:65–86 10. Evan F (2019) BE materials. ELT J 4:4 11. Subero A (2016) Introducing BE. Engl Specif Purp 43:72–74 12. Daly P, Davy D (2018) Language boundary-crossing by business school faculty using English as a medium of instruction. Eur J Int Manage 12(1/2):62
Application of AI Video Image Technology in Soft Ladder Strength Teaching and Training System Yujia Wang
Abstract Ladder strength training is an important content of athletes’ daily training, is the focus of athletes’ teaching, and one of the more difficult techniques to master. Today’s soft ladder strength teaching training is mainly based on subjective judgments and experience-based traditional teaching methods. Teachers rely on subjective observations to guide, correct and objectively evaluate students’ technical movements. They cannot communicate teaching content to students intuitively, vividly and vividly. Exploring a set of practice methods suitable for enthusiasts of ladder strength training and teaching methods suitable for teachers is the research direction of ladder strength training workers. In this paper, 24 male students in a domestic university are used as the research entity, through the use of traditional decomposition teaching mode combined with AI video image technology, a comparative study with traditional teaching methods, and related experiments on 24 students. Experiments show that AI video image technology combined with traditional teaching mode is of great help to the improvement of students’ ladder strength training performance. Keywords AI video image technology · Ladder · Teaching training · Teaching mode
1 Introduction Soft ladder, also known as rope ladder, sensitive ladder, as a kind of ladder, has the characteristics of not limited by length, light in use, and easy to carry [1]. Nowadays, by changing the material, portability, size of the ladder, the ladder is gradually applied to the field of sports, and ladder training has become one of the modern physical training methods. Ladder training is divided into three categories according to the direction: straight line, level, straight line plus level [2]. In addition, you can also practice by changing the body posture, prone position and direction. From an anatomical point of view, the ladder training mainly focuses on the lower limbs. The Y. Wang (B) Criminal Investigation Police University of China, Shenyang, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_125
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movement is maintained by constantly changing footsteps and directions. During the exercise, it continuously performs sudden stops, sudden rises, accelerations, brakes, and double pedals ground force, the ground rebound force is transmitted to the calf through the feet, and then to the thigh and torso in turn [3]. In terms of physiology, ladder training mainly improves the speed and speed by changing the efficiency of the nerve-muscle system, and improves the control ability of the nerves over the muscles through repeated stimulation of the cerebral cortex nerves, which is reflected in the improvement of various qualities. Chinese scholar Yaling pointed out that the soft ladder training method is a pioneering training method to improve flexibility. It is not limited to a single movement, but has been applied to multiple sports. At present, the related theoretical research and practical exploration of the soft ladder training method are still relatively weak [4]. Honglei pointed out that the ladder training method refers to a training method in which athletes complete multiple sets of prescribed actions within a certain period of time to improve their skills. Combining the principle of the ladder training method, through studying the application of the ladder training method in the special training, in order to enhance the athlete’s physical coordination and improve the competition performance. Ladder training method can improve the athlete’s reaction speed, movement speed and physical fitness, thereby enhancing the athlete’s competitiveness [5]. Zhongzheng et al. pointed out that ladder training and traditional training have improved the flexibility of athletes, but the effect of ladder training is more obvious. Therefore, in training, the proportion of actions in ladder training should be appropriately increased to better promote the improvement of athletes’ flexibility [6]. Video image method is a very common, vivid, intuitive and effective teaching method in physical education classroom teaching. It is mainly reflected in two aspects, image processing, comparative analysis and video production clip playback. Video production editing refers to combining the speed of technical movements, slowing down students’ practice movements by 10 times or more, so that the slowed movements can be clearly compared with the movements of the world’s top athletes, to find out the differences, and to help learners build a continuous and complete action concept [7, 8]. Today is the heyday of the development of AI video image technology. Multifunctional smart phones, digital cameras, projectors and other electronic products are constantly being used in life, as well as in various technical aspects of ladder strength teaching and training. The use of digital media functions in sports technology teaching relies on the vividness of the actual effects of the media. Similarly, the use of video image technology in the ladder strength teaching training can clearly show the students’ movement processes and essentials. It can make the body language movements that pass by in a moment change to static and turn fast to slow, replay the movements repeatedly, observe from the whole point to the point, and promote the understanding and imitation of students, so as to help improve the level of ladder strength teaching training and teaching effect [9, 10].
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2 Comparative Analysis of AI Video Image Technology Assisted Traditional Teaching and Traditional Teaching Mode 2.1 Help Students Establish Correct Visual Motion Representation There are many training skills that need to be learned when performing ladder strength teaching training. These skills are more complex in structure, and there are many technical actions to be completed in a short time. In the training of ladder strength teaching, students should carefully observe the process of skill action essentials, and repeatedly imitate these technical essentials until they have completely absorbed these action essentials [11, 12]. Using AI video image technology, through the computer, you can demonstrate the technical links that are difficult for teachers to clearly show. Use images, videos, slow motion images, stop mirrors, recurrences, to explain technical details and each action, and make the action faster and more complete present. In addition, students’ own action videos and pictures are played on the computer through three-dimensional cameras, so that students can watch the technical action videos of the world’s best players, so that students can clearly recognize their shortcomings and shortcomings, and teachers can make more complete suggestions for error correction. So as to enable students to master the technical movements proficiently and quickly, and achieve the dynamic stereotypes of the movements. Ladder strength teaching and training movements are a series of coordinated exertion movements. You can use the motion technology images of outstanding athletes as teaching demonstration materials to provide demonstration, and then play the students’ own service motion videos and pictures, so that the students can find the deficiencies in the training and make corrections.
2.2 Give Full Play to the Leading Role of Teachers and the Main Role of Students In the past process of soft ladder strength teaching and training, it was usually carried out in a teacher–student model. This training mode means that teachers need to spend a lot of time in the unilateral teaching process, such as explaining the movement essentials to the students and demonstrating the skills in person. As for the results of student training, teachers often do not have enough time to correct the errors in student training. Therefore, the traditional soft-ladder strength teaching mode prevents students from making timely corrections from the wrong training methods. It seems that students are not the main body of teaching, and the training efficiency is very low. However, the use of AI video image technology can change the traditional
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ladder strength teaching and training model and transform it into a teacher-machinestudent model. Teachers can collect teaching materials before teaching, including images, pictures, and videos, with corresponding sounds and texts. Broadcast to students in a timely manner, without spending a lot of time explaining and demonstrating to students, increasing the time for teachers and students to communicate, giving teachers more opportunities to understand the students’ thinking process, thereby improving students’ thinking ability and training students’ creativity, to improve the leading role of teachers in the training of soft ladder strength and the main role of students, and improve the efficiency of training. The use of AI video image technology to assist teaching and timely feedback can help students correct their own understanding and wrong technical actions in the process of practice, reform and innovate the soft-ladder strength teaching and training model, which is conducive to the improvement of the quality of soft-ladder strength teaching and training.
2.3 Enriching Teaching Methods and Improving Students’ Motivation The teaching methods of AI video image technology are vivid, innovative, and diverse. Compared with the traditional soft-ladder strength teaching and training mode, the teacher’s ability in demonstration and explanation has been greatly improved. The application of AI video image technology not only responds to the psychological characteristics of young people’s curiosity and innovation, but also makes students feel better during the training process. This model allows students to focus more on the ladder strength training, creating a good training atmosphere. The software created by AI video image technology can greatly optimize training content, training methods, and training demonstrations, and can display trainingrelated content vividly and intuitively. In the ladder strength teaching training, the knowledge and information taught to the students are shown to the students in the form of graphics, cartoons, music, colors, which makes the ladder strength teaching training have a certain artistic quality. In addition to teaching the essentials of soft ladder strength teaching, video images can also make students more concentrated and thinking more active. The music in the video can adjust the teaching atmosphere and reduce the mental pressure of students during the training process. The color of the image can free students’ attention from hard training and reduce fatigue. AI video image technology can perfectly realize the concepts of teaching, learning, and entertainment education, creating an active training atmosphere, which is very helpful for improving students’ training enthusiasm.
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3 The Application Process of AI Video Image Technology in the Soft Ladder Strength Teaching and Training System 3.1 Experimental Method This article takes the experiment of using AI video image technology in soft-ladder strength teaching training as the research object, taking 24 male students from a domestic college as the survey object. Among them, 12 were in the experimental group and 12 were in the control group. The results of the test before and after the experiment are processed by related software, so as to verify the specific research tasks of this research. In this paper, two multifunctional cameras are used to collect the soft ladder strength training technical movements of the experimental subjects. A and B are two digital video cameras, respectively, which are the experimental object stations. The shooting frequency of the two digital cameras is 200 frames per second, and the angle between the main optical axes of the two cameras is about 90°, which meets the shooting requirements that the angle is greater than 60°and less than 120°to form a three-dimensional shooting. The main purpose of the experimental research on ladder strength teaching training is to analyze and verify that the technical movements of the students’ ladder strength training are more standardized under the aid of AI video image technology teaching, which has a certain effect on promoting the improvement of the level of ladder strength training technology.
3.2 Experimental Principles Scientific principles: experimental principles, experimental materials, experimental methods and scientific processing of experimental results. The experimental objects and groupings are all statistically processed to ensure that there is no significant difference between the experimental group and the control group. In the experiment, the blind test experiment teaching (that is, the subject does not know the teaching plan) and the comparative analysis method within the group before and after the experiment are used to ensure the logic and the comprehensive scientific nature of the experiment. Use SPSS software to process, and the processing method is systematic and scientific. Contrast principle: the experimental research process of using AI video image technology in the soft ladder strength teaching training, the teaching plan does not involve other irrelevant variables, and the objective influence conditions are minimized in the teaching process. There will be many other unrelated variables in the experiment, in order to eliminate the influence of unrelated variables, this experiment ensures that the two groups of students are consistent in the number of individuals, the number of class hours, the content of teaching, and the field equipment. Single variable principle: In this experiment of using AI video image
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technology in the soft ladder strength teaching training experiment, the experimental group adopts the method of video image technology to assist teaching, and the control group adopts the traditional teaching method, with only this single variable. This is a very important link in this experiment, it shows that in this experiment, no matter how many variable factors there are, there must be a one-to-one correspondence principle, and one-to-many phenomena cannot occur. Furthermore, all other variable factors must be controlled in the experiment, and only one variable factor is allowed.
3.3 The Specific Implementation Process of Teaching in Control Group and Experimental Group The control group uses the traditional teaching process, which is “mainly skill teaching, students passively accept, and repeat the practice of technical movements”, and then group exercises according to the formulated unified teaching plan, and the teacher conducts collective corrections and individual corrections during the exercises. Wrong, on the 8th and 20th day of training, the training actions of the control group students were photographed, and the success rate of 10 training sessions and the standardization of the training actions were scored. The experimental group uses AI video image technology to assist the traditional teaching method. The specific implementation process: teachers explain and demonstrate, organize students to watch the slow-motion playback of the training techniques of the world’s top athletes, so that students can establish correct representational awareness and carry out technical teaching. During the teaching process, the students’ movements are tracked and filmed every day. After class, the students’ training action videos are analyzed through computer processing software. The technical movements of the world’s top athletes are used as a reference standard, and standards for improving technical movements are formulated teaching tasks. At the beginning of the second day of training, the student’s technical movements that have been filmed and processed are shown to the students for viewing. The teacher refers to the standard collective and individual error corrections of the world’s top tennis players’ serve movements. The students in the experimental group used AI video image technology to shoot training content and technical actions, and the training actions of the students in the experimental group were photographed and processed one by one every day, so as to formulate teaching tasks and plans to improve the standardization of technical actions. At the beginning of the second day of training, the video and pictures of the training movements of the students and the world’s top athletes were screened for comparison and error correction. The teaching and training tasks were completed during the training that day, so that the students’ technical movements reached the standard. The first test of the experiment was carried out in the middle stage of the experiment, that is, the 8th day, and the next test was carried out in the post-experiment stage. That is, on the 20th day, a blind test is used, the grading teacher does not know which group the students come from, and scores the standardization and success rate of the technical
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actions of the experimental group and the control group to ensure that the scoring results are objective, fair and true. The formulas used in the scoring process are: s2 =
1 [(x1 − x)2 + (x2 − x)2 + ... + (xn − x)2 ] n
(1)
(a ± b)2 = a 2 ± 2ab + b2
(2)
4 Experimental Results and Analysis 4.1 Test Results and Analysis of Related Technical Actions During Training According to Tables 1 and 2, and Fig. 1, it can be known that after 21 days of training, the technical characteristics of the ladder strength training of the experimental group and the control group are close to the world’s elite athletes. In the jump height item, both the experimental group and the control group have improved. The difference between the experimental group and the world’s top athletes is 0.12 m, and the control Table 1 Test results of related technical actions before the experiment
Group
Experimental class Control class
Table 2 Test results of related technical actions after the experiment
Jump height (m)
Elbow angle (degrees)
Knee joint angle (degrees)
3.08
106.33
141.2
3.13
108.17
141.8
T
−0.482
−0.564
−0.131
P
> 0.05
> 0.05
> 0.05
Group
Jump height (m)
Elbow angle (degrees)
Knee joint angle (degrees)
Experimental class
3.73
Control class T P World elite athletes
72.88
118.9
3.10
98.26
133.8
9.056
−12.863
−3.847
< 0.05
< 0.05
< 0.05
3.85
73.65
115.1
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test results of related technical actions during traning Experimental class Control class Elbow angle before experiment
108.17 72.88 98.26
Knee joint angle before experiment
141.2
type of test
Elbow angle after experiment
106.33
141.8
Knee joint angle after experiment
118.9 133.8
angle value
Fig. 1 Test results of related technical actions during training
group and the world’s top athletes are 0.75 m away. The difference is significant (P < 0.05). Through 20 h of training and teaching, the experimental group is closer to the technical movements of the world’s top athletes. From the elbow point of view, the two groups of students have improved, but the technical movement characteristics of the experimental group of students are closer to the world’s elite athletes, and there is a significant difference (P < 0.05). From the perspective of knee flexion, both groups of students are approaching the technical level of the world’s top athletes. Compared with the control group, the experimental group’s technical actions are obviously closer to the world’s elite athletes. The experimental group’s improvement is significantly higher than that of the control group, and there is a significant difference (P < 0.05). This shows that “AI video image technology combined with traditional teaching mode” can clearly observe the characteristics of students’ body movements in the ladder strength teaching training, which has a significant effect on the improvement of students’ training skills.
