The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy: SPIoT-2021 Volume 1 (Lecture Notes on Data Engineering and Communications Technologies, 97) 3030895076, 9783030895075

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Lecture Notes on Data Engineering and Communications Technologies 97

John Macintyre Jinghua Zhao Xiaomeng Ma   Editors

The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy SPIoT-2021 Volume 1

Lecture Notes on Data Engineering and Communications Technologies Volume 97

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 http://www.springer.com/series/15362

John Macintyre Jinghua Zhao Xiaomeng Ma •

•

Editors

The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy SPIoT-2021 Volume 1

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Editors John Macintyre University of Sunderland Sunderland, UK

Jinghua Zhao University of Shanghai for Science and Technology Shanghai, China

Xiaomeng Ma Shenzhen University Shenzen, China

ISSN 2367-4512 ISSN 2367-4520 (electronic) Lecture Notes on Data Engineering and Communications Technologies ISBN 978-3-030-89507-5 ISBN 978-3-030-89508-2 (eBook) https://doi.org/10.1007/978-3-030-89508-2 © 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

Foreword

SPIOT 2021 is the 2nd international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary agenda of Internet of things. The “Internet of things” heralds the connections of a nearly countless number of devices to the Internet, thus promising accessibility, boundless scalability, amplified productivity and a surplus of additional paybacks. The hype surrounding the IoT and its applications is already forcing companies to quickly upgrade their current processes, tools, and technology to accommodate massive data volumes and take advantage of insights. Since there is a vast amount of data generated by the IoT, a well-analyzed data is extremely valuable. However, the large-scale deployment of IoT will bring new challenges and IoT security is one of them. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Continuously evolving models produce increasingly positive results, reducing the need for human interaction. These evolved models can be used to automatically produce reliable and repeatable decisions. Today’s machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime’s worth of work. As the IoT continues to grow, more algorithms will be needed to keep up with the rising sums of data that accompany this growth. One of the main challenges of the IoT security is the integration with communication, computing, control, and physical environment parameters to analyze, detect and defend cyber-attacks in the distributed IoT systems. The IoT security includes: (i) the information security of the cyber-space, and (ii) the device and environmental security of the physical space. These challenges call for novel approaches to consider the parameters and elements from both spaces and get enough knowledge for ensuring the IoT’s security. As the data has been collecting in the IoT, and the data analytics has been becoming mature, it is possible to conquer this challenge with novel machine learning or deep learning methods to analyze the data which synthesize the information from both spaces.

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Foreword

We would like to express our thanks to Professor John Macintyre, University of Sunderland, Professor Junchi Yan, Shanghai Jiaotong University, for being the keynote speakers at the conference. We thank the general chairs, program committee chairs, organizing chairs, and workshop chairs for their hard work. The local organizers’ and the students’ help are also highly appreciated. Our special thanks are also to editors Dr. Thomas Ditzinger and Prof. Xhafa, Fatos, for publishing the proceedings in Lecture Notes on Data Engineering and Communications Technologies.

Organization

General Chairs Bo Fei (President)

Shanghai University of Medicine and Health Sciences, China

Program Committee Chairs John Macintyre (Pro Vice Chancellor) Jinghua Zhao Xiaomeng Ma

University of Sunderland, UK University of Shanghai for Science and Technology, China Shenzhen University, China

Publicity Chairs Shunxiang Zhang Dandan Jiang Xianchao Wang

Anhui University Science and Technology, China DiDi Research Center, DiDi Global Inc., China Fuyang Normal University, China

Publication Chairs Jun Ye Ranran Liu Qingyuan Zhou

Hainan University, China The University of Manchester, UK Changzhou Institute of Mechatronic Technology, China

Local Organizing Chairs Xiao Wei Shaorong Sun

Shanghai University, China University of Shanghai for Science and Technology, China vii

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Organization

Program Committee Members Paramjit Sehdev Khusboo Pachauri Khusboo Jain Akshi Kumar Sumit Kumar Anand Jee Arum Kumar Nachiappan Afshar Alam Adil Khan Amrita Srivastava Abhisekh Awasthi Dhiraj Sangwan Jitendra Kumar Chaabra Muhammad Zain Amrit Mukherjee Nidhi Gupta Neil Yen Guangli Zhu Xiaobo Yin Xiao Wei Huan Du Zhiguo Yan Jianhui Li Yi Liu Kuien Liu Feng Lu

Wei Xu Ming Hu

Coppin State University, USA Dayanand Sagar University, Hyderabad, India Oriental University of Engineering and Technology, Indore, India Delhi Technological University, New Delhi, India Indian Institute of Technology (IIT), India Indian Institute of Technology (IIT), New Delhi, India Sastra Deemed University, Chennai, India Jamia Hamdard University, New Delhi, India Institute of Technology and Management, Gwalior, India Amity University, Gwalior, India Tshingua University, Beijing, China CSIR-CEERI, Rajasthan, India National Institute of Technology, Kurkshetra, India University of Louisville, USA Jiangsu University, China Institute of Automation, Chinese Academy of Sciences, Beijing, China University of Aizu, Japan Anhui Univ. of Sci. and Tech., China Anhui Univ. of Sci. and Tech., China Shanghai Univ., China Shanghai Univ., China Fudan University, China Computer Network Information Center, Chinese Academy of Sciences, China Tsinghua University, China Pivotal Inc, USA Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, China Renmin University of China, China Shanghai University, China

Contents

Novel Machine Learning Methods for IoT Security Application of Artificial Intelligence in Arrangement Creation . . . . . . . Xiaoling Hu

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Automatic Segmentation for Retinal Vessel Using Concatenate UNet++ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhongyuan Wang, Zhengyan Xie, and Yihu Xu

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Experimental Analysis of Mandarin Tone Pronunciation of Tibetan College Students for Artificial Intelligence Speech Recognition . . . . . . . Shiliang Lyu and Fu Zhang

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Exploration of Paths for Artificial Intelligence Technology to Promote Economic Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xing He

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Influence of RPA Financial Robot on Financial Accounting and Its Countermeasures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yao Dong

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Application of Artificial Intelligence Technology in English Online Learning Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juan Ji

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Spectral Identification Model of NIR Origin Based on Deep Extreme Learning Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Songjian Dan

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Frontier Application and Development Trend of Artificial Intelligence in New Media in the AI Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ying Feng and Xiaojing Lv(U)

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Analysis on the Application of Machine Learning Stock Selection Algorithm in the Financial Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jie Wang

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Default Risk Prediction Based on Machine Learning Under Big Data Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qian Ma and Yue Wang

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Application of Intelligent Detection Technology and Machine Learning Algorithm in Music Intelligent System . . . . . . . . . . . . . . . . . . Hongyun Zou

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Application of 3D Computer Aided System in Dance Creation and Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianxing Shi

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Data Selection and Machine Learning Algorithm Application Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingyi Qiu

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Analysis of the Drawbacks of English-Chinese Intelligent Machine Translation Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 Hongmei Li and Wei Xiong Application of Neural Network Algorithm in Robot Eye-Hand System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 Xiaolei Zhang, Yaowu Shen, and Junli Chen Construction Cost Optimization System Based on AHP-BP Neural Network Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Ningning Zhang Design and Implementation of Sensitive Information Detection Algorithm Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Jianchao Fang Pruning Technology Based on Gaussian Mixture Model . . . . . . . . . . . . 137 Mengya Sun Analysis of College Students’ Behavior Based on Machine Learning and Big Data Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Siyu Ning Adaptive Sliding Mode Control of Crawler Robot Based on Fuzzy Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Zhengtao Li and Xiaoxia Liu Application of Machine Learning Algorithms in Financial Market Risk Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Yunfei Cao

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The Application of Big Data Analysis Technology in the Research of English Online Learning Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Jun Li Application of Decision Tree ID3 Algorithm in Tax Policy Document Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Chao Pang The Value Embodiment of VR Interactive Technology in Product Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Yuwen Ma, Hong Chen, and Huayun Gao Innovation of Economic Business Model Based on Particle Swarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Shaohua Zhao University Public Resource Management System Based on DBSCAN Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 Yu Guo Influence and Mechanism of Welding Residual Stress of 16MnR with Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Pengju Zhang, Wenqian Bai, Yu Wu, and Jingqing Chen Intelligent Clothing Recommendation Design System Based on RFID Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 AiQing Tang Analysis and Optimization of Tourism Landscape Pattern Based on GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Siqi Wang Hidden Markov Model for Oral Training System . . . . . . . . . . . . . . . . . 234 Jian-Gong Multi-system Platform Cooperative Electronic System . . . . . . . . . . . . . . 241 Mingyan Peng and Yan Yu Design and Implementation of Children's Cognitive Education Software Based on IOS Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 Yan Yu and Mingyan Peng Hardware in the Loop Verification System for Collision Avoidance Algorithm of Intelligent Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . 255 Jianglin Lu Multiple Evaluation System of Cloud Computing Quality . . . . . . . . . . . 263 Shuang Qiu

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Network Governance Prediction Based on Artificial Intelligence and Algorithm Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Xiaoying Ruan and Hongfu Chen Big Data Analytics for IoT Security Application of 3D Image Technology in the 3-Dimensional Reconstruction of Impressionist Oil Painting Art . . . . . . . . . . . . . . . . . . 283 Nan Gao and Liya Fu Application of 3D Technology in Garment Design Template . . . . . . . . . 291 Kaichen Zhang Application of BIM Technology in Civil Engineering Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 Jinlan Tan, Dongping Hu, Huijuan Zhang, and Jun Duan Using Information Technology to Analyze the Impact of Digital Technology on the Innovation Performance of Manufacturing Enterprises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Fan Wu An Enterprise Marketing Channel Optimization Strategy in the Context of “Internet + ” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Jingjing Qiu Painting Art Style Rendering System Based on Information Intelligent Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 Tao Zhang Application of Big Data Analysis Technology in the Construction of Cross-Border E-Commerce Supply Chain Platform . . . . . . . . . . . . . . 331 Jing Ge and Xiao Han Analysis of Industrial Pollution by GMM System Based on Big Data Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Jing Dong Application of Digital Media Art Design in Modern Advertising Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 Yu Gao and Xiaoran Chen Significance of Introducing Internet Financial Supply Chain into Cities Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . . . 353 Zhuohui Liu, Jie Ma, and Yanhong Wu The Aggregation and Development of the Internet Digital Financial Industry Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . 361 Yangmin Zhang and Jinpeng Lin

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Application of Information System Based on Big Data Technology in Fine Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Chao Chai, Tielei Liu, and He Zhang Analysis Method of Life Counseling Features of French Majors Based on Big Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 Shan Liu A Study on Classroom Teaching Practice of Art Education Based on Learning Feedback System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 Ning Yang, Xin Sun, and Shiwei Jin Urban Environmental Art Design Based on Big Data System Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Ying Bai Impact of Artificial Intelligence on New Media Operations and Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Zeyue Xie Application of Big Data Analysis Technology in Mechanical Design, Manufacturing and Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 Haoming Guo and Yan Li Design and Implementation of Rural E-commerce Training System in the Era of Mobile Information Network . . . . . . . . . . . . . . . . . . . . . . 420 Xufang Hou Construction of Digital Management Courses in the Big Data Era . . . . 427 Chonghui Li Development of Multimedia Communication Technology Under Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436 Hongjie Liu Application and Research of Curriculum Blended Learning Based on Big Data Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 Liping Zou and Fuhan Chen Development of Internet Financial Technology Based on Data Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 Xincan Li Data Analysis of Medicinal Fragrance Culture Based on Network Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 Jing Yu, Tengda Li, Hui Fang, and Hong Wu Consumer Cognition and Behavior System Based on Big Data Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 Lan Zhen and Caiyin Ren

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The Influence of Internet Tourism Information Quality on Tourism Destination Image Under Big Data Analysis Technology . . . . . . . . . . . . 473 Min Liang, Winitra Leelapattana, Prayong Kusirisin, and Jirachai Yomkerd The Application of AI Technology in English Teaching Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482 Xiao Wei The Potential of Big Data Technology in Promoting the Dissemination of Tibetan Culture in the Information Age . . . . . . . . . . . . . . . . . . . . . . 489 Juan Li Design and Implementation of Meteorological Information Service System Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Jianye Cui, Youchun Li, Jian Huang, and Zhenhua Li Research on the Application of Computer Network Security and Practical Technology in the Era of Big Data . . . . . . . . . . . . . . . . . . 505 Xin Ge and Minnan Yue Energy Big Data Application Based on Energy Big Data Center . . . . . . 511 Dongge Zhu, Rui Ma, Shuang Zhang, Jia Liu, and Jiangbo Sha Computer Big Data Analysis and Cloud Computing Network Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 Xiangju Liu, Yuan Liu, and Yang Fei Construction of Blockchain Technology Audit System . . . . . . . . . . . . . . 523 Qiong Liu Design and Implementation of Information Management System Based on Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530 Pengtao Cui Computer Information Processing Technology Based on Big Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537 Yuan Liu and Xiangju Liu Energy Big Data Storage and Parallel Processing Method Based on ODPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Rui Ma, Dongge Zhu, Xuwei Xia, Jia Liu, and Jiangbo Sha Data Mining and Statistical Modelling for the Secure IoT Countermeasure Research on the Construction of English Translation Software on the Basis of Corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Liping Huang

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English Translation Teaching Model of Computer Multimedia System Under the Background of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558 Qian Wang Employability Enhancement Network Construction Based on Multimedia Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566 Min Qiu and Minte Fan Development of Teaching Mode of Digital Electronic Technology Based on Virtual Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Yongyong Xiong Establishment of Business English Corpus Based on Foreign Economic Demand Dependent on Automatic Generation Algorithm of Conceptual Semantic Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 Liang Wei Application of Deep Learning Model Based on Big Data in Semantic Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590 Shiguang Sun and Li Li Application of PBFT Algorithm Based on Trust Model in Internet Public Welfare Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 598 Huiyan Zhou, Weibin Zhao, and Kai Zhou Integration of Digital Art Works and Virtual Reality Technology . . . . . 606 Xiaoran Chen and Yu Gao Application of Smart Wearable Devices in Elderly Care . . . . . . . . . . . . 613 Yingzhen Jia and Tianle Yin Refined Teaching Mode of Courses Based on Digital Intelligent Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623 Fang Qin, Weijia Zeng, Linlin Yu, Lin Li, and Xiaoxia Tao Design and Research of Network Database System Based on Deep Learning Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631 Zilong Xu, Lang Liu, and Yong Zhu Handwritten Font Image Design System Based on Deep Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 Yan Lin Analysis on the Application of Intelligent Multimedia Technology in the Communication System of Art Education . . . . . . . . . . . . . . . . . . 647 Xin Sun and Tianyu Jiang Construction of the Network Online Teaching System Based on the Internet Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 Wei Chen

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Analysis of the Influence of Computer Software Modeling Technology in Modeling Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664 Yugui Tang The Development of Website Video Construction in the Era of Mobile Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672 Xun Fu Construction and Application of Teaching Resources Database for Japanese Reading Based on Multimedia Technology . . . . . . . . . . . . . . . 680 Yu Ji Construction of Public Physical Training Evaluation Information System Based on Data Analysis Technology . . . . . . . . . . . . . . . . . . . . . . 686 Gang Yin Construction of English Multi-module Online Learning System Based on Intelligent Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 692 Na Sun and Hongwei Zhao Effects of Digital Communication Between Cultures Based on Intelligent Internet Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699 Dapeng Li, Jun Wang, and Wei Wei Construction of Cross Border E-commerce Comprehensive Training Curriculum System Based on Virtual Simulation . . . . . . . . . . . . . . . . . . 707 Linying Yu, Wei Wei, Jishuang Guo, and Xuqi Qin Unmanned Driving System Based on Sensor Data Fusion Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715 Wei Zhang Optimization of Orderly Charge and Discharge Behavior of Electric Vehicles in Distribution Network Based on Data Mining . . . . . . . . . . . . 723 Xiaojie Zhou, Fangzhou Xu, Zhenhan Zhou, Xintong Gu, and Yang Xuan Optimization of Power Network Investment Decision Based on Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 731 Zhiwei Liu, Jianbin Wu, Huiwen Qi, Yaju Wang, Hongtao Xu, and Jia Li Application of Internet of Things and Big Data Analysis Technology in the Dissemination and Inheritance of Light Industry Culture . . . . . . . . 738 Ying Liu Design of TCD Spectrum Analysis System Based on FPGA and ARM9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746 Donghua Luo, Junling Pan, and Xingwei Zhou Robot Localization and Scene Modeling Based on RGB-D Sensor . . . . . 753 Jiading Guo

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Construction and Training of Cloud Computing-Based Smart Grid Operation Risk Early Warning Model . . . . . . . . . . . . . . . . . . . . . . . . . . 761 Kai Wang, Rui Zhang, Hanjie Yuan, Yuting Pei, Wenwu Zhang, and Hao Xu Construction of Translation Communication Platform Based on Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769 Xue Xu and Songbo Wang Spatial Environment Adaptability and Planning of Leisure Industry Cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 Han Shen Power Line Communication Networking Method for Low Voltage Distribution Network Based on Ant Colony Algorithm . . . . . . . . . . . . . 782 Lingfeng Zeng GA Nonuniform Kriging Gradient Projection Hybrid Global Optimization Algorithm for Robot Optimization Design . . . . . . . . . . . . 788 Chaoran Chen and Dong Huang BP Neural Network for Design of Hybrid System . . . . . . . . . . . . . . . . . 796 Jie Diao Multimedia and Multifunctional Simulation Experiment System . . . . . . 802 Qing Liu Design and Research of Information Platform Based on Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809 Guiyun Chen Optimal Power Flow Calculation of Power System Based on Vector Distance Immune Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815 Shichuan Liu, Teng Mu, and Aijun Zhang Application of Image Classification Algorithm in Film and Television Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 822 Xin Zhou and Mei Zhou Online Medical Teaching Assistant System Based on Web3D Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829 Hongyan Zhu Automatic Detection Method of Construction Cracks Based on Multi-modal Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 836 Shuang Lu, Ye Wu, Xiaolei Zhong, Miao Yu, and Xuesheng Yu Cloud Technology Dual Data Center Information System Based on Disaster Recovery Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843 Feifei Niu

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Power Automatic Control Method of Microgrid Based on Particle Swarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849 Fengbing Jiang Data Confidentiality and Privacy in IoT The Application Value of Big Data Analysis Technology in Financial Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 859 Xiquan Chen Data Survey of Ancient Building Protection Based on Digital Network Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867 Rong Jia Fintech and Financial Regulation in the Context of Big Data . . . . . . . . 875 Biwei Yuan Development of Internet Green Finance Under the Background of Sharing Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 882 Yan Wang and Zhengyin Wang Construction of Financial Service Information System Based on Computer Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 891 Xianhao Chen Operation Risk Assessment of Power Trading Institutions Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899 Jianbin Wu, Huiwen Qi, Lei Xue, Qiang Li, Jingyu Zhang, Chao Wang, Junwei Wang, Wei Gao, and Xiangyu Zhang Growth at Risk of Oil Price Based on Quantile Regression . . . . . . . . . . 906 Wenbo Jia and Yingce Yang Analysis and Application of University Human Resource Management Dependent on Ontology-Based Decision Tree Algorithm . . . . . . . . . . . . 914 Xuejia Zeng The Impact of Computer Networks on Economic Development . . . . . . . 922 Yikun Li Precautions for the Development of the New College Entrance Examination Application Decision-Making System Based on the Big Data Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 928 Juxin Shao The Effect of Artificial Intelligence Auxiliary System on the Home Health Risk Management of the Elderly in the Community . . . . . . . . . . 936 Dan Xin and Weilan Xu

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Design and Implementation of Rural Three-Level Logistics Distribution System Based on Cloud Computing . . . . . . . . . . . . . . . . . . 944 Shuang Wang Automatic Generation of Distribution Network Operation Data Report and Application of Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . 953 Zongxun Song, Dongzhao Gu, Lu Li, Liping Yu, Lingyun Tao, and Hongrui Cui Easing Effect of Supply Chain Finance Constraints Based on Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961 Yuru Feng and Yufeng Wang Assistant Driving Safety Early Warning System Based on Internet of Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 969 Rongxia Wang, Fen He, Weihuang Yang, and Linling Zhao Payment System of Cross Border E-Commerce Platform Based on Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 977 Xin Wang Association Rule Mining Algorithm for Demand Attribute Data Set of Emergency Decision Support System . . . . . . . . . . . . . . . . . . . . . . . . . 985 Wenzhe Jia and Yahui Kang System Structure of School-Enterprise Cooperative Education Service Platform in Higher Vocational Education Based on Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 993 Ke Wang Bank Customer Default Risk Based on Multimedia in the Background of Internet Digital Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001 Zhengyin Wang and Yan Wang Mathematical Model of Network Center Data Hierarchical Encryption Based on Decentralization . . . . . . . . . . . . . . . . . . . . . . . . . . 1010 Bingbing Han and Zaixing Su System of Government Audit Information Integration Platform Based on Block Chain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1018 Yueying Li EMRBchain: A Blockchain-Based Electronic Medical Record Sharing Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025 Xiaze Zhang, Ying Lin, Bingchen Huang, Chenyu Cui, and Chunxia Leng Application of Internet and Information Technology in of Legal Protection About Network Virtual Property in China . . . . . . . . . . . . . . 1034 Qian Zhang

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Short Video Audience Identification Data Recommended by Multiple Neural Network Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1042 Xin Guo and Fang Deng Personalized Recommendation System Based on Vocal Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1051 Jiawei Wang Design of Intelligent Car Control System Based on Path Memory Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1058 Junru Wang Research on Application Evaluation Index System of University Enterprise Cooperation Informatization Based on CIPP . . . . . . . . . . . . 1065 Zuoming Xu Design of 1+X Modern Apprenticeship Intelligent Information Management System Based on Sequence Clustering . . . . . . . . . . . . . . . 1071 Qifeng Han, Yanchun Xu, and Chuanye Wang Choreography Algorithm Based on Hybrid Density Network . . . . . . . . 1077 Jingzi Jiang Research and Construction of Intelligent Tourism System Based on Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083 Yan Li New Media Technology in Teaching Based on VR Simulation . . . . . . . . 1090 Qing Liu Scratch Detection and Repair Method for Film Image . . . . . . . . . . . . . . 1095 Hui Sun Improvement of ID3 Algorithm in Decision Support System . . . . . . . . . 1102 Shijun Li Template Algorithm in Project Cost Management Intelligent System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1108 Zhichao Li Application of Artificial Intelligence System in Music Education . . . . . . 1115 Ting Pan E-commerce Product Recommendation Strategy Based on XGBoost Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1122 Qunzhe Zheng Research on Precision Marketing Based on Clustering Algorithm . . . . . 1129 Zhihua Gan

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The Impact of Replacing Business Tax with VALUE-ADDED Tax Based on Big Data Technology on Enterprise Financial Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136 Zhijie Huang Using Big Data to Help Computer Professional Laboratory Construction and Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 Yu Zhang, Nan Liang, and Pengfei Liu Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1151

Novel Machine Learning Methods for IoT Security

Application of Artificial Intelligence in Arrangement Creation Xiaoling Hu(&) Jiangxi Nanchang Medical College, Nanchang 330000, Jiangxi, China

Abstract. The growing maturity and innovation of information technology has promoted the development of artificial intelligence technology to a certain extent, and has been widely welcomed in all walks of life. Artificial intelligence technology can achieve a certain purpose by imitating people's thinking and behavior, which can make matter more intelligent. For example: smart home, driverless, speech recognition and automatic production are products of the era of artificial intelligence. In the field of music, more and more music engineers begin to apply artificial intelligence technology to music arrangement, which makes the form of music arrangement more diversified and intelligent. Therefore, we should strengthen the application of artificial intelligence technology in music composition, to ensure the maximum play out of its application advantages, and provide better services for the development of music. Keywords: Artificial intelligence


Composition creation
Application

1 Introduction Due to the influence of traditional cognition, the application of machine can help people complete mechanical and repetitive activities with less participation in creative activities. However, with the rapid development and update of artificial intelligence technology, it is gradually applied to music creation, music production, music analysis and music education. Among them, the key point of the application of artificial intelligence technology in music arrangement is to use AI to compose music. However, in the future development process, whether the AI technology can completely replace the artificial arrangement is also the follow-up key research topic. Through the analysis of artificial intelligence technology, we can better find the application principle of artificial intelligence technology in music composition, ensure that it can be applied to music composition with higher quality, and improve the overall level of music composition.

2 Overview of Artificial Intelligence Technology 2.1

Definition of Artificial Intelligence Technology

AI technology refers to the technology of human intelligence realized by ordinary computer program. At present, the research on artificial intelligence technology is increasing, including machine learning, deep learning, natural language processing, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 3–9, 2022. https://doi.org/10.1007/978-3-030-89508-2_1

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expert system and other fields, and has obtained strong application effect in the fields of automatic degree design, intelligent robot and so on. Music has the characteristics of thinking and artistry, and music arrangement is formed through people's thinking creation. The application of artificial intelligence technology can imitate people's thinking through computer programs, and obtain the perception of music, so as to better improve the level of music composition [1]. 2.2

Main Features of Artificial Intelligence Technology

At present, artificial intelligence technology has three main characteristics: intelligent information retrieval, pattern recognition and logic processing of complex data. Among them, intelligent information retrieval is the most significant feature of artificial intelligence technology. The application of artificial intelligence technology, through the application of some artificial intelligence means, combined with the established information retrieval system, can complete the fine acquisition of data information, so as to solve the problem that people can not complete. In daily life and work, people often face a lot of trivial data information. If manual processing is used, not only the work efficiency is low, but also the accuracy of data information processing is not high. The use of artificial intelligence technology can complete the high-quality collation and induction of data information, and also ensure the accuracy of data retrieval. In addition, in the aspect of music composition, music composition needs to use a variety of notes, and through the use of artificial intelligence technology, we can connect the correlation between the notes, and form a note library, which can provide important theoretical basis for music composition. The second characteristic of artificial intelligence technology is the recognition of various patterns. which greatly facilitates people's daily work and life. In the era of big data, the common way of communication between people is to use electronic devices. At this time, artificial intelligence technology is needed to identify all kinds of information, so as to provide reference for the application of intelligent devices [5]. The third characteristic of artificial intelligence technology is that it can process complex data logically. Artificial intelligence technology is mainly an intelligent means to simulate human thinking, so it has strong memory ability and logical analysis ability, so it can replace human to deal with some complex logical problems. Among them, the neural network structure of artificial intelligence technology can help it work like human brain, and can imitate human memory and thinking logic to complete the processing of complex problems, so that problems can be solved in a short time. Therefore, the application of artificial intelligence technology can process and analyze more complex data, help people reduce the workload, and improve the efficiency of people's work [2].

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3 The Main Application of Artificial Intelligence Technology in Music Composition 3.1

Neural Network

The main way to solve problems by artificial intelligence technology is to use the topdown thinking, and the human neural network can effectively meet the logic of this problem-solving. Among them, the basic feature of neural network is to imitate the information transmission and pattern between human brain neurons. Among them, take people's music composition as an example. The whole music creation generally needs to go through music appreciation, music perception, music imitation writing and independent creation. The study of music knowledge includes the study of composition techniques and related music theory knowledge. Learners can gradually improve their creative ideas by learning relevant theories and reasonable guidance of teachers. The whole learning process of learners can be simulated by neural network, and based on this, the composition can be completed. Among them, for the neural network, its operation needs input and output. As a kind of time art, music has a lot of information based on the time axis. Therefore, through the application of artificial intelligence technology, the creation of music arrangement can effectively reduce people's creative pressure, and also can provide more diversified inspiration for people's creation [3]. 3.2

LSTM Long-Term and Short-Term Memory Unit

LSTM belongs to the category of feedback neural network. It not only has its own characteristics, but also integrates the characteristics of RNN model, which enhances its application advantages to a certain extent. When it is used in music arrangement, the network storage should be increased appropriately, otherwise the information storage time is short, and the ideal information storage effect can not be obtained. Among them, compared with the traditional RNN, LSTM adds three gates: input gate, output gate and forget gate, which makes its application more effective and more conducive to the formation of music arrangement [4].

