Forthcoming Networks and Sustainability in the IoT Era: Second International Conference, FoNeS-IoT 2021, Volume 1 (Lecture Notes on Data Engineering and Communications Technologies) [1st ed. 2022] 9783030996154, 9783030996161, 3030996158

Citation preview

Lecture Notes on Data Engineering and Communications Technologies 129

Fadi Al-Turjman Jawad Rasheed   Editors

Forthcoming Networks and Sustainability in the IoT Era Second International Conference, FoNeS-IoT 2021, Volume 1

Lecture Notes on Data Engineering and Communications Technologies Volume 129

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

Fadi Al-Turjman Jawad Rasheed •

Editors

Forthcoming Networks and Sustainability in the IoT Era Second International Conference, FoNeS-IoT 2021, Volume 1

123

Editors Fadi Al-Turjman Innovation and Information Technologies Center, Faculty of Engineering Near East University Mersin, Turkey

Jawad Rasheed Department of Computer Engineering Istanbul University Besyol Mah, Istanbul, Turkey

ISSN 2367-4512 ISSN 2367-4520 (electronic) Lecture Notes on Data Engineering and Communications Technologies ISBN 978-3-030-99615-4 ISBN 978-3-030-99616-1 (eBook) https://doi.org/10.1007/978-3-030-99616-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

Evolution Characteristics of High-Tech Industry Innovation Efficiency Under the Background of Information Technology . . . . . . . . . . . . . . . . Xixi Feng

1

The Effect of Rapid Weight Loss on Football Players’ Health and Athletic Ability Under Computer Technology . . . . . . . . . . . Lei Zhang

9

University Art Education and Informatization Teaching Innovation in the Era of Network Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Liu

16

(CAD/CAM) Preliminary Establishment of Digital System in Dental Restoration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaobin Yin

24

Effectiveness of College Students' Physical Exercise on Improving Mood State Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zerong Jiang and Ruoguo Li

32

Influence of Big Data Information Processing Technology on English Reading Anxiety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingtai Li, Bi Zhang, and Craig Whitsed

40

Application of Big Data Information Processing Technology in the Improvement of Intercultural Sensitivity in English Teaching . . . Jingtai Li, Jiaju He, and Craig Whitsed

45

Coordinated Development of Children's Art Skills and Creativity Based on Human-Computer Interaction Technology . . . . . . . . . . . . . . . Yu Sun

50

Construction of University Teachers’ Digital Competency Model Based on New Media Communication Technology . . . . . . . . . . . . . . . . . Qi Yu

58

v

vi

Contents

Mixed Teaching of Linear Algebra Based on BOPPPS in Modern Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lijie Ma

67

Analysis of the Main Characteristics of the Outstanding Offshore RMB Bond Market with Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hanhan Zhang

74

Application of Computer Aided Instruction in Taekwondo Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Yang

83

Research on the Application of Artificial Intelligence Technology in News Space’s Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuang Wang

90

Risk Prediction and Treatment in Enterprise Management Based on Ant Colony Parallel Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaixin Shi

99

Research on the Sales Volume of Heavy Trucks in Various Usage Scenarios Based on the Analysis of the Static Market Big Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Chen Bai and Heng Zhang Analysis of Seepage and Clogging Characteristics of Rock-Soil Porous Media Based on DEM Technology . . . . . . . . . . . . . . . . . . . . . . . 116 Langhua Li, Yingjia Wang, and Yanan Yi Automobile Axle Temperature Detection Technology Based on the Matlab Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Yanbin Sun, Jianli Yang, Jing Yang, and Xiangkai Zeng Design of Marketing Data Mining System Based on AI . . . . . . . . . . . . . 133 Qiong He The Application of Computer Database System in Educational Information Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Bin Hu Intelligent Analysis and Processing Technology of Financial Big Data Based on Association Rule Mining Algorithm . . . . . . . . . . . . . . . . 149 Shuting Chen Accounting Information Quality Evaluation Based on BP Neural Network Evaluation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Shengyi Yang

Contents

vii

Design and Application of ERP System for Chinese State-Owned Enterprise Employees Based on Data Mining and Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Dejie Ma and Huilan Jing Construction of Credit Knowledge Service Model in Financial Field Based on Integrated SVM Data Stream Classification Algorithm . . . . . . 174 Yi Liu Study on Career Development and Digital Value-Oriented Countermeasures of Aesthetic Education Teachers Based on BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 Qianqian Chen and Jie Sun Lung Cancer Based on Big Data Technology Disease Data Management Status Quo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Yonghong Ma, Jiao Tan, Dongning Zhang, Ke Men, Mingjuan Shi, and Ying Cao Course Analysis and Management System Design Based on Big Data Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Dongbai Guo Application of BIM Virtual Technology in Highway Tunnel Construction Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Wanbo Qu Stock Return Analysis Based on ARMA (2,2) Model . . . . . . . . . . . . . . . 213 Haorui Yan Monitoring Method of Vertical Stress on Precast Concrete of Prefabricated Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Manli Tian Information Age, Artificial Intelligence and Virtual Reality Technology are Integrated with Logistics Teaching Reform . . . . . . . . . . 228 Rong Lu Evaluation and Improvement of Innovation Capability of Small and Medium-Sized Enterprises Based on Internet of Things Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Hui Deng Textile Industry Agglomeration and Economic Development . . . . . . . . . 241 Junlan Wang Threshold Effect of Collaborative Agglomeration of Internet and High-Tech Industry on Green Innovation . . . . . . . . . . . . . . . . . . . . 249 Huifen Wu

viii

Contents

Construction of Computer Network Security Information Leak-Proof Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Zejian Dong Techniques of Fusion Association Rule Mining Algorithm Cheerleading Training Damage Prevention Application . . . . . . . . . . . . . 265 Lin Zhu The Study on Practice of Professional Teachers in Business Administration Enterprises from the Perspective of New Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 Lina Niu Analyze the Current Direction of Administrative Management Reform from the Big Data Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Daojin Zhang Application of Information Technology in the Course Teaching of “Cost Accounting” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 Xiaoyu Yan and Hongyan Li Innovation and Entrepreneurship of Computer Application Technology Specialty Under the Vision of Smart City . . . . . . . . . . . . . . 294 Xingfeng Liu and An Qin Teaching Reform of Innovation and Entrepreneurship Education in Application-Oriented CaU Under the Background of BD . . . . . . . . . 303 Yadan Wang Development of Microgrid and Optimization of Heat Pump from the Perspective of Dual Carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Yuting Han Research and Exploration of Artificial Intelligence in Product Design in the Era of Intelligent Interconnection . . . . . . . . . . . . . . . . . . . 318 Ming Lv, Zimeng Li, and Cen Guo The Application of Product System Design in Emergency Equipment in the Era of Internet of Things . . . . . . . . . . . . . . . . . . . . . . 324 Ming Lv, Xianhao Wu, and Cen Guo The Application of Big Data and Artificial Intelligence Technology in the Collaborative Development of Art Design Education and Cultural and Creative Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 Ming Lv, Yuhan Gong, and Cen Guo Comparative Analysis of Students’ Learning Behavior in Smart Learning Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Xuegang Zhang and Qianwen Li

Contents

ix

Design and Development of AR Teaching System for Cardiac Physical Examination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Qianwen Li and Xuegang Zhang Visualization Enhancement of Parallel Coordinates Plot Based on Fisher Score and Laplacian Score . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Zhanpeng Qi Simulation Verification of Equipment Maintenance Characteristics Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 Yongling Liu, Danhong Chen, Zhen Gong, Qiushi Xiong, and Guiyong Chen Reform and Thinking of Computer Network Technology Specialty Based on Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Lei Wang and Jia Qu Application and Prospect of Big Data in the Prevention and Control of Major Epidemics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Youshen Chi The Applicability of Machine Learning Algorithms in Predictive Modeling for Sustainable Energy Management . . . . . . . . . . . . . . . . . . . 379 Shokhjakhon Abdufattokhov, Kamila Ibragimova, and Dilfuza Gulyamova Smart Home Appliance Control in the IoT Era . . . . . . . . . . . . . . . . . . . 392 Osman Abdalla Khalfalla, Suleiman Abdullahi Ali, Chadi Altrjman, and Auwalu Saleh Mubarak A Framework for Pothole Detection via the AI-Blockchain Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 Auwalu Saleh Mubarak, Zubaida Said Ameen, Paul Tonga, Chadi Altrjman, and Fadi Al-Turjman Use of Animal Waste for Heat and Produce Electricity in Form of Steam Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Da,ud Dirie Elimi, Ayub Mohamed Hussein, Abdul Mire Salad, Abdirahman Farah Ali, and Abdulaziz Ahmed Siyad A Hybrid Scheduling Approach in the Cloud . . . . . . . . . . . . . . . . . . . . 418 Adedoyin A. Hussain, Fadi Al-Turjman, Sinem Alturjman, and Chadi Altrjman Wi-Fi Feedback-Based Power System in the Heterogenous IoT Era – An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 Fadi Al-Turjman and Ahmed Ali Abdelazim Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443

Evolution Characteristics of High-Tech Industry Innovation Efficiency Under the Background of Information Technology Xixi Feng(B) College of Management, Xi’an Polytechnic University, Xi’an 710048, Shaanxi, China [email protected]

Abstract. In recent years, in the process of economic development, the importance of high-tech industries (HTI) has gradually become prominent. In the context of the continuous improvement of the level of economic growth and the further deepening of social division, the HTI will play an increasing role in promoting economic growth, and will even have a certain degree of influence on the development of the manufacturing industry in the future. HTI have become the main force in the economic growth of many developed countries. However, due to the late start of my country’s HTI, there is still a huge gap with developed countries. These gaps make the research of HTI more strategic. This article aims to study the evolution characteristics of HTI innovation efficiency under the background of information technology, and analyze the evolution of HTI in combination with supervision and empirical, qualitative and quantitative research methods. This article elaborates on the definition and characteristics of the high-tech industrial system, the evolution of the high-tech industrial system, the self-organization of the evolution and development of the HTI, and the evolution of other organizations. In the evolution and development curve of HTI, the factor analysis method is used to empirically analyze the development trend of HTI. The experimental results show that the contribution rate of innovation to the HTI reaches about 70%, which proves that the HTI innovation has grown from nothing, from small to large, from less to more, from disorder to relative, from relatively closed to completely open. Keywords: High-Tech Industry · Information technology · Industrial innovation · Evolution analysis

1 Introduction HTI is an important part of the modern economy. For our country, it is an inevitable choice to develop HTI on the road of taking a new industrialization road, applying scientific development concepts, and realizing sustainable development. Especially in the context of current economic development, resource constraints and environmental contradictions, the rapid development of HTI has important strategic significance for promoting economic growth, transforming economic growth patterns, and promoting © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 1–8, 2022. https://doi.org/10.1007/978-3-030-99616-1_1

2

X. Feng

independent innovation [1]. In this regard, this article studies the evolution stage of the HTI, and analyzes the development of the regional subsystem of the HTI at this stage. Effectively solving the problem of unbalanced growth has important theoretical and practical significance. Many scholars have conducted research on the evolutionary characteristics of HTI innovation efficiency in the context of information technology and have achieved good results. For example, Behrouz M uses the DEA model to evaluate the efficiency of the computer industry’s life cycle theory, and found that business profitability will show different levels as the product life cycle evolves [2]. Kim HS focuses on the relationship between foreign trade and the efficiency of local HTI, and points out that opening up the international market can affect the development of domestic HTI in many ways [3]. Yu J divides Italian chemical companies into high-tech companies that focus on scientific and technological knowledge, and non-high-tech companies that are less related to technology or high-level knowledge. According to the actual production situation of the industry, the DEA model aims to evaluate the efficiency of two types of companies and find high-tech companies. Intensive knowledge in technical industries will flow into non-HTI in a specific way, and knowledge has a dispersion effect from high to low [4]. These scholars have done a lot of research on the evolution characteristics of HTI innovation efficiency under the background of information technology, which provides a good theoretical basis for the research of this article. Aiming at the evolution characteristics of HTI innovation efficiency, this paper adopts the convergence experiment method and the factor extraction and naming experiment method of the evolution characteristics of HTI innovation efficiency. The evolution characteristics of HTI innovation efficiency are studied in detail and the results are obtained. It proves that the evolution characteristics of the HTI is a benign and growing trend, which further illustrates the country’s strong support for the HTI. The research on the evolution characteristics of HTI has certain practical reference significance.

2 Characteristics of HTI As an industry classified as the main industry of the national economy, the HTI naturally has the commonality of all industries, that is, it can create a certain amount of wealth and contribute to the national economy. In addition to the general characteristics of industries, HTI also have important characteristics that distinguish them from other common industries [5]. 2.1 Strategic Strategy refers to the close connection between HTI and the country’s economy and national defense capabilities, which is related to the country’s economic situation and military situation. The HTI is the driving force of a country’s economic development. It is also an important part of national strategy and has a huge impact on a country’s international political, military and economic situation [6].

Evolution Characteristics of High-Tech Industry Innovation Efficiency

3

2.2 Risk The risk is that the technical support used by the HTI is at the forefront of technology and the future is uncertain. Its huge investment has exacerbated the risk of the HTI. In addition, the speed of application of information technology in HTI has also increased the risks of HTI. Because the technology used in the HTI has the characteristics of rapid information, many high-tech technologies will be replaced by other products before they can fully perform their functions. If Microsoft invests a lot of money in research and development every year, the launch of new products is likely to mean the withdrawal of old products from the market. However, if the company does not launch new products, it will face the risk of replacing the company’s products with other companies’ products [7, 8]. 2.3 Gain High-tech applications labor labor productivity, capital, and increase productivity rates. The value-added rate of HTI has increased. The value-added utility is very obvious [9]. 2.4 Permeability High technology itself is often a comprehensive and highly intersecting technical field, including various disciplines, knowledge and talents. When HTI merge and penetrate with other sectors or traditional industrial sectors, new HTI will emerge. For example, the combination of HTI and traditional agriculture has produced many new agricultural technologies in cattle production. The combination of high-tech and agriculture not only improves the scientific and technological content of agriculture, but also promotes the transformation of agriculture to industrialization [10, 11]. 2.5 Motivation The penetration of high technology in knowledge, technology, and talents determines the driving force of the HTI. High technology can apply new technologies to traditional fields, transform old production methods, and drive technological progress in various industries. Driven by the HTI, new industries, new cattle production methods, and new consumption methods will emerge in endlessly. For example, the electronic and communication equipment industry based on the Internet is already innovating people’s consumption patterns. The emergence of online shopping has realized the dream of consumer groups to shop around without leaving home [12].

3 Experimental Analysis Method of Evolution Characteristics of HTI Innovation Efficiency 3.1 Comprehensive Analysis Method A city is a system composed of many aspects such as society, economy, and politics. There are complex internal organic connections and mutual constraints between them, and

4

X. Feng

they have comprehensive characteristics. The evolution of urban form is a complicated process. There are many factors that affect the evolution of urban morphology, and the background is complex. The process of city formation is the result of the interaction of different systems. Only in accordance with the principles of integrity, content and coordination, comprehensive analysis and understanding of the factors affecting the evolution of urban form and the methods of their interaction, can we understand its evolution trend. 3.2 The Combination of Static Analysis and Dynamic Analysis The development of HTI is a process of dynamic evolution. Only by adopting dynamic analysis methods can we better understand the characteristics of dynamic processes and their development trends. Therefore, this article first analyzes the historical development of the HTI, looks at the development model of the urban center with Chinese characteristics, and on this basis, studies the internal evolution mechanism of the HTI, understands the general law of development, and provides the future development of the HTI. Forecast and propose countermeasures. 3.3 Comparison and Comparison Methods The evolution of HTI is a long historical process, and there are significant differences in morphology in different historical periods. Only through time-consuming comparative research can we have a deeper understanding of the process and mechanism of the evolution of HTI, understand the changes of things in different periods, reveal the development process and characteristics of things, and predict future development trends. 3.4 Combining Literature Collection and Field Investigation Make full use of traditional methods and the Internet to collect domestic and foreign literature on the evolution characteristics of HTI, especially with the help of foreign related materials, to understand the latest foreign research results and trends, and to analyze and classify the received data. Combining theory with practice requires on-site inspections. On the one hand, it conducts on-site evaluation of the most mature HTI from the perspective of designers; on the other hand, it feels the advantages and disadvantages of HTI from the perspective of technology. 3.5 Convergence Analysis Method This paper draws on the existing research literature, uses the coefficient of variation and absolute convergence to test the convergence of the dynamic changes of the technological innovation efficiency of my country’s HTI, and further explains the dynamics of the dynamic changes. Technological innovation and corresponding trend characteristics of HTI in different regions. The mathematical expressions for coefficient of variation and absolute β convergence are formula (1) and formula (2) respectively: CV = δ/TFP

(1)

Evolution Characteristics of High-Tech Industry Innovation Efficiency

5

Among them, the formula (1) is the standard deviation of TFP in the technological innovation of HTI, and is the average value of the index of technological innovation; gi,t = ln(TFPi,t+T /TFPi,t )/T = α + β ln TFPi,t + εi,t

(2)

In formula (2), gi,t represent the average annual TFP growth rate of economy i from t to t + T, lnTFPi, t is the logarithm of TFP of economy i in the base period, β is the coefficient, and α is the constant term. εi,t represent the error term.

4 Analysis of the Experimental Results of the Evolution Characteristics of HTI Innovation Efficiency 4.1 Analysis of Convergence Experiment Results On the basis of the method proposed in this article, first calculate the coefficient of variation of the TFP of my country’s HTI technological innovation from 2014 to 2020. The results are shown in Table 1: Table 1. Convergence experiment analysis table Years

East

Central

West

Nationwide

2014

0.15

0.43

0.22

0.21

2015

0.11

0.05

0.60

0.25

2016

0.12

0.51

0.36

0.29

2017

0.29

0.79

0.29

0.40

2018

0.13

0.3

0.27

0.22

2019

0.14

0.05

0.15

0.18

It can be seen from Fig. 1 that the change trend of the curve can be divided into two parts by taking 2017 as the boundary. The TFP variation coefficient of my country’s eastern, central and western regions, and the average national HTI innovation in 2014– 2017 showed an upward trend in fluctuations, indicating that the technological innovation efficiency of my country’s HTI has a certain divergence in TFP changes at this stage. After the year, the coefficient of variation of the technological innovation of HTI in various regions showed a downward trend, indicating that the changes in the efficiency of technological innovation of my country’s HTI are gradually converging. Comparing the range of curve changes in different regions, it is not difficult to find that the changes in the eastern region are basically the same as those in the whole country, and the range of changes is relatively small, and the coefficient of variation in the eastern region is the smallest among all regions. The variation coefficient curve of innovation efficiency fluctuates sharply.

6

X. Feng

Fig. 1. Convergence experiment analysis diagram

4.2 Factor Extraction and Naming Experiments for the Evolution Characteristics of HTI Innovation Efficiency On the basis of the previous experiments, this paper also conducted factor extraction and naming experiments for the evolution characteristics of HTI innovation efficiency, and listed all the principal components in the table, and the characteristic roots were carried out in order from large to small. The experimental results are shown in Table 2: Table 2. Analysis table of experimental results Factor

Eigenvalues

Contribution rate (%)

1

12.15

32.3

2

8.56

20.56

3

6.68

16.57

4

4.79

6.48

5

4.68

5.26

6

4.35

3.15

It can be seen from Fig. 2 that the characteristic value and the contribution rate. The eigenvalues of the first three factors meet the criterion greater than 5. Their cumulative contribution rate reached 69.43%. Although the cumulative contribution rate did not reach 70%, it was still close to 70%. Therefore, taking the first three public factors can describe and reflect the basic situation of the development of HTI. The above three public factors have different degrees of variation in explaining the development and evolution

Evolution Characteristics of High-Tech Industry Innovation Efficiency

7

Fig. 2. Analysis of experimental results

of the HTI. It also reflects the complexity of the evolution of HTI and the quality of economic development of HTI.

5 Conclusions The position of HTI in the national economy is becoming more and more important, but at present, there are differences in the development of HTI in my country’s eastern, central and western regions and the provinces and cities within the region, and the development of HTI in various regions is not balanced. This paper studies the evolution and development status of my country’s HTI from both theoretical and empirical aspects. From the experimental data, my country’s HTI system is not static. In the evolution of my country’s high-tech industrial system, there are not only times when outstanding achievements are made, but there are also times when contradictions arise. The synergy of various forces in the internal evolution of my country’s HTI is also different. Therefore, my country’s HTI is far from equilibrium. As a complex system far from equilibrium, my country’s HTI has gradually realized the evolutionary steps from disorder to order, low-level to high-level in the process of development and evolution. However, there are still some shortcomings in the research of this article. For example, the statistical data of the HTI is not comprehensive enough, and the statistical caliber of the statistical yearbook is constantly changing, which increases the difficulty of quantitative research in the article. In the quantitative analysis of the HTI, the data the collection and sorting work is more difficult. Hope to continue to improve in follow-up experiments.

References 1. Han, C., Thomas, S.R., Yang, M., et al.: Evaluating R&D investment efficiency in China’s HTI. J. High Technol. Manage. Res. 28(1), 93–109 (2017)

8

X. Feng

2. Behrouz, M., Fegh-Hi, F.: Prioritize factors affecting the HTI investment in East Azerbaijan province using prioritization techniques VIKOR. Int. Rev. 2017(1–2), 133–142 (2017) 3. Kim, H.S., Kang, J.W.: Smart platform for microvibration control of HTI facilities. Int. J. Steel Struct. 17(1), 155–164 (2017) 4. Yu, J., Campbell, S., Li, J., et al.: Do sources of occupational community impact corporate internal control? The case of CFOs in the HTI. Account. Audit. Account. J. 32(4), 957–983 (2019) 5. Sambamurthy, V., Venkataraman, S., et al.: The design of information technology planning systems for varying organizational contexts. Eur. J. Inf. Syst. 2(1), 23–35 (2017) 6. Vanpoucke, E., Vereecke, A., Muylle, S.: Leveraging the impact of supply chain integration through information technology. Int. J. Oper. Prod. Manage. 37(4), 510–530 (2017) 7. Kher Ba, C., Kaboul, R., Deghir, Y.: Information technology and systems in transport supply chains. Eur. J. Eng. Formal Sci. Art. 1(1), 67–72 (2017) 8. Mocetti, S., Pagnini, M., Sette, E.: Information technology and banking organization. J. Finan. Serv. Res. 51(3), 313–338 (2017) 9. Yaqub, O.: Variation in the dynamics and performance of industrial innovation: what can we learn from vaccines and HIV vaccines? Ind. Corp. Chang. 27(1), 173–187 (2018) 10. Feng, K., Li, Y.: Employee ownership and industrial innovation: Huawei in the U.S.-China Technology Rivalry. China Rev. 20(4), 39–67 (2020) 11. Zhang, S., Qi, Z., Sun, Q., et al.: Genome evolution analysis of recurrent testicular malignant mesothelioma by whole-genome sequencing. Cell. Physiol. Biochem. 45(1), 163–174 (2018) 12. Xu, F., Li, H., Li, Y.: Correction to: ecological environment quality evaluation and evolution analysis of a rare earth mining area under different disturbance conditions. Environ. Geochem. Health 43(8), 1 (2021)

The Effect of Rapid Weight Loss on Football Players’ Health and Athletic Ability Under Computer Technology Lei Zhang(B) College of Physical Education at Xinyang, Normal University, Xinyang 464000, Henan Province, China [email protected]

Abstract. In recent years, with the continuous implementation of the National Fitness Program, more and more people participate in sports events. Carrying out fast weight loss training before the competition can quickly reduce the weight to the competition standard. Therefore, this article uses computer data processing technology to analyze the impact of rapid weight loss on athletes’ health and athletic ability. The purpose is to analyze the impact of rapid weight loss, so that athletes pay attention to rapid weight loss. This article mainly uses experimental method, comparative method and data method to understand computer technology, rapid weight loss and the health and ability of football players. Through experimental tests, it is found that rapid weight loss has both good and bad effects. The results showed that, in the physical fitness index, P < 0.005 for 10-min running and intermittent running was statistically significant. Keywords: Computer technology · Rapid weight loss · Football players · Health and athletic ability

1 Introduction Football has very strict requirements on the weight of players. Athletes maintain ideal weight, can continuously improve sports performance and prolong sports life. In order to achieve better sports performance goals, athletes only need to adjust their weight according to the actual situation. There are many studies on the effects of rapid weight loss on the health and athletic ability of football players. For example, Du Shulin said that some sports events need to control the weight of athletes to help athletes better adapt to the competition process [1]. An Kun said that from the characteristics of football events, football players need to have balance, strength, endurance, and speed [2]. Rong Lei further analyzed the IHT training mechanism by studying the changes in the sprint speed of high-level football players [3]. So this article studies the impact of rapid weight loss on football players, and intends to further analyze its pros and cons.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 9–15, 2022. https://doi.org/10.1007/978-3-030-99616-1_2

10

L. Zhang

This article first studies the relevant knowledge of rapid weight loss, and elaborates on the effects, principles, and methods of rapid weight loss. The second is to analyze the characteristics of football specific skills. After that, the research is about the effect of rapid weight loss on body composition and exercise capacity. The last research is the application of computer data processing technology in football.

2 The Impact of Rapid Weight Loss Under Computer Technology on the Health and Athletic Ability of Football Players 2.1 Fast Weight Loss (1) The principle of rapid weight loss The key to rapid weight loss is to follow the principle of negative calorie balance, reduce body water in a short period of time, and achieve weight loss [4, 5]. Usually, athletes use rapid weight loss methods in the process of losing weight before the competition [6, 7]. (2) The effect of rapid weight loss on body composition Rapid weight loss will significantly reduce the athlete’s body fat content, body weight and body fat percentage, but the skeletal muscle content will also be reduced[8, 9]. (3) The effect of rapid weight loss on body shape Rapid weight loss usually affects the athlete’s strength quality to varying degrees [10, 11]. (4) Quick weight loss method 1) High-intensity interval training. Do a few short-term high-intensity exercises and create a rest period between every two lower-intensity high-intensity exercises or complete rest [12]. 2) Medium-intensity training. Medium-intensity continuous exercise is the most widely used aerobic exercise method in exercise training. 3) The method of extracting time from dehydration. 2.2 The Effect of Rapid Weight Loss on Body Composition and Exercise Capacity Athletic performance is positively correlated with lean body mass. In many sports, lean body mass is closely related to athletic performance. Athletes increase the percentage of their lean body mass, thereby promoting the improvement of athletic performance. (1) The impact of rapid weight loss on athletes’ physiological functions Weight loss, especially controlling body fat at a low level, can produce good results. (2) Impact on material energy metabolism. During weight loss, athletes reduce weight by restricting diet and drinking, exercise, sweating, and loss of water. (3) Impact on the function of the endocrine system. Restricted diet can cause functional changes in the hypothalamic-pituitary-gonadal axis and growth factors of growth hormone.

The Effect of Rapid Weight Loss on Football Players’ Health

11

2.3 Application of Computer Data Processing Technology in Weight Loss Training With the development of data acquisition technology and data processing technology, a new data model has emerged. This article can sort and analyze the data obtained in weight loss training according to the mid-stream computing technology of computer intelligent equipment. Assuming that the server cluster is represented by a set Q = {Q1 , Q2 , ..., Qm }, M is the number of servers, and a is the number of the server, the server’s request will satisfy the formula (1):  D(Q )  D(Q ) n

D

T (Qm )

a

= min

D

T (Qa )

(1)

Among them, a = 1, 2, . . . , m−1. Because the weight of each server T (Qm ) cannot be 0, and the total number of current connections D of all servers is a constant, formula (1) can be simplified to formula (2):   D(Qa ) D(Qn ) = min (2) T (Qm ) T (Qa ) Assigning tasks to servers according to the ratio of load weights can solve the problem that the minimum number of connections algorithm is only suitable for applications in homogeneous systems.

3 Experimental Research 3.1 Research Objects Ten male football players were selected as the research objects, and they were randomly divided into the experimental group and the control group. The age, gender, height, weight, and training years data of 10 subjects before the intervention were sorted out. After statistical analysis of independent samples, T-test, it was found that there was no significant difference in the baseline level of measurement indicators in each group. 3.2 Experimental Process Before the intervention, the two groups of subjects received football specific physical fitness tests to obtain the pre-intervention index levels. After the pre-intervention test, all subjects received a 10-week intervention. 3.3 Test Indicators and Statistics Basic information and specific physical fitness tests were performed on all subjects two days before the intervention and two days after the intervention. Special physical fitness tests include: 10-min running, 25-m, 50-m, 100-m sprint running, intermittent running, jumping height, etc.

12

L. Zhang

4 Experimental Results 4.1 Changes in the Levels of Physical Fitness Indicators of the Experimental Group Athletes Before and After the Intervention According to experimental investigations, the 10-min sprint, intermittent running, and jumping height indicators of the experimental group before the intervention were all lower than those after the intervention. The details are shown in Table 1: Table 1. Changes in the levels of physical stamina before and after the intervention of the experimental groups of athletes Indicators

Before intervention

After the intervention

P

10 min run

0

2539.12

2574.31

25 m Sprint

4.11

3.99

0.058

50 m Sprint

5.67

5.42

0.004

100 m Sprint

10.67

9.82

Intermittent running

16.23

17.08

0

0.003

Bounce height

61.25

63.56

0.002

Fig. 1. Changes in the levels of physical stamina before and after the intervention of the experimental groups of athletes

As shown in Fig. 1, we can see that, except for the 25 m sprint, the rest of the indicators are meaningful. Before and after the intervention, the 25 m, 50 m, and 100 m sprint running time gradually decreased, while the intermittent running and jumping height increased.

The Effect of Rapid Weight Loss on Football Players’ Health

13

4.2 Changes in the Levels of Physical Fitness Indicators Before and After the Intervention of the Athletes in the Control Group According to experimental investigations, the 10-min sprint, intermittent running, and jumping height indexes of the control group before the intervention were all lower than the indexes after the intervention. The details are shown in Table 2: Table 2. Changes in the levels of physical stamina before and after the intervention of the control groups of athletes Indicators

Before intervention

After the intervention

P

10 min run

2498.57

2613.26

0

4.23

4.19

25 m Sprint

0.163

50 m Sprint

5.74

5.73

0.652

100 m Sprint

10.25

9.73

0.005

Intermittent running

16.59

16.82

0.04

Bounce height

61.11

61.58

0.203

Fig. 2. Changes in the levels of physical stamina before and after the intervention of the control groups of athletes

As shown in Fig. 2, we can know that the results of the 10-min, 100-m and intermittent running after the intervention in the control group were significantly better than those before the intervention. There was no significant difference in other indicators before and after intervention.

14

L. Zhang

4.3 Health Changes Before and After Rapid Weight Loss According to computer intelligence testing tools, it is found that athletes’ physical fitness will change before and after rapid weight loss. The specific situation is shown in Table 3: Table 3. Health changes before and after rapid weight loss Before weight loss

After rapid weight loss

Body fat percentage

10

8

Blood pressure

49

48

Fatigue index

44

43

White blood cell count

6

6.1

Blood urea

6.7

7.3

Fig. 3. Health changes before and after rapid weight loss

As shown in Fig. 3, we can see that before and after weight loss, the number of blood urea and white blood cells gradually increased, while the percentage of body fat, blood pressure, and fatigue index decreased.

5 Conclusion High-intensity interval training for rapid weight loss will enable football players to improve aerobic capacity, anaerobic capacity, lower limb explosive power and specific

The Effect of Rapid Weight Loss on Football Players’ Health

15

agility. However, due to rapid weight loss, there will be problems with physical fitness that cannot keep up, so the method of rapid weight loss needs to be carefully planned. This article believes that the combination of rapid weight loss and slow weight loss is more conducive to the improvement of football players’ physical health and athletic ability.

References 1. Du, S.: Analysis of the impact of rapid weight loss on athletes’ health and athletic ability. New Silk Road: Mid-term 000(007), 1 (2019) 2. Kun, A.: The effect of rapid expansion and contraction compound training on the sensitive quality training of college football players. Friends Human. 000(001), 58–59 (2018) 3. Lei, R.: The effect of intermittent hypoxia training on the sprint speed of football players. Sci. J. Normal Univ. 040(004), 78–81 (2020) 4. Wu, X.: The impact of core strength training on the ability of football players to change direction. Sports Fashion 000(005), 65 (2018) 5. Xu, F., Xie, H., Xu, Y.: The effect of rope skipping training on young football players’ dynamic balance and coordination. China Sports Sci. Technol. 053(003), 71–77, 107 (2017) 6. Wang, Z.: The effect of rapid expansion and contraction compound training on the speed, bounce and sensitivity of male football players in sports colleges. Slam Dunk 000(020), 49 (2019) 7. Jianmin, D.: Formula optimization of peptide-containing healthy sugar drinks and its effect on blood glucose response after football sports. Food Sci. Technol. 042(009), 66–68 (2017) 8. He, C., Zhang, X.: On the impact of football on the health of college students. Guangdong Sericulture 11(v.51), 66 (2017) 9. Jianxiong, C.: Analysis of the impact of football on the physical and mental health of middle school students——Taking some middle schools in Tianshui City, Gansu Province as an example1. Exam. Weekly 000(073), 110–111 (2019) 10. Zhang, G.: The effect of weight loss before the competition on the strength of Greco-Roman wrestlers. Sports Fashion 000(005), 218–219 (2019) 11. Zhu, L.: On the scientific methods, measures and precautions for weight loss of wrestlers. The Most Comics·School Body Music Beauty, 000(008), 1 (2018) 12. Xiaoyi, M.: The influence of Fit-light training system on the quick reaction ability of male basketball players. J. Tonghua Teach. Coll. 041(002), 71–75 (2020)

University Art Education and Informatization Teaching Innovation in the Era of Network Information Yan Liu(B) College of Information and Engineering, Anyang Vocational and Technical College, Anyang 455004, Henan, China [email protected]

Abstract. With the rapid development of computer software technology and information network technology, the huge impact of information technology on college education has become increasingly apparent. Art education has its own distinctive curriculum characteristics, and the general curriculum teaching system is often not suitable for the specific teaching implementation of art education. The purpose of this article is to study the innovation of university art education and information teaching in the network information age. First, it explained the characteristics of university learning in the network information age, and proposed relevant innovative countermeasures for university art teaching. In order to have a deeper understanding of the corresponding research questions, interviews with art teachers of University A and innovative experiments on university art informatization teaching were conducted. By comparing the experimental class of University A with the control class, the results show that the overall art work performance of the experimental class students is higher than that of the control class, with an excellent score of 62%. It is feasible to use university art teaching under the environment of information technology to carry out university art teaching innovation, and its positive impact is significant. Keywords: Network information age · College art education · Informationized teaching · Teaching innovation

1 Introduction The rapid development of the information age has brought people the space of virtual reality and convenient information resources [1]. The integration of information technology and art education helps students determine their own learning progress, select learning content and self-evaluation, and provide interactive education for teachers and students [2]. Teachers use information technology to create a variety of classroom teaching to help students solve problems, acquire knowledge and build. The purpose of modern education is to cultivate life-long learners, so that students have the ability to learn by themselves [3, 4]. Possess self-analysis and evaluation ability, reflection and criticism ability, and innovative spirit. Therefore, integrating information technology into the art classroom plays a very important role in art education [5]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 16–23, 2022. https://doi.org/10.1007/978-3-030-99616-1_3

University Art Education and Informatization Teaching Innovation

17

This kind of learning revolution under the condition of network information is essentially the innovation of learning culture, and it is an inevitable requirement for learning in the era of knowledge economy. The Ministry of Education also formulated corresponding exercises and performance standards according to the art education curriculum [6]. Under these conditions, Long Y applied data mining technology to the evaluation process of artistic achievements. First, the ID3 algorithm of the decision tree is established, which is obtained by mining the art test scores, and then the data stream is combined in the algorithm. In addition, combined with the students’ own artistic achievements, the algorithm is tested and data mining analysis is performed, and finally the student’s characteristic information is obtained [7]. Rahmat M K discussed the specific methods teachers use ICT and the factors that influence their integration of ICT into the art classroom. Participants in this study were 18 VAE teachers from three selected secondary schools in Selangor, Malaysia. The research results show that the ICT integration level of Malaysian VAE teachers is still at an intermediate level [8]. The study of university teaching model innovation in the age of network information has guiding significance for the innovation of university students’ learning concepts and learning methods [9]. In this article, we will integrate the benefits of information technology into university art education, build an open new classroom of modern art, and stimulate college students’ interest in learning art, independent learning ability and innovative ability. First of all, it analyzes the literature data of the theoretical research and practical research on learning innovation at home and abroad, and elaborates the current research status and development trend of university learning innovation. Then, under the framework of the university learning innovation system, in accordance with the in-depth ideas, we will study and explore the university learning concept, the innovation of learning methods and the innovation of teaching models in the network information age. Finally, combined with the practical research of university learning innovation under network conditions, relevant conclusions and prospects are given.

2 Innovative Research on University Art Education and Informatization Teaching in the Network Information Age 2.1 The Characteristics of University Learning in the Network Information Age (1) Digitization Digitization refers to various information and resources based on digital media. In the age of network information, college students need to fully understand that digital media is an important source of information and knowledge in terms of learning concepts [10]. (2) Individualization The university learning philosophy advocated in recent years has changed the traditional one-sided notion that teachers are the main body of education and students are the secondary objects of education. It is gradually changing to “autonomous” learning such as research learning and constructivism [11, 12]. In the process of teaching the concept of autonomous learning, educators not only regard students as the object of education, but also regard students as the subject of education.

18

Y. Liu

(3) Collaboration In the age of network information, students use computers and networks to support interactive activities among students, teachers, and students in the learning process, so that learning can break through the barriers of time and space, and achieve communication anytime, anywhere. This new cooperative learning method will help maximize learning efficiency. (4) Innovation Innovative learning is the ultimate goal of learning, and it is also the biggest requirement of learning. Innovation is not only the spirit, but also the result of learning. In spirit, innovative learning should run through the entire learning process. 2.2 Information Education Compared with traditional education, informatization education is the embodiment of modern education and education. Information education is the product of the combination of modern educational thought and modern information technology. The relationship between modern educational thought and modern information technology is not a simple “addition” relationship, but a “multiplication” relationship. To be successful, two things need to be merged. The inevitable result of education informatization is informatization education, which is a brand-new form of education. In other words, informatization education is the result of education informatization. 2.3 Construction of Innovative Art Education Classroom (1) Inquiry teaching mode based on multimedia courseware Under the creative teaching of fine arts, multimedia courseware has been effectively used in university fine arts classrooms, bringing new development opportunities and ideal platforms for traditional teachers’ teaching and students’ learning. The intuitiveness, vividness, and interest of multimedia courseware solves the understanding of some abstract concepts of art by college students, thereby improving the quality and efficiency of art teaching. The inquiry teaching mode of multimedia courseware emphasizes that under the guidance of teachers, students can construct knowledge independently according to their own hobbies and solve practical problems. The exploratory teaching model of multimedia courseware pays great attention to cultivating students’ innovative thinking ability. It uses multimedia courseware to assist art classroom teaching, and creates teaching situations with intuitive, vivid, vivid and lifelike multimedia courseware pictures, which mobilizes students’ interest in learning and is active Guide students to explore independently and acquire new knowledge through practical inquiry. (2) Web-based collaborative teaching mode The network-based collaborative teaching model means that teachers and students use multimedia and network computers to assist teaching in the classroom teaching process, forming a harmonious teaching atmosphere in which teachers and students, students and students collaborate and learn. Hope Through mutual exchanges, communication, and cooperation, a more comprehensive and deeper

University Art Education and Informatization Teaching Innovation

19

understanding and internalization of the knowledge learned are achieved. The collaborative teaching model of applying the network in classroom teaching is generally based on the needs of the teaching content and the premise of mobilizing the enthusiasm of students to participate in teaching activities, dividing class students into several groups or teams for independent and cooperative learning. In this teaching mode, teachers still play the role of instructor, guide, and facilitator, while students will work together among group members to finally complete the teaching task and achieve the teaching goal. This teaching mode advocates the mutual cooperation between the students’ collectives, and allows students to experience the psychological and emotional experience of competition, honor and disgrace among groups, etc. in the process of collaborative learning. At the same time, network technology also provides more knowledge resources for students’ collaborative learning.

3 Investigation and Research of University Art Education and Information Teaching Innovation in the Network Information Age 3.1 Research Methods The interview investigation was completed by face-to-face interviews with the art teachers in the school’s art teaching and research group. With the assistance of the faculty of University A, the author also collected relevant information on student performance provided by the art teaching staff of University A. 3.2 Data Collection Statistics on the use of informatization teaching methods in each grade, and two classes in the first year of the university, one class as an experimental class for studying the teaching structure of “art informatization innovation”, and one as its control class. The number of students in each class is 58, and the grade rankings are basically the same. Before the experiment, the students in the experimental class and the control class were evaluated on the interest in art subjects and the performance of art homework. 3.3 Data Processing and Analysis This article uses SPSS 22.0 software to count and analyze the interview survey results, and conduct a t test. The t-test formula used in this article is as follows: t= t=

X −μ

(1)

σ√X n

X1 − X2 (n1 −1)S12 +(n2 −1)S22 n1 +n2 −2

(2) ( n11

+

1 n2 )

20

Y. Liu

Among them, formula (1) is a single population test, x is the average of the sample, s is the standard deviation of the sample, and n is the number of samples. Formula (2) is a two-population test, s21 and s22 are the variances of the two samples, and n1 and n2 are the sample sizes.

4 Investigation and Analysis of University Art Education and Information Teaching Innovation in the Network Information Age 4.1 Comparison of the Situation of Using Informatized Teaching Methods Through research, the basic art courses of A University art colleges still adopt traditional teaching methods and methods, and informatization is still only used in the field of education management. Education in some grades has been digitized, but it is basically a simple extension of the general curriculum education system, which is mainly reflected in curriculum management, and does not fully reflect the educational characteristics of art courses. Figure 1 shows the percentage of art teaching methods used in each grade of art informatization teaching methods.

Freshman year 20% Fourth year of College 31% Junior College 24% Sophomore 25%

Freshman year Sophomore Junior College Fourth year of College

Fig. 1. Comparison of art informatization teaching methods adopted by all grades

Due to the distinctive characteristics of art courses, the general course teaching system is often not suitable for the specific teaching implementation of art courses. In order to correctly and effectively reflect the digital application of personalized education concepts in the art course education process, the premise is to learn from the mainstream and mature course education system at home and abroad, realize the basic education functions, reflect the characteristics of the courses, and make it a part of the art course teaching process. A powerful tool for teachers and students to become an interactive platform for teacher-student communication, improving teaching quality and teaching effectiveness.

University Art Education and Informatization Teaching Innovation

21

4.2 University Art Information Teaching and Traditional Teaching Methods The difference between the evaluation results of the two classes is shown in Fig. 2, which will facilitate the comparison between the two, and then ensure the accuracy and validity of the research results. The results after the experiment are shown in Table 1. From the results of the evaluation, it can be seen that the overall art work performance of the experimental class students is higher than that of the control class. After the students in the experimental class carried out art class teaching through the teaching structure of “Art Information Innovation” under the information technology environment, the students’ interest in art learning increased from 71% in the previous test to 98%, and 44% of the students were interested in art subjects. Very interested, only 2% are not interested in art subjects. It can be seen that the interest of students in the experimental class has changed a lot in the pre-test and post-test, while the change in the pre-test and post-test of the control class is not much. Interest is the internal driving force of students’ learning. The application of this teaching structure to university art teaching has significantly improved students’ interest in learning. Therefore, we believe that the use of the “art information innovation” teaching structure under the information technology environment to carry out university art teaching is Feasible, its positive impact is significant.

30 Number of people

25

26

26 22 18

20 15

10

10 4

5 0 Excellent

Good Poor Score Experimental class Control class

Fig. 2. Comparison of students’ art homework scores

In this way, a multi-channel, multi-angle, and all-round art classroom from text to sound and images can effectively increase students’ interest in learning and stimulate students’ learning motivation. The promotion of comprehensive ability of students in independent learning, cooperative learning, exploratory learning, and research learning

22

Y. Liu Table 1. Comparison of students’ art homework scores Excellent

Good

Poor

Number of people

Percentage

Number of people

Percentage

Number of people

Percentage

Experimental class

36

62%

18

31%

4

6%

Control class

22

37%

26

44%

10

17%

is the basis for their further adaptation to the development of society. At the same time, the research of the subject promotes the professional growth of teachers, especially the effective improvement of teachers’ classroom teaching ability and the ability to use information technology.

5 Conclusions In the context of the knowledge economy, information technology has had a significant impact on people’s production, life and learning methods. In particular, the widespread application of the latest information technologies such as networking, multimedia, and intelligence in the field of learning in colleges and universities will surely bring about comprehensive changes in learning concepts, learning content and learning methods. On the basis of theoretical research, this paper has carried out practical research on the reform of college English online teaching for individualized learning, and verified the superiority of the new teaching mode under the network condition over the traditional teaching mode. It has important theoretical significance and practical value for promoting information-oriented university learning innovation, improving China’s innovative talent training level and knowledge innovation ability in the information age, and gaining a favorable position in the fierce international competition in the information society.

References 1. Rühli, E., Sachs, S., Schmitt, R., Schneider, T.: Innovation in multistakeholder settings: the case of a wicked issue in health care. J. Bus. Ethics 143(2), 289–305 (2015) 2. Koval, T.: Students’ literary theater as an educational innovation in the context of Ukrainian and Foreign experience. Nephron Clin. Pract. 7(2), 43–50 (2017) 3. Yaroshevska, L.: Civic education of youth by means of musical art by V. Sukhomlinsky. Scientific Visnyk V.O. Sukhomlynskyi Mykolaiv National University Pedagogical Sci. 65(2), 373–377 (2019) 4. Carayannis, E.G., Meissner, D., Edelkina, A.: Targeted innovation policy and practice intelligence (TIP2E): concepts and implications for theory, policy and practice. J. Technol. Transf. 42(3), 460–484 (2015) 5. Almazroi, A.A., Kabbar, E., Naser, M., et al.: Gender effect on cloud computing services adoption by university students: case study of Saudi Arabia. Int. J. Innov. 7(1), 155–177 (2019)

University Art Education and Informatization Teaching Innovation

23

6. Popov, V.N., Kharin, A.N., Zhukalin, D.A.: The university’s innovative activity and the real sector of the economy. Vysshee Obrazovanie v Rossii Higher Education in Russia 27(8–9), 111–116 (2018) 7. Li, G., Wang, F.: Research on art innovation teaching platform based on data mining algorithm. Clust. Comput. 22(6), 13867–13872 (2018) 8. Rahmat, M.K., Au, W.K.: Visual art education teacher’s beliefs and attitudes toward incorporating ICT into art classrooms. Asian J. Univ. Educ. 15(3), 10–19 (2019) 9. Liu, L., Wang, Y.: Innovation and entrepreneurship practice education mode of animation digital media major based on intelligent information collection. Mob. Inf. Syst. 2021(11), 1–11 (2021) 10. Fu, L.: Research on the reform and innovation of vocal music teaching in colleges. Region Educ. Res. Rev. 2(4), 37–40 (2020) 11. Contreras, F., Espinosa, J.C., Cheyne, A., et al.: Entrepreneurial intention in business students: the impact of an art-based program. J. Entrepreneurship Educ. 23(6), 23 (2020) 12. Howard, F.: “It’s Like Being Back in GCSE Art”—Engaging with music, film-making and boardgames. Creative pedagogies within youth work education. Educ. Sci. 11(8), 374 (2021)

(CAD/CAM) Preliminary Establishment of Digital System in Dental Restoration Xiaobin Yin(B) Department of Medical Technology, Anyang Vocational and Technical College, Anyang 455000, Henan, China [email protected]

Abstract. Modern computer-aided design and manufacturing (CAD/CAM) systems are widely used in the field of dental restorations due to their advantages such as speed and accuracy, which have effectively improved the manufacturing efficiency and product quality of restorations. This article aims to study the preliminary establishment of the (CAD/CAM) digital system in dental restoration. Based on the analysis of the common (CAD/CAM) digital system types, CAD/CAM database technology and the functional structure of the CAD/CAM system, To understand the difference between patients’ satisfaction with traditional dental prosthesis and CAD/CAM system-based dental prosthesis, this paper selects 60 patients with implant prosthesis admitted to a hospital as the research object, and investigates their satisfaction with oral prosthesis. The survey results showed that the satisfaction of the observation group at 3 and 6 months after surgery was significantly higher than that of the control group, and the difference was statistically significant. Keywords: (CAD/CAM) Digital System · Dental restoration · All-ceramic abutment restoration · Postoperative satisfaction

1 Introduction Computer-aided design and computer-aided production, often abbreviated as CAD/CAM, are emerging integrated computer system technologies that have developed rapidly in recent decades. Among them, CAD refers to a design that uses computer technology as the main means and uses various numerical values and image information to complete, while CAM is a numerical control processing device managed by computers such as an exponential milling machine [1, 2]. CAD/CAM technology has been widely used in industrial production automation, aerospace and other high-tech technologies since 1970, greatly improving manufacturing efficiency, and now it has almost penetrated into various fields in the development of science and technology and people’s daily lives [3, 4]. Since the first set of CAD/CAM denture system came out in 1983, a lot of CAD/CAM denture system products have emerged in foreign markets. This series of products has reached the automation level of the restoration design and manufacturing process [5, 6]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 24–31, 2022. https://doi.org/10.1007/978-3-030-99616-1_4

(CAD/CAM) Preliminary Establishment of Digital System

25

However, because the operating principle of the system is still being improved, it is more expensive than the imported system, and its clinical promotion in China is also much more difficult. With the vigorous development of CAD/CAM technology, this technology has also become the dominant dental equipment maintenance market [7, 8]. Faced with such a situation, it is a general trend to develop a fixed-resolution CAD/CAM system suitable for China’s national conditions and Japan’s national conditions. My country’s research in this area has not yet reached the requirements of clinical application. Major dental hospitals are cooperating with many universities to develop a self-designed dental CAD/CAM system. A certain college is also cooperating with dental hospitals to develop an independent denture CAD/CAM system suitable for the consumption level of Chinese people. CAM system, and achieved certain results [9, 10]. At present, some universities are developing standard dental models, which require a long design cycle to design dentures that meet the actual conditions of patients, and the technical requirements for dentists are relatively high [11, 12]. In this case, it is necessary to develop a database model that contains more information to adapt to the actual situation of the patient, so as to improve the efficiency of the dentist who makes dentures and shorten the consultation time for the patient. On the basis of consulting a large number of domestic and foreign related references, combined with common (CAD/CAM) digital system types, CAD/CAM database technology and CAD/CAM system functional structure, this paper analyzes the basis of computer-aided design/computer-aided manufacturing The application effect of (CAD/CAM) system in oral restoration.

2 (CAD/CAM) Preliminary Establishment of Digital System in Dental Restoration 2.1 Common (CAD/CAM) Digital System Types (1) Cerec system Cerec series is currently the most comprehensive CAD/CAM dental system, and it is also used by the most users. It can be divided into Cerecinlab series and Cerec series by the chair. InFire unit: It can be crystallized into various materials, and the maximum temperature can reach 1600°. In Cerecinlab products, it also includes a variety of embedded design applications, VinCron software that can quickly produce inserts and full crowns by itself, WaxUp applications for complex repair designs, and also for disassembly, integration and personalized fixed arm design. The biggest difference between the Cerec system next to the chair and the inLab series is that it can use an optical scanner to scan in the oral cavity. You only need to place the lens above the abutment and the system will automatically capture the oral cavity. Inside the pictures and make a three-dimensional model, the immediate design of the chair is also easy to grind, and the equipment is very smart and simple, so it can be scanned and restored in any area of the clinic. (2) Everest system The Everest series is mainly composed of scanners, computer design software systems, grinding systems, calcification equipment, and supporting grinding materials.

26

X. Yin

The system uses a high-resolution, precise sensor camera, multi-angle and directional raster projection scanning of plaster and wax models, and the structure design is relatively reasonable. The Everest grinding control system consists of five-axis cutting and grinding devices (X, Y, Z, A, B), which can solve up to sixteen repair units, extended operating distances and large inclination angles. After the milling machine, the restoration also needs to be sintered to obtain the maximum hardness. Today, the control system can also be used to manufacture restorations using various materials such as ceramics, pure titanium, and high-strength resins. (3) Procera system Procera products also combine Procera’s unique All-Ceram technology with CAD/CAM, and scan the plaster model through a polarized scanner. At the same time, the design software of Procera products can also use the reduction function to compensate for the extrusion phenomenon formed after fusion with high-purity alumina ceramics, so as to be fastened to the edge of the base. The corresponding design data is uploaded to the central processing center through the network. The machining center first produced high-purity alumina-based crowns using dry pressing technology, and then after the high-temperature sintering was completed, they were sent to the laboratory for post-production work. (4) Cercon system Cercon Brain Cut and Cercon Heat Sintering. First, use the CerConsCan precision laser scanner to scan and repair the pattern of the steel wax pattern on the wheel, then the unsintered zirconia block is polished and worn through the Cercon brain, and the polished embryonic piece is loaded into the sintering furnace, and then again Sintering performance at 1350 °C in Cercon Heat for six hours, after firing, a zirconia abutment crown with good mechanical properties can be obtained. Cercon Base is also a special abrasive material of this series. Its main component is yttrium tetragonal zirconium (Y-TZP), which has high mechanical strength, flexural strength, fracture toughness, and good biocompatibility. Therefore, Cercon ceram KISS is a very ideal surface ceramic material, and its thermal expansion coefficient is the same as that of Cercon base material. The wide application of high-performance abrasive materials makes the Cercon system more versatile. (5) Celay system The biggest difference between the Celay system and other systems is that it can scan the prefabricated resin restoration on the oral cavity or plaster model to receive surface morphology data, and send it to the grinder for copying after processing. Through the Celay system and glass infiltration technology to process alumina ceramic blocks, it is possible to quickly manufacture alumina-based crowns and stable bridge arms with a tensile strength of more than 600 Mpa. 2.2 CAD/CAM Database Technology (1) Database technology Database system technology is the main part of China’s modern science and computer technology, and is the core technology of big data and information management systems. In the process of managing computer information systems, database technology can be used to deal with a series of issues such as data organization,

(CAD/CAM) Preliminary Establishment of Digital System

27

data storage, data collection, and information processing. In this way, resource sharing is realized, which provides a strong guarantee for safe production, and greatly improves the efficiency of data recovery and information processing capabilities. In today’s society, computer and communication science and technology are advancing rapidly. With the development of these two kinds of information technologies, database technology has also begun to be the central task of managing network information systems, and has become a key task for establishing and managing a large amount of information system data. In the CAD/CAM denture system, data storage and database management systems are also key components of the system construction. (2) Model data processing technology Model data processing is also the main part of denture CAD/CAM technology. The process mainly includes reconstruction and adjustment of data, and provides a basis for further establishing the position data of CAM tools. The application of coding technology is in the earliest stage of the development of analog data processing technology, that is to say, the restored contour can be adjusted and modified by directly using the cursor on the “model” on the display. This process takes 2–10 min. At present, data processing usually adopts automatic reconstruction technology, and it is necessary to call the standard tooth model in the database first, and then modify and adjust the crown shape according to the constraint conditions. 2.3 The Functional Structure of the CAD/CAM System (1) Data acquisition system Data collection technology is also the “bottleneck” problem of CAD/CAM dental prosthesis. The main task is to obtain all the morphological data of the tooth surface to be repaired, and submit the data information to the dentist or technician for repair diagnosis and implementation. For example, the points of the spatial surface shape of the abutment, adjacent teeth, missing tooth gap, and antagonist teeth can be used to reconstruct the more complex spatial surface shape of the tooth surface. Data collection methods can be divided into two main categories: communication data collection and non-contact data collection. At present, the most commonly used methods for obtaining clinical application data are 3D laser scanning, light reflection, and mechanical collection. The accuracy of data measurement also determines the timeliness and accuracy of dental function restoration systems such as CAD/CAM in the design and process of restorations, and is the basis and conditions for the completion of the accurate manufacturing of restorations. (2) Computer-aided design system The computer-aided design system is also the main part of the CAD/CAM dental restoration system, including data preprocessing and free surface reconstruction. The restored tooth is a typical tooth pattern in the standard database, which is different from the individual condition of the actual patient. Therefore, even if the shape of the rim of the adapter needs to be modified, manual adjustment and adjustment can be carried out according to the patient’s oral situation and the actual limitations of the standard rim data. Data processing technology, with the support of modern computer systems, contains rich connotations of design theory and dental

28

X. Yin

knowledge. The rehabilitation module designed by the doctor fully adapts to the specific needs of the patient, and can be worn and used by the patient after processing and integration. (3) Computer Aided Manufacturing System Dental prosthetic numerical control processing skills are also a key component of modern dental prosthetic CAD/CAM technology, and its main content involves the design of processing technology schemes, tool path selection, and design of CNC programming automatic manufacturing tools. CNC process accuracy is the ultimate link to improve the accuracy of the entire CAD/CAM and dental maintenance system. By manufacturing accurate restorations, the success rate of denture manufacturing can be greatly improved, but at the same time, the doctor’s adjustment work in the later period is reduced, thereby reducing the patient’s psychological pain. According to the three parts of the above-mentioned CAD/CAM system, the manufacturing process of the prosthesis is carried out through the dental prosthesis CAD/CAM system, as shown in Fig. 1.

Fig. 1. The production process of dental restoration CAD/CAM system

3 Experiment 3.1 Research Objects In this paper, 60 patients treated with implant restoration in a hospital were selected as the research object. Among them, there were 30 cases in the control group, with 32 implants, aged 20–41 years; 30 cases in the observation group, with 34 implants, aged 23–42 years. The control group used conventional implant restoration techniques, and the observation group used CAD/CAM system-based restoration techniques.

(CAD/CAM) Preliminary Establishment of Digital System

29

3.2 Statistical Analysis Use SPSS18.0 statistical application software for big data analysis, measurement data is represented by (x ± s), and comparison between groups is by independent sample t test. Percentage table was used for counting data, chi-square test was used, rank sum test was used for grade data, and P < 0.05 was regarded as the difference was statistically significant. A test formula for t can be defined as: t=

X −μ

(1)

√σX n−1

If the sample is a large sample, it can also be written as: t=

X −μ

(2)

σX √ n

4 Discussion

Table 1. Comparison of satisfaction between the two groups of patients at 1 week, 3 months, and 6 months after surgery Group ratio 1 week

Very satisfied Satisfy Dissatisfied Satisfaction χ 2

Observation 14 group

9

11

67.6%

Control group

13

8

11

65.6%

3 months Observation 18 group

11

5

85.3%

11

12

9

71.9%

6 months Observation 25 group

7

2

94.1%

10

6

81.3%

Control group

Control group

16

P

0.896 0.378

7.729 0.011

4.195 0.028

It can be seen from Table 1 and Fig. 2 that the satisfaction of the observation group at 3 and 6 months after surgery was significantly higher than that of the control group, and the difference was statistically significant.

30

X. Yin 30

100.00% 90.00%

25

80.00%

Number

60.00% 50.00%

15

40.00% 10

Percentage

70.00%

20

30.00% 20.00%

5

10.00% 0.00%

0

Group Very satisfied

satisfy

Dissatisfied

Satisfaction

Fig. 2. Comparison of satisfaction between the two groups of patients at 1 week, 3 months, and 6 months after surgery

5 Conclusions CAD/CAM stands for digital dental technology, which means that relying on the rapid development of modern computer and multimedia technology, through the seamless connection of advanced and scientific digital intelligent computer technology with modern high-end technical equipment, it greatly reduces the flow of people in the traditional dental industry. Goods delivery time and traditional production technology relying on experience, so as to achieve independent, fast, efficient, standardized, and accurate modern advanced technology for the production of precision restorations.

References 1. Kim, J., Chun, Y.S., Kim, M.: Accuracy of bracket positions with a CAD/CAM indirect bonding system in posterior teeth with different cusp heights. Am. J. Orthodontics Dentofacial Orthopedics Off. Publ. Am. Assoc. Orthodontists Constituent Soc Am. Board Orthodontics 153(2), 298 (2018) 2. Almeida, I.D., Antunes, D., Braun, N., et al.: CAD/CAM system influence marginal fit of different ceramic types? Indian J. Dent. Res. 30(1), 127–129 (2019)

(CAD/CAM) Preliminary Establishment of Digital System

31

3. Katsuda, Y., Yamada, M., Ishibashi, M., et al.: Introducing a CAD/CAM composite crown practice system into the preclinical education at Tohoku University School of Dentistry. Ann. Jpn Prosthodontic Soc. 10(4), 335–344 (2018) 4. Lu, T.C., Chen, S.L., Yang, C.Y.: Near time-optimal S -curve velocity planning for multiple line segments under axis constraints. IEEE Trans. Ind. Electron. PP(99), 1 (2018) 5. De Santis, R., Russo, T., Gloria, A.: An analysis on the potential of diode-pumped solid-state lasers for dental materials. Mater. Sci. Eng. C 92, 862–867 (2018) 6. Hong, J.M., Han, J.S., Yoon, H.I., et al.: (Korean) Full mouth rehabilitation with implantsupported fixed prosthesis via dental CAD-CAM system. J. Korean Acad. Prosthodontics 59(1), 97–106 (2021) 7. Borges, L., Lima, E., Carvalho, A.: O uso do sistema CAD/CAM para confeco de próteses fixas: aplicaes e limitaes. J. Dentistry Pub. Health 11(2), 159 (2020) 8. Nassani, M.Z., Ibraheem, S., Shams, Y.E., et al.: A survey of dentists’ perception of chair-side CAD/CAM technology. Healthcare 9(1), 68 (2021) 9. Eun-Jeong, B.: A study on the possession state of dental CAD/CAM system and usage satisfaction. J. Korean Aced. Dental Technol. 42(1), 45–53 (2020) 10. Zyer, E.K., Kahramanolu, E., Zkan, Y.K.: Evaluation of three-unit monolithic zirconia and zirconia-supported fixed partial dentures designed with CAD/CAM system by FDI criteria: a one year clinical split-mouth study. Yeditepe Dental J. 16(2), 137–146 (2020) 11. Jamayet, N.B., Nizami, M., Rahman, A.M., et al.: Fabrication of ear prosthesis with the integration of CAD/CAM system. Pediatria i Medycyna Rodzinna 15(3), 327–331 (2019) 12. Popa, D., Burde, A.V., Negucioiu, M., et al.: Internal and marginal accuracy of zirconia restorations made with two CAD/CAM systems. Hum. Veterinary Med. 10(1), 20–24 (2018)

Effectiveness of College Students’ Physical Exercise on Improving Mood State Based on Big Data Zerong Jiang1 and Ruoguo Li2(B) 1 Kunming University, Kunming, Yunnan, China 2 Pu’er University, Pu’er, Yunnan, China

[email protected]

Abstract. In recent years, “big data” has become a realistic background that we cannot ignore, and it is closely related to all aspects of people’s survival and life. As a new perspective and method for people to understand the world, big data is an important window for people to gain a new understanding of things and create new value. How colleges and universities study the effectiveness of physical exercise in improving the state of mind under the background of big data has become a new topic worthy of our research. This article is based on the working concepts of collaboration, sharing, openness, and equality of college students’ physical exercise in the context of big data, combined with the working mechanism of multiple data platforms and the work content combined with the Internet, and puts forward the practical aspects of college students’ perceptions obstacles, behavioral cues and self-efficacy are used to evaluate the effectiveness of physical exercise in improving the state of mind of college students, that is, to explore the effectiveness of physical and mental health of college students on the basis of data analysis. Mainly use questionnaire survey method, psychological measurement method, mathematical statistics method, comparative analysis method and other research methods to carry out related research on the health belief, mood state and physical exercise habits of college students in this city, through correlation and regression analysis between different dimensions. Experimental studies have shown that the physical exercise habits of college students are very poor, and only 18.3% of them have exercise habit. There is also a linear relationship between college students’ exercise health beliefs and physical exercise habits, and behavioral cues and selfefficacy are positive predictors of physical exercise habits. Keywords: Big data · Physical exercise · State of mind · Physical exercise habits

1 Introduction As one of the most important social realities today [1, 2], the background of big data always affects people’s behavior patterns and thinking habits, especially for college students [3, 4]. Furthermore, as a research object of college students’ physical exercise mental health survey, how to better integrate resources and technology in the era of big © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 32–39, 2022. https://doi.org/10.1007/978-3-030-99616-1_5

Effectiveness of College Students’ Physical Exercise

33

data to achieve their own development and innovation has become an important topic worthy of attention [5, 6]. In the research on the effectiveness of college students’ physical exercise in improving the state of mind based on the background of big data, many scholars have conducted research on it and achieved good results [7, 8]. For example, Renn KA developed the college students’ exercise health belief scale, it has perfected the health belief model and made it more detailed, which also provides a certain reference and reference for good prediction of exercise behavior [9]. Halbert CA believes that people’s judgment of their own abilities plays a major role in their self-regulation system, and thus put forward the concept of self-efficacy [10]. This article explores the research path of the effectiveness of college students’ physical exercise in improving the state of mind under the background of big data, to find a better plan for the combination of college students’ mental health and big data technology, and to improve the effectiveness of college students in improving their state of mind. Good service to the physical and mental health of real college students.

2 Effectiveness of College Students’ Physical Exercise in Improving Their Mood State Under the Background of Big Data 2.1 Under the Background of Big Data, Colleges and Universities Analyze the Changes in the State of Mind of College Students Under Physical Exercise (1) Big data provides an opportunity for colleges and universities to study the effectiveness of college students’ physical exercise to improve their mood state The emergence of big data technology can effectively make up for the shortcomings and deficiencies of college students’ physical exercise in improving mood research through a large amount of data analysis, and help educators make decisions. Based on the rich information resources of big data and professional theoretical analysis, it can provide decision-makers with relatively accurate supporting information in the process of improving the mood of college students, and better grasp the basic learning dynamics and physical and mental health of students. In the analysis of changing thoughts and behavior data, reasonably predict the problems and trends that may arise in the future. At the same time, data analysis of college students’ behavior can better understand students’ mood changes, and then scientifically formulate targeted strategies to better adapt to the individual development needs of college students’ physical and mental health. (2) Research on the practical significance of college students’ mood surveys in the context of big data From the perspective of college students, combined with the background of big data to conduct research on the effectiveness of college students’ physical exercise in improving their state of mind is more conducive to the overall development of individual students. Based on the openness and sharing characteristics of big data, in the context of big data, the channels and methods for students to receive information and provide data have greatly increased, and their horizons have become broader. But at the same time, when different information floods into the field of vision,

34

Z. Jiang and R. Li

students are inevitably affected by this information. Therefore, the screening and screening of this information requires students to have an understanding of the technology and related knowledge of big data. It also requires ideological and political education to guide students in their ideological development in accordance with the new law of development of students’ thinking. To enable them to achieve the overall development of their personal, physical, mental and various abilities. 2.2 Theories of Exercising Healthy Beliefs (1) The cognition of the benefits of behavior change: refers to the individual’s subjective judgment on the benefits of adopting a certain behavior, including reducing the risk of disease, promoting health and other benefits. When an individual believes that the greater the benefits and benefits that health promotion behaviors can bring, the greater the possibility of consciously adopting healthy behaviors. (2) Obstacle cognition of behavior change: refers to the judgment of the subjective and objective difficulties and resistance that individuals may encounter in the process of adopting a certain behavior. Even if individuals recognize that bad behaviors can cause harm and that improving behaviors are beneficial, a large perception barrier may prevent them from adopting healthy behaviors. (3) Action clues: Refers to internal and external factors that induce healthy behaviors, such as conscious physical discomfort, the role of the mass media in promoting disease prevention, health behaviors suggested by medical staff, family members or friends suffering from such diseases, etc. Prompting factors induce individuals to adopt healthy behaviors. The more clues there are, the more likely an individual is to adopt healthy behaviors. (4) Self-efficacy: refers to individuals who believe that they have the ability to change unhealthy behaviors and obtain expected results through correct evaluation and judgment of their own abilities and their own practice, or through the practical experience of others and receiving guidance from others. The higher the self-efficacy, the greater the possibility of change. 2.3 Mood State (1) Measurement of mood state 1) Mood State Profile (POMS) A self-rating scale of mood state compiled by POMS. It includes 6 subscales of nervousness, depression, energy, fatigue and panic. Each subscale includes several adjectives. There are 65 adjectives in the whole scale, which belong to the mixed arrangement of words in each subscale. Each question has 5 levels, from 0 to 5 representing “nothing” to “very many”. The scale was originally designed for patients with mental illness, and it was later discovered that it can also effectively measure the mental state of normal people. 2) BFS Mood Scale BFS is a mood measurement tool designed based on the theory that constitutes the two dimensions of mood evaluation and activation. The BFS scale contains 8

Effectiveness of College Students’ Physical Exercise

35

subscales, followed by activity, pleasure, thoughtfulness, calmness, anger, excitement, depression and inactivity. Each subscale includes 5 questions, a total of 40 questions, and all the questions are randomly arranged. 3) MACL vocabulary of various emotions. The MACL scale is a vocabulary list of 123 adjectives. Participants are required to select only those words that match their feelings. The scale consists of three non-intersecting subscales of anxiety, depression and hostility. There are two forms of MACL, which differ only in terms of instruction, one is “today’s” and the other is “usual”. (2) Mood The mood has a great influence on the subsequent behavior of a person, and in turn, some of the mental state and some external environment of the person will also affect the mood. The negative mood of a lively and cheerful person will not last too long. It can be said that the mood is caused by certain internal or external factors, and it can also be changed due to the influence of individual internal or external factors. 2.4 Application Research of Big Data Sampling Method In addition to the fact that big data and sample survey data can complement each other from the perspective of university statistics, sampling can also be used as a method to obtain data from a large-scale data set. The large-scale data set is regarded as an overall research object. Taking a sample, the sample estimated value θ of the overall parameter  can be calculated. Assuming that the sampling is repeated M times, then the bootstrap estimates of the mean value and standard error of the parameter  are: = se = [

1 M −1

1  θ M M (θ − )2 ]1/2 1

(1) (2)

Repeating the above steps several times will find multiple new estimates similar to θ, and then by analogy, find the most accurate estimate.

3 Experimental Research on the Effectiveness of College Students’ Physical Exercise in Improving Their Mood State Under the Background of Big Data 3.1 Survey Object Use the big data sampling method to survey 200 college students from grade one to grade three in five colleges and universities in this city. The questionnaires were distributed by random and stratified selection of 40 students from five universities in the city by anonymous method. 194 copies were recovered, with a recovery rate of 97%, of which 190 were valid questionnaires, with an effective rate of 97.9%.

36

Z. Jiang and R. Li

3.2 Mathematical Statistics After the questionnaire is retrieved, the invalid questionnaire is raised first, and then the questionnaire is classified, and then the data is sorted, classified and entered according to the scoring criteria. All the data are firstly counted with Microsft Excel, and then imported into the social science statistical analysis software SPS for Windows for analysis and processing. In the process of statistical analysis, statistical methods such as descriptive statistics, sample t-test, one-way analysis of variance, correlation analysis, and regression analysis are used.

4 Experimental Research and Analysis of the Effectiveness of College Students’ Physical Exercise in Improving Their Mood State Under the Background of Big Data 4.1 Correlation Analysis of College Students’ Mood State and Physical Exercise Habits This paper makes a relevant analysis of the various dimensions of the mood and physical exercise habits of college students, and the results are shown in Table 1. Table 1. Correlation analysis of various dimensions of mood state and physical exercise habits Person correlation

Exercise repetitiveness

Exercise will

Exercise habit

Tension

0.047

0.053

0.162

Anger

0.183

0.084

0.103

Fatigue

0.073

0.031

0.093

Depression

0.031

0.103

0.031

Energy

0.129

0.195

0.204

Panic

0.027

0.086

0.216

TMD

0.025

0.109

0.042

As shown in Fig. 1, the physical exercise habits of college students are very poor, and only 18.3% have exercise habits. The repetition of physical exercise is significantly correlated with anger, energy and self-esteem, and both are positively correlated; physical exercise volition is negatively correlated with depression and TMD, and positively correlated with energy and self-esteem; physical exercise habits are negatively correlated with depression and TMD, and Energy and self-esteem are positively correlated. From the data results, it can be seen that depression factors have the greatest impact on physical exercise habits. The higher the degree of depression, the lower the will and the physical exercise habits of physical exercise; the higher the TMD, the lower the physical exercise habits. Depression affects exercise behavior, the better the physical exercise habits, the better the mental health. This is very similar to the results of this data, which supports the

Effectiveness of College Students’ Physical Exercise

Exercise repetitiveness

0.3

Exercise will

Parameter value

0.25 0.2

0.162

0.15 0.1

0.05

0.053 0.047

Exercise habit 0.216

0.204 0.195

0.183 0.103 0.093 0.084 0.073

0.103

0.031

0.031 0.031

Fatigue

Depression

37

0.129

0.109

0.086 0.027

0.025

0.042

0 Tension

Anger

Energy

Panic

TMD

Person correlation Fig. 1. Correlation analysis of various dimensions of mood state and physical exercise habits

validity of the data. In addition, energy and self-esteem have a strong positive correlation with physical exercise habits and two factors. It can be inferred that positive emotions have a great role in promoting exercise habits. 4.2 Regression Analysis of College Students’ Health Beliefs, Mood State and Physical Exercise Habits There is a strong correlation between exercise health belief and mood state, mood state and physical exercise habits, exercise health belief and physical exercise habits. In order to verify whether exercise health beliefs and mood states have a detection effect on physical exercise habits, the exercise health beliefs and mood states are used as independent variables, and physical exercise habits are used as dependent variables for regression analysis. The results are shown in Table 2: Table 2. Regression analysis of college students’ health beliefs, state of mind and physical exercise habits Model

B

Standardization B

t

Perceived barrier

−0.573

−0.079

−0.132

Perceived benefit

0.327

0.178

1.28

Behavioral cues

0.221

0.173

1.38

Self-efficacy

0.461

0.204

2.38

TMD

−0.09

−0.103

−0.114

38

Z. Jiang and R. Li

t

Modle

-0.103

Standardization B

B

-0.114 TMD -0.09

Self-efficacy

0.204 0.461

Behavioral cues

0.173 0.221

Perceived benefit Perceived barrier

2.38 1.38

1.28 0.178 0.327 -0.132 -0.079

-0.573

Parameter value Fig. 2. Regression analysis of college students’ health beliefs, mood state and physical exercise habits

It can be seen from Fig. 2 that both exercise health beliefs and TMD are significant with physical exercise habits. ANOVA and t-test have reached a significant level, proving that the three have a linear relationship and the established regression equation is meaningful. Exercise health beliefs and physical exercise habits return significantly, and state of mind is also significant, while state of mind and physical exercise habits are relatively less significant, indicating that exercise health beliefs may have a mediating effect between the two.

5 Conclusions In the correlation analysis of college students’ exercise health beliefs, state of mind and physical exercise habits, perceived obstacles are positively correlated with TMD, and are not significantly correlated with the severity of the disease, but are negatively correlated with perceived benefits, behavioral cues, and self-efficacy Perceived benefits, behavioral cues and self-efficacy are positively correlated with physical exercise habits, and negatively correlated with perceived obstacles, disease susceptibility, and disease severity; exercise habits and two factors are positively correlated with energy and self-esteem, exercise volition and TMD are negatively correlated, while depression and TMD are negatively correlated with physical exercise habits. In general, there is a cross correlation between the three, the degree of correlation of each factor is different, and the direction of correlation is also different.

References 1. Xia, B., Ma, Z., Hu, Y.: Research on the relationship between physical exercise, psychological flexibility and positive emotion of college students based on computer mathematical model. J. Phys. Conf. Ser. 1578(1), 012009 (7pp) (2020)

Effectiveness of College Students’ Physical Exercise

39

2. Xun, S.: Research on college students sports associations and campus culture design based on big data analysis. Boletin Tecnico/Tech. Bull. 55(10), 272–277 (2017) 3. Ward, M.: Cognition, culture, and charity: sociolinguistics and “donor dissonance” in a baptist denomination. Voluntas 26(2), 574–603 (2015) 4. Epanovi, T., Proti-Gava, B., Spori, G., et al.: Short-term core strengthening program improves functional movement score in untrained college students. Int. J. Environ. Res. Public Health 2020(17), 8669 (2020) 5. Li, M., Fang, Q., Li, J., et al.: The effect of Chinese traditional exercise-Baduanjin on physical and psychological well-being of college students: a randomized controlled trial. Plos ONE 10(7), e0130544 (2015) 6. Lee, S.D., Lim, J.G., Yoo, S.H.: Self-directed exercise of college students for the change of physical education paradigm. J. Korea Entertain. Ind. Assoc. 11(7), 201–212 (2017) 7. Albu, A., Onose, I., Hodorc, R.M., et al.: The time for physical exercise and food habits on a number of students from Pascani National College. Timisoara Phys. Educ. Rehabil. J. 10(19), 120–124 (2017) 8. Lee, K.W., Yu, Y.K.: The effect of health exercise program on health fitness & mental health in college students. Korean J. Sports Sci. 27(6), 1241–1253 (2018) 9. Renn, K.A.: The influence of peer culture on identity development in college students. J. Coll. Char. 21(4), 237–243 (2020) 10. Halbert, C.A., Cipolle, M.D., Fulda, G.J., et al.: Admission to an observational unit improves length of stay for patients with mild traumatic brain injuries. Am. Surg. 81(2), 176–179 (2015)

Influence of Big Data Information Processing Technology on English Reading Anxiety Jingtai Li1 , Bi Zhang1(B) , and Craig Whitsed2 1 School of Foreign Languages, Jiaying University, Meizhou 514015, Guangdong, China

[email protected] 2 School of Education, Faculty of Humanities Curtin University, Perth, WA, Australia

Abstract. Reading anxiety is the fear and tension indirectly connected with the reading object. In the field of second language acquisition, the research mainly focuses on the reading anxiety of the uniqueness of the situation. Based on the specific practice of English reading teaching, this article discusses the current English reading anxiety problems, analyzes the main causes of the problems, and puts forward relevant countermeasures and suggestions to solve the English reading anxiety problems with the help of the platform combined with big data technology. Keywords: Influence · Big data · English reading · Anxiety

1 Introduction The role of reading in the learning and use of a language is obvious. At present, many scholars believe that foreign language reading anxiety is a negative emotion in foreign language learning, which is related to foreign language anxiety in part and in whole. Anxiety is an important factor that affects students’ English reading, so it is also an important aspect that affects students’ English language input. In a practical sense, the analysis and discussion of English reading anxiety will help to understand the root causes of different students’ reading anxiety from a deeper level, so as to help them overcome the influence of reading anxiety [1]. In traditional class teaching, students are restricted by many factors in the process of English reading, which dampens students’ enthusiasm for learning. Based on this, in the context of the continuous development and maturity of big data technology, teachers should actively combine the content of foreign language reading courses with modern data technology to enhance the interest of teaching and improve the teaching effect [2].

2 Concept of English Reading Teaching Mode Under the Influence of Big Data With the advancement of technology, students have more channels to obtain learning information suitable for their English reading, which brings great challenges to traditional English teaching, and at the same time brings new opportunities to English © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 40–44, 2022. https://doi.org/10.1007/978-3-030-99616-1_6

Influence of Big Data Information Processing Technology

41

reading teaching in the information age. The teaching platform combined with big data can integrate a variety of effective reading and writing learning resources, so that English classroom teaching can be enriched and extended. Especially in terms of reading comprehension exercises, students can download reading materials originally provided by traditional paper media through the mobile App, and update them in real time and practice on-demand [3]. Through the unified teaching digital platform, students also can download the reading questions of the past exams to practice at any time without being restricted by time or location, so that both the teaching of teachers and the learning of students can be extended in time and space. The wide application of the new generation of information technology has increased the enthusiasm of students for active learning, and the learning goals of students have become more clear. The development of information technology in the new era, the transformation of teachers’ traditional single functions and the abundance of teaching resources enable students to have a broader vision and understanding of the nature of English learning, especially a new understanding of reading comprehension [4].

3 English Reading Teaching Mode Under the Influence of Big Data 3.1 Select Fresh Texts to Stimulate Reading Interest Interest is the best teacher and the best learning motivation. To make students study actively, the key is to stimulate students’ interest in learning. In the era of big data, the development and utilization of curriculum resources is even more indispensable, and its impact on English reading teaching is even more immeasurable [5]. The teaching platform combined with big data technology gives the authenticity of reading text content with real language materials. On the one hand, it satisfies students’ curiosity about new things, and on the other hand, it stimulates students’ motivation to continue reading to learn more information through English as a medium, which can stimulate students’ interest and enthusiasm in English reading to the greatest extent. More importantly, this helps to improve students’ ability to process textual information, and is conducive to the cultivation of their ability to process textual information [6]. 3.2 Cultivate Inquiry Habits and Promote Individual Reading Good English learning habits are also the key to learning English well. English program reading based on big data analysis, each link is progressive every day, and the difficulty, knowledge, and thinking are gradually improved. Interest and confidence in learning English will arise spontaneously when successful learners in each part of the test will receive timely positive comments. Only by fully respecting individual differences, including English proficiency and ability, can effective English learning be facilitated. On the basis of a certain amount of reading, this interactive reading learning mode based on big data tries to cultivate students’ interest in English reading into a kind of psychological need or suggestion for learning English reading, so as to gradually form the habit of learning English actively [7].

42

J. Li et al.

3.3 Teach Reasonable Methods to Enhance English Ability Based on the accurate evaluation of big data, teachers make personalized reading learning plan, reorganize the reading text, and deeply analyze the problems in students’ English reading: vocabulary, grammatical structure, discourse; common sense (world knowledge), specific cultural knowledge, and differences in ways of thinking, etc. Teachers and learners work together to explore and develop their English reading abilities such as analysis and induction, reasoning test, and speculative logic, etc., teaching students efficient and reasonable English reading methods to enhance their comprehensive English application ability [8]. With the rapid growth of reading, students’ ability to quickly identify vocabulary and flexibly use vocabulary will be enhanced. English learning will become relaxed and meaningful, which will bring students more sense of achievement and help broaden their knowledge and vision [9]. 3.4 Build a Modern Evaluation System to Form Accurate Feedback The predictive function of big data and the super-large resource storage function of modern teaching platforms bring technical support for teachers to accurately grasp and predict students’ next stage of learning. In the era of new technology, the construction of reading teaching evaluation system has changed the traditional evaluation mode of teachers in traditional English teaching classroom and made the information between teachers and students symmetrical. Teachers can accurately understand students and grasp students’ needs to carry out teaching. Students’ learning objectives become more clear, methods are more scientific, and they also have better learning habits. The teaching of English reading based on big data analysis makes the difficulty of reading texts progressive. At the same time, big data accurately analyzes the blind spots of reading, and reading planning can be carried out step by step, which can not only push personalized learning resources and learning guidance to students, but also give timely feedback to students and bring them progress and happiness in English learning [10].

4 Problems of English Reading Teaching Mode in My Country First of all, the reading materials are out of date. The update of English reading teaching text has not been satisfactory. In the rapid development of economic and social wave, it is always slow, which is difficult to stimulate students’ interest in English learning and motivation for further learning English. Secondly, single teaching mode. The teacher’s classroom explanation often focuses on the language and cultural background knowledge, the meaning of new words, language structure and syntactic structure and other related knowledge, while the students are often just listening to the teacher’s explanation in the classroom, and always complete their own reading tasks in a passive way [11]. Thirdly, silence of classroom teaching. Reading texts are obsolete and the teaching mode is rigid. Such classroom teaching is rigid and lifeless, which can not really stimulate the enthusiasm of students to change the silence of classroom teaching, nor can it change the embarrassing situation that effective reading teaching is difficult to achieve [12].

Influence of Big Data Information Processing Technology

43

5 The Strategies for Adopting English Reading Teaching Mode Under the Influence of Big Data in My Country’s Colleges and Universities 5.1 Expand Vocabulary and Lay the Foundation for English Reading In the context of big data, students should further increase their vocabulary. On the one hand, students should strengthen the accumulation of basic English vocabulary, be familiar with common word formation, and expand the vocabulary; On the other hand, students should strengthen the accumulation of professional vocabulary, review the professional vocabulary explained by teachers in class, and consolidate them by constant reading. In the big data environment, students can also use various word memory software to strengthen the accumulation of vocabulary [13]. 5.2 Consolidate Knowledge of Grammar and Strengthen Understanding of English Reading Mastering certain grammar knowledge is the basis of reading comprehension. Therefore, students should check and fill in the gaps, consolidate their unfamiliar grammar knowledge, and be able to read long and complex sentences, so as to improve their English reading ability. In addition, students should take advantage of big data to acquire the latest professional knowledge through Internet platform and newspapers, so as to broaden their knowledge and further improve their English reading ability [14]. 5.3 Master the Correct Reading Method and Develop Good Reading Habits Students should combine extensive reading with intensive reading, understand the main idea of the article through extensive reading, and then carry out intensive reading on the basis of extensive reading [15]. In addition, students should learn the details of the article, understand the long and difficult sentences, and master the important professional vocabulary. In addition, students should also learn to fully understand and grasp the meaning of the article from the background and context, improve reading speed, and do not rely on dictionaries. In the big data environment, teachers can not only teach English reading through textbooks, but also carry out various forms of English reading activities outside of class [16].

6 Conclusion Students’ reading anxiety hinders the improvement of their comprehensive English ability, which is not conducive to the full improvement of foreign language learning. Therefore, teachers should comprehensively analyze the causes and influencing factors of students’ foreign language reading anxiety, and provide targeted solutions. In the big data environment, English reading teaching not only makes the reading forms more diverse, but also enables students to learn the latest knowledge every day with real-time and fast information transmission. Moreover, the knowledge covers a wide range and broadens the vision of English reading.

44

J. Li et al.

References 1. Abramson, L., Garber, J., Seligman, M.E.P.: Learned helplessness in humans: An attributional. Analysis. In: Garber, J., Seligman, M.E.P. (eds.) Human Helplessness. Academic Press, NewYork (1980) 2. Bachman, L.F.: Fundamental Consideration in Language Testing. Oxford University Press, Oxford (1990) 3. Barnes, T., Boyer, K., Sharon, I., et al.: Preface for the Special Issue on AI-Supported Education in Computer Science. Int. J. Artif. Intell. Educ. 27, 1–4 (2017) 4. Black, P., William, D.: Developing the theory of formative assessment. Educ. Assess. Eval. Account. 21(1), 5–31 (2009) 5. Carroll, J.: Tools for teaching in an Educationally Mobile World Abingdon. Routledge, London (2015) 6. Davies, J., Brember, I.: The closing gap in attitudes between boys and girls: A five year longitudinal study. Educ. Psychol. 21, 103–115 (2001) 7. Goksel-Canbek, N., Mutlu, M.E.: On the track of artificial intelligence: Learning with intelligent personal assistants. Int. J. Hum. Sci. 2016(1), 593–601 (2016) 8. Horwitz, E.K., Horwitz, M.B.: Foreign language classroom anxiety. Mod. Lang. J. 70, 125– 129 (1986) 9. Krashen, S.: The Input Hypothesis: Issues and Implications. Longman, London (1985) 10. Mc Arthur, D., Lewis, M., Bishary, M.: The roles of artificial intelligence in education: Current progress and future prospects. J. Educ. Technol. 4, 42–80 (2005) 11. Pinkwart, N.: Another 25 years of AIED? Challenges and opportunities for intelligent educational technologies of the future. Int. J. Artif. Intell. Educ. 2, 771–783 (2016) 12. Roll, I., Wylie, R.: Evolution and revolution in artificial intelligence in education. Int. J. Artif. Intell. Educ. 2, 582–599 (2016) 13. Saito, Y., et al.: Foreign language reading anxiety. Modern Language Journal, 83(2): 202–219 (1999) 14. Vail, A.K., Grafsgaard, J.F., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Predicting Learning from Student Affective Response to Tutor Questions. In: Micarelli, A., Stamper, J., Panourgia, K. (eds.) ITS 2016. LNCS, vol. 9684, pp. 154–164. Springer, Cham (2016). https://doi.org/10. 1007/978-3-319-39583-8_15 15. Woolf, B.P., Lane, H.C., Chaudhri, V.K., et al.: AI Grand Challenges for Education. AI Mag. 4, 61–84 (2013) 16. Zimmerman, B.J., Risemberg, E.: Self-regulatory dimensions of academic learning and motivation. In: Phye, G.D. (ed.) Handbook of academic learning, pp. 105–126. Academic Press (1997)

Application of Big Data Information Processing Technology in the Improvement of Intercultural Sensitivity in English Teaching Jingtai Li1 , Jiaju He1(B) , and Craig Whitsed2 1 School of Foreign Languages, Jiaying University, Meizhou 514015, Guangdong, China

[email protected] 2 School of Education, Faculty of Humanities Curtin University, Perth, WA, Australia

Abstract. Language is the carrier of culture, learning a language is bound to master the related culture. Intercultural sensitivity is the core competence of the emotional part of cross-cultural communicative competence. The current era of big data has brought great changes to people’s lives and work. While bringing opportunities to school teaching, it also brings challenges. Based on the analysis of the connotation of big data and the reasons for its application in English teaching, combined with the shortcomings of traditional teaching, this article puts forward some effective measures to improve the intercultural sensitivity in English teaching by using big data, so as to improve the application level of big data technology in English teaching. Keywords: Big data · Improvement · Intercultural sensitivity · English teaching

1 Introduction The concept of intercultural sensitivity was first proposed by the scholar Bennett (1981). Academia generally believes that cross-cultural competence includes cognitive, emotional and behavioral levels, and intercultural sensitivity is the emotional level of crosscultural competence. According to Chen &Starosta’s design, intercultural sensitivity has five factors: interaction engagement, respect for cultural difference, interaction confidence, interaction enjoyment and interaction attentiveness. Nowadays, most students have smart phones in their hands, and students are greatly influenced by modern information technology [1–3]. Language learning needs to be connected with the deep social and cultural environment of students, which also provides opportunities to improve the effect of English teaching and challenges the traditional teaching methods. At present, it is required to actively and effectively use information technology in education reform to promote the improvement of teaching efficiency and effectiveness [4–6]. Therefore, English teachers should think about how to effectively use new information technology to optimize the intercultural sensitivity in English teaching and improve the teaching quality [7–10].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 45–49, 2022. https://doi.org/10.1007/978-3-030-99616-1_7

46

J. Li et al.

2 Concept of the Application of Big Data Information Processing Technology in the Improvement of Intercultural Sensitivity in English Teaching With the help of big data technology, teachers can combine the teaching content to produce different forms of culture-related micro textbooks, and upload these to the campus network learning platform. Students can log in the client before class, and complete related tasks and make preparations. Teachers can understand the actual situation of students by checking and reviewing the completion of students’ tasks, and then optimize and adjust the teaching content accordingly. They can effectively combine collective explanation, centralized discussion and individual guidance outside the classroom, or supplement teaching materials, so that students can make up for their own shortcomings. After class, students can test on the Internet, upload their homework, browse relevant electronic teaching resources, supplement their knowledge, broaden their horizons, and gradually form a good habit of self-study. Teachers can use the network resources of big data in teaching, provide guidance to students in writing and assist English teaching. After self-study, the students can do the related topics designed by the teacher in the micro video, so that the students can test their learning effect; Teachers can also adjust the teaching by referring to the students’ situation [11–13].

3 Big Data Information Processing Technology Teaching Mode 3.1 Understand the Characteristics of Verbal Communication and Non-verbal Communication Big data technology is used to guide students to attach importance to the process of communication and to maintain a harmonious relationship between the two sides. In communication, appropriate ways can be used to express indirect, euphemistic and implicit expressions. In addition, due to cultural differences, some non-verbal expressions of friendly expression may be expressed in another nation with completely opposite meanings. These differences often do not attract students’ attention, which may result in more severe consequences than verbal communication. 3.2 Incorporate Cultural Knowledge in a Timely Manner People’s cultural knowledge comes from the process of socialization. Culture teaching is an indispensable part of English teaching, because culture and communication are inseparable. Therefore, in teaching, teachers should put cultural content into the cultivation of various skills, so that students can not only learn language knowledge, but also cultivate language ability, master social and cultural knowledge and improve cross-cultural communication ability. Besides, teachers should educate students on intercultural sensitivity in English teaching, so that students can understand cultural differences and avoid embarrassing situations in cross-cultural communication.

Application of Big Data Information Processing Technology

47

3.3 Improve Intercultural Communicative Competence by Cultivating Empathy Cultural empathy means that communicators get rid of the bondage of their own culture, place themselves in the target language environment, and truly feel and understand the speech and behavior of people in the other’s cultural context. Cultural empathy plays a key role in intercultural communication, which directly determines the communicative effect. Empathy requires both sides of communication to see things from the perspective of the other side. If both sides start from their own point of view, it is not conducive to smooth communication. In cross-cultural communication, we should not only think from the perspective of our own culture, but also combine with the target culture to observe, analyze and solve problems from multiple perspectives. 3.4 Promote Personalized English Learning According to the different personality and ability of students, teachers should make a comprehensive evaluation on each student, and on this basis, they should customize the targeted learning content and strategies for them, meet the requirements of students in the learning process with personalized teaching methods, fully mobilizing the enthusiasm and initiative of students, and realizing the personalized learning of students. Accurate analysis based on big data will guide schools and teachers to select the most effective private customized teaching activities for students’ individual English learning. In the process of English teaching, the school authorities and teachers should collect largescale educational data of students’ English learning situation, so as to master students’ ability and English learning situation, and adjust students’ learning content in time on this basis, so as to improve students’ autonomous learning ability [14, 15].

4 Problems of Intercultural Sensitivity Teaching Mode in My Country First of all, the concept of teaching resource design lacks innovation. Under the traditional teaching mode, there are problems such as scattered teaching resources, repeated construction, difficulty in integration and sharing, and lack of high-quality educational information resources. Secondly, English teachers’ teaching ideas are outdated, and their self-efficacy in using computers and big data is insufficient. With the continuous development of information technology, the shortage of information talents and low information literacy of English teachers are becoming increasingly prominent. Thirdly, students’ autonomous learning ability of English learning is insufficient. In the context of the continuous advancement of student-centered education reforms, there are still some students who do not know what to learn in the process of learning English, let alone how to learn.

48

J. Li et al.

5 The Strategies for the Application of Big Data Information Processing Technology in the Improvement of Intercultural Sensitivity in English Teaching in My Country’s Colleges and Universities 5.1 Improvement of Teaching Link Design First of all, the choice of English textbooks is very important. Teachers should make full use of big data technology to analyze students’ learning characteristics and the knowledge they have mastered and should master. Based on the analysis results, teachers should choose the teaching materials that students are familiar with, as familiar teaching materials can help students to discuss topics. In addition, the authenticity, cultural color and difficulty of the materials should be appropriately increased, so as to achieve a gradient in intercultural communication and learning. 5.2 Improvement of Teaching Methods Teachers should improve the traditional classroom teaching mode. Before the teaching of cross-cultural courses, teachers should provide students with rich and diverse network resources, use multimedia as much as possible, and use more video materials. Big data technology will make English culture teaching more networked and diversified. Before and after class, students can strengthen the classroom learning content and improve the learning effect through mobile APP and network platform. 5.3 Improvement of Teaching Evaluation The evaluation method no longer only depends on the final examination process. Process evaluation can scientifically and objectively evaluate students’ learning effects. Teachers can regularly collect students’ APP or network platform learning data in a certain period, analyze the data to form a process of learning performance, and then evaluate students’ learning situation in combination with the final examination. Big data records in detail the learning behaviors of students, which helps to improve students’ self-cognition ability and help teachers to carry out teaching work smoothly and effectively.

6 Conclusion The favorable factors presented by big data in promoting the positive development of English teaching are obvious. Intercultural communication is both cognitive communication and emotional communication. With the help of big data technology, teachers can start from cultural teaching to improve students’ intercultural sensitivity and crosscultural communication ability. Big data technology brings unprecedented opportunities and challenges to English education, and English education will also be increasingly personalized under the influence of big data. Big data is the development trend of the world today. In the era of big data, it is urgent for teachers to keep pace with the times, change their thinking concepts, update their knowledge structure and education concepts, and deeply integrate big data technology with English teaching.

Application of Big Data Information Processing Technology

49

References 1. Abramson, L., Garber, J., Seligman, M.E.P.: Learned helplessness in humans: An attributional. Analysis. In: Garber, J., Seligman, M.E.P. (eds.) Human Helplessness. Academic Press, NewYork (1980) 2. Bachman, L.F.: Fundamental Consideration in Language Testing. Oxford University Press, Oxford (1990) 3. Barnes, T., Boyer, K., Sharon, I., et al.: Preface for the special issue on AI-supported education in computer science. Int. J. Artif. Intell. Educ. 27, 1–4 (2017) 4. Bayne S.: Teacherbot: Interventions in automated teaching. Teach. High. Educ. 20, 455–467 (2015) 5. Bennett, M.J.: A developmental approach to training for intercultural sensitivity. Int. J. Intercult. Relat. 10(2), 179–194 (1986) 6. Chen, G.M., Starosta, J.: A review of the concept of intercultural sensitivity. Human Communication (1997) 7. Goksel-Canbek, N., Mutlu, M.E.: On the track of artificial intelligence: Learning with intelligent personal assistants. Int. J. Hum. Sci. 13(1), 593–601 (2016) 8. Holotescu, C.: MOOCBuddy: A chatbot for personalized learning with MOOCs. In: Iftene, A., Vanderdonckt, J. (eds) Proceedings of the International Conference on Human-Computer Interaction - Ro CHI 2016. Matrix Rom, Bucharest (2016) 9. Leontiev, A.: A Psychology and the Language Learning Process. Oxford Pergamon Press, London (1990) 10. Mc Arthur, D., Lewis, M., Bishary, M.: The roles of artificial intelligence in education: Current progress and future prospects. J. Educ. Technol. 4, 42–80 (2005) 11. Pinkwart, N.: Another 25 years of AIED? Challenges and opportunities for intelligent educational technologies of the future. Int. J. Artif. Intell. Educ. 2, 771–783 (2016) 12. Roll, I., Wylie, R.: Evolution and revolution in artificial intelligence in education. Int. J. Artif. Intell. Educ. 2, 582–599 (2016) 13. Timms, M.J.: Letting artificial intelligence in education out of the box: Educational cobots and smart classrooms. Int. J. Artif. Intell. Educ. 2, 701–710 (2016) 14. Vail, A.K., Grafsgaard, J.F., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Predicting learning from student affective response to tutor questions. In: Micarelli, A., Stamper, J., Panourgia, K. (eds.) ITS 2016. LNCS, vol. 9684, pp. 154–164. Springer, Cham (2016). https://doi.org/10. 1007/978-3-319-39583-8_15 15. Woolf, B.P., Lane, H.C., Chaudhri, V.K., et al.: AI grand challenges for education. AI Mag. 4, 61–84 (2013)

Coordinated Development of Children’s Art Skills and Creativity Based on Human-Computer Interaction Technology Yu Sun(B) Changchun Normal University, Changchun 130031, Jilin, China [email protected]

Abstract. With the increasing maturity of human-computer interaction technology, multi-channel human-computer interaction has become the main way of interaction between computers and humans due to its powerful functions and practicality. Interaction designs that can stimulate children’s art skills are also endless. Children’s art education is not only a functional education that cultivates the coordination of hands, eyes, and brains before school age, but also an emotional education that meets children’s aesthetic and emotional needs, and is also a creative education that cultivates children’s creativity. Preschool is a critical period for art education. At this stage, the enlightenment education for children to experience beauty, express beauty, and create beauty plays a very important role in the life-long development of people. This article aims to study the coordinated development of children’s art skills and creativity based on human-computer interaction technology, and analyzes the development trend of human-computer interaction for children, the principles of human-computer interaction design for children, the connotation of children’s art creativity, and art skills and creation. Based on the relationship of force, in order to further understand the current status of the application of human-computer interaction technology in children’s art education, this article conducted a questionnaire survey of art teachers in five kindergartens in this article. The survey results show that More and more art teachers are beginning to realize that improving children’s artistic creativity requires a variety of teaching methods The important role and significance of computer interaction technology in the process of children’s art education, and is willing to make multi-directional and multi-level attempts. Keywords: Human-computer interaction technology · Children’s art · Art skills · Creativity

1 Introduction Art education is an effective way to promote the all-round development of children. Art education essentially belongs to the category of aesthetic education. The core of art is to cultivate a quality and a way of understanding the world [1, 2]. The mastery of art skills is the basis for children to create art. Stimulating children’s creativity is the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 50–57, 2022. https://doi.org/10.1007/978-3-030-99616-1_8

Coordinated Development of Children’s Art Skills

51

essence of art education. If we strengthen the training of art skills and unify standards and requirements, it will limit the creativity of children’s art. But if you stop learning skills, the way of expressing art will be restricted, and children’s feelings and experiences will not be fully reflected, let alone any unique creativity [3, 4]. The “Guidelines for Kindergarten Education (Trial)” pointed out: “Children should be supported to express their individuality and creativity, and overcome the tendency to overemphasize skills and standardization requirements.” This clearly puts forward the content of skill learning and creativity in kindergarten art education [5, 6]. Therefore, in the art education of preschool children, we should not only pay attention to the improvement of preschool children’s art skills and skills, but also pay attention to stimulating children’s art creativity. Based on the analysis of the development trend of human-computer interaction for children, the principles of human-computer interaction design for children, the connotation of children’s art creativity and the relationship between art skills and creativity, in order to further understand human-computer interaction technology in children’s art education. The current situation of the application of the art in this article, this article conducted a questionnaire survey of art teachers in five kindergartens in this article. The survey results show that more and more art teachers have begun to recognize the important role and significance of human-computer interaction technology in the process of children’s art education, and are willing to do so.