4.2 Comparative Analysis of the Results of the Control Group and the Experimental Group After the Experiment According to Table 3 and Fig. 2, it can be known that after the experiment, the experimental group’s action technique is 61.27 points, the action standard is 31.27 points, and the total score is 92.54 points. The control group’s movement technique was 50.61 points, the movement standard was 20.83 points, and the total score was 71.44 points. It can be clearly seen that the results of the experimental group are
Application of AI Video Image Technology in Soft Ladder … Table 3 Comparative analysis of the results of the control group and the experimental group after the experiment
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Experimental group performance
Control group performance
Action technique
61.27
50.61
Action standard
31.27
20.83
Overall result
92.54
71.44
comparative analysis of the results of the control group and the experimental group after the experiment
achievement score
Experimental group
92.54 71.44
61.27 50.61 31.27 20.83
Action technique
Action standard
Total score
test index Fig. 2 Comparative analysis of the results of the control group and the experimental group after the experiment
higher than those of the control group, indicating that “AI video image technology combined with traditional teaching mode” is very helpful to students’ ladder strength training.
5 Conclusions After using AI video image technology to assist the teaching experiment, the technical movements, jumping height, elbow angle during training, and knee bending angle of the experimental subjects in the ladder training have been improved to a certain extent. The standardization of training actions in the experimental group is better than that of the control group. After using AI video image technology to assist in teaching, the experimental group and the control group were statistically analyzed from the technical evaluation and success rate of the action. The effect of the experimental group was better than that of the control group, and the standard technical actions had a promoting effect on improving the success rate. The use of video and image technology for auxiliary teaching is conducive to the innovation of teaching models
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in colleges and universities, and provides us with ideas for teaching reform, so that the methods and means of soft ladder strength teaching and training will develop in a diversified and diversified direction, which is conducive to improving soft ladder strength teaching and training the quality of.
References 1. Xuehai Z, Liang G (2018) An empirical study of “soft ladder training method” in college physical education. Sports Sci Technol Literature Bull 26(10):23–25 2. Guangpeng D (2015) Experimental research on the ladder training method in college basketball improvement class——taking yili teachers college as an example. Liaoning Sports Sci Technol 37(005):90–91 3. Nan C (2018) Analysis of the application of the ladder training method in the special training of Taekwondo. Contemp Sports Sci Technol 008(035):17–17 4. Yaling M (2019) Research on the influence of ladder training method on the sensitive quality of college aerobics students. Contemp Sports Sci Technol 009(025):67–68 5. Honglei Z (2019) On the application of the soft ladder training method in the special training of Taekwondo. Contemp Sports Sci Technol 009(012):33–33 6. Zhongzheng L, Haojie C, Zongqiang J (2019) The effect of ladder training on the agility of 8–10 amateur Taekwondo athletes. Contemp Sports Sci Technol 009(002):23–23 7. Guangwei C, Jiacheng F, Hongmiao C (2018) The effect of ladder training on the footwork ability of young table tennis players. J Nanjing Inst Phys Educ 001(005):64–75 8. Ye L (2018) The effect of ladder training on the sensitivity of badminton players’ lower limbs. Stationery Sports Suppl Technol 001(001):106–107 9. Xu W (2019) Experimental study on the influence of ladder training on the physical coordination of track and field college students. Sports Res Educ 034(004):90–92 10. Chuansheng P (2019) Research on the effect of rope ladder training on the agility of young boxers. Contemp Sports Sci Technol 009(036):32–33 11. Xihua Z (2019) Experimental research on ladder training of young male basketball players. J Nanchang Teachers College 040(006):93–96 12. Yinglin L, Wan Z (2019) Experimental research on the influence of rope ladder training on sensitivity. Stationery Sports Suppl Technol 000(014):209–210
Study on the Construction of Intelligent Supply Chain System Based on Internet of Things Technology Yanyi Deng and Rongli Dun
Abstract Since entering the 21st century, the core of supply chain management system is gradually approaching to the direction of intelligent information management, and the rise of the era of IOT has brought new opportunities for the supply chain system. The intelligent supply chain system based on the IOT technology is the key technology to realize the visualization, automation and intelligence of the supply chain system. By realizing the interconnection of everything in the construction of the intelligent supply chain system, we can effectively reduce the operating costs of enterprises, improve the market response measures, and then improve the intelligent management level of enterprises. On the basis of reviewing the concept and connotation of IOT technology and supply chain system, this paper proposes a supply chain system cooperation mode based on IOT technology. This mode perfectly integrates the complexity and high capacity of modern supply chain system, and constructs a new intelligent supply chain system with three aspects of automatic information acquisition, information transmission and big data mining, it improves the operation efficiency of the supply chain system. Relevant research can provide some theoretical and practical reference for the construction of intelligent supply chain system. Keywords IOT technology · Intelligent system · Supply chain · Operation efficiency · Strategic alliance mode
1 Background Under the impact of economic globalization, the supply relationship has undergone dramatic changes. Only enterprises that are constantly adjusted to adapt to market changes can they grow more easily. Therefore, it is important to effectively improve the sensitivity of supply chain system to global markets [1, 2]. In this form, the efficiency, flexibility and cost of information flow, cash flow, logistics and human resources between enterprises are particularly important [3]. Y. Deng · R. Dun (B) School of Business, Nanning College of Technology, Guilin 541000, Guangxi, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_126
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The IOT [4–6] is considered to be the third wave of rapid development of information after the computer and Internet. Generally speaking, the IOT connects everything in the world, that is, the connection between things, which is the most successful product of the Internet’s outward expansion. It successfully realizes the communication between objects and information systems. In addition, objects are also seamlessly connected with computers, wireless sensor networks and 4G mobile communication networks, forming a new era of interconnection of all things based on the high-quality experience of Internet users [7]. In this context, for enterprises, how to further optimize the supply chain system is imminent. With the continuous innovation of computer technology and the Internet, the IOT has already become a new force leading the international information technology revolution, and even gradually plays an important role in the supply chain system of enterprises [8]. At the same time, the change of information technology also gives birth to a new supply chain system management mode. At this time, the interconnection of all things will drive the supply chain system to produce more efficient coordination, so as to achieve a real sense of “win-win”.
2 Key Technologies of IOT and Supply Chain System 2.1 IOT Technology Up to now, there are still many differences in the definition of IOT. Therefore, the author has simply sorted out the definition of IOT, and summarized the representative views of it [9]. The definition of the IOT by the famous international organization, International Telecommunication Union, is mainly reflected in that it can solve the relationship between things, people to things and people to people. Its connotation is shown in Fig. 1. The IOT in 2020, published by the European Commission in 2008, holds that the IOT must be a worldwide IOT with a unique address on the basis of standard communication protocol, that is to say, the event must have a unique virtual personality and operate independently in the intelligent cyberspace. As early as 2010, in China’s annual government work report, the IOT was defined as the IOT developed on the basis of basic information networks such as computers and the Internet. According to the information detection equipment (such as RFID, EPC, etc.), the real world objects are connected to the Internet through the information exchange platform, so that the exchange of information between objects can achieve intelligent tracking, positioning, monitoring and management functions. According to the working principle of the IOT, the IOT can be divided into three levels, including perception layer, network layer and application layer, as shown in Fig. 2.
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Fig. 1 Definition of IOT given by ITU
Fig. 2 Architecture of IOT
In order to achieve the goal of the IOT, the relevant level is mainly composed of the following elements. First, hardware level. The hardware level mainly refers to RFID technology, GPS, far-infrared sensor and everything interconnection, intelligent identification and management. Second, software level. How to maximize the advantages of network communication technology, make full use of data processing technology to collect and analyze the data information sent to the server, so that users can use the corresponding data according to the actual needs is very important.
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2.2 Supply Chain System In the last century, the term “supply chain” came from the circulation of goods, initially referring to the military logistics supply activities in military activities. In recent years, with the rise of commercial economy, the concept of supply chain has been gradually expanded and applied to commercial activities [2]. The emergence of new concepts is bound to be accompanied by new goals. For the supply chain system, its goal is to reduce the cost of enterprises as much as possible and try to meet the needs of consumers. The connotation of supply chain gradually expands the scope of circulation and integration to the upstream and downstream members of enterprises. At this time, the concept of supply chain is born. The ultimate goal is to make the upstream and downstream enterprises and process links perfectly integrated, effectively reduce waste and duplication, and establish a stable strategic partnership through related enterprises, so as to improve the operation efficiency and service level, and enhance consumer satisfaction. However, up to now, there is no consistent definition for the concept of supply chain. Chinese scholars believe that the supply chain system should be defined as follows: the supply chain is a sharing mechanism that uses the network information technology to integrate and analyze the risks of various enterprises and organizations, and integrates the supply chain partners with various division of labor in various regions, so as to obtain the sharing economic benefits. In addition, other researchers have also defined the supply chain system, that is, the value-added chain composed of a series of upstream and downstream related enterprises such as raw material suppliers, component suppliers, manufacturers, transporters and distributors. As a result, raw materials and parts gradually become products, which are transmitted to the target consumers through procurement, production, circulation and other means, thus forming a complete supply chain system. Supply chain system is a network led by enterprises and departments to process raw materials into final products and deliver them to customers. The typical structure of the supply chain is shown in Fig. 3. The comparison of supply chain management modes is shown in Table 1. Where ✓ means good (or high or excellent), × Indicates poor (or poor or inferior).
Fig. 3 Typical supply chain structure model
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Table 1 Comparison of supply chain management mode Pattern
Business risk
Operation efficiency
Information sharing
Respond to the market
Innovative atmosphere
Decision control
Vertical integration
✓
×
✓
×
×
✓
Horizontal integration
×
✓
×
✓
✓
×
In the traditional sense, supply chain system refers to the whole process from the purchase of raw materials, to the conversion of semi-finished products and finished products, and then to the circulation to customers. In the process, the focus is usually on the value chain process within the enterprise. With the further development of supply chain theory, the application scope of supply chain system is gradually expanding. The whole supply chain system consists of network chains formed by core enterprises (including suppliers, wholesalers, retailers and customers). In the process of circulation, the target enterprises should not only pay attention to themselves, but also pay attention to the strategic partnership between enterprises, and fully realize the key of the strategic partnership in the supply chain system. Through the establishment of strategic partnership between enterprises, the operation and management can be more perfect and effective, which also promotes the transformation of the relationship between enterprises from competition to “win-win”.
2.3 Supply Chain Management Process of IOT As we all know, in the supply chain system, IOT technology has gradually played an important role and has become the main force of the supply chain system. With the support of IOT, the supply chain system can realize the real-time processing of information, which makes the information processing run through the whole process from raw material procurement to spare parts warehouse management to semi-finished products or finished products production. Specifically, all links in the supply chain system, including transportation, return, distribution, sales and after-sales service, can be anytime, quickly and quickly integrate the necessary commodity information accurately. Because all enterprises in the supply chain system can get the operation information of other members in real time, it makes the supply chain system become a transparent system, which greatly improves the transparency and efficiency of the supply chain system. In the supply chain system, the time and place of different businesses are different, and the resulting activity information is not the same, thus forming a real-time information system. Therefore, it is very difficult for enterprises to obtain and share information, and it is impossible to guarantee the timeliness and accuracy of information. At this time, the IOT technology arises at the historic moment. By applying
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the IOT technology to the supply chain system, the intelligence can be perfectly integrated into the supply chain system to form an intelligent supply chain system. The product information stored with EPC tag can be read and written at will, which is especially suitable for the supply chain system that needs to change data frequently. Furthermore, the IOT technology applied to the supply chain system has achieved numerous excellent results, the most representative is to be able to effectively deal with customers (short delivery), improve work efficiency.