4 The Concrete Application of Artificial Intelligence in Composing Music 4.1

Intelligent Chord: The Early Intelligent Form of Music Arrangement

The creation of a finished piece of music needs experience: the lyricist writes the lyrics, and the composer composes according to the lyrics, thus forming the original manuscript of music. Then the music producer or what we call “orchestration” will use a variety of musical instruments to complete the lyrics arrangement and make music accompaniment or background music according to the style of the song. This process is harmony and soundtrack. Then it is sung or played by the singer or performer, which is the second creation of art. Finally, it is recorded, and finally the final music works are elaborately polished by the music engineer. Therefore, composing music is an

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indispensable part in the process of music creation. Music arrangement is mainly to turn music score songs into sound music. If you want to use artificial intelligence technology in this link, you need to divide the whole process of music arrangement more carefully. For the composition creation, it mainly involves: chord arrangement, the choice of musical instruments, the choice and configuration of timbre, the preparation of sound texture and other steps. In the specific use of artificial intelligence technology, we should also consider these aspects of the composition process. Among them, chord arrangement is based on the measure or beat as a unit, according to a certain logical relationship, each summary is effectively connected together, and a certain chord is configured for it, and finally the instrument completes the chord performance. Therefore, the quality of chord arrangement directly affects the artistic taste of the whole music. At present, chord assistant is the most widely used chord matching tool, which can provide suggestions for chord matching according to chord relationship distance, five degree cycle and other theories, and is not limited by music style. In addition, like other popular intelligent chord tools in the market, plugin bountique scaler is also a tool for chord arrangement. It has the characteristics of fast and intuitive, with thousands of scales and arrangement modes, and supports a variety of styles and types of music. In this way, it can provide strong support for the composer's music creation. It can be seen that artificial intelligence technology has been widely used in the initial stage of music arrangement, which can effectively reduce the burden of the entry-level writers who do not have professional knowledge background and want to engage in music arrangement creation, and also effectively enrich the creative ideas of the composers [7]. Google company has released a product, a.i.duet (Fig. 1), which provides an interactive platform for composers and arrangers. It can learn and generate the corresponding melody in real time through neural network according to the notes played by human beings, so as to play the piano “duet” with human beings.

Fig. 1. Intelligent interactive composition system

Application of Artificial Intelligence in Arrangement Creation

4.2

7

Intelligent Timbre: An Important Means of Semi Intelligent Music Arrangement

After the completion of chord configuration, it is necessary to configure and play the timbre. Among them, artificial intelligence technology is widely used in the intelligent configuration of timbre, from the initial step sequence to template playing, and then to action detection, which improves the intelligent level of timbre configuration. Currently, the most popular intelligent timbre is the built-in performance template. In specific applications, only one tone or chord is needed to trigger the circular performance of the instrument. For example, the early development of Steinberg's virtual musician series can use different chord triggered instruments to perform. However, with the continuous innovation and development of artificial intelligence technology, a variety of new virtual instruments came into being. Among them, the touch virtual instrument developed by Apple integrates a variety of virtual intelligent instruments such as intelligent piano, intelligent guitar and intelligent drummer, and also develops virtual instruments with typical Chinese characteristics such as Erhu and pipa, which provides rich resources for composers. There are many kinds of intelligent timbres, and they can combine the timbres of musical instruments with the performance texture [8]. The application of artificial intelligence technology can provide a variety of business scenarios for intelligent voice configuration and performance, and also enrich the types of voice configuration, so as to better enrich the form of music arrangement. The Muse net (Fig. 2) system released by open AI belongs to this category, which provides a very good platform for human composers. The composer can interact directly with the deep neural network artificial intelligence composing system on this platform to select different music styles or musical instrument combinations. Musenet will create music works of different styles according to the needs of the composer.

Fig. 2. Muse net intelligent composing system

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Intelligent Fabric: The Actual Embodiment of Intelligent Music Arrangement

The essence of music is the flow process of the timbre of each instrument in the longitudinal treble coordinates and in the transverse time coordinates after it is arranged according to the chord, thus forming the musical texture [9]. With the support of artificial intelligence technology, automatic accompaniment came into being. Among them, compared with intelligent timbre, automatic accompaniment is more comprehensive, and makes music arrangement more intelligent. The earliest use of automatic accompaniment technology or electronic organ, electronic organ can support live performance, but can not carry out high-quality music production, in this context, can be installed in the computer automatic accompaniment software has been developed. At present, the most popular intelligent composing software on the market is band in a box. When this software is used, it needs to output chords according to the measure/beat, then select a style of performance, and set the paragraph format. At this time, a complete song accompaniment is born. Therefore, under the support of artificial intelligence technology, it makes the composition more simple and intelligent, greatly reduces the creative burden of the creators, and also provides more creative inspiration for the composers [10].

5 The Application of Music in Chorus Arrangement Choral music is composed of multiple parts. Composers or musicians need to use orchestration to arrange music between different parts. Artificial intelligence provides a new choice for it. We can find the matching chords in the big data database, and finally compose a wonderful and harmonious chorus.

6 Conclusion Continuous research and analysis of the application of artificial intelligence in music composition plays an important role in the rational use of artificial intelligence technology, comprehensively improving the level of music composition, promoting the stable development of artificial intelligence technology, and improving the artistic taste of music creation. Therefore, we should first understand the relevant overview of artificial intelligence technology and the main application of artificial intelligence technology in the composition of technical means, and then from the intelligent chord: the early form of intelligence in the composition Intelligent timbre: an important means of semi intelligent music arrangement and intelligent fabric: the actual embodiment of intelligent music arrangement, the efficient application of artificial intelligence technology in music arrangement creation can maximize the application advantages of artificial intelligence technology, reduce the creative burden of composers, and improve the intelligent development level of music creation, In this way, music creation will be more intelligent and artistic [11].

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Acknowledgements. This project is the phased research achievement of the 2021 project of Jiangxi education science “the evaluation of Chorus Conducting course teaching quality in Colleges and universities in the new era—Taking Colleges and universities of traditional Chinese medicine as an example”, Project No. 21YB383.

References 1. Chen, Y., Zhang, K., Liu, L.: The “musical personality” of artificial intelligence Music: giving the dynamic of music creation to enhance the emotional nature of music. China Natl. Expo (03), 130–132 (2021) 2. Xiao, K.: Go into deep learning and innovate teaching practice – practical research on the integration of artificial intelligence technology and music teaching in primary school. Art Eval. (02), 143–146 (2021) 3. AI transformation music creation “powerful assistant”. Invention and innovation (big technology) (11), 34–35 (2020) 4. Yang, C.: In the short video era, I am your Beethoven. Love, Marriage Family (the First Half of the Month) (10), 58–59 (2020) 5. Wenhailiang: The application of artificial intelligence in music composition. Beauty Era (2) (03), 91–93 (2020) 6. Wang, H.: The research on the professional development of Chinese pop music composition and Its Countermeasures. Shanghai Conservatory of music (2020) 7. Zeng, T.: The model of AI to realize composing. Shanghai Conservatory of music (2020) 8. Tong, Z.: Design of the recognition system of piano composition timbre based on artificial intelligence. Mod. Electron. Technol. 43(04), 183–186 (2020) 9. Zang, Y.: An analysis of copyright problems in the process of artificial intelligence editing and creation. Chin. Edit. (09), 81–86 (2018) 10. Zhou, L., Deng, Y.: Research on the current situation and trend of the development of artificial intelligence composition, vol. 032, No. 5 (2018) 11. Mao, K., Lin, Y.: Discussion on the development of artificial intelligence composition, No. 13 (2019)

Automatic Segmentation for Retinal Vessel Using Concatenate UNet++ Zhongyuan Wang, Zhengyan Xie, and Yihu Xu(&) College of Engineering, YanBian University, Jilin, Yanji, China [email protected]

Abstract. The vessel analysis of fundus image can be used to evaluate and monitor a variety of eye diseases. It play an important role in reducing the risk of blindness. At present, many model of fundus vessel segmentation still need to be improved for the segmentation results of small vessels. To address above issue, we propose a novel method called Concatenate UNet++ based on UNet++ to segment retinal vessel, which is an efficient network and can adapt for different tasks about medical image segmentation. Firstly, we use the nested skip connections to extract feature maps from shallow layer to deep layer. Then concatenate and fuse the feature maps to make the utmost of them. And in feature layer, we use atrous convolution to increase the receptive field. By verifying this method on three standard datasets, DRIVE, STARE, CHASE_DB1, the segmentation accuracy of proposed method are 0.9595, 0.9638, 0.9716 respectively. The result can demonstrate the proposed method has improvement on retinal vessels segmentation and can segment retinal vessels better than many existing retinal vessels segmentation methods. Keywords: Fundus image


Retinal vessels segmentation
Deep learning

1 Introduction The fundus image is the projection of the interior surface of the eye [1]. Retinal fundus image observing is a significant step for ophthalmologists to diagnose eye illness such as hypertension, glaucoma, diabetic retinopathy [2]. Early diagnosis can prevent vision loss. Accurate measurements of vessels in fundus images have become the basis of applications related to early diagnosis. Therefore, the vessels segmentation of fundus images have become a prerequisite for the quantitative analysis of diseases [3]. Vessel structure in fundus image is extremely complication which make vessel segmentation is very challenging [4]. At present, vessel segmentation by human consume much time and energy. Therefore, using computer to segment the vessel automatically is very valuable [5]. Many scholars have studied automatic retinal segmentation algorithms, which can mainly be divided into unsupervised algorithms and supervised algorithms. The unsupervised methods focus on the intrinsic properties of retinal blood vessels instead of original information from the manual annotation training data [3, 4]. Traditional unsupervised medical image segmentation algorithms include thresholding methods, clustering methods, and histogram-based methods and so on. The unsupervised vessel © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 10–18, 2022. https://doi.org/10.1007/978-3-030-89508-2_2

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segmentation algorithms have advantages in segmentation speed without training model steps. However there is still a large room of improvement to enhance the segmentation accuracy [2]. Supervised methods improves learning automatically by extracting the accurate vascular information marked by hand. Supervised segmentation methods are more sensitive to vascular feature information and has strong reliability and stability. They have great advantages over unsupervised methods [6]. The state-of-the-art models of supervised methods are variants of U-Net proposed by Ronneberger et al. [7] and fully convolutional networks (FCN) proposed by Shelhamer et al. [8] based on codec network. They are widely transformed in modern semantic segmentation algorithms. But there are two limitations on encoder-decoder networks. And there are limitations: 1) deeper U-Net need much computation but the results are not necessarily always better, 2) the different datasets determine the different optimal depth of the network. Therefore, U-Net still has a lot of potential for different application. Zhou et al. [9] present UNet++, which aims at overcoming the above limitations and the noticeable improvement is the nested skip connections. The UNet++ have the following advantages, 1) the architecture can capture the detailed features of the target and promote segmentation result effectively, 2) the model can adapt for many different dataset. So in this paper, a novel algorithm called Concatenate UNet++ based on the nested skip connections of UNet++ is proposed to segment retinal vessel. We use the nested skip connections of UNet++ to extract feature maps from layers on different depth. But we concatenate and fuse the feature maps to make the utmost of them, instead of using deep supervision. And in feature layer, we use atrous convolution to increase the receptive field [10]. Finally we test the model on three standard datasets, DRIVE, STARE,CHASE_DB1. This paper contributes in two aspects. 1) In our proposed method, we propose a novel model called Concatenate UNet++. Use nested skip connections, concatenate and fuse the feature maps from different depth, and atrous convolutions are used in feature layers. These can take full advantage of features map and increase the receptive field to guide the network to learn semantic information better. 2) The proposed method is an automatical method for segmentation of fundus vessels. We train and test it on three standard datasets. The results prove that the proposed method is better than many existing methods. The rest is arranged as follows: the second part is method which describe proposed deep learning architecture. The third part shows the setup of experiment, results and discussion, while the main conclusions are summarized in the fourth part.

2 The Concatenate UNet++ Model Based on UNet++ 2.1

The Architecture of U-Net and UNet++

U-Net can be divided into the left half of the encoder and the right half of the decoder. The architecture of U-Net is showed in Fig. 1. The encoder part is composed of some convolutional and down-pooling layers alternately. In the process of network training, the path of the input image can be shrunk so as to capture global information. The

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encoder part is composed of several convolutional layers and up-sampling layers alternately. During the training process, the network expands the path of the subsampled feature map so as to accurately locate each pixel point. There is no full connection layer in the algorithm, and the output uses softmax function to classify each pixel in the feature map with the same size as the input. The structure of U-Net is fixed. It has four layers and will not be changed to adapt to different datasets. The skip connection in U-Net directly fuses the high resolution feature of the encoding with the up-sampling feature of the decoding. This kind of fusion method only realizes the fusion of the shallow information, while the deeper information is not fully utilized, resulting in the gap of information between the shallow layer and the deep layer, which can’t get the best prediction. Up-sampling of deep features into shallow features or down-sampling of shallow features into deep features can shorten the semantic gap between encoders and decoders and improve segmentation performance.

Fig. 1. (a) The architecture of U-Net; (b) The architecture of U-Net.

2.2

Concatenate UNet++ Model

Fig. 2. The architecture of Concatenate UNet++. The nested skip connection is used in the model. Fusion feature is figured out by concatenation and fusion of output from shallow to deep.

In Concatenate UNet++, we keep the structure of nested skip connections and the architecture of Concatenate UNet++ is showed in Fig. 2. The feature semantic gap between adjacent levels of the encoder is little, and the low-resolution feature after

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sampling is fused with the high-resolution feature before fusion with the decoder feature. For each fusion module, features are aggregated step by step from top to bottom, and are fused with decoder features by means of nested skip connections. The structure can effectively shorten the semantic gap between encoder and decoder. The feature map of the same size is defined as the same layer, which is represented by L1*L5 from top to bottom respectively. Each node represents a feature extraction module, and each feature module is composed of two atrous convolution units, which will make the receptive field larger and is beneficial to segment tiny vessel, without loss of information. After convolution operation, the rectified linear unit (ReLU) is used. The output of each module on different layer can be expressed as: xi;j ¼ f

cð½x

cðxi1;j Þ; j ¼ 0 ; uðxi þ 1;j1 ÞÞ; j [ 0

ij1

ð1Þ

xi;j represents the result of the feature extraction unit, i represents the ith layer of the encoder, j represents the jth module in the same layer, c(
) represents convolution operation, u(
) represents up-sampling operation, [
] represents concatenation operation. In the same layer, feature extraction modules concatenate features by nested skip connections which can pass the output of the current module to all subsequent modules and concatenate with other inputs. So xi;j on the same layer can be expressed as:
j1 xi;j ¼ cð xi;k k¼0 Þ

ð2Þ

On all these counts, the input of encoder module comes from the output of the previous encoder module, while the input of other modules comes from the concatenate of all modules on the same layer before this module and corresponding up-sample result from modules on the next layer. Therefore, the output of each module on different layer in UNet++ can be expressed as: ( x ¼ i;j

c

h


cðxi1;j Þ; j ¼ 0 i  j1 xi;k k¼0 ; uðxi þ 1;j1 Þ ; j [ 0

ð3Þ

The network executes in the following order. First the encoder features are fused with sampling features on the encoder at the next layer between different network layers from shallow layer to deep layer. Then these outputs of the fusion units will continue to fuse with the corresponding up-sampling features of the module at the next layer, they will stop when there is no up-sampling unit at the next layer. The feature fusion of nested skip connection between modules of the same layer makes full use of context features. Output divided into three main parts of shallow feature, middle feature and deep feature in different depth. Finally the network reduces the semantic gap between encoders and decoders by concatenation and fusion of output from shallow to deep. The loss function is Sparse Categorical Cross Entropy. And the network can effectively capture the details of tiny vessels and better locate the backbones.

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3 The Concatenate UNet++ Model Based on UNet++ 3.1

The Detail of Experiments

Fig. 3. The graph representing loss data and validation set in orange over 55 epochs.

The simulation platform of the experiment is pycharm. And Keras and TensorFlow are used to train and test the model. The GPU is Nvidia GeForce GTX 2080Ti. We did experiments to confirm the epoch. According to Fig. 3, the loss of validation set is essentially unchanged when the number of epoch is more than 30. So the total learning epoch is set to 30. And batch size is 15. The model is trained by adam optimizer. The initial learning rate is set at 0.001. 3.2

Datasets and Pre-process

We use three standard publicly available datasets of fundus images: DRIVE, STARE, and CHASE_DB1 (CHASE). The images of DRIVE were taken from 453 individuals aged from 25 to 90. Forty fundus images were randomly selected. Seven pictures take from patients with early diabetic retinopathy, another 33 pictures take from patients without diabetic retinopathy. The resolution is 565  584 pixels. The STARE dataset includes 20 digital retinal RGB images, the resolution is 605  700 pixels. Ten of them had lesions and others had no lesions. The CHASE_DB1 (CHASE) includes 28 fundus images from 14 children. The size of each image is 960  999. In order to capture more features of small blood vessels and improve the accuracy of vascular segmentation, fundus images input into the network need to be preprocessed due to factors such as uneven illumination and low contrast. Firstly, the three channels of RGB image are extracted, and the contrast of the vessel and the background of G channel is found to be higher than other colors. Therefore, the G channel of RGB image was used to complete the gray transformation of the image to obtain the single-channel gray image, and then the fundus retinal gray image was normalized. Next, CLAHE was adopted to enhance the contrast between retinal vessels and the background without amplifying the retinal image noise, so that the structure and features of vessels in the fundus image could be more easily noticed. Finally, using local

Automatic Segmentation for Retinal Vessel

15

Fig. 4. Pre-process: (a) original RGB image; (b) G channel image; (c) CLAHE image; (d) gamma correction image.

adaptive gamma correction to enhance the darker part of the blood vessels in the retinal image of the fundus without affecting the quality of the brighter part. The results of preprocess are shown in Fig. 4. The training of the proposed model is performed on sub-images of the preprocessed images. A total of around 12000 patches, each of dimension 256  256, are obtained by randomly selecting their centers inside the full image. 3.3

Evaluation Metrics

Each pixel in fundus image will be classified as a blood vessel or a non-blood vessel. TP means that the blood vessel points are correctly regarded as the number of blood vessels; TN means the number of background points correctly regarded as the background; FP means the number of background points that were incorrectly regarded as vascular points; FN means the number of blood vessel points that are incorrectly regarded as background points. Four metrics were used to evaluate the performance of the proposed algorithm. Acc ¼

TP þ TN TP þ FN þ TN þ FN

ð4Þ

Se ¼

TP TP þ FN

ð5Þ

sp ¼

TN TN þ FN

ð6Þ

F1 ¼ 2 

P  Se P þ Se

ð7Þ

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Results and Comparison with Some Existing Methods

The segmentation images of three datasets are shown in Fig. 5. The proposed model produces abundant details of the tiny vessels segmentation. Our proposed model make the utmost of features from different layers to capture vessels’ details. It is worth mentioning in Table 1 that three indexes including Se, Sp, F1 performs best on CHASE. As we can see, the vessels distribution of CHASE is different from the other datasets. There are less tiny vessels and more backbones and branches in fundus image. Therefore the excellent result of CHASE indicates that our method can effectively extract the main part of the vessel backbones and branches. The images of fundus vessels are shown that Concatenate UNet++ has advanced results compared with other methods. So the proposed model can get good results on the task of fundus vessels segmentation.

Fig. 5. The results of different datasets, (a) original images; (b) label; (c) segmentation of Concatenate UNet++.

Automatic Segmentation for Retinal Vessel

17

Table 1. Compare with other methods in DRIVE, CHASE, STARE. Dataset Approaches DRIVE (2017) [1] (2018) [11] (2019) [12] (2019) [13] (2019) [14] (2019) [15] (2020) [16] Concatenate UNet++ CHASE (2017) [1] (2018) [11] (2019) [14] (2019) [15] (2019) [13] (2020) [16] Concatenate UNet++ STARE (2015) [17] (2016) [18] (2017) [1] (2019) [12] (2019) [13] (2020) [16] Concatenate UNet++

Acc 0. 0. 0. 0. 0. 0. 0.

9556 9561 9567 9578 9566 9581 9595

0. 0. 0. 0. 0. 0. 0. 0.

9634 9607 9610 9661 9670 9638 9560 9554

0. 0. 0. 0.

9638 9641 9673 9716

Se 0. 7897 0. 7792 0. 7891 0. 7940 0. 8038 0. 7963 0. 7991 0. 7887 0. 7277 0. 7756 0. 7641 0. 8155 0. 8074 0. 8239 0. 8585 0. 7320 0. 7791 0. 7680 0. 7735 0. 7595 0. 8186 0. 8058

Sp 0. 9684 0. 9813 0. 9804 0. 9816 0. 9802 0. 9800 0. 9813 0. 9870 0. 9712 0. 9820 0. 9806 0. 9752 0. 9821 0. 9813 0. 9820 0. 9840 0. 9758 0. 9738 0. 9857 0. 9878 0. 9844 0. 9809

F1 0. 7857 0. 8171 0. 8249 0. 8270 0. 0. 0. 0. 0.

8237 8293 8152 7332 7928

0. 0. 0. 0.

7883 8037 8191 8613

0. 7644 0. 8143 0. 8379 0. 8499

4 Conclusion We propose a novel method called Concatenate UNet++ based on UNet++ and can deal with the segmentation of fundus vessels in this paper. It is worth noting that there are three improvements to better segment retinal vessel in Concatenate UNet++. First, we use the nested skip connections are to acquire more useful multi-layer information from different deepth. Second, concatenating and fusing the feature maps are beneficial to generate fusion feature map. Third, we use dilated convolution in feature extraction units to make receptive field lager in the feature layer. To test the performance of model, the proposed models were compared with other methods in DRIVE, STARE and CHASE_DB1 datasets. The coherence of the vessels can be extracted well. The results of evaluation metrics can show Concatenate UNet++ model has better performance against some methods in recent years. In the future, we will continue to improve segmentation details of retinal blood vessels to make our proposed method to be stateof-the-art.

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References 1. Orlando, J.I., Prokofyeva, E., Blaschko, M.B.: A Discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomed. Eng. 64(1), 16–27 (2017) 2. Soomro, T.A., Afifi, A.J., Gao, J., Hellwich, O., Paul, M., Zheng, L.: Strided U-Net model: retinal vessels segmentation using dice loss. In: Paper presented at the 2018 Digital Image Computing: Techniques and Applications (DICTA) (2018) 3. Soomro, T.A., et al.: Deep learning models for retinal blood vessels segmentation: a review. IEEE Access 7, 71696–71717 (2019) 4. Lv, Y., Ma, H., Li, J., Liu, S.: Attention guided U-Net with atrous convolution for accurate retinal vessels segmentation. IEEE Access 8, 32826–32839 (2020) 5. Cai, Z., Xin, J., Liu, S., Wu, J., Zheng, N.: Architecture and factor design of fully convolutional neural networks for retinal vessel segmentation. In: Paper presented at the 2018 Chinese Automation Congress (CAC), 30 November – 2 December 2018 6. Xiuqin, P., Zhang, Q., Zhang, H., Li, S.: A fundus retinal vessels segmentation scheme based on the improved deep learning U-net model. IEEE Access 7, 122634–122643 (2019) 7. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-31924574-4_28 8. Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017) 9. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2020) 10. Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Paper presented at the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21–26 July 2017 11. Zahangir Alom, M., Hasan, M., Yakopcic, C., Taha, T.M., Asari, V.K.: Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation (2018) 12. Yan, Z., Yang, X., Cheng, K.-T.: A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J. Biomed. Health Inf. 23(4), 1427–1436 (2019) 13. Jin, Q., Meng, Z., Pham, T.D., Chen, Q., Wei, L., Su, R.: DUNet: a deformable network for retinal vessel segmentation. Knowl.-Based Syst. 178, 149–162 (2019) 14. Guo, S., Wang, K., Kang, H., Zhang, Y., Gao, Y., Li, T.: BTS-DSN: deeply supervised neural network with short connections for retinal vessel segmentation. Int. J. Med. Informatics 126, 105–113 (2019) 15. Shen, D., et al. (eds.): MICCAI 2019. LNCS, vol. 11764. Springer, Cham (2019). https://doi. org/10.1007/978-3-030-32239-7 16. Wang, D., Haytham, A., Pottenburgh, J., Saeedi, O., Tao, Y.: Hard attention net for automatic retinal vessel segmentation. IEEE J. Biomed. Health Inf. 24, 3384-3396 (2020) 17. Roychowdhury, S., Koozekanani, D.D., Parhi, K.K.: Iterative vessel segmentation of fundus images. IEEE Trans. Biomed. Eng. 62(7), 1738–1749 (2015) 18. Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J.P.W., Duits, R., ter Haar, B.M.: Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016)

Experimental Analysis of Mandarin Tone Pronunciation of Tibetan College Students for Artificial Intelligence Speech Recognition Shiliang Lyu1(&) and Fu Zhang2 1 Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China 2 Key Laboratory of Intelligent Processing of National Language in Gansu Province, Northwest Minzu University, Lanzhou, Gansu, China

Abstract. Amdo Tibetan is a silent tone language. For college students who take Amdo Tibetan as their mother tongue, the tone of Mandarin has always been a major difficulty in their Mandarin learning. In the field of artificial intelligence and Mandarin speech recognition, the speech recognition of Tibetan native speakers is the focus of the current research. Based on this, this paper takes the college students whose native language is Amdo Tibetan as the experimental subjects, extracts the fundamental frequency signals of their Mandarin pronunciation through the experiment of acoustics of speech, and converts the fundamental frequency into the value of 5° through normalization processing. The results show that the main error of their pronunciations is in the tone value, and there is little difference of the tone pattern between their pronunciations and the standard Mandarin. The main cause of their pronunciation errors is mainly affected by the negative transfer of their mother tongue. The research results can be used for reference in the fields of Mandarin teaching, speech recognition and artificial intelligence. Keywords: Amdo Tibetan
Monosyllabic tone of Mandarin
The tone errors
Acoustics of speech
The phonetic experiment

1 Introduction Chinese is a tonal language. Tone plays a distinguishing role in Chinese. Mandarin is the modern standard Chinese. It is the common language of all ethnic groups in China and has four tones. In the process of Mandarin learning and teaching, the tone part is always the key, but also the difficulty. The promotion of Mandarin is an important part of the national language undertaking. Especially in the minority areas and remote areas, the promotion of Mandarin can eliminate the barriers of communication between people. Language ability is also a kind of human resource. In the context of targeted poverty alleviation, the popularization of Mandarin is conducive to promoting local economic development and forming a stable language community of ethnic groups [1]. Both Tibetan and Chinese belong to the Sino-Tibetan language family. As one of the three major Tibetan dialects, Amdo Tibetan is characterized by consonant cluster and no tone [2]. The native speakers of Ando Tibetan have some difficulties in learning © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 19–25, 2022. https://doi.org/10.1007/978-3-030-89508-2_3

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Mandarin, especially the tone part, which will be influenced by the mother tongue and lead to the acquisition errors [3]. At present, the research on the Mandarin tones of college students who speak Tibetan as their mother tongue mainly focuses on the study of Lhasa dialect, while there are few achievements in the study of Amdo dialect and Kangba dialect. This paper will use the method of speech acoustics experiment [4]. College students whose Mandarin proficiency test scores are below Second Grade B, and whose native language is Amdo Tibetan, are the subjects of the study [5]. Through the experimental analysis of four tones of Mandarin, the error characteristics of tone pronunciation are obtained. Thus, effective teaching and learning strategies are put forward.

2 Experimental Methods 2.1

Experimental Equipment

The experimental equipment mainly consists of a collar microphone, a mixer, and a Powerlab signal collector. The softwares used are Adobe Audition CC and Praat. When recording, the sampling frequency is 40 kHz, mono channel, 16bit, and the signal is saved in Windows PCM.wav format. 2.2

Pronunciation Cooperators and Pronunciation Words

In this experiment, college students who live in Xiahe County, Gansu Province and who speak Amdo Tibetan as their native language are selected as the research objects. There are six in total, three males and three females. Pronunciation words are monosyllabic words of four tones in Mandarin. Each tone has 6 words, including the syllable of the monophthong and the one of the monophthong as the finals. When recording, the speaker pronounces each word in the spoken text with a natural intonation and speed. The speaker reads each word three times to ensure signal quality. After that, all the recordings are saved uniformly, and each speaker has 72 voice files. 2.3

Signal and Parameter Processing

After the systematic arrangement of all the signals, Praat is used to process the signals, mainly extracting the fundamental frequency parameters of the tone part. For the parameter processing process, the current technology has been mature, excluding the influence of coarticulation, removing the bending section of the fundamental frequency at the beginning and the falling section of the fundamental frequency at the end, and retaining the stable segment of the tone pattern section [6]. Through the Praat script, the values of 30 fundamental frequency points in the stable segment of the tone pattern section are extracted, and all the parameters of the value of the fundamental frequency are saved in the Excel. In order to exclude the influence of individual pronunciation differences on tone, the T-value method (Shi Feng, Wang Ping, 2006) is used to normalize the data of the fundamental frequency

Experimental Analysis of Mandarin Tone

21

and convert it into the value of 5 degrees [7]. The normalization of the T-value method is shown as follows [8]: T¼

lgX  lgðXminÞ 5 lgðXmaxÞ  lgðXminÞ

ð1Þ

In the formula, X is any extracted fundamental frequency point, Xmax is the maximum of all fundamental frequency data, and Xmin is the minimum value. By normalization, the T value corresponds to the value of 5 degrees of the tone. In these numbers, 0–1 corresponds to 1 in the value of 5 degrees, 1–2 corresponds to 2, 2–3 corresponds to 3, 3–4 corresponds to 4, 4–5 corresponds to 5 [9].