2 Research on the Coordinated Development of Children’s Art Skills and Creativity Based on Human-Computer Interaction Technology 2.1 Development Trend of Human-Computer Interaction for Children (1) "Children’s Center” is the fundamental starting point for the development of childoriented human-computer interaction technology. Child user interaction technology is child-friendly. Children are different from adults. They have their own cognitive, psychological and physical characteristics. When designing human-computer interaction technology for children, we must adhere to the design principle of “child center” [7, 8]. (2) Multi-channel sex is the technical feature of future children’s human-computer interaction. Traditional human-computer interaction technology mainly uses manual mouse, keyboard and main input device. The transmission of the interface is extremely complicated and not suitable for young children. This kind of humancomputer interaction learning method is undoubtedly very difficult for adults. The integration of physical operation interface, gesture interaction, pen language interaction, voice interaction and other multi-channel technologies to achieve physical interaction, which is also the trend of future development of human-computer interaction technology for young children. In the future, computers may become more and more invisible and smaller and smaller, and the human-computer interaction interface does not necessarily have to be very connected to the screen. As a result, traditional interactive channels may shrink, and other interactive channels may also appear [9, 10].

52

Y. Sun

(3) In the future, the emotional and sensory characteristics of children’s humancomputer interaction technology will become more natural and effective. With the development and advancement of computer sensor technology, network technology and virtual reality technology and other information systems and software and hardware technologies, the application of computer software and hardware technology in the future will become more natural and efficient, even for children who have little contact with computers. It’s the same. Young people with poor abstract cognitive ability may be able to use computers comfortably and flexibly [11, 12]. 2.2 Human-Computer Interaction Design Principles for Children (1) The use of multi-channel input combined with environmental awareness is a technical feature designed as a human-computer interactive education method for young children. Children have a wide range of knowledge, but their attention span is not very high. It is recommended to use multi-channel (including gesture, voice, posture, physical interface, etc.) fusion method for human-computer interaction operation mode to avoid resources that require a lot of attention during use. (2) The perceptual features in the design of human-computer interaction for young children will become more natural, intuitive and effective. Toddlers do not have a good understanding of the environment, and are only based on the thinking of representations, that is, thinking through images and intuition. Therefore, it is recommended that they choose to use simple and intuitive interactive operation methods such as gestures, voice, physical interfaces, and various avatar-based human-computer interactions. (3) An interactive design method based on semantics and discrete commands, which is a powerful realization for children in the design of human-computer interaction. In addition, young children only have a concrete thinking, and they cannot provide abstract operation methods in a user interface for young children. It can give children a semantic-based interaction metaphor instead of “direct operation”, protect the abstract details of the application of interactive technology in life, and allow children to focus more on the control of high-level interactions that are closely related to their scope. The physiological characteristics of children determine that it is difficult for them to perform functions such as continuous movement and precise placement. We can use individual commands to complete functions such as gestures, physical interfaces, and other interactive input methods. 2.3 The Connotation of Children’s Art Creativity (1) Definition of children’s art creativity Creation refers to the activity of destroying, personalizing and breaking the foundation of old things: building and producing new things. Everyone has the opportunity to create. The creativity of children is different from that of adults. Adult creativity refers to the ability of social, cultural and other ideas or products to bring about some qualitative changes, while children’s creativity refers to the ability to create unprecedented new ideas or products. Children’s creative art refers

Coordinated Development of Children’s Art Skills

53

to their ability to form aesthetic mental images in their minds, use artistic tools and materials to combine them, and create novel and unique works of art for specific people. (2) Educational content of children’s art creativity Creation runs through all the processes of art education. The content of preschool children’s creative art education mainly includes: First, the creation of visual images. We often see this seemingly absurd and disproportionate shape in children’s drawings and handicrafts, the colors of subjective design and imagination, and the randomly arranged composition of spaces and buildings. Because of its visibility, this type of external creation is often the one we pay close attention to. The second is the shaping of aesthetic psychological image. Not only has appeared many times in the activities of art appreciation, but also many times in the activities of children’s cultural and artistic creation. It refers to a kind of creation carried out by children in special and specific aesthetic awareness activities according to their own aesthetic goal needs and aesthetic skills. Because it is intangible, if there is not enough understanding and attention to children and their work, this kind of tacit creation is often ignored by adults. And this kind of creation is exactly the condition created by the former. 2.4 The Relationship Between Art Skills and Creativity (1) Art skills are the foundation of creativity training Art skills provide us with the technical foundation and tools for artistic creation activities. Without the cultivation and support of these art skills, the cultivation of our art creativity will be greatly restricted. From the analysis of various influencing factors included in art skills, on the one hand, more, appropriate, art knowledge and practical experience storage can increase the probability of good reflection; on the other hand, the higher the ability level, the more flexible and flexible the operation will be. More possibilities for new combinations of new things or ideas. Teaching practice shows that when children participate in art activities, the more coordinated their hand muscles are, the more they understand the nature and use of art materials, and the more they have a fuller understanding of shapes, colors, spaces and external information. Art works created through psychological processing. As far as possible to reflect children’s self-experience and understanding. I have to say that art skills provide the necessary conditions for the development of art creativity. (2) Art creativity is the experience of skill development The process of cultivating art creativity is a process of effective practical skills. For children, as long as they don’t copy or teach them the strokes, every time they make or design, they can show new things, which is more or less creative. As the drama teacher said: “The moments when a person gains joy and excitement in the game are moments when skills can be improved. This is a moment when it is very easy to focus on a particular technique.” We know that children will inevitably face when completing tasks. Difficulties that must be overcome. Most of these difficulties are the lack of knowledge of art skills. The process of dealing with difficulties through the guidance and guidance of teachers is a process of learning skills and art skills. In this process, they naturally become familiar with and master

54

Y. Sun

the skills required for a particular art creation. In this way, children can give full play to their creativity while possessing a foundation in art.

3 Experiment 3.1 Questionnaire Survey Method The questionnaire survey method is a basic and commonly used research method that uses an objective survey tool—the survey questionnaire to search for information through the issuance and recovery of the questionnaire. For the purpose of the research, based on the Johnson language learning knowledge and presentation questionnaire, the questionnaire of this article was developed. After revision and preliminary testing, the questionnaire was determined to be the official questionnaire supporting this article. 3.2 Selection of Survey Objects and Implementation of the Questionnaire The research content of this article is a study on the coordinated development of children’s art skills and creativity based on human-computer interaction technology. Therefore, this article selects art teachers from five kindergartens in this city as the survey subjects, and the questionnaire is issued by paper questionnaires. A total of 45 questionnaires were distributed, and 45 questionnaires were finally returned, with 45 valid questionnaires, and the effective rate was 100%. Subsequently, the collected questionnaire data was statistically and analyzed using SPSS20.0 statistical software and EXCEL. Through the comparison and analysis of the data, the application of human-computer interaction technology in the coordinated development of children’s art skills and creativity was studied. 3.3 Reliability Test of the Questionnaire In order to test the reliability and stability of the questionnaire, the variance of the questionnaire results was first calculated, and then the reliability of the returned questionnaire was tested by the method of “half-half reliability” test. Using formula (1) 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. S2 =

(M − X1 )2 + (M − X2 )2 + (M − X3 )2 + ... + (M − Xn )2 n r = 1 − S 2 (1 − r1 )/Sn2 r=

2rban 1 + rban

(1) (2) (3)

Coordinated Development of Children’s Art Skills

55

4 Discussion 4.1 The Application Status of Human-Computer Interaction Technology in Children’s Art Table 1. The application status of human-computer interaction technology in children’s art Small class

Top class

9.4%

2.8%

6.6%

General

14.2%

15.2%

16.7%

Less used

42.8%

46.1%

45.3%

Never used

33.6%

35.9%

31.4%

percentage

Frequently used

Middle class

50.00% 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00%

46.10% 42.80% 45.30%

35.90% 33.60% 31.40%

16.70% 15.20% 14.20% 9.40%

6.60% 2.80%

Frequently used

general

Less used

Never used

Level Small class

Middle class

top class

Fig. 1. The application status of human-computer interaction technology in children’s art

It can be seen from Table 1 and Fig. 1 that, whether it is a small class, a middle class or a large class, less than 10% of teachers often use human-computer interaction technology in art teaching; less than 20% use it, but rarely use. It can be seen from these data that most teachers rarely or never use this technology, and there is still a lot of room for the development of human-computer interaction technology in children’s art education. 4.2 The Role of Human-Computer Interaction Technology in Art Teaching According to Fig. 2, teachers believe that human-computer interaction technology can enable children to get better development in art and improve their artistic quality. At the same time, more and more art teachers are beginning to realize the important role and significance of human-computer interaction technology in the process of children’s art education, and are willing to make multi-faceted and multi-level attempts.

56

Y. Sun

Fig. 2. The role of human-computer interaction technology in art teaching

5 Conclusions Modern children’s art education is a big research topic, and its significance is not only reflected in the art teaching classroom, but also directly reflected in people’s daily life and work in modern society. Teachers should use human-computer interaction technology and other means and methods to help us improve children’s comprehensive aesthetic quality. At the same time, they should also train children to observe and analyze things in complex environments in order to extract effective information. In short, children’s art education practice activities with advanced human-computer interaction and network technology as the main media can open up a faster and broader art world for children.

References 1. Abbas, R., Marsh, S., Milanovic, K.: Ethics and system design in a new era of human-computer interaction [Guest Editorial]. IEEE Technol. Soc. Mag. 38(4), 32–33 (2019) 2. Kong, W.: Digital media art design based on human-computer interaction technology in the background of big data. Revista de la Facultad de Ingenieria 32(14), 485–489 (2017) 3. Zhang, C., Lv, R., Chen, X., et al.: Research on virtual roaming technology of urban scene based on multi-mode human-computer interaction. J. Phys. Conf. Ser. 1570(1), 012096 +5 (2020) 4. Zhou, F., Hu, E.: Human-computer interaction research in computer game interface design. J. Phys. Conf. Ser. 1915(3), 032075+8 (2021) 5. Tripathi, A.K.: Erratum to: Culture of sedimentation in the human–technology interaction. AI Soc. 31(2), 233–242 (2016) 6. Jianan, L., Abas, A.: Development of human-computer interactive interface for intelligent automotive. Int. J. Artif. Intell. 7(2), 13–21 (2020)

Coordinated Development of Children’s Art Skills

57

7. Joseph, A.W., Muru Ge, S.: Potential eye tracking metrics and indicators to measure cognitive load in human-computer interaction research. J. Sci. Res. 64(1), 168–175 (2020) 8. Daily, S.R.: 59.0 Healing arts: Cultural symbolism in children’s art in Chinese, Islamic, MiddleEastern, Native American, and African American cultures. Retour Au Numéro, 56(10), S85S86 (2017) 9. Berntsen, S., Sderstrm-Anttila, V., Wennerholm, U.B., et al.: The health of children conceived by ART: “The chicken or the egg?” Hum. Reprod. Update 25(2), 137–158 (2019) 10. Jung, C.Y.: A study on young children’s art activity program for improvement of family interaction. J. Res. Art Educ. 21(1), 131–150 (2020) 11. Savoie, A.: Essay on playfulness and play in children’s art class: A reflection based on Winnicott. Creat. Educ. 10(2), 297–307 (2019) 12. Park: Painting “Out of the Lines”: The aesthetics of politics and politics of aesthetics in children’s art. Vis. Arts Res. 45(2), 66 (2019)

Construction of University Teachers’ Digital Competency Model Based on New Media Communication Technology Qi Yu(B) Hebei University of Technology, Tianjin 300401, China [email protected]

Abstract. Under the background of new media communication technology, digital technology and the new crown epidemic, the use of digital technology to teach, such as online teaching, has become the main teaching method in the epidemic era. This has greatly initiated our country’s long-term offline teaching method. How to adapt to the current teaching methods and improve the digital competence of college teachers has become the main problem to be solved in contemporary education. The purpose of this article is to study the construction of the digital competency model of college teachers based on new media communication technology. Based on the new media communication technology, this article first analyzes the digital competency needs of college teachers, and through questionnaire surveys and interviews, establishes the three dimensions of the digital competency model for college teachers, namely: digital technology capabilities, digital professional development and innovation and the three dimensions of digital value and pursuit, and give the countermeasures for colleges and universities to improve the digital competence of teachers. According to the survey data, 8.7% of college teachers said that they did not meet the situation of frequently using new media communication technology or digital resources for teaching special research, and 21% of college teachers said that they did not meet the situation of frequently using media for teaching special research. This shows that college teachers’ performance in using digital value for teaching research and construction of digital education is not ideal. Keywords: Digital technology · University teachers · Digital competence · Education informatization

1 Introduction In the information age, new media communication technology develops rapidly [1]. With the development of social productivity and adapting to the development of the times, the state has also put forward requirements for education to keep pace with the development of the times, and the people also have high hopes for education [2, 3]. In schools, digital technology has increasingly become the dominant force in generating and shaping the mimicry reality of informationized campuses [4]. The education and teaching effects of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 58–66, 2022. https://doi.org/10.1007/978-3-030-99616-1_9

Construction of University Teachers’ Digital Competency

59

college teachers are increasingly affected by various digital technologies, and the digital competence of teachers themselves has also attracted more and more attention from the society [5, 6]. Regarding the research of digital competence, many scholars at home and abroad have conducted in-depth discussions on it. For example, Rokenes FM pointed out that digital competence includes various components, such as technology, communication, information use and multimedia literacy [7]; Kantosalo said the definition of digital competence in the strategy is summarized, and it is found that digital competence should include the technical skills of using digital technology, the ability to apply digital technology, the ability to understand the phenomenon of digital technology, and the motivation to participate in digital culture [8]; Rasi through research found that the abilities of college students are inconsistent with the demand for labor, which confirms the serious lack of professional training in digital ability [9]. This paper takes the construction of the digital competency model of college teachers as the research goal. Based on new media communication technology, first analyzes the needs of college teachers’ digital competency, and through questionnaire surveys and interviews, establishes three college teachers’ digital competency models. Dimensions, namely: digital technology capabilities, digital professional development and innovation, and digital value and pursuit of three dimensions, and investigate the current digital competencies of college teachers. Finally, this article presents countermeasures for colleges and universities to improve the digital competence of teachers.

2 Construction of the Digital Competency Model of College Teachers Based on New Media Communication Technology 2.1 Demand Analysis for the Construction of University Teachers’ Digital Competency Model (1) Multi-dimensional scrutiny of the profession of teachers in our country’s colleges and universities 1) Teacher profession at the 2.0 stage of education informatization The transformation and development of digital technology to intelligent technology has further promoted the breadth and depth of the integration of technology and education and teaching. On the one hand, it is reflected in the innovative application of learning space, learning resources and teaching tools by college teachers; on the other hand, it is reflected in the new digital technology. Innovative exploration and organization of teaching modes and teaching methods (such as project-based learning and STEAM learning, etc.) [10, 11]. The above-mentioned new requirements or new positioning for the profession of college teachers will accumulate and release the innovative efficiency of new digital technologies from the teacher side, which is an important foundation for promoting the construction of a new education ecology. 2) Teacher profession that develops students’ core literacy First, from the perspective of students, the goal of the student core literacy system is to cultivate “all-rounded people.“ The so-called “all-rounded people”

60

Q. Yu

have obviously broken through the limitations of the previous education that paid more attention to students’ knowledge, skills, and attitudes. The sound personality traits, the ability to develop independently, the awareness of serving the society, and the value orientation of responsibility are all. It is an important part of developing students’ core literacy. However, it is worth noting that the educational environment of colleges and universities has basically realized digitalization, that is, the cultivation of students’ core literacy in the school, it is difficult to get rid of the influence or support of digital technology, resources and tools. Second, from the perspective of college teachers, facing the development needs of students’ core literacy, our country’s education department has also put forward the suggestion of “strengthening teachers’ cultivating ability”, which can be briefly summarized in two aspects: on the one hand, teachers teach students the ability to improve knowledge, skills and attitudes; on the other hand, it is to develop teaching abilities at the level of students’ personality traits and value orientation. For a long period of time in the past, relative to the development of students’ teaching in the first aspect, the latter was often overlooked by teachers. To develop the core literacy of students, teachers not only need to be competent in teaching the former, but also urgently need to improve the competence in the latter. Only when a teacher is a “all-rounded person” can he be competent in the educational work of cultivating a “allrounded person”. Combining the above two aspects, the development of the core literacy of our students at this stage requires teachers to have the comprehensive competence to carry out educational and teaching activities in a digital environment, that is, the digital competence of teachers considered by this research [12]. 3) Teacher profession under the uneven development of education The imbalance of education at this stage is showing a trend of more subtle and complex development, and these problems are directly related to the professional competence of teachers. In the face of these problems, in addition to the policies of the education department, the key lies in whether teachers can give full play to their initiative, that is, have a certain ability of active learning and lifelong learning, and have a higher level of teaching pursuit and value orientation. From this perspective, these abilities should also be an important component of the digital competence of college teachers. 2.2 Countermeasures to Improve the Digital Competence of College Teachers Based on New Media Communication Technology (1) Improve the understanding of college teachers’ digital competence First of all, the majority of college teachers should look at digital competence with an open and tolerant perspective, extensively cover a wide range of knowledge fields, learn and draw on the advanced experience of others, and establish a reasonable knowledge network, that is, focus on hard power such as information technology and software skills. Promotion also pays attention to the reserve of theoretical knowledge, conceptual thinking and other soft power, and emphasizes the

Construction of University Teachers’ Digital Competency

61

horizontal and vertical connections between different abilities, so as to enhance their own comprehensive capabilities. Secondly, establish the concept of lifelong learning and constantly update knowledge. With the rapid development of modern information technology and the increasing degree of social networking and intelligence, the requirements for teachers’ digital competence are also constantly improving. The connotation is constantly enriched. The improvement of digital competence has no boundaries. College teachers must establish the concept of lifelong learning. Learn, update personal knowledge system, innovate and practice on existing models and systems, and enrich the connotation of digital competence. (2) Improve the education and training system for college teachers It is necessary to use modern information technology to carry out training and encourage college teachers to carry out digital learning independently. The information age provides a wealth of network platforms and digital resources, and innovates training models for the education and training of college teachers. Facing the problems of lack of high-quality training and insufficient learning time reported by college teachers, it is necessary to make full use of big data, “Internet+”, 5G and other technical means to give full play to the advantages of “Internet+ education”, establish and improve the network training system, build and make good use of the network learning platform, promote the informatization of education and training of college teachers’ digital competence, and share high-quality digital training resources. At the same time, college teachers are encouraged to make full use of their free time, use digital platforms and resources to flexibly carry out online self-learning, and build a team of learning-oriented college teachers. (3) Improve the evaluation and incentive mechanism of college teachers’ digital competence First, incorporate the digital competency assessment of college teachers into the daily assessment system. In the process of digital transformation of education, the digital competence of college teachers has become an important part of the overall quality. Therefore, the digital competency assessment standards for college teachers are incorporated into the daily assessment system, and the assessment will more accurately and directly reflect the digital competence level of college teachers, which will help college teachers to attach great importance to the improvement of digital competence in thought and be active in action. Carrying out digital competency learning will help college teachers play the role of autonomous learning and self-motivation, and form a joint force of education and autonomous learning. Second, link assessment results with selection, appointment, rewards and punishments. The evaluation results of college teachers’ digital competency should be used as an important basis for college teachers’ selection and appointment, management supervision, rewards and punishments, and knowledge incentives should be implemented to provide college teachers with effective learning and high digital competence with broad opportunities for further education. The knowledge-based and compound talents who understand science and technology are placed in key positions, so that the digital competency assessment mechanism of college teachers can give full play to its value, and form the internal driving force to enhance the digital competence and professional quality of college teachers.

62

Q. Yu

3 Experimental Research on the Construction of University Teachers’ Digital Competency Model Based on New Media Communication Technology 3.1 Data Collection Based on the characteristics of the research object and the social network of colleagues in this research, this research obtained interview opportunities with 12 university teachers. The survey period lasted from September 26, 2020 to November 6, 2020. The total survey recording time was 485 min, and a total of 12 university teachers were surveyed. 3.2 Interview Content and Process The content of the interview mainly focused on the expression and development level of college teachers’ digital competence; the impact of college teachers’ digital competence on individuals; and how to develop college teachers’ digital competence. 3.3 Questionnaire Survey Questionnaires were made for the interviewees, and the questionnaires were distributed randomly in each class. A total of 300 (teacher version) and 1500 (student version) questionnaires were collected, and the teacher questionnaire and student questionnaire were tested on their reliability and effectiveness. 3.4 Reliability Analysis of the Questionnaire Reliability analysis is an important process of content analysis. Only through strict reliability analysis can the content analysis results be reliable. The reliability formula of content analysis used in this study is: R=

n×K 1 + (N − 1) × K

(1)

In the formula, R stands for reliability, K refers to the degree of mutual agreement between two judges, K refers to the average of the degree of mutual agreement between the judges, and the K value calculation formula is: M is the column that both agree completely, N 1 is the number of columns analyzed by the first judge, and N 2 is the number of columns analyzed by the second judge. The overall reliability coefficient Cronbach’s Alpha of the teacher version questionnaire is 0.900, indicating that the overall reliability of the questionnaire is very high. The overall reliability coefficient of the student version of the questionnaire is 0.874, which is slightly lower than that of the teacher version, but the overall reliability of the questionnaire remains at a high level.

Construction of University Teachers’ Digital Competency

63

4 Data Analysis of the Digital Competency Model of College Teachers Based on New Media Communication Technology 4.1 Digital Technology Capability Dimension The digital technology capabilities of college teachers include whether teachers can use the teaching platform according to their needs, produce and manage digital learning resources, and collect, analyze, and integrate digital teaching data. By investigating the use of the current digital teaching platform by college teachers, the results are shown in Table 1. A survey of college teachers’ understanding of the application of online teaching platforms showed that 36.7% of teachers said they did not know much, 18.4% of teachers said they knew how to use online teaching platforms for teaching, and 44.9% of teachers said they fully understood this. Table 1. Teachers’ use of the current digital teaching platform (%) Digital teaching platform

Don’t understand at all

Don’t know much

Better understand

Fully understand

MOOC

0

23.1

19.1

57.8

Micro lesson

0

19.5

32.7

47.8

Flipped classroom

3.7

27.4

38.2

35.4

Online teaching platform

0

36.7

18.4

44.9

Fully understand

Better understand

Don't know much

Don't understand at all

Digital teaching platform

Online teaching platform Flipped classroom

Micro Lesson

MOOC -10

0

10

20

30

40

50

60

Unit: % Fig. 1. Teachers’ use of the current digital teaching platform

70

64

Q. Yu

Observing Fig. 1 we can find that college teachers have a better understanding of the more commonly used MOOCs and micro-classes, but nearly half of college teachers lack effective understanding of these emerging digital teaching methods, that is, the lack of online teaching platforms and flipped classroom application teaching aspect knowledge. Therefore, college teachers need to continuously deepen their understanding of these emerging teaching methods in order to apply them to classroom teaching more effectively. 4.2 Digital Professional Development and Innovation Dimension Digital professional development and innovation examines whether teachers can pay attention to digital technology and use digital tools for knowledge sharing and innovation. From the perspectives of students and teachers, we investigate whether they can use multimedia computer proficiency in teaching. The results are shown in Table 2. Most college teachers believe that they can use multimedia computers proficiently or completely, and only a few people are not proficient in using multimedia computers. Table 2. Can you be proficient in using multimedia computer application teaching (%) Teachers

Students

Not at all

0

1.7

Not very okay

2.6

5.6

Uncertain

4.3

12.6

More okay

34.1

40.4

absolutely okay

59

39.4

Observing Fig. 2 we can find that there is a certain gap between the students’ evaluation of teachers’ use of multimedia computers for teaching and the teacher’s selfevaluation results. That is to say, the proportion of students who believe that teachers are more able or completely able to use these digital teaching media for teaching is lower than the teacher’s self-evaluation ratio. From the students’ point of view, they are not very satisfied with the teacher’s teaching skills using these traditional teaching media in teaching. This shows that the ability of college teachers to use digital tools for knowledge teaching innovation needs to be further improved. 4.3 Digital Value and Pursuit Dimension Digital value and pursuit are teachers’ recognition of digital education and the ability to participate in the construction of digital campuses. Surveying the use of new media communication technology or digital resources for digital teaching topics by college teachers, the results are shown in Table 3: 8.7% of college teachers said that they are completely incompatible with the regular use of new media communication technologies or digital resources for teaching topic research. In fact, 21% of college teachers said that

Construction of University Teachers’ Digital Competency

Not at all

Teachers

Students

Not at all

Not very okay Uncertain

Not very okay Uncertain

0% 3% 59%

4%

More okay

65

2%6% 13% 39%

More okay

34%

absolutely okay

absolutely okay

(a)

40%

(b)

Fig. 2. Can you be proficient in using multimedia computer application teaching

they are not in line with the fact that they often use media to conduct research on teaching topics, and only about 20% of college teachers said that they are quite or completely in line with this situation. In addition, 45.3% of college teachers are unsure. Although the choices of this part of college teachers are rather vague, it can explain from another level that this part of college teachers use new media communication technology or digital resources for teaching and research. The situation is not ideal. Table 3. University teachers use new media communication technology or digital resources for digital teaching (%) Frequency

Percentage (%)

Totally inconsistent

26

8.7

Not very consistent

63

21

Uncertain

136

45.3

More in line with

60

20

Totally Suitable

15

5

Abundant Internet resources provide a certain guarantee for college teachers to carry out teaching research. It can be seen from Table 3 that the ability of some college teachers to use new media communication technology or digital resources for teaching special research is not prominent. They may not be good at or have never used relevant media for teaching research. It shows that college teachers’ performance in using digital value for teaching research and construction of digital education is not ideal.

66

Q. Yu

5 Conclusion Based on the research of the digital competency model of college teachers based on new media communication technology, this paper found that the connotation of college teachers’ digital competency is mainly divided into three dimensions: digital technology capability, digital professional development and innovation, and digital value and pursuit. Digital technology capabilities include tool application, resource production, resource management and environmental creation; digital professional development and innovation cover five specific aspects: active learning, teaching research, communication writing, critical thinking, and innovation ability; digital value and pursuit are expressed in technology identity, ethics, social service and teaching exploration. In order to improve the digital competence of college teachers, it is necessary to improve the understanding of college teachers’ digital competence, and improve the education and training system of college teachers and the digital competency assessment and incentive mechanism.

References 1. Joni, T.R.: The Prospect of Teacher Professionalization Under Law Number 14 Of 2005 On Teachers and University Lecturers. Jurnal Ilmu Pendidikan 15(1) (2016) 2. Nihuka, K.A., Voogt, J.: Collaborative e-learning course design: Impacts on instructors in the Open University of Tanzania. Australas. J. Educ. Technol. 28(2), 232–248 (2018) 3. Venkatesh, V., Rabah, J., Fusaro, M., et al.: Factors impacting university instructors’ and students’ perceptions of course effectiveness and technology integration in the age of web 2.0/facteurs Influant la Perception De L’efficacité Du Cours et De L’intégration De la Technologie L’ère Du Web 2.0. Mcgill J. Educ. 51(1), 533 (2016) 4. Berger, P.: Beyond plain acceptance or sheer resistance: A typology of university instructors’ attitudes to students’ media use in class. Teach. Teach. Educ. 67, 410–417 (2017) 5. Jang, S.J., Chang, Y.: Exploring the technological pedagogical and content knowledge (TPACK) of Taiwanese university physics instructors. Australas. J. Educ. Technol. 32(1), 107–122 (2016) 6. Castaño-Muñoz, J., Kreijns, K., Kalz, M., Punie, Y.: Does digital competence and occupational setting influence MOOC participation? Evidence from a cross-course survey. J. Comput. High. Educ. 29(1), 28–46 (2016). https://doi.org/10.1007/s12528-016-9123-z 7. Rokenes, F.M., Krumsvik, R.J.: Prepared to teach ESL with ICT? A study of digital competence in Norwegian teacher education. Comput. Educ. 97, 1–20 (2016) 8. Ali, N., Ali, O., Jones, J.: High level of emotional intelligence is related to high level of online teaching self-efficacy among academic nurse educators. Inter. J. High. Educ. 6(5), 122 (2017) 9. Rasi, P.: Media literacy education for all ages. J. Media Lit. Educ. 11(2), 1 (2019) 10. Muoz, J.C., Vuorikari, R., Costa, P., et al.: Teacher collaboration and students’ digital competence - evidence from the SELFIE tool. Eur. J. Teach. Educ. 5, 1–22 (2021) 11. Silva, J., Usart, M., Lázaro-Cantabrana, J.L.: Teacher’s digital competence among final year Pedagogy students in Chile and Uruguay. Comunicar. 27(61), (2019) 12. Martínez, J.G., Camacho, M., Gisbert, M.: Inside a 3D simulation: Realism, dramatism and challenge in the development of students’ teacher digital competence. Australasian J. Educ. Technol. 35(5) (2018)

Mixed Teaching of Linear Algebra Based on BOPPPS in Modern Information Technology Lijie Ma(B) Wuhan Donghu University, Wuhan 430212, Hubei, China [email protected]

Abstract. As an important basic course, the teaching reform of Linear Algebra will bring guidance and direction to other courses. In order to build a high-level gold course that conforms to the “once-for-both-sexes” principle, Wuhan Donghu University has established an online course on the superstar learning platform, and has adopted the teaching mode of BOPPPS off the line, fully integrating the Linear Algebra course with the professional courses, to Form New disciplines of cross-fertilization. Keywords: Linear Algebra · Teaching reform · Mixed teaching

1 Introduction As an important basic course for economic management and Science and engineering majors in colleges and universities, Linear Algebra has certain theoretical depth and wide application. It is one of the most important university mathematics courses, a must-take course for master’s Postgraduate Admission Test, the basis of subsequent courses, the carrier of scientific and technological innovation, and a powerful tool for solving practical problems. As a basic mathematics course in the field of engineering science, Linear Algebra plays an important role in the field of higher education [1]. The course can cultivate students’ ability of abstract thinking, logical reasoning and calculation. Therefore, to master the theory and method of this course is of great benefit to the students’ study of the following courses and their further study. Linear Algebra is an important basic subject in higher education, which plays an important role in engineering and other non-mathematics majors. With the advent of the information age, linear Algebra in many fields of research status is also rising [2].

2 The Present Situation of Hybrid Teaching in Wuhan Donghu University Wuhan Donghu University is an application-oriented university. The teaching reform of the course of Linear Algebra is highly valued by the school leaders, and good results have been achieved. The traditional teaching mode of blackboard writing cannot meet the needs of the new era, and cannot train the students’ self-learning ability [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 67–73, 2022. https://doi.org/10.1007/978-3-030-99616-1_10

68

L. Ma

Through the continuous teaching reform practice of the teachers in the course group, we have constructed a Linear Algebra course with the characteristics of applied education and in line with the actual needs of students. Its main features are: 2.1 Multi-measures and Precise Treatment——A Stratified Teaching Method Linear Algebra courses in our school is divided into key classes, general science and engineering classes, general economic and management classes and vocational college entrance examination classes. In order to carry out the teaching innovation, our school takes the finance major as the pilot, develops the elite type teaching. In order to meet the strong learning needs of this group of students, the course group has specially increased 16 h. As a result, the breadth and depth of the course have been correspondingly increased. It is deeply loved by the students and the learning effect is good, the final exam results are in the lead with other classes in the same major. For this useful teaching practice we will be in more professional promotion. General Science and General Economics and management class is the largest number of classes. Combined with different professional needs, starting from the professional, the introduction of Linear Algebra curriculum for practical use, the real curriculum into the professional. The students in the skill college entrance examination class of secondary vocational schools have a poor foundation and low motivation to study.According to the present situation of Linear Algebra teaching in higher vocational colleges and the powerful function of Matlab, this paper puts forward the viewpoint of introducing Matlab software into Linear Algebra teaching, at the same time, this paper discusses the specific operation and application of Matlab in linear algebra teaching with classroom teaching examples [4]. According to the different characteristics and bases of students, teaching students according to their aptitude in different levels ensures the quality of education to the greatest extent. 2.2 The Course Thought Politics, Establishes the Virtue to Set up the Person The combination of ideological and Political Education in the teaching of Linear Algebra and the proper combination of knowledge imparting and ideological education can not only improve students’ subject knowledge literacy, but also benefit students’ ideological construction [5]. Under the background of ideological and Political Education reform of curriculum, this paper analyzes and demonstrates the urgency of special subject research on ideological and political education of curriculum of Linear Algebra, and puts forward that teachers’ educational ideas should be remolded and scientific methods should be adhered to in the course of ideological and political work. Exploring the ideological and political elements of Linear algebra curriculum from the multi-angle of mathematical culture [6].

Mixed Teaching of Linear Algebra

69

2.3 Pay Attention to the Process Evaluation, Optimize the Examination System Improve the traditional assessment methods, the use of a variety of teaching evaluation design, combined with the student’s learning process and learning quality assessment and evaluation. Total score = usual score * 0.4 + paper score * 0.6. According to the check-in, complete the task point, group work, unit test, classroom performance and other ways to make full use of the background big data according to a certain weight, and finally give students fair and reasonable results. The paper score refers to the final examination score of the students who take part in the Unified Organization of the school. 2.4 With the Depth of Professional Integration, High Service Promote the interdisciplinary integration of mathematics theory and professional application, the formation of new engineering high-level curriculum. Make the student’s knowledge level and the application ability obtain the comprehensive promotion. 2.5 Keep up with the Times and Be Forward-Looking Teachers go out of the school to exchange with experts, online courses timely upload updated information about the curriculum at home and abroad, in line with international practice. To keep students abreast of the latest information and to keep abreast of the latest developments in the subject.

3 Teaching Practice Based on BOPPPS In order to implement on-line and off-line Hybrid Teaching, we adopt BOPPPS teaching model in this course. The teaching process of BOPPPS refers to the implementation of six-step teaching method in the teaching process. B: Bridge-in, Introduction. A survey of students’ autonomous learning on the learning-through platform. In this section, students will learn about the status of this course in Linear Algebra and its application to students in different majors. To form a preliminary understanding of the curriculum. O: Objective, learning goal. Students learn the teacher’s lesson plan and syllabus independently on the learning-through platform, and understand the learning tasks of the course, so that they know what they are doing. P: Pre-assessment. Students in the learning platform to complete the teacher assigned preview tasks and preview exercises. Through the completion of this part of the homework, to understand the difficult points of the teaching content, but also have a full understanding of the previous course. P: Participatory Learning.In the teaching process of linear algebra,a good learning atmosphere and free learning environment can help open the students’ ideas and cultivate their innovative consciousness [7]. Different from the traditional teaching method, we pay more attention to students’ autonomous learning ability in the process of blended teaching.

70

L. Ma

Such a flexible classroom, not only to strengthen the interaction between teachers and students, but also to strengthen the interaction between students, cram-type silent classroom into a dynamic and efficient classroom. However, since we carried out this teaching reform, the students’ interest in learning has improved significantly and the classroom atmosphere is good [8]. P: Post-assessment. After each class there will be a corresponding problem set, after each chapter will be a unit test. Finally, students must take the final exam organized by the school. Through layer upon layer testing, help students to master the knowledge points of the course. At the same time in the teaching process to focus on the combination of students with professional, exercise the ability of students to solve professional problems by means of mathematics, for the subsequent course of learning to lay a solid foundation. S: Summary. As a teacher, after completing a teaching task to sum up the experience and lessons in time for the next teaching material accumulation. The teaching process of BOPPS has been carried out successfully in our school for two semesters and good teaching results have been achieved. By the school leadership, peer experts and students alike.

4 Presentation of Results Teachers in the course group have been working to reform the teaching of Linear Algebra from a general basic course to an important basic course, which was rated as a top quality course in 2016 and a mixed gold course online and offline in 2019. This series of measures has brought about welcome changes. 4.1 Competition Results are Improving The National Mathematical Modeling Competition and the National Mathematical Competition for college students belong to the National Class A competition, competing with the students of the double first-class universities. The number of award-winning students in our school has increased steadily, especially in the 2019 mathematical modeling competition and the University Mathematics Competition have provincial first prize breakthrough. The number of winners of this year’s two competitions in our school continues to hold the first place among similar universities in Hubei Province. All these have a close relationship with the teachers’ hard work and the students’ efforts, as well as the continuous teaching reform and curriculum optimization in our school (Figs. 1 and 2). 4.2 There was a Marked Improvement in the Final Examination Results By comparing the two contrast charts, we found that, on the whole, due to the continuous improvement of our online courses, has achieved a certain teaching effect. Here’s how: The final grade of level 19 is slightly better. The reason is that our implementation of hybrid teaching more in line with the actual situation of contemporary college students. Compared with the traditional teaching mode, it can arouse students’ interest, help them to find out the missing and make up the missing, and expand their knowledge.

Mixed Teaching of Linear Algebra

71

10 5

10

0

0

2017

2018

2019

2017

2020

2018

2019

2020

Hubei Province First prize

Hubei Province First prize

Hubei Province second prize

Hubei Province second prize Hubei Province Third Prize

Hubei Province Third Prize

Total number of awardees

Total number of awardees Fig. 1. The situation of National University Students Winning Prizes for mathematical modeling

Fig. 2. The situation of winning prizes in the National Mathematics contest for college students

70

50 45 40 35 30 25 20 15 10 5 0

60 50 40

Good

Excellent

Medium

Pass

Flunk

30

18 Science and engineering capital 19 Science and engineering capital 18 Bachelor of Business AdministraƟon 19 Bachelor of Business AdministraƟon

Fig. 3. Linear Algebra final exam results by major comparison chart

20 10 0

19th Grade Finance Major General Class The 19th Grade Finance Major Key Class

Fig. 4. A comparison of the final examination between the experimental class and the ordinary class of finance major in Grade 19

Linear Algebra belongs to the category of Science, Science and engineering students to learn better than economic and management students (Figs. 3 and 4).

72

L. Ma

The excellent rate and good rate of the students in the experimental class are higher than other classes in the same major. However, due to the difficulty of the examination questions and the wide scope of the examination, some students may fail in the examination. This is because, his study difficulty is beyond his acceptable range, return to the ordinary management class may be more suitable for his study situation. Therefore, the choice of students for the experimental class must be careful, multi-faceted inspection. To some extent, the students’ examination results reflect the public basic courses. Because of the students’ different foundation, it is necessary to teach students according to their aptitude. At the same time, the experience of the experimental class type is worth popularizing in more specialties.

5 Conclusion The environment in which college students grow up has changed a lot compared with before. They are the new generation who have been edified by electronic products. Compared with the previous means of students to acquire knowledge is more diversified, if the former form of chalk and blackboard still cannot arouse students’ desire to learn. The traditional talent training mode cannot meet the requirements of the new curriculum reform background education market to colleges and universities, so “Linear Algebra” curriculum as an example, to carry out its teaching reform measures [9]. Only continuous innovation and progress can arouse students’ learning enthusiasm and achieve good learning effect. Therefore, contemporary teachers must keep pace with the times and improve their teaching level by means of information technology. As Director Wu Yan said, education in China cannot and should not return to its old epidemic state. All this shows that the online and offline hybrid teaching mode conforms to the development of the times and is the hot spot of our future teaching reform. As front-line educators, we cannot wait for the results of others, to make their own contributions. Therefore, our course department has set up a course group headed by famous teachers Professor Zheng Lie and professor He Wenxuan to study and practice continuously. With the strong support of school leaders, the teaching reform of Linear Algebra has achieved some success. It is shown in the following aspects: taking student learning as the center, making flexible use of online tools and resources to carry out online teaching, ensuring that teaching activities can be carried out smoothly, ensuring students’ quality and quantity, completing their academic tasks, and taking online teaching as an opportunity, promote the curriculum reform and construction, promote the transformation of teaching methods and students’ methods, and establish an efficient curriculum system of mixed teaching. However, there is still room for improvement. In this course, for example, mathematical knowledge is applied differently in different majors. Linear Algebra is not only a basic course but also an applied one. Teachers must promote the development of students only by paying attention to the cultivation of students’ practical ability and application ability in teaching [10]. Therefore, the selection of citation is very important when introducing new mathematical content. Need the mathematics teacher and the specialized class teacher to discuss together, the cooperation completes. How can the mathematics curriculum and the specialized curriculum better fusion, serves for the student specialized, this is our urgent need to solve the question. Need to teach teachers not only have

Mixed Teaching of Linear Algebra

73

a solid foundation in mathematics, but also have a certain professional foundation, this is undoubtedly a huge challenge for teachers. But there is a need to have the direction of the forward, forward momentum. We will take this first-class curriculum construction as the turning point, diligently innovates, trains more high-level talented person for the country!

References 1. Yang, Y., Xu, B.: Exploration on the teaching reform of Linear Algebra under the background of new engineering course. Industrial & Science Tribune, pp. 155–156 (2020) 2. Yang, Y.: Research on the teaching reform of Linear Algebra in universities in the era of big data, Times Finance, pp. 167–168 (2020) 3. Chen, H.: On the reform of online-offline mixed teaching of Linear Algebra, Computer Knowledge and Technology, pp. 117–118 (2020) 4. Huang, H.: Research on the application of Matlab in Linear algebra teaching of higher vocational education, China-Arab States Science and Technology Forum, pp. 164–166 (2020) 5. Tian, Y.: On-line and off-line blended teaching mode and its application to the teaching of curriculum——Taking teaching of Linear Algebra as an example, Science Fans, pp. 3–5 (2020) 6. Liu, F., Cao, X., Wang, Y.: A special study on ideological and Political Education in the course of Linear Algebra, PR World, 152–153 (2020) 7. Ma, L.: A Brief Analysis on the Cultivation of Students’ Innovative Consciousness in Linear Algebra Teaching International Symposium 2017--Social Science Management and Innovation, pp. 99–103 (2017) 8. Ma, L.: Implementation Plan on the Implementation Plan of the Linear Algebra Gold Course in Independent Colleges Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), pp. 433–438 (2019) 9. Diao, T., Wang, G.: On the teaching reform of “Linear Algebra” in colleges and universities under the background of new curriculum reform, Technology and Economic Guide, pp. 132– 134 (2020) 10. Ma, L.: Application of online and Offline mixed Teaching in Linear Algebra Course 2020 AsiaPacific Conference on Engineering Education, Advanced Education and Training, pp. 923– 926 (2020)

Analysis of the Main Characteristics of the Outstanding Offshore RMB Bond Market with Big Data Hanhan Zhang(B) Dagong Global Credit Rating Co., Ltd., Beijing 100048, China [email protected]

Abstract. The offshore RMB bond market has been developing for nearly 14 years and has accumulated massive of valuable data. The big data technology is used in this paper to make statistical analysis of the offshore RMB bond market. Studies have found that the offshore RMB bond market generally experienced four stages of development. At present, the outstanding offshore RMB bond market is mainly composed of offshore RMB interest rate bonds and offshore RMB financial bonds, showing the main characteristics of wide regional distribution, high credit rating and strong investment attractiveness. The scale and proportion of non-financial offshore RMB bonds are relatively small, showing the characteristics of relatively concentrated industry and regional distribution, relatively scattered credit rating distribution, and weak investment attractiveness. Keywords: Big data based analysis · Offshore RMB bond market · Outstanding offshore RMB bonds

1 Introduction In recent years, RMB bonds have become an increasingly popular RMB investable assets for foreign investors. Influenced by the incomplete opening of China’s capital account, the offshore RMB bond market has become the main market for foreign investors to engage in the investment of RMB bonds. At present, the offshore RMB bond market has been developing for nearly 14 years and has accumulated massive of valuable data. Therefore, the use of big data technology for analysis of the main characteristics of offshore RMB bond market is of great significance for projecting its future trend of development. This paper is mainly divided into three parts, the first part is on the literature review, which expounds the relevant concepts of big data and offshore RMB bond market. The second part is conducted with the use of big data technology and some financial institutions (the third party) to analyze the development of offshore RMB based bond market, and the differences of the conclusion were compared in two ways. The big data is used to analyze the outstanding offshore RMB bond market and draw its main characteristics in the third part. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 74–82, 2022. https://doi.org/10.1007/978-3-030-99616-1_11

Analysis of the Main Characteristics of the Outstanding Offshore

75

2 Literature Review 2.1 Concepts of Big Data and Applications Big data refers to data sets that fails to be acquired, cleaned, classified, stored and analyzed by conventional statistical software or data tools for massive structured, semistructured and unstructured data. Influenced by the explosive growth of Internet shared resources, the traditional analysis model of “sampling analysis”, “precise calculation” and “causal deduction” has gradually been replaced by the analysis model of “all data”, “trend calculation” and “correlation deduction”, namely, the big data based analysis. The core value of big data lies in the storage and analysis of massive data [1]. With the universal application of the technology of data, the data applications have been integrated into the economic and social development in various fields, promoting the transformation and upgrading of the rapid development of all walks of life and. At present, the big data has penetrated into retail, electricity, transportation, financial, medical, biology, education and other fields, as a result, the competitive advantage of enterprises mastering big data technology has become increasingly prominent in recent years [2]. Taking the financial industry as an example, the application of big data in the financial industry has been developed in banking, insurance, securities and Internet finance [3], of which the most common applications based on big data are: customer relationship management, precise marketing, financial products pricing, credit risk assessment, fraud identification, stock market prediction, risk monitoring and early warning, etc. [4]. In general, the use of big data technology for improvement of the operational efficiency of financial institutions themselves and the use of big data to develop new products and new applications have become the core competitive factors that determine the future development of financial institutions. 2.2 Concepts Related to Offshore RMB Bonds Offshore bond refers to the bond issued in the overseas market of the country with the local currency as the nominal value, which is an important part of the offshore financial market. Offshore RMB bonds refer to the Yuan-denominated bonds issued outside mainland China, and the market that provides the issuance, trading and investment of offshore RMB bonds is the offshore RMB bond market [5, 6]. The development of offshore RMB bond market has not only enriched the financing channels of domestic and foreign enterprises and provided convenience for enterprises to reduce financing costs, but also provided sounding RMB investment channels for investors (especially foreign investors) [7]. In recent years, with the promotion of RMB internationalization and the continuous prominence of RMB value, RMB bonds have become an increasingly popular investable asset for foreign investors, providing strong support for the long-term development of offshore RMB bond market [8, 9].

3 Comparative Analysis of the Offshore RMB Bond Issuance Market Based on Big Data and Traditional Data This part is conducted with utilization of big data technology to obtain the data related to the issuance of offshore RMB bonds since 2007 with July 31, 2021 as the deadline,

76

H. Zhang

including the name of the issuer, issuance date, issuance amount, exchange, country, etc. [10, 11]. At the same time, the author also compares the data obtained and cleaned by big data with the relevant data of the offshore RMB bond market provided by a leading financial data service institution in China to obtain the main development characteristics of the offshore RMB bond market since its emergence, and also analyze the main reasons for the differences between the two sets of data (Table 1) . Table 1. Offshore RMB bond issuance market Results of big data Year

Issuing number

Data provided by a financial data service agency Issuing scale (billion RMB)

Issuing number

Issuing scale (billion RMB)

2007

8

16.13

6

10.20

2008

5

12.00

5

12.00

2009

9

16.45

8

16.00

2010

28

45.15

23

35.76

2011

131

111.63

105

99.61

2012

201

128.35

112

102.25

2013

289

139.52

114

98.46

2014

567

309.82

176

227.44

2015

377

170.35

113

92.34

2016

372

123.20

48

62.39

2017

146

46.63

14

20.95

2018

270

124.51

67

76.66

2019

291

254.79

55

198.82

2020

359

289.97

95

267.01

July-2021

275

169.37

73

113.37

3328

1957.87

1014

1433.26

Total

By comparing the two groups of issuance data in the above table, it can be seen that although there are significant differences in the quantity and scale of issuance data obtained by the two methods, the trend proved to be relatively consistent as: From 2007 to 2010, the scale of issuance achieved incremental growth year by year. In 2011, there was an eruptive growth of nearly three times. In 2014, it reached the annual peak of issuance, and then dropped year by year. In 2017, it reached a stage low, and then annual growth was resumed. Therefore, the author believes that the development of offshore RMB bond market has generally gone through four stages: The development period (2007–2010), the rapid growth period (2011–2014), the depression period (2015–2017) and the recovery and development period (2018 till now).

Analysis of the Main Characteristics of the Outstanding Offshore

77

By comparing the exchange and country data obtained by the two methods, it can be discovered that the acquisition ability of overseas data and unlisted data of the leading financial data service agency in China is significantly weaker than in big data technology. For example, the two sets of issuance data in 2013 showed contradictory trends. After analyzing the data details of that year, it was found that in 2013, more enterprises issued offshore RMB bonds in the United Kingdom, Singapore, Taiwan, Germany, France and other countries or regions, and many of the bonds were not listed (non-public). Some companies are registered in tax havens such as Bermuda, the Cayman Islands and the Virgin Islands. These data are missing from the data sources of financial data services. In contrast, data on offshore RMB bonds issued in Hong Kong, China, are more consistent. It may be related to the opening of bond infrastructure connectivity between China and these countries. Therefore, in order to ensure a more accurate analysis of the offshore RMB bond market, only big data technology will be used to analyze the outstanding offshore RMB bond market (Outstanding bonds refer to the bonds that have not matured, excluding the matured part in the issuing market).

4 Analysis of the Main Characteristics of the Outstanding Offshore RMB Bond Market with Big Data This part, with July 31, 2021 as the deadline, utilizes big data technology to obtain all the current global offshore RMB bonds, and extracts the analysis factors such as the issuer, issue date, maturity date, issuance amount, outstanding amount, exchange, country of origin, country of registration, and credit rating of the bonds, etc., with the purpose to analyze the main characteristics of the outstanding offshore RMB bond market with big data. As of July 31, 2021, the global outstanding amount of offshore RMB bond stood at 481.3 billion RMB (excluding supranational RMB bonds issued by the Bank for International Settlements), covering 1,063 bonds, 167 issuers and more than 30 countries and regions. In light of bond in the large category, it can be divide into offshore RMB interest rate bond and offshore RMB credit bond. The following contents will give top priority of the big data based analysis on interest rate bonds and credit bonds from multiple dimensions such as bond type, country, industry, issuer and credit rating, in order to obtain the main characteristics of the current offshore RMB bond market. 4.1 Big Data Based Analysis of the Outstanding Offshore RMB Interest Rate Bonds As of July 31, 2021, the outstanding amount of offshore RMB interest rate bonds topped 249.463 billion yuan, accounting for 51.83% of the offshore RMB bond market, which is the main composition of the offshore RMB bond market. Specifically, offshore RMB interest rate bonds mainly refer to offshore RMB bonds issued by the Chinese government, the People’s Bank of China, policy banks of various countries, international multilateral organizations and local governments of various countries. As of July 31, 2021, the types of outstanding offshore RMB interest rate bonds mainly include central

78

H. Zhang

bank notes, Chinese National bonds (sovereign bonds), policy bank bonds and supranational sovereign bonds, accounting for 30.06%, 20.50%, 26.51% and 14.17% of the outstanding offshore RMB interest rate bonds, respectively. From the perspective of issuers, in addition to China’s three major policy banks that issue offshore RMB policy bank bonds, there are also policy banks in Germany, Canada, Korea, Austria, and Sweden, such as KFW bank, Export Development Canada, Export-Import Bank of Korea, Korea Development Bank, Industrial Bank of Korea, Oesterreichische Kontrollbank AG, and Svensk Exportkredit AB. Note: 1) SNAT refers to international multilateral organizations, including African Development Bank, International Bank for Reconstruction and Development, European Bank for Reconstruction and Development, Nordic Investment Bank, International Finance Corporation, European Investment Bank, Asian Development Bank, Asian Infrastructure Investment Bank, Central American Bank of Economic Integration, etc. 2) Others mainly refer to government agency bonds and regional government bonds. In terms of credit, as the issuers of offshore RMB interest rate bonds are mainly national and government-related institutions, their credit rating is kept with relative consistency with the sovereign rating, that is, high credit security and low risk on most occasions, thus attracting a large number of international investors. Virtually, offshore RMB interest rate bonds are high-quality RMB assets that international investors can buy directly in the international market (without entering the Chinese market). Given the low or negative yields on government bonds in most countries around the world, RMB bonds contribute a lot to portfolio returns of international investors.(Fig. 1)

80 60 40 20 -

Central bank bill

Sovereign debt

Policy Bank debt

Super sovereign debt

Other

Fig. 1. Outstanding offshore RMB interest rate bonds (unit: Billion RMB)

4.2 Big Data Based Analysis of the Outstanding Offshore RMB Credit Bonds As of July 31, 2021, the outstanding amount of offshore RMB credit bond is 231.836 billion yuan, accounting for 48.17% of the outstanding offshore RMB bond market. Based on the nature of issuer, it can be further divided into two categories: financial and non-financial.

Analysis of the Main Characteristics of the Outstanding Offshore

79

4.2.1 Big Data Analysis of the Outstanding Offshore RMB Financial Bonds As of July 31, 2021, the outstanding amount of offshore RMB financial bonds reaches 181.731 billion Yuan, accounting for 78.39% of the outstanding offshore RMB credit bonds. Sort by the total outstanding amount of the offshore RMB financial bonds in each country of registration (Fig. 2), the top five countries or areas are China, the United States, the United Arab Emirates, Cayman Islands and France respectively, among which the major issuers in the United States are financial institutions such as JPMorgan Chase, Goldman Sachs, Citigroup, Morgan Stanley and Wells Fargo. Major issuers in the UAE include Dubai National Bank of the United Arab Emirates, First ABU Dhabi Bank PJSC, Mashreq Bank PSC, etc. About 84.55% of the outstanding financial bonds in the Cayman Islands region are actually issued by QNB Finance Co., LTD. (the largest commercial bank in Qatar).Major issuers registered in France include Societe Generale SA, BNP Paribas SA, CIC SA, BPCE SA, Credit Agricole Corporate & Investment Bank SA, etc.. In terms of the types of financial institutions, the issuers are mainly commercial banks and diversified banks, followed by financial service institutions (such as securities companies), commercial finance (such as financial leasing companies) and insurance companies. In light of bond issuers, as of July 31, 2021, there were a total number of 99 issuers in the current offshore RMB financial bond market, of which there were 30 issuers with an outstanding scale of over 2 billion yuan. The top five issuers are QNB Finance Co., LTD., JPMorgan Chase Bank, First ABU Dhabi Bank PJSC, National Bank of Dubai of the United Arab Emirates and Bank of China (Macau).In addition, only six of the top 30 issuers are Chinese financial institutions. Therefore, it can be concluded that the international financial institutions has a greater enthusiasm for offshore renminbi bonds. To be more specific, financial institutions from Qatar and UAE might have more interests in offshore RMB bond, which might be influenced by the development of China’s “one

Property & Casualty Insurance Commercial Finance

Consumer Finance Financial Services

BRAZIL GUERNSEY

CHINA-TAIWAN NORWAY

LUXEMBOURG NETHERLANDS IRELAND

MALAYSIA SWITZERLAND CANADA SOUTH KOREA BERMUDA JERSEY

GERMANY BRITISH VIRGIN SINGAPORE

CHINA UNITED STATES UAE CAYMAN ISLANDS FRANCE AUSTRALIA BRITAIN HONG KONG

30 25 20 15 10 5 0

Life Insurance Diversified Bank

Fig. 2. Outstanding offshore RMB financial bonds (unit: Billion RMB)

80

H. Zhang

belt, one road”, followed by financial institutions from European countries, Malaysia, Singapore and other countries or regions. 4.2.2 Big Data Analysis of the Outstanding Offshore RMB Non-financial Bonds As of July 31, 2021, among the outstanding offshore RMB credit bonds, the outstanding amount of offshore RMB non-financial bonds stood at RMB 50.105 billion, accounting for 21.61% of the outstanding amount of offshore RMB credit bonds and 10.41% of the overall outstanding offshore RMB bonds. From the perspective of industry distribution (Fig. 4), the industry with outstanding offshore RMB non-financial bonds is dominated by real estate, followed by industries in automobile manufacturing, utilities, travel and lodging, etc. Among them, the outstanding scale of the real estate industry reached 22.534 billion yuan, accounting for 44.97% of the non-financial outstanding of offshore RMB bonds, which were all from the real estate enterprises in China (including Hong Kong, China). From the perspective of the country (Fig. 3), most non-financial issuers are from China (including Hong Kong, China), only a small amount of non-financial issuers are from other countries. Some countries’ non-financial issuers are only distributed in one industry (or actually only one company),such as Germany’s BSH Hausgerate GmbH (home products), Japan’s Central Nippon Expressway Co Ltd (industrial), France’s Air Liquide Finance (chemicals), New Zealand’s Fonterra Co-operative Group Ltd (food) and so on. Therefore, it can be seen that the popularity of insurance of offshore RMB bond among domestic and foreign non-financial enterprises proves low enthusiasm, which may be related to the slowdown of RMB internationalization in recent years and the offshore related policies.

18 16 14 12 10 8 6 4 2 0

CHINA-HK SOUTH KOREA

CHINA IRELAND

NETHERLANDS NEW ZEALAND

Fig. 3. Outstanding offshore RMB non-financial bonds (unit: Billion RMB)

Analysis of the Main Characteristics of the Outstanding Offshore

81

In terms of credit, as of July 31, 2021, there are 77 outstanding offshore RMB non-financial bonds, of which 49 bonds are with credit rating, accounting for about 63.64%, involving 26 issuers. Different from interest rate bonds and financial bonds, the credit distribution of non-financial bonds is relatively dispersed (Fig. 4), which might due to the small amount of rated non-financial issuers, the large credit difference of enterprises themselves, or partly related to the rating methodologies. Some large nonfinancial enterprises have high credit ratings, such as MTR Corporation (AA+) and Hanwha Solutions Corporation (AA), while some small enterprises have low credit ratings, such as Zhenro Properties Group (B+) and Redco Properties Group (B+).

15 10 5 0 AA+

AA

A+

A

A-

number of bonds

BBB+

BBB

BBB-

BB-

B+

B-

number of issuers

Fig. 4. Credit Ratings of outstanding offshore RMB non-financial bonds

Note: the rating results in the above figure are from S&P, Fitch and Moody, of which the symbols of Moody’s have been converted accordingly.

5 Conclusion This paper conducts analysis on the offshore RMB bond market based on big data, and it can be seen from the research that the development of the offshore RMB bond market has generally gone through four stages. Although the current market issuance has not recovered to the historical peak, the trend in recent years has been on the upward trend. In the outstanding market, interest rate bonds are the dominant part. In addition to the Chinese government, central bank and policy banks, policy banks and international multilateral organizations in other countries are also the main issuers of interest rate bonds, so the credit risk of interest rate bonds is in low level with high investment value. In terms of credit bonds, the outstanding scale of financial bonds is much larger than that of non-financial bonds, and most of the financial bonds are mainly issued by multinational financial institutions or state-owned banks, so their credit status is attractive to overseas investors. In contrast, the outstanding amount of offshore RMB non-financial bonds is small, showing the main characteristics of regional concentration (in China and Hong Kong), industrial concentration (in real estate) and dispersed credit ratings. In conclusion, as an investable asset with high global value, RMB assets still demonstrate great development space and potential in the future, especially in the non-financial

82

H. Zhang

field. In the future, with the continuous advancement of the “Belt and Road” construction, the continuous opening of the financial bond market and the continuous highlighting of the value of RMB assets, it is foreseeable that the offshore RMB bond market will usher in a new peak of development.

References 1. Mayer-Schönberger, V.: Big Data: A Revolution That Will Transform How We Live, Work and Think. Zhejiang People’s Publishing House, Zhejiang (2013) 2. White Paper on Big Data (2020). China Academy of Information and Communications Technology(CAICT) (2020) 3. Research on the typical application of big data in the financial field. Payment & Clearing Association of China (2018) 4. Luo, Z., Yu, C.: Analysis of the impact and prospect of big data application on corporate bond financing business. Guangxi Qual. Super. Guide Periodical. 8, 228–229 (2020) 5. Zhou, Y.: Offshore RMB Bonds. Citic Press Group (2013) 6. Ba, S., Guo, Y.: Research on the Development of Offshore Financial Market -- International Trend and China Path. Peking University Press (2008) 7. Chunwang, C.: Offshore RMB Bond market Development and Yield Model in Hong Kong. Shanghai Jiaotong University, Shanghai (2012) 8. Wang, Y.: Research on the Impact of Opening of Bond Market Opening on RMB Internationalization. Shanghai Academy of Social Sciences (2013) 9. Yuan Internationalization Report 2020. Institute of International Monetary Studies, Renmin University of China (2020) 10. Yang, Z.: Introduction to Big Data Technology (The 2nd edition). Tsinghua University Press. 1–5, 218–237 (2020) 11. (De) By Yves Hilpisco, translated by Yao Jun. Python Financial Big Data Analysis (2nd edition). Posts and Telecommunications Press (2020)

Application of Computer Aided Instruction in Taekwondo Teaching Tao Yang(B) Baotou Medical College Sports Department, Baotou 014010, Inner Mongolia, China [email protected]

Abstract. With the rapid development of science and technology, the tide of information has also brought huge impact on the field of education. The computer-aided teaching method is infiltrating into the classroom of colleges and universities at an unpredictable speed, bringing about changes for modern higher education. This paper mainly studies the application of computer aided instruction in taekwondo teaching. This paper first introduces the concept of computer-aided instruction, and makes clear the influence and function of computer-aided instruction in higher education. Then the computer aided technology is used to score taekwondo movements to improve the learning effect of students. It can be seen from the difference between the experimental class and the control class that the computer-aided teaching technology can make the teaching method more free, the learning atmosphere more relaxed and interesting, effectively improve students’ academic performance, stimulate students’ learning enthusiasm. Keywords: Computer aided instruction · Taekwondo teaching · Neural network · Learning effect

1 Introduction With the development of modern information technology, computer network technology not only has a great impact on people’s production and life, but also helps people to take the lead in information modernization. Since the implementation of computer network and the wide use of the Internet, information technology has had a great impact on people’s consumption concept, daily work, study and lifestyle. China’s information technology has penetrated into the international boundary, and has been highly valued and applied by people, giving full play to the potential of human wisdom and social material resources [1, 2]. Computer technology has brought great changes to human culture, and has been influencing people’s production and the development of modern education. Through the reform and opening up in the economic field and the progress of modernization, China’s educational system and mechanism have also undergone a series of reforms. From the initial restoration and reconstruction of the educational mechanism, the development, exploration and continuation of the reform of the educational constitution, to the deepening development of the current educational system reform, a series © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 83–89, 2022. https://doi.org/10.1007/978-3-030-99616-1_12

84

T. Yang

of improvements and deepening all put forward higher requirements for each educator and the educated [3]. After the 1950s, computer aided instruction (CAI) emerged from the traditional education. The rapid development of computer-based modern scientific electronic technology has laid a solid foundation for the realization and development of computer-aided teaching [4]. The United States, for example, highly recognizes the role of computeraided instruction in promoting educational reform. In The educational reform of Japan, it is emphasized to strengthen computer-aided instruction in teaching activities of all grades [5]. A scholar designed an Android statistical data analysis application, and took students majoring in statistics in the department of mathematics as the test subjects. After testing, the course results show that computer-aided Android statistical data application can effectively support statistical teaching activities and make it easier for students to understand the statistical analysis of mobile devices [6]. Therefore, CAI produced learning media can be used as an alternative media, as a learning resource for learning statistics in the classroom. However, computer aided instruction technology is rarely used in physical education, especially in taekwondo and other projects. Through the study of computer-aided taekwondo, we can explore the effective teaching methods and approaches, with the help of computer software, so that the teaching effect to achieve the optimization.

2 Path Optimization of Melt Deposition 3D Printing 2.1 Computer Aided Instruction Computer-aided instruction, as the name suggests, is the combination of computer technology and teaching. Computer-aided teaching has changed the traditional teaching mode and teaching environment, broken the classroom time, space fixed situation, the use of computers to spread knowledge, educators and learners to carry out knowledge exchange, help learners to master the necessary knowledge skills. Computer aided instruction (CAI) takes computer as the teaching medium and has the characteristics of interaction and individuation. It has strong applicability and can provide teaching information in time. It is a teaching method with simulation and communication functions. Computer aided teaching help teachers to realize the human-computer interaction, multimedia and personalized areas, on the premise of meet the standards of teaching to make teaching content more rich, more diversified teaching method, teaching form is more prominent, its applicability is widespread, is suitable for people of different ages, different subjects of education can be applied computer aided teaching. Computer-aided instruction (CAI) has increased the dissemination of knowledge and changed the means and ways of acquiring knowledge. Its development is a profound change in the field of education and gradually attracts people’s attention. This paper argues that computer-aided instruction is to assist a variety of teaching activities under the computer information technology. It can be a medium for educators to use computers to transfer knowledge to learners, educators use software equipment to make courseware, video, music, etc., or it can be a medium for learners to study knowledge by themselves. Through this medium, educators and learners carry out knowledge transmission, processing and communication of teaching activities. Computer Base Education (CBE) refers to educational activities that use computers as the main media to

Application of Computer Aided Instruction in Taekwondo Teaching

85

organize educational activities, and it mainly means that teachers use computers to carry out teaching activities, management, and evaluation. Information technology and curriculum integration can be summarized as large integration theory and small integration theory. Large integration is to integrate information technology into the whole curriculum, change the curriculum content and structure, and change the whole curriculum system: small integration is to integrate information technology and subject teaching, and information technology as the main tool, medium and method into all levels of teaching [7, 8]. 2.2 Motion Quality Scoring Based on Computer Aided Taekwondo postures are made up of continuous movements with a strong time relationship between front and back movements. Therefore, the LSTM neural network in deep learning can be used to mine the coherence between athletes’ movements, especially the changes of characteristics such as speed and strength. LSTM has three gating mechanisms, thus effectively solving the problems in the recursive neural network, and can be effectively applied to the research on the quality evaluation methods of potential postures [9]. Specifically, the application of LSTM to mining the consistency of product potential action is mainly determined by the structural characteristics of LSTM itself: The new time step enters the LSTM neuron through the input gate, and the short-term memory output of the previous time step is used for operation, and the more valuable information of the time step is stored in the long-term memory [10]. In boxing, for example, wrist movements change much faster and harder than shoulder movements, so the importance of wrist features can be highlighted by minimizing the shoulder features in the input door. Through the forgetting gate, the short-term memory formed in the previous time step is selectively forgotten, and the memory that contributes most to the final result is added to the long-term memory [11]. For example, when an athlete throws a punch with his right arm, each joint of the right arm is faster than each joint of the left arm, which contributes the most to the model’s final decision. As a result, every acupoint on the left arm is weakened. Through the output gate, noise and features with little contribution are excluded to output more important short-term memory (namely, the strength, speed, angle and other information of the latest moment), while maintaining long-term memory (namely, the overall posture information of the action) [12]. Through the above three gating mechanisms, LSTM can not only automatically eliminate the interference of irrelevant features, but also extract more obvious time features between frames. For example, when two athletes fight, they spend the same time, but one moves at a constant speed, and the other moves slowly and then fast. LSTM can discover the differences in speed and strength between the two actions, thus different evaluation results are given. Therefore, on the basis of obtaining human bone joints and extracting features, this method uses LSTM network to extract depth time features of potential product samples, so as to finally obtain objective scores. Specifically, through the training of LSTM network, a model that can effectively extract the characteristics of human movement time will be obtained.

86

T. Yang

In the LSTM network structure, the data of the data layer is the time step of each sample, which is input to the LSTM layer. Bilayer LSTM neurons are used here, which has the advantage of being able to extract the characteristic changes of velocity, intensity and angle in time series at a deeper level. Then the features extracted by the LSTM layer are mapped to a fully connected layer. Finally, the score was output by two neurons in the evaluation layer. The advantage of this structure is that the feature extraction of the model is more sufficient and the score is more accurate, while the disadvantage is that the training time is too long and the convergence rate of the loss function is slow, which is caused by a large number of LSTM layer and fully connected layer parameters.

3 Experiment of Teaching Activities 3.1 Experimental Methods Among the classes offering taekwondo public elective courses in grade 2020, two classes are selected by random sampling. Experimental class A and control class B are set respectively. Computer-aided teaching mode is adopted for experimental class A, while traditional teaching mode is adopted for control class B. Students in both classes have no basic knowledge of taekwondo. They are taught by the same teacher and try to keep the same teaching process. In terms of assessment method, blind examination by experts outside the school was adopted to evaluate the skills of the two classes. Finally, according to the experimental results, the application effect of flipped classroom in taekwondo public elective courses in colleges and universities is analyzed, and corresponding suggestions are put forward. 3.2 Teaching Contents and Objectives Experiment teaching content includes two parts: the part is a basic hand type, step type, taekwondo sporting potential tai chi chapter, taekwondo kick, footwork, another part is theory knowledge, basic knowledge of the teacher chose taekwondo class as a weekly compulsory course, theory basic background knowledge can be a tae kwon do, tae kwon do competition rules, tae kwon do technical training methods. The teaching objectives of experimental classroom are mainly set according to the problem videos fed back by students, common problems set with knowledge and skills as objectives, and the process and method objectives set for personality problems. Before class, teachers have an understanding of the degree to which students master the skills of this class, and can set teaching objectives more pertinently, so that the key and difficult points of teaching focus more on students’ development. It makes the teaching objectives focus more on students. 3.3 Data Statistics and Processing The data obtained from the experiment are analyzed and sorted out by the mathematical statistical analysis software SPSS22.0. According to the statistical data results, the differences between the results of the experimental class and the control class are further studied, and the styles of the two classes are analyzed.

Application of Computer Aided Instruction in Taekwondo Teaching

X −μ

t=

87

(1)

√σx n−1

Where, t is the statistical deviation between the sample mean and the population mean; X is the sample mean; μ is the population mean; σx is the sample standard deviation; n is the sample size. t=

X1 − X2

(2)

σx2 +σx2 −2γ σx1 σx2 1 2 n−1

X1 , X2 is the mean of the two samples; σx21 , σx22 is the difference between the two samples; γ is the correlation coefficient of related samples.

4 Teaching Experiment Results 4.1 Comparison of Taekwondo Assessment Results Table 1. Comparison of examination results between control class and experimental class Etiquette

Basic skills

Posture routines

Self-technology

Control class

10

14

25

26

Experimental classes

10

16

29

28

As shown in Table 1 and Fig. 1, after eight weeks of study under different education modes, students of the two classes have basically the same performance in taekwondo spirit and etiquette. In terms of basic skills, students’ learning effect of basic skills under the computer-aided education mode is better than that under the traditional classroom education mode. According to the comparison of the average score of the two classes, it is found that the students in the computer-aided education mode are more accurate and skilled in the posture exercise than those in the traditional classroom education mode, and they are more free in the speed and strength of the movement. 4.2 After the Experiment, the Sports Attitude of Experimental Class Changed As shown in Fig. 2, the experimental class has greatly improved its attitude towards physical exercise after adopting the computer-aided teaching mode. This is mainly because the computer-aided teaching mode is relatively novel, the teaching method is unique, and it is more suitable for students’ self-study characteristics, and students have a certain control over learning.

88

T. Yang

Fig. 1. Comparison of examination results between control class and experimental class

35 30

28.93

Before the trial

30.26

After the experiment

25

Value

20 15 10 4.32

5

5.51 2.297 0.028

0 Mean value

Standard deviation

T value

P value

Project Fig. 2. Physical exercise attitude before and after the experiment

5 Conclusions By using different teaching methods for the two groups of subjects and comparing the effects of computer-aided teaching method and traditional teaching method on Taekwondo learning, we can draw the following conclusions: after the experiment, the students in computer-aided teaching mode are higher than the control class using traditional teaching methods in basic skill learning, kicking posture practice and leg technique. In

Application of Computer Aided Instruction in Taekwondo Teaching

89

the aspect of attitude towards physical exercise, there are significant differences in the attitude of students using computer-aided teaching before and after the experiment. The above results show that the computer-aided teaching mode is more conducive to students’ learning of Taekwondo.

References 1. Wishnowski, L.A., Yu, C.T., Pear, J., et al.: Effects of computer-aided instruction on the implementation of the MSWO stimulus preference assessment. Behav. Interv. 33(1), 56–68 (2018) 2. Aguilar, M.A.A., Coloma, R.R., Marias, D.B.R., Patacsil. F.F.: Development of dynamic computer-aided instruction for the least learned topics in national certificate II animation. Int. J. Adv. Trends Comput. Sci. Eng. 10(1), 82-91 (2021) 3. Crooks, R.: Critical failure: computer-aided instruction and the fantasy of information. IEEE Ann. Hist. Comput. 40(2), 85–88 (2018) 4. Gao, Y.: Computer-aided instruction in college english teaching under the network environment. Comput. Aided Des. Appl. 18(S4), 141–151 (2021) 5. Bariham, I., Ondigi, S., Mueni, N., et al.: Senior high schools’ students’ perception of computer-aided instruction in North East Region of Ghana. Int. J. Adv. Res. 6(8), 40–47 (2019) 6. Eilouti, B.: A hybrid framework of computer-aided instruction and problem-based learning for metamorphic design pedagogy. Int. J. Des. Educ. 13(2), 29–46 (2018) 7. Julius, J.K.: Enhancement of chemistry self-efficacy of students using computer aided instruction among secondary school learners in Kenya. Int. J. Innov. Educ. Res. 6(8), 79–90 (2018) 8. Guo, W., Wang, L.: Application of the computer aided instruction in the english composition correction. IPPTA Quart. J. Indian Pulp Paper Tech. Assoc. 30(8), 150–157 (2018) 9. Srivastava, S., Lessmann, S.: A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Solar Energ. 162, 232–247 (2018) 10. Ergen, T., Kozat, S.S.: Efficient online learning algorithms based on LSTM neural networks. IEEE Trans. Neural Networks Learn. Syst. 99, 1–12 (2017) 11. Zhao, H., Sun, S., Bo, J.: Sequential fault diagnosis based on LSTM neural network. IEEE Access 6(99), 12929–12939 (2018) 12. Zhuge, Q., Xu, L., Zhang, G.: LSTM neural network with emotional analysis for prediction of stock price. Eng. Lett. 25(2), 167–175 (2017)

Research on the Application of Artificial Intelligence Technology in News Space’s Production Shuang Wang(B) School of Literature and Journalism, Shandong University of Finance and Economics, Jinan, Shandong, China [email protected]

Abstract. As a technology to study the behavior law of human intelligence, artificial intelligence (AI) constructs artificial systems with certain intelligence ability to complete the work that can only be qualified by human intelligence in the past, and it has been applied to many fields, including journalism. News space is composed of physical space, meaning representation system and social relationship network, and the production of news space is a more macroscopic category than the production of news content. The development of AI technology has expanded the space and time range of news transmission, the significance of space production is gradually highlighted. The application of AI technology in the production of news space, is not only shows the intelligence in the production of news content in the corresponding physical space, but also shows the diversification of production subjects and production modes in the production of virtual space and meaning representation space, the scenario-based of news space narrative, and the authenticity of users’ cognition of virtual news space under the control of algorithm. Keywords: AI technology · Production of news space · Algorithm gatekeeping · Reality of news

1 Introduction Marx’s idea of “eliminating space with time” is not specific to media technology, but it is enlightening to understand media technology. [1] In the past, when studying “time” and “space”, the role of “space” was ignored or even covered because of over-believing in “time”. [2] However, Media is not only the extension of people, but also the extension of the time and space of information transmission. The key to grasp the complexity of media activities is to expand the “objective view of time and space” of information dissemination to the “meaningful view of time and space combined with virtual reality”. From this point of view, the media-based news production is also the production activity of news meaning space. “Space production “ was pioneered by Henri Lefebvre, who proposed the core idea that “space is the product of society” and constructed a theoretical framework composed of space practice, representation of space and representation of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 90–98, 2022. https://doi.org/10.1007/978-3-030-99616-1_13

Research on the Application of Artificial Intelligence Technology

91

space. [3] Space production can be divided into three dimensions: physical space, spiritual space and social space. [4] In the era of mass communication, people focus more on the time and physical space dimensions of news production and communication for practice and research, but not enough on the spiritual space and social space of news production. News space is composed of physical space, representation system and social relationship network, and the production of news space is a more macroscopic category than the production of news content, shown as Fig. 1. After nearly 70 years of development, AI technology “changes media content production at the micro level, reconstructs media structure and layout at the medium level, and helps media participate in social governance at the macro level" [5]. How to apply AI technology in the production of news space, and what changes it brings are practical problems worth further discussion.

Fig. 1. The relationship between physical space, representational meaning space and news space

2 Intelligent Production Promotes the Diversification of Production Subjects and Modes of News Space The infiltration of AI technology into news production firstly shows that the AI robot takes part in the construction of news space as the production subject. The production subject of artificial news is human subject, and subjectivity naturally refers to human subjectivity; The direct production of intelligent news is AI (AI network), which shows “quasi-subjectivity”. [6] Intelligent news production has brought an impact on the traditional concept of “man is the subject of media”. The production subject of news space has changed from professional news practitioners to AI robots, social people and human-machine collaboration. The news production under multiple subjects also has the characteristics of distribution, fragmentation, and continuous tense. [7] Audiences, intelligent machines and news practitioners are integrated into the production subject. Through intelligent reconstruction of news discovery and information collection, data analysis and selection of topics, news generation and polishing, news control and distribution, innovative intelligent news products such as VR/AR news, AI anchor and sensor news are presented. [8] The application of AI technology in information dissemination has further amplified the dominance of users in news production, shown as Fig. 2. The

92

S. Wang

Fig. 2. The main production subjects of news.

intelligent production supported by the big data analysis of users’ behavior overturns the limitation of news production materials in time and space. Around the acquisition of users’ attention, the production mode of news meaning space under multiple subjects also presents diversified characteristics. With the help of AI software, the news writing robots can automatically generate news releases in a short period of time and quickly push them to target users, and its news space production process mainly includes three steps: data retrieval, data analysis and template matching. The professionalism, authority and discourse power of journalists on news content are decomposed by users and their behavior data, but the production categories of news space with strong sense of presence and context, which rely on inherent data shortage such as investigation and in-depth interview, still cannot be completed in a short time by relying on AI. News production and news always aim at people themselves. The ultimate subject of news production can only be the human subject, and the human is the ultimate bearer of various responsibilities in news activities. [9] Therefore, in contrast, human-machine collaboration is a news production mode in which human intelligence and AI work together. It can not only make use of the advantages of AI in data retrieval, analysis, and presentation, but also consider the “human perspective”, social situation and emotional expression brought by human intelligence.

3 Virtual and Objective Intelligent Scenes Reshaping the "Reality" in News Spatial Narrative As the goal of the production of news space, human body is the basic dimension to realize the truth of news. The body senses regulate the audience’s judgment on the truth of news. [10] The reality of news is presented by media technology, “the ‘experience truth’ at the level of media technology, the ‘acceptance of truth’ at the level of cognitive psychology and the ‘negotiation truth’ at the level of power relations” [11] all come from the scene construction of objective social reality by media technology. The transformation of media technology brings different news reality perception to users. [12] In the traditional sense, the physical scene is the main support for the survival of news space, and the print media forms such as text, pictures, audio and video are the subjective reality that users construct in the physical space and correspond to the objective social reality. The integration of virtual reality, augmented reality, holographic imaging and other technologies with the

Research on the Application of Artificial Intelligence Technology

93

production of news space makes the scene construction of news space from plane to three-dimensional, from physical space to virtual space, from the presence of reporters to the presence of users, from the producer leading to the joint construction of users, shown as Fig. 3. In VR news, the audience can watch the news from the perspective of the first person, experience different roles through the real-time manipulation of the feeling complex “replacement” and gain practical individual experience in the interaction with the news situation. [13] AI technology has promoted the seamless integration of virtual and objective news scenes, free switching, and visual transfer of symbolic meaning.

Fig. 3. The sources of users’ subjective reality

AI technology makes the physical space, virtual space and symbolic space in news production reconstruct the news space narrative together, and the hyperreal simulation blurred the boundary between the real and the virtual. [10] Physical space is the real reference source for the production of news space, while virtual space is the story space built by means of media technology and virtual scenes or virtual and real scenes, while symbolic space is the key to the news value of physical space and virtual space. In terms of the selection criteria of news events, in the traditional sense, the selection criteria of real news events are those social events that can attract public attention and could convey value. The application of AI technology extends the timeliness of real news selection and enhances the meaningful experience and interpretation of events. From the perspective of function, relying on traditional print media technology, news space lays more emphasis on real values and real-time news transmission. The application of virtual reality and AI technology in news production makes it pay more attention to users’ experience of reality, based on conveying news and information of real meaning. From the perspective of value transmission, in the traditional sense, the values transmitted by the news truth are the mainstream social values checked by the artificial or institutional, while the values transmitted by the news truth based on the algorithm are more diversified, personalized and even entertaining.

4 The Production of News Space Based on Algorithm Gatekeeping Reconstructs Users’ Information Cognition With the help of media technology, news space brings social cognition to users. Social responsibility theory advocates that news should abide by the standards of truthfulness,

94

S. Wang

accuracy, objectivity, neutrality, and trustworthiness. Gatekeeper is the key for news media to realize this social responsibility, and its most prominent role is to influence the audience’s cognition. In the age of mass communication, professional gatekeepers will influence what happens in the society by selecting and combining news information through agenda setting and opinion leader guidance, and then shape the social reality. In the age of mass interpersonal communication, individual gatekeepers can construct social reality through social relationship network and become information gatekeepers together with professional gatekeepers. News reality enters the stage of network reality from classic reality. “Deep synthesis” technology brings people into the era of virtual existence. [14] Machine gatekeeping, which is characterized by algorithm-based gatekeeping and human-machine collaborative gatekeeping, has become an important tool for shaping individuals and society’s cognition of news truth in the era of AI. Algorithm gatekeeping has shifted the gatekeeper role from artificial to symbiosis between artificial and machine, the gatekeeping standard has shifted from “true, accurate, objective, neutral and reliable” to “up-to-date, popular, interactive and correctly oriented” [15], and the core of the gatekeeping has shifted from editing and specialization to data and individuation, shown as Fig. 4.

Fig. 4. A gatekeeper in the age of AI

Algorithmic gatekeeping technology not only brings convenience to users’ personalized information needs, but also continuously constructs and strengthens users’ cognition of social reality. The algorithm control process mainly involves three links, which are algorithm capture and generation, algorithm filtering and algorithm distribution. [16] To be specific, firstly, AI technology assists news content producers to grab news hot spots, find clues and collect information from massive users’ behavior data, so as to provide production materials for news writing robots or artificial news content producers to create news space. Secondly, algorithmic filtering based on user attention drives

Research on the Application of Artificial Intelligence Technology

95

automated organization and arrangement of media presentation of news content. The algorithm classifies and sorts the stories according to pre-set indicators such as theme, timeliness, credibility, and news value, and displays them on the website. [17] Thirdly, users click, browse, read, watch, comment, and forward news content according to their personal preferences, which brings data sources for users’ social network gatekeeping and algorithmic gatekeeping. From the perspective of information screening of virtual community interactive activities, the news content that can attract a large number of users to join and participate in the interaction is more likely to become hot, and the discussion of hot types also lays a foundation for the recommendation of the community group algorithm. From the perspective of algorithm gatekeeping, the algorithm recommendation based on users’ personal preference of network trace data will actively and continuously refine to meet users’ preferences. Therefore, the convergence of the news content obtained by users through the network information port is increasing, and the probability of the news content not actively retrieved or clicked by other users appearing in their information port is greatly reduced. In the context of algorithm gatekeeping, the types, topics and forms of news content obtained by users tend to be unified, simplified and consistent, and thus the social reality of users’ subjective identification is constructed.

5 AI Algorithms for Making News There are generally three strategies for making news recommendations [18]: 5.1 Content-Based Recommendations It can also be called recommendation based on user portrait, which means that according to the user’s historical click records, the user’s preferences are summarized, that is, the user portrait, the similarity between each news and the user portrait is calculated, and the news with the highest similarity is recommended to the user, such as Table 1. Table 1. A qualitative recommendation algebra 13 o

Another good book. A better book Don’t know

Warning Another uninteresting book. Don’t know

Don’t know Don’t know Don’t know

5.2 Collaborative Filtering Recommendation It is to find a group that is like the user’s interests, and then recommend the group’s favorites to this user.

96

S. Wang

5.3 Popular Recommendation Another commonly used method for news recommendation is popular recommendation [18]. This is to set a time window to count the clicks of all news in the past period and recommend the news with the most clicks to users. The algorithm is as Table 2, Table 3 and Table 4. Table 2. A template for a quantitative recommendation algebra

1 o o o

o

Table 3. A quantitative recommendation algebra

o

1

o

o

o

o

o

o

o

o

Table 4. Another quantitative recommendation Algebra

o

1

-1

o

o

o

The question is what values to insert in the matrix in the cells now containing question marks. The simplest alternative is the one shown in Table 3. Another realization of is represented by the matrix in Table 4. We will in the experiment section below concentrate on the simpler algebra defined in Table 3 and defer further discussion of more complex algebras.

Research on the Application of Artificial Intelligence Technology

97

6 Conclusion News space is an important way for people to contact, understand and transform the world. AI is deeply integrated with journalism and fully embedded in the industrial chain of “content production - channel distribution - user consumption”. The influence of news content on users’ social cognition has changed from plane to three-dimensional, and from the fact reflection of news content to the sense of scene reality of news space. How to give full play to the value leading role of AI technology in the production of news space is an issue worthy of continuous exploration. Acknowledgement. Supported by: The Chinese social science foundation project art special general project “Study on the guiding mechanism of the values of mass cultural products in China” (2019BH01004).

References 1. Zheng, B., Ye, J., Print, F.: The telegraph to the internet-the historical evolution of the theory about the concept of media technology of marxist. Journalism Bimonthly 02, 20–28 (2016) 2. Yuan, Y.: The space imagination of the communication studies. Journalism Commun. 01, 45–50 (2006) 3. Lefebvre, H.: Translated by D. Nicholson Smith. The Production of Space: (Original work published 1974), pp. 33-39. Blackwell, Oxford (1991) 4. Chen, B., Song, S.: A study of the virtual cultural space production and its dimensional design-based on lefebaugh’s “space production” theory. J. Shandong Univ. (Philos. Soc. Sci. Ed.) 01, 37 (2021) 5. Huang, C., Ke, X.: Three dimensions of the development of the media industry driven by artificial intelligence technology. Modern Publishing 03, 43 (2021) 6. Yang, B.: Rediscussing on the subjectivity of artificial intelligence news’ production. Press Circles 08, 21 (2021) 7. Peng, L.: “Breaking the wall” and reconstruction of news ecology in the digital age. Mod. Publishing 03, 17 (2021) 8. Linghu, K., Xue, J.: News production in the age of smart media: integration Reconstruction and innovation. Chin. Editor J. 03, 71 (2021) 9. Yang, B.: Rediscussing on the subjectivity of artificial intelligence news’ production. Press Circles 08, 25 (2021) 10. Tang, Z., Zhan, C.: The concept, principle and application value of artificial mind news. Journalism Lover 02, 16 (2021) 11. Hua, W.: From interpretation to embodiment: the reproduction of news reality by virtual reality technology. Press Circles 11, 86 (2020) 12. Yang, Q., Zhou, C., Concepts, C.: Interpretive clusters and practice approaches of “journalistic truth” in the digital age. Press Circles 08, 12 (2021) 13. Hua, W.: Boundary breakthrough and reconstruction of truth: the authenticity logic of VR news. Editorial Friend 02, 71 (2021) 14. Zhao, G.: The technological logic of “deep synthesis” and the revolution of communication ecology in the artificial intelligence era. Press Circles 06, 65 (2021) 15. Wang, X.: investigating the gatekeeping criteria of weibo “hot search”: from a critical algorithms studies perspective. Chinese J. Journalism Commun. 07, 26 (2020)

98

S. Wang

16. Yong, H., Zhou, L.: The theory of gatekeeping and the reconstruction of modern society. News Writ. 08, 47 (2021) 17. Deluliis, D.: Gatekeeping theory from social fields to social networks. Commun. Res. Trends 01, 4–23 (2015) 18. Coconut, L.: The two-sidedness of information dissemination under algorithmic news promotion technology. New Media Res. 11, 43–46 (2018)

Risk Prediction and Treatment in Enterprise Management Based on Ant Colony Parallel Algorithm Kaixin Shi(B) TOJOY Wisdom Enterprise Service Co., Ltd., Shanghai, China [email protected]

Abstract. As the global economy continues to grow, the business scope of enterprises is further expanded, the environment will become more complex, the risks they face will increase, and the uncertainty will gradually increase. If an enterprise fails to manage risks properly, it will not only affect the normal operation of the enterprise, but may even lead to bankruptcy. This paper aims to study the risk prediction and processing in enterprise management based on the ant colony parallel algorithm. Based on the analysis of the current situation of risk prediction research at home and abroad, combined with the function of risk prediction and the principle of risk prediction index selection, a risk prediction system is established and reused. The parallel and distributed characteristics of ant colony algorithm establish a risk prediction model to predict enterprise management risks. The prediction results show that the relative error value of all samples is controlled below 3%, and the minimum error value is only 0.86%. Therefore, the ant parallel algorithm model can meet the accuracy requirements of enterprise management risk prediction. Keywords: Ant colony parallel algorithm · Enterprise management · Risk prediction · Risk treatment

1 Introduction With China’s entry into the WTO, enterprises are facing unprecedented opportunities and challenges. At the same time, it also leads the international economic environment, promotes the trend of economic globalization, informationization, and commercialization, and promotes the transformation of business risk management models [1, 2]. Effective management and control of uncertain factors in the operation and management process avoids the possibility of deviation between actual income and expected income and loss in the process of operation activities [3, 4]. Researchers at home and abroad have put forward many risk prediction theories and models, which is a reference that cannot be ignored in the further study of risk prediction models in this article. Some scholars have innovated and improved the multivariate model. This model was later recognized by many scholars, widely studied and applied [5]. Some scholars put forward the idea of using logistic regression method to build a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 99–106, 2022. https://doi.org/10.1007/978-3-030-99616-1_14

100

K. Shi

risk prediction model on the basis of previous research, combined with the existing risk prediction model, using macroeconomic viewpoints and concepts to do it. The high point of their model is the ability to properly isolate inappropriate alarms and appropriately consider the timeliness of financial data [6]. These two methods are developed on the basis of cumulative probability theory and are often used to estimate the maximum probability, thus surpassing the limitations of linear model assumptions in previous studies [7]. On the basis of the multivariate analysis method, logistic regression method, and BP neural network method studied by the predecessors, as well as the research results of the predecessors, a variety of mixed risk prediction models have been successfully introduced. In addition, empirical research on data samples shows that the risk prediction of the mixed prediction model is more accurate than the single prediction [8]. This paper constructs a risk prediction system based on the analysis of the characteristics of ant colony algorithm, the function of risk prediction and the principles of risk prediction index selection, and uses the ant colony parallel algorithm to establish a risk prediction model, and finally predicts the enterprise management risk.

2 Risk Prediction and Treatment in Enterprise Management Based on Ant Colony Parallel Algorithm 2.1 Features of Ant Colony Algorithm (1) Parallel distributed computing All ants search multiple points in the solution area independently and unattended at the same time. This is an inherently effective parallel search algorithm because it is very useful for parallel applications. One method is that the pheromone is distributed on both sides of the structure graph, and each ant can construct and solve according to the pheromone state of the current node, and there is no need to control the core; on the other hand, the algorithm has strong robustness, because when one or more ants have stopped running, the entire bee colony system can continue to maintain normal operation [9, 10]. (2) Powerful global optimization capability Using a random group of ants instead of a single bee will increase the algorithm to find the best solution in the world. In addition, the use of probability criteria instead of deterministic criteria to introduce search will directly cause the algorithm to deviate from the local optimization. However, the traditional optimization algorithm is very sensitive to the selection of the initial value and the replacement step size, and it is difficult to escape from it if it is within a local optimization range [11, 12]. (3) Strong adaptability Ant colony algorithm does not have special search space requirements, such as the ability to generate continuity or objective function, or precise mathematical description of objective and constraint functions.

Risk Prediction and Treatment in Enterprise Management

101

2.2 The Function of Risk Prediction (1) Crisis identification function. For companies that have fallen into a crisis, risk prediction can guide them to find the source of the crisis and the nature of the problem. This helps companies to prescribe the right medicine and solve the dilemma. If forecasting systems are not aware of corporate risks, they can also analyze data and information. (2) Adjustment function The risk prediction of the enterprise immediately corrects the decision-making errors in the daily production and operation of the enterprise, and provides a basis for the managers to make correct decisions and proposes strategies. At the same time, timely and targeted changes can be made to eliminate the dross of the enterprise. (3) Control function When an enterprise is in a critical juncture, it can effectively help the enterprise find the right direction in time, adjust the first wrong path, and formulate targeted measures for the enterprise. Even the managers of endangered companies can keep abreast of the company’s situation in real time. 2.2.1 Principles for the Selection of Risk Prediction Indicators (1) Systematic The principle of the system is to find enough factors that affect business development from the perspective of the entire enterprise, analyze the relationship between them, and identify the key factors affecting growth, mutual influence and mutual restriction. Therefore, when making risk predictions, the data integrity of the monitored items must be strictly guaranteed to effectively ensure the accuracy of the analysis results. (2) Scientificity Financial management theory is the basic principle of analyzing the financial risk prediction of enterprises. Based on the theory of financial risk management, analyze the financial status of the enterprise, discover the reasons for the existence of risks, and establish a financial forecast risk management index system. The combination of different indicators scientifically designed helps to identify the causes of risks and make scientific decisions. (3) Dynamic predictability It mainly analyzes various data during business operation to predict possible future results. Since the data generated in different stages of operation changes, the data of the entire process stage should be analyzed, and the current and historical data status should be properly considered, and the results should be fully evaluated. When we consider these monitoring indicators, we must pay special attention to how to distinguish and establish a prediction and rating system.

102

K. Shi

2.3 Establishment of Risk Prediction System (1) Solvency indicators Debt solvency is closely related to corporate financing risks. When repaying debts, they can repay their principal and interest in time, and the company also has a strong solvency. The level of debt solvency is a key factor in measuring the sustainability of a company’s growth. From the perspective of creditors, accounts payable companies are able to repay the principal and interest on time and have a good reputation. Therefore, they will raise funds to protect their rights and interests, and they will have stronger capabilities and more actively issue loans. But the solvency is not as high as possible. If the solvency of the company is high, it indicates that the company has not fully utilized internal capital, the return on equity has not reached the maximum value, and has not contributed to the achievement of business goals. (2) Operational capability indicators Operational capability is used to determine the company’s operational risks, and is mainly used to evaluate the efficiency level of the company’s existing production materials in its daily operation process. Generally speaking, the faster the capital turnover, the higher the efficiency. The efficiency of capital use determines the repeatability and profitability of the business. (3) Profitability indicators. The profitability of an enterprise determines the size of the investment risk. Profitability is also called profitability, which is the ability of an enterprise to sell products and generate income within a certain period of time. The ultimate goal of operating a for-profit enterprise is also to maximize profits. The indicators to measure a company’s profitability include total asset net interest rate, revenue-cost ratio, net asset profit rate, operating profit rate, etc., which can all be used to reflect the company’s profitability. (4) Development ability indicators Development capability is used to measure the future growth potential of an enterprise. In the unpredictable social and market environment faced by technological development and enterprises, how to adapt to different environments and maintain sustainable and strong growth is of vital importance to them. Growth ability also affects the trust and beliefs of corporate stakeholders in the company. Only companies with stable financial conditions, high profitability, and bright growth prospects can enhance all stakeholders’ confidence in the long-term development of the business, attract investors’ eyes, and look forward to the future. In summary, it can be seen that the enterprise risk prediction system is shown in Fig. 1.

Risk Prediction and Treatment in Enterprise Management

103

X1:Working capital allocation ratio X2:Current ratio Solvency index

X3:Assets and liabilities X4:Interest coverage ratio X5:Quick ratio

Operational Capability Index Enterprise Risk Forecast System

X6:Accounts Receivable Turnover Rate X7:Turnover rate of total assets X8:Net profit margin of total assets X9:Cost and expense margin

Profitability indicator X10:Roe X11:Operating profit margin X12:Growth rate of total assets Development Ability Index

X13:Growth rate of fixed assets X14:Total operating income growth rate

Fig. 1. Enterprise risk forecast system

3 Experiment 3.1 Sample Selection This paper selects the financial management data of 20 companies of different nature in a certain city to judge their financial management risk status. In this paper, the first 15 learning samples are used as training samples, and the last 5 samples are used as test samples. 3.2 Enterprise Management Risk Prediction Modeling Based on Ant Colony Parallel Algorithm The ant colony algorithm is derived from the imitation of ant colony behavior in real life. In fact, ants do not need to spend any sight to find the shortest path between their food resources and their nest. In the process of finding our way, ants keep leaving pheromone on the way they walk, and they have a great ability to sense the existence and function of pheromone on the road. The more information elements in the path, the weaker their ability to influence the ants. Moreover, the ant-bee uses its high information quality and high-intensity road as its direction of travel. In this way, finding one of the shortest paths through the giant ant is the principle of this positive feedback. The more ants that pass, the more pheromone remains on this route, and the ants below will choose this route.

104

K. Shi

Each individual can use such a pheromone communication mechanism to choose their own optimal route, thus achieving the goal of finding their own optimal route. In the optimization process, the probability of state transition is calculated according to the pheromone and heuristic information of each path. Definition pijk (t) is the state transition probability of ants choosing the next node j from k node i at time t. ⎧ α β ⎪ ⎨ [τij (t)] ·[ηikα(t)] [τis (t)] ·[ηis (t)]β k pij (t) = s⊂ allowedk (1) ⎪ ⎩ 0 In the formula, allowedk = {C − tabuk } is defined as the set of nodes selected by ant k. α can be used as a factor for estimating the relative importance of the trajectory, and β can also be used as a factor for predicting and estimating the relative importance of visibility. ηij (t) is the heuristic function, and its expression is defined as follows: ηij (t) =

1 dij

(2)

dij represents the distance between two adjacent nodes. If dij is smaller, ηij (t) is larger, and pijk (t) becomes larger accordingly.

4 Discussion The comparison curve of the actual value and the predicted value of the test sample is shown in Fig. 2, and the relative error comparison is shown in Table 1. Table 1. Comparison of the predicted results of the ant colony parallel algorithm model with the actual values Sample

Predictive value

Actual value

Relative error

16

0.4237

0.4136

0.0244

17

0.3724

0.3624

0.0275

18

0.4323

0.4224

0.0234

19

0.3267

0.3239

0.0086

20

0.3706

0.3806

0.0263

According to Table 1 and Fig. 2, the predicted value and actual value of sample data 16 are 0.4237 and 0.4136, respectively, and the relative error is 2.44%; the predicted value of sample data 17 is 0.3724, the actual value is 0.3624, and the relative error is 2.75%; The predicted value of data 18 is 0.4323, the actual value is 0.4224, and the relative error is 2.34%; the predicted value and actual value of sample data 19 are 0.3267 and 0.3239, respectively, and the relative error is 0.86%; the predicted value and actual value

Risk Prediction and Treatment in Enterprise Management

105

Value

of sample 20 are respectively 0.3706, 0.3806, the relative error is 2.63%. The relative error value of all samples is controlled below 3%, and the minimum error value is only 0.86%. Therefore, the ant parallel algorithm model can meet the accuracy requirements of enterprise management risk prediction.

0.44 0.42 0.4 0.38 0.36 0.34 0.32 0.3 16

17

18

19

20

Sample Predictive value

Actual value

Fig. 2. Contrast curve between predicted value and actual value of ant colony parallel algorithm

5 Conclusions Risk prediction is closely related to many aspects of enterprise asset management, fund security, internal control, etc. This is an important aspect of modern enterprise system management and financial management. Risk prediction, full use of management resources, and prevention of financial management risks are important guarantees for promoting the healthy and sustainable development of enterprises.