3 The Framework of IOT Applied in Supply Chain Management At present, the international academic community divides the system architecture of IOT technology into three levels, as shown in Fig. 3. The function of the perception layer in the first level is mainly reflected in the use of RFID, sensors and two-dimensional code and other information technology to integrate and analyze the external data. The function of the transport layer in the second layer is mainly reflected in the transmission of sensor data and control information. The function of the application layer in the third level is mainly reflected in the integration of intelligence into the supply chain system based on big data, fully combining the IOT technology and supply chain system, so as to realize a more intelligent supply chain system communication platform. The subject and process involved in the supply chain system are complex, which have the characteristics of long time and wide space. In the process of commodity or production transportation, there will be huge and complex information about supply, demand, production and circulation. Information should be collected and transmitted in a timely manner, and even at sometimes it needs to be at the same time. Systematic analysis of the cost structure of the supply chain system is conducive to optimizing the allocation of resources, achieving the best distribution service and payment methods, and providing effective reference information for the supply chain system. The perception layer of IOT can collect key data in supply chain system independently and realize the visualization of data. Compared with the traditional manual information input method, it has the advantages of high efficiency, low cost and high information precision. It is helpful to transfer the information of supply chain among members under different environment conditions by applying various technical means to information transmission. The sharing information platform based on big data can provide the functions of data sharing, information integration and system standardization for supply chain system. Especially in the face of a large number of supply chain system data, big data provides advanced data mining function, which can provide information integration and decision support services for the supply chain system quickly and accurately (Fig. 4).
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Fig. 4 Supply chain system optimization path
4 Information Supply Chain Strategic Alliance Based on IOT Since the 21st century, with the further development and maturity of IOT technology, it brings new opportunities to the construction of supply chain system. The high synergy effect and overall application ability of IOT technology effectively promote the unification and processing of data and information, and lay a solid foundation for the application of information technology and intelligence in supply chain system. It is worth noting that the so-called supply chain system is not a strategic partnership in the usual sense. The core of the information sharing platform of IOT is the supply chain information platform. Based on this platform, it can strengthen the communication and cooperation among the enterprises of each node in the supply chain system, and improve the efficiency and flexibility of information sharing. On the basis of this platform, all upstream and downstream enterprises can accurately monitor the status of capital flow, information flow and workflow. In addition, the enterprise can upgrade the original independent internal business procedure to the cooperation between enterprises, effectively reduce the operation time and capital cost, and promote all enterprises in the supply chain to achieve the “win-win” of the real meaning line.
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5 Suggestions of Artificial Intelligence in the Development of Supply Chain System With the rapid development of science and technology, higher-level departments should focus on improving the market competitiveness of enterprises, make full use of the advantages of network information technology, and grasp the opportunities brought by the development of IOT technology. According to the previous research, the author has the following suggestions. First, establish a data information sharing and exchange platform to reduce the difference of intelligent development among regions. The higher authorities should pay special attention to the balanced development of the central and western regions. The main objective is to build the supply chain system, provide intelligent service platform for the supply chain system, create the conditions for industrial development and improve the level of resource sharing among regions. Secondly, the intelligent management technology is added to the construction of supply chain system. All enterprises must keep up with the development of network information technology, make the most of big data technology, and accelerate the pace of supply chain system management reform. At the same time, through the intelligent professional knowledge training for enterprise employees, we strive to transform the supply chain system to the intelligent supply chain, and provide excellent talent reserve strength for it. Third, improve the behavior criterion between intelligent supply chain systems. We will vigorously support colleges and universities and scientific research institutions in training relevant technical personnel and give certain financial support. Strengthen the cooperative training between universities and enterprises, improve the practical platform for students, increase practical experience, and ensure that relevant personnel can be competent for the related work of intelligent supply chain system construction.
6 Conclusion In recent years, the era of IOT is gradually approaching us. This in-depth development not only promotes the transparency of information, the collectivization of decision-making and the intelligent control of supply chain, but also establishes an efficient information sharing platform, which makes major enterprises achieve higher efficiency with low cost. Through all these, the supply chain system becomes more flexible and transparent, and can quickly respond to the market. Starting from the IOT technology and supply chain development strategy, this paper analyzes the characteristics of intelligent supply chain system construction. Based on the IOT technology, the core technology and new management mode of intelligent supply chain system are constructed, which optimizes the three levels in the process of supply chain system construction: first, the resource allocation level of each node enterprise and the innovation of business partnership are optimized from the macro level. Second, at the information level, promote the stable construction of the supply chain system,
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promote the efficient and rapid flow of information supply and demand, and realize the integration and sharing of information. Third, timely adjust the production and manufacturing process of process management level. When the complexity of the supply chain system increases, efficient and fast operation becomes the first choice. Therefore, information sharing and data mining technology plays a key role in the innovation of the whole supply chain system construction.
References 1. Xu W, Zhang ZP, Wang HX, Yi Y, Zhang YP (2020) Optimization of monitoring network system for eco safety on internet of things platform and environmental food supply chain. Comput Commun 151:320–330 2. Angappa G, Nachiappan S, Manoj KT (2016) Information technology governance in internet of things supply chain networks. Ind Manage Data Syst 116(07) 3. Mohamed BD, Elkafi H, Zied B (2019) Internet of things and supply chain management: a literature review. Int J Prod Res 57(15–16):4719–4742 4. Chen JB, Huang Y, Xia PX, Zhang YY, Zhong Y (2019) Design and implementation of real-time traceability monitoring system for agricultural products supply chain under internet of things architecture. Concurrency Comput Pract Exp 31(10) 5. Zhou YS, Xu XJ (2019) Intelligent supply chain information system based on internet of things technology under asymmetric information. Symmetry 11(05) 6. Sun L, Zhao YJ, Sun WQ, Liu ZK (2019) Study on supply chain strategy based on cost income model and multi-access edge computing under the background of the internet of things. Neural Comput Appl (Prepublish) 7. Zhou L (2019) Thoughts on the integration of supply chain of sporting goods through internet of things. J Humanit Arts Soc Sci 2(01) 8. Manavalan E, Jayakrishna K (2018) A review of internet of things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Comput Ind Eng 127 9. Atul M (2018) Internet of things, machine learning, and artificial intelligence in the modern supply chain and transportation. Defense Transp J 74(01):14–17
Application of Internet of Things Technology in the Field of Environmental Engineering Ying Chen, Xufeng Tao, Wei Wang, Lei Liu, and Taiyang Yuan
Abstract With the vigorous development of social economy, the environmental problems facing human beings are getting more and more serious. Land degradation, water shortage and pollution, air pollution, low quality of urban environmental infrastructure, rural environmental degradation, increasing frequency of environmental accidents, climate change and other global environmental issues. These environmental problems seriously threaten people’s health and are not conducive to the sustainable development of society. Moreover, the current level of informatization in the field of environmental protection in China is still very low, and the sharing of environmental protection data and services in various regions is not open to the outside world, so many investments have been made in different regions. Therefore, the focus of environmental protection work in the future will be to promote the development of environmental protection in the direction of automation, intelligence, and networking. Informatization of environmental protection is imperative. In addition, in recent years, the application of the Internet of Things (IoT) in the field of environmental protection is an inevitable result of the development of information and communication technology to a certain stage and it is an inevitable trend of informatization in the field of environmental protection. Keywords Internet of things · Environment · Environmental engineering
1 Introduction At present, China’s environmental protection informatization level is still relatively low, far from being able to meet the needs of environmental protection and the pace of economic development. At the same time, environmental protection industries in various regions lack communication and information sharing. This has resulted in the fragmentation of the environmental industry, and some infrastructure construction Y. Chen (B) · X. Tao · W. Wang · L. Liu · T. Yuan Zhejiang Huanzhimei Environmental Protection Technology Co., Ltd., Taizhou, Zhejiang Province, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_127
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has been repeated [1]. Therefore, the development of the environmental protection industry in the direction of intelligence, automation, and networking has become the focus of the current environmental engineering construction, and the informatization of environmental engineering is imperative. The application of IoT technology in the field of environmental engineering can effectively integrate basic environmental facilities and increase the utilization rate of environmental protection industry facilities. It can be said that the application of IoT technology in environmental engineering is an inevitable trend in the development of information in the field of environmental protection [1].
2 Overview of IoT Technology The concept of the IoT, also known as a sensor network, is to connect all items to the Internet through information sensing equipment such as radio frequency identification, so as to realize intelligent identification and management. For example, when a driver makes a mistake while driving, he will automatically issue a warning. The IoT can fully perceive the world, collect various dynamic object information anytime and anywhere, and transmit perceptual information in real time via Ethernet and wireless networks [2]. Finally, it can manage and control objects intelligently and truly realize the communication between people and objects. The IoT includes a three-tier system architecture of application layer, network layer, and perception layer, as shown in Fig. 1. The sensing layer recognizes and collects various information through sensing devices such as sensors and REID electronic tags. The network layer transmits the information collected by the sensing layer through a transmission network such as a wireless network and a mobile network [2]. The application layer mainly analyzes and processes information, realizes intelligent applications and service tasks in a specific environment, analyzes and predicts various possible situations, to play a functional role.
3 Problems Faced by the IoT in Environmental Engineering 3.1 Security and Reliability Need to Be Strengthened In most cases, the sensors in the environmental protection sensing layer are located in places with harsh environments, and some places may even be corroded by chemical substances. In addition, some criminals damage the sensor, which further requires the reliability of the sensor. Therefore, the sensing layer should be able to adapt to various harsh application environments [3]. However, viruses or hackers, resulting
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Fig. 1 IoT system architecture
in data loss, easily invade the network layer. Data sharing at the application layer will further reduce data security.
3.2 Application and Industrialization Issues At present, the application scope of the IoT in environmental engineering is relatively small. On the one hand, a mature business model cannot be formed, and on the other hand, it is not conducive to the cultivation of the market. Therefore, it is necessary to select key areas and key projects in the future, and actively guide the demonstration and application of the IoT [4]. Accumulate experience in technology development,
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industrial application, management, policy implementation, etc. At the same time, the environmental engineering facing China’s IoT industry chain is still immature and still faces huge challenges. First, the industry chain has not yet formed a win-win business model, and the coordination ability of each link is poor; second, the industry chain has long links and high complexity; this makes it difficult to reduce the overall cost; finally, the industry chain enterprises are relatively dispersed and relatively large in scale [4]. Small, especially the service link. Therefore, the development of the IoT for environmental engineering in the future requires the joint efforts of the industrial chain. Only with the cooperation of upstream and downstream manufacturers such as chip manufacturers, sensor equipment manufacturers, system solution manufacturers, and mobile operators, can upstream and downstream connections be realized, thereby driving the entire industry chain. Jointly promote the construction of the environmental engineering IoT.
3.3 The Relative Shortage of Talents The application of the IoT in environmental monitoring is a system engineering involving multiple disciplines such as laws and regulations, environmental management, chemical analysis, instrumentation, automation control, information communication, software and hardware technology. There is a shortage of various professionals and talents [5]. New technology research and development, industrial application development, etc. have not yet formed a scale, which will directly restrict the further development of environmental monitoring. Strengthening personnel training is an important task of environmental monitoring in the era of the IoT [3]. At present, dozens of colleges and universities have established new IoT projects and other majors, training a large number of talents for the development of China’s IoT, and all units should pay attention to the cultivation of existing talents. In addition to being able to cope with the existing work, it can also handle many challenging tasks in the future [5].
3.4 Key Technologies Restrict the Development of the IoT There are corresponding key technological breakthroughs in the perception layer, network layer and application layer. For example, the sensitivity and accuracy of sensors may cause false warnings and directly cause unnecessary processes; the low rate of sensor networks directly affects the routing and address resources required for large-scale arrangements of nodes and terminals; currently, the IoT [6]. The network layer is multiple operators, multiple communication systems, and multiple networks, making it impossible to share environmental information in real time, resulting in coordination capabilities that cannot meet the needs of environmental engineering;
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key technological breakthroughs in cloud computing also model various application fields, data processing and data storage security have had a major impact [6].
4 Application of IoT Technology in the Field of Environmental Engineering 4.1 The Application of the IoT in Atmospheric Monitoring Atmospheric monitoring is an important part of environmental monitoring. Relevant monitoring personnel are required to regularly observe and analyze the pollutants in the atmosphere to determine whether the pollutant content in the atmosphere exceeds the standard and meet the requirements of the air quality standards set by China [7]. The application of IoT sensor technology in environmental monitoring can install sensors in areas that monitor toxic substances, or install sensors in densely populated areas, so that the range of sensor monitoring is wider. Within the monitoring range of sensors, if there is an air pollution problem, or It is the sudden and drastic changes in monitoring content that will have a deeper understanding based on sensor technology, so as to seek reasonable countermeasures and do preventive work [7], as shown in Fig. 2. According to China’s essential requirements for environmental monitoring, air monitoring capabilities should be further improved, and the scope of air monitoring should be continuously expanded in accordance with relevant policies and regulations. Due to the rapid economic development, the sources of air pollution are also increasing. While formulating relevant air monitoring policies, different pollutants
Fig. 2 The architecture of the IoT in the atmosphere monitoring system
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should also be coordinated and monitored to improve the effectiveness of IoT monitoring and realize the sharing of data and information [8]. For example, through the detection of air PM2.5 indicators, the source of pollution can be determined, so that effective monitoring strategies can be formulated, combined with actual conditions, and the monitoring mechanism will be improved and perfected, so as to achieve systematic monitoring of air pollution problems, and improve the quality of air monitoring work.