3 Analysis of Experimental Results 3.1

Analysis of the Tone Patterns of Amdo Tibetan College Students’ Mandarin

Previous studies by scholars (Duan Haifeng, Liu Yan, 2011) concluded that Tibetan students in Amdo made mistakes in the monosyllabic tone of Mandarin, including both tone type errors and pitch errors, and the order of tone acquisition was Qusheng, Yinping, Shangsheng and Yangping [10]. In this paper, after the normalization of all the fundamental frequency values by T value method, the specific tone patterns of the four tones of Tibetan college students’ Mandarin in Amdo are obtained. In order to compare the differences between the male and female, the parameters were processed separately according to gender.

Fig. 1. The fundamental frequency curves of the male pronunciation

From Fig. 1, we can see that the tones of the male college students’ Mandarin pronunciation are flat and straight on the whole, and the fundamental frequency curves of the four tones are different from that of standard Mandarin. The starting points of the four tones are higher, and the fundamental frequencies of the ending positions are generally higher. In comparison with Fig. 2, it can be seen that the tone curves of the female pronunciation are not different from the male pronunciation. According to the

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Fig. 2. The fundamental frequency curves of the female pronunciation

physiological characteristics of the pronunciation, the female’s pronunciation frequency is generally higher than the male’s. After normalization, the male and female show the same characteristics on the frequency curves of pronunciation. Yinping (tone 1), the frequency difference from the beginning to the end of the tone is little, the fluctuation is more stable, and the tone value of the female is greater than the male. The T-value for the whole tone ranges from 3 to 4, so the specific tone value is 44. Yangping (tone 2), the tone value of the female is lower than the male, and the T value of the male starts from 1.9 to 2.6, while the female starts from 1.5 to 2.1, and the tone tends to rise. From the range of T values for the male and female speakers, we can conclude that the specific tone value is 23. Shangsheng (tone 3), from the T-value trend of tone, both the male and female tones show the characteristic of zigzag tone. For the male, the T-value starts at 1.8, and breaks off at 1, and ends at 1.2. For the female, the T-value starts at 1.1, and breaks off at 0.8, and ends at 1.2. According to the value of 5 degrees and the change trend of the T value, we can conclude that the specific tone value is 212. Qusheng (tone 4), which is a high falling tone in Mandarin, and the starting points of the male and female tones are the highest among all the tones. T values range from 4.5 to 2.5 for the male speakers, and 4.5 to 2.1 for the female speakers. We can conclude that the specific tone value is 53. Therefore, it can be concluded that the value of 5 degrees of the four tones of Mandarin is the same for the male and female speakers from the tone patterns, and the specific tone values are 44, 23, 212 and 53 respectively. 3.2

Comparison of the Tone Values Between Tibetan College Students in Amdo and Mandarin

Through the analysis of the tone value, it can be concluded that the tone value of Tibetan college students’ Mandarin is lower than that of standard Mandarin, especially at the end of the syllable. There are four tones in Mandarin. The specific tone values are: Yinping 55, Yangping 35, Shangsheng 214, Qusheng 51. Their four tones are different from the standard Mandarin, due to the low level of the selected speakers’

Experimental Analysis of Mandarin Tone

23

Mandarin. In order to further compare the differences between the tone parameters and the tone values, the tone parameters of the male and female speakers are statistically analyzed. Table 1. The male’s tone parameters Tone category AVG SD Tone value

Tone1 159.96 9.39 44

Tone2 123.84 13.84 23

Tone3 105.46 8.84 212

Tone4 173.82 17.54 53

As can be seen from Table 1, the data are the average value of the fundamental frequency (AVG), the standard deviation of the fundamental frequency parameter (SD) and the tone value of Mandarin pronunciation respectively. From the mean value of the fundamental frequency parameters, Yangping and Shangsheng are lower, while the higher ones are Yinping and Qusheng. The standard deviation can reflect the dispersion degree of the mean value of the data. The standard deviations of Yangping and Qusheng of the male speakers are larger, which indicates that the parameters of each speaker show great difference. The smaller standard deviations of the four tones are Yinping and Shangsheng, indicating that there is little difference among the male speakers. Table 2. The female’s tone parameters Tone category AVG SD Tone value

Tone1 295.90 17.87 44

Tone2 213.74 11.47 23

Tone3 189.67 9.71 212

Tone4 287.06 22.45 53

Table 2 shows the tone parameters of the female speakers. As can be seen from the overall data, the fundamental frequency parameters of the female’s four tones are greater than that of the male’s. Due to the influence of the physiological structure, the pronunciation frequency of the female is higher than that of the male. Combined with the standard deviation parameters of pronunciation, the standard deviation parameters of the female’s and male’s Qusheng are the highest, while Shangsheng are the lowest. The standard deviations of Yinping and Yangping for the male and female are opposite. The standard deviation of the male’s Yinping is less than that of the female’s. The standard deviation of the male’s Yangping is greater than that of the female’s. It can be concluded from the standard deviation that the individual differences of Shangsheng are small and the fundamental frequency data are more concentrated in the process of pronunciation. The individual differences of Qusheng are great. Next comes Yinping and and Yangping respectively.

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Compared with the standard pronunciation of Mandarin, the tone patterns of the college students whose mother tongue are Amdo Tibetan are basically similar to the standard Mandarin, and their main pronunciation problems are the tone values. Amdo Tibetan college students’ specific tone value of Yinping is 44, and the standard Mandarin is 55. Both tone patterns are flat, but the former overall frequency of pronunciation is low, and the difference of T value between the starting and ending positions of the tone is 1. The tone value of Yangping is 23, and the standard Mandarin is 35. The difference of T value at the beginning of the tone is 1, and the difference of T value at the end of the tone is 2. The whole tone value is lower than Mandarin. The tone value of Shangsheng is 212, and the tone pattern is also zigzagging. The tone value of the standard Mandarin is 214. The tone values of the starting and folding points are the same as Mandarin. And the T value difference of the ending position is 2, which is lower than Mandarin. The tone value of Qusheng is 53, and the standard Mandarin pronunciation is 51. The tone pattern is consistent with that of Mandarin. The frequency of the starting position is in the same range, but the T-value difference of the ending position is 2. The tone doesn’t fall low enough. Combined with the difference of the tone value, it can be concluded that the errors of Tibetan college students’ tone pronunciation are mainly manifested in tone values, which are lower than standard Mandarin. The main cause of the tonal mispronunciation is the negative transfer of the tonal pronunciation caused by the transfer of the mother tongue. As Amdo Tibetan does not distinguish meaning through tone, it does not deliberately consider the influence of pitch change on its pronunciation in the process of pronunciation, and the pitch change is mainly manifested in sentence tone. In the process of acquiring Mandarin, although there is little difference of the tone pattern between the former and standard Mandarin, the overall pitch frequency is low. The specific tone is not pronounced in place, which is manifested in the low frequency at the end of the tone. 3.3

Teaching Strategies and Learning Suggestions

In the process of learning Mandarin, college students with Amdo Tibetan as their mother tongue have little problems in the tone patterns, which can also play the role of distinguishing the meaning correctly. However, it is still necessary to emphasize on improving the overall frequency of their pitch in the process of pronunciation. In the teaching course, students should be guided to master the tone values of standard pronunciation of Mandarin and have a comprehensive understanding of the characteristics of Chinese tones. Under the premise of accurate tone patterns, the teacher guides the students to pay attention to the change of the tone in the process of pronunciation, and compare with the correct tone pronunciation. In the process of tone learning, we can compare the standard Mandarin pronunciation with the tone of students’ own pronunciation, and use the method of repeated listening to strengthen their self-learning and judgment consciousness of tone, and strengthen their accurate tone pronunciation. Especially, students are required to pay attention to Yinping and Yangping in the process of learning pronunciation. Yinping is a high rising tone and Qusheng is a high falling tone. In the process of learning pronunciation, students are required to pay attention to the rise and fall of tone. In the

Experimental Analysis of Mandarin Tone

25

process of learning, students also need to pay attention to the pronunciation of the end position of the tone, and strengthen the training of listening sense at the same time.

4 Conclusion Through the analysis of the Mandarin tones of Tibetan college students who speak Amdo Tibetan as their mother tongue, it is concluded that the errors of pronunciation are mainly reflected in the tone value, and the tone pattern of pronunciation is not far from the standard Mandarin. After analyzing the fundamental frequencies of the male and female, it is found that the male and female have the same pronunciation errors, which are mainly influenced by negative transfer of mother tongue. In the teaching of tone, we should strengthen their tone pronunciation training, especially to improve the overall frequency of their tone pronunciation. Tone learning requires intensive pronunciation training, comparing pronunciation with standard Mandarin, paying much attention to the position of the end of syllables and the frequency of the end of the whole tone. In the future research, it is necessary to combine the listening experiment to understand the perceptual category of these college students on the four tones of Mandarin, and further analyze the causes of tone pronunciation errors, which will be more helpful to Mandarin teaching and correct pronunciation of tones.

References 1. Yue, L.: The economic value of language and its empirical research. Hunan J. Hunan Inst. Adm. 5, 81–85 (2013) 2. Kong, Y.Y., Zeng, F.G.: Temporal and spectral cues in Mandarin tone recognition. J. Acoust. Soc. Am. 120(5), 2830–2840 (2006) 3. Wei, C.G., Cao, K., Zeng, F.G.: Mandarin tone recognition in cochlear-implant subjects. Hear Res 197(1–2), 87–95 (2004) 4. Yan, L.: An Experimental Study on Chinese Tone and Intonation Learning by Ethnic Minorities in Silent Tone. Minzu University of China Press, Beijing (2009) 5. Zhu, R., Zu, Y., Wang, Y., et al.: Tibetan language tone prediction method and system (2017) 6. Zhang, J., Mcpherson, B.: Hearing aid low frequency cut: effect on Mandarin tone and vowel perception in normal-hearing listeners. Folia Phoniatrica Et Logopaedica 60(4), 179–187 (2008) 7. Feng, S., Ping, W.: Statistical Analysis of Monosyllabic Tone in Beijing, vol. 1, pp. 33–40. Chinese Language, Beijing (2006) 8. Yan, Z., Feng, S.: Statistical analysis of Mandarin tone. Chin. J. Phonetics (01), 38–45 (2016) 9. Ning, X., Feng, S.: Acoustic Experiment and Statistical Analysis of Monosyllabic Tone in Changsha, vol. 2, pp. 53–58. Language Research, Shanghai (2007) 10. Duan, H., Liu, Y.: Research on Monosyllabic tone of Tibetan Chinese learners in Amdo, vol. 14, pp. 17–18. Chinese Journal, Inner Mongolia (2011)

Exploration of Paths for Artificial Intelligence Technology to Promote Economic Development Xing He(&) School of Economics, Sichuan University, Chengdu, Sichuan, China

Abstract. After more than half a century of development, artificial intelligence technology has moved from the initial theoretical research to the market, which has greatly promoted the development of society, economy, and technology. The application of artificial intelligence to economic development has become the development of the times core. With the rapid development of science and technology today, economic development must rely on the promotion of scientific and technological forces, and the progress of science and technology also needs to be tested and confirmed in the development of the economy. This article uses qualitative research methods to focus on the development of artificial intelligence and economic development, discussing the mutual promotion of technology and economy under the current economic environment and technological development conditions, hoping to provide important academic value. Keywords: Artificial intelligence technology


Economic development
Science and

1 Introduction As a cutting-edge technology, after years of development and progress, artificial intelligence has become the forefront of the world's scientific and technological development, and it has been applied to various fields of production and life, and has even become the mainstream technology of the high-tech industry in the world's scientific and technological fields [1]. As my country's requirements for technological progress and technological exchanges have become higher and higher in recent years, as the “One Belt, One Road” initiative and the strategic development of an information technology industry powerhouse deepen, artificial intelligence will become an important force to promote my country's economic development. Economic development, as an important part of the field of national life, has always been the core of supporting my country's economic development. How to integrate artificial intelligence and real economy development and play a role in mutual development requires continuous indepth thinking and practice. At present, because the scale of application of artificial intelligence is not very large, its actual influence has not been fully demonstrated. However, people's expectations for artificial intelligence are high, thanks to its own advantages, so the application and development of artificial intelligence are currently rising sharply. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 26–32, 2022. https://doi.org/10.1007/978-3-030-89508-2_4

Exploration of Paths for Artificial Intelligence Technology

27

2 Industrialization of Artificial Intelligence Information industry refers to a type of emerging industrial chain born from the development of information technology. The information industry includes two major categories: the first category refers to the information technology equipment manufacturing industry; the second category is the information technology service industry. Information industrialization can promote economic development [2]. At present, many countries have regarded the development of industrial informationization as an indispensable part of the national economy. Some regions in China have regarded it as the main pillar of local economic development, such as Shenzhen, Shanghai, Chongqing and other places, and the economic benefits brought by it have surpassed traditional industries. In many Western countries, the information industry is regarded as the first core of the stateowned economy. For example, the annual economic output value of Silicon Valley in the United States far exceeds that of traditional industries. The article “Computer Companies That Don’t Produce Computers” published in the Harvard Business Review in 1991 mentioned that “deciding how computers are made does not create real value. Only deciding how computers are used can create real value.” This article demonstrates the importance of industrial informatization. Moreover, in the statistics of Western countries in 1991, it is not difficult to find that the total growth rate of the world’s computer market was 4%, of which the information service industry accounted for 14%, and the information service industry in the first three quarters of the United States accounted for 192% of the previous year’s profit, this also allows more countries to see the huge potential and value of information industrialization [3]. Of course, the construction process of information industrialization in different countries is also different. As far as China is concerned, after the idea of “Internet Plus” was put forward in 2011, information industrialization has undergone substantial development (see Fig. 1). Information Integration-M Electronic transaction-SD 5

Information Integration-SD Network communication-M

Electronic transaction-M Network communication-SD

Number

4 3 2 1 0 Internet banking

manufacturing Electrical other industry Hardware and appliances software equipment Artificial Intelligence industrialization types

Fig. 1. Distribution of artificial intelligence industrialization types

The development of information technology has accelerated the process of industrial informatization, and this is also the main measure of today's information technology to rapidly promote the development of the state-owned economy and enterprise economy. Industrial informatization refers to the application of information technology to the industry. Its essence is not to change the original industrial chain, but to add information

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technology to the industrial chain to improve the production efficiency of the industry [4]. Of course, industrial informatization is bound to add some departments to control the enterprise industry and promote the economic development of the enterprise industry, such as the Ministry of Information Technology. In today's industrial informatization construction, the most driving force for economic development is the advent of intelligent robots. Although it is said that it cannot completely replace humans, as long as its procedures are ensured, the robots can work uninterruptedly.

3 How Artificial Intelligence Technology Promotes Economic Development 3.1

The Mutual Promotion of Artificial Intelligence and Manufacturing

The manufacturing industry is the foundation of the economy development. Artificial intelligence has a great impact on the manufacturing industry. Artificial intelligence can promote my country's manufacturing industry from a large quantity to a high quality. The development of robots and chip technology in the 20th century promoted the transformation of the manufacturing industry. Coupled with the application of computer technology and information technology, automation and intelligence have penetrated into all areas of the manufacturing industry [5]. With the application of technologies such as man-machine dialogue and bionics, there are many types of intelligent instruments, and unmanned operation can be realized on the assembly line, which can complete tasks that many people can complete in multiple links. The development of the manufacturing industry has also promoted the advancement of artificial intelligence, such as the improvement of the refinement of equipment manufacturing, the production of artificial intelligence chips and other parts, and artificial intelligence can only be achieved if the overall level of the manufacturing industry is upgraded to the basis of hardware production. 3.2

Artificial Intelligence Upgrade in the Medical and Health Field

The integration of medical health and artificial intelligence has broken the bottleneck of traditional medical methods in many fields. As an important part of China's national economy, the medical industry has more importantly assumed social responsibilities. The development level of the medical and health industry also represents the country's comprehensive strength and technological level. In the current medical and health development, artificial intelligence has been seen everywhere in the industry. For example, the current application of imaging CD imaging technology is a convergent transformation formed after the development of artificial intelligence [6]. Through the research on the application and transformation of artificial intelligence technology in the development of China's existing medical and health business, it is found that the application of artificial intelligence technology is very important in the field of medical and health care. The development has shown a trend of integration. For example, the technical application of minimally invasive surgery is to use artificial intelligence technology to carry out scientific surgical technology applications, and realize the innovative development of medical and health technology applications.

Exploration of Paths for Artificial Intelligence Technology

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Development and Application of Smart City

The integration of the development of big data technology and the application of artificial intelligence technology has transformed into the construction and development of smart cities in the current form of technological integration [7]. The development and construction of modern cities has achieved a change in the situation of economic development and construction. Through the application and integration of artificial intelligence technology, modern cities can be transformed into shrinking landscape cities. The application of artificial intelligence technology can supervise the operation of the entire city, such as The supervision of the operation load conditions of urban traffic roads, the supervision of urban power supply operation, the supervision of urban security systems, and the supervision of the transformation and operation of urban operation hubs, etc., can use the application of artificial intelligence technology to realize the two-way transformation of technological development. Ensure the stable operation of urban development. 3.4

Development of Smart Logistics

Smart logistics is to maximize the integration of logistics units such as warehousing, roads, railways, aviation, etc., not only to maximize the use of time, but also to minimize costs. At the same time, the development of the logistics industry has greatly promoted the development of other industries and promoted the overall progress of the national economy [8]. China has been vigorously developing cutting-edge technologies in the logistics industry, and is currently at the forefront of the world, such as unmanned warehousing, intelligent loading and unloading, and intelligent screening. However, it has not yet achieved the balanced development of the logistics industry. Good connections, railways, highways, ports, and aviation have not yet cooperated tacitly, and the pressure on supply-side reforms is still huge. Therefore, further technological development is needed to promote the construction of logistics. Artificial intelligence is the use of information technology and data analysis to rationally allocate all production, logistics and consumption in the country, realize the convenience of people's material consumption, and minimize logistics costs, save social resources (see Fig. 2).

Smart logistics

Railway

Highway

Port

Aviation

Fig. 2. Composition and distribution of smart logistics

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Development of Smart Retail

The retail industry is an important part of the development of my country's real economy. After learning many advanced concepts from abroad, my country has applied artificial intelligence technology to the retail market, such as the shelf placement, unmanned warehouses, and intelligent distribution of large supermarkets in my country. Nowadays, new things like unmanned supermarkets have emerged in many cities in my country. Through the use of digital technologies such as barcodes and QR codes, as well as technologies such as fingerprint recognition and face recognition, the development of my country’s smart retail industry has achieved artificial convergence of smart technology. For example, products are numbered one-to-one through artificial intelligence codes, and product information is coded into codes. One code corresponds to an item, which can be sold online and is also anti-counterfeit [9]. Therefore, as the development of artificial intelligence technology has changed the form of the traditional retail industry, the traditional retail model has changed to coexist with virtual online commodity trading channels. The weight change of physical stores and online stores is also a result of the integration of artificial intelligence and the industry result.

4 Exploration of the Path of Artificial Intelligence Technology to Promote Economic Development 4.1

Do a Good Job in Personnel Training

The development and application of artificial intelligence is ultimately due to the cultivation of talents. The competition between artificial intelligence and economic strength is ultimately the competition of talents. Compared with the United States and Japan, my country still has a certain gap in cutting-edge core technology. For example, China's chip technology has been restricted by the United States, and the core technology of the chip is still mainly imported in the Chinese market [10]. For another example, robots that simulate human behavior have always been a technology that other countries cannot match. Therefore, to step up the development of artificial intelligence, we must start with the cultivation of talents. China's advantage lies in its market advantage. My country pays more attention to practical technology in the development of technology, but it also needs the construction of a talent team in the cutting-edge technology that represents the country's technological height. 4.2

Deepening Corporate Reforms

Most state-owned enterprises and large enterprises still have deficiencies in technology development, restricting and improving quality and structure. The frontiers of technology are often companies that specialize in technology research and development. However, small enterprises are forced by policy inclination and lack of funds, and technological development will inevitably encounter bottlenecks, which is not the direction of technological development, and from the perspective of the development of the real economy, the development of state-owned enterprises and large enterprises

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represents the development of the national economy. If state-owned enterprises and large enterprises do not take the lead in the technological revolution, the development of artificial intelligence will be slow. 4.3

Improving the Improvement of Innovation Ability

The improvement of national innovation capability is the foundation of national progress, innovation is the soul, and innovation also requires direction and practice. Various industries in our country have made a lot of contributions on the road of innovation, and have also hit many nails. For example, the research and development of technologies such as the “Urban Bus Rail” in the public transportation industry has achieved certain results, but due to the inability to solve various problems and the lack of applicability, this technology was finally abandoned. For another example, the unmanned driving technology developed by various car companies is also an important aspect of artificial intelligence research and development. However, due to traffic safety and the overall social level and the quality of personnel, it has not been promoted to the society. Therefore, the improvement of an industry or an object requires the improvement of the overall technological level of society, otherwise it will hinder each other and hinder each other.

5 Conclusion In short, although artificial intelligence has not yet shown its full strength in today’s economic development, its value has always existed. Therefore, in an era when countries around the world are secretly fighting, the development and application of artificial intelligence may be the “Industrial Revolution”. Therefore, our country should pay attention to the research on the development and application of artificial intelligence and devote ourselves to the development of our country's economy. The economic development of artificial intelligence requires continuous research and practice. The impact of artificial intelligence on people's lives and production has been revealed, and people are gradually adapting to the changes that artificial intelligence has on themselves. Therefore, the demand for artificial intelligence will gradually increase, which is also a driving force for rapid economic development.

References 1. Mohamed, A.A., Gardner, W.L.: An exploratory study of interorganizational defamation: an organizational impression management perspective. Organ. Anal. 12(2), 129–145 (2013) 2. Yang, J., Guo, L.H.: Statement or silence: a study on the difference of impression management behavior between state-owned enterprises and private enterprises after negative reports. Nankai Bus. Rev. 20(1), 83–95 (2017) 3. Mary-Hunter, M., Brayden, K.: Keeping up appearances reputational threat and impression management after social movement boycotts. Adm. Sci. Q. 58(3), 387–419 (2013)

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4. Bozzolan, S., Cho, C.H., Michelon, G.: Impression management and organizational audiences: the fiat group case. J. Bus. Ethics 126(1), 143–165 (2013). https://doi.org/10. 1007/s10551-013-1991-9 5. King, B.G., Felin, T., Whetten, D.A.: Finding the organization in organizational theory: a meta-theory of the organization as a social actor. Organ. Sci. 21(1), 290–305 (2010) 6. Carlos, W.C., Lewis, B.W.: Strategic silence: withholding certification status as a hypocrisy avoidance tactic. Adm. Sci. Q. 63(1), 130–169 (2018) 7. Cooper, S., Slack, R.: Reporting practice, impression management and company performance: a longitudinal and comparative analysis of water leakage disclosure. Acc. Bus. Res. 45(6–7), 801–840 (2015) 8. Silvester, J., Mohamed, A.R., Anderson, G.F.M., Anderson, N.R.: Locus of control, attributions, and impression management in the selection interview. J. Occup. Organ. Psychol. 75, 59–77 (2002) 9. Wayne, S.J., Liden, R.C.: Effects of impression management on performance ratings: A longitudinal study. Acad. Manag. J. 38, 232–260 (1995) 10. Elsbach, K.D., Sutton, R.I.: Acquiring organizational legitimacy through illegitimate actions: a marriage of institutional and impression management theories. Acad. Manag. J. 35(4), 699–738 (1992)

Influence of RPA Financial Robot on Financial Accounting and Its Countermeasures Yao Dong(&) Shandong Management University, Jinan, Shandong, China [email protected] Abstract. With the continuous reform of digital technology, RPA financial robot emerges as the times require. Based on the automation of financial processing process, financial robot has a great influence on traditional financial accounting because of its characteristics of precision, reliability, high efficiency, low consumption and rapid response. From the analysis of the impact of RPA financial robot on financial accounting, this paper discusses its countermeasures. Keywords: RPA financial robot


Financial accounting
Impact

1 Introduction With the arrival of big data era, financial digital transformation is a major trend [1]. In 2017, Deloitte launched a financial robot that digitally transformed the financial process [2]. After that, various industries at home and abroad have introduced financial robots. The popularization of RPA financial robot in the field of accounting work, it will inevitably have a great impact on the traditional financial accounting [3]. In the face of the great influence brought by the new technology, how to transform and develop financial accounting is worth discussing.

2 RPA Financial Robot Overview 2.1

Connotation of RPA Financial Robot

The RPA is called “Robotic Process Automation” [4]. It is robot process automation, which combs the business requirements into a process that can be executed, and then executes some customized processes through “robot” [5]. Finally, the script program is used to automatically obtain the desired information. RPA financial robot is actually a kind of software program which can deal with the financial work of the company intelligently [6]. It is based on the automation of financial processing process. Usually similar to manual accounting process, based on financial accounting knowledge, financial accounting processing principles to design a complete financial processing process. In the process of using the company, the financial robot is regarded as a virtual accounting of the financial department, and the transaction information is input in the financial robot. The financial robot can fill in the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 33–40, 2022. https://doi.org/10.1007/978-3-030-89508-2_5

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accounting voucher, register the account book, check the account, generate the report form and so on through the program setting [7]. 2.2

Characteristics of RPA Financial Robots

Because of the particularity of its technology, RPA financial robot is good at dealing with a large number of repetitive business contents and simulating the process of manual operation based on clear operation rules [8]. RPA technical features of financial robots are as follows: (1) Repetitive operation. The financial robot which relies on artificial intelligence, machine learning and other technologies, can write clear processing rules for repetitive business in the field of accounting. Thus having the function of processing financial information quickly and accurately; (2) 7  24 h working mode. With software technology and intelligent management means, financial robot makes up for the limited work energy and working time of financial accountants, and it can realize all-weather real-time processing; (3) Based on clear rules, it is required that the set amount process must have clear, digitally triggerable instructions and inputs, and that no special circumstances can be defined in advance; (4) The external hanging form is flexibly deployed on the original system with an independent user interface and will not interfere with or destroy the original financial system structure; (5) It can simulate user operation and interaction and RPA financial robots to perform accounting business intelligently instead of manual.

3 RPA the Positive Influence of Financial Robot on Financial Accounting In recent years, the RPA financial robot, by virtue of its obvious superiority over manual, has caused a great shock in the field of accounting and has a certain positive impact on financial accounting. It is mainly reflected in the following aspects. 3.1

Improving the Efficiency of Financial Accounting

Through software programming language, RPA financial robot replaces manual financial operation with automation to assist financial personnel to complete basic business. In this process, it is only necessary for the accountant to input the pipeline information of the related economic business. Through the set program, the financial robot can automatically complete the tasks of data retrieval, data migration, data recording, image recognition and processing, platform upload and download, data processing and analysis [9, 10]. For example, in the reimbursement business, the traditional financial accounting in the processing of reimbursement business, through manual input, submission, inspection, audit, approval, payment and other links. And these links require staff to have solid professional knowledge and skilled operation experience, the process is long and complex, time-consuming and labor-consuming. The RPA financial robot can complete the document scanning, log on to the reporting platform, automatically generate the reimbursement application. It can also check the

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authenticity of the invoice, check the reimbursement standard, and pay automatically according to the reimbursement application form. This process can be repeated, and finally all kinds of reimbursement documents and related information are summarized and processed to generate corresponding reports (See Fig. 1).