References 1. Hong, L., Dong, C., Pu, Z.: Research on load fluctuation of electronic information industry based on recurrence interval analysis. IEEE Access 99, 1 (2020) 2. Paiter, J., Oliveira, G.M.M.D.: Risk prediction systems: one for all or all for some. Int. J. Cardiovasc. Sci. 34(1), 39–43 (2021) 3. Stenberg, E., Cao, Y., Szabo, E., Näslund, E., Näslund, I., Ottosson, J.: Risk prediction model for severe postoperative complication in bariatric surgery. Obes. Surg. 28(7), 1869–1875 (2018) 4. Lopez-Bazo, E., Motellon, E.: Disclosure on enterprise risk and company performance: evidence from Spain. Reg. Stud. 52(5), 673–687 (2018) 5. Yang, S., Muhammad, I., Muhammad, A.: Enterprise risk management practices and firm performance, the mediating role of competitive advantage and the moderating role of financial literacy. J. Risk Finan. Manag. 11(3), 35 (2018)

106

K. Shi

6. Wang, T.S., Lin, Y.M., Werner, E.M., et al.: The relationship between external financing activities and earnings management: evidence from enterprise risk management. Int. Rev. Econ. Finan. 58, 312–329 (2018) 7. Vij, M.: The emerging importance of risk management and enterprise risk management strategies in the Indian hospitality industry: senior managements’ perspective. Worldwide Hospitality Tourism Themes 11(4), 392–403 (2019) 8. Li, L.: A study on enterprise risk management and business performance. J. Fin. Risk Manage. 07(1), 123–138 (2018) 9. Marc, M., Spri, D.M., Agar, M.M.: Is enterprise risk management a value added activity? E A M Ekonomie A Manage. 21(1), 68–84 (2018) 10. Muthukrishnan, N.: Role of operations management from the perspective of enterprise risk management in Indian industries for emerging market. Strad 8(1), 139–162 (2021) 11. Pratama, B.C., Putri, I., I Nn Ayah, M.N.: The effect of enterprise risk management disclosure, intellectual capital disclosure, independent board of commissioners, board of director and audit committee towards firm value. JurnalManajemendanKeuangan, 9(1), 60–72 (2020) 12. Febrianti, I., Novita, N.: COSO’s Enterprise risk management framework in agriculture startup to support the achievement of SDGs pillars. TIJAB (Int. J. Appl. Bus.) 5(1), 18 (2021)

Research on the Sales Volume of Heavy Trucks in Various Usage Scenarios Based on the Analysis of the Static Market Big Data Model Chen Bai(B) and Heng Zhang China Automotive Technology & Research Center Co., Ltd., Tianjin, China [email protected]

Abstract. At present, China’s heavy truck market is in a transitional stage. The impact of multiple internal and external factors makes China’s heavy truck market situation full of uncertainties. The sales analysis and forecast of the future overall market and the market space of various usage scenarios are vital for commercial vehicle companies in the market competition. It directly affects the business decision-making of auto companies. This paper aims to establish a quantitative forecasting model based on a self-built and unique static database, rationally forecast the overall sales and various usage scenarios, and provide necessary information support for companies to adjust short-term production plans and formulate mid- and long-term plans. Keywords: Heavy truck market · Sales prediction · Usage scenarios

1 Overall Market Analysis With the improvement of the Chinese people’s living standards and the introduction of some national preferential policies, the development of the automobile industry is also unstoppable, which has directly increased the production and sales of automobiles year by year. Both production and sales are showing a prosperous trend. It can be seen from this that the automobile industry and related industries have already accounted for a large proportion of the national economy [1]. In particular, the automobile industry has made a positive contribution to China’s gross domestic product. Therefore, maintaining its good development momentum is of vital importance for Chinese automotive market. Properly planning product demand can not only help companies determine the market supply and demand pattern, determine the type of product development and highlight companies’ competitive advantage, but also help effectively predict the market share and provide decision support for automotive product development, so as to maintain its good development trend.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 107–115, 2022. https://doi.org/10.1007/978-3-030-99616-1_15

108

C. Bai and H. Zhang

As a market closely related to the economic situation, the heavy truck market is different from the overall demand which shifted back in 2020 [2]. In 2021, the market atmosphere of “a decisive battle starts since the beginning” in the heavy truck market continued to June [3]. The overall performance of the automobile market in July was not good. Switching to the national phase VI motor vehicle pollutant emission standard caused a great impact, resulting in the decline of the heavy truck market production and sales. Since the second quarter, the start of infrastructure projects in various regions was not as expected, which led to a significant slowdown in terminal demand. In addition to the “national VI” regulations switch, the sales of heavy trucks in various market segments became more “downward”. From January to July of 2021, compared with the same period in 2020, sales in various market segments increased significantly year-on-year, with a slight decline in the share of tractors, which was still the largest market [4].

2 Market Analysis of Each Usage Scenario Tractors are mostly used for logistics, freight and coal transportation, with 6*4 as the mainstay. 6 × 2 tractors have basically withdrawn from the market affected by the new standard. Affected by the axle-based charging policy, express tractors are converted to 4 × 2 models. 8 × 4 mixer trucks have become the absolute main force in the market, and the 6 × 2 ones have certain demand in rural construction and rural reconstruction. The proportion of 4 × 2 yellow-card dumps has increased, mainly comes from blue-card heavy-duty dumps. In recent years, the industry has generally predicted that heavy trucks will have a large-scale quantitative trend. Judging from the terminal sales data of various market segments this year, compared with 2018 and 2019, as the largest market for heavy trucks, the market share of tractors above 440 Ps has increased, while the market share of cargo trucks below 200 Ps has dropped significantly [5]. With the promotion of policies and market demand, the domestic cold chain transportation market has developed rapidly. Accordingly, the refrigerated truck market has also grown rapidly. The average growth rate of refrigerated trucks in the past three years is close to 30%. From January to June 2021, the sales volume of refrigerated trucks reached to 21,000. Although the recent epidemic in various regions has brought negative impacts, the impact is limited to cross-border cold chain transportation. The annual sales volume is expected to be 50,000 [6]. After the cancellation of the “green channel” discount for live livestock and poultry transportation vehicles, the cost of long-distance transportation of live livestock and poultry has risen sharply. Due to the continuous increase in the price of live pigs, the short-term high economic benefits have led to a rebound in the sales of livestock and poultry transport vehicles. The national phase VI motor vehicle pollutant emission standard for heavy diesel trucks are fully implemented. Companies have increased their preferential efforts to inventory national V diesel trucks [7].

Research on the Sales Volume of Heavy Trucks in Various Usage Scenarios

109

After the full implementation of the national phase VI motor vehicle pollutant emission standard on July 1, 2021, the purchase cost of heavy diesel trucks has risen, and the cost advantages of natural gas heavy trucks have emerged. National VI heavy diesel trucks generally use lean combustion technology, and the NOx in the exhaust gas is high. Large-scale logistics and transportation companies pay more attention to the full life cycle cost of trucks, and at the same time have higher requirements for after-sales service capabilities. Daimler-Benz will mass produce high-end domestic heavy trucks in 2022 [7]. Heavy-duty truck companies with independent brands need to accelerate product upgrades to respond to market changes brought about by high-end heavy truck products [8]. In recent years, FAW Jiefang J7, Dongfeng Tianlong flagship, Sinotruk Candeka, Shaanxi Automobile Delong X6000 and other mid-to-high-end heavy truck models have been introduced to the market [9]. The prices of these products have exceeded 500,000 yuan, and their purpose is directly aimed at foreign brand models [10].

3 Sales Analysis Model Based on Usage Scenarios Through the analysis of the characteristics of the development trend of the heavy truck market and the status quo of the forecasting model, it can be seen that in order to improve the accuracy of the current heavy truck forecast research, not only the relevant characteristics of the sales of heavy trucks and the operation law of the overall market must be taken into account, but also analyze and sales related indicators with a certain weight and proportion. Comprehensively referring to the suggestions of heavy truck forecasting agencies and industry forecasting experts, and conducting one-by-one research on the influencing factors of heavy trucks, we have a preliminary judgment that we need to establish a combined model for forecasting heavy truck sales. The combination should integrate the ARIMA and LSTM coupling model prediction methods, comprehensively investigate and interpret the various indicators that affect the sales of heavy trucks, and conduct empirical research on predictions. This paper intends to apply scenario model and predict the sales volume of each scenario. The LSTM model (Long Short Term Memory) is used: This deep learning model is an advanced artificial intelligence learning method, currently used in sequence data processing and forecasting, such as stock forecasting, text context sentiment analysis and other fields. The rapid development of China’s automobile industry is closely related to the macroeconomic situation. The macro economy mainly includes total supply and total demand, the main proportional relationship in the national economy, the total value of the national economy and its growth rate, currency funds, finance, exchange rate tariffs, employment levels, and international trade surplus and deficit status. Among them, there are many factors related to the heavy truck market, which are concentrated in the GDP factor, demographic factor, urbanization factor, etc. This not only reflects the overall growth of the national economy, but also reflects the change in the level of per capita income and the level of social consumption capacity. This actually has a profound impact on the heavy truck market from the perspective of total supply and total demand. This

110

C. Bai and H. Zhang

section will also start from the macro-economy as an introduction point, and expand on two aspects of economic growth and income gap. According to correlation and data availability, six correlation factors in four categories are determined. This project selects four sub-indicators of macroeconomic GDP, GDP per capita, industrial added value, and fixed asset investment. For the category of purchasing power, it selects four sub-indicators: per capita disposable income of residents, the proportion of total savings in GDP, savings rate, and debt ratio. The consumption category selects several sub-indicators of PPI, CPI, total retail sales of consumer goods, and automobile price index. 3.1 Model Identification The original data of the influencing factors are obtained from authoritative data sources such as the National Bureau of Statistics and Statistical Yearbook, and fitted with the commercial vehicle market sales to verify the correlation. Please see Table 1 for the specific data table. Table 1. 2006–2019 Data Sheet of Each Influencing Factor Time

Per capital GDP, yuan

Highway mileage, ten thousand kilometers

Per capita disposable income of urban residents, yuan

Total savings as a percentage of GDP

Proportion of population aged 18–70

Social consumer goods, total retail sales, 100 million yuan

2006

14368

334.52

10493

46.18%

72.00%

68352.6

2007

16738

345.7

11759

48.55%

72.30%

79145.2

2008

20494

358.37

13786

50.83%

72.50%

93571.6

2009

24100

373.02

15781

52.24%

72.70%

114830.1

2010

26180

386.08

17175

50.89%

73.00%

133048.2

2011

30808

400.82

19109

51.95%

74.50%

158008

2012

36302

410.64

21810

50%

74.40%

187205.8

2013

39874

423.75

24565

49.47%

74.10%

214432.7

2014

43684

435.62

26467

48.45%

73.90%

242842.8

2015

47005

446.39

28844

48.94%

73.40%

271896.1

2016

50028

457.73

31195

46.70%

73.01%

300930.8

2017

53680

469.63

33616

45.47%

72.50%

332316.3

2018

59201

477.35

36396

46.36%

71.82%

366261.6

2019

64644

484.65

39251

46.10%

71.20%

380986.9

Research on the Sales Volume of Heavy Trucks in Various Usage Scenarios

111

Fig. 1. Analysis diagram of autocorrelation function and partial autocorrelation function

In the recognition stage, the autocorrelation function and partial autocorrelation function can be used to tentatively express the data generation mechanism with the coupling model. According to the data in the above table, Eviews is used to calculate the autocorrelation function and partial autocorrelation function of the logarithm Y of sales as Fig. 1. As can be seen from the above figure, the autocorrelation function of the logarithm of sales Y decreases slowly as the time interval increases, so the sequence Y is nonstationary. There has been an obvious growth trend in sales over the years. It can also be judged that Y is not stable, and the sequence can be differentiated. Now make a difference to Y, let y1 = d(y) then: y1t = y = yt − yt−1 Use Eviews to analyze y1, and its correlation diagram and scatter diagram are shown in Fig. 2: 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

80

85

90

95

00

05

Y1

Fig. 2. Function correlation diagram and scatter diagram

112

C. Bai and H. Zhang

From the correlation diagram of y1, no sharp decline of the correlation function and partial autocorrelation function is observed, and it is impossible to judge whether the time series is stationary or non-stationary. From the distribution diagram of the scatter diagram of y1, it can be judged that this sequence is non-stationary. Now differentiate y1, that is, the second difference of y. Let y2 = d(y1) y2t =y1 = (y) = yt − 2yt−1 + yt−2 Use Eviews to analyze y2, and its correlation diagram and scatter diagram are shown in Fig. 3:

Fig. 3. Differential correlation diagram and scatter diagram

Whether from the correlation diagram of y2 or from the distribution of its scatter diagram, it can be judged that y2 is stable, so y2 can be fitted with the ARMA model, that is, ln (sales volume) can be fitted by a second-order ARIMA process. 3.2 Estimation After obtaining a stationary time series through the difference process, the parameters of the autoregressive and moving average terms contained in the model should be estimated. For non-stationary LN (sales volume), a stationary time series Y2 is obtained by difference. ARMA fitting of Y2 is carried out according to the idea of adaptive expectation model. Regression is performed for ARMA (1, 1), ARMA (1, 2), ARMA (2, 1) and ARMA (2, 2) respectively, and AIC value of ARMA (1, 2) is obtained according to the Akechi information criterion, so the parameters of the sample model are P = 1 and Q = 2.The regression results are shown in the figure below (see Fig. 4):

Research on the Sales Volume of Heavy Trucks in Various Usage Scenarios

113

Fig. 4. Regression result diagram

The equation is: Estimation Equation: Y2 = C(1) + [AR(1) = C(2),MA(1) = C(3),MA(2) = C(4),BACKCAST = 1981] Substituted Coefficients: Y2 = −0.0002466901327 + [AR(1) = 0.2769203407, MA(1) = − 0.7255118158,MA(2) = −0.225786001,BACKCAST = 1981] 3.3 Diagnose After selecting ARIMA (1, 2, 2), the purpose of diagnosis is to see whether the selected model fits the data well. In order to select the correct ARIMA model, a high degree of skill is required. The distribution of the correlation and non-correlation functions of the ARMA (1, 2) residuals of Y2 is shown in Fig. 5:

Fig. 5. Correlation function and non-correlation function distribution

114

C. Bai and H. Zhang

The AC value of the residual autocorrelation function and the PAC value of the partial autocorrelation function all fall within the confidence interval. Therefore, the residuals obey the white noise distribution, so the model ARIMA parameter selection is correct, and the fitting effect can meet the requirements. 3.4 Prediction There are five methods for forecasting based on time series data: exponential smoothing, single equation regression, simultaneous equation regression model, autoregressive quadrature moving average model, and vector autoregressive model. Exponential smoothing is a basic method for fitting an appropriate curve from historical data of a given time series. The sample size is expanded to the prediction point of 2021 and static prediction is selected. According to the estimation results, the expected model of y2 in advance phase is as follows: y22021 = −0.000247 + 0.27692 × y22018 − 0.725512 × ε2019 − 0.225786 × ε2020 That is, it is predicted that 1.3 million vehicles will be in the heavy truck market in 2021.

4 Conclusion In 2021, the economic growth rate indicator will be set at more than 6%, which is lower than the expected indicator, showing that China will pay more attention to the quality of economic development in the future. In 2021, the impact of the epidemic will gradually fade, the macro economy has recovered significantly, and the basic demand for trucks can remain stable. It is expected that truck sales will fall to between 4.2 and 4.5 million vehicles, but still at a historical high. In 2021, the medium and heavy truck market will see a certain degree of decline. However, leading companies have adjusted their sales targets for next year to varying degrees. Market competition has further intensified, and the market space of disadvantaged companies will be further compressed, and even face the risk of elimination. Affected by the epidemic in 2020, demand for trucks exploded rapidly in the second half of the year, and the sales base during the same period was high, resulting in a significant year-on-year decline in truck sales in July this year. At the same time, in July 2021, due to the impact of the switch of emission regulations for heavy trucks, some of the demand in the first half of the year was released ahead of schedule. It is estimated that the annual sales of heavy trucks will be between 1.3–1.4 million. With the urgent need for the development of China’s heavy truck industry to transform from extensive production to lean management, various types of forecasting models have broad application prospects in the field of heavy truck market forecasting. It is worthy of the further research. It combines forecasting theories and methods, makes full use of existing data and information, and combines the ARIMA and LSTM models, so that they

Research on the Sales Volume of Heavy Trucks in Various Usage Scenarios

115

can better utilize the respective advantages of ARIMA and LSTM. The paper attempts to explore forecasting methods in new areas, and proposes a heavy truck market forecasting model based on two forecasting methods. Based on this combination model, companies make relatively accurate predictions for the heavy truck market, which can objectively reflect the trend of the heavy truck market. However, due to the correlation between the various factors used in the paper, ARIMA has certain limitations. The heavy truck market will also be greatly affected by major events in the automotive industry and some relevant national regulations and policies.

References 1. Gao, X., Chai, H., Tang, S.: Research on innovation diffusion of hybrid electric vehicles based on patent citation data. World Sci-Tech R & D 06, 784–787 (2020) 2. Zhang, Q., Miao, X.: A Study on innovation diffusion of hybrid vehicles under the influence of patent citations. Machinery 52(07), 01–04 (2020) 3. Zeng, M., Zeng, F., Zhu, X., et al.: Forecast of electric vehicles in China based on bass model. Electric Power 46(01), 36–39 (2019) 4. Ren, B., Shao, L., You, J.: Development of a generalized bass model for Chinese electric vehicles based on innovation diffusion theory. Soft Sci. 27(04), 17–22 (2019) 5. Teo, T.S.H., Yeong, Y.D.: Assessing the consumer decision process in the digital marketplace. Omega 31(5), 349–363 (2003) 6. Zhou, N.: Case study based on predictive analysis of multiple linear regression models. Guide Bus. 000(09), 124–154 (2013) 7. Wang, Z.: China’s heavy truck market demand forecast and enterprise marketing strategy research. Chongqing University of Technology (2011) 8. Zhu, X.: Car quantities prediction based on multivariate linear regression. J. Hubei Univ. Technol. 26(03), 38–39 (2011) 9. Cheng, W., Han, J.: Research on the improvement of China’s real estate market forecasting model based on grey system theory. China Urban Econ. 06, 119–121 (2010) 10. Klein, L.R., Ford, G.T., et al.: Consumer search for information in the digital age: an empirical study of pre-purchase search for automobiles. Adv. Consum. Res. 07, 98–103 (2002)

Analysis of Seepage and Clogging Characteristics of Rock-Soil Porous Media Based on DEM Technology Langhua Li, Yingjia Wang(B) , and Yanan Yi Chongqing College of Architecture and Technology, Chongqing, China [email protected]

Abstract. The seepage fouling in the rock and soil porous media is essentially the result of the coupling effect of seepage and particles. Developed a lattice Boltzmann method suitable for coupled simulation of rock and soil seepage-particlesimmersion motion boundary-DEM numerical calculation platform, and carried out a pore-scale simulation of the seepage and blocking process of rock and soil porous media, and analyzed the particle distribution of the porous media framework. Framework particle size, porosity, framework particle orientation and water pressure and other factors affect its permeability and fouling characteristics. The results show that: the spatial distribution of the framework particles in the porous media is different, and the permeability and fouling characteristics are also different; the smaller the particle size or porosity of the framework particles in the porous media, the more retained particles after fouling, and the more serious the fouling. The particle orientation has a certain influence on the fouling characteristics. The fouling is the most serious when the framework particles are strip-shaped and their long axis is perpendicular to the flow direction with the increase of water pressure, the distribution of the retained particles in the porous medium after fouling becomes more serious. Keywords: DEM technology · Rock and soil · Porous media · Infiltration · Siltation

1 Introduction According to its causes, clogging can be divided into three types: mechanical clogging, chemical clogging, and biological clogging. In geotechnical engineering, mechanical fouling is often referred to as “seepage fouling”, which refers to the phenomenon that small soil particles enter the rock and soil porous media under the action of seepage and stay behind, thereby reducing its permeability [1]. Seepage fouling has a positive effect under certain conditions, such as dam foundation fouling can reduce the leakage of reservoirs, and foundation pit water-stop curtain fouling can improve its water interception effect [2, 3]. For example, engineering drainage systems such as relief wells fail due to blockage, blockages in slopes cause landslides, and blockages of barrier dams © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 116–124, 2022. https://doi.org/10.1007/978-3-030-99616-1_16

Analysis of Seepage and Clogging Characteristics

117

induce overburden collapse, etc. [4–6]. The physical simulation test can obtain macrohydraulic parameters such as flow rate, hydraulic gradient, and permeability coefficient after blockage of the sample, but cannot understand the spatial distribution characteristics of particles retained in the porous medium after blockage and its relationship with pore-throat, that is, fluid-solid coupling. Numerical simulation technology is an important method to study such problems [4, 5]. In recent years, the DEM coupling algorithm has been used to successfully simulate complex rock-soil fluid-solid coupling problems such as contact erosion and hydraulic fracturing. To this end, this paper intends to use DEM to simulate the seepage in the rock and soil porous media, use DEM to simulate the interaction and movement of soil particles in the porous media, and use the immersion motion boundary method (IMB) to deal with the flow between the moving solid particles and the fluid [5–7].

2 DEM Basic Theory In this paper, DEM based on the soft sphere model is used to simulate the interaction between solid particles, which allows a small amount of overlap between two particles in contact. Use linear contact stiffness model and friction slip model to simulate the constitutive behavior between two particles in contact (force-displacement law), Fc = Fcn + Fct = Fn n + Ft t Fn = kn δn Ft = kt δt ≤ μFn Tc = R × Fct In the formula, Fc is the contact force between particles, n and t are the normal and tangential unit vectors of the contact, Fcn , Fn are the normal contact force vector and its magnitude, Fct , Ft are the tangential contact force vector And its magnitude, kn , kt are the contact normal stiffness and tangential stiffness, δn , δt are the contact normal overlap and the tangential relative displacement, R is the friction coefficient between particles, Tc is the torque generated by the contact force, R Is the position vector from the center of the particle to the contact point [8].  Fct + Fb ma = F n + I θ¨ =



t

Tci + Tf

t

Where m and I are the mass and moment of inertia of the particle, a and θ¨ are the linear acceleration and angular acceleration of the particle respectively, Fct and Tci are the contact force and moment generated by the i-th contact, respectively, and Fb represents other forces such as Gravity and so on.

118

L. Li et al.

3 DEM Simulation of Seepage Fouling In actual rock and soil, the internal structure of particle size, particle shape, particle orientation, and pores is extremely complicated. To study its silting characteristics systematically, a simplified two-dimensional model of permeability and silting of rock and soil porous media is established in this paper. As shown in Fig. 1. The model has a total length of 750 um and a width of 400 um. It is composed of a certain size of framework particles randomly distributed with a certain porosity, and these framework particles remain fixed in the subsequent numerical calculation process. The gray disk in the green wire frame on the left in Fig. 1 is the added pseudo-blocking particles, with a diameter of 0.8 mm [9].

Fig. 1. Two-dimensional model of seepage and siltation of porous geotechnical media

The left and right boundaries are pressure boundaries and are processed by the nonequilibrium rebound method. The upper and lower boundaries are impervious and fixed. The skeleton particles are processed by the standard rebound method [3]. The model is calculated based on the DEM numerical calculation platform to simulate the migration and deposition process of the particles to be blocked in the porous medium under the action of water flow. The main calculation parameters are listed in Table 1. Table 1. DEM simulation parameters for seepage

Analysis of Seepage and Clogging Characteristics

119

In this paper, the definition of “plugging permeability coefficient ratio” is used to characterize the severity of permeation and clogging of porous media. It is defined as the ratio of the permeability coefficient after clogging of the porous medium to the permeability coefficient before clogging. In addition, the “retention volume percentage” is also defined to characterize the number of particles retained in the porous media after fouling, which is defined as the ratio (percentage) of the total volume of particles retained in the porous media to the pore volume. Subsequently, for the porous media model before and after the clogging (Fig. 1 area), while keeping the position of the particles (including the clogged particles) unchanged, the inlet and outlet densities were set to 1000.0 and 999.0(Reynolds number). When the flow field converges to a steady state, the iteration is stopped, and the permeability coefficient is calculated by Darcy’s law. Based on this, the blockage permeability coefficient ratio of the model can be further obtained [10].

4 Simulation and Analysis This paper mainly simulates the permeation and fouling of porous media under the conditions of different framework particle distribution, framework particle size, porous media porosity, framework particle orientation, and water inlet pressure. It should be noted that the ratio of the grid unit to the physical unit is a fixed reference quantity the change law of the grid unit is completely consistent with the change law of the physical unit. 4.1 The Influence of Skeleton Particle Distribution Under the condition that the skeleton particles are circular with a particle diameter (diameter) of 5 mm, the porosity is 0.7, and the water inlet and outlet densities are 1000.0 and 999.0 respectively, by changing the random number seeds, 5 kinds of randomly distributed skeleton particles are generated. The simulation results are as follows: The plugging permeability coefficient ratios of the 5 models are 0.53, 0.45, 0.53, 0.54, and 0.51, with an average value of 0.51; the retention volume percentages of the 5 models are 4.81%, 3.44%, 3.41%, and 3.78%, respectively and 3.44%, with an average of 3.55%. The first and second models of framework particle distribution are shown in Fig. 2 before and after blockage.

Fig. 2. Flow field and retained particle distribution of porous media before and after fouling in different framework particle distributions

120

L. Li et al.

From the simulation results and Fig. 2, after permeation fouling occurs, although the volume of particles retained in the porous media only accounts for a small part of the total pore volume (less than 7.0%), these particles mostly reside at the pore throats, resulting in the number of flow channels. Significantly reduced, so the permeability coefficient after fouling is only 35% of that before fouling; in the model of framework particle distribution 1, there are more retained particles than in distribution 2, but some of the retained particles are located on the surface of the framework particles closer to the inlet And the non-flowing area, and the retained particles of distribution 2 are basically located at the pore throat, so the clogging permeability coefficient ratio of distribution 2 is rather small, that is, the clogging is more serious. 4.2 The Influence of the Porosity of Porous Media Under the condition that the skeleton particles are 40 round, and the water inlet and outlet density are 1000.0 and 999.0, respectively, the porous media models with porosities of 0.55, 0.65 and 0.75 are generated and the DEM numerical simulation of the process of permeation and fouling is performed. The model of porosity before clogging, the ratio of the permeability coefficient and the percentage of retention volume after clogging are shown in Fig. 3. The flow field and the distribution of retained particles before and after clogging are shown in Fig. 4.

Fig. 3. The plugging permeability coefficient ratio, the retention volume percentage, and the permeability coefficient before clogging of porous media at different porosities

It can be seen from above two figures that as the porosity increases, the pores in the porous medium become larger and the connectivity is better, so its water permeability is enhanced, and the fine particles are easier to pass under the water flow, and the retention volume percentage decreases accordingly. If it is small, the blockage permeability coefficient ratio gradually increases. 4.3 The Influence of the Particle Size of the Framework Particles Under the condition that the porosity is 0.7, the skeleton particles are round, and the water inlet and outlet density are 1000.0 and 999.0, respectively, a porous media model

Analysis of Seepage and Clogging Characteristics

121

Fig. 4. Flow field and retained particle distribution of porous media before and after clogging at different porosities

with a skeleton particle size of 30 um, 40 um and 50 um is generated and the process of permeation and fouling is performed. Numerical simulation of DEM. For each model, the blockage permeability coefficient ratio, retention volume percentage, and permeability coefficient before blockage after blockage are shown in Fig. 5. The flow field and the distribution of retained particles before and after blockage are shown in Fig. 6.

Fig. 5. The plugging permeability coefficient ratio, retention volume percentage, and permeability coefficient before fouling of porous media with different particle size of the framework

It can be seen from above two figures that with the increase of the particle size of the framework particles, although the number of flow channels in the porous medium before fouling decreases, their width increases significantly, and the water permeability also increases. The fine particles are carried by the water flow. It is also easier to pass, so the retention volume percentage gradually decreases after blockage, and the blockage permeability coefficient ratio keeps increasing. As shown in Fig. 6(a), fine particles quickly block the pore throats near the entrance, and subsequent particles continue to accumulate in at the entrance, eventually all flow channels are completely blocked.

122

L. Li et al.

Fig. 6. Flow field and retained particle distribution of porous media before and after blockage with different skeleton particle sizes

4.4 The Influence of the Orientation of the Framework Particles To explore the influence of the orientation of the framework particles in the porous media on its permeability and fouling characteristics, this paper established the strip-shaped particles as shown in Fig. 7, which are composed of three circles with the same area and the particle area is equal to 40 um in diameter. The area of the round particles. Under the condition that the skeleton particles are strip-shaped, the porosity of the porous medium is 0.75, and the water inlet and outlet density are 1000.0 and 999.0, respectively, three kinds of porous media with the long axis direction of the particles parallel to the flow direction, perpendicular to the flow direction and random are generated.

Fig. 7. Schematic diagram of particle shape

Through the DEM numerical simulation of the permeable fouling process, the fouling permeability coefficient ratio and retention volume percentage of these three models after fouling, and the permeability coefficient before fouling are obtained. The flow field and the distribution of retained particles before and after fouling are shown in Fig. 8 shown. It can be seen from above two figures that when the long axis of the framework particles is parallel to the flow direction, the flow channel in the porous medium is wider, the connectivity is better, and the tortuosity is small, so the water permeability is the strongest, and the fine particles are also the most under the water flow. It is easy to pass, and the fouling permeability coefficient ratio is the largest; when the long axis of the framework particles is perpendicular to the flow direction, the pore throat diameter of the flow direction in the porous medium is smaller and the tortuosity of the seepage channel is larger, so the water permeability before fouling is the weakest.

Analysis of Seepage and Clogging Characteristics

123

Fig. 8. Blocking permeability coefficient ratio, retention volume percentage, and permeability coefficient with different particle orientations of the framework

5 Summary Based on the DEM coupling algorithm, this paper simulates the process of permeation and fouling of porous media from the pore scale and analyzes the fouling of porous media by factors such as the distribution of porous media skeleton particles, the particle size of the skeleton particles, the porosity, the orientation of the skeleton particles, and the water pressure. After the influence of the percentage of post-retention volume and the ratio of blockage permeability coefficient, the internal mechanism of the influence of each factor is explored through the distribution of the retained particles in the porous medium and the change of the corresponding flow field.

References 1. Jiang, M.J., Zhang, W.: A soil CFD-DEM coupling numerical method considering the fluid state equation. Chinese J. Geotech. Eng. 36(5), 793–801 (2014) 2. Li, S., Wang, C., Wang, G.: Coarse-grained soil clogging mode identification and optimal clogging particle size interval determination. J. Hydraul. Eng. 44(10), 1217–1224 (2013) 3. Cao, H., Zhu, D.F., Fan, Z.: Experimental study on leakage change process of water-stop curtain gap. J. Hydraul. Eng. 50(6), 699–709 (2019) 4. Wu, C.Y., Li, S.S.: Experimental study on mechanical clogging mechanism and prevention methods of relief wells. Rock and Soil Mech. 30(10), 315–317 (2019) 5. Shang, Y.Q., Hou, L.G.: Stability of pipe network seepage system on gravel-containing cohesive soil slope. Chin. J. Rock Mech. Eng. 24(8), 137–139 (2015) 6. Wu, H., Chen, G.Q.: Research progress on the stability evaluation methods of barrier dams and disaster chain effects. Chin. J. Rock Mech. Eng. 37(8), 179–181 (2018) 7. Wu, X.F.: Experimental study on clogging characteristics of two kinds of plastic drainage plates. Rock and Soil Mech. 5(9), 252–254 (2014) 8. Liu, S., Wang, Y., Feng, D.: Coarse-grained soil clogging mode identification and optimal clogging particle size interval determination. Chinese J. Geotech. Eng. 11(12), 236 (2019)

124

L. Li et al.

9. Li, S., Wang, G.: Seepage blocking test and particle flow simulation of loose accumulation dam foundation. J. Hydraul. Eng. 3(10), 116–117 (2017) 10. Zhao, J.D.: Coupled CFD–DEM simulation of fluid–particle interaction in geomechanics. Powder Technol.. 9(17), 248–250 (2019)

Automobile Axle Temperature Detection Technology Based on the Matlab Platform Yanbin Sun1(B) , Jianli Yang1 , Jing Yang1 , and Xiangkai Zeng2 1 Chongqing Institute of Engineering, Chongqing, China

[email protected] 2 Chongqing University of Technology, Chongqing, China

Abstract. Based on the measurement of automobile axle temperature signals, this paper presents a combined temperature measurement method with higher measurement accuracy. This measurement method uses the different characteristics of different temperature sensors in temperature measurement to transfer the measurement volume of the combined temperature sensor to the microprocessor. In the microprocessor, the Kalman filtering technology is used to perform data fusion processing on the temperature measurement data, which further improves the measurement accuracy of the combined sensor. The measurement technology is verified by the MATLAB experimental platform. The results prove that the temperature combination sensor is effective and accurate in detecting the temperature of the automobile axle. Keywords: Data fusion · Kalman filtering algorithm · MATLAB simulation · Temperature measurement

1 Introduction In today’s society, cars have become a means of transportation for the masses. It brings convenience to human. However, while cars bring convenience to humans, they also bring many potential dangers. The temperature of the gearbox of a car in normal driving will be lower than 135°. When the car is overloaded or set in off-road mode, driving under the condition of excessive slip of the torque converter, the transmission oil temperature may reach 135°. If the gearbox oil temperature exceeds 135°, the performance of its lubricating oil will weaken as the oil temperature rises [1]. When the oil temperature is greater than 143°, the lubricating oil will lose its lubrication effect, and the internal components of the gearbox will fail due to friction, and the gear will change. The tank oil will boil or even splash. It only takes 25 to 35 s for the gearbox temperature to reach 143° from 135°. Therefore, the transmission temperature measurement and adjustment become very important. There are two methods for measuring the temperature of the automobile gearbox: one method is non-contact, which uses infrared to measure the oil temperature of the gearbox. However, this method has higher requirements for the environment around the infrared test head, and no dirt is allowed to stick to the infrared lens of the test head. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 125–132, 2022. https://doi.org/10.1007/978-3-030-99616-1_17

126

Y. Sun et al.

The other method is the contact measurement method. A temperature sensor is used to measure the temperature of the gearbox [2]. Obviously the second method is easier to implement and more accurate. This paper uses the second method to measure the gearbox oil temperature. The temperature sensor adopts a combined sensor composed of a Pt100 temperature sensor and a thermistor. The Pt100 platinum resistance temperature sensor has good linearity and high measurement accuracy, but its dynamic performance is poor, and the temperature changes quickly and cannot respond in time. The thermistor has high sensitivity, but its linearity is not good and cannot be used for precision measurement. The authors combine the two temperature sensors with their advantages to design a new sensor structure as shown in Fig. 1.They use LM358 to collect the temperature signal, and performs A/D conversion. The digital signal is collected through the microprocessor (microcontroller or ARM). They use the Kalman filtering to fuse data on the signal collected by the microprocessor and then output the temperature measurement value that is closest to the true temperature.

Fig. 1. Block diagram of the combined temperature sensor

2 Data Fusion Technology Data fusion technology was originally applied in the military field to process data information from multiple systems. The purpose of using data fusion in this paper is to eliminate uncertain and inaccurate measurement information in the temperature data of automobile axles obtained by two temperature sensors, and to obtain more accurate automobile axle temperature information. The advantage of using this method is that when a temperature sensor in the automobile axle temperature measurement system is not accurate, the system can still obtain the optimal axle temperature information through data fusion based on the information provided by another temperature sensor. The Kalman filtering algorithm used in this paper is a relatively classic data fusion algorithm. It was originally proposed to solve the linear optimal filtering recursive solution in discrete time [3]. The applies state quantities to describe physical systems uses linear system state equations to describe system changes. The so-called optimal estimation refers to the algorithm to minimize the error variance between the final estimate of the signal or state and the corresponding true value. Kalman filtering belongs to this type

Automobile Axle Temperature Detection Technology

127

of algorithm. The Kalman filtering algorithm can be simplified and summarized as the use of recursive filtering algorithm. After the new signal is measured, according to the state equation of the system itself, combined with the estimated value obtained from the previous state, the new estimate of the state will appear in a certain recursive manner [4].

3 Kalman Filtering Based on the Matlab Platform The simulation experiment of automobile axle temperature measurement is completed with the help of MATLAB experimental platform. Matlabis a tool software mainly used for image, signal processing and numerical calculation [5]. It is also a multi-disciplinary and powerful system simulation software with powerful matrix algebra operations and array operations [6]. MATLAB takes the matrix as the basic operation unit, and can call pre-defined functions to solve different types of control problems. At the same time, using MATLAB can perform intuitive modeling and simulation analysis of dynamic systems. It supports discrete, continuous, or a mixture of linear and non-linear systems, which can greatly reduce the steps required to implement the Kalman filtering algorithm. 3.1 Kalman Filtering Algorithm Kalman filtering algorithm can be expressed as a discrete control process system [7]. The system can be described by a linear stochastic differential equation: x(t) = A · (t−1) + B · u(t) + w(t) z(t) = H · x(t) + v(t) In above formulas, x(t) is the system state at time t, and u(t) is time t system control quantity. A and B are system parameters, if it is a multi-model system, it is a matrix. z(t) is the measured value at time t, H is the measurement system parameter, if it is a multi-measurement system, H is the matrix. w(t) and v(t) represent process noise and measurement noise respectively. Q and R are Gaussian white noise [8]. Kalman filtering is an iterative process [9]. It iterates once when it gets a new observation value, and constantly updates the “system state” (x) and “error covariance” (P). Each iteration is accompanied by the prediction of the next state system. For the “system state” (x) and “error covariance” (P), the state equation and measurement equation for one iteration can be obtained: x(t|t−1) = A · x(t−1|t−1) + B · u(t) P(t|t−1) = A · P(t−1|t−1) · A + Q In the above equation, x(t|t−1) is the previous state prediction result, x(t−1|t−1) is the previous state optimal result, and u(t) is the current state control quantity. P(t|t−1) is the covariance corresponding to x(t|t−1), and P(t−1|t−1) is the covariance corresponding to x(t−1|t−1). A’ represents the transposition of the system parameter A, and Q is the

128

Y. Sun et al.

system process covariance. These two equations represent the prediction of the state of the system at a later time. From the above two equations, the prediction result can be obtained, and the measured value of the current state needs to be obtained. At this point, the Kalman gain K(t) can be calculated, and another iteration on the system state x(t|t−1) and the error covariance P(t|t−1) can be done again. x(t|t) and P(t|t) can be gotten as follows: K(t) =

P(t|t−1) · H H · P(t|t−1) · H + R

x(t|t) = x(t|t−1) + K(t) · (Z(t)−H · x(t|t−1)) P(t|t) = (I−K(t) · H) · P(t|t−1) Among them, calculating the matrix which I in P(t|t) is 1, and for single-mode single-measurement, I = 1. x(t|t) is the filtered data we want, it and P(t|t) will be used as x(t−1|t−1) and P(t−1|t−1) in the next time iterating. The Kalman filtering can be realized by repeatedly calculating the above linear equations by computer programming. The Kalman filtering is used for the data generated by the Pt100 temperature sensor and the thermistor respectively. After that, there is data fusion of the data generated by these two filters. At this time, a main filter is required to combine the data generated. The Kalman filtering algorithm implemented by this filter is as follows: xm (t|t) = xm (t|t−1) Pm (t|t) = Pm (t|t−1) 1 1 1 1 = + + P(t) P1 (t) P2 (t) Pm (t) x(t) = P(t + 1)[

1 1 1 x1 (t) + x2 (t) + x2 (t)] P1 (t) P2 (t) Pm (t + 1)

3.2 Modeling and Simulation of the Kalman Filtering Algorithm This paper discusses the application of Kalman filtering in the measurement of gearbox oil temperature, assuming that the environment inside the gearbox is relatively closed, and systematically modeling the environment inside the gearbox. The temperature in the gearbox can be regarded as constant in a short period of time, so A = 1. And let u (t) = 0, then we get: x(t|t−1) = x(t−1|t−1) P(t|t−1) = P(t−1|t−1) + Q

Automobile Axle Temperature Detection Technology

129

Because the measured value is given by the sensor, it corresponds to the measured value in the equation one-to-one, so H = 1. So: x(t|t) = x(t|t−1) + K(t) · (z(t)−x(t|t−1)) K(t) =

P(t|t−1) P(t|t−1) + R

P(t|t) = (1−K(t)) · P(t|t−1) Writing a program to repeatedly iterate the above equations to realize the application of Kalman filtering to measure temperature at a constant temperature. If it is necessary to measure dynamic temperature, then modifying the formula sub-parameters and iterating again. Since this paper discusses the filtering of the data generated by two temperature sensors, it is necessary to perform data fusion for the filtering results of two Kalman filters. Repeating the above steps twice with different parameters, and then performing data fusion to perform the following calculations: 1 1 1 1 = + + P(t) P1 (t) P2 (t) Pm (t) x(t) = P(t + 1)[

1 1 1 x1 (t) + x2 (t) + x2 (t)] P1 (t) P2 (t) Pm (t + 1)

First assuming that when the temperature of the automobile gearbox is constant at 80 and the number of samplings is 100, and using MATLAB to simulate the Kalman filtering algorithm, as shown in Fig. 2:

Fig. 2. Kalman filtering effect when the temperature is constant

Then assuming that the temperature uniformly increases from 125° to 135°, and the simulation is performed again after sampling 100 times, as shown in Fig. 3:

130

Y. Sun et al.

Fig. 3. Kalman filtering effect when the temperature rises uniformly

Adjust the model parameters to make the actual temperature a random value around 135 and make the difference between the measured value and the actual temperature higher. The sampling times are also set as 60 times, and the simulation results are shown in Fig. 4:

Fig. 4. Kalman Filtering Effect When There is No Change in Real Temperature

According to the previous simulation model, variables are added to simulate the situation where the gearbox slowly rises from 80° to 135°, see Fig. 5:

Automobile Axle Temperature Detection Technology

131

Fig. 5. Kalman filtering effect when the temperature rises slowly

According to the simulation results, the Kalman filtering algorithm filters out most of the sharp waveforms. It makes the data waveforms smoother, and reduces data errors. In particular, for sensors with low accuracy, the data is determined by changes in data. The effect of the true value is very significant. The filter data of multiple sensors are fused to form a combined sensor, which can neutralize the characteristics of the sensor to make the filter waveform smoother and the data error smaller, so that the automobile control terminal can measure the temperature of the automobile shaft more accurately, so as to make timely decisions to prevent accidents.

4 Conclusion In industrial production, the requirements for measurement accuracy are getting higher and higher [10]. This paper uses Kalman filtering technology to perform data fusion processing on the measurement data of the temperature sensor. Through MATLAB simulation, it can be seen that the processed data is closer to the real value, and the stability and sensitivity of the measurement are very good. Experiments have proved the effectiveness of the algorithm and it can be applied to actual production practice. Acknowledgements. The National Natural Science Foundation of China (Grant Nos. 61575035 and 51874064), On-campus scientific research project of Chongqing Institute of Engineering (Grant No. 2018xzky10), College Students’ Innovative Entrepreneurial Training Plan Program (Grant No. CXCY144).

References 1. Fei, T., Zhang, L.: The application of Matlab in the experimental teaching of analog filter. Res. Univ. Lab. Work 04, 34–36 (2015)

132

Y. Sun et al.

2. Guan, J.: MATLAB simulation realization of Kalman filtering. Southeast Commun. 06, 178– 180 (2014) 3. Mao, J.: The design of Kalman filtering based on MATLAB. Softw. Eng. 10, 32–33 (2015) 4. Huang, Z.: Design of the digital filter based on Matlab. Shipboard Electron. Countermeasure. 34(01), 116–117 (2011) 5. Liu, Y.: The design of digital filter based on MATLAB. Comput. Knowl. Technol. 06, 1501– 1503 (2013) 6. Chai, Z.: Utilizing foreign investment, environmental constraint and TFP growth of China’s industry: comparative research based on Malmquist index and Malmquist-Luenbergerindex. Technol. Econ. 32(01), 64–70 (2013) 7. Li, C., Ma, J., Yang, Y.: Adaptive square-root cubature Kalman filter algorithm based on amending. Syst. Eng. Electron. 43(07), 1824–1830 (2021) 8. Xian, L.: Research on noise and interference of measuring system. Digital Communication World. 02, 224 & 234 (2016) 9. Xiang, Y., Feng, L., Qi, J.: A combination of Kalman filtering algorithm. Video Eng. 37(09), 168–170 (2013) 10. Lianjia, X.: Talking about the high-precision measurement method in the installation of industrial equipment. Dev. Guide Build. Mater. 18(11), 105 (2020)

Design of Marketing Data Mining System Based on AI Qiong He(B) Sichuan University Jinjiang College, Meishan, Sichuan, China [email protected]

Abstract. AI technology is an important part of the intelligent decision support system. Data mining is mainly responsible for the processing of intelligent decision support in the influence space of the intelligent decision support system. Therefore, data mining has a pivotal position in the entire intelligent decision support system. The article uses big data and data mining technology in AI technology to construct and design market research, market strategy, marketing strategy, marketing activities and other links in the marketing system. The use of AI can help companies understand customer needs more comprehensively, find market opportunities more quickly, establish business goals more accurately, and achieve smart marketing and precision marketing in the true sense. Keywords: AI. marketing · Data mining · System design

1 Introduction In recent years, AI technology has been rapidly developed under the strong support of national policies. AI is the research and development of technology for simulating, extending and expanding human intelligence, including big data, machine learning, deep learning, natural language understanding, image recognition, etc. Many traditional industries may be replaced by AI in the future if they do not use AI technology to improve the level of intelligence and complete the upgrade of the traditional system [1]. Marketing is a very important part of traditional economic life, mainly centered on customers. At present, the global economy is in a declining cycle, and business operations are facing greater difficulties. How to expand the scale of operations and reduce operating costs has become an important issue for companies to consider. To establish business goals more accurately, it is very necessary to study the application prospects of AI technology in marketing [1]. This article uses AI technology to explore the methods of transforming and constructing the traditional marketing system, and analyzes and studies the possible risks in the AI system, and proposes solutions at the end of the article.

2 The Definition of Customer Relationship Management The concept of customer relationship management can be expressed from the following three levels: it is a modern business management concept, which is a macro concept; it © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 133–140, 2022. https://doi.org/10.1007/978-3-030-99616-1_18

134

Q. He

contains a set of solutions, which is a mid-level concept, which means a set of application software system, this is a micro concept. As a management concept, customer relationship management originated from Western marketing theories, and was born and developed in the United States. To achieve this, it is necessary to integrate all aspects of customer information, and at the same time to have a series of technical means as support [2]. As a solution, customer relationship management refers to various technical means that support the concept of customer relationship management, including: Internet and e-commerce, multimedia technology, data warehouse and data mining, expert systems and AI, call centers, and corresponding hardware environments. The Internet and call centers obtain all kinds of relevant information from customers; data warehouse and data mining technology process and process this information, and multimedia technology can display this information in a more intuitive and specific way for better analysis make relevant decisions based on customer needs [2]. For enterprises, the purpose of implementing customer relationship management is to understand the current needs of customers and the needs of potential customers through a series of technical means, and then integrate all aspects of information, so that the company’s understanding of customer information is complete and consistent.

3 Data Mining Data Mining (DM) is a discipline that reveals patterns and data relationships existing in data, and emphasizes the processing of large observable databases. The emergence of data mining has led to the extensive development of AI research in the application field. This includes data mining and intelligent information extraction processes. The former digs out unknown and valuable patterns or rules from a large amount of complex real-world data, and the latter is the comparison, selection and summary of knowledge principles and rules to form an intelligent system [3]. 3.1 Research Status of Data Mining Current data mining applications are mainly concentrated in telecommunications, retail, agriculture, web logs, banking, electricity, biology, celestial bodies, chemicals, medicine, etc. It seems to be widespread, but the actual application is far from widespread [3]. According to Gartner’s report, data mining will become one of the most important technologies in the next 10 years. And data mining has also begun to become an independent professional discipline. 3.2 Research and Development of Data Mining The specific development trends and application directions mainly include: The amount of data designed for data mining will be larger, the processing efficiency will be higher, and the results will be more accurate. In terms of tools: mining tools are becoming more and more powerful, and algorithms are more and more convergent [4]. Forecasting algorithms will absorb novel algorithms (support vector machines (SVM), rough sets,

Design of Marketing Data Mining System Based on AI

135

cloud models, genetic algorithms, etc., and realize automated algorithms. selection and automatic tuning of parameters. In addition to being applied to large-scale specialized problems, the application of data mining will also be embedded and more intelligent. For example, further research on knowledge discovery methods, research and improvement of Bayes’ theorem and Boosting method, and continuous generation and improvement of commercial tool software, focusing on the establishment of an overall system to solve problems, such as Weka and other software [4].

4 The Connotation of Data Mining The so-called data mining is the process of extracting potentially useful information and knowledge hidden in it, people do not know in advance, but also potentially useful information from random, noisy, incomplete, massive, and fuzzy actual application data.. In data warehouse and data mining applications, classification is a very important method. These algorithms have good prediction accuracy on many real data sets. In data warehouse and data mining applications, clustering is also a very important method, which is widely used in business, market analysis, biology, WEB document classification and other fields [5]. Cluster analysis is an important means and method of data division or grouping processing, and it is also an important research field in data mining.

5 Application Process of Data Mining Technology in Customer Relationship Management When developing a data mining project, it is necessary to make in-depth and thorough analysis on the understanding of the problem and the data, that is, the needs of the project and which technology to use. For example, determine the application area, locate the data mining problem type; select the data mining technology that matches the problem type [5]. After analyzing, synthesizing and determining these key points, stage task assignment and process instance development are carried out. The function part is reflected in the target and the result. 5.1 Determine the Mining Target Clarifying the business goals of data mining is the first step in data mining. To determine the mining object, we must fully understand the relevant situation in the telecommunications field, and be familiar with the background knowledge of telecommunications enterprise customers [6]. In customer relationship management, customer churn management is to dig out the characteristics of churn customers from a large amount of data through predictive models (classification models), and at the same time, for non-churn customers, cluster management of customers with higher similarity.

136

Q. He

5.2 Data Preparation In the data mining process, data preparation is a very important stage. The efficiency and accuracy of data mining and the effectiveness of the final mining model are all affected by data preparation. In the telecommunications customer relationship management, due to the long-term lack of attention to customer information management by enterprises and the fact that the construction of data warehouses of Chinese enterprises is mostly in the initial stage, the original data of enterprises is not conducive to the implementation of data mining solutions, and data preparation is particularly important [6]. 5.3 The Construction and Application of Data Mining Model Based on the design of data mining telecommunications customer relationship management system, the choice of data mining model is the basic link of application development of data mining tasks. Therefore, it must be combined with specific business requirements and designed to be effective and practical [7]. The customer relationship management model, targeted mining and analysis can achieve good results. 5.4 Application and Evaluation of Data Mining Results After the data mining model is put into use, the main task of this stage is to analyze the extracted knowledge according to the decision purpose of the end user, distinguish the most valuable information and submit it to the user. At the same time, in the application and evaluation stage, not only must the knowledge be expressed in a way that can be understood by people, but also its effectiveness must be evaluated [7].

6 System Design of Market Mining Customer Relationship Management Based on AI 6.1 System Overall Structure Design Based on data mining, the telecom customer relationship management system includes the following components: (1) Make up the data source. The relational database is used as the data source of the source system. (2) Form a knowledge base. After determining the goal of data mining, select the corresponding data mining algorithm to analyze the data in the data source, apply the classification model to get the knowledge hidden in the data set, and finally form the knowledge base. (3) Provide decision support. Apply the acquired tacit knowledge to customer relationship services to provide decision support for marketing and new business promotion. (4) Feedback and improvement. The application results are evaluated and fed back to the data mining process for future improvement of data mining models and related algorithms.

Design of Marketing Data Mining System Based on AI

137

6.2 System Function Design From a functional perspective, the telecommunications customer relationship management system can provide support for decision-makers in making decisions in terms of customer characteristics and customer behavior analysis, marketing, and personalized services. 6.2.1 Analyze Different Customer Characteristics and Customer Behavior Due to differences in gender, age, occupation, etc., customer behaviors are also diverse. For example, operators can obtain three types of users through cluster analysis: Although the number of long-distance calls and network calls is higher for the first type of customers, the total call charges are lower. From a market perspective, if appropriate marketing methods are adopted to increase their use of data services, it is possible to increase the profit level of this group; and this user group will have an overall increase in consumption power after a few years [8]. Although the second type of customer calls less long-distance calls, the total call costs are higher. Therefore, this type of customer belongs to the company’s “highquality” customers, and they contribute most of the company’s profits. Such customers also have market potential. Compared with the first two types of customers, there are no special characteristics. Most of its indicators are close to the overall average [8]. For such customers, companies need to adopt active marketing strategies to provide them with more products and services. Three types of different customer characteristics, three types of different customer behaviors, and three types of different marketing plans provide support for leadership decision-making. 6.2.2 Analyze Call Data Call data mainly refers to call time, length, routing, and so on. Analyzing call data is mainly to use neural network and genetic algorithm to plan and optimize the network [8]. 6.2.3 Predict User Consumption Behavior Different customer characteristics will have different customer behaviors. From a large amount of data, the system can dig out a rule base to predict which customers will use a certain product and which customers will frequently change their phone numbers [9]. Therefore, the marketing department can be targeted Promote locally and improve services in a targeted manner, as shown in Fig. 1. 6.2.4 Provide Personalized Services Providing personalized services is one of the pursuits of communication operators. Due to the huge number of customers, personalized services can only be achieved through automated data mining technology [9]. For example, the anti-theft call service-it first

138

Q. He

Fig. 1. The main functional modules of the telecommunications customer relationship management system

obtains the law of a certain user’s phone use through data mining technology, and warns the user when this law suddenly changes. See Fig. 3. 6.2.5 System Data Structure Design The customer data for analysis mainly comes from the following Table 1 of the enterprise database. Table 1. Customer’s marketing message Item

Description

Customer ID Customer name Consumption habits Consumption habits Historical credit Credits Credit rating

6.2.6 System Technical Architecture Design The system technology architecture design must ensure good applicability-one is the expansion of hardware equipment, and the other is the expansion of the software system’s functions, and it can be compatible with system platforms of different manufacturers. The design of this system structure is achieved by adopting the popular three-tier system architecture-database server, application server, browser, and distributed technology [9].

Design of Marketing Data Mining System Based on AI

139

The system constructs commonly used analysis tool modules, mining algorithms, and models in the form of objects, and inserts them on the distributed “soft bus” in the form of universal “plug-ins” for the invocation of different analysis topics. In this way, with the system theme the functions of the system will continue to be enhanced without affecting the overall structure of the system [10]. The design concept of this soft bus greatly improves the expansion performance of the system, see Fig. 2.

Fig. 2. System structure diagram of functional module system

This system will use OLAP technology (on-ling analytical processing) to display the results of data mining [10]. OLAP has the capabilities of multiple views and dynamic views, which lays a solid foundation for successful data mining. In addition, users must often interact with the system, select appropriate algorithms, and continue to optimize the system, instead of simply relying on the data mining system to automatically generate patterns and knowledge. Users should frequently evaluate and give feedback [10]. OLAP provides a good reference for interactive data analysis and is sufficient and necessary for exploratory data mining, see Fig. 3.

Fig. 3. System structure diagram based on OLAP

140

Q. He

7 Summary In the era of AI, marketing practitioners should make full use of AI technology to change the status quo of the marketing industry, improve the efficiency and pertinence of traditional marketing activities, realize customized and intelligent design of products, and personalize customers, precise marketing, fundamentally improve the economic efficiency of the enterprise, and enhance the vitality of the product.

References 1. X.W., Cai, B.Y.: Analysis of enterprise marketing optimization strategies in the “Internet+” Era. Bus. Econ. Res. 19, 49–51 (2018) 2. Huang, C.F.: On enterprise marketing innovation in the Era of Internet Big Data. Bus. Econ. Res. 14, 72–74 (2018) 3. Party, J.Y.: Research on the ethical and legal risks of AI. Inf. Secur. Res. 3(12), 1080–1083 (2017) 4. Chen, J.H., Hao, Y.H.: Futures market price forecast modeling and decision based on DBN deep learning. Comput. Sci. 45(11), 75–78 (2018) 5. Wan, P., Wang, L.S.: Research on data mining and AI technology. Wireless Internet Technol. 10, 113–114 (2016) 6. Wang, X.: On how to use big data mining technology to promote the continued development of AI. Sci. Technol. Innov. News 14(01), 213–215 (2017) 7. Qin, Y.W.: Application of AI reasoning engine in Weibo data mining. Manage. Technol. Small Medium-Sized Enterp. (Mid-Term Issue). 5(02), 34–37 (2017) 8. Pu, D.Q.: Application of data mining in AI. Inf. Comput. (Theor. Ed.) 2(19), 315–321 (2016) 9. Li, D.D.: Data mining technology and its development trend. Comput. Appl. Technol. 02, 38–40 (2007) 10. Zhong, Z., Yin, Y.F.: Data mining and AI technology. J. Henan Univ. Sci. Technol. (Nat. Sci. Ed.) 6(3), 44–47 (2004)

The Application of Computer Database System in Educational Information Management Bin Hu(B) Sichuan University Jinjiang College, Meishan, Sichuan, China [email protected] Abstract. The development and progress of mankind promote continuous innovation in science and technology. It is the age of information and the age of science and technology at 21st century. The appearance of computers is an important milestone in the development of science and technology. It has greatly improved the efficiency of people’s work and study and has also promoted the globalization of information. Among computer science and technology, database system is very important, and its development speed is very fast, today, the application of database system is also more and more common. A good application of computer database system in education information management can effectively improve the management level and work efficiency. This article analyzes and discusses computer database system application in education information management and explains it. Keywords: Computers · Databases · Systems · Educational information · Information management

1 Introduction For every unit, enterprise, and even individual, education information management is very important. In the education information management, the computer database technology application is particularly extensive, which can provide people with convenient conditions for work and study, and It can also ensure the various information and protect important information security from being obtained by others. Database technology is the education information management technology, and the computer is the database technology that it relies on when carrying out the education information management. With social economy progress development, people pay more and more attention to education information management. The good application of computer database technology in education information management can improve the scientific nature of education information management, make education information management more modern, and improve education information.

2 An Overview of Computer Database Technology Computer database technology mainly refers to the operation of computer data storage, organization and management, and comprehensive management of computer management data, thereby storing, organizing and managing internal data, and people’s needs © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 141–148, 2022. https://doi.org/10.1007/978-3-030-99616-1_19

142

B. Hu

for information management are met. The significance of database in information management is more effective. Multiple computers can share a database system, so that in the process of information technology construction, a database can be established to maintain the use of more computer information programs, thereby improving information the quality of management ensures the effectiveness of data management. The establishment of the database during information management does not require changing the computer system program, which simplifies the system management process and improves the quality of information management.

3 Concept and Characteristics of Computer Database System At this stage, with the continuous improvement of people’s living standards, database systems have been widely used in people’s lives, and the database system in computers is generally interpreted as a data system that needs to be saved in the computer during use. content. In the data system, many files have certain formats. Under the constraints of these formats, the system content involved in the computer database is divided into the following five points: 3.1 Features of Data Sharing One of the first points is the sharing of data inside the computer. This feature mainly refers to the ability of the computer to establish a unified structure during the use of the computer. The main purpose of the establishment of such a structure is to achieve the contribution of data, so this also becomes a computer. One of the important features of the Chinese database, as shown in Fig. 1. Under the shared environment and characteristics, the database in the computer can ensure that the network of enterprises or individual users can be connected, and even can be connected all over the world, so as to achieve the purpose of transferring data and information to each other [1].

Fig. 1. Sharing of database system

The Application of Computer Database System

143

3.2 Characteristics of Data Organization The system content involved in the computer database has a very large storage capacity. Under such a huge storage capacity, the data structure in it is required to have a certain degree of organization, that is to say, the computer needs to ensure that the data connection is made. There is an association and order between the connected data. Only in such an environment can we discover the use of data in the database and the characteristics of file transfer, and help people realize the organizational structure of the computer system in an organized environment [2]. 3.3 Features of Data Independence In a computer, almost all data has a certain correlation, but after detailed analysis, it can be seen that there is still a strong correlation between the data contained in it. This situation does not hinder the independence of the data. exist. Therefore, the independence of data has become a very important content in the computer. It can continuously change its structure according to the development of the computer, and ultimately affect the logic of the data, but it will not affect other content, nor will it affect the outside world [3]. 3.4 Data Flexibility Characteristics The flexibility characteristic of the database in the computer mainly refers to the operation of the computer work. Such an operation method can ensure that the use of the computer has a very strong practicability. But observing this situation as a whole of the data will not only promote the development and progress of the computer system, but also have a variety of storage materials, and have a variety of management and operation capabilities. For example: in the stage of computer use, it can also ensure that people transfer, modify and delete data. Users can filter data according to people’s choices, which is also the main feature of database flexibility in computers [3]. 3.5 Controllability of Data In a computer database, data redundancy is a relatively complicated phenomenon between data. Such a situation will lead to a lot of waste of data and space, and even seriously affect the relationship between data. However, this situation is more likely to cause data complexity, so it is also necessary to focus on reducing the duplication of data and controlling the use of data in a good range, to ensure that the data has good controllability [2].

4 The Strategy of Computer Database Technology Application in Education Information Management 4.1 Pay Attention to the Combination of Theory and Practice With the continuous improvement of the scope of database application, its technical principles have also been promoted to a certain extent, and the fields of use are also

144

B. Hu

expanding. Therefore, in the process of database and information management, it is necessary to combine the actual situation of the enterprise and carry out advanced research [4]. The results are applied to the construction of the database, through the company’s specific time practice, constantly update the theoretical research, increase the adaptability of the database, and ensure that the company obtains greater marginal benefits when using the database. 4.2 Further Increase the Database Security Although the database defense system is constantly improving, the computer intrusion system is also constantly evolving. Once the enterprise information is illegally tampered with, it is fatal to the development of the enterprise. Therefore, the enterprise should pay more attention to the advanced security management system, strengthen the system, and introduce more advanced information protection. System to further strengthen the security of the database system [4].

5 The Current Status of Database Applications in Education Information Management 5.1 Increasingly Valued by Society Today, database technology has a wide range of applications, and because of its own characteristics, its development prospects are also very broad. Whether in work or study, people will use computers to help themselves, and education information management is no exception [5]. All in all, system of computer database applications is getting more and more attention. 5.2 Continuously Improving Safety When applying database systems in education information management, people are most worried about the security of information. With the continuous improvement of science and technology, the security of computer databases has also been improved, which is of great significance for educational information management. In educational information management, data has the highest value, and security systems in much software are still unable to achieve “zero” loss. This situation is a common defect in educational information management [5]. 5.3 Expansion of Scope The continuous development and innovation of computer database technology has promoted the continuous increase of the database system market, and with the gradual change of people’s subjective consciousness, the demand for database systems has gradually increased, which has promoted the gradual application of system of computer databases [6].

The Application of Computer Database System

145

5.4 The Rapid Development of Related Technologies The development speed of computer database technology has an important impact on its own application. Computer database technology is gradually marching towards object database through the three stages of mesh database, hierarchical database and relational database. On this basis, database technology is also constantly improving. Its own operability, safety and adaptability are improving rapidly [6].

6 Suggestions for Improving the Database Application in the Education Information Management System Computer database technology mainly refers to the operation of computer data storage, organization and management, and comprehensive management of computer management data, so as to store, organize and manage internal data, and people’s needs for information management are met. The significance of database in information management is more effective [7]. Multiple computers can share a database system, so that in the process of information technology construction, a database can be established to maintain the use of more computer information programs, thereby enhancing education the quality of information management ensures the effectiveness of data management. 6.1 Pay Attention to the Security of the Database System In education information management, the most important thing is the security of information. If the security performance of the system of computer database is relatively poor, it will cause the loss of a large amount of information, and even the deliberate destruction of criminals, as shown in Fig. 2. Therefore, people must pay attention to the security of the system of computer database to avoid security problems. When operating, they should follow the corresponding operating standards to prevent virus intrusion.

Fig. 2. Application of database in education information management system

146

B. Hu

Therefore, relevant technical personnel must continue to innovate on the security of the system of computer database to ensure the reliability of educational information management [7]. 6.2 Continue to Promote the Stable Development of Computer Databases The latest achievements in the development of computer databases must be applied to practice in a timely manner to improve the scientific nature of the practice. On the contrary, the practice must be continuously developed with the development of computer database theory and technology. This requires that in the process of development, the theoretical research of computer databases must also be based on the relevant practice of computer databases. Only in this way can the pertinence and professionalism of the theoretical research of computer databases be enhanced. While using computer databases to serve production and life, the reform and thinking of database technology is the development direction that computer databases must adhere to, as shown in Fig. 3. The good use of the system of computer database has promoted the changes in people’s production and lifestyles. When we conduct related research on education information management, we must focus on the database and improve the resource sharing ability of the database, so that the computer database can be used as educational information [8].

Fig. 3. Stability of computer database information management system

7 Strengthen the Security of Computer Data System in Educational Information Management 7.1 Strengthen the Security Performance of Computer Data Systems in the Current Stage of Social Development With computer technology rapid development, this technology has gradually begun to be valued and used by people. The most widely used environment is the management and recovery of stolen content and damaged data, which can reduce user losses. Continuously improving the’ system security of the database’ is also a very important development trend for the main system of computer database in the management of information technology at this stage and in the future development process [9].

The Application of Computer Database System

147

7.2 Strengthen Applications and Measures to Improve Safety Performance Observed under the actual use environment, the database in the computer is mainly the management system in the software, and the security and scientificity of this system are also very closely related to the user’s use [9]. Through the investigation and understanding of users, it can be seen that many users have relatively weak security awareness in the process of using computers, which will also cause the leakage of user information and seriously threaten the safety of users. Therefore, in the use of computer databases, it is also necessary to fundamentally improve and strengthen the security of the network environment to ensure that the computer is in a protected state [10]. 7.3 Strengthen the Research on the Integration of Computer Database Theory and Practice To promote and ensure the safety performance of computer use, it is also necessary to ensure the integrity of the database content. At the same time, it is necessary to pay attention to several aspects in the process of use. The first is to ensure its integrity through the data segment used by the user. It is necessary to ensure that the theory and practice of computer databases can be combined with each other in a safe environment. The second is the need to change the computer’s database through other means to complete the integrity of the data. Finally, it is necessary to ensure the integrity of the data, so that the performance of the computer can be fundamentally improved, and the operating efficiency of the computer can be improved [10].

8 Summary With the improvement of the national economy, science and technology have also continued to innovate. Computer technology has very important significance for human development. Among them, database technology is the most important thing, which not only brings people’s production and life it is convenient, and the application field of system of computer database is also expanding continuously. The system of computer database application in education information management can effectively improve work efficiency and ensure the safety and reliability of various information and data. However, there are still problems and shortcomings in the actual application process. Therefore, people should focus on the existing the problems and deficiencies should be solved by high-efficiency measures, so that system of computer database application in education information management is safer and more reliable, and the application field of database system is more extensive, which benefits mankind.

References 1. Li, T.Y., Wang, X.L.: Analysis of the application of system of computer database in educational information management. Sci. Technol. Innov. Herald 12, 102–106 (2013) 2. Luo, G., Wang, T.: Enterprise’s choice of database system. Sci. Sci. and Manag. Sci. Technol. 12, 140–143 (2011)

148

B. Hu

3. Wang, Z.F.: Discussion on the teaching of database and application courses. J. Southwest Agric. Univ. (Soc. Sci. Ed.) 4(06), 14–15 (2012) 4. Li, D.Y., Tian, Y.P.: Summary of the history and future development trend of database technology. J. Liaoning Provincial Transp. Coll. 10(01), 49–51 (2011) 5. Yan, S.: Design and implementation of community safety production supervision and management system. Geospatial Inf. 11(09), 74–76 (2016) 6. Jin, Z.: Discussion on geographic information service and service-based meteorological business system framework. Shanxi Agric. Econ. 9(10), 95–98 (2016) 7. Qiu, X.M.: Design and implementation of modern distance education teaching quality evaluation system. J. Guizhou Radio TV Univ. 11(03), 18–24 (2015) 8. Song, L.L.: Exploration and practice of LPFT teaching mode-take the course teaching of “computer application fundamentals” in secondary vocational schools as an example. J. Guizhou Radio TV Univ. 9(02), 22–25 (2015) 9. Wang, X.Y.: Application of REST architecture and workflow technology in WebGIS. J. Surveying Mapp. 11, 62–67 (2015) 10. Tian, J.B.: Research on the information construction of internal auditing from the perspective of University Governance. J. Anhui Univ. Technol.: Soc. Sci. Ed. 5(06), 110–111 (2015)

Intelligent Analysis and Processing Technology of Financial Big Data Based on Association Rule Mining Algorithm Shuting Chen(B) Central South University, Changsha, Hunan, China [email protected]

Abstract. With the development of Internet technology, more and more companies begin to obtain the financial information they need through data mining and other methods, and conduct financial risk research through data analysis. This article starts with association rules, and studies the intelligent analysis and processing technology of financial data. The purpose is to predict and judge financial risks through association rule algorithms and further improve the financial system. This article mainly uses data method, experiment method and comparison method to study data processing technology and financial analysis method, design the system and perform performance test on it. The test result shows that when the number of transactions is 80, the execution time of the system is 500s. As the number of transactions increases, the execution time becomes longer. Under association rules, parallel algorithms have more advantages than serial algorithms. Keywords: Association rule mining algorithm · Financial big data · Intelligent analysis · Data processing technology

1 Introduction With the advent of the information age, Internet technology has become an indispensable part of human life. Especially in the area of big data mining, many people have studied it. In recent years, people have gradually realized the close connection of the application of association rules to financial analysis. Therefore, it is very important to make good use of the association rule algorithm to research and forecast financial big data. The use of association rules to predict business transactions and analyze possible problems in a large number of potential and important transactions in financial activities is a way to play its role. There are many researches on the intelligent analysis and processing technology of financial big data based on association rule mining algorithm [1]. With the emergence of data analysis and data mining industries, many big data jobs have emerged in the workplace. In order to deeply analyze and predict the current situation and future development trend of big data talents, Huang Shan created a new professional dictionary related to big data and applied association rule mining algorithm to it [2]. Tong Bocheng © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 149–156, 2022. https://doi.org/10.1007/978-3-030-99616-1_20

150

S. Chen

said that since the concept of “Internet+” was put forward, it has produced significant social repercussions. The training of financial management talents must also adapt to the goals and training requirements of the big data era, and make corresponding changes and adjustments [3]. Therefore, the intelligent analysis and processing technology of financial big data based on association rule mining algorithm studied in this paper is very valuable. This article first studies data processing technology, focusing on the association rules in data mining. The second is to describe the relevance of financial analysis and discuss the problems in the analysis. Finally, a data mining technology financial analysis platform was built, and the performance of the platform was tested, and the results were obtained.

2 Intelligent Analysis and Processing Technology of Financial Big Data Based on Association Rule Mining Algorithm 2.1 Data Processing Technology Data mining refers to the analysis of large, incomplete and limited amounts of information to extract potentially meaningful and useful knowledge in order to obtain new views or solve existing problems. At present, the commonly used big data processing methods are: clustering technology, decision tree model, neural network-based algorithm, support vector machine, regression algorithm and so on. Data mining usually includes some artificial components, including data preprocessing into a form acceptable to the algorithm, and post-processing the discovered patterns to find novel and useful patterns [4, 5]. There may be more than one pattern found in a given database, and manual interaction may be required to select useful patterns. It mainly consists of the following steps: data cleaning, data integration, data selection, data exchange, data mining, pattern evaluation, and knowledge representation. Data mining tasks can be divided into two categories: description and prediction [6, 7]. In relational databases, continuous numerical values have significant mining value. At present, there are two problems that need to be solved urgently in the extraction of quantitative association rules: One is the processing of continuous numerical attributes. Efforts must be made in many aspects such as the discretization of continuous attributes, the recognition of frequent sets, and the generation and optimization of rules [8, 9]. (1) One of the most active search methods in data mining is association rule mining. There are five general steps to explore quantitative association rules: 1) Choose a discrete algorithm to determine the number of partitions. 2) Discrete interval, which maps the value of the discrete area of a categorical attribute or a numerical attribute to an integer identifier. 3) The generation of association rules. 4) Determine the association rules of interest at the output end. (2) The Apriori algorithm is one of the most classic algorithms in the field of association rule mining algorithms [10, 11]. Let X = {a1 , a2 , · · · , an } is the collection of all data items. Q is the collection of all transactions in the transaction database, where each R transaction is a collection

Intelligent Analysis and Processing Technology of Financial Big Data

151

of items, and each transaction R has a unique identifier, R ∈ X . The number of transactions containing itemsets in the transaction database Q is called the number of itemsets supported. The support of the itemset is: Support(f ) =

|F| × 100% |Q|

(1)

Among them, f is the item set, |Q| is the number of transactions in the database. The confidence of the rule R ⇒ S in the transaction database Q represents the percentage of the item set S supported in the transaction that supports the item set in Q. The confidence of the rule R ⇒ S is denoted as conf(R ⇒ S). From the definition of the confidence, the following formula can be obtained: conf(R ⇒ S) =

Support(R ∪ S) × 100% Support(R)

(2)

(3) Confidence is a measure of the accuracy of association rules. When mining massive data, not only the processing power of a single node is limited, but the storage capacity of a single node is also limited. The terabyte-level storage requirements of massive data make it difficult for a single node to deal with it. With the continuous growth of data, the scalability of a single node’s data storage is also limited. The MapReduce computing model can distribute the operations of large-scale data sets to each sub-node under the management of a master node to complete the final result [12]. 2.2 Financial Analysis Financial analysis is the use of financial statement data and other information, combined with the external environment of the company’s industry, market, etc., to analyze and evaluate the company’s past financial status, operating results, and future prospects to comprehensively and objectively evaluate the company’s operating status. The essence of financial analysis is to discover the financial problems of the enterprise and provide a basis for financial evaluation. (1) Limitations of traditional financial analysis methods 1) One-sidedness. Traditional financial analysis is mainly used to analyze the quantitative relationship between items in the company’s financial statements. Analysis methods are limited to simple calculations and summaries. With thousands of data sets, it is difficult to obtain an effective background of large amounts of data and information. 2) Hysteresis. Financial statements are the basis of traditional financial analysis methods and mainly provide the results of business activities that have occurred in the company. The company continues to generate new data, but traditional financial analysis methods cannot extract this information. The influence of the choice of different accounting and valuation methods on accounting information leads to the incomparability of financial analysis results to a certain extent.

152

S. Chen

2.3 Building a Financial Analysis Platform for Data Mining Technology Financial management analysis, as an important part of management accounting, can provide operators with information with reference value for decision-making on the basis of analyzing corporate information. Embedding data mining into the enterprise and serving the financial analysis field can condense and extract valuable knowledge from a large amount of business data and external data of the enterprise, and provide structured solutions to unstructured problems. (1) The construction of a data mining financial analysis platform should follow the following principles: 1) Guided by the basic principles of finance. The essence of data mining technology is the effective combination of data processing technology and model. However, the application of model is inseparable from artificial setting and screening. In order to maintain the scientificity and comparability of financial analysis conclusions, building a data mining financial analysis platform still needs to follow basic financial principles. 2) The principle of intelligence. Intelligentization is an important goal of building a data mining financial analysis platform, which is mainly reflected in: First, the platform should be effectively connected with the Internet of Things, accounting information systems, and external systems, and all information inside and outside the enterprise should be carried out in the form of data as much as possible. Finally, the financial analysis system based on data mining technology is no longer a simple module within the enterprise, but a networked and intelligent analysis system. 3) The principle of coordinating with the business, organizational process and culture of the enterprise. Therefore, the internal operation of the enterprise organization does not have rules that can be accurately calculated. Even advanced computer technology needs to adapt to the organizational process. At the same time, whether it is the use of data mining technology or any other advanced technology, the purpose is to serve the business and management of the enterprise. (2) Data mining financial analysis platform function 1) Index analysis function. The indicator analysis function is the most basic and most widely used function of the traditional financial analysis mode. The main improvements of the data mining financial analysis platform to the traditional financial analysis in the indicator calculation function are: first, real-time response. Second, the calculation results of financial indicators provided by the data mining financial analysis platform have higher accuracy. Third, the data source was expanded when the data mining financial analysis platform was constructed, and it was connected to external systems such as taxation, auditing, and the Internet. 2) Decision support and value discovery functions. As a branch activity of management accounting, the important function of financial analysis is to provide persuasive recommendations for decision-making needs. The application of data mining technology in the field of financial analysis has got rid of the dependence on the preset model in the traditional analysis mode. The value discovery function means that the platform, through the analysis of massive data, discovers important relationships hidden between the data that have not been discovered before. 3) Forecast budget function. Forecasting refers to the calculation of the company’s

Intelligent Analysis and Processing Technology of Financial Big Data

153

overall or single object’s operating conditions in the future based on historical data and current economic environment. Forecasts may be quantitative or qualitative. In the data mining financial analysis platform, forecasting and budgeting may be separate operating modules that can be opened to different authority or department personnel, but the two are closely linked in function and form the basis of financial early warning. The specific process is shown in Fig. 1:

Business data

Forecast business results Cost budget model

Actual results

Budget result

Financial warning

Fig. 1. Data mining financial analysis platform forecast budget function process

3) Monitoring and early warning function. Monitoring and early warning is a continuous real-time dynamic financial analysis. Financial crisis early warning is the most important application of data mining financial analysis platform in financial forecast analysis.

3 System Design Experiment Test 3.1 System Function Module Design In the business module, services such as information query, clustering, and association rule mining submitted by users are completed through the business module. The display module realizes the functions of users to view and analyze data clustering results, data generalization results, and association rule mining results. The workflow module plays a monitoring role on the work module. The data storage module puts various result data generated by mining now or historically into the file system for use by other modules. 3.2 Detailed Design on the Server Side The server side is implemented on the Hadoop platform. After the user proposes a task, the server side must complete distributed parallel computing. The main task of the data mining platform layer is to realize the parallelization of algorithms required for data association rule mining based on MapReduce. The distributed computing platform layer enables the Hadoop framework to implement distributed storage and parallel computing by configuring various parameters in the Hadoop cluster and the server that implements task submission.

154

S. Chen

3.3 System Operating Environment Set up an experimental platform, configure Hadoop and a programming environment, and the virtual machine communicates with the host through TCP/IP in a bridging manner. After the Hadoop environment is set up, use the cp command to push the program to the Hadoop platform, then use the hadoopdfs -put command to copy the data to HDFS, and finally use the hadoop jar command to send the calculation job to Hadoop. 3.4 System Performance Test In order to describe the throughput and throughput of the system’s concurrent processing, this paper tests the number of parallelism that the distributed system and the centralized system can support. The processing delay of the test system is the average submission delay of the business. This paper uses 8 experiments to test and analyze 800,000–6.4 million pieces of financial data.

4 Analysis of Test Results 4.1 Financial Platform Performance Test Results According to the system test results, the calculation of financial data in this paper is carried out by two algorithms. These two algorithms are carried out on the basis of association rules, that is, firstly apply association rules to analyze and filter financial data, and then use two algorithms to calculate. The results obtained are shown in Table 1: Table 1. Financial platform performance test results Parallel algorithm

Serial algorithm

80

500

500

160

550

600

240

596

700

320

643

800

400

691

900

480

736

1000

560

777

1100

640

814

1200

As shown in Fig. 2, the figure shows the execution time of the two algorithms under the association rules. When there are 180 transactions, the time can be the same for both. As the transaction volume increases, its execution time also becomes longer. Among the two algorithms, the distributed parallel algorithm has more advantages.

Intelligent Analysis and Processing Technology of Financial Big Data

155

80 1200

1200

1000 640

800

814

160

500 600 500

600

550 400 200 560

596 240

0

777

700 1100 736

1000 480

643 800 691

320

900 400 Parallel algorithm

Serial algorithm

Fig. 2. Financial platform performance test results

5 Conclusion The process of financial data mining mainly has the following steps: First, collect relevant information, including enterprise business, transactions, and personnel. This information is the important content that users need to understand for analysis and judgment of the company. Second, collect information related to association rules, including the development of various fields in the industry and the latest trends, etc. For the collection of these data, methods are needed for classification and comprehensive calculation, and analysis and forecasting. Therefore, this article intelligently analyzes and processes financial big data from the association rule algorithm, and designs a related platform for experiments. Experimental results show that the use of parallel algorithms has advantages in processing and analyzing large amounts of data.

References 1. Haiyang, L.: Research and application of association rule mining algorithm based on MapReduce. Inf. Commun. 194(02), 147–149 (2019) 2. Shan, H., Xuefeng, L., Jianhua, M., et al.: Analysis of talent demand in the field of big data based on association rules. Ind. Control Comput. 030(008), 78–80 (2017) 3. Bocheng, T., Minsi, Z., Jingdan, Z.: Financial big data analysis and processing ability training of financial management talents——based on the “Internet+” Era. Northern Econ. Trade 12(397), 82–83 (2017) 4. Jiani, Z.: Intelligent analysis of enterprise accounting information processing based on big data. Digital World 175(05), 91 (2020) 5. Yi, F.: Intelligent big data instigates the reform of management accounting——based on the perspective of financial analysis. Chin. Manage. Inf. Technol. 20, 13–14 (2017) 6. BaiRui, Z.N.: Problems and countermeasures in financial management in the era of big data and artificial intelligence. Accountant 000(002), 19–20 (2020) 7. Donghai, L.X.: New ideas for financial management in the era of artificial intelligence and big data. Mod. Bus. 000(006), 152–155 (2020)

156

S. Chen

8. Lingzhi, X.: Discussion on the transition from financial accounting to management accounting in the era of artificial intelligence. Nat. Bus. Situat.·Theor. Res. 000(022), 173–174 (2019) 9. Jian, X.: Intelligent analysis of accounting information processing under the background of big data. Mark. Observ. 790(02), 84 (2020) 10. Yanzhen, T.: Analysis on the intelligent development of financial management under the background of big data. Account. Study 000(013), 19–20 (2020) 11. Yong, H.: Research on enterprise intelligent financial decision support for big data. Nat. Circul. Econ. 2215(19), 54–55 (2019) 12. Xingfeng, L.: Research on the countermeasures of building intelligent financial analysis under the background of financial transformation. Bus. Account. 000(022), 109–111 (2019)

Accounting Information Quality Evaluation Based on BP Neural Network Evaluation Model Shengyi Yang(B) School of Economics and Management, University of Science and Technology Beijing, Beijing, China [email protected]

Abstract. The previous accounting information quality evaluation only used simple processing of accounting information indicators, such as averaging or artificially giving the weight of each indicator to weighted summation. The evaluation results are very subjective. Use BP network to establish a model of the accounting information quality evaluation system, obtain accounting information evaluation indicators through investigation and analysis, quantify them into definite data as its input, use BP neural network to evaluate the actual output, and use the previously obtained accounting information effect. As the desired output. When the error reaches the desired minimum value, the evaluation is considered successful. After the evaluation is successful, more accurate weights and thresholds can be obtained, and the network after the evaluation is successful is used to process another set of newly obtained accounting information evaluation indicators to obtain the accounting information quality evaluation results. This method is used in the evaluation of accounting information quality, which not only overcomes the subjective factors of experts in the evaluation process, but also obtains satisfactory evaluation results, which has a wide range of applicability. Keywords: BP · Evaluation model · Neural network · Accounting information · Quality evaluation

1 Introduction The nature, status, role, and production and operation methods of China in social and economic life are very different from those of large enterprises. In terms of accounting, the characteristics are mainly reflected in the organizational form, the establishment of accounting institutions, the quality of accounting personnel, internal accounting control, accounting resources and accounting supervision. Through the investigation of large enterprises and accounting affairs, we found that the larger the enterprise, the higher the reliability of accounting information [1]. The second is that most accountants have errors of irregularity and behavioral distortion. This is because accountants are often responsible for multiple tasks by one person, and it is difficult to maintain independence in their work. The third is that the state is not strong in supervision, which makes it easy to have a fluke mentality and manipulate accounts such as profits and taxes. Fourth, in terms of relevance, due to the lack of effective accounting information demand subjects, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 157–164, 2022. https://doi.org/10.1007/978-3-030-99616-1_21

158

S. Yang

most companies pay little attention to this. Only a few larger companies make financial budgets. Companies that have implemented computerization can disclose accounting information in a timelier manner [2]. Fifth, in terms of clarity, companies can prepare accounts in accordance with the unified national regulations, but some companies have relatively rough accounting vouchers and account books, and the digital cross-check relationship and written records are not clear.

2 The Characteristics of Accounting Information Quality Accounting information is formed in accounting and accounting analysis. The various data collected, processed, and sorted for accounting management are the description of the business activities of the enterprise using specific rules and methods. It has the characteristics of authenticity, comprehensiveness, purpose, timeliness, continuity, sociality and measurement. It is the link for the normal operation of enterprises, it reflects the occurrence of economic business in a realistic manner, and has the effect of promoting exchanges and cooperation between domestic and foreign enterprises. Information quality of accounting refers to the characteristics and the sum of the characteristics of products, processes, or services that meet regulations or potential requirements (or needs). The compatibility and comparability enable information accounting to be inspected, verify and compare with each other in different periods, which can truly reflect the objective and actual conditions of the enterprise [3]. Leake (1914) argues, the meaning of goodwill should not be confined to the existing relationship between the customer and the company, its business sense should be to obtain the present value of future expected “super profit”, “supper profit” means the future profit or advantage in excess of all expenditure in production [4]. Yang (1926) believes the recognition of goodwill must be based on the premise of increasing the earning capacity of the company [5]. The accounting standards of all countries will account the difference between the transaction cost and the carrying value or fair value of the net assets in the account of goodwill when the ownership transfer is occurring [6– 8], and internally generated goodwill will not be recognized. However, the subsequent measurement of goodwill is not such specific to compare with the initial measurement. Since the beginning of this century, accounting standards all over the world have adopted the “impairment-only approach” which carries out only impairment test at least once a year, replacing goodwill is amortized on a periodic basis [7–9]. Beatty and Weber (2006) analyze the incidence of goodwill impairment for a sample of 176 American listed companies which have goodwill balance more than the deference between company’s market value and book value, by Probit regression they concluded, debt contract, stock price earnings per share coefficient and managers’ revenue impel decision makers exercise discretion in goodwill impairment test, regulatory and the lack of goodwill reporting flexibility can inhibits the exercise of discretion [10]. Ramanna and Watts (2012) examined the annual data of 124 listed companies with evidence indicating a possible impairment of goodwill in the United States from 2003 to 2006, multiple regression findings confirmed that whether managers received cash incentives was positively correlated with the use of discretion in goodwill impairment test [12]. Glaum et al. (2015) studied the annual observational data of 8,110 companies with

Accounting Information Quality Evaluation

159

goodwill in account from 21 countries through the Logit regression model, and findings indicate that goodwill impairment incidence was positively correlated with managers’ revenue, “big bath” and “smooth” earnings management motivation, and infer discretion are used in the impairment decision test because of earnings management motivation, and also analyzed the inhibiting effect of governance intensity on the degree of use discretion by companies [10]. Lu and Qu (2016) studied 4700 observational data from 2007 to 2013 of China’s non-financial listed companies, through regression of Tobit and Probit model, they find that audit quality and ownership concentration had inhibitory effects on the use of discretion caused by earnings management motivation [10]. Kabir and Rahman (2016) studied the observational data of 1738 listed companies in Australia through the Logit and Tobit model, when the expected revenue is negative and the company has a change of CEO [11]. Glaum (2018) conducted Logit regression on the data of listed companies in 21 countries, further studied the timeliness of goodwill impairment, find governance intensity helps to improve the timeliness of goodwill impairment [12]. Table 1. The variables of goodwill impairment discretion in existing literature Author and date

Determinant of discretion exercise

Beatty and Weber (2006)

Contract; Bonus; Turnover; Stock Concern; Reporting Flexibility and Subversion

Ramanna and Watts (2012) Tenure; Contract; Bonus; Reporting Flexibility and Subversion Glaum et al. (2015)

Bonus; Turnover; Big Bath; Smooth; Reporting Flexibility and Subversion

Lu and Qu (2016)

Big Bath; Smooth; Turnover; Auditing; Equity Concentration; Subversion

Kabir and Rahman (2016)

Net Loss; Turnover; Reporting Flexibility and Subversion

Glaum et al. (2018)

Tenure; Big Bath; Smooth; Leverage; Reporting Flexibility and Subversion

This study

Operating Status; Long-Term Profitability; Reporting Flexibility and Subversion

Most of the existing studies mainly consider the impact of goodwill impairment on company’s assets, profits &losses, and managers’ income, if the company intends to avoid good impairment, and will use the discretion given by the impairment test to avoid impairment of goodwill. They believe goodwill impairment is the helpless action of the company under the pressure of supervision, or the means of earnings management when the performance is extreme. This study combined with the existing literature and the actual situation of the surge of goodwill impairment in the capital market, argues that most China’s companies have a complex attitude towards goodwill. Because in China, the issue of large goodwill balances has received widespread attention especially in recent years. The main variables of interest in the above literature on goodwill impairment discretion are shown in Table 1.

160

S. Yang

3 BP Neural Network Introduction It is a neural network evaluated based on the error back propagation algorithm. The learning rule is: use the gradient descent method to continuously adjust the network weight and threshold through error back propagation, and then the sum of squares of the error of the network is minimized [13]. BP neural network includes two processes: Input the data of information accounting evaluation indicators into the network through the input network layer, and then pass it back into the layer, repeating it again and again, until the error reaches the minimum expectation, and the network evaluation is successful [12].

4 BP Neural Network Information Accounting Evaluation Model 4.1 BP Neural Network Evaluation Principle of Information Accounting There are many factors that interfere with performance evaluation and more complex information accounting evaluation effect [13]. The performance evaluation, and the neural network is used to simulate the human brain operation, which can simultaneously realize the information transformation. First, clarify the BP neural network input and output, which are used for the information attribute management of the evaluated enterprise management performance and evaluation target; evaluate the BP neural network through a large number of known samples, obtain weights and thresholds through adaptive learning, and then describe the BP neural network. The network structure [14], and the relationship between simulation evaluation indicators and performance. Use test samples to detect until accurate results are obtained. The evaluation index of management knowledge is input in the BP neural network input layer and passed. The output results are compared and analyzed with the expected output results. If the accuracy requirements are not met [15]. 4.2 Evaluation of Information Accounting Based on BP Neural Network The input data is evaluated by neural unit operation of the hidden layer and the output layer [13], that is, the network output result. If the difference between the output result and the expected result is within the allowed error interval, the error signal will propagate backward, adjust and control the weight threshold of the neuron, ensure the output of the BP neural network and calculate the expected value through the cycle iteration process, make the mean square error of the network the minimum expected value, and end the learning process of the BP. The realization process of neural network algorithm is as follows: (1) Obtain the error function value and analyze whether the accuracy is lower than the expected error. If it meets the error requirement, then terminate the algorithm, otherwise, the BP neural network returns to the process, (2) until the value meets the error requirement, describe the expected output. The total error network calculation is performed, and the total error is adjusted for weight [16], and the processing efficiency is high.

Accounting Information Quality Evaluation

161

4.3 Neural Network Model of Information Accounting Evaluation According to the actual situation, the output layer and node number of output layer of BP neural network are set, and eight evaluation indexes of information accounting evaluation are determined. Therefore, the input layer of BP neural network has 8 nodes, among which the output layer has 1 neuron. Numerical description of information accounting evaluation results. Its value ranges from 0 to 1.The optimal number is obtained through experiments. First, you can evaluate a small number of nodes in the network. If the error obtained exceeds the expected target [5], the number of nodes will be continuously increased, and trial and error will be repeated until the error is minimal, and the final nodes number of the hidden layer will be obtained. This paper adopts trial and error method to detect. Through several iterations, when the number of nodes in the hidden layer is 10, the network has higher stability [6].

Fig. 1. Logical structure diagram of BP neural network

TANsig function and logsig function are used as transfer functions of hidden layer and output layer respectively [17]. BP network samples are divided into evaluation samples and test samples, from which a reasonable internal description can be obtained. The BP evaluation function was tested, and the results were analyzed. The input data of BP neural network contains multiple performance evaluation indexes of knowledge management. The samples for learning are used to evaluate neural network and adjust the weight to ensure the minimum output error and get the best evaluation result of information accounting, as shown in Fig. 1.

5 Application of BP Network Information Accounting Quality Evaluation Model As shown in Fig. 2, the neural network evaluation model, information accounting evaluation indicators (each indicator has a scoring range of 0–10): ×1: to be a teacher and influence students with their own behavior; ×2: appropriate amount of homework, serious correction, and patient answering; ×3: to motivate students Interest, inspire

162

S. Yang

innovative thinking; ×4: teacher’s clothing, speech and behavior, and mental state; × 5: information accounting attitude and information accounting skills; ×6: focused and clear lectures; ×7: able to express complex issues clearly; ×8: guide students Discuss and solve problems; ×9: Pay attention to information accounting interaction and teacher-student communication; ×10: make full use of modern information accounting methods. (1) Determination of the input layer number of neurons According to the information accounting evaluation indicators in our investigation, there are a total of 10 indicators. These 10 indicators can be used as the input neurons of the model, so the number of neurons in the input layer is n = 10. (2) Determination of the output layer number of neurons We use the evaluation result as the output of the network, and the number of output layers m = 1 (3) Determination of the hidden layers number of the network The hidden layer can be one layer or multiple layers. According to previous theoretical proofs, in the information accounting quality evaluation model, we choose the hidden layer as 1 layer (4) Determination of the hidden layer number of neurons In general, the hidden layer number of neurons is determined based on the convergence performance of the network. If the number of hidden layer neurons is too small, the network may not be evaluated or the network is not strong enough, but lots of hidden layer neurons will make the learning time too long, and the error may not be the best, so there is a way to determine. The problem of the proper hidden layer number of neurons [15].

Fig. 2. Evaluation model of BP neural network

After that, all the evaluation index data and the relatively complete information accounting quality evaluation results obtained before are input into the network, and the network is evaluated. We take the learning rate = 0.5, and the minimum value of the fixed error is = 0.00001. After the evaluation is over, a suitable weight threshold is obtained, and the evaluation index obtained from the subsequent investigation is processed with this weight threshold to obtain a suitable information accounting quality evaluation result. Save the 10 information accounting indicators of the above 8 samples in a txt file, read the data into the input network layer, and reach the set minimum error after 5116

Accounting Information Quality Evaluation

163

network evaluations, and get the modified weights and thresholds. And use the evaluated network to process new data (5.5 7.5 4 5 8 4.5 7 8 8.5 6) to obtain the actual output information accounting quality (6.901607).

6 Summary The BP neural network model can effectively overcome the traditional shortcomings information accounting quality evaluation methods due to its highly non-linear function mapping function and self-adaptation and self-learning capabilities and reduce the human influence factors in the determination of index weights in traditional evaluation methods, and its accuracy is high. After the above evaluation, we found that the error between the BP neural network model output value and the real value is relatively small, and the performance can completely meet the requirements of practical applications. In addition, the output accuracy of the network depends on input evaluation samples number. The larger the number of evaluation samples, the closer the evaluation value of the information accounting effect of its output is to the actual evaluation value.

References 1. Li, H.T.: Discussion on the quality characteristics of information accounting. Finance Account. Newslett. 21, 11–14 (2018) 2. Hu, L.: Thinking about the relevance and reliability of information accounting. Consum. Guid. 8(8), 127–131 (2017) 3. Zhu, Y.W.: The dilemma of relevance and reliability of information accounting quality. Account. Res. 7, 34–37 (1999) 4. Liu, X.F., Li, C.: Evaluation of service information accounting of logistics enterprises based on matter element analysis. Logist. Technol. 34(21), 94–97 (2015) 5. Zhang, R.T., Li, X.T.: Research on performance evaluation of innovation and entrepreneurship policy based on DEA model. J. Tianjin Univ. (Soc. Sci. Edn) 18(5), 385–390 (2016) 6. MOB. Accounting Standard for Business Enterprises No. 20 - Business Combination. Ministry of Finance of the People’s Republic of China, Beijing (2006) 7. IASB. Business Combinations. International Financial Reporting Standard 3. International Accounting Standards Board, London (2004) 8. FASB. Business Combinations. Statement of Financial Accounting Standards 141. Financial Accounting Standards Board, Norwalk (2001) 9. Lu, Y., Qu, X.: Earnings management motivations of goodwill impairment-the empirical evidence from Chinese a-share market. J. Shanxi Univ. Finance Econ. 38(7), 87–99 (2016) 10. Kabir, H., Rahman, A.: The role of corporate governance in accounting discretion under IFRS: goodwill impairment in Australia. J. Contemp. Account. Econ. 12(3), 290–308 (2016) 11. Glaum, M., Landsman, W.R., Wyrwa, S.: Goodwill impairment: the effects of public enforcement and monitoring by institutional investors. Account. Rev. 93(6), 149–180 (2018) 12. Zhao, S.K., Ma, Y.: Research on performance evaluation of agricultural supermarket docking supply chain based on BP neural network. Value Eng. 35(22), 88–90 (2016) 13. Tao, Z., Li, Z., Hu, P.: Construction of a learning performance evaluation model in the flipped classroom. Mod. Educ. Technol. 26(4), 74–78 (2016) 14. Li, P., Xu, T.L.: Image recognition technology of intelligent manufacturing system based on BP neural network. Mod. Electron. Technol. 39(18), 107–109 (2016)

164

S. Yang

15. Wang, B., Guo, D.Q.: Research on comprehensive evaluation of enterprise internal knowledge transfer performance based on BP neural network. Inf. Sci. 34(1), 141–145 (2016) 16. Wang, B.T., Wang, N.: Evaluation of Jiangsu Province’s innovative economy based on AHP? BP neural network. Sci. Technol. Manag. Res. 6(3), 44–47 (2004) 17. Xu, V., Mei, Q., Chen, Y., Zhu, W.: Research on the financial performance evaluation of GEM enterprises based on LMBP neural network. Sci. Technol. Manag. Res. 45(11), 75–78 (2018)

Design and Application of ERP System for Chinese State-Owned Enterprise Employees Based on Data Mining and Clustering Algorithm Dejie Ma and Huilan Jing(B) School of Marxism, Dalian University of Technology, Dalian, China [email protected]

Abstract. With the in-depth application of the ERP system, state-owned enterprises have gradually formed a large amount of data. How to use and analyze the data in the ERP system to help management decision-making has attracted people’s attention and has become one of the important goals of the ERP system construction. Data mining is a discipline that studies how to extract or “mine” knowledge from a large amount of data. Data mining and clustering algorithms are widely used. Starting from the basic concepts of ERP and data mining and clustering algorithms, this paper analyzes the problems faced by data mining and clustering algorithms applied to ERP based on the characteristics of ERP, data mining and clustering algorithms, and discusses the design and application of an algorithm-like employee ERP system. Keywords: Data mining · Clustering algorithm · State-owned enterprise · Employee ERP · System design

1 Introduction With the further changes in the global economic environment, the competitive pressure of state-owned enterprises is increasing, and ERP (State-owned Enterprise Employees Enterprise Resource Planning) embodies the most advanced state-owned enterprise management theory in the world today, and provides state-owned enterprise information integration. Therefore, it has become an inevitable choice for many state-owned enterprises to pursue management innovation and information construction. On the one hand, with the popularization and wide application of the ERP system, more and more data will inevitably be formed. How to extract useful knowledge from a large amount or even a massive amount of data to support management decision-making has become an increasingly important issue; On the other hand, data mining technology uses multidisciplinary technologies such as database systems, statistics, machine learning, visualization, and information science to study how to extract or “mine” knowledge from a large amount of data, and it has gradually developed from theoretical research to practical applications [1]. Currently, many different types of data mining systems have been produced due to data mining originates from multiple disciplines. However, most of them are general, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 165–173, 2022. https://doi.org/10.1007/978-3-030-99616-1_22

166

D. Ma and H. Jing

versatile data mining systems and general data mining algorithms. Thus, it is necessary to conduct analysis and research on data mining for specific applications in State-owned Enterprise Employees ERP system.