4.2 Application of IoT in Water Quality Monitoring In the water quality monitoring work, the water quality monitoring and evaluation work has provided more true, comprehensive and effective data for the development of water resources protection work, and promoted the water quality monitoring work to be carried out in a more reasonable and orderly manner [9]. The water quality monitoring work involves a wide range, including the monitoring of industrial drainage and natural water pollution, as well as the monitoring of non-polluted water resources. In the water quality monitoring work, it is not only necessary to analyze and judge water quality problems, but also to have a more comprehensive understanding of the toxic substances in water resources [9]. As far as the current status of water quality monitoring in China is concerned, it mainly covers two aspects: daily drinking water monitoring and water pollution monitoring. Drinking water monitoring mainly refers to the installation of sensors and related equipment at the water source. According to the daily monitoring of the water quality of the water source, Real-time analysis and grasp of water quality; the monitoring of water pollution is the monitoring of industrial wastewater, which can effectively avoid major pollution problems and effectively manage and control pollution discharge, as shown in Fig. 3.
4.3 The Application of the IoT in Solid Waste Supervision The IoT and big data can calculate the matching relationship between the generation, storage, transfer, treatment and utilization of waste through technical means such as video surveillance, spatial positioning, electronic data label scanning and identification, real-time monitoring of pollutant trends, and statistics [10]. Their recycling rate is compared with historical data and similar facilities to judge the reliability of the data, so as to provide the basis for reasonable subsidies, strict supervision, forecast and early warning, and accurate waste management, as shown in Fig. 4. It detects the amount of trash through the identification card installed in the smart trash can, and automatically performs processing such as crushing and compression, and the storage system in the bucket records the data. When the amount of garbage recorded by the storage system reaches a certain upper limit, the data information
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Fig. 3 IoT in the water quality monitoring platform
Fig. 4 Application of networking in solid waste supervision
will be uploaded to the Internet terminal [10]. The Internet terminal database automatically provides the amount of garbage generated by residents in a month based on the garbage collection path of environmental health personnel. The trash can can automatically send signals to the Internet to analyze and select the best treatment route, which improves the efficiency of the garbage treatment chain [10].
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5 Conclusion In general, in the context of the rapid development of the IoT in the new era, the development of the Internet of environmental protection is an inevitable trend to promote informatization in the field of environmental protection. Although the environmental protection IoT is still in its infancy, there are still some urgent problems in core technology, standard system construction, application and industrialization. However, with the continuous development of economy, society and technology, these problems will eventually be effectively solved, so that the IoT technology can play a greater role in environmental engineering.
References 1. Zhang ShW, Kong XF, Jiang YQ (2015) Application and research of biological monitoring technology in water environment. Environ Eng Sci 11(05):103–107 2. Liu P, Cao H (2010) Thinking of environmental monitoring informationization. Environ Eng Sci 36(03):96–98 3. Zhou YY (2017) The transformation of the internet of things to protect the environment. China Environ News 11(03):105–107 4. Liu YJ (2017) Suggestions on the development of the sensor network industry and future networks. Internet Things Technol 12(05):56–59 5. Zhao PZh (2018) Discussion on the development of internet of things technology and application. Inf Commun Technol 4(2):9–13 6. Zhang HW (2018) Research on the application of internet of things in environmental monitoring and protection. Internet Things Technol 7(08):73–76 7. Shang XB (2019) Application of internet of things technology in the field of environmental engineering. Chem Des Commun 5(07):243–245 8. Peng ShJ, Dong YY (2018) Application of internet of things technology in environmental monitoring. Private Sci Technol 3(07):51–53 9. Peng H (2015) Research on the application of internet of things technology in environmental monitoring. Low Carbon World 12(23):9–10 10. Liu F (2017) Research on the practical application of internet of things technology in environmental monitoring. Sci Technol Outlook 12(13):81–84
Application of Intelligent Optimization Algorithm in the Analysis of Golf Track Zhenjun Li
Abstract Algorithm optimization has been widely used in many engineering fields, and solving various optimization problems such as linear, nonlinear, stochastic and geometric programming has been developed rapidly. Intelligent optimization algorithm is a search algorithm that uses some similarities between things in nature and the optimization process to search. Compared with traditional optimization algorithms, intelligent optimization algorithms have significant advantages in terms of solving speed. With the rapid development of golf in China, golf lovers are increasing day by day. But this sport is not as simple as it seems. Some beginners often make mistakes because they cannot control the flight path of the ball during practice. Therefore, this article analyzes the application of intelligent optimization algorithm in golf ball trajectory estimation. Keywords Intelligent optimization · Optimization algorithm · Golf · Sports trajectory
1 Introduction Optimization problems have traditional optimization theories and modern intelligent optimization theories. Traditional optimization theories are supported by mathematical knowledge of linear programming and non-linear programming [1]. At present, they have been widely used in many production fields. The premise of the mathematical characteristics of the optimal solution can design the corresponding algorithm according to the mathematical characteristics of the optimal solution. In each step of the solution step, it must be established based on a full understanding of the optimization problem, so it has very big limitations. Modern intelligent optimization theory does not need to understand the mathematical characteristics of the optimal solution of the optimization problem. Instead, it uses a heuristic algorithm to find the optimal solution to the optimization problem. It is an approximate algorithm in itself, and it Z. Li (B) Tourism School, Zhuhai College of Science and Technology, Zhuhai, Guangdong Province, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_128
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does not guarantee that the optimal solution will be obtained. Excellent solution, but it can get the solution closest to the optimal solution in the shortest time, so it has been widely used [1].
2 Intelligent Optimization Algorithm 2.1 Definition of Intelligent Optimization Algorithm Although their principles are different, traditional optimization algorithms and intelligent optimization algorithms are based on the objective function of the optimization problem to find the optimal solution [2]. The traditional optimization algorithm is based on the mathematical characteristics of the objective function to find the optimal solution. The intelligent optimization algorithm simulates the objective function based on natural life phenomena to find the optimal solution that is closest to the optimal solution [2]. The iterative process of the intelligent optimization algorithm must include the following three steps: The first step is to determine the feasible range of the objective function in advance. Find the optimal solution strategy to find a set of initial solutions. The second step is to continue to find the optimal solution within the feasible range of the objective function according to the original strategy. The third step is to determine whether the end condition is satisfied, and then all solutions are met when it is satisfied select the optimal solution, if it is not met, return to the second step to continue execution until the end condition is met [2].
2.2 Ant Colony Algorithm The ant colony algorithm is because ants in nature always move in the direction of food during the foraging process and constantly adjust the route of travel during the process of traveling [3]. The key is to optimize the construction strategy of the solution and the pheromone update strategy. Its realization The steps of is: the first step is to set the initial values of the pheromone in all feasible schemes; the second step is to construct a set of solutions in the feasible region of the objective function according to the solution construction strategy; the third step is to update according to the pheromone. The strategy updates all path pheromone; the fourth step is to judge whether the end condition is satisfied, if satisfied, output the optimal solution, if not satisfied, return to the second step for algorithm iteration [3].
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2.3 Particle Swarm Algorithm The particle swarm algorithm is implemented by simulating the foraging process of birds in nature. Its principle is because the birds are always approaching the food source during the foraging process, but the speed and path of each bird approaching the food may be different [4]. The key is it is to determine the suitability value of each particle. The implementation steps of the particle swarm algorithm are: the first step is to determine the position and velocity of each particle and calculate the suitability value of each particle. The second step is to update the global the optimal particle and the local optimum of each particle; the third step is to update the update strategy to update the position and velocity of each particle [4]. The fourth step is to judge whether the end condition is satisfied, if it is satisfied, the optimal solution will be output, if not, it will be transferred In the first step, the fitness value of each particle is calculated to continue the iteration of the algorithm.
2.4 Genetic Algorithm Genetic algorithm is the principle of simulating the survival of the fittest in the biological evolution process in nature, but one of the optimized solutions is only for an individual in the biological group, and the set of all optimized solutions is the optimal solution for the entire biological group, so a selection is required. Crossover and mutation operators to simulate the entire evolution process of biological populations [5]. The main implementation steps of the particle algorithm are: the first step is to initialize the population to be processed; the second step is to follow the selection operator and a certain selection strategy from the population. Select an individual with a total number of n; the third step, randomly select m pairs of individuals from the population, and use the crossover operator and a certain probability to produce offspring; the fourth step, in the generated offspring according to the mutation operator and a certain [5].
2.5 Simulated Annealing Algorithm The simulated annealing algorithm is to simulate the entire process of solid annealing. It is based on the basic principle that the internal energy of the object changes with the change of temperature [6]. When the solid is at room temperature, its internal particles are in the ground state. When it descends slowly enough, the internal particles will be in equilibrium. It uses the internal energy of the solid to simulate the objective function, uses the temperature to simulate the control parameters, and uses the temperature drop to simulate the parameter changes. The main implementation steps of the simulated annealing algorithm are the first step, initialization, setting an
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initial test parameter and an initial solution; the second step, arbitrarily selecting a new solution in the neighborhood of the optimized solution [6].
3 Intelligent Optimization Algorithm Probability Model The intelligent optimization algorithm is actually a search algorithm. Compared with the intelligent optimization algorithm, there is also a probabilistic search algorithm. This is a pure random search algorithm. The pure random search is searched according to a random uniform distribution. There is no essential difference between the methods, and the intelligent optimization algorithm is a sampling model based on probability [7]. It has the following characteristics: first, its sampling method is determined, and the objective function is not easy to directly describe its mathematical characteristics. Second, in order to distinguish it from pure random sampling, the intelligent optimization algorithm adopts a sampling model with parameters, and its sampling model is only related to the current state and can be improved according to the sampling data [7]. For example, the flight path of a golf ball is a parabola with an opening downward, and when it is 20 m from the origin (parabola symmetry axis x = 20), there is a highest point (parabola maximum) 10 m. Therefore, for example, the parabola equation is y = axˆ + bx + c (because the image and the y axis intersect at 0 point, so c = 0). Equations can be set out according to the question − b/2a = 20 400a + 20b = 10 After solving, a = −1/40b = 1. (1)
The parabolic equation of the flight path of the golf ball is −x2 /40 + x = 0
(2) (3)
The maximum flying distance is 40 m and the maximum altitude is 10 m −xˆ2/40 + x = 5, the solution is √ x1 = 20 − 10 2 √ x2 = 20 + 10 2
Therefore, at the above two distances, the flying height of the ball is 5 m, as shown in Fig. 1.
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Fig. 1 The sport parabola of golf
4 Factors that Affect the Flight of Golf Balls 4.1 Swing Trajectory Swing trajectory refers to the relative relationship formed by the trajectory of the club head and the target line when the club hits the ball. Usually divided into three possibilities: from outside to inside, from inside to outside and from inside to inside [8]. The running direction of the club head will be consistent with the initial direction of the ball.
4.2 Club Head Speed Club head speed refers to the speed at which the ball is transmitted on the golf ball now of impact. The head speed is proportional to the initial speed of the golf ball, and the head speed affects the flying distance of the golf ball. Proper club head speed can make it difficult for you to thin or thicken, and it is not easy to open or close the face [8].
4.3 Face Angle The angle of the clubface is roughly divided into an open face, a closed face and a square face, which affect the curve of the ball when it flies. It should be noted that which shape of the club face angle is based on the trajectory of the club head, not the target line [9]. Because whether it is open or closed, it corresponds to the trajectory of the club head.
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4.4 Ball Angle Refers to the point of contact when the club face is in contact with the ball at the moment of impact. Through the analysis of the flying principle of the golf ball, it can be seen that in addition to the speed of the club head, the angle of the ball is affected by the flying distance [9]. A shot closer to the center can allow more power to be transferred to the ball, thereby affecting the distance of the ball. Therefore, the closer to the center, the farther the ball hits.
5 Intelligent Optimization Algorithm to Analyze Golf Trajectory Through the intelligent optimization algorithm, we found that the previous flight theory is club invalid. In fact, in the case of a sweet click, the initial flight direction of the ball depends on the direction of the face when the ball hit, and the flight curve depends on the trajectory of the club head. For the driver, the face of the club faces the starting direction that controls 85% of the ball; for the iron, the face of the club faces the starting direction that controls 75% of the ball [10]. As the face angle increases, this ratio will decrease. In the case of non-sweet clicks, the “Gear Effect” will occur. The gear effect refers to the rotation of the rod facing the ball now of hitting the ball, which drives the rotation like a gear. If the gear effect occurs when hitting the ball, it will change the ball’s trajectory and rotation [10]. New ballistic theory of intelligent optimization algorithm: (1)
(2)
(3) (4)
(5) (6)
The curve of the ball path is caused by the difference between the direction of the trajectory of the club head and the direction the club face faces when hitting the ball. If the direction of the trajectory of the club head is the same as the direction of the club face when hitting the ball, the ball you hit can be straight, or it may be curved to the left or right, depending on the direction of the shot. The ball path will not bend unless it is subjected to external forces, such as wind, during flight. The initial flight direction of the ball depends mostly on the face of the club when hitting the ball, as shown in the green arrow in Fig. 2. The orientation of the club face when preparing to hit the ball does not determine the orientation of the club face when hitting the ball, but it has a certain influence. The curve of the ball path is determined by the trajectory of the club head, as shown in the blue arrow in Fig. 2. The turf does not indicate the starting direction of the ball, the trajectory of the club head, the curve of the ball and the landing angle. In fact, turf is meaningless in this regard.