Fig. 1. Functions of RPA financial robots

RPA financial robot can repeat and mechanically move according to the script written in advance, and replace manual task processing with automatic processing. It has the characteristics of working day and night, which is equivalent to 15 times the efficiency of manual work. Therefore, the efficiency of financial accounting has been greatly improved. Taking Deloitte financial robot as an example, the traditional financial accounting invoicing takes 15 min, while the financial robot takes up to 12 min to complete. The emergence of RPA financial robots can shift most of the basic, repetitive, standardized and processed work from accountants to “robots”. It automatically completes specific financial accounting and other work at high speed, standardizes financial operations, saves business completion time, and improves the efficiency of financial accounting. 3.2

Ensuring the Quality of Financial Accounting

RPA robot follows the existing safety and data standards, simulates the human way, according to the established rules, automatically completes the repetitive process, by clicking the button or other mechanized operations can be completed. RPA financial robot can guarantee the quality of financial accounting information mainly in the following three aspects: First, compared with the traditional accounting practitioners for manual data entry, inspection, calculation and analysis, enterprises introduce RPA financial robots. And based on its perfect system structure, standardized program setting, intelligent processing mechanism of the RPA financial robots, can reduce the possibility of errors, greatly improve the traditional financial mode of manual operation error rate, greatly improve the accuracy of information output. Second, the quality assurance of financial accounting work is also reflected in the timeliness of financial accounting information processing. Compared with the operation speed of the traditional financial accountant, the RPA financial robot uses its powerful storage mechanism to store all the accounting data in the computer system. And the accounting process forms an integrated working mode. Deal with related business quickly and provide accounting information to users in time. Thirdly, RPA financial robot based on the process using regularization, standardization, task execution of each step has real-time monitoring and track record. This greatly enhance the transparency of the accounting work, to reduce the financial and

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accounting personnel for tampering with the risk of financial data and financial fraud, so as to better guarantee the quality of the financial and accounting work. 3.3

Reduce Labor Costs for Financial Accounting

The traditional financial accounting work requires the participation of a large number of financial accountants. Taking manufacturing enterprises as an example, from the material procurement process to the production and processing process to the product sales process. These large amounts of simplified and repetitive work often require the establishment of multiple financial positions. Such as accounting supervisor, cashier, wage accounting, cost accounting, income and profit accounting, fund accounting, current accounting, general ledger accounting, etc. More financial positions will lead to a large number of financial accountants, need to pay more compensation, benefits, allowances, high labor costs. The introduction of RPA financial robot, it greatly reduces the setting of many financial accounting positions mentioned above, and can reduce the labor cost from many aspects. Such as salary, welfare, allowance and so on.

4 Negative Effects of RPA Financial Robots on Financial Accounting Against the background of the better application and development trend of financial robot, the influence of RPA financial robot on financial accounting is two sides. At the same time, it also contains some negative effects. The concrete negative influence performance mainly manifests in the following four levels: 4.1

Operational Capacity Constraints

RPA financial robot can deal with a large number of financial accounting work in time and accurately through standardized program setting. It is a financial automation application that can replace the traditional manual operation and judgment specific process nodes. However, the financial robot only works according to the pre-set financial processing procedures, and can not learn like artificial intelligence. When the business scene changes greatly, it exceeds the fixed rules of the financial robot. It will not be able to “show off”. At this time, if there is no special financial accountant to supervise the operation of the financial robot, or if there is no corresponding post of financial accountant “fill”, it will affect the progress of financial accounting business processing. 4.2

Structural Imbalances in Accountants

The RPA financial robot has the 7  24 hour working mode and fast and accurate business processing ability. It can automatically complete a large number of regular and process accounting work, which to some extent replaces manual labor. This makes the total number of basic financial accounting personnel surplus and a large number of traditional basic financial posts to speed up the demise, which means that a large

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number of financial accountants may face the “hero useless” dilemma. So, is the financial robot the tool of intelligent financial innovation, or the main body of accounting work? At present, this problem is still controversial. However, whether the former or the latter, the result means that the basic financial accounting staff will soon withdraw from the historical stage in the future. At the same time, because a large number of basic accounting work is completed by the integration of RPA financial robots, the accounting functions of the recognition, measurement, recording and reporting of traditional financial accounting will also change. At this time, a large number of basic financial accounting personnel idle, management accounting personnel shortage, resulting in structural imbalance of accounting personnel. 4.3

Increased Operational Support Costs

Although RPA financial robot greatly improves the efficiency of financial accounting and reduces the cost of manpower, the application of a new technology will inevitably consume a lot of financial resources. The first is the purchase cost and installation cost of the financial robot, the second is the software upgrade and daily maintenance cost, and finally the training fee for the professional and technical personnel. The introduction of this emerging technology will bring sustained financial pressure to enterprises. 4.4

Security of Financial Information Needs to Be Tested

RPA financial robot has many advantages in financial accounting. As a new thing, it is still in the stage of groping and development, and the technology is not mature enough. With the development of new technologies such as big data, network security has always been an unavoidable problem, and the risk of important data leakage is severe. Once the information is leaked, it may cause the disclosure of trade secrets and the loss is irreparable. At present, the network information technology security monitoring ability of China is not strong enough, the network attack traceability ability is insufficient. Rashly using the financial robot for financial accounting, the security of financial information cannot be guaranteed.

5 Countermeasures of Financial Accounting Under the Influence of RPA Financial Robot Although the development of RPA financial robot has brought some negative effects to financial accounting, these negative effects can be reduced in some ways. Based on the negative impact of the above RPA financial robot on financial accounting, enterprises, accounting personnel and other subjects can adopt the following measures to reduce the impact:

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Building a “AI+RPA” Model

Based on its technical characteristics, RPA financial robot has a great role in promoting financial accounting. Therefore, the application and development of financial robot has become an irreversible trend. It is also very important to popularize the application of financial robot to meet the development trend of financial robot. However, the repetitive work of the financial robot with a set of standardized procedures is only a substitute for part of the basic manual accounting, and does not play its maximum ability. With the continuous development of artificial intelligence, the combination of financial robot and artificial intelligence has become one of the current trends. The combination of AI technology can help RPA develop from preset process, repetitive execution of simplified command to process improvement and decision optimization, which can effectively optimize the RPA operation process and improve its fault tolerance rate. In addition, further combination of machine learning will further optimize the RPA decision and execution process. The construction of “AI+RPA” mode” will help RPA financial robots to further improve their working efficiency, broaden their application scope and deepen their application. 5.2

Transition to “Intelligent” Accountants

Along with the development of economy, the RPA financial robot is constantly applied in the financial work. The financial accounting post is redefined, and the financial accounting staff are facing great difficulties. So, accelerating the transformation of financial accountants is necessary. On the one hand, find a new position for transformation. Against the impact of RPA financial robot application, financial accountants need to adapt to changes, actively transform, and improve their core competence positioning. Accounting financial accountants should break away from their original basic posts and focus their core competencies on the business that RPA financial robots cannot yet realize. From the original accounting functions to predict economic prospects, participate in economic decisions, and evaluate business performance. We should give full play to their intrinsic values and strengths, and use our work experience and skills to deal with more complex and critical work. On the other hand, strengthen cooperation with financial robot. Financial accountants and financial robots are not opposed, so it is necessary to strengthen their cooperation. Financial accountants need to learn programming language, have a certain information operation basis, understand the operation process and working principle of the RPA platform, and facilitate the daily management and maintenance of financial robots. In this process, financial accountants can use RPA financial robots to extract effective information in their efficient and accurate financial information systems for analysis, evaluation, prediction, decision-making. From the digital recorder to the digital user, we are developing towards the direction of interdisciplinary talents and broadening our ability in many dimensions.

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Cost-Benefit Risk Integrated

Because the application of RPA financial robot will increase the cost of many aspects, it is necessary to consider the cost-effectiveness comprehensively before introducing the new technology. By comparing and analyzing the cost-effectiveness of all aspects, finally to determine the optimal introduction scheme. 5.4

Establishment of Risk Early Warning Mechanisms

If enterprises want to introduce RPA financial robots, they must create an environment for the use of financial robots. It is necessary to re-establish the organizational structure, management mechanism, information technology control mechanism to adapt to the work of financial robots. Formulate relevant policies to ensure process updating and optimization. Establish exception handling mechanism and dynamic risk early warning mechanism. Ensure the safety and continuous operation of automated applications through strict management mechanisms. Because the operating environment of RPA financial robot is network, it is very important to establish dynamic risk warning mechanism. Relevant departments need to evaluate their own software, hardware and network environment. To timely update the enterprise's computer equipment, the application software configuration, improve the network running speed and page opening speed. At the same time, at the same time, in order to set the use authority of the running robot, the important information needs to be encrypted to prevent the robot system from being paralyzed by malicious attacks. Only by providing a stable operating environment for the orderly operation of RPA financial robot, can the financial robot always maintain a high speed and high quality 24-h working state. 5.5

Accelerating the Transformation from “Financial Accounting” to “Management Accounting”

The appearance and promotion of RPA financial robot make great changes in the field of accounting, such as the work flow, work efficiency, staff and so on. These changes also promote the transformation of financial accounting to management accounting, which is the trend of the times. Financial robots replace human resources to deal with repetitive and basic work, and the function of financial accounting staff changes from financial accounting function to management accounting function. By analyzing the financial information provided by the financial robot, enterprises can better provide managers with financial information related to prediction and decision-making.

6 Conclusion RPA financial robot has a great influence on financial accounting. With the continuous development of big data, artificial intelligence and other technologies, this impact will be further strengthened. In the face of this development trend, how to make full use of the positive effects and weaken all kinds of negative effects is an important issue

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worthy of attention. It is hoped that the traditional financial accounting can realize the positive influence brought by the application and development of financial robot, and through appropriate adjustment and optimization, seize the development opportunity to realize the transformation of technology, personnel and function.

References 1. Chen, T.W.: Study on the application of artificial intelligence in accounting - taking Deloitte financial robot as an example. Bus. Account. 10, 79–80 (2018) 2. Tian, G.L., Chen, H.: Research on the application of financial robot based on RPA. Technol. Account. J. 18, 12–16 (2019) 3. Fung, H.P.: Use Cases and effects of information technology process automation (ITPA). Adv Robot Autom. 3, 1–11 (2014) 4. Yin, J.F., Yin, J.M.: Enterprise accounting development in the context of artificial intelligence: challenges. Opportunities Responses Bus. Account. 18, 59–61 (2020) 5. Peng, Z., Wang, H.Q.: A study on the risks and countermeasures of accounting artificial intelligence. Friends Account. 05, 114–119 (2019) 6. Willcocks, L., Craig, M.: Robotic process automation: strategic transformation lever for global business services? J. Info. Technol. Teach. Cases 7, 17–28 (2017) 7. Li, X.: Research on the application of financial robot under the background of next generation information technology. J. Phys: Conf. Ser. 1, 1827–1830 (2021) 8. Chen, J.Y., Hu, Y.X.: Traditional accounting under the impact of artificial intelligence and intelligent accounting. China Econ. Trade Guide 04, 171–173 (2021) 9. Guo, W., Zhang, B.: Research on development strategy of news app under the background of artificial intelligence. IOP Conf. Ser. Mater. Sci. Eng. 806(1), 012031 (5 pp) (2020) 10. Zhang, Y., Xiong, F., Xie, Y., Fan, X., Gu, H.: The impact of artificial intelligence and blockchain on the accounting profession. IEEE Access, 8, 110461–110477 (2020)

Application of Artificial Intelligence Technology in English Online Learning Platform Juan Ji(&) School of General Education, Nantong Institute of Technology, Nantong 226600, Jiangsu, China

Abstract. Since the 21st century, the rapid development of computer technology and artificial intelligence technology, the needs of students to learn English knowledge, the transformation of students’ learning style, and the use of online English learning platform has become a way for students to learn English. Happy, healthy learning and all-round development are the goals of students’ learning. The online English learning platform has a variety of forms for students to freely choose content, which is making up for students’ needs for various knowledge, truly enabling students to learn independently, cultivating students’ inquiry ability, preparing for future learning and being able to adapt to the future society. This article aims to study the application of genetic algorithm in artificial intelligence technology in English online learning platform. Based on the analysis of the key technologies and platform requirements of genetic algorithm, it designs how genetic algorithm technology should be used in English online learning platform. And realized the English online learning system, and finally carried out the performance test of the system. The test results show that the system meets the expected goal, and the expected demand can be achieved no matter from the system server and the client. Keywords: Artificial intelligence technology learning platform
Genetic algorithm


English learning
Online

1 Introduction With the advent of the network information age, people’s lives and the network are more and more closely connected, and the network and people’s lives. The Internet is flooded. Every corner of human life, including students’ study life [1, 2]. With the popularization of the Internet and the widespread use of computers and mobile phones, the development and use of open learning platforms by major network companies and equipment operators have become more and more suitable for people’s life and learning, bringing great benefits to people’s lives and learning [3, 4]. Through the investigation, it is learned that the application of genetic algorithm in the online learning platform basically focuses on the automatic test paper formation, and there is almost no application in other areas. In online learning, learners will have self-tests or exams, so the test questions of the exam cannot be organized by the teacher themselves. Many online test systems or platforms have added an automatic test paper © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 41–49, 2022. https://doi.org/10.1007/978-3-030-89508-2_6

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module [5, 6]. At present, many scholars or researchers are actively studying how to improve the genetic algorithm, which can make the test paper function more scientific and reasonable, and can better test learners. The application of genetic algorithm is mainly focused on the research of the algorithm, and the more mature and improved algorithm is applied to the function of automatic test paper [7, 8]. This article aims to study the application of genetic algorithm in artificial intelligence technology in English online learning platform. Based on the analysis of the key technologies and platform requirements of genetic algorithm, it designs how genetic algorithm technology should be used in English online learning platform. And implemented an English online learning system, and finally performed a performance test on the system.

2 Application of Artificial Intelligence Technology in English Online Learning Platform 2.1

Key Technologies of Genetic Algorithms in Artificial Intelligence

(1) Coding scheme The genetic algorithm cannot directly deal with the various parameters in the space to be solved by this problem. It is necessary to encode and map the real problems to find the object that the genetic algorithm can handle, that is, the gene string. Coding design is the process of converting the feasible solution of the problem in the solution space into a chromosome code string that can be processed by the genetic algorithm for compilation. Whether the encoder is designed well or not will directly affect the operability of the genetic algorithm. Therefore, the design of the encoder is a very important and critical step in the genetic algorithm, and it is also a problem that must be considered and solved in the genetic algorithm in practice and application. The coding method not only clarifies the combination of various individual chromosomes and sequences, but also directly determines the corresponding coding method. It also has special significance for the role of selection function, crossover function, and mutation function [9, 10]. (2) Population initialization Genetic algorithm is to search for the optimized population, so we need to start from the genome before we adopt the genetic algorithm method to solve the population optimization problem. Generally speaking, the size of the initial population should be appropriate. Excessive data size will reduce the search time and space and calculation amount of the algorithm in the network, and reduce the efficiency of its algorithm implementation [11, 12]. A very small range can easily fall into some understanding of local optimization, and the accuracy of the algorithm is not high. A summary of the way and reasons for generating the initial population is expressed as: 1) In the area where the problem may be solved, individuals are selected by random search until the size of the initial population is satisfied. 2) In the possible problem-solving space, first define the constraints of the problem to be solved, and then randomly select individuals that meet the conditions to join the

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initial population, and repeat this process until the size of the set initial population is obtained. (3) Fitness function The fitness function is changed from the objective function in real problems. The genetic algorithm stipulates that the adaptability value must not be negative. But in actual problems, the value of the objective function can be regarded as either positive or negative, so the objective function and fitness function can be transformed and adjusted. The common transfer methods are mainly as follows: (1) Direct method The objective function is denoted by f(x), and the fitness function is denoted by F (x), then the relationship between them is: F ð xÞ ¼ f ð xÞ

ð1Þ

This method is simple and easy to use, but its value can be positive or negative. If the value of the fitness mode is negative, it will affect the overall performance of the algorithm. (2) Boundary method For the optimization problem of seeking the maximum value, the fitness function and the objective solution function can be transformed as follows, where Cmin represents the minimum value of the objective function estimate.
Fmax ð xÞ ¼

f ð xÞ  Cmin ; f ð xÞ  Cmin 0; others

ð2Þ

In formula (2), it can be the minimum function target value of the population that has evolved to the current generation, and Cmin can also be an externally input value. For the minimum search optimization problem, the fitness function and the objective solution function can be transformed as follows, where Cmax represents the maximum value of the objective function estimate.
Fmin ð xÞ ¼

Cmax  f ð xÞ; f ð xÞ  Cmax 0; others

ð3Þ

This method is an improvement on the direct method, but sometimes it is not easy to find a suitable value. 2.2

Analysis of Platform Requirements

(1) Performance For database performance, as the user usage increases, a large amount of information and data will be generated. The database must have the access performance to

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accommodate a large amount of data to ensure that there will be no long time when accessing data or multi-table queries. In the case of no response or server downtime, the response time should not exceed 60ms, and the response time of page jumps should not exceed 3s; the system ensures that when some modules have problems, they will not affect other modules: server resource utilization is at full load In the case of busy hours, the peak cpu load does not exceed 75%, and the memory occupation does not exceed 80%; fault-tolerant processing supports restart mode, operating mode is 7 * 24 h, multi-core computing supports multi-core parallel, which greatly improves computing speed and improves work effectiveness. (2) Security In terms of user information security, it is necessary to ensure that the user’s personal information is not leaked, and sensitive data (including the date of birth, password, email, contact information, address, etc.) should be encrypted, and sensitive information should be accessed. The user must pass the identity. The corresponding permissions can only be obtained after verification. The user’s login and personal account passwords need to be encrypted and uploaded to avoid unnecessary data leakage. Secondly, the database is regularly stored to prevent data loss due to external reasons. (3) Availability Improving reliability needs to emphasize reducing the number of system downtimes, and improving availability needs to emphasize reducing system recovery time. This system can be used under a variety of operating systems, login accounts in different systems will not cause changes in data information and files, and the deployment method is the same under different operating systems, and data loss will not occur. All the standardized processes of the system are written in the relevant manuals, and each team leader in the team has a copy. This manual also contains the contact information of the developers and managers of the relevant modules. Once there is a problem with the system, you can directly find the corresponding contact person, so that you can find the problem in the first time, quickly restore the service, improve work efficiency, and reduce communication costs. (4) Maintainability Developers need to ensure that the platform code is clear and concise, and each function has corresponding comments as much as possible. Code is the smallest component to realize system functions, and the project team is required to formulate a unified coding rule before writing the code to improve the readability of the code. So that when modifying the original function, there is no need to spend a lot of time reading and modifying the code. And the code part should be kept as concise as possible, no need to repeat the explanation of unnecessary functions, and keep the code of the whole system clean and tidy.

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3 Experiment 3.1

Build a Database

If we want to provide each learner with a set of personalized learning content, then the premise is that there must be a content library, which should include some basic attributes of each learning content. All content in the database should be compiled by professional English teachers according to certain teaching purposes and the characteristics of students. At present, the learning content of large-scale English online learning systems is presented in the form of exercises. Students learn English by doing various exercises, which can also be said to be a process of learning while testing. Then, this database should include: the type of each exercise, the level of English to which it belongs, the English module it represents, the topic it reflects, and so on. In this content library, in order to organize the content more conveniently, the content of the same module is stored in a data table in the same database. The specific design of the database is shown in Fig. 1.

English database

Listening database

Spoken language database

Read the database

Writing database

Fig. 1. Database design

After the database is built, specific content can be extracted through the constraints of content organization. The organization of English learning content is not just arbitrary stacking to meet the needs of learners. Instead, it is necessary to follow certain steps to extract content that meets the actual needs of each learner from the content library, so as to meet the individual requirements of learners and improve the quality of online learning. Combined with the research of this article, the content organization of this article should follow the following steps: (1) Understand the basic information of the students, obtain the purpose of the students’ learning, and determine the students’ English level according to the evaluation before online learning. (2) Determine the scope of each set of learning content, including four modules: Listening, Speaking, Reading and Writing, and determine the type of exercises for each module. (3) Determine the proportion of each module in the whole set of learning content. This article stipulates that each set of learning content represents an English level, including 8 units, which are all about a topic.

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Determine Learning Content Indicators

The current English online learning platform has a single learning content, all learners learn the same content, and they do not provide learning content according to their own needs, resulting in poor learning effects for many learners. The learning content of many English learning platforms is presented through exercises. These contents are stored in the database, and the contents are presented to students for learning in the form of websites, so the definition of the attributes of learning content is very important. Combined with the analysis and design of the first two sections of this chapter, the index system of learning content defined in this article includes: number, type, level, scope, and topic. 3.3

Establish a Mathematical Model of Content Organization

The organization of English learning content can actually be said to be a multicondition constraint optimization problem, which is often defined as a combination of an objective function and multiple constraints. In response to the problems studied in this article, each set of learning content mainly sets the following five indicators: number, type, level, scope, and topic. Therefore, the problem of content organization can be described as: customizing a set of learning content containing n exercises is to extract n exercises from the content library, and each exercise is determined by 5 attributes, so that a set of learning content can be defined as an n  5 matrix, the formula is as follows. 2

a11 6 a21 C¼6 4 ... an1

a12 a21 ... an1

3 . . . a15 . . . a25 7 7 ... ... 5 . . . an5

ð4Þ

Matrix C can represent a set of learning content, and the distribution of column elements in the matrix meets the needs of each learner for learning content and meets the learner’s purpose of learning English. 3.4

Chromosome Coding

Coding is the basis of genetic algorithm, and choosing a suitable coding method is the key to the success of genetic algorithm. The coding methods mainly include binary coding, gray coding, ad hoc coding designed according to actual problems, and so on. This article uses real number coding. For the problem studied in this article, each set of content is mapped to a chromosome, and the exercises that make up the whole set of content are mapped to genes. The value of the gene can be expressed by the number of the exercises. The total length of the chromosome code is determined by the total length of the set of content. The number of practice questions is determined. Using the real number coding method, it is necessary to real number all the indicators of the research problem, which requires a certain standard to support this real number. In this

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paper, the concept of measurement in statistics is used to transform the index of content organization into real numbers to complete the chromosome coding. 3.5

Calculate Fitness Value

In genetic algorithms, fitness function values are often used to evaluate the pros and cons of individuals in the population. In this paper, the fitness function is obtained by transforming the objective function. The larger the value, the better the individual. The fitness function obtained is: F ¼ 1= 1 þ

5 X

! wi ei

ð5Þ

i¼1

Among them, ei is the deviation between the expected value and the actual value distribution of attributes such as number, type, level, scope, and subject, and wi is the ratio of each deviation. It can be determined from the above formula that the smaller the error of the content individual’s constraints on the content organization, the larger the fitness value, indicating that the extracted content individual is closer to the content organization goal. 3.6

Set Algorithm Termination Conditions

Genetic algorithm is an iterative process of finding the best solution. Usually after many developments, it is slowly approaching the optimal solution. It is difficult to completely equal the optimal solution. Therefore, it is necessary to determine the shutdown state during the search. There are usually three methods for determining termination conditions. The first is to determine the number of iterations; the second is to check the deviation, that is, when the difference between the applicability value and the actual target value is less than the set allowable value, the algorithm terminates the value; the third Check for fitness changes at any time. When the suitability of the optimal person does not change or the change is small, the algorithm terminates.

4 Discussion In terms of performance testing, this test tests the related operations of the system, such as listening module, speaking module, reading module and writing module. The system adopts the benchmark test process as follows: (1) Benchmark test scenario The integrity of the system is verified through benchmark tests. The test scenarios are shown in Table 1:

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J. Ji Table 1. Benchmark test scenario test

Numbering 1 2 3 4

Features Listening module Spoken language module Reading module Writing module

VUers 2 2 2 2

Number of runs Time period Set interval 8 4 1 8 4 1 8 4 1 8 4 1

(2) Single operation scenario The response time test is shown in Fig. 2, and the system performance can be seen through this figure.

3.5

100 users

500 users

1000 users

1500 users

2000 users

3

Respons time

2.5 2 1.5 1 0.5 0 Listening module

Reading module Spoken language module Modules

Writing module

Fig. 2. Response time test

Through the test system to achieve the expected goal, both the system server and the client can achieve the expected demand.

5 Conclusions With the continuous development of educational technology and artificial intelligence technology, English online learning platforms will become an indispensable part of modern education and a powerful tool for learners and educators. The design concept and functional quality of English online learning platform will directly affect the quality of English teaching and learning. To adapt to the requirements of the development of

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the times, it is necessary to continuously promote the evolution of learning platform design concepts, architecture, and functions.