2 The Concept and Development of ERP The concept of ERP was first proposed by the American Garter Group Inc. consulting company in the 1990s. Its theory and system developed from MRPII, which greatly expanded the scope and depth of business management, and the scope of management involved all supply and demand processes of state-owned enterprises [1]. In a nutshell, ERP has the following definition: ERP is based on information technology, using the advanced management ideas of modern state-owned enterprises, comprehensively integrating all resource information of state-owned enterprises, and providing state-owned enterprises with decision-making, planning, control, and a comprehensive and systematic management platform for business performance evaluation. ERP is not only an information system, but more importantly, it is a management theory and management thought. It represents the most widely used and most effective state-owned enterprise management method in the world [2]. The core management idea of ERP is to realize the effective management of the entire supply chain, including logistics, capital flow and information flow [3]. The information system based on ERP theory mainly includes several modules: production plan management, quality management, equipment management, purchase management, inventory management, sales management, customer relationship management, cost management, and financial management. With the in-depth development of ERP applications, the scope of ERP applications has gradually expanded and is no longer limited to manufacturing. It has been applied to the financial industry, high-tech industries, post and telecommunications industries, and energy industries (electricity, oil and gas, coal, etc.), public utilities, commerce and retail, foreign trade, press and publishing, consulting services, and even healthcare, hotels, hotels and other industries [3].

3 Data Mining Overview With the development of information technology, people have accumulated more and more data, and obtaining valuable knowledge from a large amount of data has become an increasingly urgent need. Data mining in a broad sense is equivalent to knowledge discovery, while data mining in a narrow sense refers to a basic step of knowledge discovery. As a step of knowledge discovery, data mining is defined as: data mining is to dig out useful information from a large amount of data, that is, to discover hidden information from a large amount of incomplete, noisy, fuzzy, and random practical application data [4]. Compared with traditional data analysis methods (such as query reports), data mining has the following characteristics: First, data mining processes a large or massive amount of data; secondly, the purpose of data mining is to discover hidden and unknown in advance knowledge; again, data mining is more inclined to assign tasks to programs

Design and Application of ERP System

167

to complete automatically, which is also an application of artificial intelligence; finally, data mining is an interdisciplinary and an advanced data analysis method. Data mining uses a variety of different algorithms to complete different tasks. Descriptive data mining tasks describe the general characteristics of the data in the database. Predictive mining tasks make inferences on current data to make predictions [4]. The most basic and important data mining tasks are: 1) Association: Association analysis finds association rules, these rules show the relationship between attributes and attributes. 2) Clustering: Generate grouping marks, and divide the data into different clusters according to the principles of maximizing similarity within classes and minimizing similarity between classes; 3) Classification/prediction: find models that describe and distinguish data classes or concepts so that the model prediction class can be used to mark unknown object classes. At present, as an important step of knowledge discovery and the core function of business intelligence (BI), data mining has been used in many fields such as finance, telecommunications, sports analysis, and sales, but it is not widely used in manufacturing.

4 Clustering Algorithm Partitioning Under Big Data At present, big data clustering algorithms mainly include traditional clustering algorithms and sampling based clustering algorithms. 4.1 Traditional Clustering Algorithm At present, in the current public management work field, the real governance situation, and the social development needs of a large contradiction, and seriously affect the management work effect and quality. In the current national development background, China’s economic and legal construction level is constantly improving [5]. 4.1.1 Clustering Algorithm This type of division is based on the similarity of the points, and the division is performed according to the separation distance between each other in a single partition, but because it requires the user to define a parameter K without certainty in advance. Today’s representative partitioning algorithms are mainly CLARANS, PAN and K-Means [5]. 4.1.2 Hierarchical Clustering Algorithm It refers to dividing the data according to different levels, and the basis of division is based on the data bottom-up or top-down, as shown in Fig. 1. Each result after the division represents a hierarchical classification tree. Representative algorithms at this stage include ROCK, CURE, and BIRCH [6].

168

D. Ma and H. Jing

Fig. 1. Hierarchical clustering algorithms

4.2 Clustering Algorithm Based on Sampling The clustering algorithm based on sampling only needs to apply the clustering algorithm on one sample of the data set to be able to be generalized to the entire data set, focusing on smaller data, effectively reducing clustering time and saving space, and improving the economic benefits of data processing [6]. The size of the sample is estimated based on the following formula. Among them, f is the proportion of the specified data (0≦f≦1); n is the data rule; ni is the size of the cluster Ci. Sampling clustering mainly has the following three clustering algorithms. 4.2.1 Clustering Algorithm Based on Randomized Search (CLARANS) It is evolved from CLARA, inheriting the advantages of CLARA in processing largescale data, effectively saving running time and reducing the complexity of the algorithm. Its main purpose is to dig out its local optimal processing through a whole graph [7]. The method, as shown in Fig. 2, has obvious advantages in dynamic processing.

Fig. 2. Randomly selected clustering algorithm

Design and Application of ERP System

169

4.2.2 Balanced Iterative Reducing and Clustering Using Hierarchies (BTRCH) It can use its own data structure to filter all existing data points and store them in memory to improve data processing efficiency [5]. There are two important steps in this algorithm. The first is that it needs to scan the data points and build a tree in memory; the second is to use the clustering algorithm to process the leaf nodes of the established tree. 4.2.3 Clustering Using Representatives (CURE) The aforementioned algorithms generally adopt a single data point to represent a cluster. This mode is only applicable to spherical clusters. In practice, various types of clusters will appear [7]. CURE can solve this type of problem very well. Problem, use a set of scattered data points to represent this cluster, treat each data point as an independent cluster, and merge adjacent clusters in turn, based on the shortest distance, in each the stage uses the heap and KD tree to record and represent the distance between each cluster point and all the representative points of each cluster, as shown in Fig. 3. Using partitioning, local hierarchical clustering is performed on each partition until the threshold of a preset number of clusters is reached or two clusters need to be merged [8].

Fig. 3. Efficient clustering algorithm for large databases

5 The Design and Application of Data Mining and Clustering Algorithms in the State-Owned Enterprise Employees ERP System 5.1 Application Framework of State-Owned Enterprise Employees ERP System Based on Data Warehouse According to the characteristics of ERP system, combined with typical data mining system structure, a data mining application framework of ERP system based on data warehouse is shown in Fig. 4. ERP business database is an operational database in database technology, mainly dealing with online transactions, focusing on multi-transaction processing, data consistency and completeness, etc. The focus is not on the query and analysis of large amounts

170

D. Ma and H. Jing

of data [8]. The data warehouse is an analytical database, which is a long-term storage of data. The data organization, access methods and main functions of the data warehouse are all designed for the query and analysis of historical data, so the data warehouse can better support data mining [9]. The application framework of state-owned enterprise employees ERP system shown in Fig. 4 is described as follows: a) Data processing module: The data in the ERP business database is extracted, converted, and loaded, and converted into data that meets the requirements of the data warehouse. b) Data mining engine: state-owned enterprise employees use to perform data mining tasks, including association rules, clustering, classification, etc. c) Knowledge base: it help the state-owned enterprise employees to domain knowledge, used to guide the execution of data mining, and also used to evaluate the result model of data mining. d) Pattern evaluation: This module interacts with the data mining engine and also with the state-owned enterprise employees, and according to the relevant knowledge of the knowledge base, evaluates the interest of the data mining results, and filters the discovered patterns. The characteristics of the data mining application framework based on the data warehouse are: the data mining process is separated from the ERP business process [9].

Fig. 4. Data mining application framework of state-owned enterprise employees ERP system based on data warehouse

Design and Application of ERP System

171

5.2 Application Framework Based on State-Owned Enterprise Employees ERP Business Database Although data warehouse technology plays an important role in data mining, data mining is not limited to analyzing the aggregated data in the data warehouse. It can analyze more detailed business data existing in the ERP system [10]. Therefore, data mining based on the ERP business database. The application framework is shown in Fig. 5, marked as application framework. In this framework, data mining is no longer an independent system built on the data warehouse, but as an advanced module of the ERP system. On the one hand, it is an extension of the data mining analysis object, and on the other hand, it is also increasingly important [10]. In the application framework shown in Fig. 5, the ERP data mining module directly processes and analyzes business data, also performs model evaluation based on the knowledge base, and interacts with users. The necessary data preprocessing function exists as an internal function of the data mining module, which directly extracts and processes the ERP operation database [10]. In fact, large-scale database systems are increasingly developing in the direction of providing intelligent analysis and data mining functions. Therefore, it is feasible to use data mining technology to directly analyze ERP business data to a certain extent without establishing a data warehouse.

Fig. 5. Application framework of data mining and clustering algorithm based on ERP business database

5.3 State-Owned Enterprise Employees ERP Model Design The design of the data warehouse must go through three processes: conceptual model design, logical model design and physical model design.

172

D. Ma and H. Jing

5.3.1 Conceptual Model Design Determine the boundary of the system: In the research and decision-making needs of the SAP MM module, the decision makers are most interested in the evaluation of suppliers, the procurement plan of materials, and the procurement price. In order to analyze and accurately predict the data in all aspects, we set the boundary of the system as a collection of supplier management, procurement management and material management in the original SAP system MM module. Determine the subject area: the data in the data warehouse appears in the form of subject area, and each subject corresponds to the corresponding data type, which means that mining and analysis can be carried out in a space [11]. According to the analysis of the structure and related requirements of the MM module in the company’s SAP system, it is determined that the basic themes of this thesis are the supplier theme, material procurement theme and material theme. 5.3.2 Logical Model Design The logical model design is how each topic is realized, and the realization process is defined, and then the relevant content of the realization process is recorded in the data in the data warehouse. I won’t do too much introduction here. This article only takes the logical model design of the material procurement subject area as a demonstration, and the logical design of other subject areas is similarly completed [11]. Establishing the main model of material procurement includes selecting business data, defining granularity and dimensions, and determining facts and dimensions. 5.3.3 Physical Model Design The physical data model of the data warehouse is mainly to determine what structure the data will be stored in, what data search strategy to adopt, and where the data should be stored. These include: Determine the data search strategy and determine the storage location of the data.

6 Summary Based on the introduction of ERP and data mining concepts and clustering algorithms, this paper describes the application framework of the state-owned enterprise employees ERP database based on data mining and clustering algorithms. Starting from two application frameworks, the design of the state-owned enterprise employees ERP system is analyzed. Data mining technology, as an advanced technology for data analysis and decision support, will become an important content of ERP applications. With the further development of ERP systems and data mining technologies, data mining applications in ERP systems will surely help state-owned enterprises in providing faster and more effective decision support services, which will surely bring huge economic benefits for state-owned enterprises.

Design and Application of ERP System

173

References 1. Zheng, Ch.D., Wang, Q.Sh., Xi, Ch.: An empirical study on business process performance of Chinese enterprise ERP system implementation. J. Inf. 1(2), 68–72 (2010) 2. Song, X.D., Zhang, T.X., Liu, X.B.: Research on domain-oriented data mining system. Comput. Appl. Res. 25(5), 1432–1433 (2018) 3. Du, J.D., Chen, Ch.Ch., Huang, H.Y.: Design and implementation of spatial data mining system based on J2EE. Comput. Appl. 25(3), 710–712 (2015) 4. Li, B., Wang, J.S., Huang, W.: A new clustering algorithm in big data environment. Comput. Sci. 12, 247–250 (2015) 5. Zhou, L.H., Huang, C.: An algorithm for automatic fuzzy clustering. Statist. Decis. 20, 16–19 (2014) 6. Zhong, H.M.: Design and implementation of recommendation system based on clustering algorithm. Softw. Eng. 5, 3–6 (2017) 7. Liu, Y.P., Li, Z., Jiang, Z.: Fast clustering algorithm of brain fibers based on density peak search. J. Zhejiang Univ. Technol. 5, 567–572 (2019) 8. Zeng, A.L.: Research on the application of enterprise ERP based on big data technology. Dig. Technol. Appl. 11(2), 114–117 (2017) 9. Liu, Q.: Analysis and research on the new value of ERP data integrating big data technology. China Manag. Inf. Technol. 8(11), 65–68 (2017) 10. Li, X.H.: Research on the use of ERP system in coal enterprises based on data mining technology. Southern Agricult. Machin. 4(24), 221–224 (2017) 11. Tang, Y.X., Li, L.J., Wu, F.L.: Data mining technology and application in the big data era. Electron. Technol. Softw. Eng. 8(21), 118–121 (2017)

Construction of Credit Knowledge Service Model in Financial Field Based on Integrated SVM Data Stream Classification Algorithm Yi Liu(B) School of Economics, Shanghai University, Shanghai, China [email protected]

Abstract. With the widespread application of new technologies in the financial field, many new service models or companies have emerged in China. These new companies have transformed traditional financial service companies and have had a huge impact on the financial industry. Internet finance companies use mobile Internet, cloud computing, big data and other technologies to vigorously expand the fields of payment, lending, investment, asset management and other fields, and continue to expand applications and business fields, which has caused a lot of shock to the traditional financial sector. The purpose of this paper is to study the construction of a credit knowledge service model in the financial field based on the integrated SVM data stream classification algorithm. This paper establishes the construction of a credit knowledge service model in the financial field based on the integrated SVM data stream classification algorithm, and analyzes the specific content of the credit knowledge service in the financial field in detail. According to the experimental research in this article, the accuracy of financial data retrieval based on the integrated SVM data stream classification algorithm proposed in this article is very high. When the search result of the EASR algorithm of the flow classification algorithm is different from the detection result of the engine’s own algorithm, the accuracy of the EASR algorithm is higher, reaching about 92.2%. Keywords: Data flow · Integrated classification · Financial knowledge · Service model

1 Introduction Financial credit knowledge service is a financial sector credit knowledge service that combines financial data and credit knowledge to provide customers with personalized services according to customer needs. As an information-driven industry, the financial sector has a great demand for data, information and knowledge. Many financial business decisions require a large amount of information, data and knowledge as the fulcrum. With the explosive growth of data in the financial sector, how to give full play to the effective value of big data, and how to process big data computing services in a timely and fast manner, and provide users with high-value and high-efficiency services have become a hot topic of discussion today. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 174–181, 2022. https://doi.org/10.1007/978-3-030-99616-1_23

Construction of Credit Knowledge Service Model in Financial Field

175

In the research on the construction of the credit knowledge service model in the financial field based on the integrated SVM data stream classification algorithm, many scholars have studied it and achieved good results. For example, Pan Yongming, Wang Yajie, and Lai Mingzhao proposed to improve the supply chain the accuracy of credit risk prediction, based on the credit risk assessment of small and medium-sized enterprises, combined with machine training algorithms, developed a general model that can improve credit risk prediction. The model uses machine learning support (machine learning machine SVM) to develop a credit risk prediction model for small and medium-sized enterprises in the supply chain, and introduces game information (IG) to remove feature variables that have a significant contribution to the prediction results, and improves model input [1]. Liu Zijun and Fang Yue choose early warning indicators from six aspects: employment potential, growth potential, debt determination, cash flow, profit, and non-financial indicators. With performance indicators as input variables, SVM algorithm is used to develop a risk warning model for training [2]. Due to the high value and fast transmission characteristics of financial big data, it is necessary to provide more accurate and time-saving analysis services. This paper determines the specific formula of the SVM data stream classification algorithm, the credit knowledge framework in the financial field, the data screening method and the calculation parameters, and then uses two retrieval methods to compare the obtained data to prove the practicability and feasibility of the proposed scheme. And the specific financial domain credit knowledge service framework to analyze the problems that the knowledge service needs to face [3].

2 SVM Data Stream Classification Algorithm and Financial Field 2.1 SVM Data Flow (1) SVN technology was proposed by Vapnik. Because of its powerful ability to generalize the overall model, it does not fall into local minimums, nonlinear production capabilities, etc., and is appreciated by many researchers. It is based on a new structured learning method that reduces risks. It can solve the problem of using a limited number of high-dimensional input samples to build a model for the above problems, and the model is designed to perform a good promotion function. Generally speaking, its purpose is to find the best plane, which can draw the two types of samples on both sides of the plane as much as possible, and allow the data on both sides of the hyperplane to convert the farthest distance between the two points of the plane into a dual problem. In the past ten years, it has achieved full development and has become a standard tool in the field of machine learning and data mining. It has obtained a large number of successful applications and has become one of the most active research sites for sampling [4]. (2) Analysis of characteristics of data stream classification The isolation algorithms in traditional data mining are all offline algorithms, and offline algorithms must be based on known data. The semi-online algorithm of data stream classification can add the data obtained online into the calculation, such as ID3, SPRINT and other algorithms. The data contained in the data stream is a dynamic data type. This data volume is large, the update speed is fast, and it

176

Y. Liu

is constantly updated, and the total data volume cannot be calculated in advance. If you use the decision tree selection method for editing, you need to re-analyze and customize a new decision tree every time you access new data. To solve this problem, we must first analyze the characteristics of traffic data: (1) Once the data is processed, unless it is actually stored, it cannot be retrieved or processed or the cost of retrieving data is extremely high. (2) The data arrives in real time. (3) Each data increment is independent and not controlled by the application system. (4) The amount of data is large, and the maximum value cannot be predicted. Because the amount of data in the data stream is too large, not all of these data can be stored. The data flow is in low-level subtraction, and many analysis data elements can analyze high-level data. In order to analyze the technical process from the data flow, an online, multi-level, multi-dimensional, and low-cost customization method must be adopted. Because there is no unlimited data storage space, new technologies and algorithms need to balance recovery and storage space [5]. 2.2 Features of SVM Technology According to the recently published data analysis theory, many of the main advantages of this method are universal: (1) It is more versatile and can build a wide range of functions. (2) Strong stability, it is better than other methods for problem conversion, and there is no need to make many adjustments to the algorithm according to the specific problem. (3) High effectiveness and can solve practical problems. (4) Use simple optimization techniques to complete. For these reasons, it has received the attention of many scientists in the field of machine learning. Whether it is practical technical tools or scientific methods, there is still a lot of research space worthy of further discussion [6]. 2.3 Analysis and Model of Financial Knowledge Service Demand (1) Analysis of demand for credit knowledge services in the financial sector According to the analysis of references and the Internet, we can see that, according to research findings on financial credit knowledge at home and abroad, heterogeneous multi-source data can be used for more effective knowledge discovery. On the demand side, people need more professional and more intelligent and humanized financial credit knowledge services. With the advent of the era of big data, finance is a field driven by powerful data. Collecting timely and effective information to support decision-making is the core of the financial sector [7]. Financial market supervision departments also need to quickly receive and process information, discover risks and even predict risks, so as to control risk factors in advance.

Construction of Credit Knowledge Service Model in Financial Field

177

(2) Credit knowledge service model in the financial sector The structure of the model includes knowledge acquisition layer, knowledge processing layer and knowledge service function. The knowledge acquisition layer obtains configuration and unstructured data from the Internet, converts unstructured data into structured data, and provides support for knowledge processing. The knowledge management layer combines the business ontology understanding library created by the knowledge acquisition layer and the user needs and characteristics acquired by the knowledge service layer to create a user-based business knowledge base to solve specific user problems. Secondly, the knowledge service level provides users with financial services through the knowledge base of the financial sector [8]. 2.4 Service Model Composition (1) Knowledge acquisition layer Acquiring knowledge is the basis of credit knowledge. Financial information on the Internet has multiple sources and different structures. This information includes data, graphs, and text, most of which are unstructured data. Analyze, process and extract these data into structured data fusion to obtain the required credit knowledge. Therefore, the knowledge acquisition level captures unstructured and unstructured data, processes and transforms the information and then integrates, introduces important knowledge in the financial field, and establishes an understanding of the financial field ontology [9]. The knowledge obtained from different data sources has the characteristics of diversity and multi-source division of labor. Knowledge sharing can describe the greatest degree of decision-making in one sentence, and combine this knowledge into a knowledge base [10]. (2) Knowledge processing layer The knowledge processing layer combines the acquired knowledge with the user’s characteristics and user needs, and through the combination of knowledge, develops new knowledge that solves user problems and covers user needs. Personalized knowledge explores user interests through user behavior, and integrates knowledge according to relevant rules to provide users with a common understanding. (3) Knowledge service layer The knowledge service layer is the instant service unit faced by users. Through various specific business modules, user needs and questions are collected, and the user needs and questions are exported to user archives and user query data of the operating system through the local ontology, forming a template that users love. At the same time, the service design platform can create a knowledge service system based on the business intelligence database created by a specific financial service unit at the knowledge processing level to provide users with relevant financial services.

178

Y. Liu

3 Bond Evaluation Experiments Based on the Financial Sector 3.1 Experimental Setup and Process The test data is a summary of 1,500 bond lists and 1,400 new stock prospectuses. According to the search results of the Baidu search engine, each search corresponds to 10 answers and conditions. Among them, the training plan uses a test set of 1,500 contract announcements and 1,400 new product announcements. Enter the bond abbreviation and the listing announcement in the Baidu search engine, and after the search is completed, the top 10 search results of Baidu are searched, and the ranking of the search results is recorded. The Baidu search engine regards the search result ranked first as the optimal result. According to the proposed EASR algorithm to re-scoring and sort Baidu’s search results, the optimal result of the EASR algorithm and Baidu’s optimal result may be the same or different. Compare the results of various algorithms. 3.2 Experimental Formula (1) OPtf-idf feature weight calculation method The expression of the vector space model of the sample is d{t1 t2 . . . . . . tn }, the number of features of the sample is set to n, and the number of times ti , appears in the text is nti , and the value of the weight corresponding to it is wti : wti = tf (d , ti ) × idf (ti )

(1)

Among them, tf is the word frequency, and idf is the frequency of the reverse document. (2) Balance factor If ti , tj has the same tf-idf value in the second category, but the feature and text are not the same in the two categories, then the balance factor Factor is introduced to the feature:  min(df + (ti ), df − (ti )) (2) Factor(ti ) = max(df + (ti ), df − (ti )) In this formula, df − (ti ) represents the feature ti refers to the frequency of documents appearing in the negative category, and df + (ti ) refers to the frequency of documents appearing in the positive category of ti , and the value of the balance factor ranges from 0 to 1. 1 Score(d , c) ( 2

K

i=1 si

K

+

K ) N

(3)

In the above formula, N is the number of categories, C refers to a certain category, d refers to text, and K is the number of classes supported by the classifier.

Construction of Credit Knowledge Service Model in Financial Field

179

4 Bond Experiment Based on Financial Knowledge 4.1 Retrieval Combination Test The results as shown in Table 1, after machine selection of the data in the corporate bond search result set, when α = 0.3, β = 0.3, and γ = 0.4, better search results can be obtained. The detection accuracy rate is about 88%. Table 1. Best combination test table Parameter combination (0.1,0.4) (0.2,0.3) (0.3,0.3) (0.5,0.3) (0.7,0.2) (0.9,0) (1,0) Correct rate%

0.800

0.818

0.880

0.862

0.838

0.824

0.781

4.2 Retrieval Comparison Based on SVM Data Stream Classification Algorithms In the experiment of detecting EASR algorithm and Baidu retrieval, about 1,400 keywords were retrieved. When the detected results are different, they are made into a table. According to the graph data, as shown in Table 2. Table 2. SVM-based data flow retrieval table Search method

Related Irrelevant Number Accuracy of questions

Baidu’s 350 own algorithm

83

423

80.58%

EASR 390 algorithm

34

423

92.20%

As shown in Fig. 1, when the retrieval results of the two algorithms are different, the accuracy of Baidu’s own algorithm reaches about 80.58%, while the accuracy of the retrieval result of the EASR algorithm is 92.20%. This shows that in the case of different retrieval results, the retrieval efficiency of the EASR algorithm is higher. In addition, since the time of the experiment is still much later than the time when the securities are listed, the profit of the EASR algorithm is large in a short period of time, which also shows. That time factor has a very important reference value for the financial industry and the collection of financial information.

180

Y. Liu

Fig. 1. Comparison of stock search results

5 Conclusions This article introduces the combination of multi-source data knowledge and multidistributed knowledge base. Establish user characteristic data and user query data, design user preference templates according to user needs and problems, and further combine user query data and user characteristic data with basic business theories, and create business at the level of knowledge creation to understand users’ work needs and solutions Solution The solution to the user’s problem is finally sent to the user through service programming.

References 1. Pan, Y., Wang, Y., Lai, M.: Credit risk prediction of supply chain financing enterprises based on IG-SVM model. J. Nanjing Univ. Sci. Technol. (Nat. Sci. Edn) 44(01), 117–126 (2020) 2. Liu, Z., Fang, Y.: Construction of financial risk early warning model for information technology enterprises based on SVM. 2020(10), 67–73 (2021) 3. Duan, Y.: Construction of first-class university course based on artificial intelligence and neural network algorithm. J. Intell. Fuzzy Syst. 40(12), 1–12 (2020) 4. Bi-hui, C.: Construction of MLR model based on SPSS value pricing of TCM medical services in Sichuan Province. J. Phys. Conf. Ser. 1176(4), 42041 (2019) 5. Bin Da Jam, A.A., Mallick, J., Talukdar, S., et al.: Integration of artificial intelligence–based LULC mapping and prediction for estimating ecosystem services for urban sustainability: past to future perspective. Arab. J. Geosci. 14(18), 1–23 (2021)

Construction of Credit Knowledge Service Model in Financial Field

181

6. Caymaz, B., Aydin, A.: The effect of common knowledge construction model-based instruction on 7th grade students’ academic achievement and their views about the nature of science in the electrical energy unit at schools of different socio-economic levels. Int. J. Sci. Math. Educ. 19(2), 233–265 (2021) 7. Mi, K.K.: Project-based learning experience in the construction of intercultural knowledge. Mod. English Educ. 20(2), 1–18 (2019) 8. Deng, X.: Construction of industrial ecological chain for characteristic small town: a case study of VR Town at Gui-An New District. (2017-1), 89–98 (2021) 9. Huang, Y., Mai, Q.: Research on the construction of O2O teaching system of cross-cultural knowledge in College English based on MOOC. J. Intell. Fuzzy Syst. 5, 1–10 (2021) 10. Zeng, H.: A comparison study on the era of internet finance China construction of credit scoring system model. Bangladesh J. Multidiscip. Sci. Res. 2(1), 1–22 (2020)

Study on Career Development and Digital Value-Oriented Countermeasures of Aesthetic Education Teachers Based on BP Neural Network Qianqian Chen and Jie Sun(B) Wuhan Textile University, Wuhan, Hubei, China [email protected]

Abstract. The education policy requires the adherence to the five educations of morality, intelligence, physique, beauty and labor. The standpoint of aesthetic education in colleges and universities is clear, but it lacks stamina. The professional development of aesthetic education teachers and value-oriented digital learning strategies are in trouble. Analyze the actual value and future trends of the professional development of aesthetic education teachers in colleges and universities from a comprehensive perspective, realize digital education and teaching strategies, and strive to use the digital value -oriented learning mechanism of BP neural network to meet the professional development of aesthetic education teachers in colleges and universities, and use modern technology to provide aesthetic education teachers from the side. Solve the problems of discipline construction, teacher training, practice base construction, etc., make up for the shortcomings of aesthetic education in colleges and universities, and realize digital value-based countermeasures. Keywords: BP neural network · College aesthetic education · Career development · Value orientation · Countermeasure research

In recent years, due to the orientation of national policies, colleges and universities across the country have gradually paid attention to aesthetic education. With regard to aesthetic education, many people define it narrowly as art education. Although it is mainly art education, its aesthetic scope is far higher than art education. Aesthetic education in the new era is aesthetic education, which can impart aesthetic knowledge and cultivate aesthetic and creative abilities. In addition to artistic beauty, it also includes natural beauty, life beauty, scientific beauty, etc. [1]. School aesthetic education is no longer a specialty, no longer an optional subject, but basic education in all disciplines that can contribute to the all-round development of the human person and the overall progress of society. In 2018, General Secretary Xi Jinping proposed at the National Education Conference: “Cultivate socialist builders and successors for the all-round development of moral, intellectual, physical, and American labor”. “Aesthetic education” was mentioned as an important position. However, “aesthetic education” has been an indispensable part of education since ancient times. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 182–188, 2022. https://doi.org/10.1007/978-3-030-99616-1_24

Study on Career Development and Digital Value-Oriented Countermeasures

183

1 Analysis of the Development Status of Aesthetic Education in Colleges and Universities Although the aesthetic education work of colleges and universities is being valued, due to the incomplete long-term system construction, the overall promotion of aesthetic education is facing many difficulties, especially in terms of human, financial and material resources, and the development of various colleges and universities is extremely uneven. In order to further understand the current status of aesthetic education work in colleges and universities and to analyze and solve the difficulties of professional development of aesthetic education teachers in colleges and universities, the study conducted a questionnaire survey for more than 100 general colleges and universities nationwide, mainly divided into three sections: curriculum system, policy support and career development. The survey results show that 88.18% of colleges and universities have art majors, 71.82% have aesthetic teachers specializing in public art courses, 70% of colleges and universities offer public art courses for all students and include credit management (Fig. 1).

Fig. 1. Arts majors set proportions

Judging from the above data, most colleges and universities have arts majors related to aesthetic education. Among the arts majors, arts, music and dance are dominated. Other majors have developed to varying degrees, but judging from the data on the professional development of aesthetic education teachers in colleges and universities, the situation is not optimistic, analyzed from the following three aspects: 1.1 The Curriculum System Is Uneven The curriculum system is the sum of the teaching content and process of aesthetic education in schools. It measures the level of operation and overall planning of aesthetic education in a school, and determines the knowledge structure that students will acquire through learning. The school’s aesthetic education curriculum mainly includes music,

184

Q. Chen and J. Sun

fine arts, calligraphy, dance, drama, opera, film and television courses. Although most colleges and universities have art majors, most do not offer full-scale aesthetic education courses. In the survey, only 23.64% of colleges and universities offer full-scale aesthetic education courses. The curriculum system is the vehicle for achieving the goals of training and the key to ensuring and improving the quality of education. The incompleteness of the curriculum system also reflects the uneven teaching staff from the perspective. The curriculum system and the faculty are important factors in the overall strengthening and improvement of the aesthetic education work in colleges and universities. The incomplete curriculum system and insufficient teachers have largely constrained the development of aesthetic education work in colleges and universities. 1.2 Insufficient Policy Support The art majors offered by colleges and universities can only meet the needs of art students. In the case of an incomplete curriculum system, it is difficult to meet the needs of ordinary non-professional students. Aesthetics work in colleges and universities requires the planning and implementation of aesthetic education for all students through the establishment of specialized institutions, personnel and facilities, which require policy support for aesthetic work in universities. The policy support of colleges and universities is mainly reflected in the software and hardware conditions, survey data show that: 36.36% of colleges and universities have not set up a dedicated unit or department responsible for aesthetic education; 47.27% of colleges and universities have not set up executive leadership positions; 41.82% of colleges and universities do not build facilities and special classrooms to meet the needs of teaching and practical activities; 46.36% of colleges and universities cannot Meet the minimum requirement of 2 credits for all students to complete a public art course. Imperfect construction of aesthetic education institutions, insufficient venues for curriculum teaching and practical activities, and failure to meet the standard of universal education for all students will not be able to truly implement the comprehensive strengthening and improvement of aesthetic education in schools in the new era [2]. 1.3 Lack of Strength in Career Development The professional development of teachers is the process of continuous development and improvement of teachers’ ideas, knowledge, abilities, etc. as professional staff. The career development of college aesthetic education teachers is also in a very awkward situation, data shows: 74.55% of aesthetic education teachers are not promoted separately; 65.45% of college aesthetic education teachers need to take into account other non-aesthetic work; 49.09% of college aesthetic education teachers undertake extracurricular activities, after-school services and other second classroom guidance and teaching tasks are not counted in the workload, and 43.64% The proofreading of There is a limit on teaching hours for aesthetic education teachers; 55.45% of colleges and universities do not account for a certain percentage of awards and commendations such as the Teaching Achievement Award. This set of data shows that the career path of aesthetic education teachers in colleges and universities is unclear. It also reflects that teachers’ teaching and scientific research are not adequately guaranteed, and that teachers’ job title promotion,

Study on Career Development and Digital Value-Oriented Countermeasures

185

scientific research results, etc. are not supported, which directly affects the enthusiasm and creativity of college teachers in aesthetic education.

2 Value Orientation of the Integration of Industry, Education and Research With the development of science and technology, “Internet + education” has become a new form of innovation and development in the information age and knowledge society, a new form of education combined with the fields of Internet technology and education. The Ministry of Education strongly supports the integration of maternity and education, cooperation between schools and enterprises, which have become the new popular trend in education. This trend is also applicable to university aesthetic education based on full-disciplinary education. “Integration of industry, education and research” means that education and industry jointly carry out technical services, production and scientific research cooperation activities related to teaching to achieve the goal of talent training [3]. School-enterprise cooperation promotes the collaborative development of teaching and scientific research, forms an integrated platform for production, education and research, promotes the collaborative development of discipline construction and pedagogical research, and builds a platform for academic discipline construction, teachers’ teaching research, and scientific research innovation transformation. School-enterprise cooperation takes the development of applied scientific research, the transfer of scientific and technological achievements, and technical services as its hand, the promotion of research and production, and the integration of production, education and research through production. The integration of production, education and research can bring economic benefits and social value to the aesthetic education work of colleges and universities. At the same time, it will be the future guide for the professional development of aesthetic education teachers in colleges and universities. 2.1 In Line with the Professional Development of Aesthetic Education Teachers in Colleges and Universities Aesthetic education is the basic education in all education, which is adapted to the development of the times and the needs of society. The professional development of university teachers is a process of transformation from young teachers to expert teachers. It is a lifelong professional activity that explores, builds personal career paths, and achieves achievements. Under the existing environment of aesthetic education in colleges and universities, it is difficult for teachers of aesthetic education to fully develop people based on teaching and scientific research alone. If they can combine teaching and scientific research with market demand through the use of modern technology, they can break the limitations of liberal arts and sciences, promote the integration of new engineering and liberal arts, and pave the way for personal careers. The result of teaching is talent training, the result of scientific research is cultural output, “production” represents the needs of the market, and “teaching”, “research” and “production” are integrated, and the three promote each other and influence each other. In the context of the State’s policy of attaching great importance to aesthetic education in schools, the integration of education

186

Q. Chen and J. Sun

and education can bring talents and cultural output into the market in advance, explore and establish an effective market operation mechanism to meet the needs of society. At the same time, it promotes the development of “dual teacher” teachers in colleges and universities and helps them become experts in the field. 2.2 Promote the Performance of Teachers of Aesthetic Education in Colleges and Universities Most faculty of aesthetic education in colleges and universities possess the knowledge structure of a specialized subject, with high professional level and strong theoretical knowledge. However, they are not able to apply knowledge in other subject areas and have insufficient practical experience in cross-integration. Without cross-disciplinary integration, the career path and performance level of aesthetic education teachers in colleges and universities will be quite uniform. If teaching and scientific research are confined to the campus without being put into practice to society, and if they lack the practice verification, comparison, adjustment and improvement of the market, they will not be able to promote the improvement of their own business quality, nor can they help students improve their social practice ability [4]. The integration of industry, education and research is beneficial for college aesthetic education teachers to try to closely link art education with other disciplines. Through teacher training and the creation of practice bases, etc., they expand their research fields and promote better performance. The integration of maternity, education and research creates conditions for teachers of aesthetic education in colleges and universities to integrate their subject resources and provide social supply, and at the same time motivate teachers to innovate themselves, enhance their professional level and performance ability. 2.3 Enhance the Brand Effect of Aesthetic Education in Colleges and Universities The collaborative education model of integration of industry, education and research helps colleges and universities to build diversified, characteristic and high-level aesthetic education discipline systems, innovate talent training mechanisms, create high-end aesthetic education think tanks, and build a brand of aesthetic education work. The integration of industry, education and research supports university aesthetic education work to carry out discipline construction and curriculum system reform, improve teaching conditions, establish a professional experimental training platform, develop experimental teaching cases and digital resources with practical and promotional value, and create regional and even national leading experimental teaching brand projects. The goal of the integration of production, education and research reform in colleges and universities is to create brand projects that break the inherent thinking of traditional art education, and use modern technology, such as simulation teaching, virtual experience hall, etc., to create brand projects that meet the requirements of the times and the needs of contemporary students, and can benefit ordinary students. College aesthetic education can also create unique brand projects based on school professional advantages and regional characteristics, give full play to the demonstration and lead effectiveness of brand projects, and promote local economic and cultural prosperity [5, 6].

Study on Career Development and Digital Value-Oriented Countermeasures

187

3 Research on Countermeasures for Professional Development of College Aesthetic Education Teachers 3.1 “Teach” to Learn and Learn and Strengthen the Penetration of Aesthetic Education With the ever-changing era of online informatization, college aesthetic education teachers should pay attention not only to teaching, but also to their own learning and improvement in the teaching process. The teaching of aesthetic education in the new era is not about the transfer of simple music, painting, dance and other course techniques, but rather to diverging students’ thinking, improving the level of thought, developing brain intelligence, and enhancing creative ability through aesthetic education courses. Aesthetic education has the important functions of cognitive beauty, perceiving beauty and creating beauty, and can penetrate any discipline into basic education. The integration of industry, education and research assists faculty of aesthetic education in frontline colleges and universities to carry out teacher training, enrich knowledge reserves, improve professional literacy, teaching and practical ability, and help teachers to permeate aesthetic principles, humanities and emotional education into culture, politics, economics, physics, chemistry and other courses. With the help of school-enterprise cooperation platform, college aesthetic education teachers strive to shape themselves into “double teacher” teachers, create quality courses, create practical training bases, subtly let students recognize Chinese excellent traditional culture, revolutionary culture, advanced socialist culture, enhance cultural self-confidence, create value for society and contribute to the country, which is the meaning of aesthetic education work [7, 8]. 3.2 “Research” to Produce Fine Products and Exert Cultural and Creative Influence After completing the teaching, teachers use scientific research means and equipment to study and further explore their teaching results, to justify the essence and rules of teaching, and on the other hand to provide theoretical basis for the innovative results. Research work is divided into basic research, applied research and development research. When basic theory research is completed, new theories found in basic research need to be applied to specific target research, open up the application path of basic research, and turn them into practical technology [9]. In school-enterprise cooperation, college aesthetic education teachers can improve practical teaching conditions through VR, AR, and MR experimental training simulation platform or virtual technology, establish professional experimental training rooms and experience halls, develop experimental teaching bases and digital resources with practical and promotional value, and develop a comprehensive mechanism to educate people. Aiming at the teaching effect and practical application of art courses, practice bases such as concerts, group dances, textbook plays, practical workshops, and excellent traditional cultural experience pavilions benefiting all students are vigorously promoted [10].

188

Q. Chen and J. Sun

4 Epilogue College aesthetic education is a higher-level aesthetic education, with emotional education as the core, art education as the main body, and under the guidance of Marxist aesthetic principles, to educate students to strive for realistic and artistic aesthetic creation. The function of aesthetic education is reflected in quality, cultivation, ability, realm, etc. and is a basic education that can permeate other disciplines. As the main force of the establishment of morality people in colleges and universities, aesthetic education teachers can help students cultivate moral sentiments, improve aesthetic ability, establish correct world view, values and outlook on life, and stimulate innovation and creativity. Therefore, the professional development of college aesthetic education teachers is a problem related to education. In the context of the “Internet+Education” era, university aesthetic education teachers rely on the school-enterprise cooperation platform, from the perspective of integration of production, education and research, combine theory and practice, open up the boundaries of arts and sciences, establish a reasonable aesthetic education curriculum system, build quality courses and brand projects, promote professional ability improvement, use high-tech to establish practical training base, and provide support and guarantee for career development. Acknowledgements. Exploration and Practice of “Internet+” Innovative and Entrepreneurial Compound Talents “Three Learning and Three Realities” Product-learning Collaborative Education Model of the Ministry of Education in 2020 (No.: 201902247014).

References 1. Huang, G.: University Aesthetic Education, vol. 5. Peking University Press (2018) 2. Li, Q.: Research on Cai Yuanpei’s Thought of “Replace Religion with Beauty”, vol. 9, p. 74. Central Compilation Press (2017) 3. Huan, W.: Research on countermeasures of vocational education major construction under the background of integration of maternity and education. Vocat. Tech. Educ. 33, 47 (2020) 4. The General Office of the Central Committee of the Communist Party of China and the General Office of the State Council issued opinions on comprehensively strengthening and improving the sports work of schools in the new era, vol. 4. People’s Daily (2020) 5. Department of Physical Education, Health and Arts Education, Ministry of Education. Deepen reform and innovation to lead the promotion of high-quality development of aesthetic education in schools [EB/OL], December 2020. http://www.moe.gov.cn/fbh/live/2020/52806/sfcl/ 202012/t20201214_505281.html 6. Liu, W., Li, H.: The realistic dilemma and optimization path of “Five Birds” integration. J. Heilongjiang Inst. Technol. (Comprehens. Edn). 12 (2020) 7. Zhou, S., Meng, J., Jiang, S., Yang, R.: Research on early warning of IT teacher occupation depletion based on BP neural network. Comput. Digit. Eng. 42(02) (2014) 8. Liu, Q.: Exploration and practice of thinking and governance construction in musicology major courses: taking music analysis as an example. J. Beihua Univ. (Soc. Sci. Edn) 3 (2021) 9. Zhang, G.: The development path, realistic dilemma and development path of School Aesthetic Education. J. Daqing Norm. Univ. (2021) 10. Meng, W.: Logical beginnings, realistic dilemmas and breakthrough path of Aesthetic Education in Colleges and Universities. J. Natl. Acad. Educ. Admin. 12 (2020)

Lung Cancer Based on Big Data Technology Disease Data Management Status Quo Yonghong Ma, Jiao Tan(B) , Dongning Zhang, Ke Men, Mingjuan Shi, and Ying Cao School of Public Health, Xi’an Medical University, Xi’an 710021, Shaanxi, China [email protected]

Abstract. At present, lung cancer is very common, with the highest mortality rate. As for the pathology of lung cancer, we are still at the research stage. The main research of this paper is the analysis of lung cancer based on big data technology disease data management status. This article collected a total of 50 medical examination data. The field types include three types: numeric type, sub-type, and text type. The data mining model of this article is mainly realized by the data mining function of Clementine12.0. The feature selection component can calculate the importance of each field and the predicted field, and can select fields with greater importance for analysis. This paper extracts the features of the last convolutional layer of the model, and then performs batch quadratic superposition and fusion of the feature maps, and finally takes the average value as the input data for the secondary detection. Experiments have found that the parallel algorithm designed in this paper can increase the computational efficiency by about 9 times. The results show that big data technology improves the efficiency of lung cancer diagnosis. Keywords: Big data · Disease data management · Lung cancer · Data analysis

1 Introduction With the emergence and development of a new round of information technology in the world, people have ushered in a technological and economic era dominated by Internet technologies such as cloud computing, Internet of Things, and big data. With the progress of human society, the development of biomedicine, and the gradual integration and opening of information, advanced technologies can be used to answer medical questions in many biological and medical fields. Especially in the 21st century, high-throughput sequencing technology has been developed. With rapid development, people’s understanding of cancer has gradually deepened. At present, as the degree of hospital informatization has been greatly improved, the information in the hospital information system database has shown explosive growth, and a huge amount of health and medical big data has been accumulated [1]. In addition, routine lung cancer detection means radionuclide examination, metastatic lesion biopsy, mediastinotomy, etc. [2]. The above methods have more or less major limitations, such as complicated operations, high costs, long time periods, and trauma to patients, etc., and it is difficult to effectively detect early lung cancer, and they cannot meet the needs © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 189–196, 2022. https://doi.org/10.1007/978-3-030-99616-1_25

190

Y. Ma et al.

of large-scale screening [3, 4]. The data of lung cancer medical knowledge graph should be updatable, so the data source web crawler system in data extraction must be included in the knowledge graph system [5]. When the data source database is updated, the data source data is re-crawled and the knowledge graph is updated [6]. The knowledge map of lung cancer medicine needs to analyze entities through algorithms, so as to realize the display and discovery of domain knowledge, and provide suggestions for drug analysis and precision treatment [7, 8]. Users can not only fully understand lung cancer drug treatment pathways and master the knowledge of lung cancer, then they can also display the discovered knowledge intuitively through semantic retrieval or table images [9, 10]. With the continuous improvement of people’s living standards, health awareness is also gradually enhanced. The traditional medical institutions’ medical resources and services can no longer meet the growing medical needs of people. The original medical service mode is facing great challenges, and the transformation and reform of the medical industry is imminent. Many hospitals in China, especially large general hospitals, are gradually stepping into the information and digital era.

2 Big Data Technology Disease Data Management 2.1 Big Data Technology Health and medical big data has a wide range of sources, and it is also generated from Internet medical treatment related to diseases, health, and seeking medical advice. A model with the following structure is called a p-order autoregressive model, denoted as AR(p): Xt = ϕ0 + ϕ1 Xt−1 + ϕ2 Xt−2 + ... + ϕp Xp−1 + εt

(1)

In the formula, Xt represents a random variable. The expression of the partial correlation function is as follows: ⎧ ⎪ αˆ 11 = ρˆ1 ⎪ ⎪ ⎨ K K   αˆ k+1,k+1 = (ρˆk+1 − ρˆk+1 αˆ kj )(1 − ρˆj αˆ kj )−1 (2) ⎪ j=1 j=1 ⎪ ⎪ ⎩ αˆ k+1,j = αˆ kj − αˆ k+1,k+1 • αˆ k,k−j+1 (j = 1, 2, ..., k) In response to this problem, a typical solution is to parallelize the existing data processing methods, divide a huge and complex task into several independent subtasks, and hand them over to distributed computing nodes to complete, and finally summarize the results. This will greatly reduce the computational burden of a single computing node, but will produce corresponding message transmission overhead. The computing structure of traditional serial algorithms needs to be re-divided and integrated, and efficient parallel strategies have become a hot research topic today. 2.2 Lung Cancer Since lung cancer is the disease with the highest morbidity and mortality in the world, the mechanism of disease development is being studied. It has been found that mutations in

Lung Cancer Based on Big Data Technology Disease Data Management

191

the EGFR gene can permanently activate and increase downstream signaling pathways, regardless of ligand binding. The ALK fusion gene continuously expresses the ALK gene, which encodes a transmembrane tyrosinase receptor, promotes the growth of tumor cells and causes the development of lung cancer. The study of these two genes has become the main gene target for targeted lung cancer therapy today. 2.3 Data Management The purpose is to make a scientific prediction on the establishment of the project, and ensure that the development plan and implementation plan of the project are true and feasible. With a perfect management information system, managers can record the health information of residents for a lifetime and manage it permanently. This system can benefit residents’ lifelong service forever. Through the network information for residents to carry out health education, actively carry out community health services, to their chronic disease prevention and control work for example, can effectively prevent or control the occurrence of chronic diseases, such as hypertension, heart disease, diabetes and so on. With a perfect information management system, we can timely reflect the development of chronic diseases to the country, which can also facilitate the country to formulate corresponding measures to prevent, control and manage chronic diseases.

3 Lung Cancer Data Management Experiment 3.1 Parameter Configuration In order to simulate the real effect as much as possible, the experimental models and data are as far as possible based on the lowest configuration single machine. The experimental configuration parameters are shown in Table 1. Table 1. Experimental configuration parameters Name

Parameter

System

Ubuntu14.04

CPU

I7-6700

RAM

8G

Graphics card

GTX1070-8G GTX960-4G

Hard disk

SSD128G SSD256G

Frame

Python+Spark+Tensor flow+Docker

3.2 Lung Cancer Prediction Model (1) Data preparation: This article collected a total of 50 physical examination data, and the field types included three types: numeric type, sub-type, and text type.

192

Y. Ma et al.

(2) Data preprocessing: For TCGA and EDRN data, the data provider has carried out relevant preprocessing, including standardized processing in the data platform, etc. (3) Model establishment: The feature selection component can calculate the importance of each field and the predicted field, and can select fields with greater importance for analysis. 3.3 Statistics All data results are expressed as mean ± standard deviation. First, confirm the normal distribution of the data and the uniformity of dispersion, use SPSS13.0 to perform data statistics, use one-way dispersion analysis (unary configuration dispersion analysis) in the comparison between groups, and use the LSD method in multiple comparisons.

4 Discussion 4.1 Comparison of Operating Efficiency

Time

Single node 10 9 8 7 6 5 4 3 2 1 0

1

2

Spark framework

3

4

5

6

Hadoop framework

7

8

9

10

Data set size Fig. 1. Test results

Distributed algorithms are executed under the two frameworks of Hadoop and spark, and the PR value is calculated. The results are shown in Fig. 1. The calculation time of the centralized PageRank algorithm is completed in about 120 s, it takes about 20 s under the Spark framework and only about 10 s under the Hadoop framework. The experimental results show that the efficiency of the distributed PageRank algorithm is far better than the centralized method. As the number of data sets increases, the running time of singlenode algorithms increases exponentially, while the efficiency of distributed platform algorithms increases slowly. So when the number scale reaches a certain number, the distributed PageRank algorithm has greater advantages. At the same time, the execution

Lung Cancer Based on Big Data Technology Disease Data Management

193

performance of our algorithm under the Hadoop framework is slightly better than the Spark framework. This is because the method involves a lot of offline work. When Hadoop is used to perform iterations, the calculation is more accurate, and Spark is more suitable for processing online data. In the practical application, the model needs to be updated for many times according to the actual needs. It is obviously unrealistic to spend a lot of time on model training each time. The parallel algorithm designed in this paper can improve the computing efficiency by about 9 times. When the size of each training set reaches 2000, it only takes about 2 min. When each type of training set is less than 500, it can complete the training task in 10 s. Molecular resistance loci have been shown to be very important in the treatment of lung cancer. In this study, we analyzed the correlation between the main driving genes of lung cancer, EGFR and EML4-ALK, and the clinical characteristics of lung cancer patients (mainly in a large sample), and carried out a more detailed stratified analysis of lung cancer patients. 4.2 Comparison of Lung Cancer Cell Mutations After defining the objectives of the project, before the project is really launched, we should analyze the feasibility of all levels of the project according to the existing resources and project environment, including the technology of project realization, the cost required for project completion, etc. Gene mutations play an important role in the occurrence and development of lung cancer, so the study of its relationship with lung cancer pathological typing has also been given important significance. The point mutation and copy number variation are shown in Table 2. According to the data in the table, there is no difference in the point mutation rate or copy number mutation rate of the two groups of homologous genes (P > 0.05). By constructing a lung adenocarcinoma cell line overexpressing CHIAP2, we found that compared with the normal control group and the negative control group, the apoptosis of the lung adenocarcinoma cell line overexpressing CHIAP2 did not change significantly. The results show that, in the case of the same sample content, there are still differences in some mutations between the two groups of homologous genes. This shows that even though they are homologous genes with similar functions, their mechanisms of causing lung cancer are still different. Table 2. Point mutation and copy number variation Gene

Point mutation

Copy number variation

+

−

+

−

EGFR

48

753

1

65

ERBB4

9

121

4

62

χ2

0.169

0.831

P

0.681

0.362

194

Y. Ma et al.

4.3 Genetic Testing Analysis Use SVM to classify the multi-layer deep fusion features extracted by FeCNN. The result proves that the algorithm in this paper can extract better features without tedious process, and the classification accuracy is higher than traditional features. The relationship between ALK rearrangements is shown in Fig. 2. There was no difference in PFS between patients with EML4-ALK rearrangement gene and patients without EML4-ALK rearrangement gene type (p = 0.9021). The ORRs of the two groups of patients receiving crizotinib were 66.15% and 37.42% (p = 0.1362), respectively. Among patients with EML4-ALK rearrangement genes, the ORR of type V1 patients receiving crizotinib was 55.12%, the ORR of type V2 was 70.59%, and the ORR of type V3a/b was 85.66%. The ORR of patients with other non-EML4-ALK rearrangement gene types receiving crizotinib was 32.45%. After analyzing the relationship between age, gender, smoking history, pathological type, T stage, N stage, and M stage and ALK rearrangement (Fisher exact probability method), the results suggest that ALK rearrangement is more likely to occur in women younger than 60 years old and women. In patients with no smoking history and adenocarcinoma, it is related to the T stage, but not related to the N and M stages. With the construction of big data platforms in the medical industry and the gradual development and application of big data technologies such as data collection, storage, and visualization in the medical industry, the use of big data technology to evaluate the diagnosis and treatment of lung cancer has important research significance. Through the establishment of user management database and project-based SNP typing database and sample daFIGase, almost all the data can be completely recorded into the system, so that the genetic data and phenotypic data and other scattered but intrinsically related data are collected and integrated. The accurate and flexible authority management system ensures the security of the data.

Ratio

Total

ALK mutant

30.00%

100.00%

25.00%

80.00%

20.00%

60.00%

15.00%

40.00%

10.00%

20.00%

5.00% 0.00%

0.00%

1

2

3

4

5

6

7

8

9

10

11

Number Fig. 2. The relationship between ALK rearrangements

12

Ratio

ALK wild type

120.00%

Lung Cancer Based on Big Data Technology Disease Data Management

195

5 Conclusions Since lung cancer is the disease with the highest morbidity and mortality in the world, the mechanism of disease development is being studied. The rapid development of big data and artificial intelligence technology has promoted the rapid transformation of the medical system to intelligent, integrated, and efficient. This paper first analyzes the business process of data mining, studies different types of disease prediction algorithm models, and selects the appropriate analysis method according to the data of this study. Secondly, the lung cancer data acquisition and preprocessing. According to the data type and distribution characteristics of the data instance, the data is preprocessed, including abnormal value, missing value processing, text data transformation, etc., to form the data type that can be used for data mining. With the increasing incidence rate of lung cancer, it is urgent to study and control lung cancer. Pulmonary nodules have adhesion with the surrounding bronchus, chest, etc. It is small in size and complex in shape, so the detection rate and diagnostic rate of pulmonary nodules are low. Acknowledgments. This study was supported by the project of the Shaanxi Provincial Department of Education in 2018, project name: clinical diagnosis and treatment of gastric cancer gene mutations based on high-throughput gene sequencing technology (grant no. 18JK0657).

References 1. Ekong, E.E., Adiat, Q.E., Ejemeyovwi, J.O., et al.: Harnessing big data technology to benefit effective delivery and performance maximization in pedagogy. Int. J. Civil Eng. Technol. 10(1), 2170–2178 (2019) 2. Ski, Z.: The perspective of using big data technology for the purposes of educational transactional analysis. Eduk. Anal. Trans. 8(8), 81–88 (2019) 3. Dessi, A.: Designing courier service (Jastip) application by using spark-based big data technology. Int. J. Adv. Trends Computer Sci. Eng. 8(6), 3091–3094 (2019) 4. Huang, J.: Analysis on the membership management of a fashion brand by big data technology. Am. J. Ind. Bus. Manag. 09(10), 1931–1948 (2019) 5. Zhou, B., Wang, D., Sun, G., et al.: Effect of miR-21 on apoptosis in lung cancer cell through inhibiting the PI3K/Akt/NF-κB signaling pathway in vitro and in vivo. Cell. Physiol. Biochem. 46(3), 999–1008 (2018) 6. Mrcs, L.O.M., Leanne Harlingc, M., Toufektzian, L., et al.: Prognostic factors including lymphovascular invasion on survival for resected non–small cell lung cancer. J. Thorac. Cardiovasc. Surg. 156(2), 785–793 (2018) 7. Thakur, M.K., Ruterbusch, J.J., Schwartz, A.G., et al.: Risk of second lung cancer in patients with previously treated lung cancer: analysis of surveillance, epidemiology and end results (SEER) data. J. Thorac. Oncol. 13(6), e106–e107 (2018) 8. Akamine, T., Takada, K., Toyokawa, G., et al.: Association of preoperative serum CRP with PD-L1 expression in 508 patients with non-small cell lung cancer: a comprehensive analysis of systemic inflammatory markers. Surg. Oncol. Oxford 27(1), 88–94 (2018) 9. Hoyle, C., Dyer, M.: Comments on cost-effectiveness of osimertinib for EGFR mutationpositive non-small-cell lung cancer after progression during first-line EGFRTyrosine kinase inhibitor therapy. J. Thorac. Oncol. 13(5), e83–e84 (2018)

196

Y. Ma et al.

10. D’Agostino, S., Lanzillotta, D., Varano, M., et al.: The receptor protein tyrosine phosphatase PTPRJ negatively modulates the CD98hc oncoprotein in lung cancer cells. Oncotarget 9(34), 23334–23348 (2018)

Course Analysis and Management System Design Based on Big Data Technology Dongbai Guo(B) Police Skills and Tactics Training Department, Criminal Investigation Police University of China, Shenyang 110035, Liaoning, China [email protected]

Abstract. Currently, in the process of accelerating the construction of digital campuses, big data technology is being applied to the analysis of college teaching courses. Through in-depth analysis and processing of grade data, not only can teachers help teachers understand the current learning situation of students, but also based on students’ knowledge. Teach students in accordance with their aptitude, organize teaching more flexibly, and improve teaching efficiency. The purpose of this article is to study the design of curriculum analysis and management system based on big data technology. This article first analyzes the background of teaching and curriculum reform, the current situation of curriculum management, and introduces the data mining technology involved in the system. Afterwards, the course evaluation model was studied in depth, a course evaluation analysis decision-making system model was proposed, the system performance and operation requirements were analyzed, the system was designed and implemented, and the decision-making schemes and courses beneficial to teaching were outlined. The experimental results show that there are differences in the teaching behavior of teachers with different professional titles. Among them, the proportion of changes in the teaching behavior of lecturers and teachers accounts for about 63%, and the distribution of teaching activities of professors and associate professors is more reasonable. Keywords: Big data technology · Course analysis · Management system · Data mining algorithm

1 Introduction Course relevance analysis using big data technology aims to analyze actual teaching data to discover the relevance of the courses reflected in it [1, 2]. However, the relevance of the objective existence of the course is not completely consistent with the relevance of the course content itself. How to use the actual data generated in actual teaching to more accurately discover the objective relevance of the curriculum is the problem to be solved in this research [3].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 197–204, 2022. https://doi.org/10.1007/978-3-030-99616-1_26

198

D. Guo

In the research of course analysis and management system based on big data technology, many scholars have conducted research on it, and achieved good results. For example, Eichler J obtained the dependence and relationship between courses from the analysis results of students’ course scores. The degree of dependence, and can predict the student’s follow-up course academic performance. It not only provides guidance for the development direction of students in higher vocational colleges, but also provides a basis for the school’s curriculum and teacher arrangement [4]. Santos MY uses association rules and hierarchical association rules to analyze students’ school performance and graduation data, obtains the correlation between courses, core courses and important skills, and then builds a curriculum system based on the project-based curriculum teaching model for the school. The reform of the project curriculum provides reference value [5]. Through deeper excavation and more analysis of students’ courses, the purpose is to establish an effective school management model, so as to provide more help to the development of the school and the improvement of student performance. Through the research of the relevant theoretical knowledge of data mining, this paper finds data mining tools and methods suitable for quantitative analysis of curriculum relevance, and further selects data preprocessing methods suitable for curriculum information. Through simple correlation analysis, we can obtain significant related courses under the same type of courses, and further analyze and explain the significant related courses to obtain statistically related reasons, so as to obtain the curriculum relevance that our school has embodied, and the newly discovered curriculum relevance sexuality and the relevance of the course to be studied.

2 Research on Curriculum Analysis and Management System Design Method Based on Big Data Technology 2.1 Function Analysis of Course Assessment and Analysis Management System Through the analysis of the course assessment and analysis management model, this paper can conclude that the system has three main modules: basic data management module, learning attitude monitoring module and analysis decision module [6, 7]. (1) Basic data management module 1) Course management Through the course management function, courses can be added, edited, deleted, and inquired, making the system a general course assessment, analysis and decisionmaking system. 2) Student information management Add, edit, delete, and query student information so that students can correspond to course information, so that students can conduct online assessments and submit homework for their courses when they log in [8].

Course Analysis and Management System Design

199

3) Teacher information management Add, edit, delete, and query teacher information to make teachers correspond to students and course information. Teachers can log in to manage and view their courses and students. 4) Administrator management Add, edit, delete, and query system administrator information to maintain the normal operation of the entire system. (2) Learning attitude monitoring module 1) Attendance management Teachers can record students’ attendance through the attendance management function, and monitor students’ lateness, early departure, and absenteeism in a timely manner [9, 10]. 2) Learning attitude and daily work records Teachers can record students’ learning attitudes and the completion of homework through the system. 3) Teamwork ability record Record the performance of students in teamwork. After the mining model is established, it is necessary to design programs and use tools for data mining. As a complete data mining system, it must have login functions, user management functions, data management and import functions, and data mining functions [11, 12]. (3) Analysis and decision-making module 1) Learning attitude analysis and decision-making report generation function According to the records of the examination situation in the system, a report of each student’s learning attitude can be generated, and various information such as late arrival and early departure can be displayed in detail in the report. In addition, you can also see the results of the learning attitude of the entire class, and give suggestions to improve the learning attitude. 2) The function of generating decision-making report on the mastery of knowledge points in and out of class According to the results of automatic scoring by the system and manual grading by the teacher, the detailed teaching situation of the knowledge points inside and outside the course can be analyzed, for example, the scores of each student’s

200

D. Guo

knowledge points, the overall knowledge points of the class, and the knowledge that is not very good for mastering Click, the system will give hints in the teaching suggestions. 3) The function of generating the overall analysis and decision report of the course Through the analysis and decision-making of students’ learning attitudes and knowledge points in and out of class, the overall analysis and decision-making report of the course is finally obtained. The report can scientifically reflect the teaching situation of the course, the mastery of students, the opinions of teaching reform and other useful information. 2.2 Mining Association Rules Based on Educational Decision-Making In the analysis and collation of the data that analyzes the situation of the students in the school and the teaching situation of the teachers, it is found that the use of things compression algorithms is the most important theme. The following are the three main elements to achieve this theme: (1) Related processing for pre-mining Whether a collection frequently appears is mainly achieved by scanning the entire database, but because in the process of data sorting, there will be a lot of data without any clamps, so these data are deleted. Then you can minimize the number of scans of the database. The realization of this function is carried out in three ways: 1) This system mainly uses structured language sentences to determine a filtering limit, and all non-conforming data must be deleted at a time. 2) The corresponding sampling technique is done by taking the same data. 3) Divide the required data into corresponding blocks, and then perform corresponding calculations and analysis on each block of the ground. In the stage of corresponding analysis of the corresponding data, in the entire system, the main thing includes the processing of data discrete problems. (2) Improve the mining of association rules There are two main aspects of the operation of the association rules: one is the storage of candidate frequent sets; the other is processing the connection part, which is determined by the nature of the association rules: the frequent item set can also be any subset of a frequent item set. A conclusion can be drawn from this: any frequent k is contained in a frequent (k + l) item set. Based on this property, after the improved algorithm generates frequent k itemsets, it should first delete records that do not include frequent k itemsets from the database, and then continue to search for frequent k + 1 itemsets. (3) Analysis rules, export rules, filter rules Using different mining rules to analyze, based on the different mining preprocessing methods for step (l), the filtering algorithm is finally carried out.

Course Analysis and Management System Design

201

2.3 Typical Algorithms of Decision Trees Suppose attribute A has v different values {a1.a2….av}. The attribute A can be used to divide S into v subsets {S1, S2….Sv}, where Sj contains some samples in S that have the same value aj (j = 1, 2, ..., v) on A. Let Sij be the number of samples of class Ci in the subset Sj . The information entropy divided into subsets by A is given by: E(A) =

v  |Dj | j=1

|D|

∗ Info(Dj )

Dj = S1j + S2j + ......Smj D=S

(1)

(2)

|D |

The |D|j here serves as the j-th division weight. The smaller the entropy value, the higher the purity of the subset division.

3 Experimental Research on Curriculum Analysis Based on Big Data Technology 3.1 Classroom Analysis and Evaluation of Practical Courses The practical courses are D1, D2 and D3. The D1 course is set up by Department 1, and is taught by three male teachers with doctorate and associate professor titles. The learners are 23 professional junior students and the teaching time is one hour. The D2 course is set up by Department 2 and is taught by two female teachers with doctorate and lecturer titles. The learners are 12 professional sophomores and the teaching time is one hour. The D3 course is taught by three female teachers with a master’s degree and professorship. The learners are sophomore students in 11 majors, and the teaching time is one hour. Perform ITIAS analysis on the above courses. 3.2 Classroom Teaching Behavior Coding In the same way, the D1, D2, and D3 classes are coded using time sampling. D1 has a total of 1102 codes, course D2 has a total of 946 codes, and course D3 has a total of 951 codes.

4 Course Survey Analysis Research Based on Big Data Technology 4.1 Analysis of the Difference of Professional Titles of Different Teachers in Classroom Teaching In this paper, the research samples are classified according to the professional titles of teachers. The experimental results are shown in Table 1. As shown in Fig. 1, it can be seen that the proportion of changes in the teaching behavior of lecturers and teachers accounts for about 63%, while the distribution of teaching activities of professors and associate professors is more reasonable.

202

D. Guo

Table 1. Comparison of Classroom Behavior of Teachers with Different Professional Titles Parameter type

Professor

Associate professor

Lecturer

Teacher language

0.6452

0.6579

0.5163

Student language

0.2137

0.1093

0.1788

Silence in the classroom

0.0631

0.0635

0.0931

Technology use

0.1736

0.2630

0.2741

Parameter value

Professor 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2

Teacher language

Associate Professor

Student language

Silence in the classroom

Lecturer

Technology use

Parameter

Fig. 1. Comparison of classroom behavior of teachers with different professional titles

4.2 Analysis of Differences in Specific Coding Behaviors of Teachers with Different Academic Qualifications The proportion of quietness in the classroom and the use of technology are lower than teachers with a master’s degree. The differences of the specific 9 coding behaviors are shown in Table 2. Series 1 is for teachers with Ph.D., and Series 2 is for teachers with master’s degree. It can be seen from Fig. 2 that teachers with a Ph.D. degree are more direct in their teaching attitudes, such as praise, acceptance of emotions, and adoption of student opinions, while teachers with a master’s degree often use positive attitudes such as encouragement, praise, and adoption of student opinions. For classroom teaching, similarly, teachers with a doctorate’s instruction and criticism in the classroom are also higher than those of a master’s degree teacher, and the comparison of the positive integration grid and the defective grid in the transfer matrix also reflects the same problem. Teachers with a doctorate degree should adopt a positive and gentle attitude in teaching attitudes and teaching tendencies, encourage and praise students, avoid using critical language, and reduce the use of command words. In terms of questions, doctoral teachers’ questions

Course Analysis and Management System Design

203

Table 2. Differences in specific coding behaviors of teachers with different academic qualifications Coding

Series 1

Series 2

1

0.016

0.011

2

0.025

0.027

3

0.007

0.001

4

0.361

0.316

5

0.062

0.055

6

0.035

0.033

7

0.022

0.027

8

0.019

0.017

9

0.015

0.011

Fig. 2. Differences in specific coding behaviors of teachers with different academic qualifications

are much lower than those of master’s degree teachers, and the interaction ratio with teachers is low. However, in terms of students’ language, teachers with a master’s degree have a low probability of taking the initiative to speak in the classroom, and students’ active participation in learning is not as good as that of doctoral teachers. There is little difference in classroom discussion between the two types of teachers.

5 Conclusions Classroom evaluation based on classroom information analysis uses computers for data processing and mining, relies on systematic classroom evaluation standards, combines

204

D. Guo

the advantages of qualitative evaluation and quantitative evaluation, and pays attention to the multi-dimensional use of teachers, students and media to form a formative effect on the classroom. Use the classroom teaching classification system as the starting point of the research, and use the computer software as the research work to conduct multi-dimensional analysis and evaluation of the classroom teaching in colleges and universities. This article classifies high-efficiency courses from the perspective of different functional attributes, namely: tool courses, knowledge courses, skill courses and practical courses. Based on the idea of data mining, quantitative evaluation methods such as the classification of teaching behaviors A multi-dimensional comparison, analysis, and evaluation of teaching behavior has been carried out.

References 1. Liu, Y., Yu, Z., Yang, Y.: Diabetes risk data mining method based on electronic medical record analysis. J. Healthc. Eng. 2021(6), 1–11 (2021) 2. Safa, M., Hill, L.: Necessity of big data analysis in construction management. Strateg. Dir. 35(1), 3–5 (2019) 3. Elkano, M., Galar, M., Sanz, J., et al.: CHI-BD: a fuzzy rule-based classification system for big data classification problems. Fuzzy Sets Syst. 348, 75–101 (2017) 4. Eichler, J., Henbest, G., Mortezaei, K., et al.: Efficacy of an asynchronous online preparatory chemistry course: a post-hoc analysis. J. Chem. Educ. 97(12), 4287–4296 (2020) 5. Santos, M.Y., Jorge, O.E.S., Andrade, C., et al.: A big data system supporting Bosch Braga Industry 4.0 strategy. Int. J. Inf. Manag. 37(6), 750–760 (2017) 6. Iii, A.: Teaching an instrumental analysis laboratory course without instruments during the COVID-19 pandemic. J. Chem. Educ. 97(9), 2967–2970 (2020) 7. Ahmed, Y., Alneel, S.: Analyzing the curriculum of the Faculty of Medicine, University of Gezira using Harden’s 10 questions framework. J. Adv. Med. Educ. Prof. 5(2), 60–66 (2017) 8. Zhang, Y., Qiu, M., Tsai, C.W., et al.: Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1), 88–95 (2017) 9. Kiral-Kornek, I., et al.: Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine 27, 103–111 (2018) 10. Liang, Y.C., Lu, X., Li, W.D., et al.: Cyber physical system and big data enabled energy efficient machining optimisation. J. Clean. Prod. 187, 46–62 (2018) 11. Barik, R.K., Priyadarshini, R., Dubey, H., et al.: FogLearn: leveraging fog-based machine learning for smart system big data analytics. Int. J. Fog Comput. 1(1), 15–34 (2018) 12. Wang, K., Li, H., Feng, Y., et al.: Big data analytics for system stability evaluation strategy in the energy internet. IEEE Trans. Indust. Inf. 4, 13 (2017)

Application of BIM Virtual Technology in Highway Tunnel Construction Management Wanbo Qu(B) Chongqing Vocational Institute of Engineering, Chongqing 402260, China [email protected]

Abstract. The informatization of my country’s construction industry started relatively late, and BIM technology is still in its infancy and cannot be widely used, especially in highway tunnels. Therefore, applying BIM technology in the whole process of highway tunnel design makes the subsequent construction and operation meaningful. This article aims to study the application of virtual BIM technology in highway tunnel construction management. This article briefly introduces the safety management of railway tunnels. Then it systematically analyzes the characteristics, objectives, systems and methods of railway tunnel construction safety management problem management. Then, by applying BIM technology to the whole process of railway tunnel construction safety management, a BIM-based railway tunnel construction safety management model is proposed. Based on the maturity model, this document evaluates the implementation maturity of a specific construction project, analyzes the project’s BIM implementation deficiencies based on the evaluation results, and proposes suggestions for improvement to the construction company. Implement their BIM capabilities. Experimental research shows that the average score of BIM at the project construction management level is 0.33, which is close to the reuse level, indicating that BIM is at a medium level at the project construction management level. It is necessary to strengthen the training of project team talents and BIM ability training to improve overall performance. Keywords: BIM virtual technology · Highway tunnel construction · Construction management · Bim application maturity

1 Introduction With the continuous development of science and technology and Internet technology, building informatization has finally become the development trend of the construction industry [1, 2]. The emergence of BIM technology makes it possible to bring informatization to the construction industry, improve construction management technology, and improve construction management efficiency [3]. In recent years, the popularity of BIM has continued to increase, and many projects have implemented BIM technology and achieved excellent results. In the application research of virtual BIM technology in highway tunnel construction management, many researchers have conducted research on it and achieved good © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 205–212, 2022. https://doi.org/10.1007/978-3-030-99616-1_27

206

W. Qu

results. For example, Chen L proposed the engineering quality management process based on BIM technology, and realized the information association between quality management information and related software [4]. Liu first analyzed the advantages of BIM technology in engineering quality control, and then started with the quality impact of design schemes, quality supervision of materials and machinery, and quality control of engineering construction, which fully embodies the advanced role of BIM technology and expresses it for the future High-quality project construction supervision has created favorable conditions [5]. Therefore, based on BIM virtual technology, it is of great significance to the application research of highway tunnel construction management. This article analyzes the characteristics of the traditional management methods of highway tunnel projects, puts forward the problems of the traditional methods in the three stages of design, construction plan preparation and construction site management, and then introduces the application of BIM technology in municipal engineering, and studies the introduction of BIM technology. Optimize and promote the theoretical framework and application programming of the full life cycle application of the owner’s project management based on BIM technology, and then use BIM technology modeling software to create a BIM-based mechanical tunnel model.

2 Application Research on Highway Tunnel Construction Management Based on Bim Virtual Technology 2.1 Research on 3D Lining Modeling Method and Program Development of Highway Tunnels (1) Research on assembly process and program development of tunnel lining component model. When the tunnel passes through different levels of rock, the thickness of the lining will vary, and the type, size and number of components will also vary. However, under the same rock level, the lining remains the same, and the component size and installation space are also different [6, 7]. In the same way, the cladding structure of the same layer and several meters in the tunnel rock mass environment can be projected as a sample, and the construction sequence of the advanced support structure, the initial support structure and the secondary cladding structure can be created in sequence as needed. (2) Research on the method and procedure of creating an overall tunnel lining model. First, use the DtoA.exe application to extract the centerline of the tunnel, also called the skeleton line, from the topographic engineering drawing, create a 3D line model on the CATIA software platform, and assemble the lining structure along the skeleton line. This article discusses in detail the methods of creating the model: One is to use short sections of the tunnel assembled above to be placed along the frame line. (3) Research on the program of creating the overall tunnel lining model. Use the CATIA software configuration function to create the emergency parking area model and the 3D library of the side hole, and give the basic size according to the parameters that determine the shape and size of the model [8]. Use automation

Application of BIM Virtual Technology in Highway Tunnel Construction

207

technology to develop a new operating system, and call the model through the AddComponentsFromFiles function. (4) Rendering and display of tunnel lining model. In order to improve the appearance of the 3D model, it is necessary to assign corresponding feature values to each component model. CATIA real-time performance includes static performance and dynamic performance. The static performance function allows the definition of hardware specifications to be shared throughout the product development process, and allows materials to be mapped to parts and products to produce realistic performance [9, 10]. You can select hardware specifications from the material library attached to CATIA, or you can add new types and functions of hardware yourself. 2.2 BIM-Based Safety Management of Railway Tunnels Before Construction (1) Organizational assurance and resource allocation using BIM. The construction unit should adhere to the principle of “management of production must manage safety”, set up a safety management organization, appoint safety management personnel, formulate safety production rules and regulations, and implement a safety production responsibility system [11]. The primary task of introducing BIM-based railway tunnel safety management methods is to establish a corresponding organization and talent guarantee, and be equipped with corresponding computer, network and other software and hardware facilities and equipment. (2) Preparation of special construction plan based on BIM. The special construction plan is formulated according to a single construction project or one of the sub-elements of the project with complex or dangerous technology, focusing on the construction method, the use of mechanical equipment, the arrangement of work and materials, and the specific construction plan. For tunnel excavation, advanced geological forecasting, monitoring and measurement, poor geological conditions and construction of special geotechnical soils for railway mechanical tunnels, special construction plans must be formulated [12]. (3) BIM-based emergency evacuation simulation. Emergency management of railway tunnel construction has always been the core of safety management. The first is that the manpower is too theoretical to identify problems, and the second is time-consuming and resource-intensive. However, through the virtual BIM exercise between the two, problems can be found, emergency plans can be improved, and costs can be saved. 2.3 Create a Geological Model This model is the primary creation project of the entire tunnel model. Divided into three parts: (1) Coordinate data extraction. Use the coordinate extraction tool DtoA.exe to extract the point coordinates on the contour line and the coordinate data of the geological body overlay in AutoCAD, save them in an Excel table, and create a point coordinate information database.

208

W. Qu

(2) Extraction of coordinate database. Import the database into the CATIA software part design (PartDocument) module, the software will automatically read the database information and transfer it to the HyBridShapeFactory constructor for users to call. (3) Create 3D terrain. Input the corresponding model type number through the user selection interface to generate terrain point, line, and surface model, and stretch it into a terrain entity model. (4) Create a 3D geological model. Select the geological body cover layer coordinate database to generate the geological boundary surface, cut the topographic model, separate the geological body, and render each geological body to generate the geological model. According to the tunnel information in the topographic map of the tunnel, use the “text with lead line” command to mark in the three-dimensional model. 2.4 Method for Determining Maturity Weight The determination of the evaluation index weight of the construction enterprise technology application maturity model is mainly determined by the analytic hierarchy process and the fuzzy comprehensive evaluation method. The maximum eigenvalue and eigenvector of the judgment matrix are calculated and the consistency test is performed. If the consistency test is passed, both the weights of the four indicators can be obtained, otherwise the judgment matrix needs to be reconstructed. (1) Calculate the product SSS of each row element: Bi =

n 

aij , i = 1, 2, ...n

(1)

j−1

(2) Calculate the nth root BBB of XXX: Mi =

 n

Bi

(2)

(3) Normalize the vector aaa: Mi =

Mi n  Mi

(3)

i=1

Then AAA is the feature vector.

3 Experimental Research on Highway Tunnel Construction Management Based on BIM Virtual Technology 3.1 Formation of the Project Bim Team Professional leaders can be subdivided into civil engineering, steel reinforcement, and electromechanical professionals. The main job of each professional is responsible for BIM work in architecture, structure, and electromechanical.

Application of BIM Virtual Technology in Highway Tunnel Construction

209

3.2 Construction Schedule Simulation The application of BIM is conducive to improving the management efficiency of project schedule and control, analyzing the key and difficult points of the project, and using the Project software to compile the corresponding schedule, import the model and schedule into the navis work software, and repeat the simulation Test, and then determine its construction period, and then provide a basis for the reasonable preparation of sub-project schedule plans and sub-project schedule plans.

4 Experimental Analysis of Highway Tunnel Construction Management Based on BIM Virtual Technology 4.1 Three-Dimensional Calculation Based on BIM Model Corresponding modeling rules were formulated during the modeling process of this project to clarify the deduction principles for each component. Take the 10-story local component engineering quantity as an example, as shown in Table 1. As shown in Fig. 1, because we set up the corresponding model calculation rules before using Revit to increase the volume, we found that the engineering volume extracted by Revit and the engineering volume extracted by Glodon’s civil engineering calculation volume are within 3% of the error, indicating that Revit The extraction of engineering quantities can guide the actual construction of the project and also provide an effective guarantee for the cost control of the project. Table 1. REVIT extraction volume and GCL calculation volume Project name

GCL engineering quantity

REVIT model engineering quantity

C60 connecting beam

24.26

26.25

300 mm straight wall

26.35

27.31

Aerated concrete block

23.41

24.37

Fireproof board wall node

5.35

5.21

Gypsum board wall node

74.36

75.24

4.2 BIM Application Score in This Case This paper surveyed more than 30 staff members of this project, including the company’s department leaders, project department leaders, and grassroots BIM modelers. They scored the evaluation indicators one by one according to the application of the project’s BIM and finally sorted them out as follows: Table 2 shows. As shown in Fig. 2, the average score of BIM in the project organization management of construction enterprises is 0.25, which corresponds to the growth level, and the transition to the reuse level shows that the senior executives of the enterprise are

210

W. Qu

Fig. 1. REVIT extraction volume and GCL calculation volume

suspicious of the application and promotion of BIM in construction projects. Attitudes, and continue to use the traditional project management methods, resulting in BIM in the actual project construction management process is not high in execution, resulting in low scores in the project organization management. In addition, the average score of BIM at the project construction management level is 0.33 points, which is close to the reuse level, indicating that BIM is at a medium level at the project construction management level. Due to insufficient support for the application and promotion of BIM by senior leaders, project management during the construction phase Ability is not outstanding, so senior leaders must have a correct understanding of BIM, implement BIM from a strategic height, strengthen the training of project team talents and BIM ability training, and improve its overall application level. Table 2. BIM application scores in this case Evaluation index layer

Simple

Orderly

Standard

Quality management ability

0.34

0.27

0.15

Schedule management ability

0.35

0.43

0.16

Cost management ability

0.31

0.48

0.19

Safety management capability

0.25

0.34

0.23

Procurement management ability

0.22

0.24

0.45

Contract management capabilities

0.33

0.34

0.36

Risk management capabilities

0.45

0.39

0.15

Application of BIM Virtual Technology in Highway Tunnel Construction

211

Fig. 2. BIM application scores in this case

5 Conclusions Through the application of BIM in the simulation project, this paper confirms the application value and technical advantages of BIM in construction project management from the three dimensions of project construction schedule, construction quality, and construction cost. At the same time, the case was used to verify the feasibility of the maturity model, to obtain the application of BIM in the project, and to give suggestions for strengthening and modification based on the evaluation results obtained, effectively enhancing the construction enterprise’s own BIM project management Application capabilities.

References 1. Huang, Y.: Developing a modular advanced BIM course in construction management. J. Build. Construct. Plan. Res. 06(4), 198–214 (2018) 2. Yu, Z., Peng, H., Zeng, X., et al.: Smarter construction site management using the latest information technology. Proc. Inst. Civ. Eng. 172(CE2), 89–95 (2019) 3. Akinade, O.O., Oyedele, L.O., Ajayi, S.O., et al.: Designing out construction waste using BIM technology: stakeholders’ expectations for industry deployment. J. Clean. Prod. 180, 375–385 (2018) 4. Chen, L., Jing, W.: Application of the PKPM-BIM construction management platform in project security management. CADDM 27(001), 58–62 (2017) 5. Liu, Chen, Wang, et al.: Application of the PKPM-BIM construction management platform in project security management. Comput. Aided Draft. Design Manuf. 01(164), 60–64 (2017) 6. Li, X., Xu, J., Zhang, Q.: Research on construction schedule management based on BIM technology. Procedia Eng. 174(Complete), 657–667 (2017) 7. Parolise, A.: Engineered smoke control in a metro tunnel renovation using construction management project delivery. Eng. Syst. 36(1), 29 (2019)

212

W. Qu

8. Merlini, D., Stocker, D., Falanesca, M., et al.: The ceneri base tunnel: construction experience with the southern portion of the flat railway line crossing the swiss alps. Engineering 4(2), 235–248 (2018) 9. Wang, B., Zhang, Z., He, C., et al.: Implementation of a long-term monitoring approach for the operational safety of highway tunnel structures in a severely seismic area of China. Struct. Control Health Monitor. 24(11), e1993 (2017) 10. Theofilatos, A., Ziakopoulos, A., Papadimitriou, E., et al.: Meta-analysis of the effect of road work zones on crash occurrence. Acc. Anal. Prev. 108, 1–8 (2017) 11. Steinbakk, R.T., Ulleberg, P., Sagberg, F., Fostervold, K.I.: Speed preferences in work zones: the combined effect of visible roadwork activity, personality traits, attitudes, risk perception and driving style. Transp. Res. F: Traff. Psychol. Behav. 62, 390–405 (2019) 12. Hayes, P.: Superfund claim tossed: road work doesn’t prove contamination link. Environ. Rep. 49(7), 244 (2018)

Stock Return Analysis Based on ARMA (2,2) Model Haorui Yan(B) Lanzhou University of Technology, Lanzhou 730000, Gansu Province, China [email protected]

Abstract. With the rapid development of China’s economy, securities, stocks and other financial markets show a thriving trend. In the process of securities and stock investment, investors are more concerned about the investment return of stocks or securities. This paper selects the closing price of CSI 300 from January 5, 2015 to April 2, 2021 as the research object. Through the ARMA (2,2) model, the logarithmic rate of return of CSI 300 in this time period is fitted, and the rate of return series is predicted from two different angles outside the sample and inside the sample. The results show that there is a large difference between the predicted results and the actual results by using ARMA model alone, and the model needs to be optimized from other aspects to achieve the purpose of accurate prediction. Keywords: “Hushen 300” · ARMA (2,2) · Predict · Stocks’ Yield

1 Introduction Since the beginning of February 2019, China’s stock market has been showing a good trend. At the same time, risk appetite people have invested capital in the stock market in the hope of a good return in the stock market. Obviously, in the investment process, investors expect the higher the stock return, the better, and the lower the risk, the better [1]. Therefore, if we can predict the future return of stocks through relevant tools, it can not only enable investors to avoid risks, but also provide reference for the government to formulate various macroeconomic policies [2]. In recent decades, scholars have proposed many different prediction methods of stock points or returns, such as analysis of technical indicators, including trading volume curve, exponential smoothing line, K-line chart, moving average and random index, simplest chart method, ARMA model, ARFIMA model, Kalman filtering method and neural network model. These methods have played a good reference role for investors in stock selection [3]. There are many different methods for modeling the rate of return. From the initial AR model, MA model to ARMA model, it can be seen that the research methods began to transform from simple to complex [4]. In order to solve the problem of seasonal cycle, scholars began to apply seasonal difference to ARMA model to form ARIMA model [5]. At present, there are a lot of literature on the prediction of stock price, which provides convincing economic arguments and empirical results in the sample. Wu Wenfeng et al. proposed the volatility

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 213–219, 2022. https://doi.org/10.1007/978-3-030-99616-1_28

214

H. Yan

source model of stock price and applied it to the empirical study of Shanghai stock index [6]. Wang studied the stock price index of Taiwan Stock Exchange and concluded that the volatility in the data showed a trend consistent with the arch (autonomous conditional heterogeneity) model [7]. If it is mixed with artificial neural network, it can enhance the price prediction. Liu and Morley indicate that modeling with traditional econometric models often leads to very serious deviations [8]. In order to improve the accuracy of the prediction model, the economic circle has made extensive improvements to the traditional econometric model, and proposed a stock price prediction model based on GARCH (generalized econometric conditional heteroskedast). Jin Yao et al. combined AR model with Kalman filter and applied it to the stock price prediction of beacon communication [9]. Wang Huixing et al. established a time series model according to the characteristics of 50 sample stocks of Shanghai Stock Exchange and found that the model can better simulate the trend of small sample portfolio in the market [10]. Zhang Yingchao et al. also used ARIMA (4,1,4) model to predict the future stock price. The results show that the model can accurately predict the future Shanghai stock index in the short term. Cao Dong et al. integrated risk measurement into the generation process of stock price index and constructed a garchm model with high fitting degree to adapt to China’s stock market. Their research shows that Shanghai and Shenzhen 300 stock index futures can alleviate stock market fluctuations to a certain extent.

2 Prediction and Demonstration of ARMA (2,2) Model 2.1 Principle and Method of ARMA (p,q) Time series ARMA model is a commonly used random time series model, founded by box Jenkins, also known as B-J method. It is a time series short-term prediction method with high accuracy. It has been widely used in economic analysis and prediction. It is also recognized as one of the more advanced scientific time series models used in a country or region’s economic prediction. The basic form of ARMA model is as follows: Xt = φ1 Xt−1 + φ2 Xt−2 + ..... + φp Xt−p + εt − θ1 εt−1 − θ2 εt−2 − ... − θq εt−q

(1)

Deformation as: Xt − φ1 Xt−1 + φ2 Xt−2 + ..... + φp Xt−p = εt − θ1 εt−1 − θ2 εt−2 − ... − θq εt−q

(2)

Or as: φ(B)Xt = θ (B)εt

(3)

P is the order of the autoregressive model,1,2,3…p is the undetermined coefficient of the moving average model, θ1 , θ2 , θ3 . . . θq is the order of the moving average model, Obviously, when q = 0, ARMA (P, 0) is AR(P); When p = 0, ARMA (0, q) is MA (q).

Stock Return Analysis Based on ARMA (2,2) Model

215

2.2 Data Description and Statistical Test This paper selects the closing price of CSI 300 index from January 5, 2015 to April 2, 2021 as the research object, and calculates the corresponding logarithmic rate of return to make the data meet the requirements of stability. The corresponding formula is:Rt = InP t − InP t−1 , where the Rt is the corresponding stock return, Pt is the daily closing price of the stock in stage t (Fig. 1).

Fig. 1. Time series of logarithmic returns

It can be seen that the logarithmically processed closing price is roughly stable without any periodicity or cyclicity. The logarithmic rate of return will be analyzed by descriptive statistics (Table 1 and Fig. 2). Table 1. Descriptive statistics of time series data Mean

Median

Maximum

Minimum

Std

Skewness

Kurtosis

J-B

0.000229

0.000745

0.064989

−0.091542

0.015373

−0.997273

9.047676

0.000

Next, ARMA (1,1), ARMA (1,2), ARMA (2,1) and ARMA (2,2) are used to fit the yield series, and the most appropriate model is selected to fit the time series according to AIC, SC and HQC criteria. AIC, SC and HQC of the model are shown in the Table 2: According to the requirements of the information criterion, it can be determined that the time series should adopt the unit root test without intercept term and time trend term, and its p value = 0.000 < 0.05. Therefore, the original hypothesis is rejected, indicating that the original time series has no unit root and is a stable time series, so it can be predicted by the model.

216

H. Yan

Fig. 2. QQ chart of yield series

Table 2. Unit root test of yield Inspection form

Inspection form

Critical value 1%

Including intercept AIC −5.510991 item, excluding trend item SC Excluding intercept item and trend item

10%

−3.434445 −2.863236

−2.567721 0.000

−2.566484 −1.941032

−1.616559 0.000

−5.503983

AIC −5.512099

SC

5%

P

−5.508595

Autocorrelation test is used to test whether there is correlation between interest rates in each period. The correlation test method is mainly judged by calculating autocorrelation coefficient AC and partial autocorrelation coefficient PAC. The specific results are shown in Table 3. AC and PAC values are low, and the autocorrelation coefficient between RT return series and the series lagging 10 periods is −0.058, so there is a weak correlation (Table 4).

Stock Return Analysis Based on ARMA (2,2) Model

217

Table 3. Table of correlation coefficient of yield Lags

AC

PAC

Prob

1

0.036

0.036

0.000

2

−0.050

−0.051

0.056

3

0.012

0.016

0.112

4

0.037

0.033

0.090

5

0.002

0.001

0.153

6

−0.082

−0.079

0.006

7

0.021

0.026

0.008

8

0.054

0.044

0.003

9

0.030

0.030

0.003

10

−0.058

−0.052

0.001

Table 4. ARMA model fitting effect Type

AIC

SC

HQC

R-squared

Is it sequence related

ARMA (1,1)

−5.513958

−5.499948

−5.508742

0.006211

No

ARMA (1,2)

−5.510363

−5.492851

−5.503843

0.003919

No

ARMA (2,1)

−5.530165

−5.512653

−5.523646

0.023602

No

ARMA (2,2)

−5.531576

−5.510561

−5.523752

0.026308

No

According to the comprehensive judgment of information criteria, ARMA (2,2) model is more in line with the return series. Therefore, we will use ARMA (2,2) model to fit and predict the return series. Using Ljung boxq test, set the significance level of 5%, and then conduct residual test on the model. It is found that the residual fitted by ARMA (2,2) model has no sequence correlation. Therefore, the model can be used to fit the yield series. The model expression after fitting is: Rt = 0.00042536521 + 0.103697Rt−1 − 0.961187Rt−2 −0.116151εt−1 + 0.918114εt−2

(4)

2.3 Prediction of ARMA (2,2) Model When using this model for prediction, we adopt two prediction methods: (1) in sample prediction: predict the data from January 4, 2021 to April 2, 2021 through time series samples, (2) out of sample prediction: predict the data from April 5, 2021 to April 30, 2021. The results are shown in the Figs. 3 and 4:

218

H. Yan

Fig. 3. Intra sample prediction

Fig. 4. Out of sample prediction

3 Conclusion In this paper, the simple rate of return prediction ARMA (2,2) model is used to predict the rate of return of Shanghai and Shenzhen 300 stocks both inside and outside the sample. Although the specific values are successfully predicted, there is a large gap between the predicted values in the sample and the actual values. Possible reasons: (1) although

Stock Return Analysis Based on ARMA (2,2) Model

219

ARMA model is a dynamic characterization of time series, However, in fact, it is a description of linear relationship, and the actual financial data is a non-linear time series. Therefore, it is obvious that the forced use of linear relationship will be quite different from the actual situation. (2) ARMA model does not describe the volatility of financial time series, but only forecasts the future through the combination of previous historical data and independent and identically distributed errors. Therefore, the practicability and prediction results are difficult to achieve the actual situation. Simply starting from the relationship between historical data and mean value, it is inaccurate to predict time series. If you want to predict time series more accurately, you should also consider volatility, data characterization and other aspects.

References 1. Wenfeng, W., Chongfeng, W.: Discussion on stock price fluctuation model. Syst. Eng. Theory Pract. 20(4), 63–69 (2000) 2. Wang, Y.F.: Predicting stock price using fuzzy grey prediction system. Exp. Syst. Appl. 22(1), 33–38 (2002) 3. Liu, W., Morley, B.: forecasting in the Hang Seng index using the GARCH approach. AsiaPacific Financ. Mark. 16(1), 51–63 (2009) 4. Jin, Y., Cai, Z.: Application of Kalman filter based on AR model in stock price prediction. Statist. Decis. Mak. 6, 80–82 (2013) 5. Dong, C., Jia, Z.: Research on the impact of stock index futures on stock market volatility based on GARCH-M model. China Manag. Sci. 25(1), 27–34 (2017) 6. Brock, W.A.W., Dechert, D., Scheinkman, J.A., LeBaron, B.: A test for independence based on the correlation dimension. Economet. Rev. 15, 197–235 (1996) 7. Corsi, F., Lillo, F., Pirino, D., Trapin, L.: Measuring the propagation of financial distress with granger-causality tail risk networks. J. Financ. Stab. 38, 18–36 (2018) 8. Costanzino, N., Curran, M.: A simple traffic light approach to backtesting expected shortfall. Risks 6, 2–8 (2018) 9. Cotter, J., Hallam, M., Yilmaz, K.: Mixed-frequency Macro-financial Spillovers. Working Paper, Koc University, Turkey (2017) 10. Ghulam, Y., Doering, J.: Spillover effects among financial institutions within Germany and the United Kingdom. Res. Int. Bus. Financ. 44, 49–63 (2018)

Monitoring Method of Vertical Stress on Precast Concrete of Prefabricated Building Manli Tian(B) School of Road Bridge and Architecture, Chongqing Vocational College of Transportation, Chongqing 402247, China [email protected]

Abstract. With the emergence of a large number of engineering projects, people are more and more aware of the important role of construction process monitoring on project quality and safety. Monitoring methods suitable for the vertical force of precast concrete (PC) in prefabricated buildings have also emerged. Using these methods can effectively solve the common quality problems existing in traditional construction methods. This article expounds the characteristics of prefabricated PC, and analyzes the factors affecting the vertical force through the method of comparative experiment. The research results show that the vertical force of PC in prefabricated buildings (PB) will be affected by both the steel mesh and the diameter of the steel bars. The larger the spacing and diameter, the greater the vertical force load. The steel mesh with a spacing of 170 mm is at 6 At a distance of meters, the vertical force load reached 840.3, and the vertical force load of the steel bar with a diameter of 8 mm reached 936.8. Keywords: Prefabricated building · Precast concrete · Vertical force · Monitoring method

1 Introduction In recent years, prefabricated concrete buildings have become the core of the modernization of the construction industry, which solves the problems caused by traditional buildings and is a new type of environmentally friendly and efficient construction production method. The application of PB technology in my country’s construction industry has achieved initial results, especially for large-scale construction projects, which can effectively shorten the construction period and reduce design errors. If the main technology of PB is studied, it can effectively reduce waste and improve work in my country’s construction industry. Efficiency and improving building quality are of great significance. There are many researches on the monitoring methods of the vertical force of PC in PB, and great achievements have been made in construction engineering. For example, in order to realize BIM-based applications, a construction software company developed some software interfaces, optimized its own budget management software, and transferred the constructed model to computer software for workload calculation and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 220–227, 2022. https://doi.org/10.1007/978-3-030-99616-1_29

Monitoring Method of Vertical Stress on Precast Concrete

221

engineering cost. Import CAD construction drawings, use the computer system to recognize the text of the drawings for modeling, and perform the engineering cost after the construction engineer has checked the feasibility of the drawings and the model [1]. A construction company has established a complete monitoring system during the bridge construction process to monitor deflection, tensile stress and other control parameters in real time. It can adjust and monitor the on-site construction process in a timely manner, and use computer networking transmission technology to form an independent monitoring system to carry out ropes. The tension adjustment ensures the safety of high-altitude workers [2]. Although the research results in the vertical force monitoring method of precast concrete of PB are good, it is necessary to create a new monitoring method for the vertical force performance of the building construction process. This paper combines the characteristics of precast concrete in PB and uses the GM model of gray system theory to estimate the cost-benefit of prefabricated concrete in construction projects. Taking the vertical force of concrete as the research object, analyzes the factors that affect its load and draws the conclusion Appropriate materials should be selected for construction during construction.

2 Discussion 2.1 The Characteristics of Precast Concrete in Prefabricated Buildings (1) Fast construction speed After completing the design of the prefabricated components, send the corresponding drawings to the component factory for production. It can be directly installed when transported to the construction site, which is convenient and fast. The construction speed has increased significantly. If the interior decoration is integrated, the construction period will be further shortened. (2) Factory prefabrication PB components are produced in the component factory. Due to the unified supply of raw materials and standardized production, the construction errors in the cast-in-situ construction process are effectively reduced. There are centimeterlevel errors in the construction of the cast-in-place construction, while the PB is in the component production process. The error within 3 mm can be achieved, and the surface flatness accuracy of the prefabricated component can be controlled less than 0.1%. Factory prefabrication has significantly improved the accuracy and quality of components, and can prevent common quality problems such as leakage and cracking. As the prefabricated component factory uses steam curing to reduce the impact of the external environment on component production, the component production efficiency is improved [3, 4]. (3) Advanced construction technology and high safety Most of the components of the PB are prefabricated in the component factory, and then transported to the construction site. Professional construction personnel complete the related installation work. Then the connection points are cast or bolted, and finally high-strength cement mortar is used to grouting to complete the corresponding main work.

222

M. Tian

(4) The cost of prefabricated buildings is higher Some components of PB have higher material costs: the amount of concrete and steel used in PB projects is often more than that of traditional building projects. As my country’s prefabricated buildings have just started, the market has little demand for prefabricated components, and the spatial dimensions of each building project are different. The seismic fortification intensity of the location is not only the same, and the architectural design load is not only the same, it is difficult to realize a certain type of component. Repeated use in multiple construction projects makes it difficult for prefabricated components to be reproduced in large quantities like industrialized products, and for different prefabs, corresponding templates are required to complete the construction [5]. The transportation and installation costs of PB components are relatively high: as PB has just emerged, and there are not many prefabricated component factories, the self-weight and volume of PB components are relatively large, and the transportation of components requires special vehicles, which are generally semi-trailers. Prefabricated building construction site support requires special support members, and requires lifting equipment and hoisting tools with large lifting capacity, which makes the installation cost of PB higher [6]. 2.2 Main Test Methods for Vertical Force Monitoring (1) Geodetic survey method The geodetic survey method refers to the use of theodolite or total station to measure the horizontal displacement, and the level to observe the settlement, such as the use of triangulation to measure the distance and the angle between the displacement points to determine the magnitude and direction of the displacement. The main feature of this type of method is that it can use conventional measuring instruments, the measurement theory and measurement methods are mature, the measurement data is reliable, and the cost is relatively low [7]. (2) Baseline measurement method The baseline measurement method is suitable for monitoring the horizontal displacement of linear buildings, mainly including trend line method, laser collimation method, etc. The trend line method refers to the technical method of measuring the horizontal displacement of the observation point between two fixed points by regularly measuring the distance between the observation point and the reference line. The method has the characteristics of simple equipment, convenient measurement, fast speed, high precision, low cost, etc., and is suitable for the measurement of linear buildings. The laser collimation (LC) method refers to the measurement method in which the laser beam receiver is positioned at the measured point and the deviation value of the approach point is obtained by taking the laser beam as the reference line [8]. (3) Measuring robot technology The measuring robot, also known as the automatic total station, is a measuring platform that integrates automatic target recognition, automatic angle and distance measurement, automatic target tracking, and automatic recording. It can realize full automation of measurement. This method uses computer software to automate the

Monitoring Method of Vertical Stress on Precast Concrete

223

measurement process, data recording and report output, thereby achieving monitoring automation and integration to a certain extent. The appearance of the measuring robot is a milestone in the measurement industry, which has changed the way of manual measurement in the past [9]. (4) Automated monitoring technology Automated monitoring technology is a brand-new monitoring technology developed with the advancement of network technology in the information age. Automated monitoring refers to the automatic collection of data information, automatic transmission of data to a computer program, automatic analysis of the transmitted data, etc. High-precision automated monitoring instruments, massive data management and advanced and practical data analysis and processing theory provide powerful technical support for automation technology. At the same time, in order to meet the requirements of the development of informatization and automation management, safety monitoring automation is becoming an important development direction of safety monitoring [10, 11]. 2.3 Benefit Estimation of Prefabricated Buildings Based on Grey System Theory Grey system theory is the research object of the main system of scientific innovation. It has original scientific significance, is my country’s new contributions to systems engineering [12]. This article uses the GM model. For a given sequence x(0) , the magnitude and interval of x(0) ’s grade ratio σ (0) (k) are usually used to judge whether a highprecision GM prediction model can be established, that is, whether its boundary is in a suitable range. within the area. According to the pre-inspection criteria, it can be set as:   x(0) = x(0) (1), x(0) (2), ..., x(0) (n) (1) In the formula, x(0) (k − 1) ∈ x(0) , and the grade ratio σ (0) (k) is: σ (0) (k) =

x(0) (k − 1) x(0) (k)

(2)

  2 2 Then when σ (0) (k) ∈ e− n+1 , e n+1 , it means that the sequence accuracy of the sequence x(0) is reliable when performing gray prediction. If it does not meet the requirements to meet the conditions, the next step must be to perform corresponding data processing on the sequence x(0) to make it meet the requirements.