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Fig. 2 Intelligent optimization algorithm is applied to analyze golf trajectory
How to read the intelligent optimization algorithm to analyze the golf trajectory data? (1)
(2)
(3)
(4)
(5)
Club Path includes swing from the inside to the outside, coincides with the target line and from outside to inside. Negative number means from outside to inside, positive number means from inside to outside. Face Angle, a negative number means that the face is facing the left side of the target line when hitting the ball and a positive number means that the face is facing the right side of the target line when hitting the ball. Face-to-Path Ratio refers to the difference ratio between the above two. The longer the club, the greater the ratio, the more curved the course. A negative number indicates that the face is facing the left side of the trajectory, while a positive number indicates that the face is facing the right side of the trajectory. Launch Direction, a negative number means the starting direction of the ball is on the left side of the target, and a positive number means the starting direction of the ball is on the right side of the target. Spin Axis (Spin Axis) represents the rotation axis of the curve or tilt of the ball path. Negative numbers mean that the ball is curved to the left and positive numbers mean that the ball is curved to the right. You can see all of the above data in Fig. 3.
6 Conclusion This paper mainly introduces the intelligent optimization algorithm. The intelligent optimization algorithm is a very widely used algorithm to find the optimal solution without knowing the mathematical characteristics of the objective function. It mainly simulates natural phenomena in order to update information at any time and find the closest the optimal solution of the optimal solution. The research in this paper shows that intelligent optimization algorithms, such as ant colony algorithm, particle swarm algorithm, etc., are widely used in golf trajectory calculation.
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Fig. 3 Golf trajectory calculated by intelligent optimization algorithm
References 1. Yang JQ (2017) Research on evaluation model of intelligent optimization algorithm, vol 11, no 16. Zhejiang University, pp 30–34 2. Gao YCh (2016) Research on the performance and search space of intelligent optimization algorithms, vol 33, no 01. Shandong University, pp 53–56 3. Wu X, He X (2015) Enhanced damping-based anti-swing control method for underactuated overhead cranes. IET Control Theory Appl 9(12):1898–1900 4. Zhang P, Lai XZ, Wang YW (2017) Effective position-posture control strategy based on switching control for planar three-link underactuated mechanical system. Int J Syst Sci 48(3):7–10 5. Left ZY, Wang X (2013) Golf path tracking control. Control Decis 32(6):983–988 6. Xin X, She JH (2013) New analytical results of energy-based swing-up control for the Pendubot. Int J Non Lin Mech 52(3):110–118 7. Jie S, Lei HY (2016) Adaptive robust swing-up and balancing control of acrobot using a fuzzy disturbance observer. J Inst Control Robot Syst 22(5):346–352 8. Yang X, Zheng X (2018) Swing up and stabilization control design for an underactuated rotary inverted pendulum system: theory and experiments. Control Decis 65(9):72–74 9. Zhang P, Lai XZ, Wang YW (2018) A quick position control strategy based on optimization algorithm for a class of first-order nonholonomic system. Inf Sci 11(04):264–268 10. Wang PC, Fang YC (2018) A direct swing constraint-based trajectory planning method for underactuated overhead cranes. Acta Automatica Sinica 10(11):214–217
Investigation and Research into the Training Quality of Postgraduates Based on Artificial Intelligence Jiayue Cui and Changhong Guo
Abstract Optimizing the postgraduate training mechanism and methods in the field of artificial intelligence can help postgraduates establish a more comprehensive knowledge system, enhance their ability to independently undertake scientific research and innovation projects, and create more valuable output for promoting scientific progress and social development. The research on postgraduate training quality has been a hot issue of general concern in academic circles and society in recent years (Zou in Micro Survey 28, 2011) [1]. The quality of postgraduate training is a necessary condition for the life and sustainable development of postgraduate education. This paper attempts to discuss how to improve the quality of postgraduate training in terms of school hardware facilities, the construction and management of the teaching staff, scientific research, and postgraduate’s own motivation. Keywords Improvement · Postgraduate (master degree) · Quality
1 Introduction The construction of “new engineering subjects” is a new opportunity to deepen China’s engineering education reform and provides new ideas for postgraduate training in the field of artificial intelligence in China. This paper proposes a path to systematically improve the quality of postgraduate training in the field of artificial intelligence from aspects such as changing the concept of artificial intelligence talent cultivation, strengthening the discipline construction of artificial intelligence, deepening the reform of artificial intelligence education, and optimizing the education ecology of artificial intelligence. With the rapid development of science and technology in the world, China’s economy has continued to grow at a high speed, and the focus of society’s demand for talents is also constantly shifting upward. People also hope to be able to receive higher-level education, and the enthusiasm and vision for obtaining a higher degree is J. Cui · C. Guo (B) Graduate School of Harbin Normal University, Harbin, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_129
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increasing [2]. All these have provided unprecedented opportunities and vast space for the reform and development of postgraduate education in China. At present, China’s postgraduate education presents a multi-level, multiple types of training models and development directions, but with the rapid expansion of scale, the problem of postgraduate training has become increasingly apparent. Then, how to effectively ensure the quality of postgraduate training has undoubtedly become a severe project in front of postgraduate training educators. Faced with the low quality of postgraduate training and low social competitiveness, we must start with the following aspects. The author distributed 265 questionnaires and retrieved 250 valid questionnaires. Regarding the quality of postgraduate training, there are different opinions in the theoretical field, and there is no clear and unified view yet. At present, most research on the quality of postgraduate training in China is published in journals. Academic journals and papers: Academic publications related to postgraduate education are mainly concentrated in Beijing. The most representative ones are “Degrees and Postgraduate Education” sponsored by the Academic Degrees Committee of the State Council, “Postgraduates in China” and “China’s Higher Education” sponsored by the Ministry of Education. And some other publications also have a “postgraduate education column”. Some university journals and social science journals also publish relevant research papers from time to time. In journal articles, there are many studies related to the quality of postgraduate training, and the perspective is relatively broad, mainly in the following aspects.
2 Directly Conduct Research on the Quality of Postgraduate Training The quality of postgraduate training and its impact on employment: “Thoughts on Improving the Quality of Postgraduate Training” by Wang Erbao and Liu Quanju, “Some Thoughts on Improving the Quality of Postgraduate Training” by Pan Xiuhong, “Is there any Devaluation of Postgraduate” by Zhao Cairui, and “An Exploration of the Reasons for the Continued Decline of Postgraduate Employment Rate” by Gu Yongsi. Factors affecting the research ability of postgraduates: “Research on Factors Affecting Postgraduate Research Ability and Countermeasures” by Zhou Xiaoli. The impact of educational administration on the quality of postgraduate training: such as Du Zide’s “Administrative Education is the Main Reason for the Low Quality of Postgraduate Training”. Put forward measures to impeove the quality of postgraduate training, such as: “Analysis of Postgraduate Training Quality Measures” by Feng Zhiming, Liu Limei, Li Guanghui, and Sun Mei.
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3 Research of Postgraduate Training Model The research on the training model of master students involves enrollment and selection, such as: “Do a good job of enrollment to ensure the quality of postgraduate” by Feng Shuying; “What kind of students should postgraduate colleges recruit— ‘problem 2’ in postgraduate training” by Li Jun, Liu Changrong; “Problems and Countermeasures in Postgraduate Selection and Cultivation” by Ma Shenggang, Huang Tao and Li Dalei. Research on the training links and processes of postgraduates, including course learning, such as: “Developing a Reasonable Training Program to Improve the Quality of Postgraduate Training” by Kou Lan; “A Few Suggestions to Ensure the Quality of Postgraduate Training” by Xu Zhi and Hu Haoquan. Research from the training conditions of master students, including subject level: “Empirical Analysis of Postgraduate Training Conditions Affecting Postgraduate Quality” by Wang Xiaoman and Wang Zongping. The tutor team covers issues such as the number of tutors, tutor quality, tutor selection and team building, and the relationship between tutor guidance and training quality, such as: “Talking about postgraduate training quality from teacher-student relationship” by Xu Lan and Yang Lin; “The quality of postgraduate tutors is the key to determining the quality of postgraduate” by Luo Xingshan; “On the selection of tutors and their important role in postgraduate training” by Qiu Gang; “The Enlightenment of Foreign Postgraduate Tutorial System to China” by Li Bo; “The relationship between the quality of tutors and the quality of postgraduate” by Chen Xiaoming, Wu Zhi and Luo Wenbiao. “Research on Academic Degrees and Postgraduate Education Evaluation” by Wang Zhanjun (published by Higher Education Press in 2002), and “Perspective of Contemporary Postgraduate” by Ye Cheng (published by Shaanxi People’s Publishing House in 2002), etc. both give a rough or detailed description of different aspects of postgraduate education. Other works include Li Huangguo and Wang Xiuqing’s “An Introduction to Postgraduate Education” published by Science and Technology Literature Publishing House in 1991, and Wu Zhenrou and Lu Shuyun’s “History of Postgraduate Education and Degree System of the People’s Republic of China”, and “Practice and Thinking of Degree and Postgraduate Education” by Xie Guihua. Although these works do not specifically address the issue of the quality of master’s student training, they do a good job of sorting out previous related materials and can be used as the first choice for reference.
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4 Pay Attention to the Impact of the Fee System Reform on the Quality of Training, Such As: He Wenwen’s “What Is the Future of Postgraduate Fee Reform” Summarizing the domestic research results, we can get a general understanding of the current research status of postgraduate training quality. Although the existing research history is not long, it has developed rapidly, the perspective has been expanded, the depth of research has gradually improved, and the research results have become more and more scientific and more convincing, providing us with a correct understanding and grasp of the law of postgraduate education. It has established the foundation and provided a certain reference for the formulation of this survey report. Improving the quality of postgraduate training is a meticulous, arduous and time-consuming work. It is not only necessary to do a good job in all aspects of the postgraduate training process, but also to create a good ideological atmosphere, academic atmosphere and condition guarantee. We hope that this investigation and research can provide evidence that is more beneficial to the quality of postgraduate training.
4.1 Strengthen the Construction of On-Campus Hardware Facilities and Improve the Living and Learning Conditions of Students We are in an era of the explosion of knowledge and technology. The network construction of the school directly affects the acquisition of the latest resources for students. Therefore, as a place for postgraduate training, campus network construction is extremely important. Table 1 is the survey data of campus network construction. As shown in Table 1, 12.0% of postgraduates think the network construction of their school is very convenient, 24.4% of postgraduates think it is more convenient, Table 1 Campus network construction of my current school Frequency Effective
Percentage
Effective percentage
Cumulative percentage
Very convenient
30
12.0
12.0
12.0
Relatively convenient
61
24.4
24.4
36.4
General
107
42.8
42.8
79.2
Not convenient
28
11.2
11.2
90.4
Very inconvenient
24
9.6
9.6
100.0
250
100.0
100.0
Total
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42.8% of postgraduates think the construction is normal, and 11.2% of postgraduates think the network is inconvenient. 9.6% of postgraduates believe that campus network construction is extremely inconvenient. It can be seen from the data that 63.6% of postgraduates think that campus network construction is below average, and only 36.4% of postgraduates think that network usage is more convenient or more. A diverse society requires diverse talents. Therefore, the diversified training of postgraduate must first start with the enhancement of campus hardware facilities, strengthen network construction, and improve the convenient channels for postgraduate to obtain the latest information. For postgraduate training, we must not work behind closed doors without getting know the latest news. Strengthening the construction of campus network is an aspect of improving the quality of postgraduate training, and we also need to strengthen the learning and living environment of postgraduate. According to the survey, 6.4% of postgraduates think that their living and learning environment is very good, 28.1% of postgraduates think that the living and learning environment is better; 53.4% of postgraduates think that the conditions are fair; 10.0% of postgraduates believe that their living and learning environment is relatively poor, and 2.0% of postgraduates believe that their living and learning environment is very poor. Most postgraduates are dissatisfied with the living and learning environment in which they live, mainly due to the problems of students’ electricity, water and library. Only when the postgraduate living environment is more comfortable and comfortable can the quality of postgraduate training be effectively improved.