References 1. Polina, M., Lucy, O., Yury, Y., et al.: Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget 9(5), 5665–5690 (2018) 2. Chen, W.L., Lin, Y.B., Ng, F.L., et al.: ricetalk: rice blast detection using internet of things and artificial intelligence technologies. IEEE Internet Things J. 7(2), 1001–1010 (2020) 3. Onwubere, C.H.: Geospatial data and artificial intelligence technologies as innovative communication tools for quality education and lifelong learning. EJOTMAS Ekpoma J. Theatre Media Arts 7(1–2), 50–71 (2020) 4. Dudnik, O., Vasiljeva, M., Kuznetsov, N., et al.: Trends, impacts, and prospects for implementing artificial intelligence technologies in the energy industry: the implication of open innovation. J. Open Innov. Technol. Market Complex. 7(2), 155 (2021) 5. Olan, F., Suklan, J., Arakpogun, E.O., et al.: Advancing consumer behavior: the role of artificial intelligence technologies and knowledge sharing. IEEE Trans. Eng. Manag., 1–13 (2021) 6. Yee, D.H., You, Y.Y.: The impact of awareness of new artificial intelligence technologies on policy governance on risk. Res. World Econ. 11(2), 152 (2020) 7. Bhagat, K.K., Wu, L.Y., Chang, C.-Y.: Development and validation of the perception of students towards online learning (POSTOL). J. Educ. Technol. Soc. 19(1), 350–359 (2016) 8. Janssen, A., Shaw, T., Nagrial, A., et al.: An online learning module to increase self-efficacy and involvement in care for patients with advanced lung cancer: research protocol. Jmir Res. Protoc. 5(3), e147 (2016) 9. Peng, W.: Research on online learning behavior analysis model in big data environment. Eurasia J. Math. Sci. Technol. Educ. 13(8), 5675–5684 (2017) 10. Kolluru, S., Varughese, J.T.: Structured academic discussions through an online educationspecific platform to improve Pharm.D. students learning outcomes. Curr. Pharm. Teach. Learn. 9(2), 230–236 (2017) 11. Pulukuri, S., Abrams, B.: Incorporating an online interactive video platform to optimize active learning and improve student accountability through educational videos. J. Chem. Educ. 97(12), 4505–4514 (2020) 12. Haaren, F.V., Moes, N.C.C.M.: Shareworks - a ubiquitous online learning platform for project-based learning and networking. Int. J. Comput. Aided Eng. Technol. 8(1–2), 179– 197 (2016)

Spectral Identification Model of NIR Origin Based on Deep Extreme Learning Machine Songjian Dan(&) College of Continuing Education, Chongqing University of Education, Chongqing, China

Abstract. As a rapid, accurate, convenient, and non-destructive agricultural product analysis technology, NIR analysis technology has been widely used in agricultural product quality testing and raw material source identification. It is generally considered to be a non-destructive that is expected to further replace traditional agricultural chemical analysis detection technology. At present, the identification of citrus soil origin identification and crop quality monitoring technology based on near-infrared spectroscopy analysis is still relatively complicated, time-consuming, labor-consuming, and not accurate enough, and its completeness, systemicity and functionality are still not practical. The rapid identification of the origin of citrus and the effective quality supervision and inspection technology system play a very important role in promoting the healthy development of the citrus industry in our country. Based on this, this paper proposes a NIR-origin spectrum identification model based on a deep limit machine learning machine to further improve the accuracy of NIR-origin spectrum identification. First, we have a general understanding of the NIR origin spectrum identification to be studied in this article. To pave the way for the experimental part, and then propose an algorithm based on the depth limit machine (DELM) according to related technical issues, and verify it experimentally. The experimental results show that the DELM classification accuracy proposed in this paper reaches 0.932. The accuracy is better than the other two algorithms, but the PCA +machine Learning method is similar to the DELM effect, but the calculation is more complicated, requiring multiple steps such as dimensionality reduction+classifier construction, and DELM directly inputs the spectral information through depth the network and ELM are effectively integrated to construct an end-to-end feature extraction+deep classification model, which is more direct and efficient. In addition, compared with ELM, DELM can learn high-level and more abstract spectral feature expression more effectively, so it has a higher recognition rate. Keywords: Deep learning algorithm


NIR analysis
Spectral identification
Delm

1 Introductions In recent years, near-infrared spectroscopy technology has been used as a new type of non-destructive chemical analysis and detection method, which can quickly and accurately determine the raw material origin and food quality of all fruits in various agricultural products [1, 2], and has received extensive scientific research and practice. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 50–57, 2022. https://doi.org/10.1007/978-3-030-89508-2_7

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Application is generally considered to be a non-destructive testing technology that can replace traditional chemical analysis in the next few years [3, 4]. At present, there is still room for improvement in the accuracy of citrus raw material origin identification and quality evaluation based on near-infrared spectroscopy [5, 6]. In addition, its completeness, system and functionality are far from actual applications [7, 8]. In the research on the NIR origin spectrum identification model based on deep extreme learning machines, first of all, for the research of deep learning machines, some researchers have proposed that the success of machine learning algorithms often depends on the representation of data, and because of this, more and more researchers invest in the study of feature representation learning. With the rise of neural networkbased representation learning methods, especially the great progress made by deep neural networks in representation learning, machines have entered the era of automatic feature learning [9]. And some researchers pointed out that, unlike artificial neural networks, deep learning needs to use a different training mechanism from other artificial neural networks. On the basis of the artificial neural network, the reverse transfer technology is used to train the entire network. This cultivation method has obvious drawbacks and defects. That is, when the number of layers in the network network is relatively large, it will cause the diffusion problem on a certain scale, which will seriously affect the system performance of solving this type of problem due to the artificial neural network [10]. In view of the research on the near-infrared origin spectrum identification model, some researchers have proposed the characteristics of near-infrared origin spectrum identification: a wide range of target areas: the physical and chemical characteristics of all hydrogen-containing group-related samples can be effectively analyzed in the near-infrared region spectroscopy [11]. Therefore, near-infrared spectroscopy has a very wide range of research objects and cases for analysis. The size and shape of analysis samples vary: as long as we can analyze objects that can pass through the container, the almost red spectrum is used for various samples, such as gases, liquids, or various irregular solids (such as particles). The analysis process is simple and fast: no complicated sample pretreatment is required before analysis and testing. After scanning the infrared sample, using appropriate analysis models through multi-factor calibration methods or various pattern recognition methods, the samples can be quickly quantitatively or qualitatively analyzed; it does not harm the sample and does not pollute the environment: as it approaches the infrared spectrum, it is a non-destructive measurement method that will not cause damage to the experimental samples when receiving nearinfrared spectrum information, so these samples can be reused as other experiments [12]. This paper studies the NIR origin spectrum identification model based on the deep extreme learning machine. First, we have a general understanding of the NIR origin spectrum identification process on the basis of relevant literature and lays a foundation for the following experiments. It is based on the relevant key identification the technology proposes an algorithm based on the depth limit machine (DELM), which uses a multi-layer structure to integrate multiple ELMs, so that DELM can extract the deep feature spectrum information in the spectral sequence after multi-layer feature expression learning, which makes up for the common ELM model is difficult to capture the shortcomings of high correlation features due to the single hidden layer structure, which greatly improves the prediction accuracy of the prediction model, and it is verified through experiments.

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2 Research on NIR Spectral Identification Model of Origin 2.1

NIR Origin Spectrum Identification Process

(1) Spectral denoising Near-infrared spectroscopy data is usually collected manually by a spectrum analyzer, so there is inevitably a lot of noise, the sources of which include background noise, stray light, light scattering, internal response of the instrument, and artificial errors. These noises will cause a certain amount of interference to the actual data, leading to the loss of data characteristics and even the basic shift of the near-infrared spectrum. Therefore, denoising the collected raw near-infrared spectrum data is a very important part of the spectrum analysis and testing process. (2) Feature extraction based on principal component analysis In chemical and spectral analysis, principal component analysis (PCA) is one of the commonly used feature extraction methods in high-dimensional data. Through the PCA function, a small amount of raw data (PC) data can be used to replace the original thousands of dimensional near-infrared spectroscopy data. These new data are a linear combination of the original data. At the same time, these linear combinations also maximize the sample variability as much as possible, so that the new features are not related to each other, making it easier for the discrete classifier to identify different samples. (3) Feature selection based on information entropy After reducing the PCA dimension, the ability to express the original spectral data more effectively is obtained. However, the PCA method does not have classification information, that is, only the projection direction that can retain the most original information is considered. Displaying these different types of data in one direction may confuse characteristics. For example, in commonly used character recognition, when the PCA method is used to display the characteristics of the letters “Q” and “O”, because PCA considers the similarity of the two letter types more, the key may occur when determining the change. You can see that the “tail” of the Q part is removed, which reduces the recognition. Feature selection can be roughly divided into four basic steps: 1) create a feature subset: determine how to create the next feature subset to be selected, 2) evaluation criteria: determine whether the selected feature is better than the existing feature set. 3) Stop condition: decide when to end the feature selection process, 4) verify feature set: verify whether the selected subset is valid. (4) Discrimination classifier The construction of classifiers is the basic content of machine learning theory and the main method of near-infrared spectroscopy analysis based on machine learning. By learning the selected features, the classifier can construct a suitable source recognition model to identify the accurate origin classification of citrus. Different classifiers often have different characteristics, and even if the same data set is used for training, the results are often different.

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53

NIR Origin Spectrum Identification Model Algorithm of Deep Extreme Learning Machine

(1) Algorithm ideas The extreme learning machine (ELM) is a very simple and efficient machine learning algorithm. In the extreme learning machine, a single hidden layer feedforward neural network randomly selects input weights and biases according to a certain probability distribution, that is, randomly selects hidden with layer output, the output weight of the single hidden layer feedforward neural network is determined by obtaining the MoorePenrose generalized inverse of the hidden layer output matrix, which avoids iterative fine-tuning of parameters and improves the efficiency of the training algorithm. However, due to the characteristics of noise, high feature dimensions, and few samples in the spectrum, the direct use of single-layer ELM for spectral analysis is not very satisfactory; therefore, an algorithm based on the depth limit machine (DELM) is proposed, using the multi-layer structure integrates multiple ELMs, so that DELM can extract the deep-layer characteristic spectrum information in the spectral sequence through multi-layer feature expression learning, which makes up for the common ELM model's shortcomings that it is difficult to capture high-correlation features due to the single hidden layer structure, which greatly improves the prediction accuracy of the prediction model. (2) Algorithm flow The algorithm flow is shown in Fig. 1: In the DELM legend, each H is a singlelayer ELM, which forms a deep classification network through multiple ELM cascades, and finally outputs the classification results.

Fig. 1. DELM legend

(3) Algorithm calculation Unlike the visual encoder used in traditional deep learning algorithms, the input value of the automatic ELM encoder is determined by finding a random return path. The ELM learning theory shows that the randomly assigned ELM training is sufficient data. In other words, if you train the autoencoder according to the ELM concept, once

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the autoencoder is started, there is no need for tuning. In addition, in order to obtain more complete input characteristics, L1 optimization is used to construct an automatic ELM encoder. Therefore, the optimization model of the automatic ELM encoder can be expressed as:  F/ ¼ argb min k Hb  X k2 þ k b k1

ð1Þ

Where X represents the input data, H represents the random mapping output, and b represents the hidden layer weight you want to receive. In this method, X is usually based on the coding output of b and must be adapted to the optimization iteration. In the ELM autoencoder, since the output of the hidden layer feature uses random mapping, X is the initial data, and H does not need to be randomly initialized and output for customization. The description of solving the L1 optimization problem will be given below. For clarity of expression, the objective function (1) is redefined as: Fb ¼ pðbÞ þ qðbÞ

ð2Þ

3 NIR Origin Spectrum Identification Model Experiment Based on Deep Extreme Learning Machine 3.1

The Purpose of the Experiment

This paper conducts experiments on the NIR origin spectrum identification model based on the deep extreme learning machine, mainly to compare the classification effect of ELM and DELM, and to compare the classification effect of the traditional PCA + machine learning method. 3.2

Experimental Process

(1) Sample collection location In this article, the method of identifying the origin of citrus fruits and the internal prediction of fruit quality are mainly carried out. In order to conduct empirical research and verify the validity of the experimental results, this paper collected representative citrus samples from 5 provinces and 10 regions. Randomly select plants with good growth, correct number of fruits, and normal growth from the above sampling points, and select evenly mature plants to avoid large deviations in fruit maturity from different producing areas. From the outer, east, west, south, north, and middle of each tree canopy, two fruits of the same size, color and luster, normal fruit type, and no obvious signs of disease on the surface were randomly collected. 10 fruits are collected randomly per plant, so on average, about 100 fruits and 1,550 citrus samples are collected from each production area.

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(2) Citrus near-infrared spectroscopy data collection The collection procedure is as follows: first turn on the halogen light source, keep the surface of the fruit sample to be tested perpendicular to the fiber optic catheter, and the distance between the catheter and the wrist surface is 10mm. The light emitted by the light source passes through the internal tissue of the citrus fruit, and the diffusely transmitted light from the inside of the citrus fruit is detected by the detector and enters the spectrometer. For each test sample, start from the middle of the fruit, select a measuring point every 120°, and collect the diffuse reflectance spectrum of the fruit. Therefore, each sample collects the average spectral information of 3 different fruits, which is stored as the near-infrared spectral information of the citrus sample and the sample reference number.

4 Analysis of Experimental Results ELM and DELM are used to classify citrus near-infrared spectroscopy images with traditional PCA+machine learning algorithms. Calculate the classification accuracy of the three algorithms respectively, and the experimental results are shown in Table 1: Table 1. Citrus near infrared spectroscopy image classification results Overall classification accuracy Kappa coefficient ELM 0.867 0.836 DELM 0.932 0.907 PCA+machine learning 0.912 0.901

Fig. 2. Citrus near infrared spectroscopy image classification results

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It can be seen from Fig. 2 that the classification accuracy of the DELM proposed in this paper is 0.932, which is better than the other two algorithms, but the PCA+machine learning method is similar to the DELM effect, but the calculation is more complicated and requires dimensionality reduction+classifier there are multiple steps such as construction, and DELM directly inputs the spectral information, and effectively integrates the deep network and ELM to construct an end-to-end feature extraction+depth classification model, so it is more direct and efficient. In addition, compared with ELM, DELM can learn high-level and more abstract spectral feature expression more effectively, so it has a higher recognition rate.

5 Conclusions This paper uses the new generation technology NIR based on near-infrared spectroscopy as the basis of its research, and conducts in-depth discussion and research on the current application and development of related technologies for citrus fruit origin identification and internal quality detection, combined with machine learning, modern analysis methods such as chemometrics have proposed a spectral identification model of NIR origin based on a deep extreme learning machine, which improves the effective timeliness and accuracy of citrus fruit origin identification and internal quality detection. Acknowledgements. Project Supported by Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJQN20191620).

References 1. Bian, X., Li, S., Fan, M., et al.: Spectral quantitative analysis of complex samples based on the extreme learning machine. Anal. Methods 8(23), 4674–4679 (2016) 2. Bin, J., Zhou, J., Fan, W., et al.: Automatic grading of flue-cured tobacco leaves based on NIR technology and extreme learning machine algorithm. Acta Tabacaria Sinica 23(2), 60– 68 (2017) 3. Zhu, H.Y., Shao, Y.N., Jiang, L.L., et al.: Identification of microalgae species using visible/near infrared transmission spectroscopy. Spectrosc. Spectral Anal. 36(1), 75–79 (2016) 4. Yang, L., Yang, B., Jing, S., et al.: A minimax probability extreme machine framework and its application in pattern recognition. Eng. Appl. Artif. Intell. 81(MAY), 260–269 (2019) 5. Wang, W., Jiang, H., Liu, G.H., et al.: Qualitative prediction of yeast growth process based on near infrared spectroscopy. Chin. J. Anal. Chem. 45(8), 1137–1141 (2017) 6. Wang, W., Jiang, H., Liu, G., et al.: Quantitative analysis of yeast growth process based on FT-NIR spectroscopy integrated with Gaussian mixture regression. RSC Adv. 7(40), 24988– 24994 (2017) 7. Henriquez, P.A., Ruz, G.A.: Noise reduction for near-infrared spectroscopy data using extreme learning machines. Eng. Appl. Artif. Intell. 79, 13–22 (2019) 8. Li, H.-T., Chou, C.-Y., et al.: Robust and lightweight ensemble extreme learning machine engine based on Eigenspace domain for compressed learning. IEEE Trans. Circuits Syst. I Regular Papers 66(12), 4699–4712 (2019)

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9. He, C., Kang, H., Yao, T., et al.: An effective classifier based on convolutional neural network and regularized extreme learning machine. Math. Biosci. Eng. 16(5), 8309–8321 (2019) 10. Hu, L., Chen, Y., Wang, J., et al.: OKRELM: online Kernelized and regularized extreme learning machine for wearable-based activity recognition. Int. J. Mach. Learn. Cybern. 9(9), 1577–1590 (2018) 11. Utkin, L.V., Zaborovskii, V.S., Popov, S.G.: Detection of anomalous behavior in a robot system based on deep learning elements. Autom. Control. Comput. Sci. 50(8), 726–733 (2016). https://doi.org/10.3103/S0146411616080319 12. Zhou, L., Ma, L.: Extreme learning machine-based heterogeneous domain adaptation for classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 16(11), 1781–1785 (2019)

Frontier Application and Development Trend of Artificial Intelligence in New Media in the AI Era Ying Feng1 and Xiaojing Lv(U)2(&) 1

Shandong Women’s University, Jinan 250300, Shandong, China Graduate School of Management of Technology Information, Pukyong National University, Busan 48547, Korea

2

Abstract. In recent years, deep algorithms and big data and other technical means have risen rapidly, and the application of artificial intelligence has become the future development trend of various industries. In the news industry, AI is involved in all aspects of news gathering, content production, distribution and consumer consumption. This paper mainly studies the cutting-edge application and development trend of artificial intelligence in the field of new media in the era of AI. In this paper, the news media industry takes advantage of the application advantage of artificial intelligence and puts forward a news recommendation algorithm based on neural network. In this paper, the collaborative filtering method is used to recommend news by using cyclic neural network (RNN). However, in the face of the problem that the RNN network structure is repeated and difficult to recommend in parallel in collaborative filtering, this paper applies Slices Neural Network (SRNN) to the collaborative filtering recommendation system to improve the training and operating efficiency of the system. Keywords: Artificial intelligence
New media
News recommendation
Slice recursive neural network

1 Introduction With artificial intelligence, big data and cloud computing at its core, a new wave of technological drivers is upending the traditional model of media operation and Growth, The media environment and advertising ecosystem are changing, in the New Media Age, the network news broke the limitation of the timeliness and extensiveness of the traditional news [1]. The development of artificial intelligence has brought a new opportunity for the media industry, and the application of artificial intelligence in the media is also a research hotspot in the communication field. Ding Junjie (2021) thinks it is the driving force of smart elements that makes the media smart. In the 5G era, the effective integration of the three big scenes (mobile phone personal scene, Ott family scene, Iot life scene) and the three big technologies (Ai, big data, LOT) is realized. The second is moderate connection, the establishment of advertising activities and user behavior between the scene, the type of effective connection. The change that artificial intelligence technology brings to media is obvious, and run through the whole process of media industry and content production, the relation of media and artificial © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 58–64, 2022. https://doi.org/10.1007/978-3-030-89508-2_8

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intelligence is more and more close [2]. Macro sense, the structure of the media industry and even the media ecology are affected by the artificial intelligence technology, on the one hand, the media industry of the original features, structure, industrial structure and so on under the intervention of artificial intelligence technology, changing the original media industry structure is not enough to meet the demand of information market diversification and individuation, the media transformation driven by the introduction of artificial intelligence technology is the guarantee for media organizations and media people to gain a foothold in the information market [3]. On the other hand, the relationship among the media industry, the whole society and the people in the society will also change to adapt to the development of “intelligent media” while the artificial intelligence technology influences the development of the media industry, shaping a new media ecology of information industrialization [4]. Research on artificial intelligence in the field of media has attracted extensive attention in the academic circle, mainly focusing on robot journalism, media convergence, transformation of journalism education, impact on media people and other issues [5]. Some scholars pointed out that in artificial intelligence, Internet, VR/AR technology, driven by the media will be intelligent trend, put forward the three characteristics of media change, namely, everything has its media, man-machine syncretic, self evolution, this paper introduces the intelligent technology combine with news production brought about by the new production mode, the future media ecology, and in terms of human game to people the value of the stick to [6]. Other scholars emphasize that the direction of machine intelligence in the age of intelligent media is determined by people, and the power of machines lies in better connecting people, pooling people’s intelligence and expanding people’s ability with the wisdom of machines [7]. This paper studies the news recommendation algorithm in the artificial intelligence environment, and tries to put forward a development path for reference, so as to promote the healthy development of the media industry in the artificial intelligence environment.

2 Application of Artificial Intelligence in the New Media Era 2.1

News Algorithms for Accurate Content Distribution

Recommendation algorithm is a kind of program algorithm in the field of computer science. It can use some related mathematical algorithms to calculate the aspects that users may be interested in. Based on this speculation, the effective push of information can be realized. At present, the application of recommendation algorithm is mainly focused on the Internet, among which Taobao is a typical example. Traditional news communication is a mass communication mode, which is a oneway linear flow mode, that is, the media carries out information, transmission and release news reports to the public. This mode often lacks immediate and effective interactive feedback. Based on this distribution model, the audience cannot independently choose the news content according to their specific needs. Meanwhile, the media cannot accurately locate the content that the audience is interested in, resulting in greatly impaired reporting effect and user experience. With the introduction of artificial intelligence, new

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changes have taken place in the mode of interaction between transmission and receivers, and the mode of content distribution presents a trend of socialization and individuation. The news distribution platform has expanded from the traditional media platform to a variety of platforms based on the new communication mechanism, and the relationship between media and users has also been reconstructed [8]. (1) Accurate delivery of news By collecting user behavior information, analyzing user preferences and habits, artificial intelligence can accurately analyze big data, accurately grasp user needs, accurately screen mass content by means of intelligent labels, and finally realize personalized news content push according to user needs. This new mode reflects the usercentered personalized thinking, breaks the traditional media-centered information selection paradigm, realizes the customized content distribution mechanism of “thousands of people doing their own work”, and changes the previous information receiving mode of news search by the audience themselves [9, 10]. The distribution mode of algorithmic news not only optimizes users’ reading experience, but also improves users’ stickiness to the news client and strengthens the pertinence of information transmission, so as to help audiences quickly and accurately receive the content they are interested in from the massive fragmented information. (2) Classify news and improve information utilization rate Huge data-driven news algorithms can classify news content according to specific criteria and deliver different types of news to audiences with different needs. The algorithm distribution mechanism can achieve a wider range of information coverage, including the audience’s daily hobbies, life information and related long-tail news reports and other information. Information is put to better use than it is distributed by hand. In addition, the machine will constantly adjust the distribution model according to the recorded distribution behavior, guide the next news distribution more intelligently and accurately, and realize the automation of the communication channel [11, 12]. 2.2

A News Recommendation Algorithm Based on the Neural Network

This paper USES cyclic neural network collaborative filtering recommendation RNN, collaborative filtering (CF) is the most prominent in the news recommendation method, it assumes that the past now agree they recommend people would agree that in such a system, like-minded neighbor user preferences rather than the individual characteristics of the project forms the basis for all the news recommendation. The primary participants in the CF system are active users who seek rating predictions or project rankings. By using past preferences as an indicator for determining relevance between users, CF recommenders generate recommendations that rely on the tastes of compatible users. Therefore, on the basis of CF, we choose a cyclic neural network to encode user preferences as potential vectors. RNN can obtain the order information of the input sequence. It has some benefits in capturing and characterizing the time dependence of sequence data. However, in the face of diversified and large number of news, the traditional recommendation based on the cyclic neural network RNN faces two major problems: respectively, it will encounter the problem of “gradient disappearance” and

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structural duplication when processing long sequence, cannot parallel computation and the training time is too long [13]. Despite the current research makes the cycle of RNN unit is improved, with faster speed, but the change is only cycle unit, and the cycle of the sequence structure is still not changed, this paper introduces a slice (SRNN) recursive neural network to improve the sequence structure, when using linear activation function, SRNN is a special case of the RNN. In SRNN, not every current input is associated with its previous moment, but the entire sequence is concatenated by slicing. At the same time, the sequence order can be obtained from the loop unit in each subsequence, and the information can be transmitted through multiple layers so that it can be parallelized. We assumed that the time cost of each unit was a. The sequence was cut for n consultations with the same length, and the sectioning operation was repeated for k times. Therefore, the speed advantage of SRNN sequence compared with RNN sequence is recommended as follows:
n  k þ n1k  a tSRNN 1 nk R¼ ¼ k þ ¼ n T tRN T a

ð1Þ

Firstly, we associate each word in the news content clicked by User U with the entity in the knowledge graph to obtain the input sequence. The SRNN sequence recommender then splits the input sequence into several equally small sequences. In this way, subsequences can be easily parallelized. Given the interaction sequence {I1…, iT} user u, at level 0, the loop unit acts on each minimum subsequence through a join structure. Then we use the last hidden state of each subsequence at layer (p − 1) as the input to its parent sequence at layer p, and calculate the last hidden state of the subsequence at layer p:  h1t ¼ GRU 0

0



min

ð2Þ

ðtl0 þ 1Þ  t

hpt þ 1 ¼ GRU p hpti0  hpt




ð3Þ

3 Simulation Experiment of News Recommendation Algorithm Based on Neural Network 3.1

Data Sources

The data in this paper comes from the user browsing news data provided by DataCalstle as the experimental data. This data set is randomly collected from domestic websites, including a total of 12,000 browsing records of 10,000 users in a certain month. Each record includes a user ID, a news ID, the time viewed (shown as a timestamp), the content of the news, and the time the news was published. Recommended to verify the model, data needs to be set into training set and test set, this paper chose to each user’s browsing history for the last time as a test set, therefore,

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when choosing data set to ensure that the training set and testing set of users at the same time completely consistent, so the user browse number less than or equal to 2 users delete values from the dataset. 3.2

Comparison Model

In our experiment, we compared the SRNN network based on collaborative filtering with the following most advanced models: (1) DKN is a content-based in-depth recommendation framework for click-through rate prediction. DKN combines multiple channels of news expression at semantic level and knowledge level and word entity alignment. In this experiment, we use the news title as the text input of DKN. (2) Ripple is an end-to-end framework for incorporating knowledge graphs into recommendation systems. Users’ different levels of potential interest can be automatically discovered by iteratively propagating their preferences through KG.

4 Simulation Experiment Results 4.1

Model Comparison DKN

Ripple

SRNN

Data set

AUC

F1 0

10

20

30 Value 40

50

60

70

80

Fig. 1. Comparison of data from different models

As shown in Fig. 1, DKN shows excellent performance in news recommendation, indicating that they have made good use of KG’s knowledge in his algorithm. Both SKRN and Ripple perform better in terms of news recommendations. DKN and Ripple have better recommendation effect, both of which use KG as auxiliary information for recommendation. DKN uses attention network to discriminate users’ click history to capture users’ different preferences, while Ripple’s F1 value and AUC value are better than DKN, because Ripple is an end-to-end framework. The introduction of preference propagation overcomes the limitations of the existing KG perceptive recommendation methods based on embedding and path, and combines the preference propagation with the regularization of KGE in Bayesian framework to predict the clickthrough rate, which has a better news recommendation effect.

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Kg Experimental Results of Different Proportions Table 1. Results of different proportions of KG triples 0.5 SRNN 65.2 Ripple 65 DKN 63.2

AUC

70

0.6 65.8 65.4 63.7

0.7 66.4 66.1 64.1

SRNN

0.8 66.9 66.5 64.5

0.9 67.1 66.7 64.4

Ripple

1 67.2 66.9 64.8

DKN

65

60 0.5

0.6

0.7

KG

0.8

0.9

1

Fig. 2. Results of different proportions of KG triples

As shown in Table 1 and Fig. 2, the size of the Kg triplet in the four models is changed. It can be seen that in the three methods, AUC changes greatly with Kg when Kg is very sparse, while AUC fluctuates less when Kg ratio is large. We attribute this phenomenon to the tradeoff between the positive signal from long-distance dependence and the negative signal from noise: Too small a Kg ratio makes it difficult to explore interentity correlations and long-distance dependencies, while too large a Kg ratio creates more noise.

5 Conclusions The comprehensive penetration of artificial intelligence into the news media industry means that the media ecology will be rebuilt by technology. Just like in the past, the emergence of each new medium will reshape the development pattern of media, and artificial intelligence is no exception. It is rearranging the media ecosystem in unexpected ways, constantly renewing our understanding. The media industry is driven by intelligent technology to promote the intellectualization of all media industry. From information collection and news production to distribution mode and interactive feedback, news production and communication have undergone earth-shaking changes with the intervention of technology. This paper proposes and uses an algorithm based on collaborative filtering and memory enhancement. This algorithm model utilizes Slice-Recursive Neural Network (SRNN) for parallelization recommendation, and fully extends the potential knowledge-level connections between messages by using

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additional auxiliary information. Experiments show that the algorithm has good recommendation performance.

References 1. Conati, C., Gutica, M.: Interaction with an Edu-game: a detailed analysis of student emotions and judges’ perceptions. Int. J. Artif. Intell. Educ. 26(4), 975–1010 (2016) 2. Lv, J., Junjie, D.: New elements, new connections, new thinking-the value and future pattern of advertising. Mod. Advertising 6, 48–49 (2021) 3. Derakhshan, A., Beigy, H.: Sentiment analysis on stock social media for stock price movement prediction. Eng. Appl. Artif. Intell. 85, 569–578 (2019) 4. Kuo, C., Chiu, H.: Application of artificial intelligence in gastroenterology: potential role in clinical practice. J. Gastroenterol. Hepatol. 36(2), 267–272 (2021) 5. Uribe, S., Belmonte, A., Moreno, F., et al.: New access services in HbbTV based on a deep learning approach for media content analysis. Artif. Intell. Eng. Des. Anal. Manuf. 33(4), 1– 17 (2019) 6. Ramos, O.L., Rojas, D.A., Saby, J.E.: Reconocimiento de Patrones Vocálicos mediante la implementación de una red Neuronal Artificial Utilizando Sistemas Embebidos. Información Tecnológica 27(5), 133–142 (2016) 7. Tsang, L., Kracov, D.A., Mulryne, J., et al.: The impact of artificial intelligence on medical innovation in Europe and United States. Intellect. Property Technol. Law J. 29(8), 3–10 (2017) 8. Wang, Y., Tian, Y., Yin, X., Hei, X.: A trusted recommendation scheme for privacy protection based on federated learning. CCF Trans. Network. 3(3–4), 218–228 (2020). https://doi.org/10.1007/s42045-020-00045-8 9. Nechushtai, E., Lewis, S.C.: What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations. Comput. Hum. Behav. 90, 298–307 (2019) 10. Lv, P., Meng, X., Zhang, Y.: FeRe: exploiting influence of multi-dimensional features resided in news domain for recommendation. Inf. Process. Manage. 53(5), 1215–1241 (2017) 11. Bachmair, S., Svensson, C., Hannaford, J., et al.: A quantitative analysis to objectively appraise drought indicators and modeldrought impacts. Hydrol. Earth Syst. Sci. 20(7), 2589– 2609 (2016) 12. Zhou, X., et al.: Enhancing online video recommendation using social user interactions. VLDB J. 26(5), 637–656 (2017). https://doi.org/10.1007/s00778-017-0469-2 12. Chen, H., Jin, H., Cui, X.: Hybrid followee recommendation in microblogging systems. Sci. China Inf. Sci. 60(001), 1–14 (2017)

Analysis on the Application of Machine Learning Stock Selection Algorithm in the Financial Field Jie Wang(&) School of Commercial, Nantong Polytechnic College, Nantong 226000, Jiangsu, China

Abstract. With the rapid development of artificial intelligence in information processing applications, AI methods have been applied in different fields such as commerce, engineering, management, science, military, and finance. Among them, with the intelligence and modernization of the financial field, machine learning stock selection algorithms are widely used in the prediction of financial time series. The purpose of this article is to study the application of machine learning stock selection algorithms in the financial field. This article first compares and analyzes the three commonly used machine learning stock selection algorithm tools, and summarizes the opportunities brought by the application of machine learning stock selection algorithms to the financial field. This paper proposes the random forest algorithm of particle swarm parameter grid search (PSO-GRID-RF), and applies it to the stock return forecast in the financial field. This article elaborates on the regression prediction evaluation index, and verifies the superiority of the algorithm in this article. The prediction accuracy under the RF, GRID-RF and PSO-GRID-RF algorithms are 0.668%, 0.742%, and 0.870% respectively. It can be seen that this algorithm has higher prediction accuracy in stock prediction. Keywords: Machine learning algorithm
Stock prediction


Financial time series
Random forest

1 Introduction In recent years, machine learning and stock selection algorithms have been well applied in various fields [1, 2]. From traditional analysis methods to later time series forecasting, to the current machine learning algorithm forecasting, all are some of the forecasting methods for stock selection [3, 4]. Nowadays, the machine learning stock selection algorithm is widely used in the industry, and its application in the financial field has also been greatly developed [5, 6]. Many scholars have conducted in-depth research on the application of machine learning algorithms in the financial field. For example, Cai CW first decomposes the time series by discrete wavelet transform to obtain linear and nonlinear parts, and then use ARMA and Function chain artificial neural network for financial data prediction [7]; Karachun I uses support vector machine filters based on correlation to sort and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 65–72, 2022. https://doi.org/10.1007/978-3-030-89508-2_9

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select stock indicators in the feature selection part [8]; Nguyen Q established an eightfactor stock selection model The indicator system is applied in the direction of financial stock selection [9]. This article first compares and analyzes the three commonly used machine learning stock selection algorithm tools, and summarizes the opportunities brought by the application of machine learning stock selection algorithms to the financial field. This paper proposes the random forest algorithm of particle swarm parameter grid search (PSO-GRID-RF), and applies it to the stock return forecast in the financial field. This article elaborates on the regression prediction evaluation index, and verifies the superiority of the algorithm in this article.