3 Research on Vertical Force of Precast Concrete of Prefabricated Building 3.1 Research Content This article studies the factors affecting the vertical force of PC in prefabricated buildings. The comparison results are obtained by comparing the effects of steel meshes with

224

M. Tian

different spacings on the vertical force under different displacements and the effects of different steel bar diameters on the vertical force under the same displacement. What kind of concrete material should be used in the construction project has the greatest vertical force load. 3.2 Research Methods Two sets of experiments were set up using the comparative analysis method, and each set of experiments has a cross-referenced variable, and then qualitatively and quantitatively, it is used to find out which building material is more suitable. 3.3 Data Acquisition In this paper, through field investigations, in the experiment of the influence of steel mesh with different spacing on the vertical force, the vertical displacement interval of 1 m is used to measure the vertical force load of the steel mesh with 130 mm, 150 mm and 170 mm spacing. In the experiment of the influence of different steel bar diameters on the vertical force, it was ensured that the load on the vertical force for each difference of 1 mm of the steel bar diameter was recorded at the same position, that is, the vertical spacing was the same.

4 Analysis of Factors Affecting the Vertical Force of Precast Concrete in Prefabricated Buildings 4.1 The Influence of the Position of the Steel Mesh on the Vertical Force

Table 1. The influence of different spacing steel mesh on the force 1m

2m

3m

4m

5m

6m

130 mm

151.2

356.3

545.7

623.5

778.8

813.4

150 mm

165.1

372.4

563.8

637.3

796.2

829.5

170 mm

174.6

389.5

578.2

646.6

825.4

840.3

To study the influence of the steel mesh (SM) on the bearing capacity, the steel materials with the spacing between the steel mesh of 130 mm, 150 mm and 170 mm were selected as the experimental objects. According to the data in Table 1 and the line graph in Fig. 1, it can be seen that the bearing capacity curves of the SM with different spacings on the vertical force under different displacements are almost completely overlapped, indicating that the relative position of the SM affects the load of the vertical force. The effect is not big, and the ultimate bearing capacity is slightly larger when the spacing is 170 mm than when the spacing is 130 mm and 150 mm. Under the action of axial compression load, the theoretical failure of the wall panel is a sinusoidal wave failure

Monitoring Method of Vertical Stress on Precast Concrete

225

Fig. 1. Bearing capacity of vertical force under different displacements

mode. The concrete on both sides of the wall panel bulges outward under the action of pressure. Therefore, placing the steel bar on the outside can better exert its plastic deformation ability, This is also the reason why the ultimate load of 170 mm spacing is too large, so it is recommended that the spacing of the steel grids in the prefabricated concrete wall panel is 170 mm. 4.2 The Influence of Steel Bar Diameter on Ultimate Bearing Capacity

Table 2. The influence of different diameter steel bars on the force Rebar diameter

Bearing capacity

3 mm

624.5

4 mm

678.4

5 mm

781.2

6 mm

833.6

7 mm

892.7

8 mm

936.8

Three steel bars (SB) with six diameters were selected to study the influence of different SB diameters on the bearing capacity of wall panels. According to the results of Table 2 and Fig. 2, the vertical bearing capacity of the SB increases with the increase of the SB diameter. For every 1 mm increase in diameter, the load-bearing capacity increases by about 8–15%. When the diameter of the steel bar is 8 mm, the ultimate bearing capacity of the vertical force is increased by nearly half compared with that when the diameter is 3 mm. Considering comprehensively, if the design value of the

226

M. Tian

vertical bearing capacity is not too large, it is recommended that the diameter of the SB in the fabricated PC wall panel is 8 mm.

Fig. 2. The effect on the vertical force under the same displacement

5 Conclusion PB is one of the important trends in the development of the construction industry in the future. Its advantages in improving quality, shortening construction period, saving resources and energy, and reducing construction waste have made it more and more accepted in the industry. Governments of various countries also attach great importance to the development of prefabricated buildings, and actively implement compulsory policies to promote their development, promote the industrial upgrading of the construction industry, and achieve green development.

References 1. Yongliang, B., Weihua, M.: The construction method of precast concrete prefabricated stairs. Shanxi Architect. 43(9), 89–91 (2017) 2. Lishan, L.: Research on application and quality control method of environmental monitoring technology. Northern Environ. 31(6), 151–152 (2019) 3. Dyshlyuk, A.V., Makarova, N.V., Vitrik, O.B., Kul’chin, Y.N., Babin, S.A.: Features of monitoring deformation processes in ferro-concrete designs with application of the reflectometer method of recording signals of fiber Bragg gratings. Meas. Tech. 60(7), 701–705 (2017). https://doi.org/10.1007/s11018-017-1257-5 4. Sun, Q.: Design of prefabricated old-age building based on modularization: a case study of institutional elderly houses design in Taigou Village, Xi’an City. J. Landsc. Res. 10(5), 87–90 (2018)

Monitoring Method of Vertical Stress on Precast Concrete

227

5. Sun, Q.: Design of prefabricated old-age building based on modularization: a case study of institutional elderly houses design in Taigou Village, Xi’an City. Landsc. Res. English Version 10(5), 84–87 (2018) 6. Weijian, L., Ye, L., Guihao, P.: Study on determination method of working face stopping line based on stress monitoring system. Coal Eng. 51(6), 112–115 (2019) 7. Wang, Y., Haibin, G., Xiao, M.: Comprehensive evaluation of construction safety of prefabricated building based on cloud model. Eng. Econ. 29(3), 25–29 (2019) 8. Rong, H., Wen, Z., Zhongli, C.: Technical research on electrical piping of prefabricated building. Electr. Build. 037(008), 13–16 (2018) 9. Chen Wei, Y., Yangqing, Z.M.: Research on schedule buffers and robustness of prefabricated building. Constr. Econ. 39(2), 33–39 (2018) 10. Junneng, Y., Yin Tiefeng, D., Peizhen.: Monitoring method of bottom heave based on pressure difference sensing technique and its application. Hydrogeol. Eng. Geol. 44(6), 96–101 (2017) 11. Guo, L., Yuenan, J.: Analysis of the reinforced concrete prefabricated building. J. Jinling Inst. Technol. 35(1), 52–56 (2019) 12. Jun, Z., Dayang, W., Mengze, L.: Research on the collaborative design of prefabricated concrete building based on Revit. Eng. Econ. 27(9), 73–76 (2017)

Information Age, Artificial Intelligence and Virtual Reality Technology are Integrated with Logistics Teaching Reform Rong Lu(B) The Forge Business School, Chongqing College of Mobile Communication, Chongqing, China [email protected]

Abstract. This research mainly discusses the integration of artificial intelligence and VR in the information age and the logistics teaching reform. In educational and teaching activities, VR is used to construct a highly interactive, real-time, and spatially realistic virtual teaching environment. In artificial intelligence logistics teaching, students are often required to use different ways and methods to experience and feel the same thing, so as to choose the best solution strategy. The application of VR to artificial intelligence logistics teaching has created a new teaching model and learning method for the teaching of this course, enriched teaching methods and methods, and promoted the diversification of teaching practice forms. Nearly 54% of logistics teachers believe that logistics teaching is very important in artificial intelligence teaching, and nearly 46% of teachers still fail to realize the important role of logistics teaching in artificial intelligence teaching. This research will help promote logistics teaching reform. Keywords: Artificial intelligence · Virtual reality · Logistics teaching · Information age

1 Introduction Realizing education modernization and informatization, its application in the field of education. The prospects are very broad. It can reflect a real knowledge system [1, 2]. VR is very advanced [3, 4]. Virtual scenarios can provide a virtual teaching environment [5, 6]. It can be seen from the above logistics teaching activities that the use of VR can mobilize students’ learning emotions [7, 8], stimulate the imagination of teachers and students, and enhance the effectiveness of classroom logistics teaching [9, 10]. Because VR not only refers to a high-end interface for media or users, but also an application that is aimed at a specific field and solves many specific problems for this field. In order to solve the specific problems, users not only need to understand the application requirements and technology in detail, but also have rich imagination. VR is gradually being used in many fields. If VR is combined with modern teaching, it may open up more possibilities for teaching fission, improve teaching methods, and greatly enhance teaching effects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 228–232, 2022. https://doi.org/10.1007/978-3-030-99616-1_30

Information Age, Artificial Intelligence and Virtual Reality Technology

229

2 Logistics Teaching 2.1 Virtual Reality In a classroom that actually uses VR in traditional logistics teaching. VR world has become a real food, resulting in many forms such as virtual home, virtual language and virtual teaching. In the system constructed by VR, authenticity, interaction and plot are its main characteristics, and the combination of new technology and education can promote the reform of education. At present, whether it is in the field of education or in other fields, it can provide more reform and development impetus. In the future development, various information technologies should be actively used and the integration of multiple technologies should be realized. 2.2 Artificial Intelligence Teaching The content involved in artificial intelligence teaching is the cutting-edge knowledge of information technology, which has a certain depth and logic. Therefore, students may reduce their motivation to learn due to academic difficulties during the teaching process. Application of VR can solve this problem well. In traditional computer-assisted teaching, students are passive observers and can only receive audiovisual information. But in the virtual learning environment, students usually play a certain role, and then learn related content according to their own wishes, which greatly improves the learning effect of students. Therefore, VR not only brings new teaching methods and methods to school artificial intelligence teaching, but also plays a positive role in stimulating learners’ interest in learning. The teaching capabilities of artificial intelligence Q are: Q(L) =

n  i=1

R(Xi , Bi ) − φ

J 

F(Xi )

(1)

j=1

The acceptance level J of students in the teaching process is: J =

1 WL2 − κY 2 QL + D

(2)

κ, Y is the parameter value. The degree of development of VR is: T (M, N ) = Xxy (M , N ) + Rxy (M, N )

(3)

Xxy (M , N ) is empirical information. In the field of teaching, the teaching and learning process of theoretical knowledge is relatively abstract. Students can only apply theory to practice, test and understand the experience and knowledge in practice, and master and understand what they have learned. When experience, information, etc. are shared with others, it helps them to truly understand the truth contained in the theoretical knowledge points. Therefore, with the development of economy and society, the importance of practice links in the teaching process has become increasingly prominent, and the role of practice in teaching cannot be ignored.

230

R. Lu

3 Artificial Intelligence VR Integration Logistics Teaching Experiment (1) In educational and teaching activities, VR is used to construct a highly interactive, real-time, and spatially realistic virtual teaching environment. (2) The application of VR to artificial intelligence logistics teaching has created a new teaching model and learning method for the teaching of this course, enriched teaching methods and methods, improved the diversification of teaching practice forms, and accelerated education process of informatization has promoted the development of teaching reform and the improvement of teaching quality. In artificial intelligence logistics teaching, students are often required to use different ways and methods to experience and feel the same thing, so as to choose the best solution strategy. But real life often does not allow students to do this, and the use of VR just solves this problem. In the virtual real scene, students can try different schemes one by one to get different experiences and feelings.

4 Results and Discussion As can be seen from the data in Table 1, 38.4% of the schools plan to open the “Preliminary Artificial Intelligence” course in the future, 30.8% of the schools do not plan to open the course, and 30.8% of the schools said they are not sure whether they will open the course in the future. Through interviews, we learned that many schools do not plan to offer artificial intelligence courses for two main reasons: on the one hand, the school’s own teaching conditions cannot meet the needs of artificial intelligence teaching, on the other hand, teachers are relatively unfamiliar with the teaching content of this course. Unwilling to prepare new teaching content. This shows that the promotion of artificial intelligence teaching must start from the above two aspects, not only focusing on the construction of related hardware resources, but also strengthening the relevant guidance and training for teachers. Table 1 shows the establishment of artificial intelligence courses. Table 1. The establishment of artificial intelligence courses Options

Number of people (people)

Proportion (%)

Yes

5

38.4

No

4

30.8

Uncertain

4

30.8

The survey results show that nearly 54% of logistics teachers believe that logistics teaching is very important in artificial intelligence teaching, and nearly 46% of teachers still fail to realize the important role of logistics teaching in artificial intelligence teaching. This shows that at the teacher level, the knowledge and emphasis on VR and AI for

Information Age, Artificial Intelligence and Virtual Reality Technology

231

logistics teaching is not enough, and it needs to be further improved and strengthened. The importance of VR and AI to logistics teaching is shown in Fig. 1. When asked “Can I independently use VR to make courseware?”, 8 logistics teachers thought that they could basically use VR to make relevant courseware, but they could not complete it independently; 3 logistics teachers said is difficult or impossible to Number of people

Proportion 70

9

60

7 50

6 5

40

4

30

3

Proportion(%)

Number pf people

8

20

2 10

1 0

Completely production Basically can be made

Certain difficulties

Can't make

0

Options Fig. 2. Logistics professional teachers’ ability to make courseware Number of people

7

Proportion

50 45

6

35 30

4

25 3

20 15

2

10 1

0

5 Essential

Main teaching methods

Auxiliary teaching methods

Dispensable

Options Fig. 1. The importance of VR and AI to logistics teaching

0

Proportion(%)

Number pf people

40 5

232

R. Lu

make courseware using VR. Two logistics teachers said that they can make courseware completely independently. It seems that logistics teachers have basically the ability to use VR to make courseware. Even so, many logistics teachers in interviews have expressed their hope that relevant education departments can provide them with more opportunities to train in logistics teaching. The ability of logistics teachers to make courseware is shown in Fig. 2.

5 Conclusion In traditional logistics teaching, students are passive observers and can only receive audiovisual information. But in the virtual learning environment, students usually play a certain role, and then learn related content according to their own wishes, which greatly improves the learning effect of students. Therefore, VR not only brings new teaching methods and methods to artificial intelligence teaching, but also plays a positive role in stimulating learners’ interest in learning.

References 1. Massaroni, E.: Sustainability in supply chain management - a literature review. Prentice Hall Int. 8(5), 257–260 (2017) 2. Vitayasak, S., Pongcharoen, P.: Performance improvement of teaching-learning-based optimisation for robust machine layout design. Expert Syst. Appl. 98, 129–152 (2018) 3. Liou, H.H., Yang, S., Chen, S.Y., et al.: The influences of the 2D image-based augmented reality and virtual reality on student learning. Educ. Technol. Soc. 20(3), 110–121 (2017) 4. Fisher-Gewirtzman, D., Portman, M., Natapov, A., et al.: Special electronic issue: “The use of virtual reality for environmental representations.” Comput. Environ. Urban Syst. 62, 97–98 (2017) 5. Yoganathan, S., Finch, D.A., Parkin, E., Pollard, J.: 360° virtual reality video for the acquisition of knot tying skills: a randomised controlled trial. Int. J. Surg. 54(2), 24–27 (2018) 6. Dubovi, I., Levy, S.T., Dagan, E.: Now I know how! The learning process of medication administration among nursing students with non-immersive desktop virtual reality simulation. Comput. Educ. 113, 16–27 (2017) 7. Gotardelo, D.R., Bollela, V.R., Souza, A., et al.: Role-play preceded by fieldwork in the teaching of pharmacology: from “Raw Sap” to “Elaborated Sap.” Rev. Brasil. Educ. Méd. 41(3), 372–378 (2017) 8. Etminaniesfahani A, Ghanbarzadeh A, Marashi Z. Fibonacci indicator algorithm: A novel tool for complex optimization problems. Engineering Applications of Artificial Intelligence, 2018, 74(SEP.):1–9 9. Lu, H., Li, Y., Min, C., et al.: Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl. 23(7553), 368–375 (2017) 10. Pomorski, D., Perche, P.B.: Inductive learning of decision trees: application to fault isolation of an induction motor. Eng. Appl. Artif. Intell. 14(2), 155–166 (2017)

Evaluation and Improvement of Innovation Capability of Small and Medium-Sized Enterprises Based on Internet of Things Technology Hui Deng(B) Zhanjiang Science and Technology College, Zhanjiang 524094, Guangdong, China [email protected]

Abstract. In the 21st century, developing countries are facing more and more opportunities and challenges, and the development of small, medium and micro enterprises is also receiving more and more attention. Nowadays, various countries have issued various policies to promote the innovation and entrepreneurship of small, medium and micro enterprises. It is an inevitable growth trend to improve small, medium and micro enterprises’ innovation capabilities and promote overall innovation. Therefore, it is very important to study the possibility of innovation and improvement of small and medium-sized enterprises. This article uses a combination of theoretical research and empirical research to introduce the basic framework and research content of this article, and then systematically analyzes the data collected through the survey and analyzes the Internet of Things system architecture, layering and other related theories, and summarizes the Internet of Things Technical characteristics. Through the discussion of the basic technologies of the Internet of Things, understand the relevant technologies that can be applied to the innovation possibilities of small and medium-sized enterprises, and explain their characteristics. The experimental results show that the innovation ability of small, medium and micro enterprises is gradually improved under the impetus of the Internet of Things technology. Keywords: Small and micro enterprises · National economy · Innovation capability · Internet of Things technology

1 Introduction Economic globalization and the development of information science and technology have brought development opportunities to my country’s small, medium and micro enterprises, but they have also brought huge challenges. In the modern information age, the Internet of Things is widely used in business management and deeply integrated into logistics services. With the support of policies and the maturity of Internet of Things technology, automated and intelligent logistics operations have become an inevitable growth trend [1]. How to use the powerful technology of the Internet of Things to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 233–240, 2022. https://doi.org/10.1007/978-3-030-99616-1_31

234

H. Deng

enhance competitiveness, improve the innovation capabilities of enterprises, and how to integrate the Internet of Things technology into business development is the key to the growth of small, medium and micro enterprises. Therefore, studying the operational efficiency of small, medium and micro enterprises in the Internet of things to help them understand their own operating conditions has important practical significance for improving innovation capabilities. In recent years, many researchers have conducted research on the evaluation and improvement of innovation capabilities of small, medium and micro enterprises based on Internet of Things technology and have achieved good results. For example, Hudakova M believes that innovation is a business, technology, and process. Innovation is the process of transforming a series of knowledge into new products, new processes and new services, including scientific and technological activities and understanding and satisfying customer needs. Create an efficient and low-cost production and management system [2]. Yang W believes that the key to the success of SMEs is flexible business strategies, and pointed out that the most important factors affecting the success of entrepreneurship are the unique qualities of entrepreneurs in terms of personality traits, management skills and interpersonal relationships, environmental factors and compatibility with the external environment The main factors affecting the success or failure of the initial stage of entrepreneurship are the main factors affecting the success or failure of the initial stage of entrepreneurship [3]. At present, there are many researches on the innovation ability and analysis of small, medium and micro enterprises. These predecessors’ theories and experimental results provide a theoretical basis for the research of this article. This paper analyzes the far-reaching impact of the Internet of Things technology on small, medium and micro enterprises, and proposes a series of systematic studies on the innovation capabilities of enterprises. The Internet of Things is at the center of digital transformation into social and economic growth and bridges the gap between the physical world and the Internet. Gap. Today, the Internet of Things has been widely used in people’s work and life. The development of global Internet connections and data analysis applications has witnessed the continuous growth of market demand and innovation [4]. The use of Internet of Things technology can continuously innovate and improve the operation mode, logistics service and management mode of small, medium and micro enterprises.

2 The Components and Evaluation Indicators of the Innovation Capability of Small, Medium and Micro Enterprises 2.1 Elements of the Innovation Capability of Small, Medium and Micro Enterprises (1) Resource factor The investment of small, medium and micro enterprises in R&D and technological transformation is a prerequisite for technological innovation. The investment ability of an enterprise is embodied in capital investment and personnel investment. Capital investment is integrated into the preparation and operational capabilities of funds required at each stage of the R&D process. Personnel investment is integrated in the recruitment,

Evaluation and Improvement of Innovation Capability

235

training and development of scientific and technological personnel in the process of new technology development. Under the same conditions, enterprises with high investment in R&D and technological transformation are often easier to achieve technological development and take the lead in innovation. (2) Organizational factors. Generally speaking, the more flexible the organizational decision-making mechanism, the faster a company can respond to market dynamics and carry out technological innovation activities to perceive market opportunities. A good technological innovation decision-making mechanism can provide support, guarantee and early warning functions for the technological innovation process, enabling decision makers to make decisions based on as much information as possible, reducing the risk of technological innovation and at the same time being able to innovate in technology [5, 6]. The incentive mechanism of an enterprise is embodied in the process of technological innovation, manifested as a mechanism for employee evaluation, performance appraisal and rewards for scientific and technical personnel directly involved in research and development. At the same time, it also includes incentive mechanisms for relevant personnel and departments who support innovation and put forward ideas [7, 8]. 2.2 Evaluation Indicators for Innovation Capabilities of Small, Medium and Micro Enterprises The evaluation indicators of innovation ability of small, medium and micro enterprises mainly include four aspects, including evaluation of technological innovation ability, evaluation of organizational innovation ability, evaluation of market innovation ability, and evaluation of innovation environment [9, 10]. (1) Evaluation of technological innovation capability Technological innovation capability includes the intensity of R&D investment, the proportion of R&D expenditure in revenue and the proportion of R&D personnel in employees; it also includes the number of product development projects, which refers to the number of new product research and development within a certain period of time; it also includes the number of patents owned by the company And advanced equipment accounted for the proportion of existing equipment. (2) Evaluation of organizational innovation capability The evaluation of organizational innovation capability includes entrepreneurs’ desire for innovation, and an excellent leader is also an important factor affecting innovation capability. It also includes the degree of perfection of the incentive mechanism. This indicator reflects the attractiveness of the cluster incentive mechanism to the innovative behavior of employees and is a qualitative indicator. An effective incentive mechanism will stimulate the initiative and potential of employees, enhance their sense of responsibility and self-confidence, and will therefore bring inexhaustible motivation and continuous innovation to the enterprise. (3) Evaluation of technological innovation capability The evaluation of technological innovation capability includes the sales rate of new products, which is equal to the percentage of new product sales in total sales revenue, and also includes the market share of new products, which is equal to the percentage of new product sales in the same industry. Proportion. The indicators

236

H. Deng

reflect the market’s awareness of new cluster products [11, 12]. At the same time, the academic qualifications of marketing personnel are also an important factor, and high-quality talents should be introduced to enhance the innovation capabilities of enterprises.

3 Research on Experimental Preparation for Analysis of Innovation Capabilities of Small, Medium and Micro Enterprises 3.1 Experimental Method Convergence analysis research is one of the important methods to analyze the distribution of sample differences. Since the innovation ability of small, medium and micro enterprises will affect their operating performance, the evaluation of the innovation ability of small, medium and micro enterprises can be carried out by analyzing the level of their operational performance. Based on the evaluation of the operating performance of small, medium and micro enterprises in the Internet of Things, analyze and measure how the differences in operating efficiency of small, medium and micro enterprises in the Internet of Things will evolve and analyze the evolution of the differences in overall performance of related industries. Therefore, in this section, we will conduct a convergence analysis on the operational efficiency of SMEs. (1) Absolute convergence analysis CVt =

n 

(TEit − TEt )2 nTEt

(1)

t=i

Among them, CVt is the coefficient of variation in year t, TEt is the mean value of operating efficiency in year t, and TEit is the operating efficiency of the i-th company in year t. (2) In order to more accurately judge whether there is a σ convergence trend in the operating efficiency of small, medium and micro enterprises in the context of the Internet of Things, this paper uses the following expression to test cv = a1 + a0

(2)

Among them, cv represents the coefficient of variation, and t = 1,2,…,10 represents the time variable. When a1 is negative, the efficiency is converged, otherwise there is no absolute σ convergence. 3.2 Experimental Data Collection This article chooses to use the large-scale variation coefficient method to measure the convergence of some small, medium and micro enterprises. Operating efficiency, through the change trend of the coefficient of variation to analyze the technological innovation capabilities of small, medium and micro enterprises.

Evaluation and Improvement of Innovation Capability

237

4 Experimental Analysis on the Evaluation of Innovation Ability of Small, Medium and Micro Enterprises 4.1 Analysis of Coefficient of Variation Value By analyzing the change trend of the coefficient of variation of small, medium and micro enterprises in the two regions of our country, we can find the convergence or divergence of operating efficiency, and analyze the change trend of innovation ability in the two regions (Table 1). Table 1. Variation trend table of the coefficient of variation of corporate operating efficiency Year

A region coefficient of variation

B region coefficient of variation

2016

0.85

0.98

2017

0.97

1.52

2018

1.1

1.23

2019

1.05

1.78

2020

1.45

2.07

Fig. 1. The change trend of the coefficient of variation of corporate operating efficiency

According to Fig. 1, the coefficient of variation of area A in 2016 is 0.82, and the coefficient of variation in 2020 is 1.45; the coefficient of variation of area B in 2016 is 0.98, and the coefficient of variation in 2020 is 2.07. As time goes by, the coefficient

238

H. Deng

of variation presents a fluctuating trend, indicating that there is convergence and divergence overlap with σ convergence, but the overall change is on the rise. This intuitively shows that the gap in the operating efficiency of my country’s small and medium-sized enterprises between AB and my country is widening. Therefore, the operating efficiency of small, medium and micro enterprises does not have a trend of σ convergence, but has a clear trend of divergence. The future will be even bigger. 4.2 Industry Distribution Analysis of Small, Medium and Micro Enterprises in Region A Based on the Internet of Things, it is also possible to achieve integrated logistics to change the decentralized situation. The so-called integrated logistics is to deal with all small, medium and small companies in the logistics service chain as a whole. Through some institutional arrangements, they can see that such a network-based Internet of Things transmission integration has significantly improved transmission efficiency. By integrating the integration nodes of various industries, the Internet of Things can manage objects and people through scanning, mounting and tracking functions.

Fig. 2. Analysis of the proportion of small, medium and micro enterprises in area A

As shown in Fig. 2, the current small, medium and micro enterprises in Zone A are mainly concentrated in industries with low barriers to entry and low requirements for technological innovation. There are a large number of labor-intensive, resource-based, and processing trade-based enterprises, but a relatively small proportion of them are engaged in cultural creativity, characteristic agriculture, computer services, information and other competitive industries with strong expertise and high technical requirements.

Evaluation and Improvement of Innovation Capability

239

5 Conclusions This article focuses on the joint development of small, medium and micro enterprises using the Internet of Things technology, so that enterprises have related points before, and through intelligent data transmission, the connection between enterprises becomes closer, and the informationization of enterprises becomes external, which is convenient for enterprise analysis. Small and medium-sized enterprises promote the development of commercial Internet of Things technology in transportation, logistics and core business areas. The government needs to strengthen leadership and increase capital investment. Invest in the formulation of actual policies, systems and regulations, promote the establishment of corporate partnerships, and improve the operability of the Internet of Things technology in the evaluation of the actual innovation capabilities of enterprises. Acknowledgments. The first batch of Zhanjiang Non-funded Science and Technology Project in 2021, “Research on Innovation and Development of Small, Medium and Micro Enterprises in Zhanjiang City after the Epidemic - from the Perspective of Making Full Use of Tax Incentives” (Project No.: 2021b01054).

References 1. Castela, B.M.S., Ferreira, F.A.F., Ferreira, J.J.M., et al.: Assessing the innovation capability of small- and medium-sized enterprises using a non-parametric and integrative approach. Manag. Decis. 56(6), 1365–1383 (2018) 2. Hudakova, M., Dvorsky, J., Buganova, K., et al.: Analysis and evaluation of market and financial risks in small and medium-sized enterprises. Komunikacie 20(2), 16–22 (2018) 3. Yang, W.: Research on quantitative evaluation of innovation capability of intelligent grid industry cluster based on BP NNA-taking Jiangsu power grid industry cluster as an example. Revista de la Facultad de Ingenieria 32(8), 726–734 (2017) 4. Wang, K., Yan, F., Zhang, Y., et al.: Supply chain financial risk evaluation of small- and medium-sized enterprises under smart city. J. Adv. Transp. 2020(7), 1–14 (2020) 5. Huiyuan, W.: Research on evaluation of intensive economic benefits of equipment manufacturing enterprises. J. Phys. Conf. Ser. 1983(1), 012107 (2021). https://doi.org/10.1088/17426596/1983/1/012107 6. Zhang, L.: Credit evaluation of medium and small sized enterprises during supply chain finance based on BP neural network. Revista De La Facultad De Ingenieria 32(3), 776–784 (2017) 7. Oaten, M., Williams, K.D., Jones, A., et al.: Extension theory and its application in evaluation of independent innovation capability. J. Soc. Clin. Psychol. 27(5), 471–504 (2009) 8. Liu, W., Bi, K.: Dynamic comprehensive evaluation of knowledge innovation capability of enterprises. J. Appl. Sci. 13(8), 1392–1396 (2018) 9. Niu, Y.: The evaluation and comparison on the innovation capability of high-tech industries of Shandong province based on factor analysis. In: International Symposium on Technical Innovation of Industrial Transformation and Structural Adjustment. School of Economics, Yantai, P.R. China, 264005 (2019)

240

H. Deng

10. Yang, J., Ling, L., Huang, C., et al.: Evaluation for innovation ability of national agricultural science and technology parks in Jiangxi province. Am. J. Plant Sci. 09(12), 2446–2461 (2018) 11. Chen, Y., Li, W., Yi, P.: Evaluation of city innovation capability using the TOPSIS-based order relation method: the case of Liaoning province. China. Technol. Soc. 63, 101330 (2020) 12. Cao, J., Tian, J., Xu, J., et al.: Organic flow batteries: recent progress and perspectives. Energy Fuels 34(11), 13384–13411 (2020)

Textile Industry Agglomeration and Economic Development Junlan Wang(B) Lanzhou University of Technology, Lanzhou 730000, Gansu Province, China [email protected]

Abstract. Industrial agglomeration is one of the ways to balance the relationship between enterprise product cost pressure and regional adjustment pressure. The textile industry in Zhejiang Province of China has obvious characteristics of industrial agglomeration. Through the combination of the degree of agglomeration of textile industry in Zhejiang Province in recent ten years, industrial structure, employment and the development of textile enterprises, this paper analyzes the impact of textile industry agglomeration on the economy of Zhejiang Province. The research shows that the agglomeration of Zhejiang textile industry promotes the development of Zhejiang textile industry towards technology intensive structure, and will also promote the employment and the development of individual enterprises to a certain extent; At the same time, Zhejiang textile industry agglomeration will promote the economic development of Zhejiang Province in the next decade, and then the effect will disappear. Keywords: Textile industry agglomeration · Location entropy · Location economy

1 Introduction As China’s traditional pillar industry, textile industry has made great contributions to China’s rapid economic development. Under the new international situation, China’s export trade difficulties, regional allocation pressure and cost pressure bring risks to small and medium-sized enterprises. How to carry out independent innovation and improve export competitiveness has become very important, and industrial agglomeration is one of the ways to solve this problem. Industrial agglomeration uses its unique flexible production and regional collaborative innovation to drive the growth of enterprises. Domestic scholars in China have done a lot of research on the current situation of agglomeration. For the pattern evolution and development situation of China’s textile industry, Zhou X (2019) and others used Markov transfer matrix to study the development level of China’s textile industry from 1987 to 2014 [10]. It is concluded that the agglomeration of China’s textile industry is obvious in various regions, but the development level varies greatly, and the development pattern needs to be optimized. From other perspectives, Fang W (2017) and Han F (2018) analyzed the impact of textile manufacturing enterprise agglomeration on regional economy from the downstream of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 241–248, 2022. https://doi.org/10.1007/978-3-030-99616-1_32

242

J. Wang

the production chain [4]. Gao Y, Ma S, Zhang C (2020) analyzed the textile industry and related enterprises in Shaoxing, Zhejiang Province, supplemented by the diamond model, and concluded that the rich primary factor resources in the upper reaches of the textile industry in Zhejiang province make the place have huge external and internal economies of scale [3]. Since 1980, there have been many scholars studying agglomeration phenomenon in the world, and scholars of various schools have studied this kind of agglomeration phenomenon from their own perspectives. In terms of theoretical research, Kashiwagi K, Iwasaki E. (2020) explored the role of agglomeration in promoting the competitiveness of textile industry and garment industry and regional economic growth by using the method of location entropy [6]. Li C, Wu K, Gao X studied the relationship between the textile industry in Bangladesh, Turkey, Germany, China and the EU and the world economic influence from 2012 to 2020 [7]. Among them, China’s large-scale textile industry agglomeration is one of the reasons why China’s textile industry accounts for a significant proportion in the world. Rand J, Tarp F, Trifkovíc N (2019) believes that the agglomeration economy has brought lower costs to Eastern Europe [8]. Coupled with the advantages of labor and resources, it comes to the conclusion that with the expansion of agglomeration economy in Eastern Europe, its international competitiveness is also improving. Zhejiang textile industry is the leading industry in Zhejiang industrial agglomeration. By studying the current situation of textile industry agglomeration in Zhejiang Province, this paper analyzes the impact of textile industry agglomeration on the economy of Zhejiang Province, and discusses whether textile industry agglomeration is the best choice for the development of textile industry in Zhejiang Province.

2 Analysis on the Current Situation of Textile Industry Agglomeration in Zhejiang Province Zhejiang textile industry has a long history. Since 1970, the cumulative output value of textile industry in Zhejiang Province has jumped. In 2010, its output value jumped to the third place in China, and the number of enterprises ranked first in China. Due to capital and technical constraints, the internal structure of Zhejiang textile industry was relatively single in the early stage. Then, driven by market demand, Zhejiang textile industry extended to the downstream and achieved leapfrog development [1]. One of the development advantages of Zhejiang’s textile industry is the Yangtze River Delta, which is located in the two major economic belts. The two provinces and one city in this region are geographically connected, with developed economy and sufficient infrastructure, which ensures the conditions for textile technology production and consumer market in Zhejiang Province; The second advantage is sufficient human resources and raw material resources. Zhejiang Province is a populous province. At the same time, it is located in the lower reaches of the Yangtze River and has the highest cotton output in China. Coupled with the silkworm breeding and silk making technology spread since ancient times, Zhejiang textile industry has a comparative advantage in the world. Zhejiang textile industry also has obvious disadvantages. There are a large number of private enterprises in the market, but the total output value is low, the level of production technology and innovation ability are weak, and the construction of independent brands is insufficient.

Textile Industry Agglomeration and Economic Development

243

Textile agglomeration in Zhejiang Province was first developed by the agglomeration production of similar products, and then became diversified development. Then to the rise of related industries, which in turn promoted the enhancement of agglomeration. In addition, Zhejiang textile agglomeration enterprises are mainly small and medium-sized private enterprises, which are sensitive and flexible to market demand and information changes in the agglomeration area, which further promotes the coordinated development of Zhejiang Province and the textile industry as a whole. Due to the change of economic situation, Zhejiang textile industry has entered a key stage of industrial agglomeration development in recent years, which will affect and even determine the future development of Zhejiang textile industry [2].

3 Research on the Impact of Textile Industry Agglomeration on Economy in Zhejiang Province This chapter will use the location entropy method to determine the degree of textile industry agglomeration in Zhejiang Province, and analyze the impact of textile industry agglomeration on its economic development in combination with the changes of economic indicators (industrial structure, employment and enterprise development) in Zhejiang Province in recent years. 3.1 Calculation of Location Entropy of Textile Industry in Zhejiang Province This section uses the location entropy index to measure the agglomeration degree and specialization level of textile industry in Zhejiang Province from 2011 to 2017.   (1) Rij = eij ej Ei E Rij :Industrial output value of region I; eij : Industrial output value of industries in region I; ej :Total industrial output value of the region; Ei :Industrial output value of national I industry;E:National total industrial output value (Table 1). From 2011 to 2017, Zhejiang’s textile industry agglomeration carried out specialized production to a high extent. With the increase of years, the location entropy decreases year by year. The turning point in 2016 shows that some clustered industries have been eliminated in Zhejiang’s textile industry (Table 2). 3.2 Impact of Zhejiang Textile Industry Agglomeration On Industrial Structure and Employment To promote the development of tertiary industry, China must vigorously promote urbanization [11]. The development of the secondary industry has a positive impact on the development of the tertiary industry, and the independence of the tertiary industry increases year by year. Therefore, in order to increase the development of the tertiary industry, we must expand and strengthen the secondary industry and make effective use of the driving effect of informatization on industrialization. Assuming that there is a

244

J. Wang Table 1. Location entropy of textile industry in Zhejiang Province from 2011 to 2017

Particular year Output value of Zhejiang textile industry (100 million)

Zhejiang’s total industrial output value (100 million)

Output value of national textile industry (100 million)

National total Rij industrial output value (100 million)

2017

4875.01

19474.48

37976.7

275119.3

1.81

2016

6030.74

18655.12

40869.7

245406.4

1.94

2015

6026.48

17217.47

40173.3

234968.9

2.05

2014

6037.54

16771.9

38091.27

233197.4

2.20

2013

5855.93

15837.2

36160.6

222333.2

2.27

2012

5416.9

15338.02

32173.56

208901.4

2.29

2011

5805.65

14683.03

32772.66

185139.1

2.23

Table 2. Proportion of secondary industry and employment in Zhejiang Province (2010–2019) Particular year

GDP of Zhejiang Province (100 million yuan)

Output value of secondary industry in Zhejiang Province (100 million yuan)

Proportion of secondary industry

Employment of textile industry in Zhejiang Province (person)

Proportion of employment in the secondary industry (%)

2019

62352

26567

42.61%

2018

58003

25289.31

43.60%

2017

51768.26

22232.08

42.95%

634900

28.11

2016

47251.36

21194.61

44.86%

712600

28.8

2015 2014

42886.49

19711.67

45.96%

746700

29.3

40173.03

19175.06

47.73%

772400

29.9

2013

37756.58

18047.52

47.80%

790700

30.1

2012

34665.33

17316.32

49.95%

798100

30.3

2011

32318.85

16555.58

51.23%

1036900

29.5

direct relationship between the location entropy of textile industry and industrial structure in Zhejiang Province, assuming that the proportion of secondary industry is w, the model is established as follows: W = 0.2962Rij0.6228

(2)

The agglomeration of textile industry plays a positive role in promoting the economy of Zhejiang. When Rij is 1, the output value of Zhejiang’s secondary industry accounts for about 29.62%, which is in the primary stage of industrialization. The proportion of

Textile Industry Agglomeration and Economic Development

245

secondary industry output value in Zhejiang Province has decreased year by year, about 2029, Rij will be reduced to 1, that is, if the current situation remains unchanged, the degree of industrialization of Zhejiang Province will drop to the primary industrialization stage in about ten years, which is not conducive to the overall development of Zhejiang Province. At the same time, we can also see whether there is industrial structure optimization within the industry from the perspective of textile industry agglomeration in Zhejiang Province. A model function is constructed by combining the location entropy of Zhejiang textile industry and the total output value (y) of Zhejiang textile industry: Y = −16756Rij 2 + 70660X − 67025

(3)

When Rij is less than 2.1, the output value created by Zhejiang textile industry increases with the increase of agglomeration degree. When Rij begins to exceed 2.1, the opposite occurs. According to the classification method of resource intensity of industrial structure, when Zhejiang textile industry is in the former, the textile industry needs to increase the agglomeration degree of Zhejiang textile industry. On the contrary, it should turn to technology intensive and increase the role of high and new technology in the textile industry. The data of textile industry in Zhejiang Province in recent years show that it is moving towards technology intensive structure. The proportion of employees in the secondary industry in the main textile industry agglomeration areas of Zhejiang Province, such as Ningbo, Jiaxing, Huzhou and Shaoxing, has exceeded 50%. The secondary industry has created a large number of employment opportunities for Zhejiang Province, while the employment opportunities created by the textile industry account for nearly one-third of the employment opportunities created by the secondary industry, the textile industry in Zhejiang Province has provided favorable help to solve the employment of personnel in Zhejiang Province. By analyzing the data, it can be seen that the location entropy of Zhejiang textile industry and the proportion of employment in the secondary industry (T) form a two-stage shape. Take the data before (2.27, 47.8%) to build the relationship and get the model: T = 4.0458Rij + 20.837

(4)

That is, employment opportunities increase with the expansion of Zhejiang textile industry agglomeration, especially the secondary industry, which plays a positive role in the economic development of Zhejiang Province. Therefore, Rij was around 2.25, it promoted Zhejiang’s employment and economy. However, when the aggregation degree is as high as 2.25, 30.4% of the personnel complete the work of 29.5%, resulting in a waste of human resources and greatly reducing the social productivity. 3.3 Impact of Zhejiang Textile Industry Agglomeration on Enterprises There are many indicators to measure the development of enterprises. In this section, the following three representative indicators (enterprise loss rate, total profit and product sales rate) will be selected for analysis in combination with the degree of agglomeration (Table 3).

246

J. Wang Table 3. Survival of enterprises in Zhejiang Province (2011–2019)

Particular Total Number of Loss year number of loss rate enterprises making enterprises

Profit of textile industry

Total profit Proportion Total of Zhejiang of profit product Enterprises making in sales textile rate industry

Sales Sales rate rate of difference textile products

2019

18362

3135

17.07%

2018

19122

2841

14.86%

2017

20187

2138

10.59% 243.37

4605.41

5.28%

94.99% 97.37%

2.38%

2016

20201

1973

9.77% 324.12

4469.42

7.25%

97.57% 97.23%

-0.34%

2015

20525

2205

10.74% 308.35

3839.99

8.03%

96.63% 96.72%

0.09%

2014

20494

2232

10.89% 289.76

3729.13

7.77%

96.2%

0.48%

2013

20776

2203

10.60% 276.79

3561.26

7.77%

96.83% 96.98%

2012

20370

2437

11.96% 235.84

3112.65

7.58%

97.3%

97.07%

-0.23%

2011

22484

2154

9.58% 280.9

3327.29

8.44%

97.45% 97.32%

-0.13%

96.68%

0.15%

From 2011 to 2017, the total profits of enterprises in Zhejiang Province increased year by year. Taking the profit proportion of textile industry in Zhejiang Province as an important index, this paper analyzes the impact of textile industry agglomeration in Zhejiang Province on the overall enterprises in Zhejiang Province. When Rij is 2.05, the textile industry in Zhejiang Province can create a peak proportion of total enterprise profits in Zhejiang Province, which is about 8.25%. Combined with the loss rate (P) of Zhejiang enterprises and the location entropy of Zhejiang textile industry, the data of the first five periods and the last five periods are speculated based on the location entropy of 0.5, and the following model is obtained: P = 0.014Rij + 0.0763

(5)

The loss of textile enterprises in Zhejiang Province increases with the increase of agglomeration, but this situation is inevitable, even If Rij drops below 1 or even becomes 0, the overall loss rate of enterprises in Zhejiang Province is still higher than 5%. Therefore, it is sufficient to maintain a reasonable range for the agglomeration degree of textile industry in Zhejiang Province, and the calculation shows that When Rij is between 1.5 and 2.2, enterprises in Zhejiang Province maintain a relatively stable loss rate of about 10% [5]. Combined with the location entropy of Zhejiang textile industry and the difference (q) between the product sales rate of Zhejiang textile industry and the total product sales rate of Zhejiang Province, the model is as follows: Q = −11.167Rij 2 + 45.863Rij − 46.783

(6)

When Rij is between 1.88–2.24, the product sales rate of textile industry in Zhejiang Province is higher than the total product sales rate of Zhejiang Province Rij is 2.05, the difference reaches the maximum, and the promotion effect of Zhejiang textile industry on Zhejiang’s economy is the largest, when Rij is less than 1.88 or greater than 2.24, it has a reverse effect on the product sales rate of Zhejiang Province.

Textile Industry Agglomeration and Economic Development

247

Combined with the location entropy of Zhejiang textile industry and the total product sales rate (y) of Zhejiang Province, the relationship is as follows: Y = 20.879XRij 2 − 85.449Rij + 183.32

(7)

When Rij is 2.05, the total product sales rate in Zhejiang Province is the lowest, when Rij is 1–2.05, the total product sales rate of Zhejiang Province decreases gradually with the increase of textile industry agglomeration in Zhejiang Province. In Rij is greater than 2.05, it shows the opposite effect. And When Rij is between 1.88–2.24, the textile industry of Zhejiang province promotes the economic development of Zhejiang Province. Therefore, when Rij is 2.05–2.24, the agglomeration of textile industry in Zhejiang Province is not only conducive to the improvement of the total product sales rate of enterprises in Zhejiang Province, but also conducive to the economic development of Zhejiang Province.

4 Conclusion Zhejiang textile industry agglomeration Rij between 1–2.1 will promote the economic development of Zhejiang Province in the short term, and will also improve the urbanization, the development of secondary and tertiary industries, employment and the overall interests of enterprises in the textile industry of Zhejiang Province. If the concentration is too high, it will cause a waste of resources and manpower. At the same time, the current unreasonable asset structure and inefficient production of textile enterprises in Zhejiang Province are not conducive to the long-term development of textile industry and light industry in Zhejiang Province. If the technological reform and innovation of enterprises in Zhejiang Province are not accelerated, the textile industry in Zhejiang Province is likely to be eliminated within 10 years. In order to solve these problems, in the short term, it still depends on the support of the government to provide financial support and guidance to the textile industry agglomeration areas in Zhejiang Province. In the long term, it depends on the textile enterprises in Zhejiang Province to strive to create their own excellent and well-known brands, adapt to and get used to the introduction of high and new technology, constantly update their own textile technology and obtain competitive advantages in the world market [9].

References 1. Donaldson, C., Gregory, I.N., Taylor, J.E.: Locating the beautiful, picturesque, sublime and majestic: spatially analysing the application of aesthetic terminology in descriptions of the English Lake District. J. Hist. Geogr. 56, 43–60 (2017) 2. Faggio, G., Silva, O., Strange, W.C.: Tales of the city: what do agglomeration cases tell us about agglomeration in general? J. Econ. Geograp. 20(5), 1117–1143 (2020) 3. Gao, Y., Ma, S., Zhang, C.: Research on international competitiveness of manufacturing industry from the perspective of industrial agglomeration. Stat. Dec. Making 35(21), 131–134 (2019)

248

J. Wang

4. Han, F., Xie, R., Fang, J.: Urban agglomeration economies and industrial energy efficiency. Energy 162, 45–59 (2018) 5. Jianhua, Y., Mingzheng, Z., Xin, L.: Interregional transfer of polluting industries: a consumption responsibility perspective. J. Clean. Prod. 112, 4318–4328 (2016) 6. Kashiwagi, K., Iwasaki, E.: Effect of agglomeration on technical efficiency of small and medium-sized garment firms in Egypt. Afr. Dev. Rev. 32(1), 14–26 (2020) 7. Li, C., Wu, K., Gao, X.: Manufacturing industry agglomeration and spatial clustering: evidence from Hebei Province, China. Environ. Dev. Sustain. 22(4), 2941–2965 (2019). https:// doi.org/10.1007/s10668-019-00328-1 8. Rand, J., Tarp, F., Trifkovic, N., Zille, H.: Industrial agglomeration in Myanmar. UNUWIDER (2019) 9. Hasan, S., Alessandra Faggian, H., Klaiber, A., Sheldon, I.: Agglomeration economies or selection? an analysis of taiwanese science parks. Int. Reg. Sci. Rev. 41(3), 335–363 (2018). https://doi.org/10.1177/0160017616642822 10. Zhou, X., Wang, D., Wang, P.: Study on the evolution and development model of China’s textile industry agglomeration pattern. Res. Technol. Econ. Manag. 11, 89–95 (2019) 11. Zhao, H., Lin, B.: Will agglomeration improve the energy efficiency in China’s textile industry: evidence and policy implications. Appl. Energy 237, 326–337 (2019)

Threshold Effect of Collaborative Agglomeration of Internet and High-Tech Industry on Green Innovation Huifen Wu(B) School of Economics and Management, Lanzhou University of Technology, Lanzhou 730050, Gansu, China [email protected]

Abstract. Collaborative industrial agglomeration is not only conducive to innovation development, but also has environmental effects. In this paper, the threshold effect model is adopted, and the degree of collaborative agglomeration of Internet and high-tech industry is taken as the threshold variable. The purpose is to investigate the threshold characteristics of collaborative agglomeration of Internet and high-tech industry on green innovation. The study found that industrial collaborative agglomeration is very conducive to the development of green innovation. After crossing the threshold of certain co-agglomeration level, the marginal effect begins to decline, and the green innovation effect of collaborative agglomeration has been decreased. In general, the collaborative agglomeration of Internet and high-tech industry has great development space at present. Promoting the collaborative agglomeration and development of the two industries is conducive to the development of green innovation. Keywords: Collaborative agglomeration · Internet · High-tech industry · Green innovation · Threshold regression

1 Introduction The “Made in China 2025” plan clearly puts forward the concept of “innovation-driven and green development”, which provides a direction for regional development. It can be seen that green innovation development will be the general trend of the future. Nowadays, the information technology and knowledge-intensive high-tech industries are entering a “golden age” of rapid development. High-tech industries are the main innovation-driven industries in China. At the same time, the popularization of the Internet has accelerated the process of industrialization and informatization, and has become an indispensable new driving force for innovative development. The Internet and high-tech industries complement their advantages, and the deep integration of the two will greatly promote collaborative innovation. In this context, how to seize the opportunity of the Internet era and more effectively promote the co-agglomeration and collaborative development of the Internet and high-tech industry is particularly worth exploring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 249–256, 2022. https://doi.org/10.1007/978-3-030-99616-1_33

250

H. Wu

Ellison and Glaeser (1997) were the first to focus on spatial agglomeration among heterogeneous related industries. Then they put forward the concept of industrial synergy agglomeration [1]. Pei (2021) points out that different factor intensive industries have different impacts on environmental pollution through different agglomeration ways. In order to reduce pollution and protect the environment, specialized agglomeration mode is suitable for labor-intensive and technology-intensive industries [2]. However, Hao and Song (2021) found that manufacturing agglomeration did significantly aggravate environmental pollution, while service industry effectively reduced environmental pollution through agglomeration. However, the impact of synergistic agglomeration of these two industries on environmental pollution showed an inverted “U” shaped relationship [3]. In addition, Li and Han point out that limited by the efficiency of resource allocation, the collaborative agglomeration of manufacturing and producer services firstly promoted carbon emission reduction and then inhibited it [4]. Yang (2021) believes that government-led collaborative industrial agglomeration can only reduce local environmental pollution, but not affect neighboring areas. However, when industrial synergy agglomeration is market-driven, it has both local effects and significant spatial spillovers [5]. In terms of innovation, Shi (2019), taking the pharmaceutical manufacturing industry as the representative. They found that diversified agglomeration promoted the improvement of innovation efficiency of pharmaceutical manufacturing industry, whereas specialized agglomeration inhibited it [6]. Nan (2021) takes high-tech industry as the research object. Their study found that high-tech industrial agglomeration would first improve innovation efficiency, but excessive agglomeration would inhibit it. In other words, industrial agglomeration has the optimal agglomeration scale, which is most conducive to innovation [7]. Industrial collaborative agglomeration may play a more promoting role in innovation development. Therefore, Liu and Li (2019) take the collaborative agglomeration of producer services and manufacturing as the research object and found that collaborative agglomeration of these two industries is conducive to the innovative development of manufacturing industry [8]. However, Zeng and Li (2020) believe that the synergistic agglomeration of the above two industries has a hindering effect on China’s green innovation in general [9]. To sum up, many scholars have studied the effects of single industry agglomeration on environmental pollution and innovation. From the perspective of collaborative agglomeration, there are few literatures to study its impact on green innovation considering environmental factors. Moreover, in terms of collaborative agglomeration, scholars mainly focus on manufacturing and producer services. However, the advantages of the Internet are still not taken into account to study the driving effect of the Internet on hightech industry. With the development of the Internet, its integration and innovation with high-tech industry are increasingly active. Moreover, the Internet has a stronger innovation spillover effect on enterprises with higher productivity and more frequent R&D activities [10]. Therefore, it is meaningful to study the law of the synergistic agglomeration of Internet and high-tech industry on the development of green innovation. Therefore, this paper uses the threshold regression model to empirically investigate how the

Threshold Effect of Collaborative Agglomeration of Internet

251

collaborative agglomeration of the Internet and high-tech industries will affect the efficiency of green innovation, and provide a reference for the government to formulate policies on the collaborative agglomeration and development of these two industries.

2 Model Construction and Variable Description 2.1 Model Construction In order to verify the threshold effect of collaborative agglomeration of Internet and hightech industry on green innovation efficiency, this paper uses the threshold regression model. In addition, due to the fact that there is a lag in the agglomeration effect, the degree of synergistic agglomeration with one stage lag is included in the model as a threshold variable. Thus, the model is constructed as follows:     lngreenit = c + β1 lncoaggit lncoaggi,t−1 ≤ γ + β2 lncoaggit lncoaggi,t−1 > γ +β3 lnhclit + β4 lnpgdpit + β5 lnfdiit + β6 lnmartit + β7 lnerit + εit (1) Among them, green represents green innovation efficiency. Coagg represents the collaborative agglomeration level of the Internet and high-tech industries. In addition, lnhcl, lnpgdp, lnfdi, lnmart, lner are control variables, and their specific meanings are described below. 2.2 Variable Description (1) Explain the variable---Green Innovation Efficiency (green). Green innovation efficiency was measured using a non-radial and non-angular Super-SBM model. (2) Core explanatory variable---collaborative agglomeration of the Internet and hightech industries (coagg). Firstly, the agglomeration index of Internet industry and high-tech industry is calculated by the location entropy index. Then, the degree of collaborative agglomeration is calculated by the industrial collaborative agglomeration index, and the calculation formula is as follows:  Iit (2) Iaggit = It Pit Pt

Haggit = 

Hit Ht



Pit Pt

  Iagg − Hagg    it it + Iaggit − Haggit  coaggit = 1 − Iaggit + Haggit

(3) (4)

Among them, I is the region, t table time period. I represents employment in information transmission, software and information technology. H represents the number of people employed in high-tech industry; P is total employment in all industries. Iaggit represents the aggregation index of the Internet industry. Haggit represents the aggregation index of the high-tech industry, and coaggit represents the collaborative aggregation index of the two industries.

252

H. Wu

(3) Control variable Human capital (hcl): Calculate the average number of years of schooling in each region1 . Economic development level (pgdp): To measure the level of economic development by taking the GDP per capita and using the CPI price index to eliminate the impact of inflation. Marketization (mart): The measurement method of this paper is to select the market index in < MARKETIZATION INDEX OF CHINA’S PROVINCES: NERI REPORT 2018 >. Foreign direct investment (fdi): The measurement method of this paper is the proportion of actual foreign direct investment in GDP converted by exchange rate. Environmental regulation (er): This paper uses the proportion of industrial pollution control in GDP. 2.3 Data Description In this paper, relevant data from 2009 to 2017 are selected for research. Xinjiang, Ningxia, Qinghai, Tibet and Hainan are excluded due to their small scale of high-tech industry agglomeration and insignificant agglomeration effect. In the end, the remaining 26 provincial-level regions were reserved for the study. The data are mainly got from the Statistical Yearbook of China High-tech Industry, China Science and Technology Statistics Yearbook, China Statistical Yearbook, China Energy Statistics Yearbook and local Statistical Yearbook of provinces and cities over the years. Interpolation method is used to complete partial missing data. Each variable was processed logarithmically treated to reduce the heterovariance problem and subjected to descriptive statistical analysis. (See Table 1).

3 Empirical Results Analysis 3.1 Threshold Effect Test This paper uses Stata 16.0 statistical software to test the threshold effect. The results show that there is a significant single threshold effect between collaborative agglomeration and green innovation, and the threshold value is 0.8912, which is not linear. In addition, we can know that the impact of green innovation of collaborative agglomeration of the Internet and high-tech industry largely depends on the level of collaborative agglomeration in the previous period (Table 2). 3.2 Threshold Regression Results and Robustness Test As can be seen from Table 3, collaborative agglomeration of the Internet and hightech industries is conducive to the development of green innovation. When the level 1 Divide people above 6 years old into illiterate semililliterate, primary school, junior middle

school, senior high school, junior college, their education fixed number of year is defined respectively for 0 years, 6 years, 9 years, 12 years, 16 years. The weight of each age is calculated according to the proportion of the population in each age. Finally, get the average number of years of education in each region.

Threshold Effect of Collaborative Agglomeration of Internet

253

Table 1. Descriptive statistics Variable name

Representative symbols

Sample size

Mean

Standard deviation

Minimum value

Maximum value

Green innovation efficiency

ins

234

−1.1514

0.6931

−2.5784

0.1320

Level of industrial collaborative agglomeration

coagg

234

0.8689

0.6270

−0.1785

2.9359

Human capital

hcl

234

2.2039

0.0953

1.9867

2.4916

The level of economic development

pgdp

234

10.5407

0.4685

9.4932

11.4902

Foreign direct investment

fdi

234

−4.1578

0.8986

−7.3813

−2.6110

Market level

mart

234

1.8663

0.2597

1.2149

2.3224

Environmental regulation

er

234

−6.9747

0.6818

−8.6023

−5.4344

Table 2. Threshold effect from sampling test and threshold value Model

Model 1

The threshold type

F value

Single threshold Double threshold

Critical value 10%

5%

1%

Threshold estimated value (γ)

95% Of the confidence interval

39.67*** (0.0000)

17.8063

21.5611

26.3542

0.8912

[0.8860, 0.8963]

7.31 (0.6300)

16.1197

21.0831

25.0417

_

_

Note: * * *, * *, * represent the significance levels of 1%, 5%, and 10%, respectively. The following examples are also the same

of collaborative agglomeration is low and located in the first threshold interval, enterprises in the region continuously gather and gradually form a scale, and heterogeneous industries can benefit the development of regional green innovation through interactive communication and collaborative innovation. However, when the level of collaborative agglomeration becomes higher and higher and reaches the second threshold, excessive industrial agglomeration may lead to problems such as misallocation of resources and environmental pollution, making the marginal effect of collaborative agglomeration begin to decrease. However, we can find that the synergistic agglomeration of the two industries can still promote the improvement of green innovation efficiency. At present,

254

H. Wu

China’s Internet and high-tech industry have a great space for development of collaborative agglomeration. By promoting the collaborative agglomeration and development of the two industries, it will greatly benefit the development of green innovation. Control variable aspect. The improvement of human capital is conducive to local green and innovative development. As a senior factor of production, the agglomeration and flow of talents play a very important role in regional green innovation. In addition, giving full play to market competitive advantages is of great significance to the improvement of regional green innovation efficiency. The more sufficient competition and cooperation with innovation, the stronger the innovation atmosphere, but also conducive to forming a positive cycle of innovation. Secondly, environmental regulation and the level of economic development can have a positive role in green and innovative development. It is necessary for all regions to strengthen environmental governance and boost the economic development. On the one hand, the increasingly strengthened environmental regulations are conducive to the continuous technological innovation of fierce enterprises, thus reducing environmental costs. On the other hand, by strengthening their own economic development level, they can obtain more financial support to carry out innovative activities. Foreign direct investment plays a weak and insignificant role in promoting green innovation and development. This shows that enterprises can use the spillover effect of foreign investment to promote green innovation. However, at present, China’s innovation efficiency is still low, and the technology absorption capacity is weak, making the spillover effect of the industry low. Table 3. Threshold regression estimation results Variable

Model 1

Model 1–1

lncoagg (lncoaggt-1 ≤ γ)

1.4775*** (6.47)

1.2151*** (5.48)

lncoagg (lncoaggt-1 > γ)

1.0678*** (4.86)

lnhcl

1.8110* (1.80)

1.6765 (1.62)

lnpgdp

0.1578 (0.78)

0.1770 (0.85)

lnfdi

0.0386 (0.64)

−0.0114 (−0.19)

lnmart

1.6294*** (6.06)

1.6998*** (3.23)

lner

0.1654*** (4.12)

0.1407*** (6.17)

cons

−9.5954*** (−6.80)

−9.7883*** (−6.76)

Note: The numbers in parentheses are t values;

Threshold Effect of Collaborative Agglomeration of Internet

255

The process of robustness test is as follows. The Hausman test was used to select the estimation model and the test results showed that the P value was less than 0.001. So the fixed-effect model was selected for regression. By comparing with the original threshold model regression, we found that the influence coefficient size of each variable varies little and has the same direction, so the regression results can be considered more robust.

4 Conclusion Based on panel data of 26 provinces and cities in China, this paper uses panel threshold model to test the impact of collaborative agglomeration of Internet and high-tech industries on green innovation development. Through analysis, the research conclusions are as follows: The synergy of the Internet and high-tech industries is conducive to the development of green innovation. Moreover, the influence is not monotonous, and the influence coefficient is different in different agglomeration regions. When the degree of synergistic agglomeration is low, it has a great promotion effect on green innovation. When it crosses the threshold value, the crowding effect of agglomeration becomes prominent and the marginal effect decreases. But it still contributes to green innovative development. In addition, this effect largely depends on the collaborative agglomeration level of the previous period. The conclusions of the above study give us some implications. China should actively promote the deep integration of the Internet and high-tech industry, and vigorously support the policy of “Internet + high-tech industry”. By making full use of the intelligent and network characteristics of the Internet industry, give play to its radiation and driving role, improve efficiency. At the same time, the development of high-tech industries in turn further promotes the development of the Internet industr. These two industries will benefit from the process of collaborative agglomeration and better promote the development of green innovation. In addition, local governments should also pay attention to creating a fully competitive market-oriented environment and introducing high-quality talents to boost green and innovative development.

References 1. Ellison, G., Glaeser, E.L.L: Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach. Working papers (1994) 2. Pei, Y., Zhu, Y., Liu, S., Xie, M.: Industrial agglomeration and environmental pollution: based on the specialized and diversified agglomeration in the Yangtze River Delta. Environ. Dev. Sustain. 23(3), 4061–4085 (2020). https://doi.org/10.1007/s10668-020-00756-4 3. Hao, Y., Song, J., Shen, Z.: Does industrial agglomeration affect the regional environment? evidence from Chinese cities. Environ. Sci. Pollut. Res. 1, 16 (2021). https://doi.org/10.1007/ s11356-021-16023-6 4. Li, T., Han, D., Feng, S., et al.: Can industrial co-agglomeration between producer services and manufacturing reduce carbon intensity in China? Sustainability 11(15), 4024 (2019)

256

H. Wu

5. Yang, H., Zhang, F., He, Y.: Exploring the effect of producer services and manufacturing industrial co-agglomeration on the ecological environment pollution control in China. Environ. Dev. Sustain. 23(11), 16119–16144 (2021). https://doi.org/10.1007/s10668-021-01339-7 6. Shi, J.: Diversified agglomeration, specialized agglomeration and innovation efficiency of pharmaceutical manufacturing. Open J. Soc. Sci. 07(7), 147–158 (2019) 7. Nan, S.: Research on the influence of high-tech industry specialization agglomeration on innovation efficiency. E3S Web Conf. 235, 02026 (2021). https://doi.org/10.1051/e3sconf/ 202123502026 8. Liu, S., Wen-Xiu, L.I., Chen, X.Y.: The impact of co-agglomeration of producer services and manufacturing industries on the enterprise’s innovation. J. Guangdong Univ. Fin. Econ. 34(03), 43–53 (2019) 9. Zeng, W., Li, L., Huang, Y.: Industrial collaborative agglomeration, marketization, and green innovation: evidence from China’s provincial panel data. J. Clean. Product. 279(2/3), 123598 (2020) 10. Paunov, C., Rollo, V.: Has the internet fostered inclusive innovation in the developing world? World Dev. 78, 587–609 (2016)

Construction of Computer Network Security Information Leak-Proof Management System Zejian Dong(B) Shandong Vocational College of Light Industry, Zibo 255300, Shandong, China [email protected]

Abstract. In recent years, computer network security has gradually penetrated into various industries in our society, and different companies and individuals have increased their efforts to prevent information leakage in terms of computer network security. This paper analyzes the importance of computer network information security at this stage, and through the establishment of network information security anti-leakage management system, the information received or transmitted is encrypted and improved, and the construction of computer network security information management system is improved. Based on the preliminary analysis results, a security management model based on the computer information leakage prevention system is designed. This model improves the original information encryption algorithm. After the improvement, both ends of the data transmission can be more secure than the original anti-information leakage model. The improved model can be used not only in the field of leakage prevention, but also in the construction of major computer network information security, realizing allround management of computer network information leakage prevention. In order to ensure the normal operation of the network security information anti-leakage management system, it is necessary for the administrator to protect the security system and solve some problems that may arise in a timely manner. With the rapid development of high-amplitude construction of computer networks, the two classic encryption algorithms in computer network communication systems can effectively strengthen the security of network information leakage prevention. Keywords: Information leakage prevention · Network security · Information encryption algorithm · Network communication · Computer system

1 Introduction Computer network information security protection technology can improve the utilization efficiency of information resources, the sharing ability of resources and the transmission efficiency of information data [1]. With the emergence of a variety of informationbased transaction modes such as e-commerce and Internet finance, the construction of computer network information security management systems and subsequent protection issues have become an important content of current network information security [2]. Therefore, while the computer information security anti-leakage management system uses computer network information technology to improve work efficiency, it must also © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 257–264, 2022. https://doi.org/10.1007/978-3-030-99616-1_34

258

Z. Dong

pay attention to computer network information security issues and establish corresponding emergency protection systems, so as to avoid sudden information security problems [3, 4]. In order to achieve the integration of computer users and network security models, companies have begun to actively introduce advanced network information systems [5, 6]. Compared with the previous traditional model, the construction of the computer network security information leakage prevention system has played an important role in operational efficiency, management and control [7]. When the computer system is infected with a virus, the computer virus will destroy the program data in the system, and some viruses will also damage the integrity of the file data, which will cause the loss of important information in the power system and affect the normal operation of the control system [8, 9]. The current research on computer network data security has not yet solved the problem [10]. Therefore, the construction of the computer network security information leakage prevention management system has aroused widespread attention in all major fields of society [11]. With the development and improvement of the encryption algorithm in the computer network information leakage prevention concept, the current information security system in the computer network uses multiple encryption methods to coexist. All messages are transmitted based on information encryption, making them protected by information. The leakage management is more accurate, effective and safe [12].

2 Establishment of Computer Information Encryption Algorithm 2.1 Artificial Fish School Algorithm The artificial fish school algorithm is actually a new optimization algorithm based on the fish school system proposed in recent years. It has been found that it can be used in the computer information encryption process. Therefore, the network information can be encrypted by the artificial fish school algorithm to obtain the objective function: PER = 1 − (1 − BER)

(1)

The probability of error of the computer network security information encryption and leakage prevention management system is BER. PER is the metric index for computer network information encryption and leakage prevention. It is very necessary to use the entire anti-leakage management system to encrypt the metric index, but this needs to get the corresponding BER parameter from PER. After using this encryption method, the error probability formula for the used transmission mode can be calculated as follows: PERQAM = 1 −

log2 i j

Np

(1 − P(k)) log2 (i−j) − 

PERn (y) ≈

log2 j x=1

1, 0 < y ≤ yn an exp(−gn y), y ≥ yn

1 ≤ G ≤ srLs − 1

Np

[1 − P(x)] log2 (i−j)

(2) (3)

Construction of Computer Network Security Information

259

D × S∗L × YL∗   x = y1 m1−1 modp, y2 m2−1 modp

(4)

3 Network Information Encryption and Vulnerability Detection Model Establishment 3.1 Network Information Encryption Vulnerability Detection Model Define the set of receiving vectors as {r}, and define S as the conditional probability of information bits in the received bits of {r} and S in the event that the information node X satisfies all check equations including xj is Pr (xi |{r}, S) Bit xj takes 0 or 1, consider the following relationship: Pr (xi = 0|{r}, S) ≥1 Pr (xi = 1|{r}, S)

(5) 



When the above formula is true, the received bit value xj = 0, otherwise xj = 1. Let’s analyze the network information in detail below. Define the probability of an even number of 1 s in an n-length binary sequence as follows:   1 1 + 1 − 2Pji (6) 2 In the formula, Pji represents the probability that the j-th bit in the sequence is 1. 0 as the test message, which represents the probability of satisfying the j-th Define Ri−j test equation when xi = a. When xj = 0, it can be obtained from (6): R0i−j =

  1 1 + 1 − 2Pij 2

(7)

Corresponding to xj = 1, the above probability is: 1 0 = 1 − Ri−j Ri−j

(8)

In the formula, N(i) represents the set of all information bits connected with the check bit zi . Since each bit is statistically independent, the conditional probability of event S with respect to xj can be achieved by expressing the product of all probabilities in (7) and (8):   1 (9) Pr S|xj = 1, {r} = Rk−j   0 Pr S|xj = 0, {r} = Rk−j The left side of the inequality can have the following forms:    Pr S|xi = 0, {r}, P xj = 0, {r} Pr (xi = 0|{r}, S)   =  Pr (xi = 1|{r}, S) Pr S|xi = 1, {r}, P xj = 1, {r}

(10)

(11)

260

Z. Dong

Assuming that the information passes through the AWGN channel after BPSK modulation, its noise power spectral density is N/2 = σ; then the maximum likelihood probability function of the received bit r, is expressed as follows:   1 e P rj |xj = 1, {r} = √ πN0

(12)

  1 P rj |xj = 0, {r} = √ e πN0

(13)

From the Bayesian formula:   a Qj0 (t + 1) = KJ 1 − Pj Ri−j (t + 1) The above formula leads to the following ruling:  xj = 0 xj = 1

(14)

(15)

At this point, the entire decoding process is over. After introducing the likelihood ratio measurement to the artificial fish school algorithm, the vulnerability detection algorithm can be obtained, and the minimum sum algorithm can be obtained after further improvement. The improved algorithm has the same principle, but the calculation process is more simplified. 3.2 Analysis of Classification Results The formula is as follows: 2 1 n  RMSE = yi − yi i=1 n 

(16)

Testing the information security protection system requires precise removal of fibers, and dynamic spatial positioning of the protection system. First, after segmentation, median filtering, and morphological analysis of the acquired samples in the acquired protection system, the formula is as follows: N 1

xm = xi N i=1

1 N ym = yi i=1 N

(17)

The decisive factor of threshold segmentation is how to obtain the best threshold, which means that the segmentation method is actually a process of finding a threshold that meets the experimental requirements through a certain algorithm and then segmenting the target.

Construction of Computer Network Security Information

After thresholding, the parameter g(x,y) can be obtained as:  1 f(x, y) > T g(x, y) = 0 f(x, y) ≤ T

261

(18)

The purpose of Eq. 13 is to mark the background pixel in the fiber sample as 0 and the target pixel as 1. The first moment can be expressed as:  m10 = x∈s y∈s x (19) m01 = x∈s y∈s y Then the regional centroid (x, y) can be expressed as:  10 x0 = m m00 m01 y0 = m00

(20)

Commonly used methods of threshold segmentation include: local statistical threshold segmentation, global threshold segmentation, threshold segmentation based on seed growth, adaptive threshold segmentation and optimal threshold segmentation.

4 Evaluation Results and Research Unprocessable , 0.23%

Unknown processing data, 0.07%

Can handle, 99.70%

Fig. 1. The processing degree of network communication security data based on artificial fish swarm algorithm

Analyzing the data shown in Fig. 1, using artificial fish swarm algorithm to intelligently analyze the processability of network communication security data, and after data mining processing, a relatively small amount of unknown processed data and unprocessable data are found. And, for the experiment, what we value is this part of the non-processable data, because this is the data we must use for the experiment retrieval.

262

Z. Dong

Table 1. Part of the training data table of the management system based on the clustering analysis algorithm Downstream bytes

Upstream bytes

Maximum number Unlimited of RRC connections utilization

Data load level

4234234

632543

123

82

High load 2

2642424

553622

147

53

Low load 2

4797284

242425

667

98

High load 2

4032423

426234

393

79

Low load 2

5925424

145242

614

63

Low load 1

As shown in the training data set based on the cluster analysis algorithm listed in Table 1, after analyzing the data, a new type of artificial fish is proposed for the problem of poor stability and slow convergence in the application of the cluster analysis algorithm. After a period of training, we found that the algorithm improves the stability and learning speed of computer network information in the anti-leakage system, and the actual effect is very good. Figure 2 is a performance analysis diagram of two different information encryption algorithms in practical applications. It can be seen from the figure that the performance of the RSA algorithm is lower, and the higher performance is the artificial fish school algorithm. Time efficiency is an important performance of the real-time remote sensing image processing algorithm. At the same time, the time efficiency of the algorithm in the training and testing phases is also considered in the experimental testing process (Table 2).

Measured Aata Analysis RSA 120 Artificial fish school algorithm

100 80 60 40 20

Algorithms Name

0 5 10 15 20 25 30 35 40 45 50

Fig. 2. Comparison of measured data of different algorithms for different line trends

Construction of Computer Network Security Information

263

Table 2. Comparison of measured data of different algorithms for different line trends Algorithm name

Line trend 1

Line trend 2

RSA Algorithm

111.23

13.15

Artificial Fish Cchool Algorithm

255.12

319

Method prediction

215.37

312

5 Conclusion In summary, based on the perspective of computer networks, information technology is developing relatively rapidly. When computer networks and information security systems encounter problems, they must thoroughly study the root cause of the problem, implement security protection awareness, and stay vigilant at all times to prevent the network information security system from being invaded and maintain legitimate interests. Therefore, effective protective measures should be adopted to enhance the security and stability of the computer network anti-leakage system, improve quality and efficiency, and promote the prosperity and development of the industry. Clarify the application points of symmetric encryption and digital signature technology, packet filtering technology, gateway technology and firewall technology, and gradually improve the level of maintenance work by enhancing security awareness, strengthening virus protection, and optimizing the operating environment. Through the computer network information security anti-leakage management system, the specific network security approach should be explored, that is, encryption technology, facility performance, authority differences, etc. should be optimized, and the adjustment of the system’s operating status will promote the development of network information.

References 1. Kong, P.Y.: Cost efficient data aggregation point placement with interdependent communication and power networks in smart grid. Smart Grid, IEEE Trans. on 10(1), 74–83 (2019) 2. Zhang, Z., Niu, Y., Cao, Z., Song, J.: Security sliding mode control of interval Type-2 fuzzy systems subject to cyber-attacks: the stochastic communication protocol case. IEEE Trans. Fuzzy Syst. 29(2), 240–251 (2021). https://doi.org/10.1109/TFUZZ.2020.2972785 3. Zhou, C., Liao, X., Wang, Y., Liao, S., Zhou, J., Zhang, J.: Capacity and security analysis of multi-mode orbital angular momentum communications. IEEE Access 8, 150955–150963 (2020). https://doi.org/10.1109/ACCESS.2020.3010957 4. Verma, P.K, El Rifai, M., Chan, K.W.C.: Signals and Communication Technology Multiphoton Quantum Secure Communication || Preliminary Security Analysis of the Multi-stage Protocol, pp. 119–130 (2019). https://doi.org/10.1007/978-981-10-8618-2. (Chapter 7) 5. De, A., Khan, M.N.I., Nagarajan, K., Ghosh, S.: HarTBleed: using hardware Trojans for data leakage exploits. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 28(4), 968–979 (2020). https://doi.org/10.1109/TVLSI.2019.2961358 6. Zhan, K.: Design of computer network security defense system based on artificial intelligence and neural network. J. Intell. Fuzzy Syst. 9, 1–13 (2021)

264

Z. Dong

7. Qi, Y.: Computer real-time location forensics method for network intrusion crimes. Int. J. Network Secur. 21(3), 530–535 (2019) 8. Wang, Y., Ma, J., Sharma, A., et al.: An exhaustive research on the application of intrusion detection technology in computer network security in sensor networks. J. Sens. 2021(3), 1–11 (2021) 9. Singh, O.P., Singh, A.K.: A robust information hiding algorithm based on lossless encryption and NSCT-HD-SVD. Mach. Vis. Appl. 32(4), 1–13 (2021). https://doi.org/10.1007/s00138021-01227-0 10. Guo, X., Jingyu Hua, Y., Zhang, D.W.: A Complexity-reduced block encryption algorithm suitable for Internet of Things. IEEE Access 7, 54760–54769 (2019). https://doi.org/10.1109/ ACCESS.2019.2912929 11. Rekha, C., Krishnamurthy, G.N.: An optimized encryption algorithm and F function with dynamic substitution for creating S-box and P-box entries for blowfish algorithm. Comput. Sci. Inf. Technol. 2(1), 16–25 (2021) 12. Zhang, Q., Han, J.T., Ye, Y.T.: Image encryption algorithm based on image hashing, improved chaotic mapping and DNA coding. IET Image Proc. 13(14), 2905–2915 (2019)

Techniques of Fusion Association Rule Mining Algorithm Cheerleading Training Damage Prevention Application Lin Zhu(B) Xuzhou Vocational Technology Academy of Finance and Economics, Xuzhou 221008, Jiangsu, China [email protected]

Abstract. In order to reduce the damage in the training process of cheerleaders in sports, this article applies the Association Rules Mining Algorithm (ARMA) to the training of technical support teams to prevent injuries, and seeks to prevent injuries through new vision, to build a correct analysis mode for cheerleading training games. According to the association rule mining algorithm, methods to reduce training injuries are proposed from four aspects: athletes, physical environment, social environment, and media. Keywords: Association rule mining algorithm · Technique cheerleading · Injury prevention introduction

Technique cheerleading Exercises is a kind of Lala Exercises that integrates technical skills and complex movements. Because the movement Technique cheerleading Exercises have a certain degree of difficulty, strong skills and risk factors [1], so the Technique cheerleading Exercises so making the technique cheerleading sports higher„ and the corresponding damage probability is also higher in comparison. And not only in the training process, but also in the warm-up or game process in time [2, 3]. In addition, it may happen not only to new players, but also to some old players. Therefore, under the rapid development of cheerleading, it is necessary to pay more attention to the sports risk [4, 5], which can effectively reduce the athlete’s injury probability, reduce the sports risk coefficient, ensure that the athlete can practice the difficult movements and increase the athlete’s career time. The efficiency and quality of licking dog training are also conducive to the steady, scientific and sustainable development of cheerleading sports in China [6, 7]. Association rules have become a hot research topic in various industries in recent years. The algorithm is used to analyze the social system engineering of athletes in the process of preventing injuries. It is used to prevent injuries in stretching exercises and prevent possible sports injuries from a macro perspective. Preventing sports injuries provides a new analytical perspective. In this thesis, by training cheerleaders to discover new relevant rules, the algorithm is applied to the training of technical cheerleaders to prevent injuries. Increasing the research on different factors of athletes participating in sports training is an effective preventive method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 265–273, 2022. https://doi.org/10.1007/978-3-030-99616-1_35

266

L. Zhu

1 The Principle of Association Rule Mining Algorithm At present, the mining algorithm of relevant rules not only has a variety of injury control in the fields of transportation and public health, but also is widely used. It also has important application value for preventing and restraining sports injuries. When Haddon analyzes the relevant rules and mines the algorithm, the first thing is to explain theoretically the reasons for the damage in the three stages and three models, and accurately prevent the damage in different stages. Carrying out their own prevention [8]. Taking a car accident as an example, the driver may have an accident due to drinking, brake failure, low environmental visibility and other reasons. In a traffic accident, if you do not wear a seat belt, it will cause damage to the bumps, hard objects, edges, and environmentally flammable building materials of the car. The result of a traffic accident is determined by the depth of the wound, the condition of the car’s injury, and the response of emergency medical treatment. This model was originally only used for traffic injuries, but now it is widely used for research and control of various injuries. This matrix provides theoretical models and tools to better understand the role of the host and the environment, and plays a great role in preventing and controlling damage [9]. In the domestic sports and education fields, studies on sports injury prevention using this theoretical model have not been discovered, but overseas scholars have tried this before. It can be seen that the use of related rule mining algorithms to prevent sports injuries is social systems engineering. The author believes that this model system can also be used in the training of technical assistance groups to prevent injuries. Data mining of cheerleading training behavior is carried out in a big data environment, and a big data information resource network database is built for cheerleading training behavior. The edge sequence of the tree structure of the cheerleading training behavior resource database is {e1 , e2 , · · · , er }, where ei = (oi , pi+1 ), 1 ≤ i ≤ r, oi ∈ {p1 , p2 , · · · , pi }, The seasonal influencing factors of cheerleading training meet: dist(ei ) = dist(oi , pi+1 ) = dist({p1 , p2 , · · · , pi }, pi+1 )

(1)

At this time, {dist(e1 ), dist(e2 ), · · · , dist(er )} is called the health benefit index of cheerleading training behavior. In the cheerleading training behavior benefit index information storage model, the health benefit index parameter system is fitted by constructing a feature data entity set [1]. The big data information fitting model is described as:    Rβ X = U E ∈ U R|c(E, X ) ≤ β (2)    Rβ X1 = U E ∈ U R|c(E, X1 ) ≤ 1 − β

(3)

bnrβ (X ) = Rβ X − Rβ X1

(4)

The characteristic data of cheerleading training behavior is affected by factors such as fitness equipment, seasons, school physical education courses, etc. It is necessary to mine and analyze the characteristics of these information data to obtain the static and dynamic query template set for the characteristic data mining of cheerleading training.

Techniques of Fusion Association Rule Mining Algorithm

267

2 Application of Association Rule Mining Algorithm in Cheerleading Training 2.1 Skills Injuries in Cheerleading Training Technical support is a sport with high difficulty and high probability of injury. I conducted a pre-match practice tracking survey on the technical cheerleaders of Guangxi High School, and found that only 3 of the 23 players were not injured. The sports injury rate is 90.19%. According to a 10-day survey, the average number of injuries per day is 1.5 times. According to statistics, due to injuries, players cannot practice 4 times. These injuries are mainly manifested as work-related injuries, contusions, sprains, and injuries. Mainly occurred 36% of the waist 28 times, 16% of the ankle 12 times, 16% of the arm 12 times, 15% of the knee joint 11 times, 7 times of the shoulder 10%, 9% of the first 7 times, and 4% of the second times of the chest. 2.2 Application of Association Rule Mining Algorithm in Injury Prevention in Cheerleading Training Labrador gymnastics is a young sport, and basic theoretical research is still in the exploratory stage. With the establishment of the Lala Gymnastics International Organization, Lala Gymnastics is increasing worldwide. There are also world championships in the competition. It is a worldwide game. Therefore, how to prevent losses in cheerleading gymnastics training has become a common concern in the sports world. Based on this paper, we mainly mine algorithms from related rules [10, 11]. It is necessary to confirm how the technical support staff was injured during the training. Therefore, regarding the training phase, we intercepted the early stage of the mining algorithm of the relevant rules, and changed several parameters accordingly, and made a matrix table suitable for preventing injuries during the training phase. In order to verify whether the mining algorithm of the new relevant rules can reduce the damage in the training of the cheerleading team, I once again surveyed 22 players of the technical cheerleading team of Guangxi University. In this investigation, the author mining algorithm parameters according to the new relevant rules, aiming at the training of cheerleaders before the competition, carried out targeted injury prevention. After ten days of statistics, the total number of injuries dropped from 79 to 38, an average of 3.8 per day. Six people were not injured. The total injury rate dropped to 72.75%. Because of the injury, the player can only practice once. In order to facilitate comparative analysis, the two statistical data are summarized in a table (Table 1). Analyzing the injury situation of each part in the table, it is found that there is a new algorithm for discovering relevant rules in the training competition of the support team. To a certain extent, it reduces the injuries in cheerleading training and plays a role in preventing injuries.

268

L. Zhu Table 1. Percentage of damage in each site.

Injury site

waist

Ankle joint

Wrist

Knee joint

Shoulder

Neck

Chest

Total

First time

28

12

14

11

7

7

2

79

Second time

12

9

5

7

3

5

0

41

Percentage drop

56.2%

31.3%

56.2%

43.8%

54.1%

40.5%

99%

48.6%

3 Combined with the Research and Analysis of the Association Rule Mining Algorithm in the Pre-Match Training Phase 3.1 Sportsman’s Perspective (1) Get ready to warm up In the training process of stretch gymnastics, there are high-intensity physical training and high-tech breakthrough training. For example, the difficulty of the jumping category includes triple jump lotus jump, hurdle jump and foot jump. During these training sessions, the temperature of the muscles will become higher [12]. The muscle group behind the buttocks also has stretching, etc., which effectively makes the stretch and elasticity of the muscles in the best state, reduces the stickiness of the muscles, and improves the effectiveness of training. Therefore, by performing warm-up training, accidental injuries during training can be reduced. (2) Pay attention to the injury history of athletes This is also an element that hurts the player’s history. Mild injury refers to normal training in accordance with the training plan after the injury, without aggravating the injury. Moderate injuries can be trained after the injury, but the activities of the affected area must be stopped or reduced. Therefore, athletes must adjust their physical conditions while participating in training with illness, and control exercise time, exercise intensity, and exercise width when participating in training for light and moderate losses [13]. Severe injury gives up training and accepts treatment. Training shall be participated in after recovery. With regard to the knowledge of some technical cheerleading theory, lectures and lectures can also be accepted in schools and technical cheerleading halls. Students from multiple classes and faculties gather together to teach in a large classroom equipped with association rule mining algorithm facilities. The host can play the association rule mining algorithm, or explain through the association rule mining algorithm. The application form of this association rule mining algorithm mainly exerts some skills of the professional expertise of cheerleaders, saves other teachers’ resources, and creates space for other teachers’ professional development and research. In fact, the application of association rule mining algorithm has a great influence on the management training and training of high school sports games [14]. The College Skills Cheerleader has realized the transition from pure teaching to comprehensive training. Training methods have also changed from traditional teacher and student experience to technical training, and the forms of competition are also diversified. In other words,

Techniques of Fusion Association Rule Mining Algorithm

269

association rule mining algorithms have been widely used in all walks of life. The specific applications of high school sports are as follows. The details are shown in Fig. 1. The application of association rule mining algorithm in skill cheerleading training provides a new learning method for skill cheerleading learners. The application of association rule mining algorithm in some skills such as cheerleading and entertainment greatly reduces the dangerous sports such as skiing and racing. The association rule mining algorithm can be used for training indoors. Please avoid accidents such as avalanches and collisions. The association rule mining algorithm achieves a realm that is difficult to achieve by other objective factors. As an athlete’s trainer, to a certain extent, the player’s winning rate has been improved. In some countries and regions, it is difficult for training and athletes to reach each other’s level. The association rule mining algorithm effectively solves such problems. In addition, the association rule mining algorithm can also simulate natural sports fields, allowing people living in cities to feel nature and relax. (3) Broad teaching horizons First of all, teachers’ use of modern training techniques has improved. As a high vocational skills cheerleading teacher, you must not only master the use of various skills cheerleading equipment and modern support facilities, but also master the technology of association rule mining algorithm training facilities. For example, in the management training of the high school sports meeting, experience the editing of the production video of the lecture, the video of the personal training of the music selection, and the recording of the model video. According to the management and training needs of the sports meeting, it is necessary to grasp as much as possible. Creative use of school skills cheerleading hall department and personal training plan. For example, the use of indoor gymnastics training, the use of theoretical training, the use of high-quality training videos of other teachers and experts, etc. Secondly, the teacher’s skills in cheerleading competition management and training techniques themselves are improved. In the practice of skill cheerleading management training, the application of association rule mining algorithm is still shown as a supplementary training method. That is to say, it mainly focuses on the exemplary practical training activities of skill cheerleading teachers. Therefore, the association rule mining algorithm plays a different role in the higher vocational skills cheerleading training according to the different training content. There are generally two applications. The first category is auxiliary, that is, to help complete the demonstration of the teacher’s actions. Or after activating the students’ enthusiasm for learning, it is no longer used. Or use it multiple times in a very short period of time during classroom training. The second category is the main training method. As mentioned above, the whole course is completed by the association rule mining algorithm. That direct effect enriches the teaching methods of teachers. 3.2 External Environment With the development of modern information technology and communication technology, the association rule mining algorithm has also realized networked and remote application. For teachers and students, it is also based on the actual situation of training to expand the horizon of training and learning. One is that the vision of professional

270

L. Zhu

knowledge is very broad. The content of the association rule mining algorithm is the large-capacity teaching of theoretical knowledge in the teacher’s classroom, the demonstration teaching of related technical skills and actions, which are more professional, accurate, and show standardized action images and technical analysis. This kind of training method goes beyond the limitations of the teacher, and expands in the way of learning, learning content, and vision in an all-round way. Second, the field of vision for training techniques is very broad. This technical vision includes association rule mining algorithm related equipment and facility application technology, association rule mining algorithm software technology development and application technology, association rule mining algorithm content playback technology, and association rule mining algorithm video recording technology. Students are accepting association rules. While training on mining algorithm production results, teachers also appropriately introduce association rule mining algorithms and other related technologies to students. In order to reduce the risk of athletes training in high temperatures, try two points. First, conduct multi-stage and short-term training. This training method can shorten the time interval between athletes’ training and rest, so that the physical strength of the exercise can be quickly restored, and the training injury caused by the decline in physical strength can be reduced. On the other hand, high-energy foods and beverages can supplement the energy loss of the human body. Or directly drink salt water with similar human sweat and salt content (one liter and two grams of salt) to maintain the athlete’s physical strength. Even if the weather is very cold, the stickiness will become stronger during training and the contraction speed will slow down, so it is easy to get injured. When training at temperatures below 11 °C, athletes’ muscles tend to become stiff and lack elasticity. The coordination of movements is reduced, and the ligaments are also prone to injury. Therefore, first of all, we must fully exercise the relevant parts in the training, so that the muscles and ligaments of the related sports (especially the small joints such as the wrist and ankle joints) must be moved. However, it is important to avoid warming the body and causing fatigue and injury more easily. Secondly, you must put on clothes and keep your body temperature at the end of the training break to prevent cold wind and cold from invading. (1) The influence of environmental humidity on human exercise ability According to research, people are suitable for training in an environment with a relative humidity of 30% to 75%. However, if the relative humidity of the training environment exceeds 80%, people will have difficulty breathing, the skin will be moist and sticky, and the body will not dissipate heat well. This will quickly affect the athlete’s training state, and it is easy to damage training. However, if the humidity of the training environment is low, the players will be thirsty, swollen throat and nosebleeds. Therefore, you must train in a proper humidity environment during training. (2) Social environment The influence of social environment mostly affects the mental state of athletes. More specifically, the competitive pressure of the game will affect the athlete’s psychology. On the one hand, this kind of pressure will make superior leaders have expectations of the training team. On the other hand, defeated the opponent according to the coach’s request. The other is the competition between players. When

Techniques of Fusion Association Rule Mining Algorithm

271

competitors are strong, coaches also require athletes to achieve expected results. When leaders are still looking forward to the rankings, athletes will unconsciously carry a burden on their hearts. If an athlete has an ideological burden and participates in high-intensity and difficult technical training, it is likely to be injured.

4 Experiment and Result Analysis Taking a certain system’s cheerleading training event rehearsal set as the experimental data set, the selected cheerleading training event rehearsal set contains 48841 cheerleading training event rehearsal times, and the length of each cheerleading training event rehearsal is about 14 items. The performance analysis of Apriori, FP-Growth, Eclat, Relim and ARMA algorithms are carried out, and the test results are shown in Fig. 1.

(a) The number of rehearsals for cheerleading training events is 48,842, and the degree of support changes

(b) The support rate is 20%, and the number of rehearsals for cheerleading training events changes Fig. 1. Performance test of Apriori, FP-Growth, Eclat, Relim and ARMA algorithms.

If the support degree changes, then the performance of the Apriori algorithm will become worse, especially when the support degree is not more than 5%, the time for obtaining frequent sets will be longer than other algorithms, if the support degree is not less than 10%, pass and The search time of other algorithms is close to that of the Apriori algorithm, and when the support is not less than 5%, it needs the most time to fetch frequent sets, compared to other algorithms; the ARMA algorithm requires the least time, especially when cheerleading training events When the number of rehearsal sets becomes larger and larger, the performance advantage of the ARMA algorithm is more obvious and it takes less time.

272

L. Zhu

5 Conclusions In order to effectively reduce the risk of sports accidents, athletes should improve their overall quality according to the actual situation to master sports skills as much as possible, strengthen teamwork, strengthen risk prevention awareness, continuously improve their own protection awareness, and develop better living habits. Coaches should the support athletes should formulate a reasonable training plan for their own situation, and reasonably arrange the load and exercise intensity of the support group members to fully prepare for daily habit warm-up training. Must be prepared in advance for exercise risk avoidance. Labrador gymnastics can develop in a higher quality, faster and longer direction.

References 1. Kovácsik, R., Szabo, A.: Dynamics of the affective states during and after cheerleading training in female athletes. Pol. Psychol. Bull. 50(1), 29–35 (2019) 2. Maslyak, I.P., Krivoruchko, N.V.: Physical development of students of teacher training college as a result of exercises of cheerleading. Phys. Educ. Stud. 20(1), 55–63 (2016) 3. Masliak, I., Krivoruchko, N., Bala, T., Horchaniuk, Y., Korchevska, O.: Efficiency of using cheerleading for flexibility development at female students of teacher training college. J. Phys. Educ. Sport 19(1), 1–22 (2019) 4. Mervis, J.: Analysis: senator’s attack on “cheerleading” study obscures government’s role in training scientists. Science 10(5), 1–22 (2016) 5. Grigoroiu, C., Pricop, A., Wesselly, T., Netolitzchi, M.: Optimizing the physical training of the female students in the cheerleading team of the university politehnica of bucharest. Gymnasium XX(2), 68 (2019). https://doi.org/10.29081/gsjesh.2019.20.2.06 6. Pelin, R.A., Grigoroiu, C., Mezei, M., Branet, C.: The utilisation of plyometric means in the development of the explosive force in the UPB cheerleading team. Revista romaneasca pentru educatie multidimensionala – J. Multidimen. Educ. 10(1), 1–9 (2018) 7. Bertram, S., Brixius, K., Brinkmann, C.: Exercise for the diabetic brain: how physical training may help prevent dementia and Alzheimer’s disease in t2dm patients. Endocrine 53(2), 350– 363 (2016) 8. Carvalho, E.E.V., et al.: Pilot study testing the effect of physical training over the myocardial perfusion and quality of life in patients with primary microvascular angina. J. Nucl. Cardiol. 22(1), 130–137 (2014). https://doi.org/10.1007/s12350-014-9949-6 9. Stolberg, C.R., Mundbjerg, L.H., Funch-Jensen, P., Gram, B., Bladbjerg, E.M., Juhl, C.B.: Effects of gastric bypass surgery followed by supervised physical training on inflammation and endothelial function: a randomized controlled trial. Atherosclerosis 273, 37–44 (2018) 10. Dechêne, A., Zöpf, T., Antoch, G., Paul, A., Hilgard, P., Gerken, G., et al.: Frequency and duration modulate anticarcinogenic effects of a physical training in the colon. Int. J. Sports Med. 36(09), 710–715 (2015) 11. Ounis, O.B., Elloumi, M., Zouhal, H., Makni, E., Zaouali, M., Lac, G., et al.: Effect of an individualized physical training program on cortisol and growth hormone levels in obese children. Annales Dendocrinologie 72(1), 34–41 (2015) 12. Pedroza, A.S., Lopes, A., Mendes, D., Braz, G.R., Nascimento, L.P., Ferreira, D.S., et al.: Can fish oil supplementation and physical training improve oxidative metabolism in aged rat hearts? Life Sci. 137, 133–141 (2015)

Techniques of Fusion Association Rule Mining Algorithm

273

13. Sondag, A., Moody, V., Mangan, A.: Examining barriers, motivators and injury related to physical training in wildland firefighters. Int. J. Wildland Fire 28(9), 678 (2019). https://doi. org/10.1071/WF18134 14. Horber, F.F., Kohler, S.A., Lippuner, K., Jaeger, P.: Effect of regular physical training on age-associated alteration of body composition in men. Eur. J. Clin. Invest. 26(4), 279–285 (2015)

The Study on Practice of Professional Teachers in Business Administration Enterprises from the Perspective of New Engineering Lina Niu(B) Zhengzhou Shengda Economic and Trade Management University, Zhengzhou, Henan, China [email protected]

Abstract. New engineering attainments in training professional and technical talents with strong practical ability are both opportunities and challenges for private colleges and universities. The selection of English major teachers in private colleges and universities is somewhat deficient, and the selection ignores the background of management practice. There is insufficient incentive and support for professional teachers to participate in organizational management practice. In order to strengthen management skills training quality, private colleges should be way more fulfilling professional teachers, change the traditional hiring standards, establish an effective incentive and constraint mechanism, promote the professional teachers to actively participate in organization and management practice, realizes the teachers “static” and “dynamic teaching” the benign interaction, from teachers in the business management practice of enterprises, analyzing the core competencies of the professional teachers in the new situation. Keywords: New engineering · Professional teachers · Business administration · Teacher construction

1 Introduction New engineering promotes the pace of integration of disciplines. At present, the employment of college graduates majoring in business administration has encountered a variety of difficulties. One of the main reasons is that students “can’t use learning” after they go out of the campus, so they have less mastery of the management skills required in the workplace and cannot quickly integrate into the high standards of the enterprise. In fact, this has nothing to do with the fact that the teachers of business administration in colleges and universities pay too much attention to theoretical research and lack of practice. In the face of business administration, which is a practical major, it is difficult to train and cultivate effective business administration skills for business administration students, making them unable to timely deal with the real work problems they face. Then, from the perspective of strengthening the quality of management skills training for students majoring in business administration, strengthening the construction of professional teachers has become a key issue to be solved urgently in the cultivation mode of business administration professionals in colleges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 274–280, 2022. https://doi.org/10.1007/978-3-030-99616-1_36

The Study on Practice of Professional Teachers

275

2 The Significance of the Construction of Professional Teachers in Colleges and Universities In view of the current employment dilemma faced by graduates, undergraduate majors of business administration in private universities should take the cultivation of applied management talents facing the needs of local market economy as their own talent training objectives [1]. Of course, this requires the support of a team of professional teachers who both have a deep professional knowledge background and know how to transform professional knowledge into management skills. First, the construction of teaching staff is conducive to improving the ratio between the knowledge and ability of teachers in business administration, promoting the formation of a reasonable human capital structure of teachers, and significantly increasing the connotation of teachers. Secondly, the construction of teaching staff can help to reverse the tendency of emphasizing “knowledge” over “ability” in business administration professional education, break through the dilemma of “mere talk” in classroom teaching, and effectively improve the quality of education and teaching. Thirdly, the construction of teaching staff is beneficial to students. By relying on high-quality teachers, students can learn how to build a management skill system from three aspects, namely concept, interpersonal relationship and technology, and strive to build the core competitiveness of employment. It can be said that local colleges and universities should strengthen the cultivation quality of students’ management skills and promote students’ employment [2]. The construction of teachers of business administration is the first step.

3 Difficulties in the Construction of Business Administration Faculty in Private Colleges and Universities in Henan Province From the perspective of strengthening the cultivation of students’ management skills, many private colleges and universities are still unable to keep up with the pace of highquality business administration education in terms of the construction of professional faculty, and cannot adapt to the high standard work requirements of business administration enterprises, which are mainly reflected in the following three aspects. 3.1 Insufficient Recruitment of Professional Teachers Because of industry and commerce management professional education fee and other science and engineering major, fixed investment is relatively small, the cultivation of marginal cost is low, so some private colleges consider economic benefit, on the one hand, human “create conditions for approval to open the” business management professional, and constantly expand the scale of enrollment [3]. On the one hand, there is no corresponding increase faculty recruitment, result in teachers’ quantity apparent lag. In this case, some local colleges and universities tend to ignore the teaching effect and training quality. The typical manifestations are as follows: first, training plans and courses should be established according to the existing teaching conditions rather than the characteristics of the business administration major, and some courses related to management skills training and development cannot be established; Secondly, the class

276

L. Niu

system is adopted in professional teaching, which severely restricts the use of various teaching means conducive to the formation of management skills, such as group discussion, classroom games, situational simulation and role playing. Thirdly, a teacher who teaches several courses at the same time can hardly impart knowledge to a high standard, let alone help students to complete the transformation from management knowledge to management skills. In short, due to the lack of recruitment of business administration teachers, it is difficult to ensure the number of teachers required by management skills-oriented talent cultivation. 3.2 Ignoring Management Practice Background in the Recruitment of Professional Teachers The practice shows that, due to some mechanism obstacles, the most important way for local universities to recruit business administration teachers is to recruit doctoral or master’s graduates to teach directly. In this way, teachers of business administration are often just out of school and immediately enter the school. Although they have a master’s degree or a doctor’s degree, they have a single subject, major and working background [4]. Although I have strong theoretical knowledge of management, I lack practical experience and experience. In the absence of professional practice, teachers of business administration only complete the simple role transformation from “being cultivated” to “cultivating” in the traditional talent cultivation mode. They may be good at “researching” management rather than “engaging in” management. Theory and practice cannot be well combined. Moreover, the process of faculty selection for business administration is relatively simple, which focuses on the applicants’ educational background, graduation school, scientific research achievements and trial speaking performance, etc., while failing to check the management practice background. If professional teachers do not gradually accumulate a large number of relevant experience and practical operation skills in the practice of business administration, they will be difficult to understand the weight of the actual management situation, and it will be difficult to help students complete the introspection of management knowledge and acquire the management skills that their future career depends on. 3.3 Insufficient Incentive and Support for Teachers’ Participation in Organizational Management Practices At present, there are some private colleges and universities professional teachers to participate in the practice of business management enterprise organizational management to give incentives and support is often insufficient. First, in the title appraisal system, the teaching effect and quality of talent cultivation because of the lack of visibility and quantitative sex like scientific research achievements and is difficult to enter the evaluation standard, so business management professional teachers focus on the topic, thesis, awards, and in the enterprise management practice in order to get the insufficiency of power management skills. Secondly, although there are some local colleges and universities have been pushing for industry and commerce management professional teachers to try on one’s credentials the exercise to the enterprises and institutions, but the overall incentive intensity is not big, the coverage is not wide, especially in the

The Study on Practice of Professional Teachers

277

target enterprise search, connect and exchange negotiation, contract signing, and subsequent security aspects of less substantial support. Third, in terms of teacher training and continuing education for business administration, teachers still focus on attending academic lectures and conferences, and there are not many opportunities for teachers to participate in the organization and management practice [5]. In a word, even if teachers of business administration are willing to participate in organizational management practice, if they cannot get the positive incentives and background support provided by the school in terms of information, funds, systems and resources, such personal willingness is basically difficult to achieve.