4.2 The Construction and Management of Teaching Staff (1) Overall quality of teaching staff. According to the survey, 11.2% of postgraduates believe that the overall quality of the faculty of their college is very high, 47.2% of postgraduates think it is relatively high, 34.4% of postgraduates think it is fair, and 7.2 postgraduates think it is relatively low. It is not difficult to analyze that 58.4% of postgraduates believe that the comprehensive quality of their training school is relatively high and relatively satisfactory. Almost half of postgraduates believe that their overall quality is not high, and they may give rise to some dissatisfaction emotions and behaviors. Therefore, the school must improve and strengthen the construction of the school’s teaching staff. (2) Number of academic lectures per year. According to the survey, the number of lectures is investigated for the department of postgraduate, as shown in Table 2. From Table 2, it can be seen that 9.7% of postgraduate departments have not even held an academic lecture within a year. In terms of postgraduate training, we must increase academic exchanges, increase the circulation of information, and increase academic exchanges between students. 40.3% of departments only hold an academic exchange lecture occasionally. In response to this phenomenon, the colleges and departments trained by postgraduate must pay attention to it and fully realize the importance of direct academic exchange activities by postgraduate. Moreover, the
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Table 2 Number of academic lectures per year Frequency Effective
Effective percentage
Cumulative percentage
0
24
9.6
9.7
9.7
1–2
76
30.4
30.6
40.3
3–4
56
22.4
22.6
62.9 100.0
More than 4 Total Missing
Percentage
92
36.8
37.1
248
99.2
100.0
2
0.8
250
100.0
System
Total
school should create conditions and it is best to have the opportunity to invite some professors from other schools to give lectures. This will not only increase the awareness of the differences in academic research methods, but also stimulate students’ interest in and enthusiasm for research. (3) Frequency of meeting tutors. According to the survey, Table 3 shows the frequency of postgraduates meeting their tutors. The data in the table clearly show the situation, and no more specific explanation will be added here. At present, the trend of younger postgraduate tutors is becoming more and more obvious. Rejuvenation of tutors can indeed bring vitality and vitality to postgraduate education. But at the same time, young tutors also have problems such as insufficient guidance experience, less investment, and insufficient sense of responsibility. Therefore, it is necessary and realistic to establish a tutor training system. Through training, tutors can realize that they play an irreplaceable role in all aspects of postgraduate enrollment, training, moral education, and degree. Tutors are the first responsible person to ensure and improve the quality of postgraduate training and the key force in the implementation of comprehensive quality education for postgraduates, so as to further realize their responsibilities and obligations. Table 3 Frequency of meeting tutors Frequency Effective
Once a week
Percentage
Effective percentage
Cumulative percentage
138
55.2
55.2
55.2
Once every two weeks
64
25.6
25.6
80.8
Once every three weeks
13
5.2
5.2
86.0
Once a month
24
9.6
9.6
95.6
Rarely meet
11
4.4
4.4
100.0
250
100.0
100.0
Total
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4.3 Scientific Research (1) Main purpose for postgraduates to participate in scientific research. According to the survey, 20.6% of postgraduates participate in scientific research just to get enough credits. 16.1% of postgraduates are to obtain scientific research subsidies, and 52.4% of postgraduates are to improve scientific research capabilities. 10.4% of postgraduates participated in scientific research for other reasons, possibly at the request of their tutors or due to the course requirements. At present, insufficient attention is paid to the quality of postgraduate training and the requirements are not strict. As long as students meet the basic requirements, or barely meet the basic requirements, they can graduate and get the degree. Although the country has a basic requirement in terms of the quality of postgraduate training (or degree awarding), different universities have different standards for implementation, and it is difficult to achieve the same standard [3]. The author believes that no matter what the purpose of postgraduate research is, it is from education or self. As long as students participate in scientific research, it is definitely conducive to students. Therefore, mobilizing the enthusiasm of students to participate in scientific research will help improve the competitiveness of postgraduates and also help improve the quality of postgraduate training. (2) Academic innovation and social practice. Postgraduate education was originally based on research, with the purpose of cultivating students’ innovative ability [4]. But many students do not go to graduate school for scientific research. Postgraduate students are a great source of practical talents. Therefore, the academic performance and social practice in the postgraduate training process are very important for students. However, according to the survey, only 32.5% of postgraduates feel that they have participated in more practical activities and are relatively satisfied. There are also 67.5% of postgraduates who feel that their own practical activities and innovative academic activities are insufficient to meet their own needs. With the development of degree and postgraduate education in China and the implementation of the strategy of “rejuvenating the country through science and education” [5], how to ensure and improve the quality of postgraduate training has become a problem to be solved. Because quality is the lifeline of degree and postgraduate education, it is the ultimate goal of developing postgraduate education. Without quality, there can be no sustainable postgraduate education. Therefore, we must increase the intensity of postgraduate training and strengthen practice [6]. The postgraduate training quality survey is as follows (Table 4).
4.4 Postgraduates’ Own Motivation (1) Factors with the greatest influence on postgraduates. According to this survey, the factors that have the greatest impact on postgraduate training are shown in Table 5.
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Table 4 Academic innovation and social practice Frequency Percentage Effective percentage Cumulative percentage Effective Very high High General Low
22
Missing
8.8
59
23.6
23.7
32.5
51.6
51.8
84.3
33
13.2
13.3
97.6 100.0
6
2.4
2.4
249
99.6
100.0
1
0.4
250
100.0
System
Total
8.8
129
Rarely Total
8.8
Table 5 Factors with the greatest impact on me Frequency Effective
Professional rudimental
Percentage
Effective percentage
Cumulative percentage
59
23.6
23.6
23.6
The ability to independently engage in scientific research and solve problems
129
51.6
51.6
75.2
Creative thinking and innovative spirit
62
24.8
24.8
100.0
250
100.0
100.0
Total
According to survey statistics and analysis, 23.6% of postgraduates believe that the biggest factor for self-cultivation is basic professional knowledge; And 51.6% of postgraduates believe that the most important factor in their own training is the ability to independently engage in scientific research and the ability to solve problems; 24.8% of postgraduates believe that the most important factors for their own training are creative thinking and innovation. From the data, only a small number of postgraduates believe that the cultivation factors that have the greatest impact on them are innovative spirit and creative thinking [7]. Through analysis, it can be concluded that in the part of postgraduate training, in the eyes of students, they do not value the creativity and innovation of their own abilities. The creativity and innovation spirit are relatively the most in need of strengthening among the national spirit [8]. Therefore, when cultivating postgraduate in schools, we must pay attention to the innovative training of postgraduate, which is not only a means to improve the quality of postgraduate training, but also a necessary way to enhance the national soft power [9]. (2) Purpose of pursuing a master’s degree. The investigation results are shown in Table 6.
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Table 6 Purpose of pursing a master’s degree Frequency Effective
Percentage
Effective percentage
Cumulative percentage
Get a master’s degree
97
38.8
38.8
38.8
Change a major or work
75
30.0
30.0
68.8
To get a title
20
8.0
8.0
76.8
Out of interest
43
17.2
17.2
94.0
Others
15
6.0
6.0
100.0
250
100.0
100.0
Total
From the survey and analysis data, it can be seen that 38.8% of postgraduates study for a master’s degree only to obtain a diploma, 30.0 postgraduates are for changing majors or jobs, and 8.0% of postgraduates are for the purpose of assessing professional titles. 17.2% of postgraduates pursue a master’s degree out of their own interests, and 6.0% of postgraduates pursue a master’s degree for other reasons. We can see from the data analysis that 76.8% of postgraduates are pursuing a master’s degree out of a certain utilitarian need, while a small proportion of them rely on their own interests. This kind of utilitarian and externally purposeful postgraduate study is not conducive to the development of China’s education cause, nor is it conducive to the improvement and improvement of the quality of postgraduate. Artificial intelligence is a strategic technology leading a new round of technological revolution and industrial transformation [10]. Colleges and universities are the meeting point of the first productivity of science and technology, the first resource of talents, and the first driving force of innovation. The cultivation of artificial intelligence talents and achievements in colleges and universities can provide a strong support for a country that is strong in science and education and a country in talent. In order to promote the rapid development of China’s artificial intelligence field and the rapid development of the artificial intelligence industry, colleges and universities should accurately grasp the talent training rules in the field of artificial intelligence and optimize the discipline layout. In addition, it is necessary to promote the scientificization of the talent structure in the field of artificial intelligence and establish and improve a three-dimensional training system in which universities, scientific research institutions and enterprises participate in and support each other. To improve the quality of postgraduate training, many factors such as the improvement of the quality assurance system, the effective operation of the supervision mechanism, the training conditions, the training environment, the academic atmosphere, and incentive measures cannot be ignored. Therefore, ensuring the quality of postgraduate training is a complex system engineering that requires cooperation and joint efforts from many aspects. It should be noted that to mobilize the subjective initiative of postgraduate and actively improve its own quality, only by mobilizing the intrinsic
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motivation of the educational objects can the quality of postgraduate training be better improved. Acknowledgements The resonance of scientific research and education at the same frequency, SJGY20200390.
References 1. Zou JM (2011) Investigation and analysis of the quality of postgraduate training in my country. Micro-Survey 28 2. Liu YQ, Zhang SH, Jia Q (2001) Deepen reform and strive to improve the quality of postgraduate training. J Hebei Agric Univ (Agriculture and Forestry Education Edition) (in Chinese) 3. Feng ZM, Liu LM, Li GH, Sun M. Analysis of postgraduate training quality measures. J Adult Educ Coll Hebei Univ Technol (in Chinese) 4. Yin XD (2012) Research on quality problems and Countermeasures of postgraduate training. J Natl Acad Educ Adm 000(007):69–73 (in Chinese) 5. Huang CY (2000) How to improve the quality of postgraduate training. High Sci Educ 2 (in Chinese) 6. Wang YM, Hu D, Sun MY (2021) An analysis of labor education ideas for college students in China. Educ Sustain Soc (ESS) 4(1) 7. Pawlik J, Lemmer G, Selch S et al (2019) Longitudinal evaluation of female and male medical residents’ career satisfaction. Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen, pp 147–148 8. Schaaf W, Zwissler B (2012) Evaluation of further training in anesthesiology. Will we meet the demands? Anaesthesist 61(9):759–769 9. Eymann A, Durante E, Carrió S, Figari M (2012) To improve postgraduate training: medical residents’ input in residency quality improvement. Educ Health 25(3):208–210 10. Ren WJ, Wang YQ, Dong HL, Huo FC, Kang CH (2015) On improving full-time professional degree postgraduate training quality of control engineering. In: 2015 international conference on management science and management innovation (MSMI 2015)
Application of Computer Technology in the Management of College Students Hui Yang
Abstract With the development of computer technology and Internet application and the expansion of the scale of students in college education, college education has been developing rapidly in this situation, which makes the management of students in college education more and heavier, and the difficulty and complexity of management greatly increased. This paper mainly studies the application of computer technology in university student management. In this paper, the development of university student information management system using Java language development, for the department of statistics in this paper, including structure, application system security architecture, system platform architecture, a summary of business architecture and deployment scheme design foundation, thus gradually as a system for the detailed design, better prepare to complete the design of the system each function module. The design content of this paper covers every link of the whole cycle management of students from enrollment to employment, covering various functions with student management as the core, such as student management, course management, examination room management, performance management and other functional modules. Finally, through the conceptual structure design, logical structure design, finally obtained the student information management system database physical design results, solved the student information management system data design problem thus laid the foundation for the realization of the system. Keywords Computer technology · Student management · Information management · Java language
H. Yang (B) Hubei Business College, Wuhan, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_130
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1 Introduction Entering the twenty-first century, the whole era has stepped into the information age. With the rapid development of information technology and network technology and the rapid popularization, the wisdom to change work idea thorough popular feeling gradually, the concept of artificial intelligence and large digital gradually be recognized, and in many other aspects of social production and living of universal application, the traditional way of working and learning style and way of life are faced with the impact and change, gradually improving and upgrading of the old model, new ways of working and way of life, step by step in a rapid way into every aspect of modern society. With the rapid development of digital and intelligent campus concept as well as computer technology and network technology, people have more channels to obtain information, more ways of information transmission and faster speed of information transmission, and students’ life style and learning style are also changing. In view of the learning environment in colleges and universities has a wide range, high degree of liberalization, active thinking, the characteristics of large amount of information, management and service of university students’ work way cannot limit the bondage of traditional thinking, should be combined with the mobile terminal and PC terminal, large data background such as the characteristics of the new ways and new technology transformation of the mode of traditional higher education and train of thought, reduce the work form of direct interaction, people advocate work for students by means of informatization and network provide services and support [1, 2]. It is an inevitable trend in the new era of social development, in line with the era development and social progress of student work in colleges and universities put forward new requirements, facing the change of society and the needs of the students’ way of life, the informationization of the management and service work of university students and the intelligent construction is a necessary choice to enhance work efficiency and educational quality. The origin of computer, the germination of information technology, the rise of artificial intelligence and big data technology all started from the western developed countries. No matter in the support of hardware, or in the development of software, or even in the application of social production and life, the western developed countries are ahead of China. Therefore, China’s information and intelligent construction obviously lags behind the western developed countries, and the application of social production and life and the change of production and life style are also relatively backward. Therefore, the information construction and intelligent application of colleges and universities in China are characterized by insufficient hardware and lagging software. The application system of student work management and service in foreign universities has basically realized the intensive management mode and technical application system [3]. The characteristic of intensive management mode is that it can realize the effective convergence of departmental data, ensure the sharing and relevance of data, and give play to the overall function, which is manifested in high organizational efficiency, quick response speed, high information accessibility, convenient and safe information management [4].
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In this paper, the use of Java language to create a university student information management system, to achieve automatic information management, comprehensive query, analysis and decision-making, the system to achieve the students and teachers of information to add, modify, delete and query functions, effectively improve the teaching management work with students as the core of the quality of all kinds of information.
2 Information Management System for College Students 2.1 System Architecture (a) Overall structure. Student information management system is designed in this paper a through J2EE industry standard enterprise distributed technology architecture to realize completely the three-tier architecture of application software system, the system USES JAVA language development, by making full use of the JAVA language is cross-platform, scalability, security, stability, reliable technical functions and characteristics, combined with system also implements a large number of integrated application interface, not only makes the system has the powerful technical advantages, but also promoted the flexibility of the system for the most part and expansionary. The system architecture design determines the application characteristics of the system implementation, so the architecture of the application system should be carefully considered and selected. When selecting a system architecture, the application scenario of the system should be fully analyzed, rather than the more complex and new the architecture is, the better. In fact, choosing the most appropriate architecture is the ideal architecture. According to the basic principles of modern software engineering proposal, without affecting the main features and the main application system performance requirements, design as simple as possible, may be a better solution, because of the relatively simple design scheme, the system usually has improved the reliability and performance, and are easier to implement the system, the complexity of application development can also be reduced. At present, the basic architecture available for selection is C/S architecture or B/S architecture, or some parts of the application system adopt C/S architecture, and other parts adopt B/S architecture, that is, a mixed architecture [5, 6]. How to choose ultimately should be carefully authenticated. Only after the analysis of the characteristics of the application and the characteristics of the two architectures, it is possible to make a more appropriate choice. Combined with the characteristics of the application of educational administration management, it is decided to select C/S architecture to realize the functional modules of student achievement entry, student achievement identification, student status information entry, system administrator’s teaching resource basic information management, because these basic data have high security requirements. Most
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of the functional modules, such as the input of appraisal information, the query of online results, the printout of student status table, the printout of course schedule, the query of school calendar, and the printout of graduate file table, choose B/S structure to realize. Therefore, this application system adopts the implementation of hybrid architecture. (b) Business architecture. The business architecture of the student information management system consists of the presentation layer, the business layer and the data support layer. The presentation layer is mainly for personal work platform management and comprehensive inquiry. It supports the customization of concerned information for different levels (such as the dean of the school, the dean of the department, etc.), and can customize query and display through various dimensions, so as to ensure that managers at all levels keep up-to-date with the dynamics of the school and carry out relevant work [7]. According to the characteristics of college education student management, the business layer carries out various functions of student management, teacher management, professional management, teaching management, etc. manage the whole process of students. Data support layer: it provides structured data of the system, such as basic data, setting of roles and permissions of organizational personnel, and setting of work approval process. It is the basis for the system operation and other applications of the above layers.