2 Application of Machine Learning Stock Selection Algorithm in the Financial Field 2.1

Machine Learning Stock Selection Algorithm

(1) Support vector machine model The support vector machine model first analyzes and recognizes the training data, and finally can determine the sample classification and regression analysis on the test sample set. The support vector machine algorithm can not only support linear classification, but also use “core” technology to support nonlinear classification. The difference between the support vector machine algorithm and the previous statistical methods is that the support vector machine algorithm performs well in the case of small samples. This algorithm is not like the traditional process from induction to deduction. It realizes the process of direct derivation and calculation from sample to prediction, thereby achieving the purpose of simplifying the classification problem and simplifying the regression problem. At the same time, it can be found that when the sample data set of the support vector machine becomes larger and larger, the algorithm model will take up a lot of computer memory and consume a lot of learning time, and when the sample data set increases, the support vector machine model will be affected to a certain extent [10, 11]. But obviously, the amount of stock data in my country's stock market has become very large, so the training and prediction effect of support vector machines on such a large sample data set remains to be investigated. (2) XG boost The XGBoost machine learning algorithm model is an optimized distributed gradient enhancement library. The biggest feature of the XGBoost algorithm is its efficiency and efficiency, the algorithm is very flexible, and the algorithm is also relatively portable. The XGBoost machine learning algorithm provides the improvement of the parallel tree, so that this algorithm can solve many problems accurately and quickly [12]. This is about some good characteristics of the XGBoost algorithm, and its application is also very extensive and successful. The algorithm can be applied to almost all kinds of scenes and backgrounds, and can be expanded, and its performance in various competitions is also extremely good.

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(3) Random forest, Random Forest uses bootstrap random sampling to obtain a lot of small data sets (pockets) from the overall data set, uses each small data set to train the weak learner separately, and finally adopts the method of voting or taking the average to obtain the strong learner. Bagging is a parallel learning method. Since the focus of Bagging is to reduce variance, the effect of Bagging on decision trees and neural network learners is more obvious. 2.2

Advantages of the Application of Machine Learning Stock Selection Algorithms in the Financial Field

(1) Convergence of financial technology The establishment of the traditional financial transaction model is achieved by manual formulation and feature selection by the strategy department and the quantitative technology department in cooperation with the quantitative technology department, and then the programming of quantitative strategies. The development of machine learning and computer science has led to the financial field. The machine learning stock selection algorithm can teach the system how to re-recognize the data on the basis of the original system, and recognize and classify the pattern, automatically find out the scheme that can obtain the benefits, and automatically realize the system in the system by reading the input of the training set data and outputting the model. Algorithm, and can be generalized to different future market data. Therefore, after the entire system is trained, manual intervention or algorithm modification is not required. (2) Help improve efficiency For the financial industry, combining machine learning stock selection algorithms can not only improve the overall mining efficiency, but also provide a good opportunity for the transformation of the entire company's information system to keep up with the industry leaders. Ninety percent of the world's data was generated in the past two years. The era of information explosion has arrived. The efficiency of manually processing data through fund managers is no longer enough. Combining machine learning algorithms can greatly liberate the entire automated process. (3) Conform to the trend of the times The research scope of institutional investors represented by fund companies on quantitative trading and automated trading has also shifted from the original simple strategy to a more high-frequency strategy combined with artificial intelligence. At the same time, it further drives investors to invest funds in vectorized investment funds. The combination of intelligence and big data in financial asset allocation has gradually become the trend of market development. For financial companies to maintain their advantages in the fierce competition in the fund industry in the future, it is indispensable to optimize the existing multi-factor model in combination with machine learning stock selection algorithms.

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Prediction of Stock Return Based on Machine Learning Stock Selection Algorithm

(1) PSO performs parameter optimization Using RBF function as the kernel function of SVM, the definition of RBF function is as follows: 

k x  x0 k K ðx; y Þ ¼ exp  2r2 0

 ð1Þ

VM needs to optimize two parameters, the kernel parameter y ¼ 2r1 2 and the penalty parameter C > 0 of the error term, and the particles contain these two parameters. The PSO algorithm is used to select the parameters of the support vector machine. Particles are used to represent y, which is xi ¼ fyg. Each particle has a corresponding position and velocity V. The position and velocity can be used to evaluate the movement trend of the particle in space. There are two substrings in the particle for encoding C and y respectively. Assuming that the substring S is used to encode C and y, the length of S is L, and the unsigned integer value represented by S is i, the true values of the parameters C and y are determined by the following formula. C ¼ Cmin þ

i ðCmax  Cmin Þ 2i  1

ð2Þ

y ¼ ymin þ

i ðymax  ymin Þ 2i  1

ð3Þ

3 Experimental Research on the Application of Machine Learning Stock Selection Algorithm in the Financial Field 3.1

Algorithm Flow

The algorithm first uses the PSO algorithm to perform feature selection on the input feature set, extracts useful features, and then selects the optimal parameters of RF through the GRID algorithm to obtain a set of parameters with the largest RF prediction accuracy, reducing the amount of calculation, shortening the calculation time, and improving RF accuracy of the forecast. The steps of this experiment are: (1) Download stock data through the website. (2) Derive the characteristic parameters of stock data as input data, and normalize the data. (3) Use the PSO algorithm to select attributes. The selected attributes are used to predict the stress and are updated according to the position of the particles. 1 is

Analysis on the Application of Machine Learning Stock Selection Algorithm

(4)

(5) (6) (7)

3.2

69

selected and 0 is not selected. The number forming the subset of attributes F and FO is the feature number. Suppose that if the number of repetitions exceeds the maximum number of repetitions, it will jump out of the loop and exit the subset of optimized features and optimized parameters according to the historical optimal position. If the maximum number of iterations meets the conditions set in step 4, exit the optimal mode, otherwise return to the previous step. Construct a data matrix based on the optimal features selected by the particle algorithm in the previous step. Cross-validate the training set and test set to optimize the random forest parameters. After determining the optimal parameters, use the test data to test the trained random forest algorithm model to get the prediction result. Evaluation Index

Accuracy is expressed as PAccuracy , which is the ratio of the sum of the number of correct records as correct and the number of incorrect records as wrong to the total number of samples. The recall rate (Recall) is recorded as PRecall , which is the ratio of the number of correct predictions as correct and correct predictions as correct, and the number of incorrect predictions as the sum of errors. Precision is recorded as PPresion , which is the ratio of the number of correct predictions to correct predictions, and the number of incorrect predictions. The comprehensive evaluation index (F-Measure) is calculated by precision and recall rate, and the calculation formula is as follows: F¼

3.3

2 1=PPresion þ 1=PRecall

ð4Þ

Data Sources

The source of the stock data set used in this article is CSI 300, with a total of 1500 data.

4 Application Data Analysis of Machine Learning Stock Selection Algorithm in the Financial Field 4.1

Forecast Accuracy of Different Algorithms

The random forest parameter is determined to be 50, and the prediction accuracy results of the three algorithms are shown in Table 1: The prediction accuracy under the RF, GRID-RF and PSO-GRID-RF algorithms are 0.668%, 0.742%, and 0.870%, respectively. The comprehensive evaluation index F values of the three algorithms are 0.512, 0.543, and 0.627 respectively.

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J. Wang Table 1. Forecast accuracy of different algorithms PAccuracy (%) F

Algorithm

RF 0.668 GRID-RF 0.742 PSO-GRID-RF 0.870

Accuracy（%）

0.512 0.543 0.627

0.87

F

0.742

unit: %

0.668

0.627 0.543

0.512

RF

Algorithm

GRID-RF

PSO-GRID-RF

Fig. 1. Forecast accuracy of different algorithms

It can be seen from Fig. 1: When using PSO-GRID-RF, the accuracy and F value are better than the other two algorithms. This is because the number of parameters selected by PSO is small, so that the complexity of grid search parameters is greatly reduced, and the prediction accuracy is improved through effective feature selection. 4.2

Predictive Performance of Different Algorithms

Table 2 shows the error results between the predicted stock closing price and the actual stock closing price of the three algorithms in the next 1 day, 10 days, 20 days, and 30 days. The RF algorithm is in the next day, 10 days, 20 days, and 30 days. The predicted error scores are 24.04%, 30.43%, 37.16%, 39.13%. The errors of the PSOGRID-RF algorithm are 5.65%, 7.53%, 15.68%, and 13.26%, respectively. Table 2. Errors predicted by different algorithms Algorithm 1 10 RF 24.04 30.43 GRID-RF 11.04 18.37 PSO-GRID-RF 5.65 7.53

20 37.16 21.40 15.68

30 39.13 28.06 13.26

Analysis on the Application of Machine Learning Stock Selection Algorithm 13.26 15.68

Algorithm

PSO-GRID-RF

71

30

7.53 5.65

20 10

28.06 21.4 18.37

GRID-RF

1

11.04

39.13 37.16

RF

30.43 24.04 0

5

10

15

20 25 Unit: %

30

35

40

45

Fig. 2. Errors predicted by different algorithms

It can be seen from Fig. 2 that when the SVM model is directly used for prediction, the RF algorithm is more complicated in parameter selection, resulting in large errors in the prediction of 1 day, 10 days, 20 days, and 30 days in the future. After using the GRID algorithm for parameter search, the prediction effect is significantly improved, and the error is significantly reduced. The error of the PSO-GRID-RF algorithm proposed in this paper is reduced by more than 50%, indicating that the improved method is a very effective prediction method.

5 Conclusion The application of machine learning and stock selection algorithms has important research significance for the development of the financial field. The purpose of this article is to study the application of machine learning stock selection algorithms in the financial field. This article first compares and analyzes the three commonly used machine learning stock selection algorithm tools, and summarizes the opportunities brought by the application of machine learning stock selection algorithms to the financial field. This paper proposes a random forest algorithm of particle swarm parameter grid search (PSO-GRID-RF) and applies it to stock return forecasting. This article elaborates on the regression prediction evaluation index, and verifies the superiority of the algorithm in this article.

References 1. Zhu, Y., Zhou, L., Xie, C., et al.: Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. Int. J. Prod. Econ. 211(MAY), 22–33 (2019) 2. Zhu, Y., Xie, C., Wang, G.J., et al.: Predicting China’s SME credit risk in supply chain finance based on machine learning methods. Entropy 18(5), 195 (2016)

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3. Gogas, P., Papadimitriou, T.: Machine learning in economics and finance. Comput. Econ. 57 (1), 1–4 (2021). https://doi.org/10.1007/s10614-021-10094-w 4. Ferrati, F., Muffatto, M.: Entrepreneurial finance: emerging approaches using machine learning and big data. Found. Trends® Entrepreneurship 17(3) (2021) 5. Shivarova, A., Dixon, M.F., Halperin, I., Bilokon, P.: Machine learning in finance from theory to practice. Financ. Markets Portfolio Manag., 1–3 (2021) 6. Johnson, K.: Artificial intelligence, machine learning, and bias in finance: toward responsible innovation. Fordham Law Rev. 88(2), 5 (2019) 7. Cai, C.W., Linnenluecke, M.K., Marrone, M., et al.: Machine learning and expert judgement: analyzing emerging topics in accounting and finance research in the Asia– Pacific. Abacus (3) 2019 8. Karachun, I., Vinnichek, L., Tuskov, A.: Machine learning methods in finance. In: SHS Web of Conferences, vol. 110, no. 5, p. 05012 (2021) 9. Nguyen, Q., Diaz-Rainey, I., Kuruppuarachchi, D.: Predicting corporate carbon footprints for climate finance risk analyses: a machine learning approach. Energy Econ. (3), 105129 (2021) 10. Rundo, F., Trenta, F., Stallo, A., et al.: Machine learning for quantitative finance applications: a survey. Appl. Sci. 9(24), 1–20 (2019) 11. Kolchinsky, E.: Machine learning for structured finance. J. Struct. Finan. 24(3), 7–25 (2018) 12. Fernández, J.A.F.: United States banking stability: an explanation through machine learning. Banks Bank Syst. 15(4), 137–149 (2020)

Default Risk Prediction Based on Machine Learning Under Big Data Analysis Technology Qian Ma(&) and Yue Wang College of Economics, Sichuan Agricultural University, Chengdu, Sichuan, China

Abstract. The core of big data is to use the value of data. Machine learning is the key technology to use the value of data. For big data, machine learning is indispensable. Big data and machine learning complement each other, a wide range of application scenarios and good application performance make it continue to develop. Among them, the convenience of online credit has attracted more and more people. Coupled with multi-dimensional lending information, the huge amount of platform data has become the research object of big data analysis technology. Based on the data of Renren loan from 2014 to 2018, this paper uses machine learning method to predict the default risk of borrowers and compares it with logistic regression. The results show that in the aspect of risk measurement, SVM has the best measurement effect, while KNN algorithm has poor measurement performance. In this result, we can improve it from four aspects, that is, using more algorithms, adjusting parameters many times, looking for more effective indicators, and adjusting sample data for different algorithms. Keywords: Big data analysis technology prediction


Machine learning
Default

1 Introduction With the development of Internet technology, data types are increasingly rich. One of the characteristics of modern big data is that the amount of data stored exceeds the analysis and calculation ability of human beings [1]. The emergence of big data provides a cost-effective prospect for decision-making improvement in key development areas such as health care, economic productivity and security [2]. In terms of economy and finance, data is the core asset. From a large perspective, the application of big data technology can achieve precision marketing, strengthen risk management, and innovate services and products. From the perspective of risk management, big data plays an important role in the prediction of economy, scoring of personal credit and corporate credit, and default prediction. With the increase of the number of loans, the economy is in a changing environment, and the loan default rate is also rising [3]. At present, there are many ways of lending, such as bank loans, peer-to-peer social lending [4] and P2P network lending. Due to the progress of technology, P2P platform greatly reduces the cost of borrowing. However, this improved allocation brings higher credit risk [6]. Although credit scoring plays a certain role in evaluating the reputation of the lender, the scoring model relies more on the accuracy of the classification model [7]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 73–78, 2022. https://doi.org/10.1007/978-3-030-89508-2_10

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In terms of credit risk, machine learning algorithm has been used to calculate and predict credit risk by evaluating personal historical data [8]. Due to the rapid development of advanced technologies related to data mining, data availability and computing power, most banks are also updating their business models and turning to machine learning methods [9]. Traditional prediction mainly uses probit, logistic models. However, some studies have pointed out that the results of advanced nonlinear classifier based on big data method and machine learning training are better than traditional methods [10]. When the machine learning algorithm is applied to the loan data to predict the possibility of loan default, artificial intelligence can improve the existing credit risk model [11]. Ji Yoon Kim’s experiments on online loan data show that convolutional neural network has the highest accuracy compared with other machine learning methods. In this paper, machine learning algorithm is used to analyze the default situation of Renren loan, explore the applicability of different methods and put forward relevant suggestions.

2 Default Prediction Based on Machine Learning 2.1

Data Selection Table 1. Variables and definitions in datasets

Type

Variable

Default Default Order Information lnRate lnAmount Type Variable Order Information lnMonths Borrower Edu Information Marriage Inc lnAge Male Worktime Borrower Score Information Car_l House_l Whitewash Real_name Information lnWords Invest Turnover Consump

Note Default or not, default = 1, no default = 0 Logarithm of lending rate Logarithm of loan amount Note Loan maturity logarithm Educational background, take 0–3, the larger the number, the higher the educational background Marital status, married = 1, unmarried = 0 Income, take 0–6, the larger the value, the higher the income Logarithm of age Gender, male = 1, female = 0 Working years, take 0–3, the larger the number, the longer the time Credit score divided by maximum Car loan, car loan = 1, no car loan = 0 Mortgage, mortgage = 1, no mortgage = 0 Real name: real name = 1, not real name = 0 Logarithm of text words Investment and entrepreneurship is 1, otherwise it is 0 Short term turnover is 1, otherwise it is 0 Personal consumption is 1, otherwise it is 0

There are three kinds of “Renren loan” targets: credit certification target, on-the-spot certification target and institutional guarantee target, among which the on-the-spot and institutional guarantee targets are less in default, which is not suitable for research.

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At present, “Renren loan” has all used the on-the-spot certification target, so the recent data is not applicable. Therefore, this paper selects the data of “Renren loan” on-thespot certification target from January 2014 to March 2018.After removing the orders with repayment, incomplete information and more than 200000, and excluding the orders with the first loan, 13871 orders were obtained, including 1986 default samples (see Table 1). The selected data have the following characteristics: (1) the sample default rate is 14.32%. (2) The maximum number of overdue samples is 46, and the average is 4.46. Among the orders that have been paid off, that is, the proportion of overdue orders is 64.98%. (3) The minimum credit score is −99 and the maximum is 234. The credit gap of borrowers on this platform is large. 2.2

Model Selection

KNN is k-nearest neighbor. The implementation steps are as follows: obtaining data, splitting training set and test set, calculating distance, selecting the nearest K instances, obtaining relatively large classification and calculating accuracy. KNN classification has the advantages of simple implementation and good classification effect for rare events and multi classification events, but it also has obvious disadvantages, that is, the classification effect is poor when dealing with unbalanced samples. Support vector machine (SVM) is a two class classifier. Nonlinear mapping is the theoretical basis of SVM method. SVM uses inner product kernel function instead of nonlinear mapping to high-dimensional space, which has good applicability for binary classification nonlinear model to distinguish default or not. BP(back propagation) neural network model is a neural network model that can be transmitted backward. Neural network is widely used in machine learning and has good performance in classification. The important concepts in this algorithm are excitation function, loss function and gradient descent. BP network is composed of a fixed number of input layer, output layer and several hidden layers. Its working mechanism is similar to that of human neuron. The working signal is transmitted forward and the error signal is transmitted backward, just like a continuously adjusted feedback mechanism, and finally the output is realized (see Fig. 1).

Fig. 1. Working diagram of BP neural network

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Evaluation Index

In order to evaluate the model and compare the effect of machine learning on credit default prediction, we need to establish an evaluation system. The performance comparison of machine learning is a complex thing, we pay more attention to the generalization performance, which has a lot to do with the selection of training set. At the same time, the machine learning algorithm itself has a certain randomness, even if the same parameters run on the same test set for many times, the results can be different. In order to facilitate the comparison, this paper selects several basic indicators of machine learning. Because the selected machine learning methods are supervised learning, that is, given the training set, the results of the training set are applied to the test set to get the output, and the real situation is known in advance. Four main indexes of supervised learning are selected in the evaluation: accuracy, precision, recall and F1 score. Accuracy is the ratio of the number of correctly classified samples to the total number of samples, which is the most common and basic evaluation index. However, in the case of unbalanced samples, especially when we are more interested in default samples, that is, relatively small samples, accuracy evaluation basically has no reference value, which is not included in the rating system in this paper. Precision is the proportion of true positive cases in all true cases, recall rate is the proportion of true positive cases in total positive cases, F1 is the harmonic average of accuracy rate and recall rate. In this example, we pay more attention to the predictive ability of the model for default samples. Macro AVG is more easily affected by the smaller party in the unbalanced samples, so we choose macro AVG instead of micro AVG. 2.4

Algorithm Implementation

Taking 9922 pieces of data from January 2014 to June 2015 as the training set and 3949 pieces of data from July 2015 as the test set, the proportion of training set and test set is about 7:3, and the proportion of default samples in both training set and test set is 14%, which is equivalent to the default rate in the total sample. In this way, the consistency of the data distribution between the training set and the test set is maintained, and the influence on the final result caused by the extra deviation introduced in the data partition process is avoided. The variables in the data sample include default, order information, borrowers’ personal information, year effect and regional effect. In order to eliminate the dimensional effect between indicators, the data is normalized. The data of training set is used to train the algorithm model, and the algorithm model is applied to the training set and the test set to see the estimation and prediction effect of the model respectively. In order to compare the prediction effect of logistic regression and machine learning algorithm, we use Python to realize logistic regression and obtain the values of four kinds of evaluation indexes. There are four key points in KNN classification, which are quantifying the non numerical type, normalizing, determining the distance function to determine the distance between samples, and determining the value of K. The number of neighbors is the most important parameter. Generally, the prediction effect of using the default value is better. If it is set too large, it is easy to introduce some distant samples. Therefore, this paper makes a validation in the range of 1–5, and finds that the

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prediction effect of the model is better when k = 1 and K = 5. Compared with the accuracy rate and accuracy rate, this paper uses the default value k = 5, and selects the Euclidean distance as the distance function. When doing SVM, select one of the support vector classification SVC implementation, kernel function choose the default RBF. The implementation of BP neural network is based on tensorflow environment and keras package. This paper uses the main sequential model in keras. After the model is built, the model is compiled. The excitation function is relu and the loss function is binary. The optimization function is rmsprop, the performance evaluation function is accuracy, and the threshold value is set to 0.5. 2.5

Comparison of Experimental Results

Table 2. Estimation results of four methods Logistic Estimate Precision 0.82 Recall 0.84 F1-score 0.83

Forecast 0.81 0.82 0.81

KNN Estimate 0.87 0.7 0.75

Forecast 0.76 0.61 0.64

SVM Estimate 0.83 0.85 0.84

Forecast 0.82 0.84 0.83

BP Estimate 0.81 0.83 0.82

Forecast 0.79 0.84 0.81

I According to the above Table 2, except KNN algorithm, the precision of the other three methods is better, all above 0.8. Model estimation refers to the applicability of the trained model to the data in its own samples. From the three indexes of the model estimation effect, SVM has the best effect. It has a weak advantage in precision, recall and F1 value, followed by logistic regression. The estimation effect of BP is weaker than logistic regression. In this case, under fitting is not obvious of logistics. For the training model, we focus on whether it has good prediction performance. In comparison, the prediction ability of SVM is still the best of the four methods, while logistic regression and BP neural network have their own advantages, but we can not task for higher recall rate when we require precision. From the value of F1, the prediction performance of the two methods is similar. KNN algorithm still has the worst prediction effect. The reason is that KNN algorithm has poor prediction ability for sample imbalance problem. Although logistic model also has good performance in dealing with symmetric distribution, the shortcomings of KNN are more obvious in the data used in this paper.

3 Conclusions P2P network lending, as an internet financial model, is an important role in financial development. Although it has gone through nearly 15 years of development, reform and innovation, there are still some problems in the actual operation, such as information asymmetry, lack of sufficient constraints on the behavior of both sides of the

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lending, which affect the healthy development of P2P.In the past two years, machine learning has achieved good results in the research of P2P online loan platform default. This new classification method also shows good applicability, enriches and optimizes the default early warning model of P2P online loan platform. In this paper, KNN, SVM and BP neural network algorithm are used to estimate and predict the data of “Renren loan”, and the results are compared with the traditional logistic results. The results show that the SVM algorithm is better than the logistic results in estimation, and the applicability of KNN is not strong in prediction, so it is not suitable for default prediction. Big data technology has made great changes in people's lives, and it will also improve the quality of credit reference. Compared with the traditional bank credit score, the footprint of the Internet can effectively predict the default probability of consumers. The combination of credit score and Internet footprint can greatly increase the prediction of default risk. On the other hand, for big data prediction, the model is very important. In the future research, we should constantly try and strengthen new models, so as to obtain higher accuracy.

References 1. Gutmann, M.P., Merchant, E.K., Roberts, E.: “big data” in economic history. J. Econ. Hist. 78(01), 268 (2018) 2. Hilbert, M.: Big data for development: a review of promises and challenges. Dev. Policy Rev. 34(1), 135–174 (2016) 3. Madaan, M., Kumar, A., Keshri, C., Jain, R., Nagrath, P.: Loan default prediction using decision trees and random forest: a comparative study. IOP Conf. Ser. Mater. Sci. Eng. 1022 (1), 012042 (2021). (12pp) 4. Kim, A., Cho, S.B.: An ensemble semi-supervised learning method for predicting defaults in social lending. Eng. Appl. Artif. Intell. 81(MAY), 193–199 (2019) 5. Eria, K., Subramanian, P.: Decision support credit scoring model to improve loan default prediction in financial institutions. J. Comput. Theor. Nanosci. 16(8), 3514–3518 (2019) 6. Giudici, P., Misheva, B.H.: P2p lending scoring models: do they predict default? J. Dig. Bank. 2(1), 353–368 (2018) 7. Aslam, U., Ilyas, H., Sohail, A., Batcha, N.K.: An empirical study on loan default prediction models. J. Comput. Theor. Nanosci. 16(8), 3483–3488 (2019) 8. Lahmiri, S., Bekiros, S.: Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design. Quant. Financ. 19(9), 1569–1577 (2019) 9. Turiel, J.D., Aste, T.: Peer-to-peer loan acceptance and default prediction with artificial intelligence. Royal Soc. Open Sci. 7(6), 191649 (2020) 10. Canfield, C.E.: Determinants of default in p2p lending: the Mexican case. Indep. J. Manage. Prod. 9(1), 001 (2018) 11. Kim, C.: Towards repayment prediction in peer-to-peer social lending using deep learning. Mathematics 7(11), 1041 (2019)

Application of Intelligent Detection Technology and Machine Learning Algorithm in Music Intelligent System Hongyun Zou(&) College of Art, Hubei Polytechnic University, Huangshi 435000, Hubei, China

Abstract. In recent years, intelligent detection technology and machine learning algorithms have been widely used in various fields. Music is an important part of people's entertainment life and an important way to relax. The music field is combined with it, and the derived music intelligent system is loved by more and more users. The purpose of this article is to study the application of intelligent detection technology and machine learning algorithms in music intelligent systems. This article applies intelligent detection technology and machine learning algorithms to the music intelligent system. First, the overall framework of the system is designed, and people's functional and non-functional requirements for the music intelligent system are analyzed. Then according to the function of the system, it is divided into four modules: data processing, music learning, song recommendation module, and login and registration. The system modules are introduced in detail, and the functions of the system are tested. Experimental data shows that when the number of users is 20, 50, 100, 150, the success rate of user access is 100%, and the average response time is 1.46 s, 3.83 s, 1020 s, and 13.24 s, respectively. It can be seen that the system can basically provide services normally, and the non-functional test of the system has reached the target plan. Keywords: Music retrieval Neural network


Machine learning
Intelligent music system


1 Introduction With the development of artificial intelligence technology and machine learning algorithms, more and more smart products appear in people's lives [1, 2]. Music is a form of artistic expression that people love to hear, and the development of the digital music industry puts forward higher requirements for music products [3, 4]. Through intelligent detection technology, it breaks the constraint of unclear description of music content when using text description information to retrieve, and reduces the cost of large-scale manual labeling of music databases; the application of machine learning algorithms can help users in massive music resources look for personalized music to recommend [5, 6]. Many scholars have conducted in-depth discussions on the research of music intelligent systems. For example, Castillo JR has established a user-centric music intelligent search system, which finds texts that reflect the semantics of music from the user's music log to expand the audio features [7]; Panwar S collects editable metadata © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 79–87, 2022. https://doi.org/10.1007/978-3-030-89508-2_11

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information on the website, such as artist, album name and other tags, and expands it into the corresponding audio features [8]; Rhodes C designed a combination of music content and user context features statistical model to meet the short-term listening needs of users [9]. The purpose of this article is to study the application of intelligent detection technology and machine learning algorithms in music intelligent systems. This article applies intelligent detection technology and machine learning algorithms to the music intelligent system. First, the overall framework of the system is designed, and people's functional and non-functional requirements for the music intelligent system are analyzed. Then according to the function of the system, it is divided into four modules: data processing, music learning, song recommendation module, and login and registration. The system modules are introduced in detail, and the functions of the system are tested.