4 Strategies for Private Colleges and Universities to Strengthen the Construction of Faculty of Business Administration Private Universities after less then ten years of development, already has a solid research foundation, set up “teacher development center”, “lead, hire, excitation mechanism [6], to continuously strengthen the business management professional teachers team construction, to enhance students management skills training quality, build management oriented business management personnel training mode teachers lay a solid foundation. 4.1 Enrich the Faculty of Business Administration in Multiple Ways No matter what kind of talent training mode, the quality of teachers is always a necessary condition in running a school. At present, the employment of graduates majoring in business administration is gradually difficult, and the source of students is also beginning to be tight. Therefore, the business administration major of private colleges and universities is also at the critical time of “strengthening physical fitness”. Private colleges and universities should set up a long-term vision, break the traditional restriction that academic qualification is the only priority, and enrich the faculty of business administration from the perspective of strengthening the quality of students’ management skills from the perspective of student demands and market needs. Recruit doctoral or master’s graduates from colleges and universities to change ever single mode, correcting “degree” only “academic” only selecting talents bias, to broaden the vision, to enterprises and institutions for turning willingness “knowledge + skill” type of management talent [7], or to the research institutions employ those a lot of contact and staff involved in the practical project, or to the various enterprises and institutions training institutions recruiting experience in training of trainers, join the business management professional talent training. 4.2 Optimize the Recruitment Criteria The expansion of the professional teaching staff should not only improve the original teachers’ ability to integrate their specialty and practice, but also enhance their core competitiveness by recruiting teachers with rich practical experience into the professional teaching team. Of course, in order to avoid the incremental teachers to reproduce the defects of the original teachers, we should pay attention to the professional course

278

L. Niu

teachers in the knowledge and skills of the double assessment. In fact, there is no evidence that there is an inevitable positive correlation between teachers’ research papers and management skills. It can be seen that some of the so-called “high quality” business administration papers are filled with convoluted mathematical models that are far from the management skills that students need to master [8]. Business administration teachers need to tell students how to do a good job of management, rather than how to study management. Instead of focusing solely on the former, the correct approach is to change the traditional selection criteria and examine both “academic ability” and “management skills”. Under the premise of qualifications such as education background, degree and trial teaching, the emphasis should be on whether the teacher has received formal training in management skills and whether he has engaged in organizational management practice. Under the same conditions, the teacher who has enterprise work experience and practical management skills and is good at presentation and training is preferred. 4.3 Establishing an Effective Incentive and Constraint Mechanism Now, the most urgent thing is how to encourage teachers of business administration to make up for their lack of management experience and skills. Firstly, Universities should set up an evaluation mechanism which can fully reflect the teaching effect and training quality of business administration major in the teacher management system of performance assessment, title review and position promotion. Second, industry and commerce management professional students need to know about the concept of three management skills, interpersonal and technology in the future management of the core values, guide them to help management skills formation as the main standards to evaluate the efficacy of the business management professional teachers’ teaching and training quality, so as to reverse constraints for teachers’ teaching, encouraging them to focus on how to use language, context setting, classroom training, help students to complete the transformation of the cognition to the management skills. Third, it is necessary to fully mobilize the enthusiasm and initiative of teachers in business administration to seek management experience and management skills [9]. To be specific, it can develop the positive interaction between schools and society, encourage and support teachers to undertake horizontal projects of enterprises, and understand the real operation mode and law of enterprises and accumulate practical experience through participating in enterprise management decision-making, providing management consulting, temporary job training, exchange and learning and other forms in enterprises and social organizations. Considering the lack of individual strength of teachers, local colleges and universities should actively contact and negotiate with local enterprises in the name of units, so as to provide organizational guarantee for teachers majoring in business administration to participate in management practice.

5 Conclusion Under the general trend of new engineering, under the guidance and cultivation of professional teachers, college teachers can use their professional knowledge to guide students’ professional practice more effectively. Students should really apply what they

The Study on Practice of Professional Teachers

279

have learned into practice as soon as possible, so as to realize the benign interaction between “static teachers” and “dynamic teachers”. The so-called “static teachers” refer to the part of full-time teachers with a fixed establishment and relatively stable work in colleges and universities, while the “dynamic teachers” refer to those who have no fixed establishment, sign long-term or short-term contracts with the school, or temporarily hire teachers, and are in a state of flow. At present, “static teachers” of business administration major in local colleges and universities generally act as the sole teaching force. In fact, in addition to completing the transformation of “static teachers”, we should also flexibly use the power of “dynamic teachers” to strengthen the quality of students’ management skills training and promote the education of business administration professionals [10]. For example, qualified local colleges and universities can invite some personnel who are deep in the front-line management and familiar with enterprise management affairs to the school to explain the actual operation and management experience of enterprises to students. Or, through long-term or short-term part-time contracts, external professional managers with certain theoretical basis and practical working skills can be hired as practice mentors to guide students’ practical training activities; Or cooperate with some competent training institutions and management consulting institutions to create a more effective platform for students’ management skills training through teacher exchange and teacher exchange. The addition of “dynamic teachers” can not only enrich the teaching content, so that students majoring in business administration can hear the real voice from enterprises, but also enrich the level of professional teachers to form a contrast and competition with “static teachers”, from which students majoring in business administration can draw their strengths. The interaction between the two sets of teachers is of great significance for enhancing the quality of students’ applied management skills.

References 1. Qi, X., Thang, X.: Discussion on the Teaching Reform of business Administration Major in local Universities. Education. Explor. 7 (2015) 2. ZhouZhou: Countermeasures and Suggestions for cross-border Cultivation of Business Administration Major under the background of “Internet +”. Jac Forum 3 (2017) 3. Pan, Z., Li, J., Bai, J., et al.: Exploration on the Cultivation Model of Undergraduate Talents For Business Administration Majors Guided by “Four-Yuan Coupling”. University of China Teaching, 3 (2018) 4. Liu, S., Zhang, J.: Research on the construction of internal education quality assurance system in business schools -- from the perspective of AACSB certification. Shanghai Manag. Sci. 05 (2014) 5. Pu, X.: Exploration on the new applied talent Training mode of business administration major. Value Eng. 15 (2014) 6. Carol, S., Emile, A., Kalliopi, B. et al.: Digital support for academic writing: A review of technologies and pedagogies. Comput. Educ. 131 (2018) 7. Anne, H. van den, B., Esther, T.: Effects of anonymity on online peer review in secondlanguage writing. Comput. Educ. 142(C) (2019) 8. Jinfen, X., Li, X.: English language teacher education: a sociocultural perspective on preserver teachers’ learning in the professional experience. Asia Pacif. J. Educ. 40(4), 578–581 (2020). https://doi.org/10.1080/02188791.2020.1734291

280

L. Niu

9. Braat-Eggen, E., Reinten, J., Hornikx, M., Kohlrausch, A.: The influence of background speech on a writing task in an open-plan study environment. Build. Environ. 169, 106586 (2020). https://doi.org/10.1016/j.buildenv.2019.106586 10. Maxwell, J., Weill, C., Damico, J.: Investigating the use of appropriation in the writing of a child with autism: a case study. J. Commun. Disord. 65, 10–21 (2017). https://doi.org/10. 1016/j.jcomdis.2016.12.002

Analyze the Current Direction of Administrative Management Reform from the Big Data Perspective Daojin Zhang(B) Department of Economic Management, Xuchang Ceramic Vocational College, Xuchang, China [email protected]

Abstract. At present, the application of big data and cloud computing technology in the field of public governance is increasing. The governance ability of the government directly determines the breadth and depth of the application of big data, and determines the promotion effect of big data strategy. From the perspective of big data, government governance capability includes big data decisionmaking capability, technology implementation capability, policy evaluation and adjustment capability, as well as big data learning capability, resource integration capability and business process transformation capability. Recently, the term “big data” has been used to describe and define the massive amount of data produced in the era of information explosion, as well as the related technological development and innovation. Administrative organs at all levels also attach great importance to the application of big data when promoting administrative work. However, there are still many problems in the decision-making mechanism of administrative management. In order to solve these problems, the development strategies of administrative departments under the background of big data era are put forward in order to provide reference for improving the traditional administrative management mode. Keywords: Big data era · Administration · Development strategy

1 Introduction With the development of the Internet, the Internet of Things, big data, cloud computing and other information and communication technologies, human society has entered a higher stage of the information society, namely the so-called “big data era” [1]. The rapid development of information technology has spawned another wave of governance reform after the “New public Management” movement, which can be called “digital government”, “smart government”, “Internet + government”. 2015, the government issued a series of policy documents to leverage the positive role of information technology in the modernization of the country’s governance system and capacity. These policy documents not only define the development strategy of big data, but also make detailed and detailed deployment of the corresponding specific work. Faced with the technical difficulties of big data and the pressure of self-reform of the government, the academia and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 281–287, 2022. https://doi.org/10.1007/978-3-030-99616-1_37

282

D. Zhang

the industry have carried out relevant research and exploration on how the government departments will implement the party’s big data development strategy, what aspects to start from, and what are the key points and difficulties. Then, the basic connotation of government governance capability from the perspective of big data is that clarifying these problems is the premise and basis for government departments to analyze big data governance reform and determine the task and direction of governance reform. From the perspective of big data, the governance ability of government is not only reflected in the implementation ability of big data technology, but also in the learning ability, decision-making ability, policy evaluation and adjustment ability, resource integration ability and business process transformation ability of big data. These capabilities are reflected in different parts of the governance process. Contemporary China is in the historical stage of leaping from the middle to the late stage of industrial society. Due to the rapid development of new technologies, human society has entered the era of big data. At the present, China is at the node of the development of management theory. The development of management theory has promoted the transformation of government governance model, and the emergence and development of big data technology has provided technical support for the transformation of government governance model. Through understanding the reform of government administration, as well as the existing problems, combined with the knowledge to put forward the corresponding solutions. In government administration, the research and application of big data will greatly improve the efficiency of administration and reduce the expenditure of administrative costs [2]. It is of great research value in the context of streamlining administrative departments and introducing innovative talents. This paper analyzes the characteristics of the network era and the problems existing in the administrative work, and puts forward the reform direction of the administrative work under the background of big data.

2 The Theoretical Dimension of Government Governance Ability Construction Focusing on large data requirement for government behavior ability construction of governance ability can’t just stay in the big data of government governance, governance processes and results of the analysis of the opportunities and challenges, and should further develop this opportunity and challenge which new requirements are put forward for the governance body, and then on the basis of these requirements to build the components of government governance ability, Mining the new connotation of government governance ability under the background of big data, so as to improve the government’s governance ability. Focusing on the Subjective Initiative of the Government Governance ability is the ability of the governance subject to deal with external matters, which is reflected in the ability of the governance subject to transform and use objective things, and the action ability of the subject to act on and serve the object. Therefore, attention to the behavior ability of people needs to pay attention to the subjective initiative of people. Explore the ability of governance subject to actively learn and transform external environment. Focusing on the components and Interrelationships of governmental behavior capacity Governmental governance capacity is influenced by the components of governmental

Analyze the Current Direction of Administrative Management Reform

283

behavior capacity, the status, functions and interrelationships of different components and presents different levels. The construction of government governance capacity also needs to clarify the positioning, role and mutual relationship of different behavior capacities in the process of government governance, so as to help government departments to carry out capacity building in steps and with emphasis [3]. In terms of the process dimension, we also need to pay attention to the governance process of the government. From the nature of big data, big data not only refers to the effective use of diverse data sets; It also refers to the big data that can be used as a governance resource, representing the governance thinking, governance technology and capacity of the government, and promoting the governance reform of the government. Accordingly, the application ability of big data is not only reflected in the technical processing of big data, but also in the decision-making of big data policies, the integration of resources, and the governance reform for the effective use of big data. From the perspective of the government’s application of big data, the application of big data contains the value orientation of government governance, including the government’s understanding and cognition of big data; At the same time, big data is embedded in the public policy process as a tool. The formulation, implementation, evaluation and adjustment of big data policies all reflect the government’s ability and level of big data governance. Therefore, the construction of government governance capacity needs to focus on the whole process of government big data governance, including not only the policy process of big data application, but also the learning link of government big data, resource integration link and administrative system reform carried out by the government to implement big data strategies [4]. The construction of government governance capability needs to start from the whole governance process of big data being applied to public affairs, pay attention to the requirements of each link on the government work itself, and take this as the basic elements of government behavior capacity, so as to clearly show the governance capability that the government needs to have in the big data environment.

3 Major Problems in Administrative Management Under the Background of Big Data Although great progress has been made in administrative management, there are still four problems in decision-making mechanism. Ideological solidification, Lack of administrative innovation Consciousness, the Chinese government has been restricted by traditional administrative thoughts in the administrative process, there has been a serious phenomenon of ideological solidification. The reform of the administrative system has continued. Under the requirement of administrative innovation, the basic innovation consciousness is still lacking. Government administrators, on the other hand, are in many cases older and have earlier educational and administrative experience and find it difficult to accept new ideas and knowledge. Therefore, the concept of administrative reform is weak. On the other hand, it is difficult for the government to completely correct the long-formed administrative ideas in a short period of time. Without denying long-term ideas and methods, their ability to learn as a group is limited, making it difficult to explore new avenues in the administrative process [5]. The establishment of ideology has become one of the important factors that

284

D. Zhang

hinder the innovation of our government. Management philosophy establishes the government’s management consciousness, management purpose and management thought for the government’s management idea. In the innovation of government management, the most basic elements such as government idea, service idea, responsibility idea, law idea and limited government idea are value idea. This directly affects the choice of the system. The choice of the system will affect the behavior of the system, the quality of decisions, and the structure of the system. Therefore, advanced management innovation concept is the basis of government management window. Since the reform and opening up, Chinese administrative system has undergone fundamental changes. With the rapid development of the market economy, governments at all levels should actively transform their functions, implement the policies and measures of the central government, and make contributions to the realization of various social undertakings, the enhancement of overall national strength, the improvement of people’s living standards and the development of the national economy [6]. However, the Chinese government does not value its effectiveness on the international stage. Compared with developed countries such as Singapore, the level of government efficiency is low. The management mode of the government is formed by the planned economy system. The government’s ability to intervene and information is lacking, and its actions are inefficient. In the process of daily administration, Chinese government mainly serves the development of local economy, society, culture and ecology, and there are more and more daily affairs. However, over the years, the number of government departments has been increasing. Many departments are staffed and their numbers are growing. From this, the government appeared grass-roots cadre disorderly and other serious phenomenon. The resistance of government departments in the process of administrative management is increasing, and the local financial burden is also increasing. Most of the leaders and relatives of these departments and staff have low policy implementation capacity and serious behavior. The institutional change makes the government in the decision-making process is subject to many restrictions, law enforcement is difficult to promote effectively, it is difficult to achieve part of the administrative innovation. Miscellaneous personnel hindered the construction of the new government, and its administrative functions need to be further strengthened. Backward functions, the lack of perfect supervision of the performance of government functions is directly related to the construction of the government, which puts forward higher requirements for the government’s administrative functions. At present, the management of our government lacks functions. The phenomenon of the government’s inaction and chaos is very obvious, which is the reason why the government lacks perfect supervision and management. On the other hand, the government is far away from the central government, and the monitoring and management are relatively low, which makes it impossible to effectively implement the supervision and management system. There is no mutual supervision between the two. On the other hand, the supervision ability of the government is very limited and the government does not accept their opinions and suggestions. It is therefore inevitable that the Government will achieve results in the exercise of its administrative functions.

Analyze the Current Direction of Administrative Management Reform

285

4 Development Strategy and Reform Direction of Administrative Management Under the Background of Big Data Era 4.1 Administrative Development Strategy Innovation decision support system, the control decision-making power in view of the present administrative management decision-making mechanism disorder, the decisionmaking power abuse, bad situation, we must establish an effective decision-making as soon as possible safeguard mechanism, control power, prevent the abuse of administrative power, decision-making power, and public participation, the scenes enthusiasm of degradation. To innovate the decision-making guarantee mechanism, we can start from the following aspects: to establish the hearing system, strengthen the institutional guarantee, provide the people with opportunities and places to hear politics, and make the party committees and governments at all levels make their decisions open and transparent; Establishing a public inquiry system to provide the public with opportunities for consultation and decision-making; Perfecting the accountability system of administrative decision-making mechanism; Grass-roots management should be transformed from the subordinate relationship of absolute obedience to the communication between the upper and lower levels, and the upper and lower relationship mode should be transformed from the comprehensive dependence to the relationship network between interests; Relevant administrative regulations shall be formulated so that decision-makers have laws to abide by when exercising their decision-making power, so that they can make decisions according to regulations and according to law, and the legal responsibilities that decision-makers must bear shall be specified in the law, so that they can be strict with themselves and make scientific decisions [7]. Innovative management style, improve the administrative efficiency under the background in the era of big data, the administrative departments at all levels and the relationship between the individual or organization to gradually shift to depend on each other, interdependent relationship, when making administrative decisions, must keep up with the development of The Times, practical and innovative management style, the concept of people-oriented decision to implement the administrative management work, Adhere to the party’s purpose of “serving the people wholeheartedly”, and constantly improve administrative efficiency. Innovation of administrative management mode needs to achieve three changes: To speed up the government management style from closing to opening, with the help of big data, Internet technology, increase the intensity of the government information publicity, make sunshine government, guide the social public published politics with the aid of network platform, expressing the political will, to participate in political activities, supervision and administration management, improve the level of government management, realize the government decision-making, scientific decision-making; Accelerate the transformation of government management from control-oriented to service-oriented, with the help of big data technology, implement egovernment, better perform administrative functions relying on e-government, provide more high-quality public service products, continuously simplify administrative processes, and change “people running” to “data running”; Establishing and improve the early warning system, evaluation system and problem handling system of government management, and realize the dynamic and scientific management of government.

286

D. Zhang

4.2 Direction of Administrative Reform Such scientific nature is reflected in the following aspects: First, big data provides a large enough number of data, based on which decisions can more accurately reflect the reality. China’s two-child policy is a typical case of adjustment based on big data. The government has mastered the proportion of the elderly population in China, the rate of the elderly population increase, and the birth rate of new births. Only by comparing the data of these two aspects can the country decide when to open the two-child policy. Second, big data provides accurate data screening capabilities, which lays the foundation for fine management. For example, China’s public security system has been building its own data-based information base for many years. Once the key information about a criminal suspect is mastered, it can be compared and searched in its information base, thus providing a basis for scientific case handling [8]. Third, big data is not only data collection, but also data analysis, which can be carried out from different perspectives and provide a basis for government management from more perspectives. For example, in the population data, we can look at the total number or divide it by gender. The imbalance of gender ratio is caused by the lack of big data on population sex. Can also be seen from age, can also be seen by regions, different perspectives have different reference significance.

5 Conclusion These studies are helpful to understand the necessity of improving government governance capacity under the background of big data, as well as the methods and approaches to improve it. However, there is a lack of theoretical definition of government governance capacity from the perspective of big data, and a lack of direct and systematic research on the construction of government governance capacity [9]. These deficiencies are not conducive to government departments to recognize the new requirements of big data environment on their governance ability, to accurately evaluate the current situation and existing problems of government governance ability, and to improve their own capabilities in a targeted way. Big data analysis is becoming a key change point for various industries to participate in competition, and also an important basis and breakthrough for China’s administrative reform [10]. Effective integration of structured, semi-structured and unstructured data, analysis and exploration of their potential value is a necessary path to improve administrative efficiency and competitiveness in the era of big data. Administrative organs at all levels derive their power from the people. In the process of administrative management and administrative decision-making, they should be responsible to the people and make scientific management and democratic decisionmaking. In the era of information explosion and big data, we should actively adapt to the trend of The Times, actively change the administrative management system, improve the administrative management mechanism, improve the pertinence and effectiveness of administrative management work, so as to continuously enhance the credibility of governments at all levels and win the support and trust of the people.

Analyze the Current Direction of Administrative Management Reform

287

References 1. Ni, X.: Research on the reform of Administrative Law Enforcement mode under the background of big Data. Legal Expo, 19 (2019) 2. Research on the training program of health management professionals based on market demand. Modernization of Education. (A0), Wang Dahong, Hei Qiming (2019) 3. Yang, S., Zhang, H.: Research on the reform of Government management model in China under the background of big data. Statist. Manag. 9 (2017) 4. Xu L.: Research on innovation of government administrative management mode under the background of big Data. China Econ. Trade 000(009), 57 (2017) 5. Yijingyi: Research on the innovation strategy of university administration under the background of big data. Sci. Technol. Econ. Guide 27(10), 252 (2019) 6. Liu, F., Xu, X., Lei, M.: The overall optimization and teaching reform of course group based on the cultivation of data analysis ability in information and computing science major. Math. Learn. Res. 24 (2020) 7. Chen, X.: Research on administrative management problems under the background of big data era. J. Kaifeng Inst. Educ. 5 (2017) 8. Goth Adeline Yen Se Learning cultures: understanding learning in a school-university partnership. Oxford Rev. Educ. 47(3) (2021) 9. Chris, G., Richard, U.: Promotion and uptake pathways for research output: a review of analytical frameworks and communication channels. Agricult. Syst. 55(2) (1997) 10. Max, R. Stuart, H.G., Chris, R.P. et al.: Collaborative research praxis to establish baseline acoustics conditions in Gitga’at Territory. Global Ecol. Conser. 7(C) (2016)

Application of Information Technology in the Course Teaching of “Cost Accounting” Xiaoyu Yan and Hongyan Li(B) Department of Economics and Management, Jilin Agricultural Science and Technology University, Jilin, Jilin, China [email protected]

Abstract. As a core course of the accounting major, cost accounting has a strong theoretical, practical and comprehensive nature. By analyzing the current situation of cost accounting teaching, it is found that the traditional teaching mode has not met the requirements of modern information teaching, which is not conducive to the cultivation of compound cost accounting talents. Combining with the problems of traditional teaching mode, this research further analyzes the significance of information technology applied to course teaching, and on this basis, proposes the specific application of information technology in the pre-class preparation phase, the class implementation phase, and the post-class feedback phase. Keywords: Information technology · Cost accounting · Application

With the rapid development of my country’s economy and the continuous updating of information technology, more and more traditional industries integrate information technology into their own development and construction. The management of corporate finance has also become more complicated, and the management of cost accounting has undergone major changes. In order to adapt to the development of economy and management, enterprises have shown a higher demand for compound cost accounting talents [1]. However, in reality, accounting talents cultivated by colleges and universities often lack practical ability and perfect knowledge system. Therefore, in order to adapt to market demand, it is necessary to reform cost accounting, apply information technology to teaching, and improve teaching quality and efficiency [2].

1 Current Status of Cost Accounting Teaching Cost accounting is a major professional subject of accounting. Compared with other accounting professional courses, it pays more attention to choosing appropriate accounting methods and calculating costs. It has a strong theoretical and practical nature. The teaching status of cost accounting shows the following characteristics: 1.1 Single Teaching Form The main performance of the cost accounting classroom is that the teacher teaches on the podium, and the students study below. The teacher teaches the knowledge points of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 288–293, 2022. https://doi.org/10.1007/978-3-030-99616-1_38

Application of Information Technology in the Course Teaching

289

the whole book to the students, and the students practice what they have learned through homework after class. Although teachers apply information technology in the teaching process, they are only limited to a tool for displaying courseware and have not played the role of information teaching [3]. 1.2 Variety of Course Content Cost accounting courses generally last from 48 class hours to 64 class hours. Students are required to be proficient in various methods of product costing, including the application and filling of a large number of calculation formulas and tables. The content of the course seems to be divided into chapters, and the actual connection is close. If you do not master the knowledge points of a link, you will not be able to carry out the overall process of cost accounting. 1.3 Insufficient Interest in Learning During the learning process of cost accounting courses, students show a state of passively receiving knowledge. The calculation forms are usually large and cannot be reflected in the courseware and blackboard. Students are required to follow the teacher’s ideas for cost accounting. If students are not paying attention, they will not be able to follow up the progress of the class. In addition, the content of the course is large, difficult, and highly relevant, which can easily make students feel tired and lose interest in course learning [4]. 1.4 Lack of Effective Practice Due to the limitation of teaching time and teaching conditions, cost accounting teaching is mainly based on theoretical teaching, and the knowledge covered by practical training cases is small, too simple, and lacks comprehensive teaching cases. At the same time, practical teaching is based on calculations, and paper bills are often used for practical training [5]. Students cannot fully understand the production and processing processes of enterprises, and cannot meet the needs of enterprises for compound cost accounting talents. 1.5 Delay in Learning Feedback Cost accounting courses under the traditional teaching model need to complete the teaching of all the knowledge points within a limited number of hours. The teaching tasks are heavy, the classroom interaction time is short, and there is a certain distance between teachers and students [6]. For the questions raised by the teacher, some students dare not take the initiative to answer, and dare not ask the teacher for their own questions. Teachers are unable to understand students’ learning in a timely manner through the classroom.

290

X. Yan and H. Li

2 Significant of the Application of Information Technology 2.1 Increasing the Rationality of the Curriculum Cost accounting courses are rich in knowledge points and diverse in content. Some of the content overlaps with subjects such as financial management and management accounting. It is impossible to complete the detailed teaching of all knowledge points within a limited class time. The application of smart teaching tools such as “SPOC” and “Rain Classroom” can help teachers to reasonably split and integrate the teaching content according to the course objectives and students’ learning situation. Make full use of fragmented time and intensive time to carry out online and offline teaching activities [7]. 2.2 Meeting the Goal of Training of Professional Talents At this stage, cost accounting courses mostly use a combination of theoretical lectures and paper-based practical training to teach the content of each chapter, and students cannot fully understand the selection of cost accounting processes and methods. Using information technology to share teaching resources such as a comprehensive case library and building a training platform can help students understand the entire process of enterprise production and processing, and independently complete cost accounting [8]. 2.3 Stimulating Students’ Interest in Learning Cost accounting courses are mainly based on teachers’ use of traditional teaching methods to impart theoretical knowledge, requiring a lot of calculations and filling in forms. Many students feel that the courses are boring and difficult to understand and lose their interest in learning. The application of information technology such as short videos and animations can create learning situations and attract students’ attention [9]. Case training can enable students to experience the usefulness of the courses firsthand and increase their interest in learning. 2.4 Improving Teaching Quality and Efficiency The study of cost accounting courses requires a combination of pre-class preparation, in-class study, after-class review and practical training. The traditional teaching methods can’t better supervise and keep abreast of students’ learning situation in time. The application of information technology can push high-quality learning resources to students in every link, reflect the progress of students in real time, and summarize and analyze the completion status. It is convenient for teachers to understand the learning situation in time and implement teaching in a targeted manner.

Application of Information Technology in the Course Teaching

291

3 Specific Application of Information Technology 3.1 Pre-class Preparation Phase Before class, teachers use “Rain Class” and other smart teaching tools to distribute class tasks, materials and related notices. Students can understand the key points of learning through the assignment of tasks, and effectively participate in the learning and interaction in the classroom. The materials are generally short videos, and the content contains the knowledge points and learning situations that students need to master before class, and the time should not be too long. In the preview process, students can mark the parts they do not understand to clarify the key points of classroom learning. Since students have no business experience, short video playback alone cannot play a positive role. At this time, it is particularly important to create learning situations through videos and animations [10]. Based on this, students can understand the production process of the enterprise and how the relevant accounting documents are passed between various departments, so as to clarify the process of cost accounting. Teachers can use the learning monitoring function of the smart teaching tool to understand the students’ preview situation, and at the same time can post simple exercises for testing, and adjust the focus of the course explanation in real time based on the answers. 3.2 Classroom Implementation Phase Before class, teachers can use smart teaching tools to post sign-in tasks to save time on roll call. In the classroom, the teacher will explain the problems that students respond to during the preview and the important and difficult points in the chapters. The courseware and videos are used in the explanation process to help students understand the content of the course while attracting students’ attention. For the important and difficult points in the course, teachers can throw a small case through the smart teaching tool, and the group will discuss it, and then submit the answer content. The teacher displays the content of each group’s answers by casting a screen, briefly explains and analyzes the distinctive points and areas that need improvement in the content of each group’s answers, and finally makes a summary to deepen students’ understanding and memory of key knowledge. Every time an important knowledge point is taught in the class, the smart teaching tool can be used to release choices or judgment exercises within a limited time, so as to urge students to learn about the understanding of the knowledge point in time while listening to the class. After each lesson is finished, a response link can be set up, through this method to summarize the content of the lecture, and at the same time increase the enthusiasm of the students. 3.3 Post-class Feedback Phase After the course is over, the teacher will release the courseware, related materials and expansion materials of this course through the wisdom teaching tool, so that students can choose the review materials suitable for them according to their own learning situation and characteristics. The forms of homework are diversified, and the system can directly correct objective questions, make analysis, and directly announce the results.

292

X. Yan and H. Li

For knowledge points with strong expansibility, teachers can use Weibo or WeChat group chat to post discussion topics to stimulate students’ enthusiasm for learning, and at the same time to draw distance from students. Information technology occupies an indispensable position in cost accounting training. After students have completed the theoretical knowledge of a certain chapter, they can carry out special training through practical training software. Teachers can first release the video introducing the production process and accounting process to cultivate students’ cost accounting thinking, and then release the actual operation process. After the cost accounting course is taught, teachers can follow the same steps to release comprehensive training. The application of information technology in cost accounting teaching is shown in Fig. 1:

Fig. 1. Chart of the application of information technology in cost accounting teaching

Application of Information Technology in the Course Teaching

293

Acknowledgements. This work was supported by Jilin Association for Higher Education Project “Research on the Construction and Practice of Online and Offline Mixed Curriculum of Management Professional Courses in Applied Universities” (JGJX2021C64).

References 1. Brusca, I., Labrador, M., Condor, V.: Management accounting innovations in universities: a tool for decision making or for negotiation? Public Perform. Manag. Rev. 42(5), 1–26 (2019) 2. Waiganjo, I.N.: Teachers’ perceptions and use of information and communication technology in teaching and learning: Kadjimi Circuit, Kavango West, Namibia. Open Access Libr. J. 08(03), 1–21 (2021) 3. Agatep, J., Villalobos, R.N.: Villalobos: project-based learning approach in teaching information and communications technology. Sci. Insights 35(2), 216–221 (2020) 4. France, D., Lee, R., Maclachlan, J., Mcphee, S.R.: Should you be using mobile technologies in teaching? Applying a pedagogical framework. J. Geogr. High. Educ. 45(2), 221–237 (2021) 5. Kuatbai, K., et al.: Use of innovative pedagogical technologies in teaching economic disciplines. ACADEMICIA: An Int. Multidiscip. Res. J. 11(4), 1336–1339 (2021) 6. Chisango, G., Marongwe, N., Mtsi, N., Matyedi, T.E.: Teachers’ perceptions of adopting information and communication technologies in teaching and learning at rural secondary schools in Eastern Cape, South Africa. Africa Educ. Rev. 17(2), 1–19 (2020) 7. Jayachithra, J.: Information and communication technology in teaching and learning: perspectives on e-learning at higher education level. Int. J. Recent Technol. Eng. (IJRTE) 8(5), 4084–4086 (2020) 8. Miao, Sirui: Path analysis of promoting accounting teaching reform in vocational education by information technology. Trans. Comput. Sci. Technol. 7(1), 61–65 (2019) 9. Dilyana, M.: Accounting treatment of software development costs according to applicable accounting standards. Ikonomika i Kompût rni Nauki 3(2), 50–60 (2017) 10. Gaol, F.L., Abdilla, H.L., Matsuo, T.: Adoption of business intelligence to support cost accounting based financial systems — case study of XYZ company. Open Eng. 11(1), 14–28 (2020)

Innovation and Entrepreneurship of Computer Application Technology Specialty Under the Vision of Smart City Xingfeng Liu(B) and An Qin International School of Education, Guangzhou Railway Polytechnic, Guangzhou, Guangdong, China [email protected]

Abstract. With the continuous development of smart cities, there are more and more talents for innovation and entrepreneurship. In recent years, under the vigorous promotion of government departments, the innovation and entrepreneurship education of universities, especially computer science, has developed strongly and has achieved many gratifying achievements results. But there are also some problems that cannot be ignored, such as a lack of understanding of idealism and research systems. Therefore, this article focuses on the dual innovation of computer application technology under the view of smart city. First, we have a general understanding of innovation and entrepreneurship capabilities on the basis of literature data, and then conducts research on some problems existing in the current stage of computer application technology, and finally conduct a survey on the current situation of dual innovation in computer application technology, and draw relevant conclusions through the survey. The survey results show that under the construction of smart cities, more than 43% of computer application students are willing to participate in innovation and entrepreneurship. The main obstacles at this stage are the shortage of funds, the lack of professional guidance and relevant experience. Keywords: Innovation and entrepreneurship · Computer major · Innovation and entrepreneurship ability · Improvement strategy

1 Introductions With the deepening of reform and opening up, new ideas are increasingly accepted and sought after by the majority of students [1, 2]. The pursuit of individuality, self-esteem, and the courage to accept challenges are the characteristics of this era [3, 4]. In the process of pursuing and achieving life goals, they pay more attention to self-realization and selfesteem [5, 6]. Cultivating students’ innovation and entrepreneurship can provide students with growth opportunities to improve their overall quality, stimulate creative thinking, and meet the challenges of the future. It can also provide a lot of development space for the pursuit of hobbies and careers, allowing them to pursue their professional knowledge © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 294–302, 2022. https://doi.org/10.1007/978-3-030-99616-1_39

Innovation and Entrepreneurship of Computer Application Technology

295

and goals. Expectations and other pursuits to achieve complete self-transcendence and self-realization [7, 8]. In response to the research on innovation and entrepreneurship of college students, some researchers have pointed out that the current innovation and entrepreneurship education in colleges and universities face many problems such as the lack of coordination between internal departments, insufficient integration of resources, blocked education courses, and defects in systems and mechanisms. In response to these problems, the researchers proposed to promote innovation and entrepreneurship educational reform colleges and universities should strengthen the concept of coordination, build an institutional mechanism between the government, universities and enterprises, and stimulate multidisciplinary synergy [9]. Some researchers also pointed out that smart cities are spreading at this stage, and higher new requirements are put forward for innovative and entrepreneurial talents. Therefore, as bases for training innovative and entrepreneurial talents, universities have become particularly important for innovation and entrepreneurship education reforms, and proposed relevant measures to explore and practice innovative corporate culture education from the four dimensions of optimizing support: guarantee system, optimizing evaluation and incentive mechanism, and playing a leading role [10]. Some scholars advocate that the training program for independent innovation and entrepreneurial talents for college students is an important way and platform for the transformation and reform of the management mode of college student talent training. It enhances the ability of college students for independent innovation and cultivates more innovative talents with higher levels in the process of building an innovative country, so we have conducted researches on the talent training plan for university students’ independent innovation, summed up the work of training innovative enterprises in recent years, and put forward process management and target management compound management model [11]. To sum up, there are still many research results on innovation and entrepreneurship of college students, but there are relatively few studies on innovation and entrepreneurship of computer application technology. This article examines the innovation and entrepreneurship of computer technology from the perspective of smart city, summarizes the problems faced by innovation and entrepreneurship of computer technology based on related literature, and discusses the relationship between innovation and entrepreneurship of smart city and professional computers. Then, we use the survey method to investigate the current innovation and entrepreneurship status of high-tech computer applications, and draw relevant conclusions through the survey results.

2 Research on Innovation and Entrepreneurship in Computer Application Technology 2.1 Problems of Innovation and Entrepreneurship in Computer Application Technology (1) Practical resources Practicing entrepreneurial activities is inseparable from abundant practical resources, seamless channels and practical platforms. Practical resources are mainly

296

X. Liu and A. Qin

used for platform construction and action guidance [12]. It includes the following aspects: First, laboratory and supporting equipment. Workshops can provide students with ample space for innovative experiments. Advanced and complete laboratory equipment helps students verify the latest experimental results. The second is a professional innovation and entrepreneurship guidance agency. The quality of innovation and entrepreneurship of engineering students has been continuously improved. Innovation and entrepreneurial development are complex and comprehensive activities. As individuals, students often need professional guidance agencies to provide them with professional information and knowledge to help them. The mentor organization is also an important communication platform for engineering students to carry out innovation and entrepreneurship activities and even cross-regional exchanges. The third is the special fund for innovation and entrepreneurship. (2) Student literacy Student literacy is a summary of students’ advanced knowledge and skills. The different levels of knowledge and ability of engineering students, as well as their cognition of innovation and business education, inevitably have different effects on the quality of innovation and entrepreneurship of engineering students. Especially in terms of theoretical application or ability and personality, students with a higher literacy rate will have great advantages in participating in innovation and entrepreneurship activities and improving the quality of innovation and entrepreneurship. Abundant knowledge and theoretical reserves can effectively guide students to engage in practical innovation and entrepreneurial activities, and practical and innovative internships are accompanied by students’ innovation. However, students with high literacy rates tend to be more relaxed and unable to make sustained efforts. This is a concrete obstacle to innovation and improving the quality of business, and needs to be paid attention to. However, students with inadequate ability and personality are facing the improvement of innovation and entrepreneurial quality. Of course, disadvantages will give students a sense of urgency and can encourage them to make continuous progress and improvement. In addition, students’ knowledge and interest in innovation and entrepreneurship activities and the influence of personal will on the quality of innovation and entrepreneurship cannot be ignored. (3) Insufficient effectiveness of innovation and entrepreneurship activities Today, different business activities take place in many places: different hatcheries, business parks, commercial cafes, etc. Entrepreneurship conferences, seminars, breakfast meetings and other activities with a business atmosphere can attract many business students, because they have a wide range of business contacts, so it is still very effective to find one or two potential partners here. However, these “rush to market” businesses often require entrepreneurs to spend a lot of time, but many people find it more practical to spend limited time on developing their own products. In business, entrepreneurs often encounter non-customers and need to focus on activities involving a large number of potential customers. Therefore, undergraduate entrepreneurs need to adjust their time and engage in high-quality activities that are truly conducive to their own business. Through effective activities, find suitable partners or employees at the early stage of development, promote your products, and obtain the first batch of “user angels”. At the same time, they can

Innovation and Entrepreneurship of Computer Application Technology

297

alleviate the pressure of entrepreneurship through collective psychological mutual support, and participate in various business activities, with an innovative attitude, advancing with the times, learning while walking, and walking at the forefront of technology and industrial development. 2.2 The Relationship Between Smart City and Computer Application Technology Professional Innovation In different historical periods, there are different content carriers. In the context of the economy entering the smart city, under the strategy of innovation-driven development, innovation and entrepreneurship have become the era content and new carrier of the smart city consistent with the spirit of the times and the concept of social development. In terms of content, innovation and entrepreneurship education in computer application technology is closely related to the establishment of smart cities. In terms of function, the establishment of a smart city can be the practice of innovation and entrepreneurship in computer application technology, focusing on the cultivation of students’ practical ability, unifying personal value, social value and future development, and becoming a new carrier of innovation and entrepreneurship education. 2.3 Data Processing The true variance is an abstract concept. In other words, it is a possible variable. Observations made in actual measurement X and T may not exactly match, and there will always be errors. Among them, accidental errors cannot be avoided, and systematic errors should be avoided or reduced as much as possible. Since it is difficult to decompose systematic errors in actual questionnaire surveys, the actual values in the reference book also have decomposition types that include systematic errors. In other words, the expression X = T + B + E is simply written as X = T + E. For the measurement error of E, it is generally considered that the expected value is 0, regardless of the actual score. Under this assumption, it can be proved: The real score is equal to the overall mean of the measured score, and the variance of the measured score σx2 is equal to the sum of the weak variance of the real score σT2 and the variance of the error σE2 , expressed by the formula: E(X ) = E(T )

(1)

σX2 = σT2 + σE2

(2)

3 Investigation on the Status Quo of Innovation and Entrepreneurship in Computer Application Technology Under the Smart City Vision Valve 3.1 Purpose of the Questionnaire Survey This article focuses on the current willingness of computer application students to participate in innovation and entrepreneurship in the construction of smart cities and the

298

X. Liu and A. Qin

obstacles they face, so as to explore the current status of entrepreneurship in the field of computer application technology from the perspective of smart cities. 3.2 Questionnaire Survey Process (1) Survey object In order to make the survey accurately, concisely and objectively reflect the current situation of innovation and entrepreneurship of students majoring in computer application, this article strictly limits the subject of the survey, basically adopting the survey method, sampling in groups, and extracting computer majors in the city. In order to ensure the diversity of the results, three universities were selected, covering the key universities of the city, one college and two colleges. (2) Survey sample In this survey on independent innovative enterprises of computer application college students, a total of 127 questionnaires were sent to them, and 124 were recovered, with an average recovery rate of 96.0%. After excluding some incomplete and randomly filled out invalid questionnaires, there are 120 valid questionnaires left. The actual investment recovery rate reached 77.8%. After collecting, verifying and approving the survey data, we mainly use spss21.0 and excel software to input the effective survey data results into the computer for relevant analysis and investigation.

4 Analysis of Survey Results 4.1 Result Analysis (1) Participation in entrepreneurship and innovation under the construction of smart cities This article uses a questionnaire survey method to investigate the participation of computer application majors in the construction of smart cities in the construction of smart cities. The relevant data results are shown in Table 1: Table 1. Participation in entrepreneurship and innovation under the construction of smart cities A college

B college

C college

More interested, often participate

46%

45%

44%

Very interested and actively involved

18%

17%

19%

It doesn’t matter whether you participate or not

13%

12%

14%

Not very interested, rarely involved

17%

16%

15%

6%

10%

8%

No interest, never participate

It can be seen from Fig. 1 that in the context of building a smart city, students majoring in computer application are more involved in entrepreneurial innovation, and they are

Innovation and Entrepreneurship of Computer Application Technology

299

percentage

more interested. The number of frequent participants is about 44%. 17% of students are interested and actively participating. This shows that more than half of the students are actively involved in innovation and entrepreneurship and can independently participate in practical innovation and entrepreneurship activities. In addition, 6% of the students made it clear that they were not interested and never participated, 18% of the students said they were not interested and rarely participated, and 13% of the students said it did not matter whether they participated. Some students still show a lack of initiative and hesitate to independently participate in innovation and entrepreneurship. 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0%

More interested, often participate

No interest, Not very Very interested It doesn't never and actively matter whether interested, involved you participate rarely involved participate or not

Participation intention A college

B college

C college

Fig. 1. Participation in entrepreneurship and innovation under the construction of smart cities

(2) Obstacles faced by entrepreneurship and innovation This paper uses questionnaire surveys to investigate the obstacles faced by computer application majors, and the relevant data results are shown in Table 2: Table 2. Obstacles facing by innovation and entrepreneurship A college

B college

C college

Capital shortage for innovation and entrepreneurship

55%

54%

53%

Lack of entrepreneurial experience and interpersonal relationships

23%

22%

25%

Lack of necessary professional knowledge and ability

14%

14%

15%

8%

10%

7%

Lack of professional teacher guidance

It can be seen from Fig. 2 that the main obstacle faced by innovation and entrepreneurship is the shortage of funds, which accounted for more than 54%, accounting for more than half, and the lack of relevant experience and personal connections in technology accounted for about 23%.

300

X. Liu and A. Qin 60%

percentage

50% 40% 30% 20% 10% 0%

Capital shortage for innovation and entrepreneurship

Lack of entrepreneurial experience and interpersonal relationships A college

Lack of necessary professional knowledge and ability

Lack of professional teacher guidance

problems

B college

C college

Fig. 2. Obstacles facing innovation and entrepreneurship

4.2 Strategies to Improve the Innovation and Entrepreneurship Ability of Computer Application Technology Majors (1) Carry out social practice with special fund to help innovation and entrepreneurship Social practice with special fund is an important part of the practice of moral education in colleges and universities, and it plays a central role in cultivating students’ ability and comprehensive quality of combining theory and practice. Innovation and entrepreneurship training is also a practice, requiring specific practical activities to achieve educational goals. To this end, universities can start teaching innovation and entrepreneurship in the following ways: Universities should make rational use of the golden period of winter and summer vacations and organize organized social practice activities. During the holidays, the school can organize student speeches, competitions and social practice activities in the form of units or groups. (2) Strengthen base construction and experience innovation and entrepreneurship The effective development of innovation and entrepreneurship education requires a work-based platform. On the one hand, universities need to integrate resources and build a campus practice base. The school extensively solicits students’ opinions, creates a green channel for innovation and entrepreneurship, surveys all students in the school through questionnaire surveys and interviews, showcases the status of students’ innovation and entrepreneurship, exchanges business ideas with students, and supports students’ initiative and creativity to fully mobilize and stimulate Students’ enthusiasm for innovation and entrepreneurship.

5 Conclusions This article focuses on the research on the innovation and entrepreneurship of computer application technology under the smart city vision valve. After analyzing the problems faced by the innovation and entrepreneurship at the current stage, the current situation

Innovation and Entrepreneurship of Computer Application Technology

301

of the computer application technology professional innovation and entrepreneurship is investigated, and the survey results are obtained. Under the establishment of the Smart City, the willingness of computer application majors to participate in innovation and entrepreneurship is still quite high. Students who are interested in innovation and entrepreneurship and frequently participate account for more than 43%. Then the main obstacles facing innovation and entrepreneurship at this stage are the lack of funds, the lack of professional guidance and relevant experience, so related mechanisms of the finance, platforms and practical bases at the social level need to be established. Acknowledgements. The authors have received financial support from the Programmes of Research and Practice Project of Innovation and Entrepreneurship Education for Computer Application Technology Major under Smart City Vision (2019KC213); Research on the Transformation Mode and Collaborative Innovation Mechanism of Scientific and Technological Achievements of Higher Vocational Colleges in Rail Transit under the Construction of “Guangdong-HongkongMacao Greater Bay Area” (2020GZQN62); Research on connotation Construction of Higher vocational Colleges in the New Era (GYXYR1804); Research on the Operation Mechanism of the Industry-Education Integration Alliance of Vocational Education—Take the Southern China “One Belt and One Road” Rail Transit Industry-Education Integration Alliance as an example. (Characteristic Innovation Project of Ordinary Colleges and Universities in Guangdong Province) (2020WTSCX228)(Philosophy and Social Science).

References 1. Kazuyuki, M.: Innovation and Entrepreneurship: A first look at linkage data of Japanese patent and enterprise census. Discus. Pap. 29(1), 77–78 (2016) 2. Schmitz, A., Urbano, D., Dandolini, G.A., de Souza, J.A., Guerrero, M.: Innovation and entrepreneurship in the academic setting: a systematic literature review. Int. Entrepr. Manag. J. 13(2), 369–395 (2016). https://doi.org/10.1007/s11365-016-0401-z 3. Czarniewski, S.: Small and medium-sized enterprises in the context of innovation and entrepreneurship in the economy. Polish J. Manag. Stud. 13(1), 30–39 (2016). https://doi. org/10.17512/pjms.2016.13.1.03 4. Bhagavatula, S., Mudambi, R., Murmann, J.P.: Management and organization review special issue ‘the innovation and entrepreneurship ecosystem in India.’ Manag. Organ. Rev. 13(1), 209–212 (2017). https://doi.org/10.1017/mor.2017.11 5. Cusumano, M.A.: The puzzle of Japanese innovation and entrepreneurship. Commun. ACM 59(10), 18–20 (2016) 6. Yan, Y.: Teaching research on higher vocational pre-school education of professional art course based on innovation and entrepreneurship education. Creat. Educ. 9(5), 713–718 (2018) 7. Salter, A.J., McKelvey, M.: Evolutionary analysis of innovation and entrepreneurship: Sidney G. Winter—recipient of the 2015 global award for entrepreneurship research. Small Bus. Econ. 47(1), 1–14 (2016). https://doi.org/10.1007/s11187-016-9702-4 8. Barroso-Tanoira, F.G., Tanoira, F.: Motivation for increasing creativity, innovation and entrepreneurship. An experience from the classroom to business firms. J. Innov. Manag. 5(3), 55–74 (2017) 9. Qian, H.: Knowledge base differentiation in urban systems of innovation and entrepreneurship. Urban Stud. 54(7), 1655–1672 (2016)

302

X. Liu and A. Qin

10. Mcphee, C., Saurabh, P.: Editorial: innovation and entrepreneurship in India (January 2018). Technol. Innov. Manag. Rev. 8(1), 3–4 (2018) 11. Oyelakin, O., Kandi, U.M.: The moderating role of government policies on the relationship between technology, innovation and entrepreneurship development in Nigeria: a partial least square approach. Univ. J. Manag. 5(10), 477–484 (2017) 12. Chen, Z.: Language learning strategies based on the educational concept of innovation and entrepreneurship. Open Access Libr. J. 03(6), 1–6 (2016)

Teaching Reform of Innovation and Entrepreneurship Education in Application-Oriented CaU Under the Background of BD Yadan Wang(B) Cultural and Tourism Industry Research Center of Wuhan Business University, Wuhan, Hubei, China [email protected]

Abstract. The rapid development of the Internet has ushered in new opportunities and development possibilities for college students (CS)’ innovation and entrepreneurship (IaE) education. It is inevitable that there are uncertain factors in the process of IaE. How to make CS improve their comprehensive quality and IaE ability in the era of financial media has become a common problem that CaU need to overcome. Based on this, this paper carries out the research of IaE education curriculum reform and innovation under the background of Internet. This paper uses the method of literature analysis and questionnaire survey to carry out the research. In the research, this paper establishes the research mechanism group of IaE education, and carries out the investigation on the innovation of IaE education curriculum reform in Colleges and universities (CaU). It takes the CS’ understanding of IaE, the graduates’ IaE situation as the evaluation index, and carries out the evaluation analysis. At the same time, the group members invited professionals to discuss the meeting, further analyzed the experience of IaE education in CaU, and analyzed the collected data. The results show that in the era of financial media, 75.7% of the respondents know about IaE education, and 86.4% know about the supporting services of IaE. It can be seen that under the background of the Internet, the development of IaE education curriculum reform in CaU has made some progress, and the research on its work is of great value. Keywords: Big data · Innovation and entrepreneurship · Teaching reform · Questionnaire survey

1 Introduction Because of the rapid development of computer technology and the widespread popularity of the Internet, human society has entered a network era of rapid progress, that is, the era of BD [1, 2]. The era of big data (BD), with its rich and diverse information resources and convenient ways of interaction, has aroused the general concern of CS, thus affecting their study and life [3]. The emergence of the era of BD has changed the traditional mode of IaE Education [4, 5]. IaE education in CaU needs to abandon some outdated practices © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 303–310, 2022. https://doi.org/10.1007/978-3-030-99616-1_40

304

Y. Wang

under the traditional education mode, seek new changes under the background of BD, adapt to the requirements of the era of comprehensive media, and deeply strengthen and improve the ways and methods of IaE Education Curriculum Reform [6]. At present, the IaE education in CaU in China has achieved certain results, but there have been some problems, such as the convergence of IaE in CaU and the difficulty of IaE success [7, 8]. In order to achieve the teaching reform goal of IaE education in CaU under the background of BD, we must focus on personnel training, seize the current development opportunities of IaE education, actively adapt to the development environment of BD, and explore practical IaE education curriculum reform mode and path [9]. Under the background of BD, IaE education in CaU should actively change the education concept, highlight their own characteristics combined with data advantages, and improve the IaE education methods, so as to promote the improvement of the overall education quality and the transformation of University achievements [2]. In order to better carry out the research, this paper first briefly summarizes the status quo of IaE education in China, and further analyzes the REFORM of IaE education in CUHK from the perspective of the Internet. Secondly, we analyze the problems existing in China’s IaE curriculum reform through questionnaire survey and interview, and propose corresponding reform measures in the context of the Internet. To test the effectiveness of the proposed measures, we conducted an in-depth analysis of the survey data.

2 Curriculum Reform and Mechanism Innovation of IaE Education in CaU 2.1 Mechanism Innovation of IaE Education Curriculum Reform in CaU Under the Background of BD In the context of BD, it is a double-edged sword of university IaE education. It doesn’t matter whether the advantages are greater than the disadvantages or the disadvantages are greater than the advantages. It’s important for university IaE educators to analyze the characteristics of the Interne era. Grasp the development law of the era of integrated Internet, actively look for the combination point of university IaE education and the era of integrated Internet, and promote the development of IaE Education under the background of Internet. (1) The use of financial media technology. In order to improve the IaE education system of CaU in the era of media convergence, and to develop and improve the IaE education system of CaU, we must first change our attitude towards media convergence. Financial media is not a disaster, but an objective weapon to promote the development of education. Financial media should be used as an effective tool to carry out IaE Education under the new situation. On the one hand, we seriously study and think about the characteristics of media integration technology, actively explore the characteristics and laws of College Students’ IaE education in the era of integrated media, and integrate media integration technology into practical education and teaching activities. On the other hand, we should set up life websites

Teaching Reform of Innovation and Entrepreneurship Education

305

to serve CS’ study and life, such as literature, postgraduate entrance examination, work and so on, to help CS get the information they need. Educators in CaU should always pay attention to students’ ideological trends, and avoid students’ behavior of value dislocation and moral deficiency. (2) Establish the dominant position of CS. The traditional IaE education emphasizes the dominant position of educators. If educators transfer information to students, students’ autonomous ability will not be fully developed, and the effect of education will be greatly reduced. In order to carry out IaE education in the era of integrated media, we must change the original traditional concept and establish an education mode with CS as the main body. (3) The era of BD needs to establish a complete public opinion supervision system. In today’s BD information flooding, CaU need to establish a public opinion monitoring system supported by integrated media technology, accelerate the establishment of integrated media information dissemination laws and regulations, and strengthen the control of computer networks and mobile phones. The supervision and management of the network can prevent the spread of illegal information from the source. On the one hand, we should improve the construction of comprehensive media system, form a public opinion supervision team represented by student cadres and active backbones, supervise and guide some bad information and bad behaviors on campus network, and form a harmonious campus network atmosphere. On the other hand, we should cultivate CS’ habit of using the Internet correctly, clarify their network responsibilities and obligations, and improve their awareness of network self-protection. 2.2 Statistical Methods The number of IaE projects, the number of students benefited from IaE education, and the number of registered companies are taken as the judgment elements of the standard level, and different elements are compared according to the weight meaning table. The basic judgment matrix is obtained by calculating the ranking weight vector of the comparison elements: 1  akj i = 1, 2 . . . , n wi = n  n j=1 akj n

(1)

k=1

SPSS software was used for statistics. Mean ± SD was used for count data, and percentage (%) was used for count data. X2 and t test were used for comparison between groups, with P < 0.05 as the difference. The formula is as follows: (1) Arithmetic mean value: n  −

x=

i=1

n

xi (2)

306

Y. Wang

(2) Standard deviation:   N 1  σ = (xi − μ)2 N

(3)

i=1

3 Research Methods and Data Sources 3.1 Research Materials and Experimental Design The research group is composed of experts from IaE teaching and Research Section of our university, combining questionnaire collection and experimental control. The IaE training results of CaU in recent years were compared and analyzed, and the questionnaire developed was published online. A total of 506 questionnaires were recalled, 6 invalid questionnaires were deleted and 500 valid questionnaires were recalled. At the same time, according to the results of the questionnaire survey, the team members invited the elderly to a meeting to discuss the specific experience of IaE training and tested the results of the questionnaire. 3.2 Analysis Method and Evaluation Content Statistical and comparative analysis of data using statistical SPSS software, the difference between groups was statistically significant (P < 0.05). Among them, the evaluation indicators mainly include the understanding of IaE, the IaE of graduates, etc.

4 On the Teaching Reform of IaE Education in Application Oriented Universities 4.1 Analysis of IaE Education in CaU In order to make a detailed analysis of the current situation of IaE in CaU, this paper makes a comparative analysis of graduates’ understanding of it, as shown in Table 1. Table 1. Current situation of IaE education Degree IaE education Supporting services for IaE

Very understanding

Understanding

Generally understanding

Not understanding

9.7%

21.5%

44.5%

24.3%

10.3%

24.8%

51.3%

13.6%

As shown in Fig. 1, in the context of BD, respondents’ knowledge of IaE education reached 75.7% and their knowledge of IaE support services reached 86.4%. The cultivation method of innovative and entrepreneurial talents is a teaching activity combining

Teaching Reform of Innovation and Entrepreneurship Education

Innovation and Entrepreneurship Education

307

0.8 0.6

51.30% 44.50%

0.4

24.80% 9.70%

Degree

Supporting services for innovation and Entrepreneurship

24.30%

21.50%

13.60%

0.2

10.30% Very

Understanding

Generally

Not

0

Category Fig. 1. Current situation of IaE education

theory with practice. Just leaving the IaE talent culture in books does not reflect the IaE’s influence. Although some universities have built a platform to serve students’ IAE, they cannot provide long-term support for the management and maintenance of the platform. In addition, some national universities lack financial support for student IAE activities, and the physical construction of student IaE is absent. Some experimental facilities are backward and the site is too small. As employment has become more difficult in recent years, achieving an IaE education has become an ongoing goal of higher education reform and social attractiveness. Many CaU gradually require students to innovate by establishing credit system and holding competitions, and formulate reasonable education plans to explore the future development mechanism and further ability channels of IAE students, so as to adapt to the current situation. Economic and social development. IaE education in CAU can make use of the characteristics of financial media to accelerate the transformation of IaE education mechanism, achieve new progress in work and education goals, influence the delivery of education in a variety of ways, and create a new breakthrough. In the age of financial media, IaE teaching from CaU. 4.2 Research on the Curriculum Reform of IaE Education in CaU Under the Background of BD Under the background of BD, in order to further explore the development of IaE education curriculum reform in CaU in China, the comparison of output factors of IaE education in CaU in recent three years is shown in Table 2. As can be seen from Fig. 2, the development of IaE education in CaU in China is not very rapid, but it has also undergone good changes. In most domestic CaU, the cultivation of IaE talents has been in a marginal state, and has not been included in the talent training plan of related majors. At present, domestic CaU have the concept of utilitarian IaE education. Most university managers believe that the IaE work is only limited to the exact practice of some students. The arrival of the new era has brought unprecedented impact on college education, especially on College Students’ IaE, even on the whole society. In the new situation, the traditional IaE mechanisms and methods

308

Y. Wang Table 2. Output factors of IaE education in CaU

Output factor

2017

2018

2019

Number of entrepreneurship and innovation projects

7.1

6.3

7.2

Number of students benefited

8.8

9.7

8.8

Number of registered companies

3.8

3.7

3.7

Output factor weighting

7.1

6.8

6.9

10

8.8

7.1

6.3 7.2

7.1

6.8 8.8

6

3.8

3.7

4

6.9

2

3.7 Projects

Students benefited

8

Output factors

12

9.7

Registered companies Output factor weighting

2019

2018

0

Index 2017

Fig. 2. Output factors of IaE education in CaU

have not adapted, mainly in the progress of traditional education methods and the gap between the development of modern science and technology is growing. Therefore, the IaE education in CaU is not in line with the current education goal that CaU want to achieve. Facing the problems and challenges in the working mechanism of College Students’ IaE seriously, it has become an area that educators should constantly attack to continuously explore new channels. IaE mechanism innovation, as a part of the training plan of innovative talents in CaU, has added new power and energy to the teaching reform. It is an effective way to cultivate students’ innovative spirit, practical ability and comprehensive quality. Whether this talent training activity can be carried out for a long time needs the strong support and help of school leaders at all levels, relevant departments and teachers. Only when we work together to strengthen the investment and management in the training process of IaE talents, as well as new exploration and research, can this new talent training mode be carried out effectively and orderly, and has more possibilities for tapping the potential of students’ entrepreneurship, innovation and employment development. Therefore, we need to improve the teaching management mechanism of IaE Education under the background of BD, and further improve the teaching management. (1) Improving teaching mode With the progress of the times, the teaching mode should be constantly innovated. The requirements of society for talents are constantly changing, which

Teaching Reform of Innovation and Entrepreneurship Education

309

requires the innovation and reform of teaching management, teaching philosophy, teaching mode and knowledge structure. Teachers use multimedia teaching, change the traditional teaching mode, can improve students’ interest in learning. CaU should actively cultivate students’ management ability, establish the sense of ownership, enable students to participate in the process of teaching management while learning, give full play to their intelligence, improve students’ subjective initiative, so as to realize the supervision of teaching management. (2) Strengthen cooperation between departments The college should follow the principle of connecting educational management with student management, establish the collaborative development mechanism and cooperative relationship between departments, and promote the management level of “learning-based teaching”. “Management” through the intermediary of management services, to create a harmonious, healthy and positive macro environment for the healthy development of students, to ensure that all departments can organize daily work in a harmonious, healthy and positive way and abide by rules and regulations. (3) Improving the mechanism of teaching management In the process of teaching management, we need to further improve the management mechanism. First of all, CaU need to set up a reasonable management organization, effectively adjust the traditional management objectives and specific work content, to ensure that the teaching needs are met to the greatest extent. Secondly, we need to effectively supplement the teaching management mechanism, realize the reasonable integration of teaching management content, further refine the management tasks, and ensure the maximum realization of teaching objectives. On this basis, educators need to effectively stimulate the main role of students, and implement targeted management for different students, so as to ensure that students can maximize their own value in integrating into the enterprise.

5 Conclusions Under the background of BD, it is a very important and valuable task to explore the reform of IaE education curriculum in CaU. This paper studies the innovation of IaE education curriculum reform in CaU from the perspective of “BD”. In the research, this paper makes an investigation on the innovation of IaE education curriculum reform in CaU. The evaluation and analysis are made on the understanding of IaE of CS and the IaE of graduates. This paper holds that, combined with the background of BD, it requires not only the educational staff to have strong business ability and comprehensive quality, but also high requirements for the educational staff. This will enhance people’s trust in IaE, make IaE achieve qualitative leap, and actively promote the development process of IaE education curriculum reform.

References 1. Colorado Technical University et al.: Project management education in online environments. Int. J. Strateg. Inf. Technol. Appl. 7(4), 110–119 (2017)

310

Y. Wang

2. Davis, H., Graham, C.: Navigating a career in tertiary education management in an era of unceasing transformation. J. High. Educ. Policy Manag. 40(2), 97–106 (2018) 3. Shibata, K., Ichikawa, K., Kurata, N.: Knowledge of pharmacy students about doping, and the need for doping education: a questionnaire survey. BMC. Res. Notes 10(1), 396 (2017) 4. Sadewo, Y.D., Purnasari, P.D., Dimmera, B.G.: Development of entrepreneurship education teaching material based on the national resilience and Amare culture. JETL (J. Educ. Teach. Learn.) 5(1), 41 (2020) 5. Nijhuis, S.A.: Exploring project management education. Eur. J. Soc. Sci. Educ. Res. 9(1), 44–61 (2017) 6. Zheng, Z., Zhao, W., Liu, G., et al.: Research on the results of the advanced mathematics teaching of CS of science: the effect of STC teaching mode based on “Internet +”. J. Phys. Conf. Ser. 1592(1), 012080 (2020) 7. Hua, S., Ren, Z.: “Online + Offline” course teaching based on case teaching method: a case study of entrepreneurship education course. Int. J. Emerg. Technol. Learn. (IJET) 15(10), 69 (2020) 8. Malach, J., Kristová, K.: The impact of school education and family environment on pupils’ entrepreneurial spirit and attitude to entrepreneurship. New Educ. Rev. 49(3), 101–114 (2017) 9. Peter, O.I., Janet, O.M., Ojo, O.A.: Perception of undergraduate students of the relevance of entrepreneurship study to science and technology education in Southwest, Nigeria. J. Educ. Soc. Res. 10(3), 186 (2020)

Development of Microgrid and Optimization of Heat Pump from the Perspective of Dual Carbon Yuting Han(B) North China Electric Power University (Baoding), Baoding, Hebei Province, China [email protected]

Abstract. With the continuous deterioration of the environment, many countries around the world are committed to reducing carbon emissions and controlling resource utilization, so as to better protect green waters and mountains. Based on the background of “double carbon” era, this paper discusses the necessity and innovation of microgrid development from the perspective of environmental problems. And then we analyzes the current microgrid development. At the same time, in order to ensure the efficiency of microgrid, taking Baoding Cogeneration microgrid as a specific case, the essay establishes a variety of models, comparing the efficiency of heating pump and non-heating pump through particle swarm optimization algorithm. Finally, from the perspective of economy and environmental protection, it is found that increasing heat pump has high efficiency and can reduce energy loss in the operation of microgrid. Keywords: “Double carbon” · Development of microgrid · Heat pump · Microgrid’s efficiency

1 Introduction The development of microgrid has always been a hot topic aiming at making better use of existing resources. Since China put forward the “dual carbon” goal, the development of microgrid has attracted more attention. However, there are few articles on the combination of environmental problems and Microgrid. Combining theory with practice, while analyzing, this paper takes a city’s investment in a cogeneration microgrid as an example to effectively analyze the microgrid.

2 Microgrid Development in the Context of “Double Carbon” 2.1 Background of the Times - “Double Carbon” With the development of science and technology, climate change has become one of the global problems facing mankind. Carbon dioxide emissions everywhere in life make greenhouse gases soar and threaten the global life system. In this context, countries © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 311–317, 2022. https://doi.org/10.1007/978-3-030-99616-1_41

312

Y. Han

all over the world aim at reducing greenhouse gases in the form of global agreement. Therefore, China puts forward the goals of carbon peak and carbon neutralization, that is, strive to achieve no growth in carbon dioxide emissions before 2030, and then slowly reduce it after reaching the peak, so as to offset all the carbon dioxide emissions by planting trees, energy conservation and emission reduction before 2060. In such an environment-friendly and green perspective, both the development of new energy and the reuse of existing resources are of great significance to reduce carbon emissions. 2.2 Introduction to Microgrid Microgrid is a new network system composed of micro source, storage equipment, monitoring equipment, load, safety device, etc. At the same time, it is an automatic system that can self-repair, self-monitoring and protection management. In the current transportation, it can not only operate independently, but also connect the external power grid system for grid connected operation. The important function of power electronic equipment in microgrid is to provide power and energy for microgrid and strengthen the control of the system. In terms of traditional power grid, microgrid is one of the sub concepts. It is a controllable unit, which can give external power transmission response in a very short time to meet the requirements of power transmission and distribution. At the same time, load and micro power supply, that is, distributed power generation in micro grid, common are wind power generation, photovoltaic power generation, etc. The micro electric network is composed of a certain number of distributed generators carrying the load according to the relevant power. During operation, the whole micro electric network is controlled by static switches. The vigorous development and application of micro electric system can not only connect distributed power generation with new energy, but also realize the reuse of power load energy, and improve the power supply of power enterprises. The reliable supply of various energy forms for load is not only an effective means of power system reform, but also an inevitable stage from traditional power grid to microgrid. Microgrid has a very broad development prospect [3]. Microgrid can not only adjust and control under the large power grid system and smoothly connect into the large power grid system, but also realize the unified operation of electric energy and voltage within it. In addition, microgrid has small scale and scattered system composition. Therefore, it is necessary to use advanced technology and new energy for power production, which is conducive to improving construction efficiency, reducing production cycle, saving production cost and meeting power supply demand. At the same time, the microgrid directly connects the energy storage equipment and power supply on the user side, reducing the occupied area of the transmission corridor [9]. 2.3 Microgrid Development in the New Era Microgrid will form a power grid in the region to make full use of the natural distributed resources that are difficult to make full use of before, such as wind energy and light energy [8]. To a certain extent, it reduces the burden of energy consumption and better

Development of Microgrid and Optimization of Heat Pump

313

turns the energy of nature into its own use, which meets the requirements of the double carbon goal in the new era. However, in the stochastic programming involving solar energy, wind energy and other renewable energy, there are uncertainties and correlations in various random variables at the same time, so it is still developing in many aspects to ensure the quality of power generation [1, 6] at the same time, with the continuous improvement of the requirements for the power supply quality and operation economy of microgrid in the field of smart grid, the optimal operation of microgrid highly depends on the real-time energy scheduling of network system. Therefore, making full and efficient use of microgrid is still one of the goals [4]. At present, China is in the transition period from the second largest power source to the first largest power source, and is gradually improving the proportion of new energy and regulation flexibility. Based on the changes of production structure and system technology, the new power system will present different new characteristics, and the “source follows the load” will change to “source load interaction” [7]. Distributed power generation widely exists on the power consumption side, and the load has changed from a single “consumer” of power consumption to a “producer consumer” of power generation and consumption; Electricity, cooling, heat, gas and other energy sources are coupled and flexibly converted, showing obvious differences and complementarities in load, and have the ability to provide a considerable degree of regulation and support.

3 Heat Pump Optimization of Cogeneration Microgrid 3.1 Summary From the perspective of the new era, it is not only the higher requirements for our technology, but also the pursuit of higher efficiency. Therefore, taking the cogeneration microgrid as an example, from the perspective of economy and environmental protection, heat pumps are added to the cogeneration microgrid on the basis of the basic cold and heat cogeneration system, so as to reduce the energy loss in the operation of the microgrid. It is mainly aimed at the cogeneration microgrid in northern cities of China. Here, another algorithm particle swarm optimization algorithm is used to optimize the capacity of the microgrid. 3.2 Model Establishment and Particle Algorithm Description The main components of typical microgrid are: wind turbine, energy storage device, micro gas turbine, photovoltaic cell, etc., so we establish models respectively [5, 10]: 1. Power supply and heat output model of micro gas turbine (using micro gas turbine as cogeneration microgrid has great advantages, so micro gas turbine is selected as the core of microgrid CHP unit);

314

Y. Han

Ptn = Gtn ηne

(1)

Htn = Gtn (1 − ηne − ηn1 )δnh q

(2)

2. Corresponding model of wind turbine: the wind direction is difficult to predict and measure, so the generation power of wind turbine fluctuates greatly, so we adopt the widely used Weibull wind speed fitting model. 3. Power generation model of photovoltaic device: most of them are silicon solar cells based on silicon. Therefore, for fixed assembled solar cells, it is suitable to calculate the output power of the battery by estimating the module temperature; Tmod = Tamd + PPV = PSTC

30GAC 1000

GAC [1 + k(Tmod − Tr )] GSTC

(3) (4)

4. Energy storage device: as a key part of cogeneration microgrid, establish a model, analyze its discharge and charging process, and construct the relationship between charge state and time; S(t + 1) = S(t) − S(t + 1) = S(t) −

Ptb ηc t Cbat

Ptb t ηd Cbat

(When charging) (When discharging)

5. Finally, the heat pump model is analyzed: considering the types and efficiency of heat pumps, the ground source heat pump system is selected for simple formula description. According to the conditions of power balance and output constraints, particle algorithm is used for analysis. Particle algorithm, also known as particle swarm optimization algorithm, [2] is a random search algorithm based on group writing developed by using the foraging behavior of birds. Here, particle algorithm is used to verify and analyze the optimization of heat pump. 3.3 Algorithm Practice Cases and Analysis Firstly, we collected the annual temperature and wind speed data of Baoding in 2019, and drew the Weibull distribution map, as shown in Fig. 1. At the same time, the temperature change diagram is drawn, as shown in (Table 1) Fig. 2.

Development of Microgrid and Optimization of Heat Pump

315

Fig. 1. Weibull distribution of wind speed

Table 1. Wind turbine parameters Wind turbine model

Rated capacity

Cut in wind speed

Rated wind speed

Cut out wind speed

Extreme wind

price

maintenance cost

FD25-100

100 kW

3 m/s

11 m/s

18 m/s

52.5 m/s

¥80000

¥2元/kW

Fig. 2. Broken line chart of temperature variation of time

According to the above data, FD25-100 wind turbine, tsm-dd14a (II) photovoltaic cell, aerto-rt10kb lead-acid battery, CHP micro gas turbine and HP heat pump are used

316

Y. Han

respectively. According to the actual simulation analysis of particle swarm optimization algorithm, it can be seen that while generating the same benefits, adding heat pump requires fewer micro gas engines (47 < 72), photovoltaic cells (9 < 71) and fans (82 > 77) than non heating pump, so the investment of total cost is greatly reduced and the efficiency is improved. At the same time, according to the drawn power diagram, it can be compared that the power of gas engine is higher by adding heat pump for iteration (Figs. 3, 4 and 5). The same conclusion can be drawn by comparing the following figures.

Fig. 3. Heat pump power

Fig. 4. Gas turbine power

Development of Microgrid and Optimization of Heat Pump

317

Fig. 5. Iterative fitness graph

References 1. Hu, J., Li, H., Liu, Z.: Scenario reduction based on correlation sensitivity and its application in microgrid optimization. Int. Trans. Electr. Energy Syst. 31(3) (2021) 2. Imran, M., Hashim, R., Abd Khalid, N.E.: An overview of particle swarm optimization variants. Procedia Eng. 53, 491–496 (2013) 3. Bakhsh Narejo, G., Acharya, B., Sarban Singh, R.S., Newagy, F.: Microgrids: Design, Challenges, and Prospects, CRC Press, London (2021) 4. Yang, X., Wang, Y., Zhang, Y., Yao, W., Wen, J.: Impact analysis of cyber system in microgrids: perspective from economy and reliability. Int. J. Electr. Power Energy Syst. 135 (2022) 5. Amin, M.-S., et al.: Multi-objective IGDT-based scheduling of low-carbon multi-energy microgrids integrated with hydrogen refueling stations and electric vehicle parking lots. Sustain. Cities Soc. 74 (2021) 6. Suganthi, S.T., Arangarajan, V., Veerapandiyan, V., Deepa, A., Mohamed, A., Mariammal, T.: Detection and classification of multiple power quality disturbances in Microgrid network using probabilistic based intelligent classifier. Sustain. Energy Technol. Assess. 47 (2021) 7. Li, Q.: New energy has entered an era of high proportion, and large power grid and micro grid are complementary and symbiotic. Energy 7 (2021). (in Chinese) 8. Shi, J.: The boundary of new energy participating in the power market needs to be clarified. China investment (Chinese and English), (Z7) (2021). (in Chinese) 9. Gong, C., Jia, W., Wu, D., Pan, K.: Optimization of development prospect of natural gas power generation industry under the goal of carbon neutralization. Nat. Gas Ind. 41(06) (in Chinese) 10. Guo, Y., Hu, B., Wan, L., Xie, K., Yang, H.: Short term optimal economic operation of cogeneration microgrid with heat pump. Power Syst. Autom. 39(14) (2015). (in Chinese)

Research and Exploration of Artificial Intelligence in Product Design in the Era of Intelligent Interconnection Ming Lv, Zimeng Li(B) , and Cen Guo Shenyang Jianzhu University, Shenyang, Liaoning, China [email protected]

Abstract. In the era of intelligent interconnection, artificial intelligence has brought tremendous technological changes to people’s production and life. Including smart home, smart city, intelligent design, and artificial intelligence has been applied to all areas of our life, its application in the field of product design and creation is more and more extensive. Through the analysis of the concept of artificial intelligence, combined with the development of artificial intelligence and product design tools driven by new technological revolution such as Internet of Things, big data and artificial intelligence, this paper determines the value of artificial intelligence in product design and development, and explores the application strategy and future development prospect of artificial intelligence in product design process. Through the creative integration of artificial intelligence and product design, it can not only bring innovative means and paradigms for product design, but also bring more choices and possibilities for designers and users. However, AI cannot completely replace human product design and creation. Artificial intelligence will be the focus of product design development in the new era, and some empirical applications of artificial intelligence in product design are given. Analyzing the application of AI in product design in the era of intelligent interconnection can help us better understand the real demand and development direction of artificial intelligence. Keywords: Intelligent interconnection · Artificial intelligence · Product design · User needs · Innovation and development

1 Introduction In the era of intelligent information interconnection and knowledge drive [1]. With the deep integration of the Internet, big data and artificial intelligence with the real economy, new forms of digital industry such as industrial Internet and intelligent manufacturing continue to emerge. Today, intelligent interconnection has become an important direction of the new round of industrial revolution, with data-driven, intelligent manufacturing and the Internet of everything as the core support, driving great changes in information technology and related industries. In this context, product design innovation is also constantly changing and upgrading. At present, Currently, China is in the context of an © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 318–323, 2022. https://doi.org/10.1007/978-3-030-99616-1_42

Research and Exploration of Artificial Intelligence

319

AI environment, where AI and design show a high degree of synergy in design, whether it is finding solutions to complex problems or the appearance of products. In the process of using AI in modern product design, it first imitates the creative design process of human beings, and then assists designers in solving complex data analysis. From the current situation of artificial intelligence development in the context of the Internet of Things, product design and data analysis based on human creation and design process is already within reach. China’s huge population base provides an effective support for the development of Internet data, which can effectively improve the accuracy of AI algorithms.

2 Overview of Artificial Intelligence Artificial intelligence is to collect and summarize certain data, and make intelligent analysis and selection for the database. In the process of product design exploration, people need to improve the ability of their brain to work. The emergence of artificial intelligence has greatly satisfied the deficiencies of individuals in biology and greatly improved the breadth and depth of people’s design. After decades of development, artificial intelligence has gradually changed from an idea to a reality. For example, the birth of Alpha Dog is an important achievement of AI. The development of intelligent robots has gradually entered a mature stage. In the era of big data, the massive data generated by intelligent products has the characteristics of super-high dimension [2]. We are currently in the process of enhancing AI so that programs can receive information and request it in an intelligent way.

3 Application of Artificial Intelligence in Product Design In recent years, AI has ushered in the peak of research and development. Solutions through big data analysis and intelligent computing are gradually being adopted, making it convenient and easy to process large amounts of data [3]. People’s demand for product design is also increasing, designers can no longer rely on personal efforts to meet people’s demand for products. Artificial intelligence has been widely applied in the field of product design and creation, which not only promotes the diversity and diversity of product design, but also brings a good prospect for the development of product design. The amount of data generated is huge and diverse, and the quality of big data needs to be improved due to the fast data acquisition [4]. In the development of product design in the new era, designers need to constantly improve technical development to meet a wide range of needs. Artificial creativity has a positive impact on product design. Integrating artificial intelligence design technology into product design can help designers reduce work pressure and work time, and promote the development of product design education. With the help of artificial intelligence technology, students can learn about product design. In the future, it is possible that only designers need to complete the design of part of the line outline, and the computer can automatically generate a more delicate picture; The computer can understand the light, shade and color of each part only by completing the representation of black and white light and shadow. AI ecosystem has development, integration, openness and versatility. Good application of AI can continuously promote the development of product design.

320

M. Lv et al.

4 Application Value of Artificial Intelligence in Product Design 4.1 Product Design Tools Are Intelligent Human society cannot exist without tools. Data mining tools can access all data sites, which is an effective way to organize and solve the data [5]. Under the background of intelligent interconnection, design tools are gradually changing from physical objects to digital objects and from tangible objects to intangible objects. This intelligent transformation has a profound impact on designers’ thinking, content and design process in the process of product design. In the traditional design background, the design tool is to use ink, pigment and other tools to create. With the development of artificial intelligence technology and graphics processing equipment, artificial intelligence aided product design is gradually formed. Artificial intelligence not only provides a basis for teaching different types of product design, but also provides help for innovative methods and forms of product design, ultimately improving the limitations of traditional design. 4.2 The Work Presents Intelligence In recent years, virtual reality technology has gradually matured. In the era of intelligent interconnection, big data system has the characteristics of real-time and effectiveness [6]. Designers can effectively convey the design concept expressed in the design works through the communication and experience of the virtual environment, and apply this technology to show the works to people, so that people can have a sense of being on the scene. At the same time, people can see the details of the products more intuitively,thus can better show the structure and function of the product. 4.3 Intelligentization of User Needs The advantage of ARTIFICIAL intelligence is that it can efficiently and conveniently mine and analyze data. In the Internet system, data can be stored for short and long time by big data-driven technology [7]. To make information transmission more diversified, realtime interaction stronger, and making product design more intelligent while improving user experience. Most of traditional analysis approach for the users to take the form of questionnaire survey, and artificial intelligence can get the user requirements from the big data, user preferences and user behavior data, the potential demand of the user’s data mining, development, and data analysis, so that we can better analyze the user interaction, to provide users with service, make the product design of the product more targeted, In the face of some fixed groups can better reflect the practicality of the product, and effectively meet the needs of different user groups.

5 Application Practice of Artificial Intelligence in Product Design Big data analysis must transition from static and centralized mode to dynamic and distributed mixed mode to play a leading role in intelligent interconnection [8]. “Artificial intelligence home appliances” refers to the products supported by artificial intelligence

Research and Exploration of Artificial Intelligence

321

technology. All artificial intelligence home appliances belong to a set of home appliances system. According to the basic functions, forms and other characteristics of home appliances, the design of home appliances under “artificial intelligence home appliances” is not very different from ordinary home appliances. By analyzing the artificial intelligence air conditioner, artificial intelligence refrigerator and artificial intelligence laundry machine launched at present, it is found that the function content of artificial intelligence home appliances has been greatly expanded, and the function content of other categories of home appliances has appeared in a single intelligent home appliance, such as the security monitoring function of Internet of Things air conditioner. Therefore, it is not perfect to take the basic functions and morphological characteristics of household appliances as the basis to determine the system types of household appliances with artificial intelligence. The internal elements of the product system of each AI home appliance are closely linked, and it and other products or environment will form a larger system set. So by according to each home appliance products in the system function and application of elements such as technology, and combined with the operation of home appliance habits etc., can be roughly will artificial intelligent home appliances products system is divided into three types: type of artificial intelligence appliance system, professional home appliance system of artificial intelligence and recreational appliance system of artificial intelligence. 5.1 Management-Type Artificial Intelligence Home Appliance System In the managed target system, smart home appliances have the following characteristics: products are usually placed in the “central” position of the home space, such as the living room; Its shape volume or interface window is relatively large, such as air conditioning and TELEVISION; And it has the possibility and space to expand the functions of other home appliances. The Internet of Things, search engines, and more are all based on large-scale data aggregation [9]. The representative intelligent product under the management system is the artificial intelligence air conditioner. In addition to the main functions of controlling and adjusting indoor temperature and air optimization, it also has the functions of intelligent security and remote monitoring, and can realize remote operation and management. On this basis, air conditioning can be used as a management platform for all household appliances, and it can monitor and manage the operation and consumption of other household appliances through it. It can be used as the “center” for household appliances information collection and release. The Google company is also a highly valued company in the field of artificial intelligence, including Google clip is a AI camera, the product through the artificial intelligence technology screening active pictures, active scene, master be fond of according to the video and so on. 5.2 Professional Artificial Intelligence Home Appliance System Smart home appliance products in professional system have the following characteristics: the product is representative in the functional category of home appliance, its representative function cannot be realized in other categories of smart home appliance products, users’ needs and problems for the product are also more focused on the functional factors

322

M. Lv et al.

of the product. For example, the representative professional intelligent home appliances – artificial intelligence washing machine, through sensors and electronic tags, “identify” the information of washing clothes, “judge” and the corresponding washing program, water temperature and time and other functions, better in the washing machine’s basic function optimization upgrade to a higher quality. 5.3 Entertainment Artificial Intelligence Home Appliance System Artificial intelligence household appliances under the entertainment system set have the following characteristics: products in storage, use, convey information at the same time also receive new information in real time; This type of product interface window proportion is relatively large, the product system has its corresponding “convey” and “accept” components. Artificial intelligence TV is a representative product of entertainment system. Big data analysis improves product quality and service demand, creating more value for society [10].

6 Conclusions Artificial intelligence brings new changes to product design and creation. As a medium, AI is driving change across industries. The expression form of product design is diversified, and the change of this form will change with the change of The Times, but eternity is the essence of the product. Therefore, in the era of intelligent interconnection, the injection of artificial intelligence into product design can bring new vitality and vitality to products. Artificial intelligence is therefore a major and systematic upgrade in product design. With the intervention of artificial intelligence in different fields, products have new attributes and definitions. In a subtle way, big data Internet of Things is used to make systematic analysis. In the process and method of product interaction design, the efficiency of product interaction design and user experience are constantly improved. In the future, AI will surely be applied to more industries, helping designers to make more and more high-end products.

References 1. Chiheba, F., Boumahdi, F., Bouarfa, H.: A new model for integrating big data into phases of decision-making process. In: Proceedings EDI40 Conference, pp. 636–642 (2019) 2. Sen, D., Ozturk, M., Vayvay, O.: An overview of big data for growth in SMEs. In: Proceedings ISMC Conference, pp. 159–167 (2016) 3. Iqbal, R., Doctor, F., More, B., Mahmud, S., Yousuf, U.: Big data analytics and computational intelligence for cyber-physical systems: recent trends and state of the art applications. Future Gener. Comput. Syst. 105, 766–778 (2020). https://doi.org/10.1016/j.future.2017.10.021 4. Ghasemaghaei, M., Calic, G.: Can big data improve firm decision quality? The role of data quality and data diagnosticity. Decis. Support Syst. 120, 38–49 (2019). https://doi.org/10. 1016/j.dss.2019.03.008 5. Gandhi, K., Schmidt, B., Ng, A.H.: Towards data mining based decision support in manufacturing maintenance. In: Proceedings CIRP Conference, pp. 261–265 (2018)

Research and Exploration of Artificial Intelligence

323

6. Cheng, Y., Chen, K., Sun, H., Zhang, Y., Tao, F.: Data and knowledge mining with big data towards smart production. J. Ind. Inf. Integr. 9, 1–13 (2018). https://doi.org/10.1016/j.jii.2017. 08.001 7. ur Rehman, M.H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P.P., Perera, C.: The role of big data analytics in industrial Internet of Things. Future Gener. Comput. Syst. 99, 247–259 (2019). https://doi.org/10.1016/j.future.2019.04.020 8. Mourtzis, D., Vlachou, E., Milas, N.: Industrial big data as a result of IoT adoption in manufacturing. In: Proceedings CIRP Conference, pp. 290–295 (2016) 9. Jabbar, A., Akhtar, P., Dani, S.: Real-time big data processing for instantaneous marketing decisions: a problematization approach. Ind. Mark. Manag. 90, 558–569 (2020) 10. Hamilton, R.H., Sodeman, W.A.: The questions we ask: opportunities and challenges for using big data analytics to strategically manage human capital resources. Bus. Horiz. 63, 85–95 (2020). https://doi.org/10.1016/j.bushor.2019.10.001

The Application of Product System Design in Emergency Equipment in the Era of Internet of Things Ming Lv(B) , Xianhao Wu, and Cen Guo Shenyang Jianzhu University, Shenyang, Liaoning, China [email protected]

Abstract. As a new information industry, the Internet of Things can be interconnected at any time and at any place to realize intelligent interaction. It has immeasurable practical and social significance for human health and safety. With the development of information technology, the emergency industry is also undergoing a process of transformation from informatization, digitization and intelligence. The design of traditional emergency equipment generally emphasizes the shape, function, interface, and human-machine dimensions of emergency equipment. Whether it is the components, elements, and components that constitute the product entity, or the function, form, and environment that realize the value of the product, there are all It is connected with a certain structural form, law and order and constitutes an organic whole. On this basis, the design of emergency equipment for the Internet of Things should pay more attention to the product system. Product system design is one of the important concepts of modern product design. This article starts from the product system design in the Internet of Things era, based on system thinking, and emphasizes the systematic and organic integrity of emergency equipment and emergency management. Keywords: Internet of Things · Product system design · Emergency equipment · Emergency management

1 Introduction The Internet of Things refers to a network of physical devices embedded with sensors and software to collect data and communicate with each other. In the field of emergency management, the Internet of Things can be used to enhance data collection in the physical environment and quickly transmit these data to different departments in the city. Emergency is an action aimed at major emergencies that may occur, in order to ensure timely rescue operations and reduce personnel and economic losses. The broad concepts of emergency management include prevention of danger, emergency preparedness (safety planning and training), emergency response (evacuation and rescue), and disaster recovery (restoration of basic services and lifelines) [1]. My country is a country with frequent disasters, but the development of emergency management system and emergency equipment industry is relatively slow. The emergency work practices of a series of major © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 324–329, 2022. https://doi.org/10.1007/978-3-030-99616-1_43

The Application of Product System Design

325

emergencies such as typhoons, floods, earthquakes, and so on have made governments at all levels increasingly aware of the importance and urgency of developing emergency response industries and improving government crisis response and handling capabilities. Prepare the corresponding emergency plan in advance, you can identify and evaluate potential hazards and the development status during their occurrence in advance, and make specific arrangements in advance. Wang et al. [10] proposed a scenario-based system framework of natural disaster emergency management. It clarified the basic idea of identifying key situational elements and the detailed procedure of “scenario-response” emergency management. Chen et al. In avoiding, mitigating, responding to and recovering from natural and man-made hazards [4]. And the severity of the consequences and impact of the accident can be predicted. Before and after the accident, the corresponding emergency countermeasures are clarified, who is responsible for what to do, when to do it, and to what extent. At the same time ensure the corresponding strategy and resource preparation. The multi-hazard theoretical studies explored the mutual relations and influence mechanism among different hazards from the perspectives of basic theory, coupling effects and risk analysis [3]. Weather-related natural disasters, such as typhoons or floods, sometimes prevent emergency response teams from reaching certain areas on time. This hindrance reduces the emergency team’s ability to track disasters, inform the public of the latest information, and respond in a timely manner. However, if IoT emergency devices exist in these areas, they will be able to broadcast signals and transmit key data, such as air quality, water quality, or temperature more easily. With these data, relevant emergency departments can make more informed decisions on how to deploy disaster relief resources.

2 Application of Product System Design in Emergency Equipment of the Internet of Things Product system design mainly includes three aspects: product design target system, product design method system and product design process system. The core content of the product design target system is the product system. The product system, like any system, is composed of a number of interconnected elements. These elements are the product system elements. In general, the design, function and interface of the general emergency product system are the elements that constitute the product system. Under the influence of the application of the Internet of Things technology on the emergency equipment, the product system of the Internet of Things emergency equipment some elements have changed. In the architecture of the Internet of Things, the uppermost layer is the perception layer of the Internet of Things. The perception layer is also a basic part of the Internet of Things system to perceive the outside world. This layer is mainly a network composed of a large number of sensor nodes. The perception layer can realize the dynamic perception of the external world [5]. Such as “early warning elements”, the monitoring and early warning platform of the intelligent IoT emergency equipment is composed of the IoT monitoring platform and the IoT monitoring and early warning APP. The main functions of the Internet of Things monitoring platform include: information collection and storage, data intelligent analysis, real-time monitoring and monitoring, hierarchical early warning and alarm, information security transmission,

326

M. Lv et al.

and big data statistical analysis. The main functions of the IoT monitoring and early warning APP include: real-time risk early warning, historical risk records, and realtime enterprise monitoring. General emergency equipment is not closely related to other system elements under the emergency equipment system, and is generally not included in the range of system elements concerned in the design of emergency equipment. The service elements of the Internet of Things emergency equipment system are greatly different from those referred to by ordinary emergency equipment. “Forewarning”. Due to the influence of technical elements (Internet of Things technology), there are more elements to be paid attention to in the design of emergency equipment for the Internet of Things than in the past. At the same time, the relationship between the elements and their location and structure in the system are worth studying. Due to the interrelationship and influence between early warning elements and other elements under the IoT emergency equipment system, it has changed from the form of existence to the way of use.

3 The Significance of IoT Emergency Equipment Although science and technology have developed rapidly in the world, they have even developed to the extent that natural disasters can be predicted. Based on multi-scale experimental facilities, experimental techniques such as scaled experiments, full-scale experiments, and field measurement experiments are often used to simulate fires, earthquakes, and earthquakes. The true reproduction of various disasters such as tsunamis and hail [2]. However, there are still some things that people cannot predict. For example, the current epidemic caused by the new type of coronavirus is difficult to predict and forecast. This requires proper handling. Faced with the huge casualties and property losses caused by such emergencies, we need to resolve and deal with them quickly. Among them, funerals, sanitation and epidemic prevention, rescue of patients, and resumption of work and production all require a lot of materials, which are inevitable. The focus of urban emergency management is still mainly preparation and response [6]. Otherwise, the affected area, number of people, and economic losses will expand and become more serious disasters. This is different from a war. There will be sufficient preparations before the war to build fortifications in advance to prevent the enemy from attacking. Will conduct tactical discussions to reduce unnecessary losses in the war. Carry out resource reserves to fully guarantee the soldiers’ battery life in battle. It can be said that in the war, there is a complete and thorough logistics system and rescue plan. However, in certain unexpected events, various departments are unable to make adequate preparations. Emergency equipment plays an important role and significance in the emergency process, and can become an important guarantee for timely, orderly and effective emergency rescue work. It has a strong scientific nature. The so-called scientific nature means that the function of emergency equipment should conform to scientific principles and does not violate scientific facts. There is also a guiding nature, which means that in the emergency process, it is more about people’s own actions. Guidance and coordination with the emergency department, rather than systematic and mandatory indoctrination. Emergency equipment has become a medium that stimulates people’s alertness and crisis awareness through product system design, and inspires people to face emergencies. The

The Application of Product System Design

327

product system design enables emergency equipment under the Internet of Things technology as a link between “emergency” and “life”, which can produce “reasonableness”, respect “human”, explore “things”, and give play to it in emergency Due function.

4 Internet of Things Emergency Equipment Application Field 4.1 Government Coordination Field The key to responding to sudden natural disasters and public health emergencies is for the government to effectively coordinate, mobilize and utilize various international, national, regional and surrounding resources. Make emergency instructions or suggestions in time, organize the allocation of emergency relief supplies and funds, mobilize and assist relevant units to manufacture emergency supplies according to actual needs; adopt all methods and means to coordinate, guide or eliminate irresistible factors in emergencies. Obstacles caused by disaster management. 4.2 The Field of National Mobilization Mobilization is the broad participation of the people in social development and the realization of specific mass movements by relying on their own strength. It is based on the needs of the people and uses the principle of social participation as a means for self-improvement. In the emergency mobilizer mechanism, the state can tell people what technical means are through the media, such as communication, time and location, type, scope, difficulty and progress of disaster relief, etc., so that people can participate in disaster relief: First, In the context of emergency management, serious games raised awareness of disaster risks across diverse audiences, helped identify hazards and preventive actions, and triggered empathy by simulating disasters in realistic ways [7] Funds to purchase emergency supplies. The third is to provide necessary human resources for disaster relief. 4.3 Green Channel When a major emergency occurs, it will inevitably cause traffic jams and crowded people. Therefore, we must establish a “green channel” mechanism to ensure that there are multiple dedicated channels for critical emergency times, which can effectively simplify emergency traffic processing and speed up customs declaration. Airport border inspection, convenient and rapid establishment of regional checkpoints, so that emergency supplies, emergency personnel and emergency equipment can reach the disaster area unimpeded and accurately, thereby improving emergency response efficiency, shortening post-disaster rescue time, and greatly reducing personnel injuries and property losses.

5 Analysis of the Characteristics of the Emergency Equipment of the Internet of Things One of the characteristics of emergency response is that it reflects “emergency and fast”. How to realize emergency materials and deliver them to the expected location quickly and safely. Consider the following aspects:

328

M. Lv et al.

5.1 Fast Transportation Through the emergency support mechanism and road management signed an agreement in advance or temporary agreement orders, to implement emergency transportation requirements, contactable military rescue is necessary, the use of military transportation equipment, especially military transportation lines and related facilities, Can realize the rapidization of traffic emergency supplies. 5.2 Establish Material Reserves in Advance The demand for emergency material reserves in the disaster-stricken areas is very important. If their fast information capacity is limited, they can transfer, collect, and purchase online as quickly as possible from the production unit, the central government or the public health emergency mechanism. For ordinary materials, transportation should be carried out within a period of time after the occurrence of sudden natural disasters, and purchase agreements should be signed with manufacturers or professional companies with good reputation, affordable quality and reasonable prices [8, 9]. These agreements collect the influence of the purchasing unit, and can deliver emergency supplies to the agreed location within the specified time.

6 Conclusions From the perspective of product system design, this article explains various factors of emergency equipment under the Internet of Things technology. At present, the emergency industry has shown rapid changes. This paper studies the application of Internet of Things technology in emergency management, which can realize real-time monitoring and intelligent processing of safety elements in safety production, and build a service-oriented intelligent Internet of Things monitoring and early warning platform that covers pre-warning, in-process disposal and post-inspection. Thereby transforming traditional passive processing methods, realizing active supervision and real-time monitoring, improving safety supervision level, reducing management costs, reducing safety accidents, and providing comprehensive and true data for various businesses of emergency management work. Emergency equipment is in emergency management. It has an indispensable role and provides useful exploration for building a new generation of emergency management system and promoting the construction of smart cities.