2.2 System Function Module (a) Student management module. The student management module mainly includes student basic information management, payment rules setting, student status management and query statistics management and other sub-functions. Students’ basic information management to achieve the basic situation of the management of students, these information is basically static, as the college student management of the basic data. At the same time, it can also be updated with the occurrence of some events, such as the adjustment of students’ major, the major attribute can be updated. The fee payment rule setting realizes the setting management function of the fee payment rule for students, and serves as the basis for the fee collection in the process of students from enrollment to graduation. The student status management mainly realizes the functions of student status change management, major transfer management, student status information audit and so on. When a student’s status changes, such as graduation or withdrawal, such information will be recorded and kept. Query statistics is mainly realized through student-centered, comprehensive query of various information of students, and statistical analysis according to class.
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(b) Course selection management module. The course selection management module mainly includes course setting, optional course management and other sub-functions. The curriculum setting realizes the management of all courses offered by the college, mainly the management of the basic information of the course, which is basically static and serves as the basic data for the college students to select courses. The optional course management implementation generates a preliminary course schedule according to the rules and teaching plan of the major, and then it can be adjusted manually to get the course schedule for students to choose. Each student selects the courses he or she needs to learn according to the course arrangement obtained in the optional course management for his or her major, and generates the course schedule for each student. (c) Examination management module. The examination management module mainly includes the examination rules setting, the examination room arrangement, the invigilator arrangement and other sub-functions. The setting of examination rules realizes the setting of course examination rules, which is mainly the setting of information such as the number of candidates in each subject, the demand of examination arrangement and the length of examination. The arrangement of the examination room mainly realizes the management function of the examination place during the examination process, including the maximum capacity of the examination room and the available time. The arrangement of invigilators mainly realizes the setting of rules such as invigilators, invigilators’ time and invigilators’ subjects. According to the setting of the examination rules, the arrangement of the examination room and the invigilator’s arrangement, the examination plan is generated, and then the actual situation is adjusted to get the examination arrangement of the students. (d) Achievement management module. The achievement management module mainly includes the student achievement information management, the achievement comprehensive inquiry and the analysis, the student status graduation early warning management and so on. Entering students’ scores realizes the function of registering students’ test scores of various subjects, which is the basis of comprehensive inquiry and analysis of scores. The result comprehensive query realizes the multi-dimensional query and analysis of students’ results, which lays the foundation for summarizing the advantages and disadvantages of teaching activities and provides reference for further improving the teaching quality. The early warning of school status graduation mainly realizes according to the preset early warning conditions. When the early warning conditions are triggered by the students’ examination results, the early warning information is sent to the tutors and students, so as to avoid the influence of the students’ school status and graduation due to the unqualified results. (e) Relevant formulas. In the security management system, the correlation coefficient is often used to measure the similarity between users [8]. The formula is as follows:
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sim(a, b) =
i∈I
i∈I
(rai − r a )(rbi − r b ) 2 i∈I (r bi − r b )
(1)
sim(a, b)(rbi − r b ) b∈N sim(a, b)
(2)
(rai − ra )2
Evaluation formula: p(a, i) = r a +
b∈N
Similarity formula: = sim( a , b)
a · b | a | ∗ b
(3)
3 System Test 3.1 System Test Scheme The practice of information system development shows that defects are inevitable in software development. Therefore, if the software can be tested, as many as possible to find out the various defects, can greatly reduce the maintenance cost in the life cycle of the information system. The test scheme mainly includes the design and selection of test type and test content, test environment and test tool, test method and test case, etc. The design of test cases is very important for a software test, and it is closely related to test methods, test types and test contents. The design requirements and focal points of test cases for black-box test and white-box test, and test cases for functional test and performance test are often different [9, 10].
3.2 System Testing Tools and Environment In terms of the selection of testing environment and testing tools, in order to improve the level of automated testing, this test selected Selenium testing tools for Web application for functional testing and Load Runner testing tools for performance testing. Selenium supports popular browsers, such as Internet Explorer, Mozilla Suite, and others, and can simulate user actions in the browser. As a commonly used performance testing tool, Load Runner can simulate a large number of concurrent users, monitor the Load, concurrency and performance of the system in real time, and find the system bottleneck more conveniently [11, 12].
Application of Computer Technology … Table 1 System function module test table
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Use case number
Test module
Test results
1
Automatic arrangement
Pass
2
Query information
Pass
3
Collision detection module
Pass
4
Teaching task inquiry
Pass
4 System Test Results 4.1 Functional Test As shown in Table 1, the test results of some functional modules are listed. The goal of automatic test scheduling module test is to check whether the system can successfully export the test scheduling data from the database, and arrange the test table correctly, and the test result is passed; the goal of query information module test is to test the correct query of the invigilation table, including the whole school invigilation table, the class invigilation table, the teacher’s invigilation table and so on, the test result is passed; the goal of the conflict detection module test is to test whether the system can find the problem when the basic information in the database is artificially modified, and send out the warning message that the data integrity is damaged. The test result is passed. The goal of the teaching task query module test is to check whether the system can correctly query the teaching task data of each department or the whole school in the specified semester or school year, and the test result is passed.
4.2 Performance Test (a)
Stress test
As shown in Fig. 1, the pressure test tool was used to simulate the simultaneous request of 20 people for 1 min, and the success rate of request was greater than 99%. Average time is less than 1 s, maximum time is 1.908 s, and query time is less than 1 s. The system can meet the requirements under high concurrency. (b)
CPU Occupancy
As shown in Fig. 2, the CPU occupancy rate of the system under 5, 10 and 20 concurrent requests was recorded by using the detection tool. The CPU utilization of 5 concurrent requests was up to 27%. CPU utilization of 10 concurrent requests is up to 36%; CPU usage peaked at 57% for 20 concurrent requests. The maximum CPU occupancy rate is less than 70%, indicating that the system designed in this paper consumes little information resources and can meet the campus usage situation.
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a) Stress test Max
2500
Min
Average
Median
Time consuming(ms)
2000
1500
1000
500
0 5
20
40
60
Time(s) Fig. 1 System response time diagram
b) CPU Occupancy 60%
5
10
20
Occupancy rate
50% 40% 30% 20% 10% 0% 15
30
45
Time(s) Fig. 2 CPU usage test
60
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5 Conclusions The system designed in this paper provides a one-stop comprehensive service platform for college students, a comprehensive business platform for the student work team, and a data support platform for the relevant decision-making of school leaders. System covers the main job function of student work to realize the automation, intelligence and refinement, students work to improve the efficiency of student management and service work, simplify the procedure of work, implements the process of growing up to the student management, make the student worker team pay more attention to education quality, saving a large amount of manpower and material resources, financial resources, management, service for students and education provides a powerful guarantee for the fine, automation of. Due to the limited level of software design and time relationship, the system also has many bad places, such as: how to improve the system security, to the information system level protection regulations, how to improve the performance of the system, in the business problem such as high concurrency can still run smoothly, it is to be constantly improve and improve in the future.
References 1. Alameri IA, Radchenko G (2017) Development of student information management system based on cloud computing platform. J Appl Comput Sci Math 11(2):9–14 2. Adekola OD, Idowu SA, Adebayo AO (2016) Component based software engineering in student management system domain: a development for reuse approach. Int J Softw Eng Appl 10(9):149–162 3. Fratto V, Sava MG, Krivacek GJ (2016) The impact of an online homework management system on student performance and course satisfaction in introductory financial accounting. Int J Inf Commun Technol Edu 12(3):76–87 4. University RM, Pittsburgh PA, Pittsburgh UO (2016) The impact of an online homework management system on student performance and course satisfaction in introductory financial accounting. Int J Inf Commun Technol Educ 12(3):76–87 5. El_Rahman SA, Shabanah SS (2016) Course and student management system based on abet computing criteria. Int J Inf Eng Electron Bus 8(3):1–10 6. Jasmis J, Aziz AA, Jono M, Zamzuri ZF, Elias SJ (2021) An analysis model for an integrated student activities management system for higher education during rmo/cmco/pasca covid-19 period in Malaysia. Procedia Comput Sci 179(2):798–803 7. Surendran K (2020) Student academic management system using blockchain technology. J Adv Res Dyn Control Syst 12(SP3):1410–1415 8. Songsom N, Nilsook P, Wannapiroon P et al (2020) System design of a student relationship management system using the internet of things to collect the digital footprint. Int J Inf Educ Technol 10(3):222–226 9. Purnamasari F, Ashaari NS (2018) User centered design approach to redesign graduate student management information system. Data Sci J Comput Appl Inf 2(2):109–114
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10. Shi Q (2017) Design and implementation of student information management system based on B/S and C/S. C e Ca 42(3):1054–1058 11. Yang YL (2019) Feasibility analysis of using internet for student management. Comput Knowl Technol 15(03):63–64 12. Li JH (2015) Research on college student management based on network environment. J Jimusi Vocat Coll 11:150
Transformation from Financial Accounting to Management Accounting in the Age of Artificial Intelligence Xianfeng Liu
Abstract As the traditional financial accounting work is greatly replaced by artificial intelligence, it is the general trend to transform to management accounting. Now more and more business managers are eager for management accountants to deeply participate in the company’s operation and decision-making, so as to greatly enhance the economic benefits and wealth creation of enterprises. According to the current economic situation, this paper takes 180 accountants and 20 accounting experts as the experimental objects, analyzes the significance of the artificial intelligence era for the transformation and development of accounting, puts forward the transformation path combined with the environment of the artificial intelligence era, and probes into the specific strategies for the transformation and upgrading to management accounting in the artificial intelligence era. The results show that the probability of accountants being replaced by artificial intelligence is 97.6%; 65% of the obstacles to the current financial digital transformation come from enterprises’ concerns about data security, 50% from enterprises’ consideration of cost, and 40% from enterprises’ priority of process management in financial management; in practice, we should deepen the connection between functional departments and financial departments with the help of artificial intelligence technology, and adjust accounting information according to the characteristics of the times In order to ensure the high-quality development of enterprise accounting, we should carry out risk control in combination with artificial intelligence and transformation process. Keywords Artificial intelligence · Financial accounting · Management accounting · Transformation research
1 Introduction With the development of modern science and technology, the era of artificial intelligence has come quietly [1, 2]. Especially in recent years, the wide application X. Liu (B) Jiangxi University of Applied Science, Nanchang, Jiangxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_131
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of big data and Internet technology has brought new trends to China’s industrialization. With the help of intelligent robot, all departments in the enterprise have changed completely. No matter the financial department or the management department, there are new working directions. The transformation of financial accounting to management accounting has made new development [3, 4]. As a necessary key department of an enterprise, the development of financial industry is directly related to the life and death of enterprises. Traditional financial accounting has been transformed like a new type of accounting. Therefore, the extensive application of artificial intelligence technology can reduce the human demand of enterprise financial accounting and reduce the human resource cost of the enterprise [5, 6]. In addition, under the background of new business trend, new technology brings new business status. Unlike traditional accounting methods, intelligent technology makes the industry have a new trend of change. In order to gain competitive advantage in the fierce market competition, enterprises must change their original working methods, keep pace with the times, seek their own changes in the world, learn to use them “Two legs” walking, namely operation management and financial management go hand in hand [7, 8]. With the change of the development environment of enterprises, the requirements of accounting personnel are higher and higher. The managers expect that the accountants can achieve the goal of integrating industry and finance, leading business and creating value together on the basis of accounting work. Therefore, it is imperative to change from financial accounting to management accounting [9, 10]. This paper analyzes the practical significance of the transition from financial accounting to management accounting from the difference between financial accounting and management accounting, and then elaborates the strategy of transition from financial accounting to management accounting, and provides meaningful reference for enterprises. Based on this, this paper first expounds the accounting work in the era of artificial intelligence, and studies and analyzes financial accounting and management accounting. Combining with the environment of artificial intelligence era, this paper puts forward the transformation path, takes 180 accountants and 20 accounting experts as the experimental object, and then analyzes the problems in the transformation and puts forward solutions, aiming to promote the innovation and reform of accounting industry in an all-round way Leather [11, 12].