2 Application of Intelligent Detection Technology and Machine Learning Algorithm in Music Intelligent System 2.1

Demand Analysis of Music Intelligence System

(1) Description of system function requirements In theory, a music intelligence system that can provide users with music services mainly includes five major functions: 1) Account management: the main functions include user login, password management, personal homepage, personalized settings, friend management, etc. 2) My music: including local music management, music download management, my collection and playlist management. Users can listen to songs in local music offline, or select music from their own playlists to listen to songs online, add their favorite music to the playlist created by themselves, and classify and label the playlist. 3) Now playing: operate the music being played, such as play, pause, loop, track control and other functions, and also perform operations such as collecting, sharing, downloading, commenting on the current song, adding to the playlist, etc. 4) Music search: allow users to search in the music library when there is a song they want to listen to find the music that meets their expectations. 5) Discovery: it mainly contains two contents: “Guess what you like” and “Music Radio”, and can make personalized music recommendations for users. (2) System non-functional requirements 1) The principle of ease of use: Considering the user-friendly design requirements and thinking from the user's perspective, the user should see a music recommendation platform that does not require too much thinking and can easily and quickly use the services provided by the system.

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2) The principle of efficiency: when users use the music system, they often perform auditioning and downloading. The amount of music in the music library is very large. If you want to quickly complete music retrieval, you must have a classification of the music library. The perfect database and index allow users to quickly find the songs they want to listen to. 3) Principle of continuity: an embarrassing aspect of music recommendation systems is that users usually do other things while listening to music. If the user does not perform any operation again after listening to a song, our processing method here is usually to choose to play a song similar to the song or a song of the same artist, so as to ensure the continuity of the playlist and ensure that the user can compare satisfaction. 4) The principle of scalability: in addition to the basic functions and recommendation algorithms of the system, the current functions of the system can be improved and expanded in many ways. Therefore, when implementing the system, we must fully consider the possibility of adding new functions and adding/improving algorithms in the future. Therefore, when designing the system as a whole, it must conform to the high cohesion, and the function modules must conform to the principle of low coupling. The system has good scalability. 5) Security principle: User data should be fully protected. Music data and user data can be made public in order to facilitate research, but it is necessary to strictly ensure that users’ private information is not leaked back to ensure system security. 2.2

The Overall Module Design of the System

(1) Data processing module design The data processing module is an intermediate module whose main function is to prepare the required data for the subsequent machine learning recommendation algorithm. This module first preprocesses the data, and needs to read the behavior record table between the user and the music, and process the behavior record table. According to the user's behavior record and the user.s personal information, the songs marked by the user as disliked or not of interest are filtered out. Among the remaining songs, the preference matrix between the user and the song pair is formed according to the user's preference information. Because it is also necessary to calculate the weights of preference pairs, it is also necessary to perform a score conversion on the user behavior record table, and obtain the score table between the user and the song according to certain processing rules, and the generated two matrix tables are given to the machine learning module for the machine Learn to make recommendations. (2) Music learning module design The music learning module is the most important module in the recommendation system. According to the processed data provided by the data processing module using intelligent detection technology, it calculates a sort order of songs by the target user, and selects the top songs as the target user. Provide better

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recommendation in the recommendation process, the neighbors of the target user need to be used to make recommendations for the user [10, 11]. Therefore, in the entire recommendation module, we need to read the preference matrix of all usermusic deviation pairs and all the user’s rating matrix is also the reason why the recommendation process is calculated in the background and does not need to be updated in real time. However, in the calculation process, each user only needs to maintain its own related data, because the real-time data does not require other users’ data, only the corresponding summary table needs to be updated regularly. (3) Design of the result module of music intelligent recommendation The music smart recommendation result module mainly displays recommendation results for target users. When displaying the recommendation results for the target user, it is necessary to read the user recommendation list in the database to match the recommendation list of the current user. At the same time, read the personal user behavior record table maintained for the user. According to the behavior record, follow the recommendations in the recommendation list. In order, the songs that the user has listened to are displayed as old songs recommendation, and the songs that the user has not listened to are displayed as new song recommendations. The recommended results are displayed in pages, and when the user clicks the arrow on the next page, the user changes the page. At the same time, considering that there will be a cold start problem for new users, hot recommendations are made for them. Quantify the user's operations on songs and artists, including browsing, searching, listening, tagging, collecting, deleting, etc., fusing explicit and implicit data to obtain the user’s preference for known songs and the similarity between songs, according to this predicts the user's preference for unknown songs, thereby generating a recommended list [12]. The recommendation result module mainly includes three types of recommendations, namely popular recommendation, new song recommendation and old song recommendation. The latter two recommendations have been introduced above and are mainly implemented for old users. The recommendation results are also obtained using machine learning algorithms. Since new users have cold start problems, if they are new users, they will filter out their favorite music library according to their personal hobbies. According to the popularity of these songs in the system, the number of times they have been listened to the songs, and the list is sorted and recommendations are made for users. (4) Design of login and registration module The main purpose of this module is to collect user information and obtain user information to facilitate subsequent learning and related music recommendations. There are five main functions of this module: 1) Account registration: after entering the system, each user who uses the music recommendation system needs to register first. When registering, assign a user id to the user, and then record the user's personal information and some information about the user's music preferences, such as favorite singer or music language, music characteristics, etc., in order to give more accurate recommendations.

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2) User information preservation: after obtaining the above-mentioned registration information, store it in the user information table in the database for subsequent recommendation. 3) Account login: When the user needs to log in to the software, according to the user name and user password entered by the user, search for a match in the user information table in the database, and enter the main interface of the system after the match is successful. In this way, the system can also know the users currently using the system, and then provide corresponding recommendation services for each specific user according to the user id. 4) User information query: provide users with a query service of their own information, and users can view their registered information. 5) User information modification: as some of the user’s information may change over time, such as the user’s occupation, interests, etc., the above provides users with information modification services. This can also provide users with the most realistic current information when recommending to the system. 2.3

Application of Machine Learning Algorithms in Music

An important subject in machine learning-deep learning is increasingly integrated with various fields. Convolutional neural networks in the field of deep learning can help users quickly and accurately obtain music they are interested in in an unprecedented music library of tens of millions. For the training of the model algorithm, the main goal is to minimize the loss. The CNN network model can be used to predict the potential factor features of music audio. There are many ways to calculate the mean square error of the function in the regression model, as follows: (1) Mean Squared Error Loss (MSE) can be regarded as another calculation method of Euclidean distance. The calculation method is shown in formula (1):
 1 XN loss Y; Y^ ¼ ð^y  yi Þ2 i¼1 i N

ð1Þ

(2) Mean Squared Log Error Loss (MSLE) is to take the logarithm of the data first and then find the mean square error. The calculation method is as shown in formula (2):
 1 XN loss Y; Y^ ¼ ðlog ^yi  log yi Þ2 i¼1 N

ð2Þ

Although there are many loss functions for measuring regression models, MSE is still the most widely used loss function; compared to MSE, MAE can effectively punish outliers and is more suitable for situations with more data outliers.

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3 Investigation and Research on the Application of Intelligent Detection Technology and Machine Learning Algorithm in Music Intelligent System 3.1

System Environment

The experimental system environment uses Window10+Python, and uses Anaconda to manage the installation environment and various toolkits. The processor is IntelCorei5, and the system memory capacity is 8GB. This article uses the back-end deep learning framework Keras based on Tensor Flow. During the model training process, the loss function obtains the minimum value after 30 iterations. 3.2

Experimental Data

The experimental data set is Million Song. This data set has 384,546 music and 1,019,318 independent users and contains 48,373,586 user records of music playback times. If the behavior of the experiment uses 1,000 users to play 1,000 pieces of music as the experimental data set, then at least 25 pieces of music are played by the user at least once. 3.3

Experimental Project

(1) User access test Perform the stress test of the login module. After entering the Load Runner Controller, record the script and select the itinerary edit to set the user's login method. Here we set to log in 5 people every 10 s, and the logout method is also set to log out 5 people every 10 s. After completing the environment configuration, run the script to start the test. (2) Module test By testing the music recommendation function module in the system function module, the accuracy rate, recall rate and coverage rate are obtained, and the superiority of the system is obtained.

4 Application Data Analysis of Intelligent Detection Technology and Machine Learning Algorithm in Music Intelligent System 4.1

User Access Test

Table 1 shows the results of the concurrent user access system test: when the number of users is 20, 50, 100, 150, the success rate of user access is 100%, and the average response time is 1.46s, 3.83s, 1020s, 13.24s.

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Table 1. User access test User number 20 50 100 150

Average response time(s) 1.46 3.83 10.20 13.24

Maximum response time (s) 2.01 5.64 11.64 17.92

Success rate (%) 100% 100% 100% 100%

Average hit rate per second (%) 45.679% 84.367% 144.62% 200.34%

200.34% 100%

150

17.92

number of users

13.24 144.62% 100%

100

10.2 84.37% 100%

50

5.64 3.83

Maximum response time (s)

45.68% 100% 2.01 1.46

20 0

11.64 Average hit rate per second (%) Success rate(%)

Average response time(s) 5

10 Number

15

20

Fig. 1. User access test

It can be seen from Fig. 1 that the average click rate per second of the number of users increases and increases with the number of users, and the success rate is 100%. It can be seen that the system can basically provide services normally, and the nonfunctional test of the system has reached the target plan. 4.2

Function Module Test

Table 2 shows the application effect of the music recommendation module of the music intelligence system: when the number of users is 20, 50, 100 and 150, the accuracy of the system is 89.58%, 90.12%, 88.41%, and 90.69%.

Table 2. Function module test data 20 50 100 150

Accuracy (%) Recall rate (%) Coverage rate (%) 89.58 52.14 86.74 90.12 36.78 84.23 88.41 29.48 82.61 90.69 26.46 81.83

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unit: %

89.58

86.74

90.12

84.23

88.41

82.61

90.69 81.83

52.14 36.78 29.48

20

50 Number of users 100 Accuracy(%) Recall rate (%)

26.46

150 Coverage rate (%)

Fig. 2. Function module test data

It can be seen from Fig. 2 that the coverage and accuracy of the music intelligent system using machine learning algorithms and intelligent detection technology are both above 80%. It can be seen that the system has the function of recommending personalized music for users.

5 Conclusion With the increase of work pressure and the acceleration of the pace of life, more and more people like to listen to music to relax. Music is gradually becoming a way of life for people. While people love music, they also make people pursue the intelligence of music products. The combination of artificial intelligence detection technology and machine learning into music not only brings people a lot of convenience, but also enriches people’s lives. This article applies intelligent detection technology and machine learning algorithms to the music intelligent system. First, the overall framework of the system is designed, and people’s functional and non-functional requirements for the music intelligent system are analyzed. Then according to the function of the system, it is divided into four modules: data processing, music learning, song recommendation module, and login and registration. The system modules are introduced in detail, and the functions of the system are tested. The final test results show that the system can basically provide services normally, and the non-functional test of the system has reached the target plan.

References 1. Foti, P.: The cultivation of musical intelligence and its contribution to child's development -a digital music lesson in kindergarden with parents’ input Eur. J. Educ. 6(12), 12 (2020) 2. Maenner, M.J., Marshalyn, Y.A., Kim, V., et al.: Development of a machine learning algorithm for the surveillance of autism spectrum disorder. Plos One 11(12), e0168224 (2016)

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3. Brewster, L.R.: Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data Mar. Biol. 165(4), 1–19 (2018) https://doi.org/10.1007/s00227-018-3318-y 4. Park, Y.W., Klabjan, D.: An aggregate and iterative disaggregate algorithm with proven optimality in machine learning Mach. Learn. 105(2), 199−232 (2016) https://doi.org/10. 1007/s10994-016-5562-z 5. Wang, Y., Dou, Y., Yang, W.: A new machine learning algorithm for numerical prediction of near-earth environment sensors along the inland of east Antarctica. Sensors 21(3), 755 (2021) 6. Lavanchy, J.L., Zindel, J., Kirtac, K.: Author correction: automation of surgical skill assessment using a three-stage machine learning algorithm Sci. Rep. 11(1), 8933 (2021) 7. Ramírez, J., Flores, M.J.: Machine learning for music genre: multifaceted review and experimentation with audioset J. Intell. Inf. Syst. 55(3), 469–499 (2019)https://doi.org/10. 1007/s10844-019-00582-9 8. Panwar, S., Rad, P., Choo, K.K., Roopaei, M.: Are you emotional or depressed? learning about your emotional state from your music using machine learning J. Supercomput. 75(6), 2986–3009 (2018)https://doi.org/10.1007/s11227-018-2499-y 9. C Rhodes R Allmendinger R Climent 2020 New interfaces and approaches to machine learning when classifying gestures within music Entropy 22 12 1384 10. Zhao, C., Yan, T., Wang, G.: Research on the application of computer intelligent detection in civil engineering technology. J. Phy. Conf. Ser. 1915(3), 032077 (2021) 11. Amezquita-Sanchez, J.P., Park, H.S., Adeli, H.: A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform Eng. Struct. 147, 148–159 (2017)https://doi.org/10.1016/j.engstruct.2017. 05.054 12. Guétin, S.: A patient-controlled, smartphone-based music intervention to reduce pain—a multi-center observational study of patients with chronic pain Eur. J. Integr. Med. 8(3), 182– 187 (2016)https://doi.org/10.1016/j.eujim.2016.01.002

Application of 3D Computer Aided System in Dance Creation and Learning Jianxing Shi(&) Baotou Teacher’s College, Baotou 014030, Inner Mongolia, China

Abstract. Dance is an art form of body movement accompanied by music rhythm, which has a profound impact on our life. At present, the rapid development of information technology is increasingly extensive and far-reaching impact on our work, study, life in all fields, 3D computer aided (3DCA) technology, perception technology also appears, the emergence of these computer aided technology to promote the change of dance creation (DC) and learning. In the study of DC, the use of 3DCA technology can meet the needs of digital DC, let the creators study their own creation with a new and more comprehensive vision, and provide a better creation platform for the creators. This paper proposes a 3DCA system, which uses motion capture technology to help the creators achieve DC and learning. This paper expounds 3DCA technology, describes the application of 3DCA system in DC, illustrates the differences between 3DCA DC and traditional human DC, and analyzes the complexity of dance, arrangement time, musculoskeletal wear coefficient and reasonable control coefficient of movement. The results show that the complexity coefficient of traditional human DC is 9.07, and that of computer-aided 3D DC is 8.48. There is no significant difference between the two. However, the time of computer-aided 3D DC is only one hour, while the time of traditional human DC is 10 times of it, the traditional human DC needs the creator to choreograph, and the musculoskeletal wear coefficient is 10, while the musculoskeletal wear coefficient of computer-aided 3D DC is 2.31. But correspondingly, because computer-aided 3D DC is machine choreography, its reasonable control coefficient of action is 10.57, and the reasonable control coefficient of traditional human DC is 26.31. Keywords: Dance creation
Three dimensional computer technology Auxiliary system
Dance movement




1 Introduction Nowadays, the development of information technology has entered a heyday, and the information technology with computer and network as the core is promoting the change of traditional Internet technology with unprecedented strength. With the emergence of this change, the application of computer technology in DC is more and more widely [1, 2]. With the development and progress of information technology in the new era, a new generation of creators and dancers constantly break through the traditional DC mode and teaching methods. At the same time, with the assistance of computer technology, it helps to improve the creators’ creative consciousness and help them understand the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 88–95, 2022. https://doi.org/10.1007/978-3-030-89508-2_12

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dance action more intuitively [3, 4]. The introduction of computer technology in DC learning can extend the connotation of dance, constantly innovate movements, and organically combine motion capture technology with DC by using 3DCA technology, which can enrich the creators’ senses and promote their cognition of dance movements [5, 6]. At the same time, the combination of virtual reality can stimulate the creators’ imagination, Improve their creative ability of dance innovation, so that creators can make better use of dance forms and taste the connotation of dance [7, 8]. The application of 3D computer-aided system in DC and learning provides a good independent learning environment for creators. 3D motion capture technology can provide basic dance skills through character collection and dance motion collection to provide indepth understanding and learning for dance creators [9, 10]. In the research on the application of computer-aided system in DC learning, many scholars at home and abroad have studied it and achieved certain research results. Wang J and others pointed out that due to the attraction of dance forms, the teachers’ oral and personal teaching of many inherent cultural charm, and the limitation of students’ own aesthetic ability, students are generally not used to studying the cultural meaning of works or difficult to understand the connotation behind the form. With the help of multimedia graphics, pictures, music, dynamic video materials, it is easier to guide students into the level of thinking and exploring the artistic charm, cultural and philosophical connotation of dance [11]. Salaris P and others pointed out that in the practice of dance teaching, mainly some amateur training institutions, due to the lack of teachers, more use of multimedia technology such as video materials to assist teaching, which is not an internal active state of building the discipline frontier [12]. This paper mainly studies the application of 3DCA system in DC and learning. This paper expounds the 3DCA technology, describes the application of the 3DCA system in DC, and carefully expounds the differences between the 3DCA DC and the traditional human DC, and analyzes the complexity of the dance, the arrangement time, the musculoskeletal wear coefficient and the reasonable control coefficient of the movement. At the same time, this paper investigates and analyzes the creator's satisfaction with the use of computer-aided 3D system.

2 The Research of 3DCA System in DC and Learning 2.1

3DCA Technology

The most important thing in the study of DC is the study of dance action creation. Through the use of three-dimensional auxiliary technology to analyze the creator's dance action, with the support of action collection, we can deeply understand the choreography of dance action design, so that the creator can have a more comprehensive understanding of dance. Motion capture technology tracks and records human motion data with high spatial and temporal resolution, which can greatly reduce the time consumed by key frame (KF) method in animation production. The research of motion capture can be traced back to the use of rotary scanner. Invented by Max

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Fleischer in 1914, it is a device that allows animators to track human movements frame by frame. The video of human motion is captured by the camera, and then the animator tracks every frame in the video through a transparent image board, which can produce a very real human motion. Nowadays, with the development of science and technology, there are a variety of motion capture devices, which can be divided into two categories: node based motion capture system and node free motion capture system. In the node based motion capture system, actors need to wear nodes in all parts of the body. According to the different characteristics of these nodes, the node based motion capture system can also be divided into several categories. In the optical motion capture system, the infrared camera can track the reflective nodes on the actor, so as to record the actor's action. The optical tracking can track a large area. However, when capturing the action data of multiple people, it is deeply affected by the inclusion problem. The mechanical node is installed on the movable part of the human body. When a part is bent, the corresponding mechanical node will output signals. These signals contain the structural information of the part. Through these structural information, the corresponding part of the human body can be located. For mechanical motion capture system, users do not need to worry about the problem of hiding. In the magnetic motion capture system, the position of each node in the magnetic field is used for motion capture. When using the magnetic motion capture system, users also do not need to worry about the problem of hiding. The form of motion data with joint coordinates as nodes is very intuitive, and it is also conducive to measure the changes between the joints. However, there are large numerical differences between people of different body types who capture the same action. Therefore, the three-dimensional rotation of joints has become a common measurement method. For an action M with m frames, it can be expressed as follows: MðtÞ ¼ fpðtÞ; r 0 ðtÞ; r 1 ðtÞ; r 2 ðtÞ; :::; r jJ j ðtÞg; t ¼ 1; 2; :::; m

ð1Þ

Among them, P(t) and r0 ðtÞ respectively represent the three-dimensional absolute coordinates and initial direction of the root joint relative to the predetermined coordinate system in the capture area at time t, jJj represents the number of selected key nodes, and rj ðtÞð1  j  jJjÞ represents the angular rotation value of the j-th joint relative to its parent node. The whole action M can be stored in m-row matrix, each row represents a frame, and its length formula is as follows: Mh ¼ jJ j  m þ m

ð2Þ

On the other hand, nodeless capture system does not need to install any nodes on actors. With the development of computer vision (CV) technology, nodeless capture scheme is becoming more and more mature. For example, the nodeless motion capture systems developed by Stanford University, University of Maryland and Massachusetts Institute of technology are very famous nodeless capture solutions. In addition, Kinect developed by Microsoft is a very mature nodeless capture solution. The development of nodeless motion capture will bring a new era for human-computer interaction (HCI). In

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the learning of DC, 3D motion capture technology can collect and transmit the dance movements of the creator to the computer. Through the corresponding computer-aided software, the collected dance movements can be arranged and combined freely, which can try all kinds of possibilities of dance movements and will not damage the body of the creator because of long-term DC. 2.2

Application of 3DCA System in DC

(1) Large scale choreography Dance digitization is the fusion and collision of modern science and technology and dance art, and it is an inevitable trend of the development of science and technology and art. Digital dance uses 3D computer-aided technology to change the information collection of dance movements, and uses digital form for creation, which enriches the forms of expression and means of performance of traditional DC. 3DCA system can be integrated in large-scale dance choreography, reduce the time required for dance rehearsal and the financial and material resources required by the creative team, and take pains to select dance choreographers. The 3DCA system can realize the feasibility of large-scale dance choreography through technical software. It can simulate the real dance framework of large-scale dance through multiple arrangement and combination of dance action models captured by motion capture. At the same time, it can also simulate the stage effect, so as to provide more choices for large-scale dance choreography and reduce the human and material resources consumed by rehearsal. The application of computer three-dimensional technology in large-scale dance can break through the space-time limit of the stage, and make the dancers and the audience get the inner self satisfaction through the virtual. Through the combination of virtual and reality, digital dance fully shows the artist's design concept, which adds wings to the rich imagination of DC, and endows its artistic creation with aesthetic feeling and realistic reproduction effect. (2) Digital dance teaching In digital dance teaching, the use of computer-aided three-dimensional teaching, the use of multimedia technology, performers can be transformed into spectators, better grasp the aesthetic of dance art. The application of multimedia image materials in dance teaching can enable students to have an aesthetic grasp of the works from the perspective of bystanders. Through the transformation of roles, students can understand the dance from the connotation, and then more consciously think about how to express and move the audience, so as to achieve a more accurate grasp of each action element and the aesthetic essence of an excellent dance work. In dance teaching, fully mastering the use of CAI multimedia technology is conducive to the mutual promotion of teachers’ teaching practice and students’ dance art learning efficiency. In the process of using image and other multimedia materials for teaching, teachers can make up for the loopholes and deficiencies in

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the demonstration process by using image materials after completing the demonstration action. Teachers can record all kinds of classic moments in the process of demonstration by words and deeds in the form of images. Through the continuous playback, fine play and freeze frame of multimedia technology, the specific movements and dance forms constantly appear in the students’ field of vision, which can accurately demonstrate the same movement for the students, and let the students figure out the elements of the action repeatedly until the students master and really appreciate the essence and essence of dance movements. In this way, teachers’ physical strength and energy can be greatly saved, and it is also convenient for students to make full use of their spare time. After intense training, they can recover their physical strength and review the learning content by watching the video materials, so as to consolidate their knowledge. Furthermore, we can also record students’ training images in class for observation, so that students can see their own problems intuitively, so as to help teachers guide them according to the situation, suit the remedy to the case, help students shorten the time cycle from understanding dance details and elements to perceiving dance culture, and achieve the best effect of teaching and learning. Therefore, it is necessary to use all kinds of modern multimedia technology to assist and improve dance teaching.

3 Research and Analysis 3.1

Research Objects

The main research object of this paper is the creators of 3DCA system. In order to understand the feelings of the creators on the use of 3DCA system, this paper issued a network questionnaire in the form of questionnaire survey. A total of 100 questionnaires were issued, of which 96 valid questionnaires were collected and integrated. 3.2

Research Process Steps

This paper studies the computer three-dimensional technology, analyzes the application of computer three-dimensional aided system in DC learning, and carefully expounds the differences between computer three-dimensional aided DC and traditional human DC, analyzes the complexity of dance, arrangement time, musculoskeletal wear coefficient and reasonable control coefficient of movement. At the same time, this paper investigates and analyzes the creator's satisfaction with the use of computer-aided 3D system.

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4 Research and Analysis on the Application of 3DCA System in DC and Learning 4.1

Correlation Coefficient Analysis of DC Under 3DCA System

Table 1. Analysis of the correlation coefficient of DC under the 3DCA system Factors Action design time Musculoskeletal wear coefficient Control coefficient of action rationality Action complexity factor

Traditional manpower design 10 10 26.31

Computer aided design 1 2.31 10.57

9.07

8.48

The new form of DC brought by the 3D computer-aided system only requires the use of modern software in front of the computer to complete the choreography and creation of a dance, and can clearly see the effect after the production of dance works. This undoubtedly improves the speed of DC, saves dance resources, and reduces the labor cost, The specific differences between computer-aided 3D DC and traditional human DC are shown in Table 1.

Fig. 1. Analysis of the correlation coefficient of DC under the 3DCA system

As can be seen from Fig. 1, the complexity coefficient of traditional human DC is 9.07, and that of computer-aided 3D DC is 8.48. There is no big difference between the two. However, the time of computer-aided 3D DC is only 1 h, while that of traditional human DC is 10 times of it. The traditional human DC needs the creator to choreograph, and the musculoskeletal wear coefficient is 10, while the musculoskeletal wear coefficient of computer-aided 3D DC is 2.31. But correspondingly, because computeraided 3D DC is machine choreography, its reasonable control coefficient of action is 10.57, and the reasonable control coefficient of traditional human DC is 26.31.

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Satisfaction Analysis of Computer Aided DC

Computer aided DC meets the needs of digital dance, has a good prospect, and provides a more interactive creation platform for creators. In order to understand the satisfaction degree of the creators to the computer-aided DC system, this paper studies the attitude of the creators through a questionnaire survey. The data results are shown in Table 2. Table 2. Satisfaction analysis of computer aided DC Very satisfied Satisfied Neutral Dissatisfied Very dissatisfied Number 37 43 12 4 0 Proportion 38.54 44.79 12.5 4.17 0

Fig. 2. Satisfaction analysis of computer aided DC

As can be seen from Fig. 2, 83.33% of the creators think that the computer-aided DC system is satisfactory and helps them create better dances, 12.5% of the creators hold a neutral attitude towards the computer-aided DC, and only 4.17% of the creators are dissatisfied with the computer-aided DC system.

5 Conclusions With the development of information technology, the application of 3D computeraided system in DC and learning is deepening, which is caused by the tide of the times. This kind of DC method is used by dance creators because of its high-speed creative efficiency and convenience. However, because of its reasonable arrangement and controllability, the 3DCA system still needs to be improved to provide a better platform for creators. In this paper, through the study of 3D computer-aided technology and its application in all aspects of DC, we explore the integration of information technology and art, and show the correct blend of art and science.