References 1. Chiheba, F., Boumahdi, F., Bouarfa, H.: A new model for integrating big data into phases of decision-making process. In: Proceedings EDI40 Conference, pp. 636–642 (2019) 2. Sen, D., Ozturk, M., Vayvay, O.: An overview of big data for growth in SMEs. In: Proceedings ISMC Conference, pp. 159–167 (2016) 3. Iqbal, R., Doctor, F., More, B., Mahmud, S., Yousuf, U.: Big data analytics and computational intelligence for cyber-physical systems: recent trends and state of the art applications. Future Gener. Comput. Syst. 105, 766–778 (2020). https://doi.org/10.1016/j.future.2017.10.021

The Application of Product System Design

329

4. Ghasemaghaei, M., Calic, G.: Can big data improve firm decision quality? The role of data quality and data diagnosticity. Decis. Support Syst. 120, 38–49 (2019). https://doi.org/10. 1016/j.dss.2019.03.008 5. Gandhi, K., Schmidt, B., Ng, A.H.: Towards data mining based decision support in manufacturing maintenance. In: Proceedings CIRP Conference, pp. 261–265 (2018) 6. Cheng, Y., Chen, K., Sun, H., Zhang, Y., Tao, F.: Data and knowledge mining with big data towards smart production. J. Ind. Inf. Integr. 9, 1–13 (2018). https://doi.org/10.1016/j.jii.2017. 08.001 7. Rehman, M.H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P.P., Perera, C.: The role of big data analytics in industrial Internet of Things. Future Gener. Comput. Syst. 99, 247–259 (2019). https://doi.org/10.1016/j.future.2019.04.020 8. Mourtzis, D., Vlachou, E., Milas, N.: Industrial big data as a result of IoT adoption in manufacturing. In: Proceedings CIRP Conference, pp. 290–295 (2016) 9. Jabbar, A., Akhtar, P., Dani, S.: Real-time big data processing for instantaneous marketing decisions: a problematization approach. Ind. Mark. Manag. 90, 558–569 (2020) 10. Hamilton, R.H., Sodeman, W.A.: The questions we ask: opportunities and challenges for using big data analytics to strategically manage human capital resources. Bus. Horiz. 63, 85–95 (2020). https://doi.org/10.1016/j.bushor.2019.10.001

The Application of Big Data and Artificial Intelligence Technology in the Collaborative Development of Art Design Education and Cultural and Creative Industries Ming Lv(B) , Yuhan Gong, and Cen Guo Shenyang Jianzhu University, Shenyang, Liaoning, China [email protected]

Abstract. Art education is a key condition for the development of China. In the Internet information technology industry, big data has become a hot term, so big data can play a role in education.it is an important area that can be studied. This article aims to study how to realize the development of art education and the new momentum of cultural and creative industries. On this basis, discuss how big data and artificial intelligence technology can provide them with new methods and tools to support them. These methods and tools can be used to enhance the potential of education, training, and human performance to enable them to better complete tasks and activities. Promote the development of art education, and can develop in coordination with the cultural industry. This new idea can be provided to art design education. Keywords: Art design education · Big Data Technology · Artificial intelligence

1 Introduction The cultural and creative industry is very important as an industry of creative ability, and it is very important to cultivate cultural and creative talents. Online education and many online public courses have become popular learning methods for students in recent years, and more and more similar platforms have appeared. This new learning mode can save a lot of unnecessary time for students. For example, some students who are ill or cannot go to school for some reason can continue to study in this way. Time is precious. In addition to this advantage, there are many other advantages. Everyone has different time to understand new knowledge. Some people may take a lot of time to understand new things. This model effectively solves the problem of not being able to keep up with the progress. The education field with big data as the core technology gives the education field more room for development. As a result, the learning mode of students and the teaching methods of educators have also changed. As the driving force of cultural and creative industries, art education promotes the coordinated development of art education and cultural creative industries. In this context of the development of the educational field, it is necessary to seek new ways of art education to effectively promote the common development of creative industries. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 330–335, 2022. https://doi.org/10.1007/978-3-030-99616-1_44

The Application of Big Data and Artificial Intelligence

331

2 Opportunities and Challenges of Art Education and Cultural Creative Industry in the Context of Big Data 2.1 The Development of Intelligent Education The development of artificial intelligence technology is rapid, and more and more artificial intelligence tools are used in the field of education, becoming effective assistants for educators and people who use them to learn. More and more educational robots are used in the education field. They become smart assistants to help people. For example, the robots developed by some educational institutions can help teachers complete auxiliary or repetitive tasks in the classroom, such as reading texts, calling names, invigilating exams, sending and receiving test papers, etc. It can also help teachers collect and sort out information and make comparison tables. To assist teachers in preparing lessons and scientific research activities, which not only reduces the burden on teachers, but also improves work efficiency. At the same time, students can clock in, interact and communicate with teachers, and exchange learning problems with classmates, which improves students’ autonomous learning ability and provides a positive learning method. Not only for students, this robot can also be used for childcare. Now that many people are busy with work, this kind of robot can help parents choose reading materials suitable for their children. Some parents are not good at singing or foreign languages. It is also possible to interact with children through robots. Recording life is a habit of many people. It would be very happy if I could see the photos of myself when I was a kid when I grow up. So you can also set functions other than learning, such as recording life or learning records. For example, what songs have been learned this month, and how much new knowledge has been learned. These statistics are very shocking to read after a long time. It will also make people feel a sense of accomplishment, and you can look back at these data to encourage yourself when you are confused. 2.2 The Challenges of Big Data and Artificial Intelligence in Education There is a lot of room for development in applying big data to education, and at the same time it will face various problems. The combination of education and big data is a field that has just begun to research and develop in our country. Big data is very distinctive in the field of education. And has a great influence on its research and application. Therefore, it is necessary to work hard to integrate the two. In order to better complete the research in this field, we need to be fully prepared for the challenges that may be encountered in the future, and we need to work together on the system and mechanism, so that our education and big data industry can be brought to the next level. AIEd faces basic problems in the field of general education [1]. It cannot guarantee good educational effects and high-quality learning [2]. The use of technology should be closely linked with education and learning theory [3]. Changes in artificial intelligence research trends bring new challenges [4].

332

M. Lv et al.

3 The Art Education and the Cultural Creativity Industry Concept Elaboration and the Development Present Situation 3.1 The Concept and Current Situation of Art Education and Cultural Creative Industry Art Education not only solves the problem of how to continuously transfer sustainable development talents to cultural and creative industries, but also provides more possibilities for people to realize their artistic ideal. Family Environment is one of the factors influencing art education. In the growth of the family environment on the Creators influence, will guide their future creation, to give their own ideas and values. If the children are forced to learn or to learn for certain purposes, it is not a good way to learn art nowadays many parents have begun to attach importance to children are learning, but the way of learning guidance is not perfect. Parents should hold a peace of mind to see the results of their children. In the process of growing up, the family environment directly affects the development of a person’s life, especially from the early childhood stage to the adolescent stage. These factors also indirectly affect adulthood. It often determines the person’s thoughts, conduct, character, etc. In addition to the impact of the family environment, a teacher system with professional ethics is also necessary. They can’t just care about academic performance problems and ignore students’ psychological problems. But when you realize that students are not in a high mood, they should understand the situation and deal with it actively. The social issues of students are also very important, while teaching students knowledge. Everyone has to learn how to treat others correctly. 3.2 The Inner Relationship Between Art Education and Cultural Creative Industry With the joint efforts of art education and modern technology, the new cultural and creative industry has developed into one of the dazzling stars. Cultural Creative talents need to invest a lot in cultural creative training; the development of cultural creative industry is based on art education. Therefore, the cultural creative industry from the beginning and art education is inextricably linked; art education is the source and foundation of the development of cultural creative industry. Some data show that in 1998, only 0.4% of the employed persons in China cultural industry were employed. In many developed countries, the cultural industry has become one of the pillar industries of economic growth and labor absorption. The cultural industry employs 10 percent of people in Australia, 6 per cent in Canada and 5 per cent in Finland. At present, not only the lack of high-quality cultural and creative industry managers, emerging industry professionals, but also the lack of a large number of complex talent, creative talent. There is a reason for the huge impact that traditional art faces, and that is the emergence of contemporary high-tech. At the same time, the development of the modern global market is also one of the reasons. This has led to an upgrade and a new generation of traditional art and culture. A large number of unprecedented art forms and varieties of art have appeared. The traditional education model has shortcomings for cultivating talents. As long as you are aware of these, you can understand why art education should be vigorously enhanced. It is the

The Application of Big Data and Artificial Intelligence

333

good wish of mankind to improve the overall quality and creative ability of people. Art majors are relatively popular majors among college admissions majors. An unstoppable expansion momentum is expanding in the establishment of art majors. And it is expanding in a large-scale manner. The cultural and artistic education industry is a major part of the cultural and creative industry chain. Let art education be part of it. In this way, the cultural and creative industries can develop in reverse. Promoting the development of art education in colleges and universities gave birth to a large number of new art majors. At the same time, it also helped the rapid development of art education. Talent is the key to the cultural and creative industry. As an industry where human creativity is the main economic growth model, the quantity and quality of talents are very important. At present, it is far from meeting the needs of the rapid development of cultural and creative industries. The chosen profession is also very important to everyone. If you choose a profession that is not suitable for you, it will be very painful. On the contrary, only by finding a major you like can you be more engaged in learning. Only in this way can the quality of the school’s training of innovative talents become higher. A professional report formed by using big data before students choose a major. For example, data such as the type of work you will be engaged in after graduation, the proportion of work locations, etc., can be used to understand these majors.

4 The Path of Applying Big Data to Art Design Education 4.1 Realize the Individuation Education Many countries and universities in the world are building big data analysis infrastructure [5]. By mining meaningful information suitable for the education field, customized education services can be provided [6]. The chosen profession is also very important to everyone. If you choose a profession that is not suitable for you, it will be very painful. On the contrary, only by finding a major you like can you be more engaged in learning. Only in this way can the quality of the school’s training of innovative talents become higher. A professional report formed by using big data before students choose a major. For example, data such as the type of work you will be engaged in after graduation, the proportion of work locations, etc., can be used to understand these majors. In art education, schools can use this educational model of big data application to carry out communicative learning, provide hardware support for the generation of students’ creative ideas, and form a learning environment that is conducive to students’ creativity and customized recommendations for communication. At the same time, the development direction of the market will be the same as the development level of the cultural and creative industry, and it will be better affected by the new art education model. The art education that highlights the training of art skills must be actively transformed into an innovative art education. The “comprehensiveness” of teaching content is the focus of art education. In terms of teaching content, it is necessary to add comprehensive courses. For example, thinking training courses and some related or integrated courses. In this way, it can adapt to the development of art that shows a trend of interdisciplinary. Let students have knowledge of psychology, sociology, economics and other related majors.

334

M. Lv et al.

4.2 Learning Analysis and Intelligence Assessment Through Big Data On the issue of school management, big data has important value. Which is helpful to realize the precision and science of school management. Other non-traditional modes of cultivating talents can be used. For example, to cultivate innovative talents in the mode of industry-university cooperation in running schools. Some colleges and universities have begun to adopt the “studio system”. It is also a very effective mode of running a school. Make art education and social development, studio can meet the completion of professional teaching tasks. To make education and cultural consumption closely linked requires the best integration of theory and practice. Big Data Technology can track and follow all the teaching process on educational platforms, and record the digital traces of teachers; and students; classroom performance and extracurricular behavior, through the catch of micro-behavior in educational activities, we can provide the most direct, objective and accurate evaluation of educational results for educational management institutions, schools, teachers and parents. Need for research on intelligent learning analysis [7], for smart assessment, teachers provide textbooks and slides. Through repeated testing and practice [8], Students can enhance their memory of what they have learned [9]. Recommendations for the purpose of learning analysis, recommendation and review [10]. Cultivating students’ personal innovation ability is very important for students. In art education, you can often hold various humanities lectures and conduct art education on the side. Training students in a guidance-based training mode will effectively improve students’ independent learning ability. In addition, they can also hold some humanities history, art appreciation, etc. Related open classes. Through big data analysis, we can understand the specific situation of students, based on the initial ideas and creativity of the students, the guidance will make the realization of the creativity more smooth. It is also possible to implement a visit plan and create a smart visit ticket card. This smart card can visit some exhibitions, museums, planetariums, etc. for free. And can give explanation function and question and answer function. At the same time, it can also record and organize big data to understand the user’s preferences. It is helpful for parents to discover their children’s interests in certain areas.

5 Conclusions The reform of design and art education in universities and the cultivation of cultural and creative industries with Chinese characteristics are of great help in promoting the development of Chinese cultural undertakings. Need for research on intelligent learning analysis [11]. We must learn to use these new methods rationally and create new educational models. Let these educational models effectively help improve students’ learning styles. At the same time, it also reduces mechanical work for educators, allowing them more time to pay attention to other aspects such as students’ psychology. Effectively improve students’ comprehensive quality and practical ability, to achieve the goal of cultivating cultural and creative talents, to strengthen the soft power of national culture and the international influence of Chinese culture, to achieve the coordinated development of art education and cultural and creative industries.

The Application of Big Data and Artificial Intelligence

335

References 1. Boulay, B.: Can we learn from ITSs? In: Gauthier, G., Frasson, C., VanLehn, K. (eds.) ITS 2000. LNCS, vol. 1839, pp. 9–17. Springer, Heidelberg (2000). https://doi.org/10.1007/3540-45108-0_3 2. Castañeda, L., Selwyn, N.: More than tools? Making sense of the ongoing digitizations of higher education. Int. J. Educ. Technol. High. Educ. 15(1), 1 (2018). https://doi.org/10.1186/ s41239-018-0109-y 3. Bower, M.: Technology-mediated learning theory. Br. J. Educ. Technol. 50(3), 1035–1048 (2019) 4. Yang, S., Yang, J.H.: Precision education: new challenges for AI in education [conference keynote]. In: 27th International Conference on Computers in Education (ICCE), XXVIIXXVIII. Ken ting, Taiwan: Asia-Pacific Society for Computers in Education (APSCE), 4 December 2019 5. Lin off, G.S., Berry, M.J.: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley Computer Publishing, Hoboken (2011) 6. Kwonyongock, Data Analytics in Education: Current and Future Directions. J. Intell. Inf. (2013) 7. Stephen, J.H., Yang, H.O., Matsui, T., Chen, N.-S.: Human-centered artificial intelligence in education: Seeing the invisible through the visible. Comput. Educ. Artif. Intell. 2, 100008 (2021). ISSN 2666–920X 8. Stenlund, T., Sundström, A., Jonsson, B.: Effects of repeated testing on short- and long-term memory performance across different test formats. Educ. Psychol. 36(10), 1710–1727 (2016) 9. Siemens, G., Long, P.: Penetrating the fog-analytics in learning and education. Asynchron. Learn. Netw. 46, 30 (2011) 10. Chatti, M.A., Dychhoff, A.L., Schroeder, U., Thus, H.: A reference model for learning analytics. Int. J. Technol. Enhanc. Learn. 4, 318–331 (2012) 11. Zawacki-Richter, O., et al.: Systematic review of research on artificial intelligence applications in higher education – where are the educators? Int. J. Educ. Technol. Higher Educ. 16(39), 1–27 (2019)

Comparative Analysis of Students’ Learning Behavior in Smart Learning Environment Xuegang Zhang1 and Qianwen Li2(B) 1 Information Center, Guangdong Food and Drug Vocational College,

Guangzhou, Guangdong, China 2 College of Traditional Chinese Medicine and Health, Guangdong Food and Drug Vocational

College, Guangzhou, Guangdong, China [email protected]

Abstract. This paper analyzes the important influence factors of intelligent learning environment on students’ learning behavior in higher vocational colleges, compares and analyzes the specific influence data, and finally discusses the significance of intelligent learning environment on education and teaching. Keywords: Smart learning environment · Learning behaviour · Comparative analysis

1 Introduction Through the teaching environment of perception, experience, exploration and collaborative innovation, the intelligent learning environment realizes the accurate learning, accurate teaching and accurate application, and realizes the intelligent education of data guiding teaching, teaching by learning and teaching by person. With the support of the research project, the research group analyzes the important influencing factors of the intelligent learning environment on the learning behavior of students in higher vocational colleges, compares and analyzes the specific influencing data such as classroom interaction participation, the number and time of raising hands in class and voting activities, and finally discusses the significance of intelligent learning environment on education and teaching [1, 2].

2 Influence Factors of Intelligent Learning Environment on Important Learning Behaviors of Higher Vocational College Students According to the research of the research group, the pre class content preview of ecommerce majors in our school before the creation of the intelligent learning environment, that is, only 12% of the students prepare the information required by the classroom according to the teacher’s requirements, and most of the students’ pre class preview score © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 336–341, 2022. https://doi.org/10.1007/978-3-030-99616-1_45

Comparative Analysis of Students’ Learning Behavior

337

is 0; in the classroom, 38% of the students abide by the classroom discipline, listen carefully and raise their hands, 26% of the students listen carefully and take notes, 11% of the students review the course content after class, 71% of the students complete the tasks assigned by the teacher on time, and the excellent rate of the final examination is only 9%.Compared with the traditional teaching environment, the intelligent learning environment has a greater impact on the important learning behavior of higher vocational college students [3–5]. 2.1 Online and Offline Hybrid Teaching Platform The hybrid teaching platform realizes the whole process of online teaching before class, in class and after class, realizes the preview of online courses and the real-time transfer of knowledge by reversing the classroom learning mode, and promotes students’ in-depth understanding of knowledge by convenient teaching interaction and discussion, truly realizes the student-centered innovative teaching mode, and completely solve the classroom design and teaching out of touch, lack of classroom interaction means, students’ classroom participation is low, discussion organization efficiency is low, classroom data no record, no reservation of results [6]. 2.2 Timely and Effective Process Evaluation Ensures the Real-Time Fairness of Students’ Process Performance The intelligent learning environment realizes and records a variety of teaching process evaluation methods, such as adding points in the process of selecting people, adding points in the process of answering questions, adding points in the process of discussion, and students’ mutual evaluation. It combines objective evaluation with subjective evaluation, permeates the whole process of interaction, realizes the establishment of common goals for teachers and students, establishes positive interdependence around goals, promotes members to share results and help each other, and through the scoring evaluation, it is fair and transparent to generate statistical reports in real time, which promotes members to share results and help each other. Timely and effective process evaluation ensures the real-time and fairness of students’ process performance, and effectively improves students’ learning motivation and self-confidence [7]. 2.3 Detailed Data Analysis and Teaching Evaluation System Drive the Fine Teaching Improvement In the traditional classroom, the above data mainly through the face-to-face observation of teachers to roughly make qualitative judgment. In online teaching, because almost all communication and learning behaviors will form data, which can be found in the background, it is possible to determine the relationship between participation and learning effect through quantitative research. The intelligent learning environment automatically analyzes students’ classroom performance, task execution after class, and learning engagement through teaching process data. It realizes all-round real-time control of real and effective learning information, generates targeted adjustment of teaching

338

X. Zhang and Q. Li

strategies, and through normal, timely, multidimensional and accurate data presentation and intelligent analysis, it can provide objective data for personalized teaching, teaching self-diagnosis, learning situation detection and other teaching supervision activities, and provide strong support for diversified teaching evaluation. Then it can effectively help teachers to analyze the activity design from a task, feedback the teaching effect from a student, reflect the teaching design from a classroom, and deeply detect the whole teaching process from macro to micro, so that students’ learning situation can be traced, and effectively drive the improvement of refined teaching [8].

3 A Comparative Study of the Influence of Intelligent Learning Environment on Important Learning Behaviors of Students in Higher Vocational Colleges 3.1 A Comparative Analysis of the Results of Homework Between the Smart Learning Environment Teaching Class and the Previous Class The students of this year’s grade 2019 are compared with the students of previous years in their homework scores, and the eight homework scores with the same difficulty and conditions are used as the measurement parameters of learning effect. The results of the comparative analysis of the after-school homework scores of grade 2019 e-commerce students and grade 2018 students are as follows (1) (Fig. 1):

Fig. 1. Comparative Analysis of homework scores of two grades

3.2 Comparative Analysis of Classroom Participation Between Smart Learning Environment Teaching Classes and Previous Sessions As for the definition of learning participation, Hu Yaqing and other scholars believe that learning participation is a basic form of learners’ active and continuous participation

Comparative Analysis of Students’ Learning Behavior

339

in related teaching activities. In the process of learning participation, learners not only participate in unilateral behavior, but also involve multi-dimensional comprehensive participation such as cognition and emotion [9, 10]. Based on the characteristics of the teaching design of this course, we adopted the number of students’ hands raised as an indicator of students’ classroom participation. This value represents the enthusiasm of students to participate in classroom activities and discussions in behavior, and reflects that students are highly involved in learning in terms of cognition and emotion. Therefore, comparing the number of hands raised by students in different learning environments can reflect changes in student participation. First, the normal test is performed on the number of hands raised by students in class, and the results are as Table 1: Table 1. One-Sample Kolmogorov-Smirnov Normal Test on the number of hands raised Total N Most Extreme Differences

114 Absolute Positive

.255 .240

Negative Test Statistic

-.255 .255

Asymptotic Sig.(2-sided test)

.000a

a. Lilliefors Corrected

P < 0.05, there is a significant difference between the distribution of the number of students’ raised hands and the normal distribution. Therefore, the independent sample rank sum test (Mann-Whitney U test) is used to compare the number of hands raised by the two graders, and the results are as follows (Table 2) (Fig. 2): Table 2. Independent-Samples Mann-Whitney U test summary of the number of hands raised by students in two teaching conditions Total N

114

Mann-Whitney U

2681.500

Wilcoxon W

4059.500

Test Statistic

2681.500

Standard Error

172.230

Standardized Test Statistic

6.210

Asymptotic Sig.(2-sided test)

.000

340

X. Zhang and Q. Li

Fig. 2. Independent sample Mann-Whitney U test

U = 0.000 < 0.05, so the difference in the distribution of the number of hands raised by students under the two learning conditions is statistically significant. It reflects that the student’s classroom participation in the smart learning environment is significantly higher than that in the ordinary learning environment. 3.3 Comparative Analysis of the Final Examination Results Between the Smart Learning Environment Teaching Class and Previous Sessions

Table 3. Comparison of the total scores of the final assessment between the intelligent learning environment teaching class and the previous one Independent sample t-test for final examination scores of two grades Number of cases

Average value

Standard deviation

t

Freedom

Sig. (doublestern)

18 E-commerce

62

79.15

11.55

-2.248

112

0.027

19 E-commerce

52

83.8

10.295

Class Final exam results

The average score of grade 18 was 79.2 ± 11.6, and that of grade 19 was 83.8 ± 10.3. There was significant difference between the two classes by independent sample t test (P < 0.05) (Table 3).

Comparative Analysis of Students’ Learning Behavior

341

4 Conclusion The intelligent learning environment promotes the “learner centered, teacher led” teaching mode, and shapes the “Internet plus” classroom teaching system, which greatly improves the teaching efficiency, stimulates the students’ enthusiasm for classroom participation, and makes students’ learning behavior easier and more effective. Acknowledgements. This work was supported by: Natural science research project of Guangdong Food and Drug Vocational College in 2020 (2020ZR25). 2020 humanities research project of Guangdong food and Drug Vocational College (2020RW6). Quality engineering research project of Guangdong Provincial Department of education in 2018 (GDJG2019205). Characteristic innovation project of Guangdong Provincial Education Department in 2020 (2020KTSCX257).

References 1. Huang, A.: Analysis and thinking of College Students’ learning behavior. National business information (Theoretical Research), (07), 69–71 (2012) 2. Deng, M.: Improvement analysis of College Students’ sense of learning efficacy and negative learning behavior. Teaching Res. 37 (06), 7–10 + 123 (2014) 3. Xiaoping, L., Jianglan, G.: The correlation between learning attitude and learning behavior. Psychol. Behav. Res. 04, 265–267 (2005) 4. Zhongxiong, F., Cong, C.: A strategic analysis of the application of virtual reality technology in vocational colleges. J. Beijing Polytech. Inst. 20, 65–68 (2021) 5. Yiyu, H.: Overview of research on the application of Virtual Reality (VR) in education. China Educ. Inf. 01, 11–16 (2018) 6. Hafiz, I.M., Akhter, S.S., Abdul, M.M.: Rethinking theories of lesson plan for effective teaching and learning. Soc. Sci. Humanities Open 4, 2021 (2021) 7. Liao, J.: Discussion on the application of effective teaching theory in college physical education, 4 (2019) 8. Pablo, P.J., Mauro, R.C., Nicolas, S.: Interacting particles systems with delay and random delay differential equations. Nonlinear Anal. 214, 2022 (2022) 9. Zhang, Y., Yuan, L.-J., Zhang, Q., Sun, X.-Y.: Multi-objective optimization of building energy performance using a particle swarm optimizer with less control parameters. J. Building Eng. 32, 2020 (2020) 10. Euschi, S., Khaldi, A., Kafi, R., Kahlessenane, F.: A Fourier transform based audio watermarking algorithm. Appl. Acoust. 172, 2021 (2021)

Design and Development of AR Teaching System for Cardiac Physical Examination Qianwen Li1 and Xuegang Zhang2(B) 1 College of Traditional Chinese Medicine and Health, Guangdong Food and Drug Vocational

College, Guangzhou, Guangdong, China 2 Information Center, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong,

China [email protected]

Abstract. Cardiopulmonary physical examination is an important part of the professional learning of rehabilitation treatment technology in higher vocational schools. Through the application of AR technology, students can be better guided to complete the professional skills of applying the knowledge they have learned to practical case diagnosis. Therefore, our team designed and developed an AR teaching system for cardiac physical examination to assist teaching. Keywords: Augmented reality technology · Cardiac physical examination · Teaching system

1 Introduction Cardiac physical examination is an important part of the diagnostic science for higher level rehabilitation therapy technology majors. The traditional lecture method for this course is usually using pictures and videos. The students need to imagine the changes in lesions based on their knowledge of the anatomical structure of the heart, and the process involves multiple imaginations and multiple logical transformation and reasoning processes, which makes the explanation and learning process very difficult. Based on augmented reality technology, the AR teaching system for cardiac physical examination is designed and implemented as a real-time interactive visualization application software for the part of cardiac physical examination in diagnostic courses related to medicine, which transforms the abstract and difficult theoretical course content into virtual reality content that is easy to understand and visualize, and realizes real-time interaction to improve the teaching effect of teachers and learning efficiency of students and achieve the teaching objectives [1–4].

2 Design and Implementation of AR Teaching System Modules for Cardiac Physical Examination Under the funding of the research project, our team members completed the software architecture design according to the front-line teaching needs, analyzed the functional © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 342–346, 2022. https://doi.org/10.1007/978-3-030-99616-1_46

Design and Development of AR Teaching System

343

modules to be implemented and their requirements, and completed the basic system flow design, software visualization design, and interaction design to ensure that the teaching system application is consistent with the usage habits of front-line teaching staff in both the overall and detailed aspects. Finally, based on the software module design, visualization design, interaction design and system flow design, we developed and implemented the cardiac physical examination teaching system, which was imported into the wearable AR device Hololens to run (Fig. 1).

Fig. 1. UI design of the software

2.1 Visualization Design According to the Requirements of AR Technology Based on the existing 3D modeling of the heart, we made the corresponding visualization design for the key teaching contents of the cardiac physical examination part (including mitral stenosis, mitral valve insufficiency, aortic stenosis, aortic valve insufficiency and other pathological states) according to the characteristics and requirements of AR technology, and established the corresponding dynamic 3D models. Because it involves dynamic modeling of several common cardiac disease states, we initially chose the way of modeling several major structures of the heart separately (left atrium, left ventricle, right atrium, right ventricle, and each valve) for efficiency and material reusability considerations, and then combined them in the run, so that the modeling of the affected lesion parts can be run by modifying them in different disease states. However, since it is a dynamic model, separate modeling appears to have very slight time differences during dynamic display, which accumulate to a non-negligible degree after several repetitions of playback and affect the synchronization (Fig. 2).

344

Q. Li and X. Zhang

Fig. 2. Complete re-modeling approach for each different disease state

Finally, we chose to model the heart in a way that each different disease state was completely re-modeled to ensure the display effect as much as possible (Fig. 3).

Fig. 3. The vortex of blood flow

For this part of the teaching, the visualization of normal and abnormal blood flow is also the focus of this software design. Based on the heart modeling, we used particle swarm [5–8] with animation to show the vortex of blood flow in different valve pathologies, so that students can observe the corresponding heart valve and blood flow changes while learning to listen to heart sounds and master the difficult knowledge.

Design and Development of AR Teaching System

345

2.2 Designing Real-Time Interaction Methods Related to Each Teaching Content The real-time interaction methods that Hololens can recognize include gaze focus following, voice interaction, and gesture interaction. Voice interaction can recognize keywords, and gesture interaction mainly includes click, grab, move, zoom in, zoom out, rotate, play and other functions. The AR teaching system for cardiac physical examination realizes the teaching content used in teaching through the above interaction methods. In the process of software design, the functions that need to be used are finely designed according to the teaching requirements and technical characteristics of AR technology to ensure that the gesture real-time interaction can efficiently meet the teaching requirements. 2.3 Audio Standardization Design and Implementation The existing cardiac auscultation audio is basically the actual recording of the patient’s physical examination, which is not sufficiently standardized. In order to meet the teaching needs, the cardiac physical examination AR teaching system imports different heart sounds, depicts the heart sound map using real-time Fourier transform [9, 10], and combines the heart ejection cycle to seamlessly match the heart state with the heart sound characteristics (Fig. 4).

Fig. 4. Heart sound characteristics

3 Conclusion The AR teaching system of cardiac physical examination makes full use of virtual simulation technology to build practical training conditions, and introduces AR technology into the teaching of cardiac physical examination reasonably, which effectively “visualizes” the abstract theoretical knowledge under the body surface and reduces the difficulty of teaching and learning, and provides a reference experience for solving similar problems in medical basic courses.

346

Q. Li and X. Zhang

Acknowledgements. This work was supported by: Natural science research project of Guangdong Food and Drug Vocational College in 2020 (2020ZR25). 2020 humanities research project of Guangdong food and Drug Vocational College (2020RW6). Quality engineering research project of Guangdong Provincial Department of education in 2018 (GDJG2019205). Characteristic innovation project of Guangdong Provincial Education Department in 2020 (2020KTSCX257).

References 1. Fang, Z., Cheng, C.: A strategic analysis of the application of virtual reality technology in vocational colleges. J. Beijing Polytech. Inst. 20, 65–68 (2021) 2. Huang, Y.: Overview of research on the application of Virtual Reality (VR) in education. China Educ. Inf. 01, 11–16 (2018) 3. Hafiz, I.M., Akhter, S.S., Abdul, M.M.: Rethinking theories of lesson plan for effective teaching and learning. Soc. Sci. Humanities Open 4, 2021 (2021) 4. Liao, J.: Discussion on the application of effective teaching theory in college physical. Education 4, 2019 (2019) 5. Soft Computing; Researchers from Amirkabir University of Technology Report on Findings in Soft Computing (Modeling Energy Flow in Natural Gas Networks Using Time Series Disaggregation and Fuzzy Systems Tuned by Particle Swarm Optimization), Energy Weekly News, pp. 1945–6980 (2020) 6. Guo, J., Sato, Y.: A dynamic allocation bare bones particle swarm optimization algorithm and its application. Artif. Life Robot. 23(3), 353–358 (2018). https://doi.org/10.1007/s10015-0180440-3 7. Pablo, P.J., Mauro, R.C., Nicolas, S.: Interacting particles systems with delay and random delay differential equations. Nonlinear Anal. 214, 2022 (2022) 8. Zhang, Y., Yuan, L.-J., Zhang, Q., Sun, X.-Y.: Multi-objective optimization of building energy performance using a particle swarm optimizer with less control parameters. J. Building Eng. 32, 2020 (2020) 9. Euschi, S., Khaldi, A., Kafi, R., Kahlessenane, F.: A Fourier transform based audio watermarking algorithm. Appl. Acoust. 172, 2021 (2021) 10. Pourhashemi, S.M., Mosleh, M., Erfani, Y.: Audio watermarking based on synergy between Lucas regular sequence and Fast Fourier Transform. Multimed. Tools Appl. 78(16), 22883– 22908 (2019). https://doi.org/10.1007/s11042-019-7595-3

Visualization Enhancement of Parallel Coordinates Plot Based on Fisher Score and Laplacian Score Zhanpeng Qi(B) Jack Baskin School of Engineering, University of California, Santa Cruz, California, USA [email protected]

Abstract. Over the past decades, high-dimensional data visualization analysis has always been a hot topic in the field of data science. PCP (Parallel Coordinate Plot) is a very commonly utilized tool in the field of data analysis. To be specific, each feature of the dataset can be illustrated in a Cartesian Coordinate System. To complete the recording on one data from a dataset onto the chart, one needs to find the numerical value of each feature belonging to one data on each feature axis and connect those points on each feature axis together. However, when using PCP to deal with and analyze a large amount of data and features, overlapping and crossing between segments would strongly affect the visualization performance of the chart and therefore increase the difficulty of data analysis. To address such issue, this paper presents a visualization enhancement method that can reorder feature axes on the plot and remove unnecessary feature axes automatically. To reorder and remove feature axes automatically in PCPs, we employed the Fisher score and Laplacian score to reorder features based on the corresponding weight. By comparing the visualization result of reordering for each method, features with low priority among the reordering result of both methods can be observed. After using this method on PCP, the visualization performance of PCPs considerably improved, which demonstrates that the methods based on feature selection are beneficial to optimize the PCP performance. Keywords: Parallel Coordinates Plots · Visualization analysis · Performance enhancement · Fisher score · Laplacian score

1 Introduction In recent years, the visualization of high-dimensional datasets has become a hot topic of research. PCP (Parallel Coordinate Plot) is a commonly used tool in the field of data analysis, specifically data analysis for high dimensional data. With its assistance, one can compare the numeric value of the feature of several data (series) with ease. However, it also has certain disadvantages: when dealing with datasets with a large number of features or large datasets in general, the crossing and overlapping between segments can cause quite an obstacle for data analysis. To solve this issue, a number of methods of modifying PCP have been proposed. In [1], for instance, Raidou et al. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 347–355, 2022. https://doi.org/10.1007/978-3-030-99616-1_47

348

Z. Qi

visually enhanced parts of each PCP pipeline with respect to its slope and simplified the interaction of selecting an individual data; In [2], they proposed Pargnostics (Parallel coordinate diagnostics) to serves as the bridge between the last stages of the visualization pipeline and the user’s perceptual system for the specific case of parallel coordinates; Also in [3], certain researchers present a set of novel, smart brushing techniques that enhance the standard interactive brushing of a parallel coordinate plot. This brushing technique is a modified, data-guided, smart brush that provides real-time feedback to the users based on the current mouse pointer location and surrounding data. Those methods above are mostly trying to improve the visualization performance by deploying visual enhancement on the plot, which can certainly enhance the visualization performance of the plot considerably [4–6]. However, there are only a few methods that presented machine learning as a tool to optimize PCP performance. In this paper, we present a new solution for enhancing PCP by optimizing the dataset for the cause of presentation on CPC by adapting the Fisher Score and Laplacian Score [7, 8]. The main aspects of this work can be summarized as follows: 1: The visualization performance of original PCPs can be enhanced by utilizing feature selection methods such as the Fisher score and Laplacian score. 2: Comparing the result of each rank and remove features that don’t comply with both ranks. The rest of this paper is organized as follows. In Sect. 2, we introduce the basic concept and structure of a PCP (Parallel Coordinate Plot) in detail. In Sect. 3, we introduce and show the process of utilizing Fisher Score and Laplacian Score, which are the feature selection algorithms that can be used to apply on the original PCP. In Sect. 4, we show and analyze an enhanced PCP and compare it with the original PCP. Finally, Sect. 5 concludes this paper.

2 Formulation of Parallel Coordinates Plot

Fig. 1. The illustration of PCP without data

PCP is always used for analyzing the visualization of high-dimensional datasets. To be specific, PCP (Parallel coordinate plot) consists of multiple unique feature axes. Each axis in PCPs represents one feature from the dataset, respectively. The corresponding

Visualization Enhancement of Parallel Coordinates Plot

349

features of each data can be connected in PCPs with a broken line manner. A segment would appear, which represents one data from the dataset. Then, if we record all the data from the dataset in this way, we obtain a graph (namely PCP) with multiple feature axes with segments connecting between them. Since it has feature axes, PCP is especially useful when we are comparing the specific feature values between data from a dataset. In data analysis, PCP is a usually used tool for the analysis of high dimensional datasets due to its convenience in verifying the difference between feature values belongs to different data (Fig. 1).

3 Feature Selection Algorithm 3.1 Fisher Score Fisher Score [9] is a supervised learning method that can be used to address feature selection problems. Based on the scores of each feature, those corresponding features can be ranked by the Fisher Score. The calculation of Fisher Score can be represented as the following:  nj (µij − µi )2 Si = (1)  nj ∗pij2 Where µij denotes the mean of the i-th feature in the j-th class, and pij denotes the variance of the i-th feature in the j-th class. Accordingly, nj and µi are the number of data instances in the j-th class and the mean of the i-th feature. 3.2 Laplacian Score To compute LS of r-th feature, following steps are required: Table 1. The formulation of Laplacian Socre algorithm

LS (Laplacian Score) [10] is a method used to express the local preserving power of each feature on a dataset. LS is created according to the observation that if two data points are closed to each other, they are probably related to a similar topic.

350

Z. Qi

LE (Laplacian Eigenmaps) and LPP (Locality Preserving Projection) are the calculation fundament of the LS algorithm.

4 Experimental Results Visualization and Analysis To show how datasets would be presented normally on a PCP, we utilize three datasets (Iris, Seeds, and Glass) on the PCP, which are listed/ shown in Table 2 in detail. Table 2. The datasets utilized in the experiments Datasets

Number of samples

Number of features

Number of categories

Iris

150

4

3

Seeds

210

7

3

Glass identification

214

9

7

Tables 3 and 4 show the top 4 ranks of features for each sample dataset based on the Laplacian score (from the smallest to the largest) (see Table 2) and Fisher Score (From the largest to the smallest) (see Table 3) for each feature. FN indicates the position of a specific feature in the original dataset, and the number after that is the Laplacian Score/Fisher Score (4 decimal places after the dot) for each feature presented in those two tables. For example, if a feature originally belongs to the second position of a dataset, and its Laplacian Score/Fisher Score value is 0.1, then we would represent it as F2/0.1 in this table and rank it accordingly by comparing the Laplacian Score/Fisher Score of it and other features. Table 3. The top 4 features ranked based on Laplacian Score of three datasets and the numeric value of Laplacian Score for each feature Datasets

Top-1/LS

Top-2/LS

Top-3/LS

Top-4/LS

Iris

F3/0.0785

F4/0.1011

F1/0.2821

F2/0.3919

Seeds

F1/0.2835

F2/0.2835

F5/0.3221

F4/0.3301

Glass identification

F7/0.8844

F1/0.9229

F9/0.9349

F8/0.9516

Visualization Enhancement of Parallel Coordinates Plot

351

Table 4. The top 4 features ranked based on Fisher Score of three datasets and the numeric value of Fisher Score for each feature Datasets

Top-1/FS

Top-2/FS

Top-3/FS

Top-4/FS

Iris

F3/16.0566

F4/13.0613

F1/1.6626

F2/0.6688

Seeds

F1/5.2965

F2/5.2327

F5/3.9256

F7/3.5647

Glass identification

F3/1.5756

F8/0.9369

F4/0.8588

F2/0.6863

To demonstrate the effect of PCP axes reordering more clearly, here we present the PCPs on three raw datasets (Iris, Seeds, and Glass) and those three datasets being reordered based on its Laplacian Score/Fisher Score. To better verify and visualize the effect on PCPs based on feature selection, we only present the top-4 features on the PCP based on Laplacian Score/Fisher Score (Figs. 5–10).

Fig. 2. Visualization performance of original PCP on Iris dataset without the preprocessing of feature selection

Fig. 3. Visualization performance of enhanced PCP on Iris dataset based on Laplacian Score

As we can see from the above Figs, feature axes reordering based on Laplacian Score/Fisher Score effectively decreased the cases of segment overlapping and crossing

352

Z. Qi

Fig. 4. Visualization performance of enhanced PCP on Iris dataset based on Fisher Score

Fig. 5. Visualization performance of original PCP on Seeds dataset without the preprocessing of feature selection

Fig. 6. Visualization performance of enhanced PCP on Seeds dataset based on Laplacian Score

Visualization Enhancement of Parallel Coordinates Plot

353

Fig. 7. Visualization performance of enhanced PCP on Seeds dataset based on Fisher Score

Fig. 8. Visualization performance of original PCP on Glass dataset without the preprocessing of feature selection

Fig. 9. Visualization performance of enhanced PCP on Glass dataset based on Laplacian Score

354

Z. Qi

Fig. 10. Visualization performance of enhanced PCP on Glass dataset based on Fisher Score

on corresponding PCPs. Especially in the Iris’ pair of Figs (Fig. 2, Fig. 3, and Fig. 4), since the Iris dataset only has four feature axes, it can be seen that a completed visualization of Iris dataset after feature axis reordering based on Laplacian Score/Fisher Score ranking. Clearly, we note that the visualization performance of PCP is significantly enhanced. If we compare Fig. 2 with Fig. 3 and Fig. 4, we and clearly spot that Fig. 3 and Fig. 4 obtain much fewer cases of overlapping and crossing in general when comparing with Fig. 2. Interestingly, the feature re-rankings based on Laplacian Score and Fisher Score are actually the same. This can be deemed as both supervised feature selection method and unsupervised feature selection method can achieve the same result on enhancing visualization performance of PCP. Those visualization results above demonstrate that the proposed method of reordering feature axes can effectively decrease the cases of crossing and overlapping between segments in a PCP.

5 Conclusion This paper presented a new enhancement to PCP to improve visualization performance when dealing with high dimensional data by reordering and removal of its feature axes based on feature selection methods (Laplacian Score and Fisher Score). Firstly, we calculated and ranked the Laplacian Score/Fisher Score of each feature on three datasets (Iris, Seeds, and Glass). Then, we took the top 4 features in each rank to update empty PCPs. Finally, we validated the difference between PCPs before the enhancement and PCPs being processed by Laplacian Score/Fisher Score. The result of the comparisons shows that feature selection methods could improve the visualization performance on PCPs. This paper shows the potential of feature selection or machine learning in general in the field of enhancing and adjusting statistics tools. In the future, a series of meaningful work can be conducted subsequently. For instance, when optimizing a data analysis tool, instead of enhancing the tool itself, one can choose to optimize the dataset that is presented in order to obtain the best tool performance possible. As for the visualization of high-dimensional datasets, one can certainly try to use feature selections merely to

Visualization Enhancement of Parallel Coordinates Plot

355

discover the priority of those features included, based on which, eventually increases the visualization performance.

References 1. Raidou, R.G., Eisemann, M., Breeuwer, M., Eisemann, E., Vilanova, A.: Orientationenhanced parallel coordinate plots. IEEE Trans. Visual. Comput. Graphics 22(1), 589–598 (2016). https://doi.org/10.1109/TVCG.2015.2467872 2. Dasgupta, A., Kosara, R.: Pargnostics: screen-Space metrics for parallel coordinates IEEE Trans. Visual. Comput. Graphics 16(6), 1017–1026 (2010). https://doi.org/10.1109/TVCG. 2010.184 3. Roberts, R.C., Laramee, R.S., Smith, G.A., D’Cruze, B.T.: Smart brushing for parallel coordinates. IEEE Trans. Visual. Comput. Graphics 25(3), 1575–1590 (2019). https://doi.org/10. 1109/TVCG.2018.2808969 4. Zhou, L., Weiskopf, D.: Indexed-points parallel coordinates visualization of multivariate correlations. IEEE Trans. Visual. Comput. Graphics 24(6), 1997–2010 (2018). https://doi. org/10.1109/TVCG.2017.2698041 5. Guo, H., Xiao, H., Yuan, X.: scalable multivariate volume visualization and analysis based on dimension projection and parallel coordinates. IEEE Trans. Visual Comput. Graphics 18, 1397–1410 (2012). https://doi.org/10.1109/TVCG.2012.80 6. Johansson, J., Forsell, C.: evaluation of parallel coordinates: overview, categorization and guidelines for future research. IEEE Trans. Visual Comput. Graphics 22, 1 (2015). https:// doi.org/10.1109/TVCG.2015.2466992 7. Srivastava, S.: A review paper on feature selection methodologies and their applications. Int. J. Eng. Res. Dev. 7, 57–61 (2013) 8. Bachu, V., Anuradha, J.: A review of feature selection and its methods. Cybern. Inf. Technol. 19, 3 (2019). https://doi.org/10.2478/cait-2019-0001 9. Gu, Q., Li, Z., Han, J.: Generalized fisher score for feature selection. In: Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011 (2012) 10. He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. Proceed. Adv. Neural Inf. Process. Syst. 18 (2005)

Simulation Verification of Equipment Maintenance Characteristics Based on Big Data Yongling Liu, Danhong Chen(B) , Zhen Gong, Qiushi Xiong, and Guiyong Chen School of Economics and Management, Shenyang Aerospace University, Liaoning, China [email protected]

Abstract. The research on the simulation and verification technology of equipment maintenance characteristics based on big data is aimed at the possible failures and their causes in the actual operation training process of the technology-intensive complex system integrating mechanical, electronic, hydraulic, information and control technologies. The failure mode and impact analysis method (FMEA) is adopted for large equipment. This paper studies the electromechanical and electrohydraulic faults in large weapon equipment, analyzes the basic properties and propagation characteristics of the faults, establishes the fault attribute description model database, carries on the fault propagation simulation analysis by using neural network. This study makes full use of the characteristics of big data virtuality, increases the number of repeated verification, reduces unnecessary costs and saves costs. Keywords: Large equipment · Fault attribute · Simulation function model · Data structure

1 Introduction With the development of equipment complexity, integration, high speed, information, intelligence and data application technology, the traditional fault diagnosis technology cannot meet the demand of fault diagnosis of large complex equipment and fast, must break through the security key technology, fault diagnosis and maintenance scientific planning guarantee mechanism, Make full use of big data information and integrated network technology to form equipment support database, improve the ability of rapid fault detection, diagnosis and maintenance support of equipment, achieve intelligent equipment support, and give full play to the effectiveness of equipment [1]. In the light of the test equipment to collect/analyze the simulation data, mining correlation between abnormal data and equipment failure, the fault diagnosis based on the deep study and analysis of large data prediction technology, the research based on the deep learning of neural network architecture, to build fault diagnosis based on fuzzy inference mechanism and deep learning model, for equipment maintenance and inspection of [2].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 356–362, 2022. https://doi.org/10.1007/978-3-030-99616-1_48

Simulation Verification of Equipment Maintenance

357

2 Construction of Simulation Verification Mode of Equipment Maintenance Characteristics Based on Big Data 2.1 Main Sources of Fault Data There are three main sources of failure data, as shown in Table 1. Table 1. Source of data Maintenance data

Technical documents and maintenance manuals provided by relevant units

Expert experience It is mainly the maintenance experience knowledge of experts in the field System simulation Analysis is made through Mul Tisim circuit simulation and fault tree

2.2 Fault Diagnosis System and Method Application of large data analysis platform, to carry out the analysis of failure mode and characteristic parameters acquisition and modeling technology of fault diagnosis and prediction, provide the typical fault characteristic parameters (such as temperature, voltage, running state, etc.) collection of monitoring and analysis method, the basic characteristic parameters of a typical fault model based fault diagnosis strategy and model, generate diagnostic rules and knowledge, The basic characteristic value sampling method, change trend and threshold determination method of typical fault modes are provided. Using the weight relation between the analysis efficiency measure index to analyze the problem, the accuracy is high. First of all, the weight relationship between the efficiency measures is analyzed. According to the empirical data given by experts, Delphi method is used to calculate the weight between the efficiency measures and establish the weighting matrix (Fig. 1).

Fig. 1. Fault diagnosis implementation process

358

Y. Liu et al.

There are n evaluation indexes selected in the draft of the index system l1 , l2 , ..., ln . After expert advice consultation and statistical results, the total importance of comprehensive expert advice is respectively X1 , X2 , ..., Xn . The ranking of the total imporn  xj . The normalized ranking vectance of indicators was normalized ωj = xj / i=1

tors of the number of n evaluation indexes are obtained that is (ω1 , ω2 , ..., ωn ) and ω1 + ω2 + ... + ωn = 1. Secondly, the simulation parameters of basic evaluation indexes are dynamically measured, that is, the parameters required to construct the initial super matrix are obtained by simulation analysis of the correlation degree between some quantitative indexes. If the index parameters are linear, the least square method can be used. The basic process based on the least square method: assume that a set of basic estimation indicators of the input are A = (a1 , a2 , ..., am ). Among them, ai is the intermediate indicator. This group of indicators affect the upper-level performance measurement. The indicator is y. Aj (j = 1, 2, ..., n) is the experimental index value at moment j. The least square method was used to establish the fitting relation regression model between each index value and n  bk ak . Then the influence coefficient vectors of efficiency measure index y = b0 + k−1

n basic evaluation indexes are obtained B = [b1 ,b2 ,...,bn ]T . By pairwise comparison of these influence coefficients, the judgment matrix composed of relative importance can be formed as C = (bi /bj )m*n = (cij )m ε1 m(U ) < ε2 ⎩ m(A1 ) > m(U )

(2)

A1 is the decision result, where ε1 and ε2 are the pre-set threshold. 3.3 Construct the Fault Diagnosis and Prediction Model with Multi-source Feature Verification D-S evidence combination algorithm can fuse identity information of different information sources, eliminate part of contradictory information, obtain new identity information, and modify the algorithm with correct diagnosis results [6]. Where, the evidence reliability can be calculated by the following formula:    ⎫ ⎧ αti = exp −ε1 ∗ μti + φti ⎪ ⎪ ⎪ ⎪ ⎬ ⎨ t γ μti = γit − minN j=1 j  (3)

2   t ⎪ ⎪ ⎪ t , i = 1, 2, ..., N ⎪ ⎭ ⎩ φti = exp −ε2 ∗ N1 N ∗γ γ − γ i=1 i i

3.4 Establish Bilinear Convolutional Neural Network The bilinear convolutional neural network was established and trained with big data, and the fault classification and diagnosis technology integrating deep learning and big data analysis was studied. Through comparative analysis, Theano and Lasagne deep learning framework was adopted for research, and the bilinear convolutional neural network was established. Make full use of big data such as application data and environmental data generated during equipment test and operation to train the deep learning network and realize the ability of fault diagnosis and classification [7].

362

Y. Liu et al.

4 Conclusion In this paper, the large-scale equipment using failure mode and effect analysis (FMEA), [8] the large weapons and equipment of mechanical and electrical failure, failure analysis and the basic properties and propagation characteristics, establishing database of its fault attribute description model, fault propagation simulation analysis by using neural network, one failure mode, failure cause, The simulation of fault phenomenon and occurrence process and calculation of fault occurrence probability provide technical reference for fault analysis and research of large equipment [9]. In the end. Promote the development and revolution of equipment maintenance industry [10]. Acknowledgments. This research was supported by the college students’ innovative entrepreneurial training plan of Shenyang Aerospace University under grant X202110143102. Danhong Chen is the corresponding author and instructor of this paper.

References 1. Dunk, N.M., Lalonde, J., Callaghan, J.P.: Research on modeling and simulation theory of equipment maintenance support team. Sci. Technol. and Innov. 209, 130–132 2. Shida, Q., Lu, Y., Pu, Z.: Review on military-civilian integration distribution guarantee mode of equipment maintenance equipment. Logistics Technol. 209, 16–119 3. Jiang, Z., Shi, W., Ma, J., Cheng, Y.: Symbol recognition algorithm of electrical components based on deep neural Network. J. Electr. Power Syst. Automation 209, 1–2 4. Zheng, J., Nie, R., Wang, S., Tan, Y.: Information Network Security (2021) 5. Liu, G.: Application of fuzzy reasoning mechanism in electrical engineering design. Equipment Manage. Maintenance, 98–99 (2017) 6. Xie, B.: Emperor of the palace. review on turnout fault diagnosis and prediction based on machine learning. Railway Commun. Signal Eng. Technol. 209, 93–99 7. Yao, Y., Liu, D.: Prediction of short-term wind power based on convolutional neural networklong short-term memory network. Modern Electr. Power, 2–7 (2021) 8. Liu, G., Chen, C., Wan, B., Guo, T.: J. Astron. 209, 531–538 9. Song, W., Wu, J., Dong, Z., Zhang, J., Zhou, K.: Equipment maintenance scheduling optimization model based on cost analysis. Modern Defense Technol. 209, 1–9 10. Liu, Q., Huang, Z., Liao, Y.: Application of simulation technology in the development of anti-corrosion performance of vehicle frame. New Technol. New Process, 29–31 (2017)

Reform and Thinking of Computer Network Technology Specialty Based on Internet of Things Lei Wang1(B) and Jia Qu2 1 Department of Management and Information, Shandong Vocational College of

Communications, Weifang 201206, Shandong, China [email protected] 2 Department of Navigation, Shandong Vocational College of Communications, Weifang 201206, Shandong, China

Abstract. In order to further improve the effect and quality of computer teaching in engineering schools, cultivate students’ autonomous learning ability, and obtain successful experience in the construction of computer and network disciplines, it is necessary to analyze the actual needs for the transformation of massive computer networks in colleges and universities in the Internet of things (IoT) era on the basis of summarizing the overview of the Internet of things, and study the specific Transformation Countermeasures in detail. Based on the in-depth research on the educational reform of the computer network technology specialty of the Internet of things, this paper summarizes the teaching status of the computer network technology specialty on the basis of literature. In order to further understand the actual situation, this paper uses the method of questionnaire to investigate the teaching status of the computer network technology specialty at the present stage, Teachers and students majoring in computer network technology believe that it is necessary to carry out teaching reform under the background of Internet of things. Keywords: Internet of Things · Computer network · Computer major · Teaching reform

1 Introductions Under the background that the country attaches great importance to the development of higher vocational education, higher vocational colleges have achieved rapid development [1, 2]. But can our education keep up with the development of the times? The answer is not only that the pace has not kept up, but obviously lags behind [3, 4]. According to incomplete statistics, the computer network majors currently used in high schools are at least five years behind the society, and some are even ten years behind [5, 6]. The main reason why the university has not developed rapidly is that the goal of talent training cannot keep up with the pace of the times, the teaching method is still traditional, and the education of book knowledge has nothing to do with theory and practice, causing the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 363–370, 2022. https://doi.org/10.1007/978-3-030-99616-1_49

364

L. Wang and J. Qu

knowledge learned by students to lag behind the development of society, and the talents cultivated by universities cannot adapt to the development of society [7, 8]. Regarding the research on the teaching reform of the computer network technology major, some people pointed out that although many technical students have received vocational training in schools, they may not be able to meet the needs of enterprises after they leave the society. Therefore, it is necessary to create a classroom in the training area to create a practical educational situation, so that education, learning and collaboration can be combined, and theory and practice can be combined. To build such a classroom requires that the selection of materials for the completion of the course should be close to the work need, close to the professional environment, consistent with the company’s technological development. When opening courses, front-line teachers participate, and the definition of vocational courses should be based on actual work and work process, with due consideration to student acceptance. The focus of comprehensive education is to create a hands-on educational situation, provide students with real learning scenarios, let them practice as soon as possible, and help them from school to factory, and comprehensive education is to adhere to the cognitive law of the educated and effectively solve the problem of vocational education. High-quality vocational training courses for the discontinuity of theory and practice [9]. Other researchers put forward that the development of vocational training curriculum is a university policy reform based on vocational skill development guidelines and vocational skill development guidelines, which includes all aspects of curriculum, teachers, educational activities and materials. The purpose of vocational training course is to cultivate students’ basic vocational skills. The course needs to break the tradition and let students experience work, business and society in a realistic or completely realistic environment. In this way, the students not only have basic professional quality and basic professional skills, but also have good professional quality, good work style and innovative spirit [10]. In summary, there are many research results on the teaching reform of the computer network technology major, but there are fewer researches on the reform of the computer network technology major under the background of the IoT. This article studies the reform of the computer network technology major based on the IoT. After analyzing the current teaching situation of the computer network technology major, the actual situation is investigated by the method of questionnaire survey, and the specific measures for the teaching reform are proposed through the document survey results.

2 Computer Network Technology Professional Research 2.1 The Status Quo of Professional Courses of Computer Network Technology (1) The setting of educational goals is unreasonable and does not meet social needs Educational goals are the foundation of education, teaching, implementation and evaluation, and are the educational achievements and standards that teachers and students should achieve in educational activities [11]. The wrong direction will inevitably lead to insufficient talent training. The biggest difference between technical colleges and ordinary colleges is that graduates of technical colleges have skills. Therefore, cultivating technical talents is the current goal of cultivating

Reform and Thinking of Computer Network Technology Specialty

365

talents in engineering colleges. However, in the actual education process, most engineering colleges emphasize the systematicness and completeness of subject knowledge when formulating educational goals, while ignoring practical skills, and there is a phenomenon of emphasizing theory and neglecting practice. Therefore, the students did not show their advantages when they were hired. What companies need are technical talents for application implementation, not the “nerd type” who only understand theory and don’t know how to operate. (2) The purpose of education is not suitable for the actual level of students Education in our country is deeply influenced by traditional education. Educational objectives and content are generally based on the curriculum or education plans of relevant subjects announced by the state for the educational objectives and content of schools and localities. However, due to differences in regions and student quality, such courses or projects cannot fully adapt to the actual situation of students, and there is a contradiction between the educational goals and the actual level of students. (3) The teaching method is not systematic Computer network education should not be seen as a technique for teaching mechanical replication, but as a process of students’ conscious learning, textbook analysis and self-development. Many schools now recognize the importance of the reform of computer network professional education. Nowadays, many schools adopt task-oriented teaching methods, learning incentive education, model education and other methods to meet the needs of talent development in key disciplines of computer networks. However, reforms cannot be accomplished overnight, and systematic theoretical knowledge guidance and training are required. When the education reform is only on the surface, there are many misunderstandings and even opposite effects in the use of these new teaching methods. If you simply instill knowledge in students according to the old methods, it will still cause many students to sleep in the classroom. 2.2 The Necessity of Computer Network Technology Professional Reform in the Context of the IoT In the era of the IoT, the school has implemented the reform of the computer and network majors in higher vocational schools. It not only provides intuitive teaching scenarios through multimedia, but also reflects the difficult content of teaching more realistically, which helps students understand and improves students’ independent learning ability provides a lively and interesting teaching process for their learning process, and also establishes a new model of student participatory teaching, which further supplements the shortcomings of the school’s traditional teaching.

366

L. Wang and J. Qu

3 Investigation on the Teaching Status of Computer Network Technology Major 3.1 Purpose of the Investigation This article explores the current state of computer network technology education, primarily to investigate the awareness of computer network technology reforms and related suggestions for reforms in the context of IoT. 3.2 Investigation Process (1) Survey object Three universities are randomly selected in the city because the subjects of this article are determined to be students and teachers majoring in computer network technology to ensure the accuracy of the subjects. University levels vary to ensure universality of findings. (2) Number of survey samples The number of survey samples is an important factor that affects the results of the survey. Therefore, based on the relevant literature and the actual situation of the survey, the number of survey samples is determined to be 143. After the survey samples were issued, 140 samples were recovered. After sorting out the recovered samples, the effective number of samples was 140. 3.3 Data Processing For the missing value of the variable, calculate the average of the valid records for this variable and use this average to enter in [12]. Equation (1) shows the formula for calculating the average value when the average value filling method is used, n 

x=

ai xi

i=1

m

(1)

In the formula, a represents a vector, a = 1 indicates that the data at x is not missing, a = 0 indicates that the data at x is missing, x represents the i-th observation value of the data sample, and m represents the number of valid observations in the data sample. Obviously, the mean value of the overall data after restoration is the mean value of the valid observations, and the variance of the overall data is calculated by formula (2). 1  m−1 2 s (xi − x)2 = n−1 n−1 1 n

s2 =

(2)

i=1

In formula 2, s represents the variance of the effective observation data. Because the mean of the observations is used to fill in the missing data, the overall data variance after restoration is inconsistent with the actual variance, and the variance of the restored data is smaller than the original data, that is, the restored data is distributed too concentrated.

Reform and Thinking of Computer Network Technology Specialty

367

4 Analysis of Survey Results 4.1 Recognition of Reform by Computer Network Technology Majors in the Context of the IoT This paper uses questionnaire surveys to investigate the current teaching status of computer network technology majors, and obtains the recognition of students and teachers for reforms in computer network technology majors in the context of the IoT by sorting out the survey samples. The data results are shown in Table 1. Table 1. The recognition of reform in the computer network technology under the background of the IoT A college

B college

C college

Necessary

43%

45%

44%

Generally

36%

33%

34%

No need

21%

22%

23%

Fig. 1. The recognition of reform in the computer network technology under the background of the IoT

It can be seen from Fig. 1 that teachers and students majoring in computer network technology are necessary for teaching reform in the context of the IoT, accounting for more than 44%, and generally accounting for about 33%. From these data, we can see that the teaching reform is trending. It is imperative. 4.2 Reform Related Suggestions This paper uses questionnaire surveys to investigate the current teaching status of computer network technology majors, and obtains the teacher’s suggestions on reforms of

368

L. Wang and J. Qu

computer network technology majors in the context of the IoT by sorting out the survey samples. The data results are shown in Table 2: Table 2. Reform related suggestions A college

B college

C college

Revise talent training goals

34%

35%

37%

Reform the curriculum system

42%

45%

43%

Strengthening of faculty

24%

20%

20%

Fig. 2. Reform related suggestions

It can be seen from Fig. 2 that the reform of the curriculum accounted for more than 42% of the suggestions given, and then the reform of the talent training target accounted for about 35%. 4.3 Specific Reform Measures (1) Curriculum reform Traditional computer network technology majors must have network-level and application-level knowledge and abilities of the IoT, and only require the addition of a few courses on the perception level. Even college students studying on the IoT have a wide understanding of human cognitive level, and the time for college students to study on campus is limited, and they cannot fully grasp the cognitive level, so the curriculum should be targeted at their actual professional field to carry out inclined teaching. When setting up the IoT major in the department of Computer Science, perceptual level teaching often focuses on software. Hierarchical teaching is regarded as a professional extension of large-scale networking technology.

Reform and Thinking of Computer Network Technology Specialty

369

“Embedded System Linnux”, “Wireless Sensor Network Technology” and “Wireless Sensor Network Technology” “Application”, “Radio Frequency Identification (RFID) Technology and Application” and other courses, as well as part of the core content of the profession. Network technology large-scale expansion courses, such as “Network Security Technology” is “Universal Wiring Technology” that adds content related to the IoT. This will help students majoring in computer network technology to learn IoT-related technologies and enable graduates to engage in specific jobs in the IoT. (2) Establish a systematic work process research system Universities are experimenting with curriculum reforms such as integration of work and study, dual system, integration of production and education. The only purpose is to solve the problems in the research curriculum system. However, based on the progress of actual vocational training, none of these can be vocational training. Therefore, the development of a systematic curriculum system for the work process involves job analysis, task standardization, scope definition, learning field conversion, curriculum career selection, learning framework to achieve training modules and learning settings. The learning system created by integrating the knowledge of different disciplines can not only reflect the company’s work flow, but also optimize the implementation of educational goals. (3) New setting of human resources development goals The deployment of computer network technology is to cultivate the skills and talents needed to create and manage business and organizational networks. Vocational schools select the development direction of computer network technology major through surveys of enterprises, graduates, students, parents and schools. The definition of student employment work follows the basic principles of “professional → practical work tasks” and “professional basic abilities and professional skills” ideas. (4) Teaching method reform Throughout the teaching process, students adopt a variety of teaching methods such as project teaching method, case teaching method, and work-based teaching method to realize the combination of education, learning, and execution modules, focusing on cultivating professional skills. The project teaching method is applied in the courses of network technology, integrated cable, web page design, configuration, server management and so on. In the case teaching process, students carefully analyze the requirements and final results of the task, list the knowledge points and operating skills needed to solve the problem, formulate an implementation plan, set the task goal and step-by-step implementation, and pay attention to process management and monitoring, and write related reports.

5 Conclusions This article focuses on the research on the reform of the computer network technology major based on the IoT. After a general understanding of the relevant theories, the current teaching status of the computer network technology major is investigated, and the survey results indicate that the computer network technology major students and

370

L. Wang and J. Qu

teachers believe that teaching reform is necessary in the context of the IoT, and then give relevant reform suggestions, and put forward relevant collective measures based on the reform suggestions.

References 1. Abdulghaffar, A.A., Mostafa, S.M., Alsaleh, A., et al.: IoT based multiple disease monitoring and health improvement system. J. Ambient. Intell. Humaniz. Comput. 11(3), 1021–1029 (2020) 2. Wei, X., Qiong, G., Luo, Y., Chen, G.: The reform of computer experiment teaching based on O2O model. Comput. Appl. Eng. Educ. 27(1), 102–111 (2019). https://doi.org/10.1002/ cae.22060 3. Jing, Z.: Research on the Teaching Reform of Computer C Language under the Mode of MOOC. In: DEStech Transactions on Social Science Education and Human Science, 2017(ssme), pp. 66–68 4. Zhang J.: Research on the Teaching Reform of Computer C Language under the Mode of MOOC. Int. J. Technol. Manag. 000(006), 66–68 (2017) 5. Churchill, E., Bowser, A., Preece, J.: Teaching and learning human-computer interaction: past, present, and future. Concur. Comput. Pract. Exp. 28(4), 1291–1309 (2016) 6. Foerster, A., Dede, J., Koensgen, A., et al.: TEACHING THE IoT. Mobile Comput. Commun. Rev. 20(3), 24–28 (2016) 7. Wan, J., Tang, S., Shu, Z., et al.: Software-Defined Industrial IoT in the Context of Industry 4.0. IEEE Sens. J. 16(20), 7373–7380 (2016) 8. Burgin, S.R., Sadler, T.D.: Learning nature of science concepts through a research apprenticeship program: a comparative study of three approaches. J. Res. Sci. Teach. 53(1), 31–59 (2016) 9. Blair, G., Schmidt, D., Taconet, C.: Middleware for Internet distribution in the context of cloud computing and the Internet of Things: Editorial Introduction. Ann. Telecommun. 71(3–4), 87–92 (2016). https://doi.org/10.1007/s12243-016-0493-z 10. Annan, EdD, Dusti: Effectiveness of the sloppy mountain medical center computer-based escape room game for teaching interprofessional teamwork concepts. Collab. Health. Inter prof. Pract. Educ. Eval. (JCIPE), 10(1), 6–6 (2019) 11. Farhaoui, Y.: Teaching computer sciences in morocco: an overview. IT Prof. 19(4), 12–15 (2017) 12. Callaghan, M.N., Long, J.J., Es, E.V., et al.: How teachers integrate a math computer game: Professional development use, teaching practices, and student achievement. J. Comput. Assist. Learn. 34(1), 10–19 (2018)

Application and Prospect of Big Data in the Prevention and Control of Major Epidemics Youshen Chi(B) Human Resources and Social Security Information Center, Yantai 264001, Shandong, China [email protected]

Abstract. In recent years, various epidemic viruses have seriously threatened the safety of human life. Big data network technology follows the law of network evolution and is an inevitable choice for effective prevention and control of major epidemics. This article takes the epidemic data at the time of the COVID-19 outbreak as the research object, and analyzes the application of big data technology to people’s travel, communication and life in the epidemic from the perspective of big data. This article analyzes the correlation between crowd activities and the spread of the epidemic, the crowd mobile network model, the spatial clustering of cases, and the development trend of the epidemic. The results of the study showed that the number of online communities increased from 17 before the outbreak to 21 after the outbreak, and the average community space was reduced to 80.95% before the outbreak. The ratio of the amount of activity between communities to the amount of activities within the community was changed from before the outbreak. The reduction of 0.31 from 0.31 to 0.20 after the outbreak indicates that the scope of crowd activities has shrunk after the outbreak, and population activities among communities have weakened. Keywords: Big data technology · Major epidemics · Epidemic prevention and control · New coronavirus

1 Introduction At present, big data technology has matured in the detection technology of known diseases, but it is still difficult to detect new and unknown infectious diseases. Stevens H proposed an infectious disease diagnosis and antibiotic treatment system (IDDAT), which uses a decision support system based on big data to build a model [1]. Clarke R proposed a method framework for applying big data technology to medical treatment, which can store patient information and connect with institutions [2]. Cui L uses location and social network information to build a model to achieve the monitoring of infectious diseases [3]. Drosou M designed a data model for real-time monitoring of infectious diseases, which can reflect the changes of infectious diseases to a certain extent [4]. This article analyzes the correlation between crowd activities and the spread of the epidemic, the crowd mobile network model, the spatial clustering of cases, and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 371–378, 2022. https://doi.org/10.1007/978-3-030-99616-1_50

372

Y. Chi

the development trend of the epidemic. It lays a foundation for further analysis of the application of existing big data technology in the prevention and control of new major infectious diseases.

2 Current Status and Methods of Big Data Research in the Prevention and Control of Major Epidemics 2.1 Application of Big Data Technology in the Prevention and Control of New Major Infectious Diseases In view of the current deficiencies in the prevention and control of major infectious diseases and strategies, existing research teams at home and abroad have applied big data and blockchain technology to the monitoring and prevention of major infectious diseases [5]. Select and transform the cleaned data, and realize knowledge discovery in big data sets through data mining and mode selection. Due to the specificity of individual patients and the large number of cases, the accuracy of manual experience review of case information is not high. These problems will affect the early detection of new and major infectious diseases, not conducive to timely control of the epidemic [6]. 2.2 Ideas for Constructing the Model of Epidemic Spreading Community Unit Based on Big Data Agent-based modeling method (ABM) is a type of distributed artificial intelligence, and its operation mode is to establish a series of agents with independent analysis and decision-making capabilities (agent), and simulate the operation of the real world through the behavior and interaction of these agents [7]. Traditional observation and research methods are very different. It is difficult to quantify and summarize its operating rules, and the agent-based model can describe and predict the overall spread and spread of the epidemic through distributed simulation of the subject and its interactive activities. Expression of various prevention and control measures from the perspective of this new coronavirus epidemic, the main prevention and control measures adopted by the government mainly include three categories: The first category is publicity and education; the second category is monitoring; the third category is isolation control. The relationship between crowd activity and the spread of the epidemic is manifested in two aspects: on the one hand, the time correlation between crowd activity and case growth, using the total daily population movement and the daily number of real-time reproductive cases (Rt) and the number of new cases per day. On the other hand, the correlation between population activity and the spatial distribution of cases is measured by the pearson correlation coefficient between the total intensity of crowd movement in each street after the outbreak and the number of cumulative confirmed cases and the number of local infections [8].

Application and Prospect of Big Data

373

2.3 Combined Prediction Model for Big Data Epidemic Prevention and Control Decomposition and Reconstruction of Major Epidemic Epidemic Data Decompose the collected large-scale major epidemic data by wavelet analysis to obtain multi-scale components. At1 = SoftMax (kt1 )

(1)

In the formula, A is the phase space dimension. Eliminate the mutation part, and the remaining high frequency part can accurately describe the change characteristics of major epidemic data: vj = squash(sj ) ikjl =

n 

ikjl (ε)

(2)

(3)

δ=1

The chaos theory is used to reconstruct the decomposed multiple scale components, which reduces the prediction error and computational complexity of large-scale nonlinear major epidemic data: ht = tanh(wc xt + uc (rt ht−1 ) + bc )

(4)

yt = σ (W0 · ht )

(5)

x is the next input, ht−1 is the suspension of the last import, ht is candidate status at the current moment,W0 is the hidden state at the current moment,yt is output for the current moment.

3 Application Research Design of Big Data in the Prevention and Control of Major Epidemics 3.1 Application Research Design of Big Data in the Prevention and Control of Major Epidemics Based on the mobile phone signaling data provided by a communications company, the movement of people between streets is calculated. The company has a market share of approximately 21%, with a total number of 15 116 289 users, and an expansion factor of 4.76 for data expansion [9].

374

Y. Chi

3.2 Content (1) Analyze the development trend of the epidemic. According to the time of arrival of imported cases to the epidemic area, draw daily case input charts and analyze the status of imported cases in the early stage of the epidemic [10]. In addition, in order to understand the dynamic trend of the infectious power of the disease, the Lotka-Euler equation is used to calculate the real-time reproductive number of COVID-19 local cases Rt at different time periods: Rt =

1 M (−rt )

(6)

Rt represents the growth rate of the local cumulative number of cases in different periods, and the local cumulative number of diagnoses after an average incubation period is used as a proxy. This article uses the incubation period as 7d, M is the intergenerational time distribution function, and the moment generating function of g(t). (2) Analyze the crowd mobile network. According to the mobile phone users’ movement between streets, construct a crowd mobile network between streets. The formula is as follows: MIi =

n 

Mij

(i = j)

(7)

j=1

Where: MIi is the total intensity of crowd movement in the i-th street; Mij is the intensity of crowd movement between streets i and j; n is the number of streets. The formula for calculating the proportion of human movement change is: Ri =

MIi (a) − MIi (b) MIi (b)

(8)

In the formula:Ri is the proportion of people moving in the i street; MIi (after) is the total number of people moving on street i after the outbreak, and MIi (before) is the total number of people moving on street i before the outbreak.

4 Big Data Analysis in the Prevention and Control of Major Epidemics 4.1 Big Data Analysis of the Temporal and Spatial Evolution Characteristics of the Epidemic As shown in Fig. 1, from the time the imported cases arrived at the epidemic area, it can be seen that from January 6th, people infected from other places have been imported into the epidemic area one after another, and reached a peak on January 19th, and then declined; January 20.

Application and Prospect of Big Data

375

Fig. 1. The time evolution of the COVID-19 epidemic

Fig. 2. The cases have shown a relatively clustered pattern in all districts and counties

As shown in Fig. 2, big data technology is of great significance for epidemic prevention and control. Family doctors or grassroots community hospitals can detect signs of an epidemic at the earliest stage, and classify and refer patients according to the criticality of their illness, and treat the epidemic in a relatively appropriate way.

376

Y. Chi

Fig. 3. The Moran’s I index follows the development of the epidemic

Table 1. The changing trend of the total daily movement of people Time

Imported cases

Import case

I&L

Local case

No infection

Symptomatic

1.21

1.98

2.15

2.16

1.89

2.41

4.13

1.23

2.35

2.31

2.8

2.86

2.6

4.27

1.25

2.84

3.32

3

3.47

3.01

7.82

1.27

4.19

4.32

4.27

4.47

3.89

4.22

1.29

4.84

5.05

5.45

5.05

5.48

6.99

2.1

4.98

4.96

4.97

5.02

5.43

9.47

The results of the spatial clustering analysis of cases are shown in Fig. 3. It can be seen that the global Moran’s I index fluctuates up with the development of the epidemic, and finally stays at 0.24, indicating that the distribution of cases shows a pattern of increasing clustering around high-high clustering. It can be seen that after the outbreak, with the implementation of the government’s prevention and control measures, crowd mobility was significantly reduced. The total number of people moving over time is shown in Table 1. Before the outbreak, the total number of people moving every day showed a gradually increasing trend. After the first case appeared on January 21, the number of people moving began to be significant decrease, and with the government’s first-level response on January 24. 4.2 Big Data Analysis of the Relationship Crowd Activity and Epidemic Spread As shown in Fig. 4, the correlation coefficient between the total daily population movement on a logarithmic scale and the real-time reproductive number (Rt) of cases is 0.97

Application and Prospect of Big Data

377

Fig. 4. Total daily population movement and real-time reproduction number of cases

(P < 0.01), and the daily newness after an average incubation period (7d). The correlation coefficient of increasing the number of cases was 0.89 (P < 0.01), indicating that there is a strong correlation between population activity and case growth.

Fig. 5. Digital health use a series of digital technology methods

As shown in Fig. 5, comparing the distribution of the population mobile network community and the case cluster area after the outbreak, it can be found that the population mobile community distribution is basically consistent with the distribution of the case cluster area. Different high cluster areas of cases correspond to different online communities.

378

Y. Chi

5 Conclusions Although many technical methods for applying big data to infectious diseases and medical care have been proposed in recent years, the monitoring accuracy of big data needs to be improved due to the inability to reliably share data between medical institutions. In the early stage of the outbreak of a new major infectious disease, there is a problem of information islands. The early outbreaks of major infectious diseases are separated from each other, resulting in the inability to horizontally share data comparisons of the same symptoms, disease information, and special epidemic data. It will reduce the accuracy of monitoring models based on big data, and affect the early detection and control of new major infectious diseases. Accuracy and efficiency of surveillance of new major infectious diseases. For unknown new major infectious diseases, it is necessary to optimize existing models, improve data analysis methods, and further improve the accuracy and efficiency of monitoring and early warning of new major infectious diseases.

References 1. Stevens, H.: Big data, little data, no data: scholarship in the networked world. J. Assoc. Inf. Technol. 67(3), 751–753 (2016) 2. Clarke, R.: Big data, big risks. Inf. Syst. J. 26(1), 77–90 (2016) 3. Cui, L., Yu, F.R., Yan, Q.: When big data meets software-defined networking: SDN for big data and big data for SDN. IEEE Network 30(1), 58–65 (2016) 4. Drosou, M., Jagadish, H.V., Pitoura, E., et al.: Diversity in big data: a review. Big Data 5(2), 73–75 (2017) 5. Hans, J.P.: Big data in food safety: an overview. Critic. Rev. Food Sci. Nutr. 57(11), 2286–2295 (2016). https://doi.org/10.1080/10408398.2016.1257481 6. Salmond, J.A., Tadaki, M., Dickson, M.: Can big data tame a “naughty” world? Can. Geogr. 61(1), 52–63 (2016) 7. Taglang, G., Jackson, D.B.: Use of “big data” in drug discovery and clinical trials. Gynecol. Oncol. 141(1), 17–23 (2016) 8. Deng. Z.Q., et al.: Analysis on transmission chain of a cluster epidemic of COVID-19, Nanchang. Ch. J. Epidemiol. 41(23), 23–26 (2020) 9. Kensler, T.W., Spira, A., Garber, J.E., et al.: Transforming cancer prevention through precision medicine and immune-oncology. Cancer Prev. Res. 9(1), 2–6 (2016) 10. Jason, W.C., Ng, C.Y., Brook, R.H.: Response to COVID-19 in TaiwanBig data analytics, new technology, and proactive testing. JAMA 323(14), 46–49 (2020)

The Applicability of Machine Learning Algorithms in Predictive Modeling for Sustainable Energy Management Shokhjakhon Abdufattokhov1(B) , Kamila Ibragimova2 , and Dilfuza Gulyamova2 1 Turin Polytechnic University in Tashkent, Little Ring Road Street 17, Tashkent, Uzbekistan [email protected] 2 Tashkent University of Information Technologies, Amir Temur Street 107, Tashkent, Uzbekistan https://tuit.uz https://polito.uz/control-and-computer-engineering/

Abstract. The world’s energy sector is having difficulties governing the best management synthesis because of challenges such as a request in production supply and demand design changes. Mapping data of the energy sector to machine learning (ML) can effectively alleviate the problem. ML algorithms can analyze equipment data, build predictive models and solve issues regarding sustainability. Innovative areas designed with ML algorithms can naturally react to fluctuations in power costs and control energy utilization. Frameworks dependent on ML can help energy providers to get ready to stay up with fluctuating sustainable power supplies through predicting energy demand, forecasting the maintenance period of pieces of equipment in energy plants such as sunlight based PVs, wind power and hydrogen sources enabling to eliminate the applicability limits of these renewable energy sources around the world. Designing smart grids in combination with advanced control techniques, such as model predictive control (MPC) enables to comfort satisfaction of consumers while handling constraints needed to meet sustainability. This paper is devoted to use of ML algorithms in different renewable energy sources and bridging ML with MPC to achieve sustainable energy management . Keywords: Machine learning · Predictive modeling energy · Model predictive control

1

· Sustainable

Introduction

In the last few decades, the world’s energy sector has faced adding challenges, similar to a demand and effectiveness, supply and demand pattern change, and the absence of the best management analysis. In addition, numerous greenhouse gases are contributing a vital role to global warming due to the burning of coal, oil, and gas are creating a harmful greenhouse effect that is causing global warming and climate change [1,2]. To eliminate this climate change, it is necessary to c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 379–391, 2022. https://doi.org/10.1007/978-3-030-99616-1_51

380

S. Abdufattokhov et al.

reduce produced greenhouse gases like CO2 emission from fossil fuel and to use alternative renewable energy sources (RES) like solar photovoltaic (PV) panels, wind turbines and water dams to generate electricity with minimal cost of operation and green energy environment. Cities taking advantage of smart grids (SG) are being integrated both energy sources to get an uninterrupted power supply and optimize resource management by the data-driven control system. However, there can be a shortfall or excess energy generation by RES since solar and wind power generation depends on sunshine and wind speed. Thus, for sustainable power supply to load and to combat voltage and frequency fluctuation problems, the local onsite micro-grid is integrated into the primary power grid called the macro-grid. When RES generates less power, the macro-grid will supply the remaining power, and when RES generates excess power, it can sell it to the macro grid. A lithium-ion battery can store excess energy by RES, but it is relatively expensive. For this reason, researchers from the energy sector argue that there is a considerable need to sustain our natural resources like coal, oil and gas for further energy production [3]. The mentioned worldwide energy and environmental challenges have led megacity governments to gradationally modify their programs, opinions, and strategies towards a greener and energy-effective approaches [4]. An increase in monetary savings, cut off in producing greenhouse gas, and improvement in energy security can be achieved by reducing energy demand using the energy efficiently. The authors of [5] proposed a methodology to identify energy baselines and performance indicators in investigating energy shortening potentials. Their findings revealed that potential annual energy savings for the educational buildings were up to 9.6 %. Another method was offered by [6] to quantitatively predict the effect of greening on building energy consumption to support decision-makers for implementing urban greening policy. From the economic and technical challenge point of view, the smooth running of the power system by switching between macro grid to microgrid is challenging since it requires weather forecast to be accurate to produce enough renewable energy. Thus power production, transmission, distribution and delivery to the consumer have to be more tractable, viable and cost-efficient for all parties like stakeholders, government regulators, clients and consumers. Transferring energy sector data to artificial intelligence (AI), especially machine learning (ML), can gradually solve the concerns mentioned above and help achieve sustainable energy management. A smart grid is an intelligent digital electric grid that bridges a variety of technologies and customer services. The technologies and services used in the smart grid depend on the load type served and electricity system networks type. Figure 1 illustrates that the smart grid facilitates the consumers and prosumers’ increased choices in controlling their electricity use and production. ML is prevalent in almost every renewable energy research for design, estimation, optimization and distribution. The proposed ML algorithms for renewable energy research are complex and expensive, hence need to be simplified and cost-effective. Energy use information improvement with worker banks and other gadgets running constantly should be planned

Sustainable Energy with ML

381

Fig. 1. Overall infrastructure of smart grid for sustainable energy [7].

without cooling the energy and cost reserve funds. Energy forecasting and planning are essential for different kinds of stakeholders for making the decisions of sustainable future energy development globally. An accurate predictive models of energy demand constructed by ML models like support vector machines, artificial neural networks, Gaussian processes, K-nearest neighbour and others, enable to avoid unexpected power blackouts, reduce operating costs and overcome complexity problems in modern energy[8,9]. The remainder of this paper is organized as follows: After briefly discussing motivations and necessities to achieve sustainable systems in Sect. 1, we highlight commonly used machine learning algorithms in Sect. 2. Section 3 deals with the importance of predictive modelling in different energy sources, while Sect. 4 bridges machine learning and advanced control technique to achieve sustainable energy management. Finally, the conclusions are drawn in Sect. 5.

2

Machine Learning Algorithms

Machine learning is becoming commonly used technique to design smart systems in different fields during the past few decades. ML uses training data to build a predictive model for given future data. For this task, we guess some models and structure, and find the set of model parameters, which to be optimized. In order to design a proper predictive model, it is required to find parameters that make minimum the error of the model for the training data and then test the model with test data that was not used to find the model’s parameter. There are three main ML methods types: supervised learning (predictive), unsupervised learning (descriptive), and reinforcement learning. The supervised learning methods aim to discover a mapping from inputs to outputs, given a labelled set of input-output

382

S. Abdufattokhov et al.

pairs. An optimal chosen model enables the algorithm to correctly determine the class labels for unseen samples. In the unsupervised learning methods, the purpose is to recognize related patterns for a given input data. Unlike supervised learning, this is a much less specific problem since there is no confidence and specific measure of what patterns to match and of the error to use, accordingly. Unsupervised learning is not only closely related to the problem of density estimation in statistics, but also summarizes and explains key features of the data. The schematic diagram of commonly used machine learning algorithms is depicted in Figure 2. Below, we provide preliminary concepts about these methods. 2.1

Regression Model Algorithms

Regression methods are widely used not only for providing with good prediction model, but also for ability to clarify which variables are correlated or not correlated to each other. In practice, the performance of regression algorithms rely on the form of the data collection procedure since the proper form of the data-generating process is generally not known. Regression models for prediction are often applicable although they may provide with suboptimal values [10]. The most popular regression algorithms are ordinary least squares regression, linear regression, logistic regression and multivariate adaptive regression splines. 2.2

Instance-Based Algorithms

An instance-based learning model is a decision problem with instances or examples of training data deemed essential or required to the model. This type of learning, also called memory-based learning, is lazy because it compares new samples with instances stored in memory instead of performing explicit generalization. The standard instance-based algorithms are k-nearest neighbor (kNN) [10], learning vector quantization, locally weighted learning and support vector machines (SVM) [11]. 2.3

Decision Tree Algorithms

Decision tree algorithms build a predictive model based on actual values of attributes in the data. Depending on the data type two types of trees can be generated, namely classification and regression. In classification tree structures, class labels are assigned in leaves, while the features that lead to different label classes are represented in branches [12]. Decision trees where the target value is continuous are called regression trees. The widely implemented decision tree algorithms are classification and regression tree (CART), Chi-squared automatic interaction detection (CHAID), decision stump, conditional decision trees.

Sustainable Energy with ML

2.4

383

Bayesian Algorithms

Bayesian algorithms are a statistical inference method that updates the probability measure as acquired evidence using Bayes’ theorem [13]. The Bayesian method is an essential technique in many areas such as mathematical statistics, stochastic modelling, robust control and so on. Bayesian updating is crucial in the dynamic analysis of a data sequence as it provides a rational method for updating beliefs. The most popular Bayesian algorithms are Naive Bayes, Gaussian Naive Bayes, multinomial Naive Bayes, Bayesian belief network and Bayesian network.

Fig. 2. Schematic architecture of commonly used machine learning algorithms.

2.5

Artificial Neural Network Algorithms

Artificial neural networks models can be understood as a surrogate function approximation method that adopts the structure of biological neural networks since they present systems of interconnected “neurons” which share messages mutually [14]. These connections consist of weights optimized during the training process with the help of an adequately chosen loss function. This learning method is widely implemented for regression and classification problems thanks to its universal approximator ability. Single layer perceptron, multilayer perceptrons, backpropagation, Hopfield network and radial basis function networks are considered as the most popular ingredients in artificial neural network architecture.

384

2.6

S. Abdufattokhov et al.

Deep Learning Algorithms

Deep learning architecture consists of several shallow networks(hidden layers) connected through parameters including weights and bias [10]. Each layer of these complex architecture represents different features, and their sum tries to match the target in the output layer. The composition of a layer of nonlinear processing units used in a deep learning algorithm rely on the problem. The familiar deep learning algorithms are feedforward, convolutional, recurrent neural networks, long short-term memory networks and stacked auto-encoders. 2.7

Clustering Algorithms

Clustering algorithms describe the class of problem and the class of methods. In this method, centroid-based and hierarchal modelling approaches form different types of labelled clusters [10]. All methods are concerned with using the adopted structures in the data best to organise the data into groups of maximum commonality. Common notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or specific statistical distributions. The most popular clustering algorithms are: k-means, k-medians, expectation maximisation and hierarchical clustering. 2.8

Dimensionality Reduction Algorithms

In machine learning, dimensionality reduction is the process of shortening the number of random variables through analyzing feature selection and feature extraction that results in transforming the data from the high-dimensional space to fewer dimensions [10]. Many of these methods can be adapted for use in classification and regression, such as principal component analysis (PCA), principal component regression, multidimensional scaling, linear, mixture and flexible discriminant analysis.

Fig. 3. Model evaluation process in machine learning.

Deploying a proper ML model consists of several processes, as depicted in Fig. 3, starting from raw data analysis and ending with a model accurate enough.

Sustainable Energy with ML

385

The main time-consuming parts in the model evaluation are data processing (i.e. feature selection) and model training that involves choosing the proper model structure, an efficient optimization algorithm and a correct loss function. We refer [10] to the reader for a more detailed explanation of each section.

3

The Importance of Predictive Modelling in Different Energy Source Sectors

Environmentally friendly energy generation comprises synchronous and asynchronous power sources. The former mainly consists of hydropower and biomass, while the latter includes generation such as solar and wind, whose nature creates difficulty in making the total output of the grid supply predictable, resulting in system instabilities. These two issues require the accurate predictive modelling of asynchronous power assets and the utilization of new techniques to improve framework strength. With the extension of information technology, advanced ML algorithms has been experimented to perform different intelligent behaviour in the energy industry and have made ML become the primary technological system such as power system cybersecurity, nuclear power plants, electrical grids, manufacturing, building construction and so on. Figure 4 shows the impact of AI(mainly ML) on the energy and economic sectors [15] from where one can conclude that energy demand will soon play a vital role in the world economy.

Fig. 4. The impact of AI(mainly ML) on the energy and business sectors [15].

386

S. Abdufattokhov et al.

This section reviews the use of ML in central renewable energy source systems such as wind, solar and hydrogen. 3.1

Hydrogen Energy

Low carbon and economics are the primary and friendly indexing terms for selecting hydrogen production technology. Producing hydrogen through electrolyzing water is the most straightforward source of hydrogen and the most economical in terms of energy production. Nowadays, industrial hydrogen generation technology mostly takes advantage of using petrochemical energy and considers above 95% of the world’s total hydrogen production. Nevertheless, the primary disadvantage coming from producing hydrogen based on petrochemical energy is the by-product of a large amount of carbon dioxide. It is required to make full use of industrial by-product hydrogen to face a green environment by properly developing coal gasification, developing minor oil and natural gas cracking to produce hydrogen, and restricting the aimless use of electrolytic water to produce hydrogen. The main advantage of implying ML in hydrogen energy is the forecast of CO2 hydrogenation activities [16], the output voltage of the microbial fuel cell [17], hydrogen power density forecast [18], solid oxide fuel cell [19]. The approaches established in these researches proved ML’s efficiency in energy generation modelling and increased effectiveness of energy waste. 3.2

Solar Energy

Solar energy is also considered as promising sustainable energy resources because of environmental friendliness and virtual inexhaustibility. The most widely used solar technologies are PV devices that convert light directly into electricity without engine intervention. The power generated by PV is associated with the irradiance problem, thus implementing prognostic algorithms may bring benefit to the operation of PV-containing power systems and grids. The predictive modelling feature of ML algorithms is highly dependent on the quality of the data acquisition process and the model type. Hybrid methods can be used in integrating various data sources and modelling techniques to improve prediction accuracy [20]. The authors of [21] investigated modelling solar cells for the simulation analysis of PV systems. A feature extraction method from photovoltaic data is investigated in [22] to choose the proper vector of inputs an SVM classifier can reconstruct the weather types database. The study of building a predictive model with an ML approach in electric power production by a solar photovoltaic system is proposed by [23,24]. 3.3

Wind Energy

Among the widely used renewable energy sources, wind energy is the third most crucial natural source coming after hydrogen and solar PV. The wind turbines are the energy-generating devices mounted at specific heights to capture most

Sustainable Energy with ML

387

of the available energy, are rotated by kinetic energy in power plants, spinning the generator rotor to produce electricity. Investigations on applying ML algorithms to wind energy technology has been carried out for more than 20 years. The authors of [25] proposed a feature selection in wind power prediction systems where artificial neural networks and k-NN algorithms were implemented. In [26], the authors presented a wind speed forecasting system relying on an SVM approach. A prediction model provided by combining machine learning several techniques for short-term wind speed prediction problems were developed by [27].

4

Predictive Control for Sustainability

Modern energy system automation mainly covers three fields for providing sustainability: predictive maintenance, optimal control and efficient management. While good efficiency and quality of the networks are achieved by implementing proper control methods, for high availability and reliability, and economic benefits are maintenance and management responsibilities, respectively. In order to meet sustainability requirements, they have to be integrated simultaneously and should be incorporated into an intelligent maintenance-control-management system. Moreover, power companies and utilities try to match uncertain variable loads to control the smart grid’s sustainability and satisfy their real-time demand responces. Hence the implemention of an elaborate version of predictive maintenance control is needed. On the other hand, the benefits of ML algorithms has been implementes for increased enery processing and the producing of big data. ML tools extract informative features from collected data and conscructs a predictive model to control complex energy systems to provide stability of energy sources. The interconnection between large data and ML is a primary requirement for an intelligent tool to efficiently analyze datasets generated from power systems to design a robust control with high system accuracy and satisfaction. Figure 5 illustrates this concept and real-time applications in energy systems controlled by intelligent optimal control method called as Model Predictive Control (MPC). MPC is a multivariable control technique that optimizes the objective function mostly by driving the predicted plant output to the desired reference, while handling constraint satisfaction if a physical or an accurate and suitable predictive surrogate model of the real plant is available. Due to this ability, MPC has been satisfactorily applied to solve complex control industrial problems [28–30]. During the last decade, control engineers have been trying to bridge MPC with ML to overcome difficulties in modelling the system dynamics. We refer [31–33] to the reader for MPC-based ML algorithms’ applications to enhance efficient energy management. The use of ML can mitigate the challenges mentioned above and increases the share of producing renewable energy in the country’s energy supply system. Additionaly, it is expected that ML techniques and the innovative energy distributing framework will improve centralized energy control centres and provide new integration capabilities for smart grids.

388

S. Abdufattokhov et al.

Fig. 5. Bridging ML and MPC for energy efficient management.

5

Conclusion

This research mainly focused on the applicability of machine learning methods in different renewable energy source sectors for sustainable development. The studies mentioned above showed that machine learning technology has high potentials to demonstrate green engineering by forecasting the maintenance period of equipment in energy plants such as sunlight-based PVs, wind power, and hydrogen sources, enabling eliminating the applicability limits of these renewable energy sources around the world. The advancements in machine learning improve the integration of renewable energy and increased computational power solve many problems in the energy industry. To optimize the consumption of renewable sources, integrating ML and MPC technologies with the power grid can significantly increase the power system’s stability, reliability, load planning and management. Future work will be devoted to the comprehensive analysis of machine learning methods in solving problems related to green manufacturing processes.

Sustainable Energy with ML

389

References 1. Debnath, K.B., Mourshed, M.: Forecasting methods in energy planning models. Renew. Sustain. Energy Rev. 88, 297–325 (2018) 2. Vasylieva, T., Lyulyov, O., Bilan, Y., Streimikiene, D.: Sustainable economic development and greenhouse gas emissions: the dynamic impact of renewable energy consumption, GDP, and corruption. Energies 12, 32–89 (2019) 3. Erdinc, O., Uzunoglu, M.: Optimum design of hybrid renewable energy system: overview of different approaches. Renew. Sustain. Energy Rev. 16(3), 1412–1425 (2012) 4. Cajot, S., et al.: Obstacles in energy planning at the urban scale. Sustain. Urban Areas 30, 223–236 (2017) 5. Ocampo, B., et al.: A methodology to estimate baseline energy use and quantify savings in electrical energy consumption in higher education institution buildings: Federal University of Itajuba (UNIFEI) case study. J. Clean. Prod. 244, 1 (2020) 6. Qiao, R., Liu, T.: Impact of building greening on building energy consumption: a quantitative computational approach. J. Clean. Prod. 246, (2020) 7. Kumar M., et al.: Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids. Energies. 13(21), (2020). https://doi.org/10.3390/ en13215739 8. Barhmi, S., Elfatni, O., Belhaj, I.: Forecasting of wind speed using multiple linear regression and artificial neural networks. Energy Syst. 11(4), 935–946 (2019). https://doi.org/10.1007/s12667-019-00338-y 9. Houimli, R., Zmami, M., Ben-Salha, O.: Short-term electric load forecasting in Tunisia using artificial neural networks. Energy Syst. 11(2), 357–375 (2019). https://doi.org/10.1007/s12667-019-00324-4 10. Harrington, P.: Machine Learning in Action. Manning Publications Co., Shelter Island, NY, USA (2012) 11. Karatzoglou, A., Meyer, D., Hornik, K.: Support vector machines in R. J. Stat. Softw. 15(9), 1–28 (2006) 12. Kotsiantis, S.B., Zaharakis, I.D., Pintelas, P.E.: Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26(3), 159–190 (2006) 13. Abdufattokhov S., Muhiddinov B.: Probabilistic approach for system identification using machine learning. In: International Conference on Information Science and Communications Technologies (ICISCT), pp. 1–4, https://doi.org/10.1109/ ICISCT47635.2019.9012025 (2019) 14. John G., Kohavi R., Pfleger K.: Irrelevant features and the subset selection problem. In: Proceedings of the 11th International Conference on Machine Learning. July 10–13, New Brunswick, NJ, (1994) 15. Ransbotham S., Kiron D., Gerbert, P., Reeves, M.: Is your business ready for artificial intelligence? Jt. BCG-MIT sloan manag. Rev. Surv. impact Artif. Intell. Bus. (2017).https://www.bcg.com/publications/2017/strategy-technologydigitalis-your-business-ready-artificial-intelligence.aspx 16. Liu, Y., Cao, T., Han, S., Xu, G.: Design of CO2 hydrogenation catalyst by an artificial neural network. Comput. Chem. Eng. 25, 1711–1714 (2001). https://doi. org/10.1016/S0098-1354(01)00714-1

390

S. Abdufattokhov et al.

17. Garg, A., Vijayaraghavan, V., Mahapatra, S.S., Tai, K., Wong, C.H.: Performance evaluation of microbial fuel cell by artificial intelligence methods. Expert Syst. Appl. 41, 1389–1399 (2014). https://doi.org/10.1016/j.eswa.2013.08.038 18. Tardast, A., et al.: Use of artificial neural network for the prediction of bioelectricity production in a membrane less microbial fuel cell. Fuel 117, 697–703 (2014). https://doi.org/10.1016/j.fuel.2013.09.047 19. Marra, D., Sorrentino, M., Pianese, C., Iwanschitz, B.: A neural network estimator of Solid Oxide Fuel Cell performance for on-field diagnostics and prognostics applications. J. Power Sources 241, 320–329 (2013). https://doi.org/10.1016/ j.jpowsour.2013.04.114 20. Aguiar, L.M., Pereira, B., Lauret, P., D´ıaz, F., David, M.: Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting. Renew. Energy 97, 599–610 (2016). https://doi.org/10.1016/j.renene.2016.06.018 21. Patra, J.C., Modanese, C., Acciarri, M.: Artificial neural network-based modelling of compensated multi-crystalline solar-grade silicon under wide temperature variations. IET Renew. Power Gener. 10, 1010–1016 (2016). https://doi.org/10.1049/ iet-rpg.2015.0375 22. Wang, F., Zhen, Z., Mi, Z., Sun, H., Su, S., Yang, G.: Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting. Energy Build 86, 427–438 (2015) 23. Rana, M., Koprinska, I., Agelidis, V.: Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Convers Manag 121, 380–390 (2016) 24. Yadav, A., Chandel, S.: Solar radiation prediction using Artificial Neural Network techniques: a review. Renew. Sustain. Energy Rev. 33, 772–781 (2014) 25. Jursa, R., Rohrig, K.: Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models. Int. J. Forecast. 24, 694–709 (2008) 26. Kong, X., Liu, X., Shi, R., Lee, K.: Wind speed prediction using reduced support vector machines with feature selection. Neurocomputing 169, 449–456 (2015) 27. Feng, C., Cui, M., Hodge, B.M., Zhang, J.: A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. Appl. Energy 190, 1245–1257 (2017) 28. Abdufattokhov, S., Ibragimova, K., Gulyamova, D., Tulaganov, K.: Gaussian Processes Regression based Energy System Identification of Manufacturing Process for Model Predictive Control. Int. J. Emerg. Trends Eng. Res. 8(9), 4927–4932 (2020). https://doi.org/10.1109/ICISCT47635.2019.9012025 29. Bruni, G., Cordiner, S., Mulone, V., Rocco, V., Spagnolo, F.: A study on the energy management in domestic micro-grids based on model predictive control strategies q. Energy Convers. Manag. 102, 50–58 (2015) 30. Patrinos P., Trimboli S., Bemporad A.: Stochastic MPC for real-time market-based optimal power dispatch. In Proceedings of the 50th Conference on Decision and Control, Orlando, USA, 7111–7116, (2011) 31. Neto, A., Fiorelli, F.: Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Build 40, 2169–2176 (2008)

Sustainable Energy with ML

391

32. Jain A., Mangharam R., Behl M.: Data Predictive Control for peak power reduction. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, 109–118, (2016) 33. Abdufattokhov, S., Ibragimova, K., Khaydarova, M., Abdurakhmanov, A.: Datadriven finite horizon control based on gaussian processes and its application to building climate control. Int. J. Tech. Phys. Prob. Eng. (IJTPE) 3(2), 4927–4932 (2021)

Smart Home Appliance Control in the IoT Era Osman Abdalla Khalfalla1 , Suleiman Abdullahi Ali2(B) , Chadi Altrjman1 , and Auwalu Saleh Mubarak1(B) 1 Artificial Intelligence Engineering Department, Research Center for AI and IoT,

AI and Robotics Institute, Near East University, Mersin 10, Turkey {20195032,chadi.altrjman,auwalusaleh.mubarak}@neu.edu.tr 2 Center for Research and Development, Jamhuriya University of Science and Technology, Mogadishu, Somalia [email protected]

Abstract. Many nations are trying to develop smart cities. In these cities, all appliances will be connected through a communication network, the main challenge is building a system that will be flexible and efficient at the same time. In this study a mobile application was developed to control home appliances through ESP32 controller. The mobile application was developed in such a way that the user can use fingerprint, voice control, and switch buttons control. The mobile application was tested on several phones from a different manufacturer and it performs greatly based on the performance criteria specified in this work. Keywords: Smart buildings · Internet of Things · Smart cities · Mobile applications

1 Introduction Smart cities are one of the major research topics in the Internet of Things(IoT). Smart cities are designed to make life easier and to have things connected to the internet. The applications of smart cities ranges from City-planning, transportation, communication, education, health, and tourism are some of the most common applications [1, 2]. The primary goal of technology has always been to increase efficiency while reducing effort. Today the world is pushing towards ubiquitous computing in all aspects of life since the emergence of the ‘Internet of Things in the last decade. As a result, it’s critical to make human-technology interaction as simple as possible. Automation is an example of a technology that seeks simplicity while also enhancing efficiency [3–5]. Several studies were carried out on home automation or smart homes by using different controllers, communication channels and different command types. Communication can be done through Bluetooth [6, 7], Wi-Fi [8, 9] or mobile data to communicate between the mobile app, controller and the device to be controlled, while for the commands, buttons on mobile applications [6], voice control [10] and automatic control based on detections by IR sensors. It is very important to have a system that will incorporate these different types of commands (Buttons, Voice and Sensors) and communication (Bluetooth, Wi-Fi and Mobile Data). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 392–397, 2022. https://doi.org/10.1007/978-3-030-99616-1_52

Smart Home Appliance Control in the IoT Era

393

In this study a smart home control system was developed to control home appliances from a mobile application, below are the contributions of the study: • We developed a mobile application to control home appliances with flexible control such as fingerprint, voice command and buttons • We developed a system that uses different network communication to the user for round the clock usage. 1.1 Related Works There are several research papers published, based on smart lighting and smart houses. In this study, efforts are being made to improve the smart home automation in such a way that appliances can be controlled at a close range and from anywhere around the globe. In[11] authors worked on the smart home Sinric pro website, this helps to connect with the sinric pro IOT platform to perform device operations remotely. This system allows the user to stay connected with their home by using Google Assistant Smart Things and Amazon Alexa platforms too. The system improves user’s flexibility with the feature of real-time feedback on any platform and they worked on a good project but the disadvantage of this project is that sinric website will give you one month for free and then will charge you have to pay 2–3$ for each device, this is expensive. In [12] authors worked on Using Esp32 IoT Based Wi-Fi Enabled Smart LED Systems to reduce energy wastage (Established street lighting systems have certain drawbacks as they are operated manually. If this system is not monitored properly, this may lead to more energy consumption. This system requires proper monitoring and energy management techniques to reduce energy wastage. In [13] authors worked on an Internet of Things Based Air Conditioning and Lighting Control System for smart homes, a sample smart home application was implemented using IoT technology and NodeMCU embedded system microcontroller. This system, which can be controlled by the Blynk smart home interface from any environment where the Internet is available, provides a comfortable and smart climate-lighting system. There is an Android-based home automation system that makes use of Bluetooth and voice commands, and it’s called an Android Based Home Automation System Using Bluetooth and Voice commands [14]. The authors in [15, 16] developed a system that can decode the user’s voice command and extract the exact meaning of the user demand for voice commands. There are several ways to create a successful Home Automation System using IOT and devices, as described in Anand Kishore Azad’s “IOT Based home automation via Bluetooth” with security enhancement [17]. In [18–20], it is possible to increase the conventional living standard of homes by implementing a home automation system. The fundamental architecture makes use of a remote Bluetooth device that allows remote access to Smartphones over the Internet [21–23].