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2 Overview of Management Accounting 2.1 Meaning and Characteristics of Management Accounting 2.1.1
The Meaning of Management Accounting
Management accounting is quite different from financial accounting. On this basis, management accounting is a kind of internal report audit. His main objective principle is management. Therefore, there is a significant difference between the way of thinking of management accounting and financial accounting. It is an organic combination of thinking and management ideas, so as to help enterprises achieve correct business decisions.
2.1.2
Characteristics of Management Accounting
One is to better serve enterprise management. According to the different needs of various departments, products, projects or business processes within the enterprise, management accounting needs to conduct in-depth analysis of the financial reports, provide effective information for managers at all levels, help them make decisions and improve economic benefits. Second, the methods are flexible. In today’s big data era, management accounting makes full use of intelligent financial data to obtain useful information through multiple channels, so as to use various analysis tools to further process the collected basic financial accounting information, which provides a huge database for enterprise management. Third, grasp the present and face the future. To provide support services for the internal management of enterprises, on the one hand, management accounting needs to carry out cost analysis and income calculation for the operation, investment and financing schemes formulated by enterprises; on the other hand, it needs to carry out cost analysis and income calculation for the operation, investment and financing schemes formulated by enterprises, so as to ensure the realization of the strategic objectives of enterprises On the basis of material and other resources, we should make overall planning, be down-to-earth, and steadily achieve a higher level of development.
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2.2 Differences Between Financial Accounting and Management Accounting 2.2.1
They Are Used for Different Purposes
The main purpose of financial accounting is to explain the operating results that have been produced in the past period. It is to effectively calculate the factors such as the raising, use and appreciation of enterprise funds according to the accounting standards, so as to comprehensively and accurately reflect the current financial situation, operating results and cash flow of the enterprise. The purpose of management accounting is not only to pay attention to the business activities and financial accounting information of the enterprise, but also to analyze and summarize the management accounting information related to the enterprise in combination with some non-financial accounting information, so as to assist the enterprise management to make timely, rapid and accurate judgments and make effective decisions with the best scheme.
2.2.2
Timeliness of Information Provided
Different financial accounting information generally reflects the summary of historical records. For example, listed companies in China should disclose their financial reports within four months after the end of the accounting year, with a relatively fixed time. Management accounting can not only reflect the current information, but also reflect some future information, and all the information can provide timely basis for decision-making. Due to the information involved in the future, the form and time of the report should be relatively flexible.
2.2.3
Information Types and Focus Are Different
The basic characteristics of financial accounting information generally include: objective, continuous, true and accurate. It mainly provides pure financial information, which is used to record the data information of various economic businesses of enterprises. Management accounting information needs to provide financial and nonfinancial information at the same time from beginning to end, including more human subjective judgment factors. The focus is to carry out deep processing and refining induction on the basis of enterprise information provided by financial accounting, and then analyze and summarize the enterprise management information needed by the management. This process often needs to be applied to a variety of tools and more professional It’s the best way.
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2.3 Statistical Methods Using SPSS statistical software, the count data was expressed by mean ± standard deviation, and the count data was expressed by percentage. The comparison between groups was performed by x2 and t test, P < 0.05, with statistical significance. The formula is as follows:
2.3.1
Arithmetic Mean Value
n x=
2.3.2
2.3.3
Standard Deviation
i=1
xi
n
N 1 (xi − μ)2 σ = N i=1
(1)
(2)
Rectifier Linear Unit (Relu)
In the data-driven culture mechanism, the proposed activation function is rectifier linear unit. The relu function has the following form: r elu(x) = max{0, x}
(3)
3 Ideas and Methods 3.1 Subjects Taking 180 accountants and 20 accounting experts as the experimental objects, this paper analyzes the significance of the era of artificial intelligence for the development of accounting transformation, puts forward the transformation path combined with the environment of the era of artificial intelligence, and then analyzes the problems in the transformation and puts forward some suggestions.
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3.2 Experimental Methods 3.2.1
Questionnaire Survey and Interview
The questionnaire is divided into two parts, including the replacement rate of different occupations by artificial intelligence in the era of artificial intelligence and the obstacles of financial digital transformation. A total of 200 questionnaires were distributed and 190 valid questionnaires were collected. According to the current economic situation, this paper analyzes the relationship between financial accounting and management accounting, and discusses the strategies of transformation and upgrading to management accounting in the era of artificial intelligence.
3.2.2
Data Analysis and Experimental Evaluation
This study uses SPSS to deal with the data of the questionnaire, and at the same time uses incomplete induction to analyze the data collected by questionnaire, interview, observation and note taking, and then analyzes the practical significance of the transition from financial accounting to management accounting, and further expounds the transition strategy from financial accounting to management accounting, so as to provide meaningful reference for enterprises to transition from financial accounting to management accounting.
4 Discussion on the Transformation from Financial Accounting to Management Accounting in the Age of Artificial Intelligence 4.1 Analysis on the Replacement Rate of Different Occupations by Artificial Intelligence in the Era of Artificial Intelligence In this paper, questionnaire survey and interview are used to understand the situation of different occupations being replaced by artificial intelligence in the era of artificial intelligence in China. The survey results are shown in Table 1 and Fig. 1. According to Table 1 and Fig. 1, the probability of telephone salesman being replaced by AI is the highest, 95%; the probability of accounting staff being replaced by AI is 97.6%. When artificial intelligence is involved in the accounting industry, the tedious repetitive work will be greatly simplified. The high-efficiency processing advantages of AI have a great impact on the traditional financial and accounting functions. The enterprise will find that some basic accounting posts can be replaced
Transformation from Financial Accounting to Management … Table 1 Replacement rate of different occupations by artificial intelligence
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Occupation
Percentage (%)
Ratio
Telemarketer
99
89
Typist
98.5
86
Accountant
97.6
85.50
Bank clerk
96.8
84.60
Customer service
95
83
Ratio
Percentage
Customer service
Occupation
Bank clerk Accountant typist Telemarketer 75%
80%
85%
90%
Percentage
95%
100%
105%
Fig. 1 The replacement rate of different occupations by AI in the era of AI
by artificial intelligence, so the enterprise will greatly reduce the demand for traditional Accountants. Therefore, if we want not to be eliminated by the times, we can not stop. Accountants must follow the situation and combine artificial intelligence with financial management to realize the transformation from financial accounting to management accounting. In the process of changing the financial and accounting functions, accountants should pay more attention to the induction, integration, processing and analysis of data, which can not only predict the risks of enterprises, but also put forward optimization suggestions in time to the deficiencies of enterprise management.
4.2 Analysis of Obstacles to Financial Digital Transformation in the Era of Artificial Intelligence The survey results of financial digital transformation obstacles in the era of artificial intelligence are shown in Table 2 and Fig. 2.
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Table 2 Obstacles to financial digital transformation Obstacle
Proportion (%) Reliability test
Worry about data security
65
Consider the cost
50
0.33
Give priority to process management in financial management 40
0.28
Proportion
140%
0.45
Reliability test
100% 80% 60% 40%
Proportion
120%
20% 0%
Worry about data security
Consider the cost
Give priority to process management in financial management
Obstacle
Fig. 2 Obstacle analysis of financial digital transformation in the era of artificial intelligence
It can be seen from Table 2 and Fig. 2 that 65% of the obstacles to the current financial digital transformation come from enterprises’ concerns about data security, 50% from enterprises’ consideration of cost, and 40% from enterprises’ priority of process management over financial management. With the rapid development of market economy, the competition between enterprises is becoming increasingly fierce. If enterprises want to obtain competitive advantage, they must improve the management quality and efficiency. But at present, most of the small and medium-sized enterprises are limited by various conditions and their own resources, the accounting data of the financial department and other business departments and other relevant documents can not form a complementary and insufficient Integration and efficient and rational use can not play the overall efficiency of 1 + 1 > 2.
4.3 The Transition Path from Financial Accounting to Management Accounting Based on Artificial Intelligence Era 4.3.1
Strengthen the Integration of Industry and Wealth
In the division of enterprise functional departments, the financial department has the characteristics of strong professionalism. In the process of accounting transformation, due to the “information island” problem of financial accounting, there is less
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communication between the financial department and each functional department. Under the condition of mutual independence, the accounting business activities can not be carried out smoothly. At the same time, due to the information differences between departments, it seriously hinders the financial accounting from turning to management. The change of accounting shows that in the era of artificial intelligence, accounting is undergoing reform and development. We should deepen inter departmental exchanges and strengthen the integration of industry and finance.
4.3.2
Accelerating the Process of Enterprise Financial Information Construction
With the rapid development of big data, cloud computing, artificial intelligence and other new technologies, enterprises should realize the importance of establishing their own information management system. In the daily work of management accounting, we should face a large number of complex data, sort out various information hidden behind the data, and use the information management system, can automatically complete the basic data sorting and information mining, break the barriers between the systems, and promote the construction of enterprise information system, and then realize the management accounting information system and business system Communication and sharing, to realize the intelligent development of enterprise management accounting.
4.3.3
Strengthen the Construction of Management Accounting Talents
From the perspective of enterprises, the core competitiveness of enterprises is not only talents, but also the ability to train and retain talents. Because the strategies formulated by the management of enterprises need talents to implement. Therefore, we should speed up the construction of financial team and ensure that some people can use it in the process of enterprise development. To build a financial team with strong comprehensive ability, we should not only increase the training of professional level, thinking mode, management ability and comprehensive quality, but also cultivate the overall outlook of financial personnel to improve their business ability. At the same time, enterprises should also actively introduce compound talents, promote the optimization of the structure of financial team, strengthen the understanding of the importance of management accounting to the value creation of enterprises, and promote the effective implementation of the transformation from financial accounting to management accounting.
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4.3.4
X. Liu
Strengthening the Promotion of New Management Accounting System
The enterprise managers need to further improve the operation system of enterprise management accounting. With the aid of big data information technology, the enterprise organization structure and financial information operation process can be optimized and improved. The professional team of management accounting of the enterprise and other functional departments can be deeply integrated, and relevant management accounting work system and standard shall be established and perfected, and the cost, budget and operation shall be strengthened Item management ensures that the implementation of management accounting can be supported by the enterprise departments, give full play to their own advantages, improve the order and efficiency of enterprise information, and provide reliable, timely and accurate data for the decisions of enterprise managers.
5 Conclusions In summary, China is in the stage of economic transformation and upgrading. The emergence of new technologies such as artificial intelligence is providing new impetus for the development and transformation of enterprises. Similarly, financial management plays an important role in the development of enterprises. Financial accounting transformation management accounting plays an important role in the process of economic transformation and upgrading. In practice, the relationship between functional departments and financial departments should be deepened by means of artificial intelligence technology. The construction of accounting information should be adjusted according to the characteristics of the times. In order to ensure the quality and efficiency of transformation, the accounting system should be implemented in depth, and the risk control system should be carried out in combination with the process of artificial intelligence and transformation, so as to ensure the high-quality development of enterprise accounting. I believe that the future of the development of artificial intelligence will enhance the high attention to management accounting, will better integrate the management accounting concept and all management links of the enterprise, then realize the refinement of various management work, effectively promote the continuous improvement of enterprise management, and realize the steady and benign development of the enterprise. Acknowledgements This research was financially supported by Key scientific research projects of Jiangxi Provincial Department of Education (Grant No. GJJ GJJ191100) and “13th five-year” plan of education science of Jiangxi province (Grant No. 18YB295).
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The Role of Artificial Intelligence and Virtual Reality Technology in the Training Mode of Rehabilitation Professionals Changqiu Duan and Haili Song
Abstract Rehabilitation therapeutics started late and developed slowly in China, and there are many shortcomings in the talent training mode. The trained rehabilitation talents cannot fully meet people’s increasingly diverse needs for rehabilitation services. In order to better promote the development of rehabilitation medicine, we urgently need to reform and innovate the training mode of rehabilitation talents, and cultivate professional talents with rehabilitation specialty characteristics. The introduction of artificial intelligence and virtual reality technology is the key to achieve this goal. Keywords Artificial intelligence · Virtual reality · Rehabilitation professional · Training nowadays
1 First, the Current Situation and Demand of Domestic Rehabilitation Personnel Training The training of rehabilitation talents is the first essence of the development of rehabilitation and the key to the sustainable development of rehabilitation. At present, the quantity and quality of rehabilitation talents are far from meeting the growing needs of rehabilitation and clinical patients. The contradiction between the technology and treatment effect of rehabilitation talents and the diversification and frontier of rehabilitation needs has become increasingly prominent. Talent is the key to solve this problem. In addition to the corresponding policy support from the state, such as opening a green channel, providing assistance in rehabilitation personnel training, rehabilitation professional title evaluation and employment, rehabilitation industry salary (From March to July 2015, a survey was conducted on the rehabilitation institutions of the National Disabled Persons’ Federation, the health system, the C. Duan Changchun University of Humanities, Changchun, Jilin Province, China H. Song (B) Changchun Special Education School, Changchun, Jilin Province, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. Atiquzzaman et al. (eds.), 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Lecture Notes on Data Engineering and Communications Technologies 102, https://doi.org/10.1007/978-981-16-7466-2_132
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1198 Table 1 Qualifications of workers in rehabilitation institutions
Table 2 Wages of practitioners in rehabilitation institutions
C. Duan and H. Song Education background
Number
Specialized subject the following
509 (34.2%)
Undergraduate B
652 (43.8%)
Postgraduate or above
329 (22.1%)
Total
1490 (100.1%)
Monthly salary level
Frequency