2 Methodology In this study, a mobile app was developed using a kodular evinronment for mobile application, the mobile applicatiom will control home appliances using an ESP32 controller

394

O. A. Khalfalla et al.

that was coded using C++. The mobile app can communicate with the ESP32 using Ethernet, Wifi, Bluetooth or mobile data. The appliances of the house are connected to the ESP32 through a relay. The system works in different modes, if the user is in the Bluetooth range, all communications will be carried out through the Bluetooth, while if the user is somewhere else around the user can communicate to the controller using the internet. In the mobile app, the user can use his fingerprint to turn on the light for some seconds and turn it off or use the buttons provided for each appliance. For the lights, the user can also give a voice command to turn on/off the light. The visual representation of the whole method is presented in Fig. 1.

Fig. 1. The overall system architecture.

3 Results and Discussion To know the efficiency of the proposed work, different mobile phone brands Samsung S20s, Samsung Note9, Samsung S9+, HUAWEI Y9, HUAWEI Y8P, Tecno and also on laptop windows OS performances were compared.. all the mobile phones with the exception of techno has an android 11 OS. After testing the app on those devices owned by 7 different individuals, we collected a response time-based data using different factors such as the phone type, network type and RAM capacity. Though, using laptops scored the best results because laptops have a big size of RAM however Note9 is the fastest among the smart mobile phones. Huawei and Samsung S9+ score the second-best result while Samsung A20S came in third. Tecno mobile is the slowest among smartphones, and it came in the last, and that is because of the performance of this device (Fig. 2). From the network test, it was learnt that the Ethernet tends to achieve the highest speed, followed by the wifi though it depends on the number of users and speed of the network, and lastly, the mobile data which also depends on the network carrier. The results are presented in Fig. 3. Also Fig. 4 shows the performance based on the RAM size.

Smart Home Appliance Control in the IoT Era 0.7

RESPONSE TIME(mSec)

0.6 0.5 0.4 0.3 0.2 0.1 0 0

1

2

3

4

5

6

7

8

NUMBER OF USERS S20S

S9+

HUAWEI Y9

TECNO

NOTE9

LAPTOP

HUAWEI Y8P

Fig. 2. Users and device response time NETWORK TYPE

RESPONSE TIME(mSec)

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

1

2 ETHER

3

4

5

6

7

8

NUMBER OF USERS WIFI DATA

Fig. 3. Types of the communication network RAM RESPONSE TIME(mSec)

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

1

2

3

4

5

6

NUMBER OF USERS 4GRAM

6GRAM

8GRAM

Fig. 4. Response time and RAM size

7

8

395

396

O. A. Khalfalla et al.

4 Conclusion As we know, a lot of countries are trying to build smart cities to make life easier and to connect all devices through the internet, it is also very important to have a system that will be cheap, efficient and very flexible. In this study, a system that gives users different command and network communication options was developed to cater for peoples need when it comes to controlling the appliances in the house. The performance of the system was tested based on the mobile phones, facilities, number of users at a time and the network type. The system shows great performance and can be deployed to perform the desired task.

References 1. Al-Turjman, F.: 5G-enabled devices and smart-spaces in social-IoT: an overview. Futur. Gener. Comput. Syst. 92, 732–744 (2019). https://doi.org/10.1016/j.future.2017.11.035 2. Al-Turjman, F., Abujubbeh, M.: IoT-enabled smart grid via SM: an overview. Futur. Gener. Comput. Syst. 96, 579–590 (2019). https://doi.org/10.1016/j.future.2019.02.012 3. Rasheed, J., Alimovski, E., Rasheed, A.: MManagement: Wi-Fi Hotspot based attendance application using android smartphone. In: 1st International Informatics and Software Engineering Conference: Innovative Technologies for Digital Transformation. IISEC-2019Proceedings, pp. 1–5 (2019). https://doi.org/10.1109/UBMYK48245.2019.8965588 4. Zaro, F., Tamimi, A., Barakat, A.: Smart home automation system. Int. J. Eng. Innov. Res. (2020). https://doi.org/10.47933/ijeir.781091 5. Anitha, G., Sathya, M., Suganeswar, S., Vignesh Raja, G.: Bluetooth based home automation and security system. J. Phys. Conf. Ser. 1916(1) (2021). https://doi.org/10.1088/1742-6596/ 1916/1/012106 6. Sy, J.B., Irfan, S.: Bluetooth based automation system using Android App. Int. J. Eng. Res. Technol. 14(1), 332–337 (2020) 7. Pandya, B., Mehta, M., Jain, N., Kadam, S.: Android Based Home Automation System Using Bluetooth & Voice Command-Implementation (2016) 8. Ahmed, H.H.I.: A simple smart home based on IoT using NodeMCU and Blynk, p. 14 (2019) 9. Shirisha, E., et al.: IOT based home security and automation using google assistant. Turkish J. Comput. Math. Educ. 2(6), 117–122 (2021). https://doi.org/10.17762/turcomat.v12i6.1275 10. Patil, H., Mangal, M., Salunke, S., Bhoir, A., Patil, K.: Voice assistant home automation using Arduino. Int. Res. J. Mod. Eng. Technol. Sci. 3, 2582–5208 (2021). (http://www.irjmets.com @International Res. J. Mod. Eng.) 11. Bhavsar, A., Pawar, M., Dudhe, A., Shirbahadurkar, D.S.D.: Smart home. Int. J. Adv. Res. Sci. Commun. Technol. 5(2), 725–733 (2021). https://doi.org/10.48175/ijarsct-1327 12. Sai Priyanka, S., Ayyappa Vijay, K., Chandu, M.: Using Esp32 IoT based Wi-Fi enabled smart LED systems. Int. J. Recent Trends Multidiscip. Res. 1(01), 1–08 (2021) 13. Tastan, H., Gokozan, M.: An Internet of Things based air conditioning and lighting control system for smart home. Am. Sci. Res. J. Eng. Technol. Sci. 50(1), 181–189 (2018) 14. Pandya, B., Mehta, M., Jain, N.: Android based home automation system using bluetooth and voice command. Int. Res. J. Eng. Technol. 03(03), 3–5 (2016) 15. Devikanniga, D., Ramu, A., Haldorai, A.: Efficient diagnosis of liver disease using support vector machine optimized with crows search algorithm. EAI Endorsed Trans. Energy Web 7(29), 1 (2020). https://doi.org/10.4108/EAI.13-7-2018.164177

Smart Home Appliance Control in the IoT Era

397

16. Anandakumar, H., Umamaheswari, K.: Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Clust. Comput. 20(2), 1505–1515 (2017). https://doi.org/10.1007/s10586-017-0798-3 17. Azad, K., Tech, M.: IOT Based Home Automation Using Bluetooth with Security Enhancement (2019) 18. Ramesh, K., Vara Prasad, M., Hemachandran, K.: Design and implementation of advanced ARM7 based biometric security system using wireless communication (2018) 19. Mittal, M., Sinha, S., Darsipudi, K.K., Vishwakarma, S., Nalini, N.: Smart home automation system using bluetooth and infrared. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 7(4), 269–273 (2017). https://doi.org/10.23956/ijarcsse/V7I4/0215 20. Liu, S., Hua, Y., Xiang, Z.J.: Smart Home Based on the ZigBee wireless (2012). https://doi. org/10.1109/ICINIS.2012.48 21. Vignesh, T., Selvakumar, D., Prasath, C., Manikandan, N., Sri Viswanath, R.: Design and fabrication of home automation. IOP Conf. Ser. Mater. Sci. Eng. 623(1), 012002 (2019). https://doi.org/10.1088/1757-899X/623/1/012002 22. Lai, T.W., Oo, Z.L., Than, M.M.: Bluetooth Based Home Automation System Using Android and Arduino (2019) 23. Venu Prabu, C.R.: IoT based home automation system (Smart Light). Int. Res. J. Eng. Technol. 6, 1088 (2019)

A Framework for Pothole Detection via the AI-Blockchain Integration Auwalu Saleh Mubarak(B) , Zubaida Said Ameen, Paul Tonga, Chadi Altrjman, and Fadi Al-Turjman Artificial Intelligence Engineering Department, Research Center for AI and IoT, AI and Robotics Institute, Near East University, Mersin 10, Turkey {auwalusaleh.mubarak,zubaida.saidameen,20204500,chadi.altrjman, fadi.alturjman}@neu.edu.tr

Abstract. Potholes are a major cause of road accidents. In smart cities, developing a system that will help in detecting the potholes in real-time while warning the motorist before reaching that location is very important. Lightweight EfficientDet family models were proposed in the literature to detect potholes in real-time. The work is divided into two parts, first a mobile application was developed using these trained models and it can efficiently detect the potholes. Also, in the second part, a blockchain was proposed to keep a record of the location of the pothole in real-time so that it can be updated on the network. The blockchain serves as a security mechanism so that the potholes coordinates can be altered and become up to date. Keywords: Artificial intelligence · Blockchain · Computer vision · EfficientDet

1 Introduction Roads contribute significantly to an economy’s overall growth. Asphalt, concrete, or both-surfaced roads are commonly utilized as transportation platforms across the world. Potholes, crack skid resistance, uneven manholes and other faults are all examples of road conditions. A pothole is a depression on the pavement or road surface that is bowlshaped and has a 150 mm minimum plane diameter [1]. The detection of faults (e.g., cracks and potholes) on the road section and evaluation of the extent of the flaws are the main components of a road safety survey [2]. Potholes are an essential sign of road defects among numerous types of pavement distresses, and they should be discovered as soon as possible for asphalt-surfaced pavement maintenance and rehabilitation [3]. The reason for this is that this type of issue causes major traffic delays and puts vehicles in a dangerous situation. Potholes can arise as a result of low-quality materials, a poor design that allows surface water to collect, ice formation in cracks, and other factors [4]. Because roads are so important in people’s everyday lives, they must be maintained regularly to ensure that they remain functional and safe. Because there are so many roads in a country, it’s difficult to keep track of them all. As a result, it’s impossible to predict when potholes will © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 398–406, 2022. https://doi.org/10.1007/978-3-030-99616-1_53

A Framework for Pothole Detection via the AI-Blockchain Integration

399

appear [5]. Pavement potholes are frequently discovered manually by local transportation agency inspectors during periodic field examinations in developing countries. Although this traditional approach can aid in the proper assessment of potholes, it is inefficient in both data gathering and data processing. Because a single pavement inspector can only check fewer than 10 kms every day, this is the case [6]. Keeping track of such a long stretch of road is a difficult undertaking that is nearly impossible to accomplish with only human resources. Integrating an automated pothole detection system into automobiles would aid in the identification of potholes and, as a result, would alert motorists and schedule repairs [7]. Blockchain is a distributed ledger technology (DLT) that records data in an encrypted chain of blocks that can never be changed or fabricated. Although certain cryptocurrencies, like Bitcoin, have utilized it as the public ledger, it has a broader notion [8]. To use a blockchain, one must first establish a network with all interested computational nodes. When a node fulfils a transaction, it signs it with a unique signature, confirming the transaction’s integrity, and then sends it to its peers. The transmission block includes legitimate transactions and uses the corresponding hash to refer to the preceding block in the chain. The block is rejected if at most one of these requirements is not satisfied. Otherwise, the nodes update the transactions by adding the block to their chain. Because altering a block necessitates changing all prior blocks, this technique ensures data authenticity [9–12]. The public and private blockchains [8] are the two forms of blockchain. The major distinction between them is who is allowed to join the network, run the consensus mechanism, and store the shared data. Anyone may join these networks and view their public transactions since public blockchains are completely open. In this situation, the network is likely to have an incentive system in place to encourage more people to utilize it. Access to the information held in a private blockchain, on the other hand, requires authorization. As a result, the parties authorized to perform transactions, be present in the network, and create new blocks in the chain might be limited. A blockchain requires a means to obtain a decentralized consensus because there is no central authority to validate transactions. There are two major models for this at the moment: Proof-of-Work (POW) and Proof-of-Stake (POS) are two types proof systems [8]. The POW concept is used by Bitcoin, Ethereum, and practically all cryptocurrencies [13]. The contributions in this study are as follows: • A pothole detection model was developed using three different EfficientDent model families (i.e. D0, D2 and D4). • The performance of the models was compared to find the best performing model. • The tradeoff between speed and accuracy in detection was considered for the model that will serve as the backbone of the mobile application. • Blockchain was proposed to keep a record of the pothole locations to avoid tampering with the data and also for real-time updates.

2 Related Works Different approaches are being used to automate the detection process of the pothole in roadways, including sensor-based techniques [14–16]. Vibration sensors are used in

400

A. S. Mubarak et al.

sensor-based systems to identify potholes. False-positive and false-negative readings from the vibration sensor identifying joint inroads as potholes or not detecting potholes in the centre of a lane, respectively, may impact the accuracy of detecting potholes. Image processing techniques [17–19] is another approach. Traditional image processing algorithms for pothole identification are accurate, but they also require difficult activities such as manually extracting features and modifying image processing settings and steps for various road conditions. Authors in [20] and [21] have proposed computer vision-based models that use median filtering and morphological procedures. [18] developed a pothole identification system that uses texture extraction and pixel comparison approaches to detect potholes as well as normal pavements. Laser-based 3D reconstruction techniques [22, 23] and stereo vision-based [24, 25] are been utilized for pothole detection, 3D reconstruction approaches utilize 3D road data. They need a time-consuming setup and computational efforts to recreate the pavement surface, and they may be affected by camera misalignment, lowering detection accuracy. Traditional image processing algorithms for pothole detection are accurate, but they also require difficult activities such as manually extracting features and modifying image processing settings and steps for various road conditions. 3D point clouds generated by stereovision algorithms with the use of a pair of video cameras are used to develop 3D reconstruction-based approaches [26]. To identify simulated potholes, [27] proposed combining a vision and motion system. To locate potholes in 2D photos, [28] used the elliptic form, grain surface texture, and image segmentation. The area of artificial intelligence has led to the use of model-based techniques [29– 34]. Model-based pothole detection systems have been spurred by the development of improved image processing techniques and the availability of low-cost camera sensors. To detect potholes in 2D digital photos, traditional machine learning (ML) approaches were used to create a trained model. They achieve considerable processing power while achieving substantial accuracy. Experts are also required to manually extract characteristics in order to increase the accuracy of ML approaches for detecting potholes. Deep learning (DL) approaches make use of deep convolutional neural network (CNN) operations, which may automate the extraction and categorization of features at the same time. DL object detectors are divided into two types: one-stage detectors and two-stage detectors [20]. Several one-stage pothole detectors have been published as a result of research [35]. You Only Look Once (YOLO) [36] and Single Shot Multibox Detector (SSD) [37] are two examples of one-stage detectors that have been published to identify potholes. They have a reasonable level of accuracy and a quick detection speed. However, few research attempts, such as Faster R-CNN [38], have been published to create two-stage detectors [42] to identify potholes [7]. According to [39], Geo-fences should be defined in smart contracts as part of Location-based Services (LBS), and current geographical locations given by mobile users should be analyzed to see if they are within the geo-fence or not. Their method makes use of well-known location encoding technologies (such as geo-hashes and S2 cells) to convert polygons into a grid of cells, which are then saved in the smart contract as a representation of the geo-fence. The authors compared the storage and processing costs of these two location encoding methods in Ethereum-based smart contract implementation, concluding that the codification as S2 cells is far more economical.

A Framework for Pothole Detection via the AI-Blockchain Integration

401

Authors in [40] present a method for creating a proof of location system by decentralizing the blockchain in order to preserve users anonymity, the decentralized verification is better than the centralized approach, this method will testify to the users’ location and generate a digital certificate of the location. In addition, envision an LBS peer-to-peer network with mobile nodes that are linked to the Internet and may communicate with one another through short-range Bluetooth communication. They identify two responsibilities for nodes in the network in their paper: prover and witness. A prover is a node that seeks to gather proofs of position from its neighbours, while a witness is a node that has already given the proper proof of location. Every peer has a unique identification, or public key, and may use its identifier’s private key to digitally sign content.

3 Pothole Detection 3.1 Dataset and Data Preprocessing In this study 600 images from the internet and the potholes on the roads from the Republic of Northern Cyprus were used, the images annotation was carried out using the roboflow web tool, bounding boxes were drawn on each image, image augmentation was carried out by performing scaling and shear augmentation technique, more images were generated to make a total of 2500 images, the original images were 800, 80% of the images were used for training, 10% for validation and 10% for testing. 3.2 Methodology In this study, AI and blockchain were being used to develop a warning system for motorists on the road. The study has two parts: computer vision-based sensing and the blockchain for data recording. An object detection model for detecting potholes was developed using EfficienDet: D0, D2, and D4. The EfficientDet [41] is a neural network for object detection. It’s a TensorFlow object recognition API that supports many model families, including CenterNet. [42], MobileNet [43], ResNet, and Fast RCNN. Despite being 4× to 9× smaller and using 13x to 42x fewer FLOPs than earlier detectors, EfficientDets achieves 55.1mAP (mean average precision) on COCO testdev. The EfficientDet models were trained using the pothole dataset to find the best performing model by comparing their accuracy based on the mAP, mAP-50 and mAP75, 80% of the data was used for training,10% for validation and 10% for testing. The lightweight model can be deployed on mobile phones. In the current study, the model can detect the potholes while the coordinates will be saved on the blockchain, each user will serve as a node, the information will be saved in real-time and will be updated in the blockchain if maintenance work is carried out and another user passes the same location, the information based on that coordinate will be changed and the system will not give motorist information that there is a pothole in that location. So far we have implemented the pothole detection method and proposed the blockchain concept. The whole system will be deployed to a mobile application and only the user that has access to the application. The idea of incorporating a blockchain is to keep a record of the location of the potholes on the road so that users of the application can be alerted before reaching the position. This can reduce the occurrence of an accident on the road and can save people’s lives.

402

A. S. Mubarak et al.

4 Results and Discussion Three models were employed to detect potholes in real-time, the models after training the D0 from Table 1 achieves an mAP of 77, mAP-50 of 90 and mAP-75 of 80. The D2 model achieves an mAP of 79, mAP-50 of 97 and mAP-75 of 82. The D4 model achieves an mAP of 84, mAP-50 of 100 and mAP-75 of 87. The D4 achieves the best performance but is larger and slow in detection compared to the other models, to have an efficient model with reasonable speed, D2 was adopted for this study. The results for D0, D2 and D4 are presented graphically in Figs. 1, 2 and 3 respectively. Figure 4 shows a detected pothole by the mobile application. Table 1. Models performance evaluation Model

mAP

mAP-50

D0

77

D2

79

97

82

D4

84

100

87

90

mAP-75 80

Accuracy

mAP 100 90 80 70 60 50 40 30 20 10 0 D0

D2

D4

Models

Fig. 1. mAP Accuracy of the proposed models.

mAP-50 100

Accuracy

80 60 40 20 0 D0

D2

D4

Models

Fig. 2. mAP-50 Accuracy of the proposed models.

A Framework for Pothole Detection via the AI-Blockchain Integration

403

Accuracy

mAP-75 100 90 80 70 60 50 40 30 20 10 0 D0

D2

D4

Models

Fig. 3. mAP-75 Accuracy of the proposed models.

Fig. 4. Example of a detected pothole

5 Conclusion Amongst the rapidly growing research areas are Artificial Intelligence and Blockchain. Several object detection models have been developed to efficiently detect objects with high accuracy. In this study, firstly, EfficientDet families D0, D2 and D4 were proposed to detect potholes. The trained model was used to develop a light mobile application. The model’s performance demonstrates that it is capable of doing the desired job. Secondly, a blockchain integration with the AI model was proposed to take a record of the location of the pothole on the road so that users can be alerted when they approach the location. Also, the proposed model will update the records if maintenance work is being carried out at a previously pothole location so that users will not be alerted since there is no more pothole. In the future, we will like to develop the blockchain and integrate it into the mobile application so that motorist will be informed of potholes in real-time.

References 1. She, X., Hongwei, Z., Wang, Z., Yan, J.: Feasibility study of asphalt pavement pothole properties measurement using 3D line laser technology. Int. J. Transp. Sci. Technol. 10(1), 83–92 (2021). https://doi.org/10.1016/j.ijtst.2020.07.004

404

A. S. Mubarak et al.

2. Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., Fieguth, P.: A review on computer visionbased defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv. Eng. Inf. 29(2), 196–210 (2015). https://doi.org/10.1016/j.aei.2015.01.008 3. Federal Highway Administration (FHWA).Variability of pavement distress data from manual surveys, Maint. Manag, no. 202, pp. 0–3 (2000) 4. Tamrale, G.S., et al.: Analysis of existing road surface on the basis. Glob. J. Res. Eng. E Civ. Struct. Eng. 19(3), 17–22 (2019) 5. Shaghouri, A.A., Alkhatib, R., Berjaoui, S.: Real-Time Pothole Detection Using Deep Learning, pp. 1–10 (2020) 6. Oliveira, H., Lobato, P.: Supervised crack detection and classification in images of road pavement flexible surfaces. Recent Adv. Signal Process. (2009). https://doi.org/10.5772/7448 7. Ahmed, K.R.: Smart pothole detection using deep learning based on dilated convolution. Sensors 21(24), 8406 (2021) 8. Zheng, Z., Xie, S., Dai, H., Chen, X., Wang, H.: An overview of blockchain technology: architecture, consensus, and future trends. In: Proceedings of the 2017 IEEE 6th International Congress Big Data, BigData Congress 2017, pp. 557–564, (2017). https://doi.org/10.1109/ BigDataCongress.2017.85 9. Deebak, B.D., Fadi, A.T.: A robust and distributed architecture for 5G-enabled networks in the smart blockchain era. Comput. Commun. 181, 293–308 (2022). https://doi.org/10.1016/ j.comcom.2021.10.015 10. Šarac, M., Pavlovi´c, N., Bacanin, N., Al-turjman, F., Adamovi´c, S.: Increasing privacy and security by integrating a blockchain secure Interface into an IoT device security gateway architecture. Energy Rep. 7, 8075–8082 (2021). https://doi.org/10.1016/j.egyr.2021.07.078 11. Ullah, F.: A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities. Neural Comput. Appl. 6, 1–22 (2021). https://doi.org/10.1007/s00 521-021-05800-6 12. Deebak, B.D., Al-turjman, F.: Journal of Information Security and Applications Privacypreserving in smart contracts using blockchain and artificial intelligence for cyber risk measurements. J. Inf. Secur. Appl. 58, 102749 (2021). https://doi.org/10.1016/j.jisa.2021. 102749 13. Nakamoto, S.: Bitcoin: A Peer-to-Peer Electronic Cash System, vol. 15, no. 4, pp. 580–596 (2020) 14. Yu, B.X., Yu, X.: Vibration-based system for pavement condition evaluation. In: Applications of advanced technology in transportation, pp. 183–189 (2006). https://doi.org/10.1061/407 99(213)31 15. Mednis, A., Strazdins, G., Zviedris, R., Kanonirs, G., Selavo, L.: “Real Time Pothole Detection using Android Smartphones with Accelerometers Research domain Road infrastructure as blood vessels (2011) 16. Tai, Y.C., Chan, C.W., Hsu, J.Y.J.: Automatic road anomaly detection using smart mobile device. In: Conference on Technologies and Applications Of Artificial Intelligence (2010) 17. Sai Ganesh Naik, M.B., Nirmalrani, V.: Detecting potholes using image processing techniques and real-world footage. In: Mallick, P.K., Bhoi, A.K., Marques, G., Hugo C. de Albuquerque, V. (eds.) Cognitive Informatics and Soft Computing. AISC, vol. 1317, pp. 893–902. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1056-1_72 18. Koch, C., Jog, G.M., Brilakis, I.: Automated pothole distress assessment using asphalt pavement video data. J. Comput. Civ. Eng. 27(4), 370–378 (2013). https://doi.org/10.1061/(asc e)cp.1943-5487.0000232 19. Muslim, M., Sulistyaningrum, D.R., Setiyono, B.: Detection and counting potholes using morphological method from road video. AIP Conf. Proc. 2242(August), 2020 (2021). https:// doi.org/10.1063/5.0008282

A Framework for Pothole Detection via the AI-Blockchain Integration

405

20. Huidrom, L., Das, L.K., Sud, S.K.: Method for automated assessment of potholes, cracks and patches from road surface video clips. Proc. Soc. Behav. Sci. 104, 312–321 (2013). https:// doi.org/10.1016/j.sbspro.2013.11.124 21. Radopoulou, S.C., Brilakis, I.: Improving patch defect detection using vision tracking on video data. In: Proceedings The 21st EG-ICE Workshop on Intelligent Computing in Engineering, no. September (2014) 22. Yu, X., Salari, E.: Pavement pothole detection and severity measurement using laser imaging. IEEE Int. Conf. Electro Inf. Technol. (2011). https://doi.org/10.1109/EIT.2011.5978573 23. Moazzam, I., Kamal, K., Mathavan, S., Usman, S., Rahman, M.: Metrology and visualization of potholes using the microsoft kinect sensor. In: 16th International IEEE Conference on Intelligent Transportation Systems ITSC, no. October, pp. 1284–1291, (2013). https://doi. org/10.1109/ITSC.2013.6728408 24. Staniek, M.: Stereo vision method application to road inspection. Balt. J. Road Bridg. Eng. 12(1), 38–47 (2017). https://doi.org/10.3846/bjrbe.2017.05 25. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2016-December pp. 770–778 (2016)https://doi.org/10.1109/CVPR.2016.90 26. Hou, Z., Wang, K.C., Gong, W.: Experimentation of 3D pavement imaging through stereovision. In: International Conference on Transportation Engineering, ICTE 2007, vol. 2007, no. Icte, pp. 376–381 (2007). https://doi.org/10.1061/40932(246)62 27. Karuppuswamy, J., Selvaraj, V., Ganesh, M.M., Hall, E.L.: Detection and avoidance of simulated potholes in autonomous vehicle navigation in an unstructured environment. In: Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision, vol. 4197, no. 513, pp. 70–80 (2000). https://doi.org/10.1117/12.403788 28. Koch, C., Brilakis, I.: Pothole detection in asphalt pavement images. Adv. Eng. Inf. 25(3), 507–515 (2011). https://doi.org/10.1016/j.aei.2011.01.002 29. Mubarak, A.S., Ameen, Z.S.I., Tonga, P., Al-Turjman, F.: Smart tourism: a proof of concept for Cyprus museum of modern arts. In: The IoT Era, pp. 49–53 (2021). https://doi.org/10. 1109/icaiot53762.2021.00016 30. Yousaf, M.H., Azhar, K., Murtaza, F., Hussain, F.: Visual analysis of asphalt pavement for detection and localization of potholes. Adv. Eng. Inf. 38, 527–537 (2018). https://doi.org/10. 1016/j.aei.2018.09.002 31. Hoang, N.D.: An artificial intelligence method for asphalt pavement pothole detection using least squares support vector machine and neural network with steerable filter-based feature extraction. Adv. Civ. Eng., 2018 (2018). https://doi.org/10.1155/2018/7419058 32. Hoang, N.D., Huynh, T.C., Tran, V.D.: Computer Vision-Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic-Based Investigation Metaheuristic. Complexity 2021 (2021). https://doi.org/10.1155/2021/3511375 33. Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H.: Road damage detection using deep neural networks with images captured through a smartphone, pp. 4–6, (2018). https:// doi.org/10.1111/mice.12387 34. Kotha, M., Chadalavada, M., Karuturi, S.H., Venkataraman, H.: PotSense - Pothole detection on Indian roads using smartphone sensors. PervasiveHealth Pervasive Comput. Technol. Healthc. (2020). https://doi.org/10.1145/3377283.3377286 35. Zhang, Z., Ai, X., Chan, C.K., Dahnoun, N.: An efficient algorithm for pothole detection using stereo vision. In: IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP, pp. 564–568 (2014). https://doi.org/10.1109/ICASSP.2014.6853659 36. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once : unified, real-time object detection (2016). https://doi.org/10.1109/CVPR.2016.91 37. W. Liu et al.: SSD : Single Shot MultiBox Detector (2015)

406

A. S. Mubarak et al.

38. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN : towards real-time object detection with region proposal networks, vol. 8828, pp. 1–14 (2016). https://doi.org/10.1109/TPAMI.2016. 2577031 39. Victor, F., Zickau, S.: Geofences on the blockchain: enabling decentralized location-based services. In: IEEE International Conference on Data Mining Workshops ICDMW, vol. 2018November, pp. 97–104, (2019). https://doi.org/10.1109/ICDMW.2018.00021 40. Amoretti, M., Brambilla, G., Medioli, F., Zanichelli, F.: Blockchain-based proof of location. In: Proceedings of the 2018 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2018, pp. 146–153 (2018). https://doi.org/10.1109/QRSC.2018.00038 41. Tan, M., Pang, R., Le, Q.V.: EfficientDet: Scalable and efficient object detection. In: Proceedings IEEE Computer Social Conference Computer Vis. Pattern Recognition, pp. 10778–10787 (2020). https://doi.org/10.1109/CVPR42600.2020.01079 42. Duan, K., Bai, S.: CenterNet: keypoint triplets for object detection, pp. 1–10 (2019) 43. Howard, A.G., Wang, W.: MobileNets: efficient convolutional neural networks for mobile vision applications https://arxiv.org/pdf/1704.04861.pdf (2017)

Use of Animal Waste for Heat and Produce Electricity in Form of Steam Power Da,ud Dirie Elimi1 , Ayub Mohamed Hussein1 , Abdul Mire Salad1 , Abdirahman Farah Ali2(B) , and Abdulaziz Ahmed Siyad2 1 Department of Electrical Engineering, Faculty of Engineering, Jamhuriya University

of Science and Technology, Mogadishu, Somalia 2 Center for Research and Development, Jamhuriya, University of Science and Technology,

Mogadishu, Somalia {Afarah,Abdulaziz}@just.edu.so

Abstract. The steam power system is the main source of electricity in decades, and using dungs from various animals to study their capacity in contributing steam power is still ongoing, in this paper we implemented a mini steam power system to compare variety of animal dungs. This system uses animal waste especially Camel manure, Cow dung and goats’ dung by using a three-step process to generate electricity from steam: first , we burned camel manure, cow manure, and goat manure; second, water is converted to high-pressure steam; then, high-pressure steam is rotates a turbine shaft which drives the electric generator and producing electricity. and it does greatly reduces the dependence on Fuel and provides the way that benefits for the community. The results indicate that per kg (kilogram) of cow manure generates more power than other animal’s dung because of durability and duration of burning each of them. The system used to implement is affordable, flexible and has fast monitoring energy. Keywords: Animal waste · Steam power · Heat

1 Introduction This project aims to create a way to use animal wastes into electricity especially camel manure, cow dung and goat dung using established steam power system. This project uses wastes from animals to generate electricity, as you know waste management was already necessary all over the world, which is why all countries spend a lot of money to manage it, so this project first manages waste and next we use it to generate electricity as commercial, making energy from wastes of animals cheaper than using fossil fuels, If the energy produced by this project is stored it can be used in many places like rural area for lighting and charging mobiles and also it will be useful for people with no access of electricity. We designed system that encourages to the electricity and contributes the economic growth.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 407–417, 2022. https://doi.org/10.1007/978-3-030-99616-1_54

408

D. D. Elimi et al.

2 Literature Review A. Overview Waste creation is a major concern in all countries, but it is especially acute in developing countries. Livestock dung (a combination of animal excrement, urine, and/or animal bedding mate- material) is a type of biological waste and a low-value cattle by-product that is unavoidable. The process of dung composting is a method of converting such waste ingredients into manure compost as a brilliant organic fertilizer and soil alteration, with or without mixing other crop residues. Japan generates approximately 97 Mt of manure annually from livestock [1]. Wastes have been divided into two kinds by age, whether fresh or stored, under the parameters previously mentioned. Illustrations were obtained by taking samples, A minimum of 30 handfuls of farmyard manure, each with a mass of approximately 10 g, using gloved hands, from houses or farmyard storage manure tons. Handfuls a small number They were collected from various depths in the stuff, an equal number of subsamples of slurry or dirty water was acquired, once again from the various zones and depths of each slurry vessel, and mixed to form a single sample of wastewaters. Previously, comprehensive procedures, shipment conditions to the laboratory, and laboratory analysis methodologies have been published dirty water was obtained, again from the various areas and depths of each slurry vessel, and combined to form a single sample of liquid waste. Previously, comprehensive procedures, shipment conditions to the laboratory, and laboratory investigation procedures have been published [2]. Animal dung is one of the methane processing materials for livestock. This produces bacteria, parasites and microorganisms. In other words, if not harnessed, animal dung has an exceedingly high risk of causing water contamination. Owing to the health and respiratory consequences of air and water pollution, over 1.6 million people die each year. This sense, air pollution could be related to the gases emitted into the atmosphere during fossil fuel burning. Therefore, it becomes essential to use the dung for better use and alternative fossil fuel equipment. The solution is given by the use of methane. In addition to its other benefits, the process of anaerobic digestion produces methane. Anaerobic digestion inactivates bacteria and parasites and decreases the occurrence of water contamination very effectively. Biogas production and usage would decrease the constant burning of fossil fuels, thereby minimizing air emissions as it is environmentally friendly [3]. Animal waste is in place, Therefore, for the successful implementation of such anaerobic processes, the effects of mixing or co-digesting animal wastes for biogas manufacture must be properly evaluate. B. Waste to Electricity History The project aims to create a way to us animal wastes into electricity especially camel manure, cow dung and goat dung using heating process. This Need for renewable energy sources for the purpose of the decentralized and centralized generation of power from these sources has contributed to the production of research into different energy sources. In recent years, anaerobic digestion (AD) has received significant attention as one such way of meeting these regionalized and central power sources [4]. Fossil fuels produce much of the world’s electricity. The majority of this electricity (coal, oil and natural gas) is produced by thermal power plants. For

Use of Animal Waste for Heat and Produce Electricity

409

around 100 years, thermal power plants (TPP) have been used in various classes of industrial procedures. The major energy resource for these systems is heat extracted from fuel. In a steam power plant, water is heated in the furnace and converted to a high-pressure vapor before passing through turbine blades. [5]. The generator is spun around as a result, and power is generated. Fossil fuel use has negative environmental consequences, including air pollution, direct effects on human health, and disturbance of bionetworks. Long-term methods and actions are required for Sustainable Growth to address today’s global environmental issue. C. Conversion of Animal Dung to Useful Energy In addition, the renovation of biomass into usable energy will generate heat and electricity and can also be transformed into bio fuels (e.g., biogas). There are three ways to transform waste into biomass energy and bio fuels that are useful. Thermal conversion is the method in which heat is used to transform different bio-organisms to chemical formations. This conversion can be carried out in many ways, including combustion, torrefaction, gasification, prolysis, hydro-processing and hydrothermal upgrade. 20% to 30% productive is a typical thermal biomass plant. Chemical conversion is the method of transforming, using chemical reactions, different waste materials and liqusid slurring into fuels. The molecules are made up of natural materials such as biomass. Living enzymes such as bacteria and other micro-organisms are used to break up these molecules. Such micro-organisms are useful in processes such as fermentation, composition and digestion [4]. Two mechanisms are involved in convection heat transfer. Energy is conveyed not just by random molecule motion (diffusion), but also by the bulk or macroscopic motion of the fluid. Convection is usually classified based on the flow characteristics. “Free convection” occurs when a flow is created by buoyant forces caused by density changes caused by temperature variations in the fluid. Force convection, on the other hand, happens when an external source, such as a fan, induces flow [6]. D. Radiation: The energy emitted by nonzero-temperature materials is known as thermal radiation. Solids, liquids, and gases can all emit this gas. The Stefan-Boltzmann law governs the emissive power’s upper limit. E. Waste and Electricity Relationship The association between electricity and waste via a multi-level process, regimes changed. The method was multi-level as current waste managing and electricity generation regimes modified in response to wider changes in the landscape, such as the oil crisis, increasing understanding of the environment and European integration, as well as the engagement with the creation of niche alternatives such as the use of biogas at landfill sites and farms and the combustion of waste at industrial sites. Technological, institutional, and social networks were all affected by the transformation process in a socio-technical regime. This finding is consistent with the multi-level perspective’s basic assumption that conversion processes are invariably multi-level and mixt. The importance of government

410

D. D. Elimi et al.

rules and regulations, as well as outsider pressure, is particularly relevant in this scenario. Landscape changes do not transform regimes on their own; they require translation [5, 7]. F. Steam Power Plant Catalytic steam gasification at a low temperature of livestock dung compost in a fluidized bed. The mixture of a fluidized bed reactor with a high-performance catalyst will provide a feasible device for [8, 9]. We built a small steam electric power plant out of water, which is then converted to high-pressure steam with the help of a Cooker. The Cooker, a boiler that generates heat by burning waste such as camel, cow, and goat dung, produced a large amount of steam after boiling the water, as seen in the diagram below [6].

3 Methodology The project aims to create a way to us animal wastes into electricity especially camel manure, cow dung and goat dung using heating process. This Need for renewable energy sources for the purpose of the decentralized and centralized generation of power from these sources has contributed to research into alternative energy sources is growing. Fossil fuels produce much of the world’s electricity. The majority of this electricity (coal, oil and natural gas) is produced by thermal power plants. For around 100 years, thermal power plants (TPP) have been used in various kinds of industrial processes. The major energy resource for these systems is heat extracted from fuel. By heating the water in the furnace, it transforms to a high-pressure vapor in a steam power plant and formulates to pass through the turbine blades. As a result, the generator is turned around and electricity is generated. The use of fossil fuels causes detrimental environmental impacts, such as air pollution, direct effects on human health, and disruption to ecosystems. For Sustainable Growth to address today’s global environmental issue, long-term approaches and actions are required. Biogas production from cow dung and horse dung It was designed to work at room temperature in this case. The use of the modified Gompers equation to the study of biogas production was able to predict the trend of biogas production over time. The digester B substrate (25% cow dung and 75% horse dung) produced the most biogas, followed by a mixture of substrates (100% horse dung), and finally a mixture of substrates containing a mixture of substrates containing a mixture of substrates containing a mixture of substrates containing a mixture of substrates containing a mixture of substrates containing a mixture of substrates containing a mixture of substrates containing a mixture of substrates containing (50% cow dung and 50% horse dung). Digesters D and E were labeled as ineffective digesters because they failed to break down the food at the end of the digestion cycle Produce whatever amount of biogas that can be detected (Figs. 1, 2, 3, 4, 5, 6, and 7). A. Power Plants To create energy, power plants use steam from geothermal reservoirs. Three main processes are used to convert hydrothermal fluids to electricity: dry steam, flash steam, and binary cycle geothermal power plants. The sort of conversion employed at California’s Geysers is determined by the state of the fluid (steam or water) and its temperature

Use of Animal Waste for Heat and Produce Electricity

411

Fig. 1. Flowchart of the system

(selected during development). Hydrothermal fluids, usually steam, are used in dry steam plants. The steam is directed straight to a turbine, which drives an electric generator. Instead of fossil fuels, steam is used to power the turbine (also eliminating the need to transport and store fuels). Excess steam and severe heat are emitted by these plants. Excess steam and very modest amounts of gases are all that these plants emit. The earliest geothermal power plants to be developed were dry steam power plants (they were first used at Lardarello in Italy in 1904). The Geysers in northern California, the world’s greatest single source of geothermal energy, are still utilising steam technology today. Flash steam plants are the most common form of geothermal power producing plant in use today. Fluid with a temperature greater than 360°F (182 °C) is injected at high pressure into a tank at the surface with a much lower pressure, causing some of the fluid to flash evaporate quickly. The vapor is then used to power a turbine, which in turn drives a generator. If there is any remaining fluid in the tank, it can be flashed in another tank to extract even more energy.

412

D. D. Elimi et al.

Fig. 2. General design of the system

4 Implementation and Testing This paper will present all the implementation include an energy production, waste reduction, and manure utilization, common comparison between three types of animal waste and steam energy as well as at the end of the paper will be talked about the result. A. Construction We build the steam power construction part of our system and their cooperation which consists of the following component combustion of dung, cooker, evaporating tube and shaft fan. B. 1 System Construction As depicted in the diagram above, the relationship between components and how they work together is demonstrated. We used a three-step process to generate electricity from steam: first, we burned animal dung, particularly camel manure, cow manure, and goat manure; second, Water is turned to high-pressure steam, which is then converted to mechanical spinning of a turbine shaft, which drives an electric generator. a) Step 1: Combustion of Dung During our research we used three types of animal waste namely Camel manure, cow dung and goat dung, each of these three has given us a level of energy which we will present here and we will finally make a comparison. (1) Camel’s Manure Camels have manure which can be dried 10 to 15 days after it has been warned this period is the time taking the manure can dry in the sunlight but when there is no sunlight Camel manure can dry within 20 to 25 days when it does not rain, during our research we burned 1 kg of camel manure which flames completely for 6 to 8 min while we are blow also we put kitchen a Cooker in 3 L of water it can boil the water for 17 to 19 min

Use of Animal Waste for Heat and Produce Electricity

413

and Water can be at its maximum boiling point 7 to 9 min after starting to boil so the steam produced by the Cooker we used for eight minutes directly the cooker produced the required steam which can drive the Shaft and Motor we used.

Fig. 3. Camel manure

Goat are one of the domesticated animals that eat grass and leaves of plants and lay manure fewer than camel manure, we dried the goat manure to the sunlight for 15 to 17 days and the days there is no sunlight can dry the manure 27 to 29 days. we burned 1 kg of goat manure which flames for 13 to 15 min then we putted the Cooker on the fire containing one liter and a half of water, the water boiled for 20 to 22 min and they continued to boil for six minutes then the cooker produced the steam we need to transfer the system we were using like the motor and shaft.

Fig. 4. Goats manure

(2) Cow Manure The cows are the grass-eating animals which lays very wet manure, during our research we knew Cow dung can dry out in the sunlight for 9 to 11 days and the absence of sunlight cow dung can dry out in 21 to 23 days. we burned 1 kg of cow dung that flames in 2 to 3 min and we putted on the kitchen a Cooker contained 3 L of water after that the water boiled within 15 to 17 min then the

414

D. D. Elimi et al.

water continues to boil its maximum temperature 3 to 4 min and then easily dissolve the cow dung.

Fig. 5. Cow manure

C. Comparison of Combustion Manure We burned 1 kg of camel manure which flames completely for 6 to 8 min while also we put kitchen a Cooker in 3 L of water it can boil the water for 17 to 19 min and Water can be at its maximum boiling point 25 to 27 min after starting to boil so the steam produced by the Cooker, we used for 25 min directly the cooker produced the required steam and also we burned 1kg of cow dung that flames in 2 to 3 min and we putted on the kitchen a Cooker contained 3 L of water after that the water boiled within 15 to 17 min then the water continues to boil its maximum temperature 3 to 4 min and then easily dissolve cow dung, the steam which came from goat dung can’t drive the Shaft and Motor and also we burned 1 kg of goats manure that flames 13 to 15 min and water takes to boil 20 to 22 min the water can be its maximum boiling point for three minutes and dissolve after 52 min. Table 1. Comparison of manure Type of manure/dung

In kilogram

Water used to boil

Time it flames

Time to boil water

Its maximum boil

Time to dissolve dung

Camel manure 1 kg

3L

6–8 min

17-19 min

25–27 min

54 min

Goat dung

1 kg

3L

0

0

0

52 min

Cows manure

1 kg

3L

2–3 min

15–17 min

3–4min

36 min

b) Step Two: Water to Steam We made a modest steam electric power plant using of water, which is generally transformed to high-pressure steam using a Cooker. a boiler that generates heat by burning waste such as camel dung, cow dung, and goat dung, the Cooker produced high steam after boiling the water, as indicated in the diagram below.

Use of Animal Waste for Heat and Produce Electricity

415

Fig. 6. Creation of steam

c) Step Tree: Mechanical Rotation of Turbine Shaft: The shaft received mechanical rotation from the steam, which was converted to mechanical rotation by Steam Turbines. In the following process, Steam turbines convert 10–40% of the combination input pressure potential energy and linear kinetic energy of steam to the turbine shaft’s output rotational kinetic energy: -High-pressure steam (potential energy at high pressure) enters the turbine and is sucked through a series of turbine blades before exiting at atmospheric pressure. A set of stationary blades that are also converging nozzles send the steam towards the following set of blades first. The steam then comes into contact with rotor blades, which are connected to the turbine shaft and spin as the rotor blades move (rotational kinetic energy). - A stage consists of a collection of nozzles and rotor blades. To convert as much steam energy as possible into shaft energy, some turbines use a succession of phases. The amount of energy converted when the steam flows through the blades depends on whether the turbine is an impulse or a response turbine. These two types of turbines are distinguished by their blade configurations. Each blade arrangement used a distinct primary force (impulse or response) to move the rotor blades, but also used another type of force (reaction or impulse) as a secondary force. Both can be used together to produce an impulse-response system that primarily relies on both impulse and reaction. D. Discussion An electric motor is a machine that converts mechanical energy into electrical energy. By interacting between the motor’s magnetic field and electric current in a wire winding, electric motors generate force in the form of torque applied on the motor’s shaft. Their power output per hour is 100.39 watts when we used cow dung and 63.36 watts when we used camel manure and output was 0 when we used coat manure, steam power from the Cooker makes the motor produced voltage around 0.55 V when we used cow Dung and voltage was 0.72 V when we used camel manure, the current that produced the motor is 30–35 milli amps and approximately 39 milli amperes all experiments we used this test through digital multimeter. The cow manures flammability and duration of their steady pressure is more compared to the camel which makes it generate more power.

416

D. D. Elimi et al.

Fig. 7. Mechanical rotation of turbine shaft

Table 2. Output of the system Type of manure/dung

Dung in kilogram

Air pressure

Voltage

Current

Power/h

Camel manure

1 kg

3.4 m/s

0.550V

32 mA

63.36 W/h

Cow dung

1 kg

4.2 m/s

0.715V

39 mA

100.39 W/h

Goat dung

1 kg

0

0

0

0

5 Conclusion Steam power system has been considered and constructed using dung of animals, In the Waste Heat system a Combustion of dung is indicated for electricity production through a steam came from cooker and rotating to the shaft. The prototype of the system performed as expected and satisfactorily; the system components are readily available, reasonably inexpensive, and run pretty reliably; the system converts steam to electricity; and the system is ready to use lights in homes, the rotating turbine shaft drives an electric generator. This power steam system based on comparison between common three types of animal waste and steam electricity from generator, in this system two of three types of animal waste especially camel manure and cow manure that we used gave us the steam which can drive the Shaft and Motor. Water is one of the most significant components of this project; water is usually converted to high-pressure steam using a Cooker, which generates heat energy from waste combustion. We used an electric 12 DC motor, which is an electrical machine that converts mechanical energy into electrical energy by generating force in the form of torque on the motor’s shaft through the interplay of the motor’s magnetic field and electric current in a wire winding. The main point interest of this project will be to avoid fuel and use waste to get electricity. It is advised that greater steam turbine capacity and a large Cooker be installed and used in order to achieve high pressure, which allows for the production of more energy. The main factors that require attention include a focus mix of cow and camel manure because cow manure is faster than camel manure in terms of fulmination and Camel

Use of Animal Waste for Heat and Produce Electricity

417

manure takes a long time to burn, and we also recommend using at least a 240 Dc motor to obtain more electricity that can be used in various locations.

References 1. Ackermann, T., Andersson, G., Söder, L.: Distributed generation: a definition. Electr. Power Syst. Res. 57(3), 195–204 (2001). https://doi.org/10.1016/S0378-7796(01)00101-8 2. El-Khattam, W., Salama, M.M.A.: Distributed generation technologies, definitions and benefits. Electr. Power Syst. Res. 71(2), 119–128 (2004). https://doi.org/10.1016/j.epsr.2004. 01.006 3. Amsterdam, H., Thopil, G.A.: Enablers towards establishing and growing South Africa’s waste to electricity industry. Waste Manage. 68(2013), 774–785 (2017). https://doi.org/10.1016/j.was man.2017.06.051 4. Raven, R.: Co-evolution of waste and electricity regimes: multi-regime dynamics in the Netherlands (1969–2003). Energy Policy 35(4), 2197–2208 (2007). https://doi.org/10.1016/j.enpol. 2006.07.005 5. Ndinechi, M.C., Onwusuru, I.M., Ogungbenro, O.A.: Economic potentials of animal dung as a viable source of biomass energy. Acad. Res. Int. 2(1), 83–89 (2012) 6. Abdeshahian, P., Lim, J.S., Ho, W.S., Hashim, H., Lee, C.T.: Potential of biogas production from farm animal waste in Malaysia. Renew. Sustain. Energy Rev. 60, 714–723 (2016). https:// doi.org/10.1016/j.rser.2016.01.117 7. Yusuf, M.O.L., Debora, A., Ogheneruona, D.E.: Ambient temperature kinetic assessment of biogas production from co-digestion of horse and cow dung. Res. Agric. Eng. 57(3), 97–104 (2011). https://doi.org/10.17221/25/2010-rae 8. Ofoefule, A.U., Nwankwo, J.I., Ibeto, C.N.: Biogas Production from Paper Waste and its blend with Cow dung Biogas Production from Paper Waste and its blend with Cow dung, vol. 1, no. May, pp. 1–8 (2016) 9. Xiao, X., Dung, D., Li, L., Meng, X., Cao, J.: Catalytic steam gasification of biomass in fluidized bed at low temperature : conversion from livestock manure compost to hydrogenrich syngas. Biomass Bioenerg. 34(10), 1505–1512 (2010). https://doi.org/10.1016/j.biombioe. 2010.05.001

A Hybrid Scheduling Approach in the Cloud Adedoyin A. Hussain1,3(B) , Fadi Al-Turjman2,3 , Sinem Alturjman2,3 , and Chadi Altrjman3,4 1 Computer Engineering Department, Near East University, Mersin 10, Nicosia, Turkey

[email protected]

2 Artificial Intelligence Engineering Department, Near East University, Mersin 10,

Nicosia, Turkey {fadi.alturjman,sinem.alturjman}@neu.edu.tr 3 AI and Robotics Institute, Research Centre for AI and IoT, Near East University, Mersin 10, Nicosia, Turkey 4 University of Waterloo, Waterloo, ON, Canada [email protected]

Abstract. The cloud has grown in wide popularity in recent years. It provides metered resources to end-users. The user gets to pay for the resource being utilized. The provision of these resources has become an issue that is to be addressed in the cloud in order to guarantee customer certification. The introduction of task scheduling will assist in curbing this issue. Has scheduling the task to the appropriate resource will guaranty users’ quality of service. In this work, a hybrid genetic algorithm is being proposed. Genetic algorithm works on the basis of natural selection. The work is being validated by using computational parameters from both the user desired and provider desired criteria like time, cost, and throughput, and resource utilization. Then the proposed technique is compared to other scheduling techniques like first come first serve, short job first, and round-robin for more validity. The proposed technique has the best execution rate with a rate of 32.47 ms. The results show that the proposed hybrid GA reduces time and cost. This is a convincing technique for cloud computation. Keywords: Cloud · Genetic algorithm · Task scheduling

1 Introduction The cloud is making progress with the improvement of PC and other gadgets. A reasonable planning strategy is used to allot cloud tasks to cloud resources. To work on the viability of the task planning for the cloud stage, a scheduling strategy reliant upon a genetic algorithm is presented. Task computation is a kind of progressive estimation to improve the arrangement of data, create space for big data, and use that data to control the transformative pattern of the cloud stage [1, 2]. The fundamental rule of the cloud is that the system handles requests introduced by customers into subtasks. Subsequently, task planning for the cloud is one of the basic advances of cloud computing, which impacts the whole execution of the cloud stage. At the point when execution of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 418–431, 2022. https://doi.org/10.1007/978-3-030-99616-1_55

A Hybrid Scheduling Approach in the Cloud

419

all subtasks is done efficiently, it achieves a quality of service for both users and cloud providers [3–6]. This reduces the waiting period and decreases the cost, and increases the productivity of resource utilization. Authors in [7] created a scheduling process in the cloud for tending to the cloud task reliant upon further developed computation, which can secure lesser time and lower cost for wrapping up the job. It is the vital development process that uses the possibility of business execution of delicate product designing with public customers [8]. A colossal number of studies show that the issue of cloud planning is related to the NP-hard problem, which has been analyzed by various analysts. Cloud is one more advancement gotten from grid computing and implies using enlisting resources as an organization and providing for beneficiaries on interest through the Internet [9, 10]. Authors in [11] revised the PSO to consider the idea of customer’s organization, which has achieved extraordinary results in arranging resources to cloud tasks, finishing a tremendous number of consistent enlistments. It relies upon splitting resources between customers utilizing virtualization. Regardless, there are various troubles normal to distributed computing, Authors in [11] created a hereditary approach strengthening estimation for task arranging with two-fold fitness, which can effectively change the solicitations of the customers for the properties of tasks and work on the customers’ satisfaction. High execution can be given by processing, scattering remaining weights across all resources, and effectively getting less holding time, execution time, and high throughput [12]. Authors in [13] utilize the procedure for tending to the cloud task planning by implementing the ACO, which has a better waiting time in handling the cloud task. Task scheduling and load balance are seen as the principal factors that control other execution models, for instance, availability, flexibility, and power usage. It incorporates the moderate speed of gathering and is basically caught in a nearby ideal. Task planning is one of the focal issues in distributed computing. The significance of good scheduling of cloud resource simulation is that it progresses real utilization of resources [14]. The contributions in this work are, • • • • •

An extensive review of task scheduling in the cloud. Key design feature for scheduling in the cloud is discussed. A hybrid task scheduling technique is proposed. The results are discussed and analyzed. A summation of key issues and difficulties is discussed.

The paper is introduced as follows. Section 1 provides an introduction to the paper. An extensive literature review is introduced in Sect. 2. While in Sect. 3, it depicts the methodology with the proposed algorithm. In Sect. 4, sets forth the outcome and results gotten from the experiment. At long last, Sect. 5 concludes the paper. Table 1 gives an outline of the abbreviation utilized and its definition.

420

A. A. Hussain et al. Table 1. Abbreviations used Terms

Meaning

IaaS

Infrastructure as a service

ICT

Information and communication technologies

QoS

Quality of service

GA

Genetic algorithm

TEL

Technology-enhanced learning

TET

Total execution time

DEWS

Dropout early warning systems

NP

Nondeterministic polynomial

RR

Round robin

TFT

Total finish time

ML

Machine learning

TWT

Total waiting time

LR

Linear regression

PaaS

Platform as a service

VS

Virtual studio

2 Literature Review Various experts have proposed various ideas to overcome the issue of scheduling and resource assignment. In any case, further upgrades can even be made presently [15]. This section gives a succinct overview of scheduling and resource allocation. This current procedure gives a cost and time model approach for scheduling. Authors in [16] proposed a reasonable weight-changing computation by using hybridization of ant Colony technique, with the min-max method. Authors in [17] proposed a multi-object approach that uses the further developed differential estimation. The development is balanced using Ant Colony Optimization (ACO) computation. Regardless, assortments in the tasks are not considered in this system. They developed this strong method to restrict the cost of migration of VM and keep up the SLA (Service Level Agreement) which is a QoS factor. They considered the invigorate periods of the task in fulfilling the request. This processes the number of virtual machines from other applications. Authors in [18] proposed a load changing and arranging computation that doesn’t consider work sizes. As GA hardly picks the processors and a while later applies the inherited computation, the fittest processors track down the VM which has lower need. This methodology doesn’t show the real utilization of resources. Authors in [19] thought about the system that deals with the starvation issue in work change. Authors in [20] presented the preparation of tasks reliant upon a lining model. Through this, the need is assigned to VM to extend the response

A Hybrid Scheduling Approach in the Cloud

421

period of the structure and to achieve better weight changing [20, 21]. A nonlinear programming model has been formed to circulate resources for tasks. This strategy perceives the sound chromosome, packs the resources, applies a rivalry assurance, and makes another general population. In any case, in [22] they proposed the booking of tasks while thinking about information transmission as a resource. With this, in [23] they have proposed an Improved GA by using the PPRM. They have illustrated the association between task booking and energy assurance by resource distribution. Subsequently, it helps with growing the response time which prompts better execution of the system and cares for consistency. Authors in [24] proposed AHP situating-based task arranging. Later in this cycle, GA is applied to the new generation and observes the wellbeing rate. Authors in [25] a moving horizon scheduling plan for persistent endeavors was proposed. Authors in [26] have assessed smart cloud estimations to change the stack and proposed AntLion Optimizer (ALO) to give better results in changing the pile in the cloud. This gives a more critical course of action in the cloud. Along these lines, in [27] they proposed anticipating equivalent extraordinary weights. ALO and GA follow a bunch of filtrations and dispense with the less critical course of action. They have used the FCFS method for managing demand occupations when resources are available. Authors in [27] have audited transdevelopmental GA for making a response. It is following three fundamental exercises. Multi estimates decisions and various credits are considered. Fundamental GA having terms called individuals, chromosome, quality, and fitness work were discussed. By this idea, they ac-accomplished better typical response time and augmentations cloudlets with change in encoding. Authors in [28] proposed a need-based work booking computation for use in conveyed registering. They have considered a need-based early survey. Authors in [29] proposed the usage of meta-heuristic improvement to decrease execution costs through arranging. This bleeding-edge convinces the authors of this assessment to coordinate additional exploration on scheduling and resource assignment. Authors in [30] have proposed a Cloud-based by and large Storage and dynamic Multimedia Load Balancing (CSdynMLB) system to change the pile of a specialist bunch. In [31] they introduced the high-level cost of energy and covered delay objectives. It helps with reducing overhead time [32]. Regardless, this model is inadequate in deciding the capacities of centers and the absolute structure has no reinforcement, in a like manner achieving a single motivation behind frustration [33].

3 Methodology This section depicts the design and gives more perspectives on the proposed system as a whole. It shows the participant in the scheduling on the Cloud platform. The user requests a task and he is responded to accordingly by the service provider, by providing the appropriate resource based on its scheduling algorithm. For the techniques in booking cloud resources, the pattern of Task Scheduling trains the scheduler to get tasks from the customers and get a solicitation from the cloud information service (CIS) for open resources and their properties. The customer request the resources, and the cloud provider is answerable for the assignment of that assets to the customer to avoid the encroachment of the Service Level Agreement (SLA). Cloud scheduler is proficient to plan different virtual machines (VMs) for different tasks. The scheduling structure in the

422

A. A. Hussain et al.

cloud is depicted in Fig. 1. The proposed scheduling technique for examination is made in three sections, static scheduling, dynamic scheduling, and the proposed hybrid genetic algorithm. The results are analyzed based on different related boundaries (user desired and provider desired criteria) and the best result from these calculations are discussed in detail.

Fig. 1. Diagram of the scheduling process in the cloud.

3.1 Hybrid GA Here, for a GA to be hybrid its regular mode of operation has to be altered. Here we propose the combination of the traditional optimization technique known as the roundrobin with the GA to form a hybrid GA. Here we will be using the dynamic form of the RR and GA approach. The proposed approach is shown in Fig. 2. The process is described below. • First population: For the proposed hybrid algorithm, the first population is generated randomly, and then it is encoded. VMs and the task are encoded e.g., VM2: - TS3-TS7TS9. Therefore, each chromosome comprises VM and ID for every task execution. • The round-robin operation and the fitness calculation: After encoding the round-robin operation is performed. The aim is to reduce the completion time for each task on the resource. Round robin works on the basis of quantum time. Here, a time is selected so each process works simultaneously in parallel. In this work, the median of each chromosome is selected as the quantum time. This method provides efficient task fairness and can provide an efficient fitness value. The quantum will be given as the median of all the processes. The quantum will also provide justice for the task with

A Hybrid Scheduling Approach in the Cloud

423

minima and longer execution time. When execution halts, the fitness is then calculated using the equation below. The completion time for task Ts on Ra is given using this equation. Total Completion Time = max (CT s,a ). Then, to minimize the time, the time for every task must be calculated. Where for each task Psa is the processing time. The processing time of every task in the VM is calculated using Ps . Ps =

n 

Psa

ı=1

• Selection operation: In this manner, the tournament technique is used in case of any limitation in population size. The tournament technique is productive computationally and efficient for a parallel process. Two chromosomes are selected from the population indiscriminately. The fitter chromosome is chosen to be a parent; or else, the less fit one is chosen. The unselected ones are returned to the initial population and can be chosen again.

Fig. 2. HGA approach.

424

A. A. Hussain et al.

• Mating process: Two chromosomes are chosen for crossover after the selection process, to produce new children. The parents will be selected also as children even after the crossover. In this way, it will produce four children. After this, out of the four, the best two will be selected. Afterward, mutation can be applied for a fitter value. • Initialize subpopulation and termination: There might be a good fittest solution, however, it isn’t chosen during the crossover. After each iteration, elitism and a local search approach are performed where subpopulations are added into old populations. The local search is the hill-climbing where a point on the search space is chosen at random towards the uphill move. This process will speed up the search for the best-fit individual. This progression is considered a great method as a portion of the cycles can create the best solution. Then this chromosome isn’t eliminated from the populace, yet it is picked and added to the populace when the next iteration starts. When finally, a fit solution is gotten or the stopping criterion is met the process ends.

4 Results The parameters used to measure the QoS of the scheduling algorithm are explained in this section. Several experimentations using the most reassuring traditional optimization technique were utilized. To validate the feasibility of the proposed technique, it is contrasted with various traditional task scheduling optimization algorithms like round robin, first come first serve, and shortest job first. Generally, optimization criteria are distinguished into two; cloud providers and cloud users’ desire criteria. Thus, this work takes into consideration parameters from both the cloud service provider’s desire and the cloud user’s desire to solidify the proposed method. These optimization criteria are not prominently addressed by other researchers. Each baseline gives its proficiency during scheduling. The models were tried with various settings and parameters. Afterward, contrasting arguments were put forth. 4.1 Computational Environment The assessments done in this paper were executed utilizing the Eclipse IDE, which is an open-source system. Java is maybe the most remarkable programming language, and it offers different libraries that can oversee information science. It is also the most trademark and effective language and it was utilized in this evaluation. Cloudsim is a simulator that presents different exhaustive bundles concerning cloud processes and it is utilized for this simulation. The test was reviewed on a pc with, intel center i7, Disk: 1TB, RAM: 12 GB, Processors: 2.3 Ghz.

A Hybrid Scheduling Approach in the Cloud

425

4.2 Computational Parameters TWT (Total Waiting Time): It is the time an assignment sits tight for execution when other tasks are fighting for execution. It is the outright time spent by the task in the pre-arranged state ready to be executed. TFT (Total Completion Time): It is the distance in time from the start of a task till it wraps up. It is the absolute time at which a task or a cycle completes its execution. TET (Total Execution Time): Total execution time is the total amount of time spent by the cycle from coming in the pre-arranged state to its wrapping up. The execution time implies the time between the accommodation of a task/process and the hour of its completion. Thusly what measure of time it needs to execute a cycle is similarly a critical element. Cost: This monetary expense will be founded on the amount of time spent by the client on a specific asset. This shows the monetary expense which portrays the aggregate sum that should be paid by the client to the cloud provider for the asset being used. The equation beneath shows how it is determined where T hints at the time the asset is being used and C implies the monetary expense of the asset per unit time.  {C × T } × 100/1 Cost = i∈resources

Status/Availability: This is a critical component in finishing up how to scatter and administer the right assets for a given VM. The accessibility status is a triumph when the right resource is being consigned to the VM and knowing which resources are accessible at a given time. Resource availability accepts a huge part in the planning of tasks. Throughput: Throughput is the proportion of work completed in a unit of time. In this manner, throughput is the cycles executed to a few tasks completed in a unit of time. Throughput is a way to find the capability of a resource. It is expected to grow the number of tasks dealt with per time unit. It might be described as the quantity of cycles executed in a given proportion of the time. Resource Use: Resource usage is one more boundary that shows the maximum of the use of assets. Though, specialist co-ops need to accomplish maxima gains by delivering a restricted measure of assets. Assets will be kept occupied. This boundary is one of the fundamental importance in task scheduling. It signifies how many assets in the cloud are being occupied, this aids in following how the resources are used. Average resource utilization =

Time is taken by resource i to finish all the task × n Makespan

426

A. A. Hussain et al.

5 Result Discussion Table 2 shows the connection of our model with different models. The table shows the proposed model surpasses other models massively. Figure 3 is portraying the TET, TFT, and TWT of all the scheduling models. This shows the relationship be tween’s each model against the TWT, TET, and the TFT. These boundaries are exceptionally viewed to accomplish a higher QoS. These are the utilized parameters to legitimize how effective the model is. In the wake of rehashing a similar test, our proposed model beats different models when it comes to these parameters. In RR, each work gets a comparable proportion of time, yet there are a couple of circumstances where typical holding up time can be an issue as displayed in the outcomes. Also, the model delivers the least execution time which makes the execution of errands quicker contrasted with different models. This waiting time keeps clients from holding up that long which evades terminations. Figure 4 is portraying the asset usage versus the scheduling models. Along these lines, the holding up time will be diminished in the proposed hybrid calculation against different models. This shows the connection of asset usage for the scheduling model. Besides, the proposed model uses the assets that are free during the run time by picking another task. Additionally, the asset used is upgraded reparably. The asset used is analyzed under different aggregate counts of the makespan. In any case, when different assets are used then it becomes ideal. The hybrid and RR have an increment in the asset used and afterward stay in a consistent state. As the size of the asset, or how much assignment increment, there is an ordinary ascent in the normal waiting time. In this manner, we can infer that the hybrid is the most productive rather than the other analyzed models. From the figure, we can likewise find the effectiveness of different models rather than our model, the normal resource used by different models remains practically comparable, which implies it is impacted by the number of accessible assets. Table 2. Result comparison with other models Traits

FCFS

SJF

RR

HGA

Throughput (ms)

0.73

0.72

0.74

1.23

Total Finish Time (ms)

100.18

101.67

99.31

76.6

Total Execution Time (ms)

54.68

55.36

54.31

32.47

Availability

Success/40

Success/40

Success/40

Success/40

Total waiting Time (ms)

41.72

42.21

40.92

40.16

Resource Utilisation (%)

44

44

42

69

Cost (%)

0.28

0.27

0.28

0.16

A Hybrid Scheduling Approach in the Cloud

427

Figure 5 is portraying the throughput of all the scheduling models. The throughput is one of the most amazing legitimizing boundaries to show the exhibition of a cycle for every unit time. This shows the relationship against the throughput has we can see the best model with the best throughput is the hybrid model. Later, a series of 40 tasks were made to boost the throughput. This outcome portrays how proficient our model is. Each undertaking was parted into their tenth to show the presentation. The throughput is the highest number of assignments that can be completed per unit time, the hybrid model outflanks other models. During this course, we can see our model outflanks other models even during the split. Figure 6 is portraying the monetary expense versus the scheduling models. This shows the economic cost factor. Cost is to see the effect of the charged rate over the pre-owned methodology for information conveyance. The outcome gotten shows the hybrid model per task has a lesser expense factor. This obstructing advantage makes it more fascinating for clients without the feeling of dread toward being cheated. It is an assessing factor for each center in the cloud stage. The hybrid model shows a promising benefit where the percentage rate was on a comparable worth for each task. We can close by expressing the hybrid model outperforms and has a base efficient expense as opposed to other scheduling models.

Fig. 3. Time comparison with other models.

428

A. A. Hussain et al.

Fig. 4. Resource utilization comparison with other models.

Fig. 5. Throughput comparison with other models.

A Hybrid Scheduling Approach in the Cloud

429

Fig. 6. Economic cost comparison with other models.

6 Conclusion In recent years, the cloud is unquestionably one of the most stimulating topics for scientists, and researchers. This paper presents the significance of the task scheduling calculations in the cloud. It also provides an adequate understanding of the static and dynamic task scheduling techniques in the cloud. This work utilized a hybrid method by combining round-robin and genetic algorithms to form a hybrid GA. This work utilized notable criteria’s from both user and provider desired criteria for proper validation. Several computation parameters like time, throughput, and cost, and resource utilization were experimented on. The proposed technique has the best execution rate of 32.47 ms and the lowest waiting time in contrast to other models. This result provides adequate QoS to both user and cloud providers. Future works as to be considered like upgrading the work utilizing other parameters and comparing the results as they will show up. More hybrid techniques can also be considered and surveyed. It is accepted that this undertaking will help experts at whatever point considered.

References 1. Zhou, Z., Hu, Z.-G., Yu, J.-Y., Abawajy, J., Chowdhury, M.: Energy-efficient virtual machine consolidation algorithm in cloud data centers. J. Central South Univ. 24(10), 2331–2341 (2017). https://doi.org/10.1007/s11771-017-3645-z 2. Masdari, M., Salehi, F., Jalali, M., et al.: A survey of PSO-based scheduling algorithms in cloud computing. J. Netw. Syst. Manag. 25(1), 122–158 (2016) 3. Almaamari, A., Omara, F.A.: Task scheduling using PSO algorithm in cloud computing environments. Int. J. Grid Distrib. Comput. 8(5), 245–256 (2015)

430

A. A. Hussain et al.

4. Hussain, A.A., Al-Turjman, F., Sah, M.: Semantic web and business intelligence in big-data and cloud computing era. In: Ben Ahmed, M., Rakıp Karas, , ˙I, Santos, D., Sergeyeva, O., Boudhir, A.A. (eds.) SCA 2020. LNNS, vol. 183, pp. 1418–1432. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-66840-2_107 5. Eberhart, R. Kennedy, J.: A new optimizer using particle swarm theory,” in Micro Machine and Human Science, 1995. In: MHS 1995, Proceedings of the Sixth International Symposium on, pp. 39–43 (1995) 6. Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inf. J. 16(3), 275–295 (2015) 7. Hasan, M.Z., Hamed, T.Y., Al-Turjman, F.: Particle swarm optimization for adaptive socialdistance of neighborhood in the IoT and COVID-19 era. Int. Conf. Artif. Intell. Things (ICAIoT) 2021, 7–14 (2021). https://doi.org/10.1109/ICAIoT53762.2021.00009 8. Sekaran, R., Kumar Munnangi, A., Rajeyyagari, S., Ramachandran, M., Al-Turjman, F.: Ant colony resource optimization for industrial IoT and CPS. Int J Intell Syst. 1–20 (2021). https:// doi.org/10.1002/int.22636 9. Al-Turjman, F.: AI-powered cloud for COVID-19 and other infectious disease diagnosis. Pers. Ubiquit. Comput. 1, 4 (2021). https://doi.org/10.1007/s00779-021-01625-1 10. Palanikkumar, D., Gowsalya, E., Rithu, B., Anbuselven, P.: An intelligent water drops algorithm based service selection and composition in service oriented architecture. J. Theoret. Appl. Inf. Technol. 39(1) (2012) 11. Awad, A.I., El-Hefnawy, N.A., Abdel-kader, H.M.: Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments. Proc. Comput. Sci. 65, 920–929 (2015). https://doi.org/10.1016/j.procs.2015.09.064 12. Hussain, A.A., Dimililer, K.: Student grade prediction using machine learning in Iot Era. In: Ever, E., Al-Turjman, F. (eds.) FoNeS-IoT 2020. LNICSSITE, vol. 353, pp. 65–81. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69431-9_6 13. Gan, Y., Yin, C., Fan, Q., Li, A.: Improved T-matrix method for simultaneous reconstruction of dielectric and perfectly conducting scatterers. Access IEEE 8, 143622–143631 (2020) 14. Hussain, A.A., Dawood, B.A., Al-Turjman, F.: Application of AI techniques for COVID-19 in IoT and big data era: a survey. In: Al-Turjman, F. (ed.) Artificial Intelligence and Machine Learning for COVID-19. SCI, vol. 924, pp. 175–211. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-60188-1_9 15. Jayadivya, S.K., Mary Saira Bhanu, S.: QoS Based Scheduling of Workflows in Cloud Computing. Int. J. Comput. Sci. Electr. Eng. 1(2315-4209) (2012) 16. Pardeep, K., Amandeep, V.: Scheduling using improved genetic algorithm in cloud computing for independent tasks”, conf (2012) 17. Kumar, P., Verma, A.: Scheduling using improved genetic algorithm in cloud computing for independent tasks. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics, pp. 137–142 (2012) 18. Tsai, J.-T., Fang, J.-C., Chou, J.-H.: Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput. Oper. Res. 40(12), 3045–3055 (2013) 19. Maguluri, S.T., Srikant, R.: Scheduling jobs with unknown duration in clouds. IEEE/ACM Trans. Netw. (TON) 22(6), 1938–1951 (2014) 20. Cheng, C., Li, J., Wang, Y.: An energy-saving task scheduling strategy based on vacation queuing theory in cloud computing. Tsinghua Sci. Technol. 20(1), 28–39 (2015) 21. Hussain, A.A., Dawood, B.A., Al-Turjman, F.: IoT and AI for COVID-19 in scalable smart cities. In: Paiva, S., Lopes, S.I., Zitouni, R., Gupta, N., Lopes, S.F., Yonezawa, T. (eds.) SmartCity360° 2020. LNICSSITE, vol. 372, pp. 3–19. Springer, Cham (2021). https://doi. org/10.1007/978-3-030-76063-2_1

A Hybrid Scheduling Approach in the Cloud

431

22. Wen, X., Huang, M., Shi, J.: Study on resources scheduling based on ACO allgorithm and PSO algorithm in cloud computing. In: 2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science. pp. 219–222 (2012) 23. Arkhipov, D.I., Di, W., Tao, W., Regan, A.C.: A parallel genetic algorithm framework for transportation planning and logistics management. Access IEEE 8, 106506–106515 (2020) 24. Lin, W., Liang, C., Wang, J.Z., Buyya, R.: Bandwidth-aware divisible task scheduling for cloud computing. Softw.: Pract. Exper. 44(2), 163–174 (2014) 25. Ergu, D., Kou, G., Peng, Y., Shi, Y., Shi, Y.: The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. J. Supercomput. 64(3), 835–848 (2013) 26. Zhu, X., Yang, L.T., Chen, H., Wang, J., Yin, S., Liu, X.: Real-time tasks oriented energy-aware scheduling in virtualized clouds. IEEE Trans. Cloud Comput. 2(2), 168–180 (2014) 27. Liu, X., Zha, Y., Yin, Q., Peng, Y., Qin, L.: Scheduling parallel jobs with tentative runs and consolidation in the cloud. J. Syst. Softw. 104, 141–151 (2015) 28. Shamsollah, G., Othman, M.: Priority based job scheduling algorithm in cloud computing. Procedia Eng. 50, 778–785 (2012) 29. Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014) 30. Liu, X.F., Zhan, Z.H., Deng, J.D., et al.: An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. Evol. Comput. 22(1), 113–128 (2018) 31. Zhou, Z., Abawajy, J., Chowdhury, M., Hu, Z., et al.: Minimizing SLA violation and power consumption in cloud data centers using adaptive energy-aware algorithms. Future Gener. Comput. Syst. (FGCS) 86(6), 836–850 (2017) 32. Xie, G., Zeng, G., Jiang, J., Fan, C., Li, R., Li, K.: Energy management for multiple real-time workflows on cyber-physical cloud systems. Future Gen. Comput. Syst. 27(7), 1915–1928 (2016) 33. Ahmed, H.A., Al-Turjman, F.: Artificial intelligence and blockchain: a review. Trans. Emerg. Telecommun. Technol. 32, 2 (2021). https://doi.org/10.1002/ett.4268

Wi-Fi Feedback-Based Power System in the Heterogenous IoT Era – An Overview Fadi Al-Turjman and Ahmed Ali Abdelazim(B) Artificial Intelligence Engineering Department, Research Center for AI and IoT, AI and Robotics Institute, Near East University, Mersin 10, Nicosia, Turkey [email protected], [email protected]

Abstract. Electric power has recently been optimized and witnessed a significant transformation in many aspects to providing sustainable clean, distributed energy for the fast-paced, growing world. Modern technologies such as the internet of things (IoT) technology is at the forefront of this digital revolution. Besides, heterogeneous IoT has enabled and offered a variety of applications in power systems since it has enhanced power efficiency, distribution of energy, especially in rural areas. Furthermore, it has accelerated transmitting and receiving the signals from the main electric power grid to the emergency backup system via a smart WI-FI feedback signal to sustain critical loads such as hospitals, schools, plants, banks, etc., working without interruptible. Also, IoT contributes to increasing the volume of the energy shared. This review paper discusses the impact of IoT technology in evolving electric power and energy systems at an unprecedented pace.

1 Introduction Recently, the concept “Internet of things (IoT)” has drawn the world’s attention and become a high profile. Besides, it’s considered the upcoming major evolution with the internet that would occur a considerable impact in our everyday life and transform the planet into an electronic skin. Furthermore, these modern technologies such as intelligent robots, artificial intelligence, communication systems are expected to be the fourth industrial revolution that will be integrated into all reproduction of an operation power system’s physical structure and the real-time description, technical performance [2]. Internet of things technology has been integrated into electrical power systems in order to enhance its transmitting, receiving, and processing ability via a massive smart network composed of many power devices such as information sensing devices or smart sensing of the physical things around us, enabling them to be interconnected with each other [1]. The digital power system (DPS) was addressed in 2000 year by Professor Lu, and it indicates the Readers such as laser scanning and various devices, global positing system, radio frequency identification (RFID) devices, Infrared sensors [2] (Fig. 1). Internet of things technology (IoT) plays an essential role in the construction process of the smart power grid due to several reasons. First, it enabled the smart grid to emulate the human body by ensuring that each component of the smart grid can talk and listen and sense any disruption, allowing the grid to be self-automated. Conversely, the traditional power grid, the electrical company cannot know about the malfunction until the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 432–442, 2022. https://doi.org/10.1007/978-3-030-99616-1_56

Wi-Fi Feedback-Based Power System in the Heterogenous IoT Era

433

Fig. 1. IoT Smart grid (SG) architecture presenting power systems, power flow, and information flow [3].

customer informs them. In IoT smart grid, the utility company will be issued automatically about the disruption since the embedded smart components into the smart grid will immediately send a Wi-Fi signal to the company to take the right procedures because all the grid components must have an IP address that can communicate in two ways and that enhances the efficiency significantly [3]. Also, the application of the IoT in smart grids is classified into three types regarding the three–layered IoT architecture. Firstly, deploying various IoT smart devices for monitoring the states of the equipment, and this process mainly happens in the perception layer. Secondly, collecting the information from equipment through the smart devices sensors and happens at the network layer. Thirdly, controlling the smart gird via application interfaces in the application layer. The sensing devices in IoT technology are composed of Wi-Fi sensors such as machine to machine (M2M) devices, RFIDs, cameras, laser scanners, GPSs, etc. Furthermore, the smart grid construction is divided into four sub-systems. Power generation, transmission and distribution, and the utilization process. Integrating IoT into these subsystems enables us to monitor and control energy consumption. In addition to gas emissions, power production prediction, and many other vital processes [3]. The world and the environment are inextricably connected to its other half, energy. Under the high demand of energy, it requires a strong communication infrastructure that can run a high volume of energy. IoT is proposing a solution in order to overcome this obstacle by using a low-power wide-area network in the phase of wireless that enables an immense number of wireless connections that can cover a wide range of area with minimum power consumption and maintenance. The narrowband internet of things (NB-IoT) and long-range (LoRa) technology are the two technologies that IoT provides. The Narrowband internet of things (NB – IoT) is based on the cellular communication technology that allows the mobile phone to connect to the internet. Low power wide area networks (LPWAN) offer a practical and economical way of creating an IoT network that relies on receiving and transmitting big data in the power and energy systems [4]. Hence,

434

F. Al-Turjman and A. A. Abdelazim

internet of things (IoT) technology will shift the world power to another interconnected network that transforms the world into a pool of data flow.

2 Background Through this section, an overview about the IoT Assisted Energy–constrained platforms from big data repositories. In addition to the applications of IoT in smart-cities regarding the rapid increase in the number of mobile devices conducting a case study that depicts the femtocells models of wireless communication systems to provide better coverage as well as energy consumption. A. Retrieving images based on IOT-assisted energy-constrained devices. Processing and retrieving the images by the aforementioned method could be divided into three core steps. Firstly, detecting the image using Viola-jones algorithm and cropped. Each image has a different number of stored faces. Hence, many based image retrieval methods have been emerged and used in many fields, especially the education, defense, IoT surveillance cameras, and medical sciences research area. Enabling such a technology allowed most of the connected devices to transmit a high volume of data to cloud servers. As a result, it raised the efficiency of image and data processing as well as the increasing the storage capacity since the Smartphone users are not capable of performing a complex program when dealing with high-resolution images [5]. The Viola – jones algorithm method is preferred to be used for any reason. Firstly, it’s based on the face regions and presents a true positive detection in addition to, extracting convolutional features efficiently. Secondly, its open-source and fast face detection algorithm helps to reduce the complexity of smartphone on the internet of things (IoT). Despite the advantages, Viola-jones method has a limited false-positive detection in case of processing a complex background image. Consequently, in order to overcome this effect, two classes have been trained and they divided into two classes (Non face, Face) classifier as verification step for face detection. It provides us with only true- positive detected faces in the image. A five hundred face images and five hundred non-face images are used in the classification model. Binary patterns texture features are extracted for training a binary classifier which can discriminate between false-positive and true-positive detection of faces. The chosen data tested on three algorithms including quadratic SVM, linear SVM, and decision tree with ten folds cross- validation. The results were 90%, 92%, and 95% validation accuracy. Therefore, the proposed method of detecting the images using the internet of things (IoT) has proved a high credibility and accuracy [5]. B. IoT technology models in smart cities. The fast-paced development in heterogeneous wireless technologies in order to support different Radio Access technologies and to connect mobile users to the internet. The current technology such as iPad, smartphones, and tablets have made it easier to connect people to the internet in any time. As a result, mobile users expect to find a high-quality service from the infrastructure since the number of mobiles connected to the internet will increase immensely. Hence, mobile operators have been working to find a solution to the problem of coverage and capacity. In

Wi-Fi Feedback-Based Power System in the Heterogenous IoT Era

435

addition to, the growth in energy consumption. Many models have been used to solve the problems and one of these methods is the Matrix Geometric Method that is used to solve the network model by reducing the number of channels into one. In smart cities. Femtocells will contribute to realizing the data exchange, including ubiquitous devices, personal and environmental monitoring. This comprehensive model requires an energy-consumption policy. Considering, a set of low data which are deployed to provide a sufficient capacity to the user since the mobile user might be static or While shopping in the mall or driving through the city, they can use their smartphones and mobile applications such as mobile video while working shopping [6]. C. IoT application in Electric transportation and smart park: Day by day, electric transportation is becoming a vital part of our life, and the automotive industry is offering an energy benefit. Governments are currently urging vehicles manufacturers to produce more Electrical vehicles. The integration of the electrical vehicle into the electric grid via vehicle to grid (V2G) and grid to vehicle (G2V). Hence, the smart park is composed of a fleet of plug-in vehicles performing vehicle to grid power transactions. As shown in Fig. 2 there is two smart parks linked to the utility grid via a set- up transformer. The smart park can accommodate hundreds of vehicles that can take part in the power transactions with the grid. There are multiple grid services provided by smart parks, such as maximum utilization of renewable energy sources, load-peak shaving, reducing emissions, and minimizing the cost of energy. However, there are several challenges regarding the tremendous distributed Smart Parks. Furthermore, the cyber-security and grid stability. In order to overcome these challenges, intelligent computing connected with advanced control and protection is crucial. Internet of things (IoT) technology in the electric transportation sector reduces and controls the power systems’ energy consumption and provides sophisticated technology into people’s lives [7].

Fig. 2. [8] Smart Parks connected to the electric power network.

436

F. Al-Turjman and A. A. Abdelazim

D. IoT in smart home power systems. The emerging technology of (IoT) has been integrated in several aspects of our life such as our household devices that are equipped with wireless communication system. Each home has a wireless sensor network (WSN), and the obtained data from each device is transmitted to a central station which is called a home hub. These smart devices have nodes that have communication capabilities and moderate computation, and this home hub could be anyone device such as (PC, tablet, smart meter, etc.) any of these devices that has a storage capacity can handle performing local processing and make a connection with devices out of the home range area. This smart home environment, as depicted in Fig. 3 is made up of numerous IoT sensors and actuators (e.g., motion sensor, temperature sensor, and humidity sensor). In addition to the warless communication technology such as Wi-Fi, Bluetooth, zigbee and IPv6). Besides, the control systems that could be smartphone, and tablet). All of these combinations create a sense of decision-making, and the different adaption abilities to the various home appliances in smart households. The hidden power that imposes all of these devices and components is the IoT wireless sensors offering several advantages when applied in our environment by minimizing the energy consumption, reducing the cost, and monitoring the home environment [9]. All the data received from different device source is gathered and accumulated in the cloud. A massive data storage should be provided in the cloud in order to process the infrastructure. The upcoming generation of IoT applications considered as the cloud of the central part of this system considering this framework as a cloud centric. With this smart technology, the house owners can monitor the movements inside and outside the house helping to protect the house from any danger. Also, these smart sensors reduce the energy wastage by controlling lighting [9].

Fig. 3. Multi-level IoT framework for smart home [8].

Wi-Fi Feedback-Based Power System in the Heterogenous IoT Era

437

3 Iot Smart Use of Energy in the Industy IoT has been integrated into the industry production process designing a connected and flexible system reducing the energy consumption and optimizing the production process. A lot of energy is wasted in the traditional factories to reach the final process. Furthermore, monitoring the production process constantly requires a tremendous number of human resources. Recently, the current technology has introduced an innovative solution to replace the human factor in the monitoring process, which is the IoT technology that helps to recognize and analyze disruptions and failures through the production process from the beginning till the end. Consequently, action could be taken to prevent the production of waste and energy waste. IoT and its technology play an essential role in this process via smart mobile devices that could be an example of monitoring equipment. A Wireless communication such as ZigBee, Z-wave, Wi-Fi could be used to connect all equipment. Moreover, to utilize IoT effectively, there should be smart sensors that connected to the devices, hence; the components that consume energy more than the standard energy level can be detected And analyzed. In smart factories that apply the smart technology such as IoT the main element in the whole system is the data processing in the cloud platform. To help managers in the production process and make smart decisions. IoT systems can offer collaboration between customers, companies, and manufacturers. As a result, a specific product will be produced according to customers’ preferences [9] (Fig. 4).

Fig. 4. The complexity of communication in industrial automation systems [9].

3.1 Methodolgy The application of IoT in different fields has been reviewed and discussed in the Introduction. In addition, the deployment process of IoT technology has been addressed with respect to the challenges and opportunities. The survey mainly focused on Wi-Fi

438

F. Al-Turjman and A. A. Abdelazim

feedback in power systems utilizing the IoT technology in order to enhance the signal transmission and receiving process, transforming the typical power grids into smart grids that connected to sensors in order to detect any disruption. Besides, the application of Wi-Fi based on feedback signal via IoT in smart cities and mobile phones has been studied, showing the significant enhancement of IoT in these sectors. To conduct this paper, an extensive search was carried out to gather the recent publications on the contribution of IoT in the power systems or energy sector. The search process passed with various stages to cluster the data. First, the term “Internet of things” was widely searched, then internet of things application in power systems, but the title was not specified, and the abstract. The scope of search was limited to engineering. The next step was to priorities the papers from the recent to oldest and the most relevant. The papers were collected in categories of power systems (smart grids, smart cities, smart homes, mobile phones with energy-constrained). In this paper we focused on the IoT applications that can generally be applicable to most of the electrical power systems that rely on Wi-Fi feedback signals.

4 Results and Discussion of Iot Wi-Fi Applications 4.1 Femtocells in Smart Cells In smart cities the concept of femtocells has been expanded to realize the tremendous heterogeneous data exchange such as data centers, ubiquitous devices, and personal and environmental monitoring devices, personal and environmental monitoring devices connected to mobile users. These femtocells are meant to provide a multitude of services to enhance the quality of life in smart-cities as well as the residential experience, hence; femtocells in such settings will be distributed and available on public and private buildings and roads. The obtained results of the proposed study that focused on the femtocell, performance metrices are compared for three different sections categorized as low, medium, and high velocity mobile users if the femtocell can accommodate up to 2000 requests as shown below in Fig. 5 the effects of velocity of the mobile users on the mean queue length since as mobile users move faster in the cell the mean queue length decreases (MQL). Consequently, HetNet model will be essential in order to offer an energy-consumption model to serve numerous numbers of static/mobile users. A Hetnet case study is visualized for smart cities in which many scenarios have been studied. In Fig. 5 a set of femtocells have been deployed within an area of a microcell to provide sufficient capacity to the users. In the meanwhile, to supply an adequate QoS in terms of throughput, mean queue length, energy consumption, and respond time. Considering the different cases of the mobile users in which they might be static/mobile and use their smartphones or streaming online. Each of these femtocells has a range pf transmit power of 20 mW and a bandwidth of 5 MHz. the users are assumed to be uniformly distributed in the specified are range. In addition, the femtocell radius is 30 m. This case study implies that the application of IoT Wi-Fi feedback signals in smart cities will have a significant impact on all digital life aspects [10] (Fig. 6).

Wi-Fi Feedback-Based Power System in the Heterogenous IoT Era

439

Fig. 5. Femtocells serving mobile phones in a smart city [10].

Fig. 6. The effects of velocity of mobile users on throughput [10].

Hence, with the internet of things (IoT) Wi-Fi technology, the aid of femtocells in such situations, particularly with increasing traffic load will be very efficient and vital. In addition, as the results depict the essential parameters that should be taken into consideration which are the traffic load and velocity of mobile users [10]. • Mobile user velocity: As mobility is a vital issue in the deployment process, including velocity is always valuable in performance evaluation. In this study case, the velocity of mobile users has been classified as related to low-speed mobile users such as pedestrians and stationery, mobile users with a velocity up to 15 km/h. the medium

440

F. Al-Turjman and A. A. Abdelazim

speed mobile users such as the bicycle riders with velocity in the range of 15 to 30 km/h. High-speed mobile users with a velocity above 30 km/h [10]. 4.2 An Experimental Study of a Reliable IoT Gateway. IoT technology has allowed a user to remotely control a few properties such as the TV, refrigerator, light, etc. Hence, in this experimental study, IoT gateway using the IoTivity framework is designed. Besides, the development of an in-home-scale testbed for evaluation for the proposed IoT gateway. The proposed method includes three different types of devices, which are the master server, the three server devices used in the testbed are built on a Raspberry-pi-embedded machine using a general Linuz-kernal-based (Raspbian). As a result, the devices can be easily configured. Raspberry pi is used to design IoT-based smart environments. Andriod 6.0 code name Marshmallow has been used as well as the IoTivity platform which is an open-source-software framework that enables device-to-device (D2D) for easy-to-control wireless IoT devices. A network considers the connection between the master and slave servers as shown in Fig. 7 [11].

Fig. 7. Laboratory-scale IoT testbed [11].

When connecting the master server to IoTivity, then the IoTivity configures which machine is connected. The master server sends a message to the slave servers when IoTivity cannot find any entry. The measurements of the testbed, the recently published IoTivity framework (version 1.1.1) has been used. Also, Wi-Fi and LTE interfaces have been used to communicate with the user’s smartphone. A performance evaluation has been conducted to address the proposed gateway, each device’s registration time in the wireless network environments (Wi-Fi and LTE) has been measured and the number of slave devices is increased after every measurement. Also, the number of the measurements have been raised to 100 as shown in Fig. 8. The results showed that the introduced gateway registers each device within 2.4 s. If these measurements are conducted in the condition of wired-network condition, hence; the registration time will be enhanced significantly [11].

Wi-Fi Feedback-Based Power System in the Heterogenous IoT Era

441

Fig. 8. Average response time for attribute registration [11].

5 Conclusion In this paper, the applications of Wi-Fi technology on the internet of things era have been presented, showing how the proposed solutions might transform human life into a new era that is safer, faster, and accessible. The power system model is considered as a vital sector to enhance its production, transmission, and distribution process through the IoT technology by the embedded smart sensors that emulate the human’s body by detecting any malfunction or disruption in the smart grid transmitting a signal via Wi-Fi to the production electric utility company in order to the take the required procedures instead of the current power system grid technology that needs to be checked frequently. To maintain the power delivering to the subsystems. Furthermore, applying the IoT technology into the mobile phones to make an interruptible accessible internet everywhere to everyone even if with the high data flow usage such as the video camera and a high volume of data sharing connecting the user to the physical things around him. Besides, energy consumption has been considered in order to offer a better power system that saves energy. Internet of things will be a new life revolution to put humans in the equation of the unprecedented pace of developed technology.

References 1. Motlagh, N.H., Mohammadrezaei, M., Hunt, J., Zakeri, B.: Internet of Things (IoT) and the energy sector. Energies 13(2), 494 (2020). https://doi.org/10.3390/en13020494

442

F. Al-Turjman and A. A. Abdelazim

2. X. Li, “The application of IOT in power systems. https://www.researchgate.net/publication/ 289944013_The_application_of_IOT_in_power_systems#:~:text=The%20IoT%20technol ogy%20speeds%20up,and%20the%20prevention%20of%20damage. http://b-dig.iie.org. mx/BibDig2/P11-0392/files/PESGM2011-000231.PDF 3. Rehmani, M., Davy, A., Jennings, B., Assi, C.: Software defined networks-based smart grid communication: a comprehensive survey. IEEE Commun. Surv. Tutor. 21(3), 2637–2670 (2019) 4. Song, Y., Lin, J., Tang, M., Dong, S.: An Internet of Energy Things Based on Wireless LPWAN. Engineering, 17-Oct-2017. https://www.sciencedirect.com/science/article/pii/S20 95809917306057. Accessed 09 Mar 2021 5. Mehmood, I., et al.: Efficient image recognition and retrieval on IoT-assisted energyconstrained platforms from big data repositories. IEEE Internet of Things J. 6(6), 9246–9255 (2019). https://doi.org/10.1109/JIOT.2019.2896151 6. Bedi, G., Venayagamoorthy, G.K., Singh, R., Brooks, R.R., Wang, K.: Review of Internet of Things (IoT) in electric power and energy systems. IEEE Internet Things J. 5(2), 847–870 (2018). https://doi.org/10.1109/JIOT.2018.2802704 7. Mwasilu, F., Justo, J.J., Kim, E., Do, T.D., Jung, J.: Electric vehicles and smart grid interaction: a review on vehicle to grid and renewable energy sources integration. Renew. Sustain. Energy Rev. 34, 501–516 (2014). https://doi.org/10.1016/j.rser.2014.03.031 8. Shrouf, F., Ordieres, J., Miragliotta, G.: Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management, Selangor, Malaysia, pp. 697–701 (2014). https://doi.org/10.1109/IEEM.2014. 7058728 9. Gharbia, M.B., Bouallegue, R.: Handover decision algorithm in femtocell long term evolution networks. In: 2018 Seventh International Conference on Communications and Networking (ComNet), 2018, pp. 1–6https://doi.org/10.1109/COMNET.2018.8622167 10. Ever, E., Al-Turjman, F., Zahmatkesh, H., Riza, M.: Modelling green HetNets in dynamic ultra large-scale applications: a case-study for femtocells in smart-cities. Comput. Netw. 128, 78–93 (2017). https://doi.org/10.1016/j.comnet.2017.03.016 11. Kang, B., Choo, H.: An experimental study of a reliable IoT gateway. ICT Express 4, 130–133 (2018). https://doi.org/10.1016/j.icte.2017.04.002

Author Index

A Abdelazim, Ahmed Ali, 432 Abdufattokhov, Shokhjakhon, 379 Ali, Abdirahman Farah, 407 Ali, Suleiman Abdullahi, 392 Altrjman, Chadi, 392, 398, 418 Al-Turjman, Fadi, 398, 418, 432 Alturjman, Sinem, 418 Ameen, Zubaida Said, 398 B Bai, Chen, 107 C Cao, Ying, 189 Chen, Danhong, 356 Chen, Guiyong, 356 Chen, Qianqian, 182 Chen, Shuting, 149 Chi, Youshen, 371 D Deng, Hui, 233 Dong, Zejian, 257 E Elimi, Da,ud Dirie, 407 F Feng, Xixi, 1 G Gong, Yuhan, 330 Gong, Zhen, 356

Gulyamova, Dilfuza, 379 Guo, Cen, 318, 324, 330 Guo, Dongbai, 197 H Han, Yuting, 311 He, Jiaju, 45 He, Qiong, 133 Hu, Bin, 141 Hussain, Adedoyin A., 418 Hussein, Ayub Mohamed, 407 I Ibragimova, Kamila, 379 J Jiang, Zerong, 32 Jing, Huilan, 165 K Khalfalla, Osman Abdalla, 392 L Li, Hongyan, 288 Li, Jingtai, 40, 45 Li, Langhua, 116 Li, Qianwen, 336, 342 Li, Ruoguo, 32 Li, Zimeng, 318 Liu, Xingfeng, 294 Liu, Yan, 16 Liu, Yi, 174 Liu, Yongling, 356

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Al-Turjman and J. Rasheed (Eds.): FoNeS-IoT 2021, LNDECT 129, pp. 443–444, 2022. https://doi.org/10.1007/978-3-030-99616-1

444 Lu, Rong, 228 Lv, Ming, 318, 324, 330 M Ma, Dejie, 165 Ma, Lijie, 67 Ma, Yonghong, 189 Men, Ke, 189 Mubarak, Auwalu Saleh, 392, 398 N Niu, Lina, 274 Q Qi, Zhanpeng, 347 Qin, An, 294 Qu, Jia, 363 Qu, Wanbo, 205 S Salad, Abdul Mire, 407 Shi, Kaixin, 99 Shi, Mingjuan, 189 Siyad, Abdulaziz Ahmed, 407 Sun, Jie, 182 Sun, Yanbin, 125 Sun, Yu, 50 T Tan, Jiao, 189 Tian, Manli, 220 Tonga, Paul, 398

Author Index W Wang, Junlan, 241 Wang, Lei, 363 Wang, Shuang, 90 Wang, Yadan, 303 Wang, Yingjia, 116 Whitsed, Craig, 40, 45 Wu, Huifen, 249 Wu, Xianhao, 324 X Xiong, Qiushi, 356 Y Yan, Haorui, 213 Yan, Xiaoyu, 288 Yang, Jianli, 125 Yang, Jing, 125 Yang, Shengyi, 157 Yang, Tao, 83 Yi, Yanan, 116 Yin, Xiaobin, 24 Yu, Qi, 58 Z Zeng, Xiangkai, 125 Zhang, Bi, 40 Zhang, Daojin, 281 Zhang, Dongning, 189 Zhang, Hanhan, 74 Zhang, Heng, 107 Zhang, Lei, 9 Zhang, Xuegang, 336, 342 Zhu, Lin, 265