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References 1. Ming, L., Hong, P., Jia, G.: Silk road dance exhibition system based on motion capture. Comput. Aided Drafting Des. Manuf. 04, 7–10 (2016) 2. Wei, Z.: Application and implementation of motion capture and motion analysis technology in dance teaching. Rev. Fac. Ing. 32(16), 474–480 (2017) 3. Owen, C.B., Dillon, L., Dobbins, A., et al.: Computer literacy through dance: the dancing computer project. Int. J. Pervasive Comput. Commun. 13(1), 26–40 (2017) 4. Li, Y.: Dance motion capture based on data fusion algorithm and wearable sensor network. Complexity 2021(1), 1–11 (2021) 5. Hsia, L.H., Huang, I., Hwang, G.J.: Effects of different online peer-feedback approaches on students’ performance skills, motivation and self-efficacy in a dance course. Comput. Educ. 96(5), 55–71 (2016) 6. Kumar, K., Kishore, P., Sastry, A., et al.: Computer vision based dance posture extraction using slic. J. Theor. Appl. Inf. Technol. 95(9), 21–33 (2017) 7. Gratsiouni, D., Koutsouba, M., Venetsanou, F., Tyrovola, V.: Learning and digital environment of dance – the case of greek traditional dance in youtube. Eur. J Open Distance E-Learn. 19(2), 80–95 (2016). https://doi.org/10.1515/eurodl-2016-0009 8. Laraba, S., Tilmanne, J.: Dance performance evaluation using hidden Markov models: dance performance evaluation using hidden Markov models. Comput. Anim. Virtual Worlds 27(3– 4), 321–329 (2016). https://doi.org/10.1002/cav.1715 9. Gingrasso, S.: Practical resources for dance educators! digital technologies for dance as an art form. Dance Educ. Practice 5(1), 33–36 (2019) 10. Musa, N.: Digital preservation for malay folk dance expression: developing a framework using motion capture, aesthetic experience and laban theory approach. J. Adv. Res. Dyn. Control Syst. 12(01), 995–998 (2020). https://doi.org/10.5373/JARDCS/V12SP1/20201152 11. Wang, J., Miao, Z., Guo, H., et al.: Using automatic generation of labanotation to protect folk dance. J. Electron. Imaging 26(1), 011028 (2017) 12. Salaris, P., Abe, N., Laumond, J.P.: Robot choreography: the use of the kinetography laban system to notate robot action and motion. IEEE Rob. Autom. Mag. 24(3), 1 (2017)

Data Selection and Machine Learning Algorithm Application Under the Background of Big Data Jingyi Qiu(&) Tongda College of Nanjing University of Post and Telecommunications, Yangzhou 225002, Jiangsu, China

Abstract. At present, machine learning, as an important tool in data mining, is not only the exploration of human cognitive learning process, but also the analysis and processing of data. Facing the challenge of large amounts of data, part of the current research focuses on the improvement and development of machine learning algorithms, and another part of the researchers is devoted to the selection of sample data and the reduction of data sets. These two aspects of research work are parallel. Training sample data selection is a research hotspot in machine learning. Through effective selection of sample data, more informative samples are extracted, redundant samples and noise data are eliminated, so as to improve the quality of training samples and obtain better learning performance. This article aims to study data selection and the application of machine learning algorithms in the context of big data. Based on the analysis of machine learning implementation methods, the construction process of random forests, and random group sampling integration algorithms, the application of random group sampling methods is used to accurately select bases. Compared with the previous algorithms, the RPSE algorithm greatly improves the calculation speed of the data in the classifier and training samples, and ensures that the base classifier performs random calculations on the samples during training. According to the integrated gap spacing, a support vector machine training data can be selected, and the selected data set that needs to be filtered is used as a classifier for the support vector machine for training, so as to obtain the final classification. The experimental results show that compared with the more common traditional data selection algorithms, the RPSE algorithm greatly accelerates the accuracy and speed of data selection, and reduces the accuracy and precision of the support vector computer classification under the necessary conditions. Keywords: Big data vector machine


Data selection
Machine learning algorithm
Support

1 Introduction With the widespread popularization of digital technology and the Internet, a large amount of data has been collected, entering the era of data explosion. A very natural question raised by the explosive growth of data is “now we have collected a lot of data, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 96–103, 2022. https://doi.org/10.1007/978-3-030-89508-2_13

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how do we deal with it?” Raw data is rarely used directly, and manual analysis cannot keep up with the rapid growth of data [1, 2]. Data mining and knowledge discovery (KDD) emerged as an emerging field to solve this problem, which includes subjects such as databases, statistics, and machine learning [3, 4]. Many factors make data selection necessary. First, the data is not collected purely for data mining or a specific algorithm; second, there are missing data, redundant data, and incorrect data during collection and recording; third, sometimes the data may be too large to be processed [5, 6]. For data selection, one case is to solve the problem by using different feature selection methods. Its main task is to delete columns of the data set that are of little significance to machine learning, and to reduce the feature vector; in the other case, it is through reduce the insignificant rows in the data set for data selection, that is, reduce the number of sample data. In the process of consulting the information, it is found that the research of the second situation has attracted more and more scholars’ attention and has gradually become a new trend [7, 8]. There are many reasons for this trend: First of all, some aspects of data reduction involved in selecting rows cannot be covered by feature selection. For example, feature selection just selects attributes and does not remove errors and redundant data. These data participate in training and directly affect training. The accuracy of the model; secondly, it is now possible to select data rows through advanced statistical knowledge and accumulated experience, making the selection of training samples more efficient; thirdly, this has many advantages in machine learning training, such as the selected. The data is more representative and the time required for training is shorter [9, 10]. Based on the analysis of the machine learning implementation method, the construction process of the random forest and the random group sampling ensemble algorithm, this paper uses the random group sampling method to accurately select the data in the base classifier and the training sample, and compare it with the previous algorithm. In contrast, the RPSE algorithm greatly improves the calculation speed in the process of data selection efficiency, and ensures that the base classifier performs random calculations on the samples during training. According to the integrated gap spacing, a support vector machine training data can be selected, and the selected data set that needs to be filtered is used as a classifier for the support vector machine for training, so as to obtain the final classification.

2 Research on Data Selection and Machine Learning Algorithm Application in the Context of Big Data 2.1

Machine Learning Implementation Method

(1) Traditional parameter estimation method Traditional statistical theory is the basis of parameter estimation. The format of the parameters is known in advance, and the training samples are used to estimate the parameters. However, this method has disadvantages: First, the distribution of the training samples must be known in advance, but many practical problems cannot meet this requirement; the second is that traditional statistics study edge cases where the number of training samples tends to infinity. If the training sample has

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many features, a large-scale training sample set is needed to get an accurate estimate. However, it is difficult to obtain unlimited training samples in practical problems. Therefore, some methods and theories seem logical, but they are not practical in practice [11, 12]. (2) Non-linear method based on experience The main representative of the experience-based nonlinear method is the artificial neural network, which is also considered to be one of the important implementation methods in the field of machine learning technology. Neural network is a way to solve this problem by imitating human thinking. The ability to deterministic problems can make them extremely sensitive to noise and data, which enables them to process unnecessary information. This method has been widely used in technologies such as model sequencing, knowledge accumulation, intelligent control and function approximation. However, before the neural network completes the setting and learning of each classification task, it must artificially set and determine the number of nodes, initial weights, polarization values, and the number of all hidden layers of each input and output layer. In addition, neural networks can also use gradient-based machine learning techniques, which will cause the weight to change. It has become a local minimum, and the training process is extremely complicated and time-consuming. These problems are precisely based on the research and development of machine learning technology represented by neural networks, etc., in a certain state of stagnation. (3) Statistical learning theory The statistical learning theory is based on the theoretical analysis of the principle of minimizing the occurrence of risks in experience, and is a set of theoretical frameworks designed and created specifically for the study of small samples. After in-depth analysis of the relationship between empirical risk and real empirical risk, the theory gives the generalized attributes of statistical learning models. According to the results in these cycles, the corresponding definitions are obtained. Based on the results in these cycles, a new theory of reasoning and the principle of induction on small samples have been established, which provides modern people with a more systematic exploration of the machine learning theory of limited samples, provides a solid technical basis and scientific support. 2.2

The Construction Process of Random Forest

(1) Sampling without replacement: Assuming that a training object data set contains n and one object is selected as the data, this data is immediately deleted from all the data and objects contained in it. This sampling method is called sampling without replacement, also called simple random sampling. For this sampling method, each sample has the same designed probability. If the total number of samples is large, it is difficult to find a large enough container to stir all the samples uniformly, so the representativeness of the samples drawn from it will be very low. The random number method refers to sampling using dice or random numbers obtained through computer software. The main feature of non-replacement sampling is that the original data of each sample drawn is non-repetitive.

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(2) Sampling with replacement: It mainly refers to a sampling processing method in which a sampled object is sampled by the entire population without any deletion from the entire population, so that the number of samples in the original entire sampled population is there will not be any changes, but the samples obtained after the entire sampling have the opportunity to have a mutually repeated sampling. In addition, the sampling self-service processing methods can be roughly divided into two types: one is weighted self-service sampling and unweighted self-service sampling. The method of unweighted self-service sampling is also called selfservice sampling, which is randomly selected from an original source. Samples are drawn in the data set, and each sample is drawn without any replacement. The number of samples drawn in each sample in the set is the same as an original data set. Because they all have replacement sampling, some samples are likely to be withdrawn multiple times at a time, but others may not be sampled at once. 2.3

Random Group Sampling Ensemble Algorithm

The RPSE algorithm reduces the time of the entire SVM data selection process by reducing the training data of the base classifier. The time complexity of the RF algorithm to select the support vector is O 23 XNlogN , N is the number of training sets, and X is the number of base classifiers. Generally speaking, when the training data is very large, X < < N. The RPSE algorithm uses 23 of the training set as the training data of the base classifier, so the time complexity is about 

2 O XNlogN 3

 ð1Þ

The time complexity of the entire training process is  
2 3 O XNlogN þ O N s 3

ð2Þ

3 Experiment The data structure during the experiment is shown in Table 1. This article mainly analyzes and studies the globel algorithm using 9 UCI data sets of different capacities and a data set synthesized by manual assembly. The results of this analysis experiment are the rf algorithm, the svis algorithm and the rpse algorithm proposed in this article have been compared with each other for a comprehensive performance calculation and analysis. In the algorithm, we have added another more successful k-nearest neighbor method npps (nearest neighbors) to the running time based method) and another statistical algorithm beps (border-edge pattern selection).

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Attributes 3 9 12 3 3 1 18 20 15 8

Class 2 5 1 4 1 1 6 2 9 2

4 Discussion 4.1

Classification Results

From the results of the experiment in Table 2, we can clearly see that the same method is selected for comparison with the same data set used by the other two methods: (1) The accuracy and accuracy of data classification from these methods can be seen that in 10 data sets, only 6 rpse algorithms can be used to obtain better experimental results of data classification; (2) In screening the total amount of each training data set, the rpse algorithm uses 7. The total amount of training data set obtained after filtering the data should be the lowest. Therefore, the rpse algorithm has been implemented and has achieved good results whether it is in the analysis and selection of a large amount of data, or in the accuracy and accuracy of svm’s classification of data. From the overall execution results of these 10 training sets, it is clear that compared with the svm algorithm, the rpse algorithm only uses 43.8% of the training set and loses about 0.46% of the training set division accuracy. Table 2. Training data and classification loss selected by three data selection algorithms Dataset

SVM RF Acc Loss lris 97.32 1.33(2) Glass 96.27 1.85(1) Heart 91.19 0.38(3) Balancc 99.67 0.01(3) Bank 97.89 −0.02(2) Globle 99.62 0.40(2) Segment 97.75 0.45(1) Waveform 95.37 0.89(2) Pendigits 96.69 0.21(2) Shuttle 97.92 0.34(2) Avg loss 0.58

Size 59.9(2) 74.7(2) 66.6(1) 52(2) 24.7(2) 4.4(2) 51.8(1) 46.7(2) 24.2(1) 50.5(2) 45.25

SVIS Loss 1.56(3) 2.33(3) 0.35(2) 0.01(2) 0.45(3) −0.04(1) 0.59(2) 0.49(1) 0.51(3) 0.64(3) 0.69

Size 68.3(3) 77.5(3) 74.8(3) 53.6(3) 32(3) 4.7(3) 53.6(3) 48.6(3) 36.3(3) 59.7(3) 50.1

RPSE Loss 0.58(1) 1.88(2) 0.19(1) 0(1) −0.44(1) 0.47(3) 0.61(3) 0.93(3) 0.2(1) 0.16(1) 0.46

Size 54.7(1) 70(1) 70.3(2) 47.8(1) 21.8(1) 4.2(1) 52.2(2) 44.4(1) 26.7(2) 45.9(1) 43.8

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From the results of the experiment in Table 2, we can clearly see that the same method is selected for comparison with the same data set used by the other two methods: (1) The accuracy and accuracy of data classification from these methods can be seen that in 10 data sets, only 6 rpse algorithms can be used to obtain better experimental results of data classification; (2) In screening the total amount of each training data set, the rpse algorithm uses 7. The total amount of training data set obtained after filtering the data should be the lowest. Therefore, the rpse algorithm has been implemented and has achieved good results whether it is in the analysis and selection of a large amount of data, or in the accuracy and accuracy of svm’s classification of data. From the overall execution results of these 10 training sets, it is clear that compared with the svm algorithm, the rpse algorithm only uses 43.8% of the training set and loses about 0.46% of the training set division accuracy. 4.2

Time Complexity Analysis

Class RF training time

250

RPSE

RF

200 150 100 50 0 1

2

3

4 5 6 10 different kinds of data sets

7

8

9

10

Fig. 1. Training time of two ensemble classifiers on 10 data sets

Figures 1 and 2 show the comparison between the training time of the algorithm and the time required for data selection in the 10 data sets in Table 1. From this result figure, we can clearly see that in the case of a relatively small training data set, the corresponding training time ranges between the two types of training data selection algorithms are not much different, but when the training time range corresponding to the training data set is very large, the corresponding training time of the rpse algorithm is significantly shorter than rf. The actual operation duration of the rf algorithm on the shuttle data set is about 250 s, and the actual operation time of the rpse algorithm is about 150 s. It can be simply explained that the more data set, the more obvious the advantages of rpse algorithm in calculation.

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the total training time

300

RPSE

RF

250 200 150 100 50 0 1

3 4 on 10 different 5 6kinds of 7data sets8 the 2total training time

9

10

Fig. 2. The running time of the two data selection algorithms on 10 data sets

5 Conclusions At present, machine learning, as an important tool in data mining, is not only the exploration of human cognitive learning process, but also the analysis and processing of data. Facing the challenge of large amounts of data, part of the current research focuses on the improvement and development of machine learning algorithms, and another part of the researchers is devoted to the selection of sample data and the reduction of data sets. These two aspects of research work are parallel. At present, research scholars pursuing research on sample selection are to select samples with a large amount of information as much as possible on the basis of compressing samples, so as to improve the training and classification of classifiers.

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8. Byrd, R.H., Chin, G.M., Neveitt, W., et al.: On the use of stochastic hessian information in optimization methods for machine learning. SIAM J. Optim. 21(3), 977–995 (2016) 9. Singh, A., Ganapathysubramanian, B., Singh, A.K., et al.: Machine learning for highthroughput stress phenotyping in plants. Trends Plant Sci. 21(2), 110–124 (2016) 10. Ying, S., Babu, P., Palomar, D.P.: Majorization-minimization algorithms in signal processing, communications, and machine learning. IEEE Trans. Sig. Process. 65(3), 794– 816 (2016) 11. Shanks, D.R.: Regressive research: the pitfalls of post hoc data selection in the study of unconscious mental processes. Psychon. Bull. Rev. 24(3), 752–775 (2016). https://doi.org/ 10.3758/s13423-016-1170-y 12. Chen, H., Guo, B., Yu, Z., et al.: A generic framework for constraint-driven data selection in mobile crowd photographing. IEEE Internet Things J. 4(1), 284–296 (2017)

Analysis of the Drawbacks of English-Chinese Intelligent Machine Translation Based on Deep Learning Hongmei Li1(&) and Wei Xiong2 1

2

Department of Nursing, Leshan Vocational and Technical College, Leshan 614000, Sichuan, China Department of Electronic and Information Engineering, Leshan Vocational and Technical College, Leshan 614000, Sichuan, China

Abstract. Machine translation is an interdisciplinary subject. In terms of subject area, it belongs to an application field of computational linguistics. However, the research of machine translation is based on the three disciplines of linguistics, mathematics and computing technology. Nowadays, the advantage of machine translation is speed, but there are problems such as text grammar errors. Therefore, this article analyzes the disadvantages of English-Chinese intelligent machine translation based on deep learning. First, this article explains the principles of machine translation, and analyzes the shortcomings of EnglishChinese intelligent machine translation; then, researches on deep learning algorithms, designs a model for detecting shortcomings of English-Chinese intelligent machine translation, and conducts performance testing on it. The final detection results show that the model can detect long-translated articles, and can well detect sentence grammatical errors caused by machine translation malpractices. Keywords: Deep learning
English-Chinese learning drawbacks
Intelligent machine translation


Translation

1 Introduction People expect machine translation to overcome language barriers that can be traced back to the beginning of the computer. The basic idea is to use the computing speed and memory capacity of the computer to support or replace translators in complex translation tasks, so as to realize automatic conversion from one language to another [1, 2]. Deep learning was first proposed by researchers in 2006, and took the lead in the rapid development of image processing and speech recognition in 2013. In recent years, researchers have found that deep learning can better alleviate the linear inseparability of statistical machines, translation, lack of proper semantic representation, features that are difficult to design, difficulty in making full use of non-local context, scarcity of data, and error propagation. Machine translation has become a hot spot of current research [3, 4].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 104–111, 2022. https://doi.org/10.1007/978-3-030-89508-2_14

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Nevertheless, machine translation still has serious challenges, such as insensitivity to source language sentence length, incomplete sentence structure ordering, and mistranslations and omissions. Therefore, it is necessary to analyze the shortcomings of English-Chinese intelligent machine translation based on deep learning, which can be optimized for the current deficiencies of machine translation, can promptly discover problems such as mistranslation and missed translation of sentences after machine translation, greatly improving translation efficiency and accuracy.

2 An Overview of the Drawbacks of English-Chinese Intelligent Machine Translation Based on Deep Learning 2.1

Basic Principles of Machine Translation

In the context of globalization, connectivity is the key to build a world of diversity. International education requires higher-level education that promotes cross-cultural communication. In the translation project, the target audience of the translated text is the administrative staff who intends to learn the experience of foreign courses. The goal of translation services is to provide accurate, readable, acceptable, high-quality and efficient text. Translators must emphasize their reaction and course selection after reading the translated text [5, 6]. According to the requirements of translation service providers, there are four levels of translation requirements. One is applicable to unpublished content, which is expected to give a comprehensive understanding of information. The second is suitable for unpublished content, such as international documents, which requires accurate translation of the source text, and consideration of style or final use. The third is suitable for localized, professional content, such as marketing materials, which require high-quality professional users. The fourth is a source text that is suitable for creativity and market use, and aims to maintain the same intent and style. In actual work, the most important factors are accuracy, timeliness and readability [7, 8]. 2.2

Development Trend of Machine Translation

If you look at the development trend of machine translation, the leading role of humans in the translation process is basically declining. The rule-based approach completely relies on translation rules created by human experts. Statistics-based methods require manual design of features and hidden structures in order to automatically learn translation knowledge from data. Deep learning-based methods can directly provide end-toend ideas to describe the entire translation process. In fact, this is a process of increasing requirements for computer modeling functions. Traditional statistical machine translation methods often build implicit structures and features based on linguistic theories when designing models. The difference is that end-to-end neural machine translation focuses on designing neural network structures. Very similar to the convolutional neural network design for image-specific attributes, the design of a new neural network architecture based on language knowledge is a huge challenge for neural machine translation [9, 10].

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Disadvantages of English-Chinese Smart Translation

Machine translation output also has limitations. The error analysis of Google Translate enables translators to improve the quality of machine translation. Previous studies on errors in machine translation are as follows: Of course, the output of machine translation also has limitations. Google Translate’s error analysis enables translators to improve the quality of machine translation. Previous studies on machine translation errors are as follows: (1) Incorrect translation of technical terms Machine vocabulary translation has the lowest error rate, but term translation is an exception. In the context of vocational training, these terms usually have a fixed and appropriate meaning. In the absence of a professional corpus, the output of machine translation will generally refer to it. For example, the ambiguous “unit” is translated from machine to “unit”. In the context of education, the correct meaning is “credits.” The term “general education” has been mistranslated as “general education” in the education circle, and the correct translation should be “general education” [11, 12]. (2) Wrong translation of proper nouns and abbreviations The abbreviation of the proper name is often mistranslated because machine translation cannot recognize the context of the proper name. For example, in the past 20 years, both the NSF and the engineering community have called for systematic changes in engineering education. The result of machine translation “In the past 20 years, NSF and the engineering community have called for systematic changes in engineering education”. Another example is the correct terminologyOlin University (Olin University) was incorrectly translated as “Olin University” Olin University refers to their background and should be referred to as “Olin University” for short. (3) Part of speech translation errors Words in one language belong to a specific part of speech and do not necessarily need to be converted to the same part of speech in another language. Such errors appeared earlier in rule-based machine translation. For example, when it comes to “students have a lot of flexibility in choosing courses”, Google returns “students have a lot of flexibility in choosing courses” and the word “easy to use” remains unchanged, which makes it more flexible than “students.” You can choose courses to be “more flexible”. Flexible course options are “easier to read”. (4) Grammatical errors: consistent tense, voice, article, number English is characterized by more frequent and systematic use of folding to express grammatical relationships, while Chinese often uses function words, auxiliary verbs, and word order changes to express syntactic relationships. Chinese has no time, no variants, no singular and plural forms, all exist in English. The automatic result of the sentence “Your research field provides in-depth research topics, mainly referring to personal occupation” is for example. “Your research field provides in-depth research on a topic, usually referring to a certain occupation”, the source sentence article “a” and “one” always appears in the translation, forcing the translator to omit unnecessary English articles in the translation.

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(5) Repeat Machine translation editors have almost no wording changes. English words are usually associated with the same Chinese word. For example, the three sentences of the word “provide” have different meanings in Chinese, but the same word appears over and over in machine translation. However, human translators can use deep learning algorithms to correct this. (6) Original sentence structure Although Google Translate improves sentence comprehension, there are still differences in avoiding ambiguity and adjusting sentence structure. Machine translation can return general information when processing a single sentence. However, the machine translation of long and complex sentences retains the structure of the original sentence. For example: “The college is located in Needham, Massachusetts, adjacent to Babson College. It covers about 7 acres. It was approved by the Massachusetts Higher Education Commission in 1997 to provide undergraduate degrees in mechanical, electronic, and computer engineering”. The machine translation is as follows: “The college is located in Needham, Massachusetts, about 70 acres from Babson College. It was authorized by the Massachusetts Higher Education Commission in 1997 to provide bachelor’s degrees in mechanical, electrical and engineering engineering, and computer science and engineering. “Obviously, the translation strictly follows the sentence structure of the original text without adjusting the sentence structure, making the sentence too loose. (7) Symbol error Formatting errors are mainly typesetting errors and punctuation errors. In some cases, English and Chinese have similar punctuation marks, but they are used in different ways. Machine translation often causes such errors, but they are usually ignored by users. For example, a comma in English will not be translated by a pause in Chinese. “Mental ability, knowledge quality” is incorrectly translated as “ability, knowledge, quality” and “ability, knowledge, quality”. In short, although there is a lot of work to learn, Google Translate has not adapted and created it. It only satisfies formal equivalence, not functional equivalence, and provides mechanical and direct translation of words and phrases. Although machine translation is fast and efficient, the results are robust and original, requiring modification, adjustment, and polishing. 2.4

English-Chinese Machine Translation Algorithm Based on Deep Learning

Intelligent optimization algorithm is a special heuristic optimization algorithm, which is generally a comprehensive optimization method gradually developed by taking advantage of some similarities of complex problems such as natural and social optimization systems. Such as fish artificial school algorithm, simulated annealing algorithm, particle school algorithm, ant colony algorithm, in addition to: cuckoo algorithm, immune optimization algorithm, bee colony algorithm, bat algorithm, root system algorithm and weed invasion algorithm. These algorithms combine the universal laws that exist in nature with real models, presenting a more intuitive and comprehensive analysis that is easier to understand, giving the algorithms more flexible content.

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Establishing an optimization model is the key to solving parameter optimization problems. It has a wide range of applications in various fields of science and technology and in all aspects of social life, and is the most extensive and active branch of mathematics applications. Least squares estimation is the most common problem in parameter estimation, and it has the following general form: min

Xn i¼3

min

½f ð xÞ  f ðx; a1 ; a2 ; . . . . . .ak Þ2

X ði;jÞ¼X

lðy; ½uv þ orðu; vÞÞ

ð1Þ ð2Þ

Here, l represents some loss function, represents the index range defined by the data, or (u, v) is a regularization term to ensure that the model has good generalization and numerical stability. The main limitation of the MF method is the non-convexity of its main function. These defects often cause the MF current to fall into a local minimum, especially in the case of abnormal values and noise.

3 Experiments on the Drawbacks of English-Chinese Intelligent Machine Translation Based on Deep Learning 3.1

Design of Analytical Model of Malpractice of English-Chinese Intelligent Machine Translation

Output probabilities

Words

Softmax

Sentences

Masked Multi-head attention Linear

Multi-head attention Multi-head attention

Add&Norm

Feed forward

Multi-head attention

Fig. 1. Translation and detection model structure

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It can be seen from Fig. 1 that the central idea of the deep learning mechanism is to give the model priority attention to the input information that needs more attention at a certain moment in the task. In some sequences, humans usually use network structures such as recurrent neural networks as model encoders. If the input sequence is too long, it is difficult for a model with few parameters to encode the input information well, and if a deep learning mechanism is used, the information required by the task can be stored and used well. In previous translation machines, deep learning mechanisms are often combined with network structures such as recurrent neural networks. The network translation model based on the deep learning mechanism of previous translation software cannot be combined with other common models such as convolutional neural network or recurrent neural network. Instead, it adopts a pure attention mechanism and performs better than other models. 3.2

Intelligent English-Chinese Translation Process Based on Deep Learning

Input

Word

Word

Word

Gram mar

Gram mar

Gram mar

Sente nces

Output

Fig. 2. The English and Chinese intelligent translation process

It can be seen from Fig. 2 that the input of each unit of the recurrent neural network is the vector representation of the current word and the output of the previous unit. The system input vector and the superimposed current word are used as the input of the current unit of the recurrent neural network. The idea of the system is not limited to a specific model, any pattern whose input is a word vector can be recognized by the system.

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4 Experimental Analysis of the Drawbacks of EnglishChinese Intelligent Machine Translation Based on Deep Learning 4.1

Effectiveness Verification of Translation Malpractice Analysis Model Table 1. The effectiveness of Analysis model Accuracy rate Recall rate F1 The part of speech is Baseline The part of speech is Baseline The part of speech is Baseline embedded embedded embedded

PER ORG LoC MIsc Overall

74.35% 65.49% 76.57% 74.97% 73.13%

56.65% 63.9% 82.58% 69.48% 67.67%

82.42% 68.92% 82.61% 72.35% 77.83%

63.2% 60.12% 76.62% 67.83% 67.38%

78.18% 67.16% 79.47% 73.64% 75.4%

59.75% 61.95% 79.49% 68.65% 67.53%

It can be seen from Table 1 that the analytical ability of the model is greatly improved under the optimization of the deep learning algorithm. It can detect textual errors in English at multiple levels, and can well reduce the translation errors caused by the malpractices of machine translation. 4.2

Performance Comparison of English-Chinese Intelligent Translation Algorithms

Effecve rate

80 60

Deep learning

58 38

35 25

40

14 20 0

0-20

20-40

40-60 Sentence length

60-80

80-100

Fig. 3. Translation performance of the model on different long sentence distributions

As can be seen from Fig. 3, as the length of the sentence increases, the quality of the translation decreases significantly. The quality of translations in the range of more than 20–40 declines significantly, indicating that the model can detect long-translated articles and can well detect sentence grammatical errors caused by machine translation malpractices.

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5 Conclusion With the continuous development of globalization, science and technology are constantly advancing, and Internet technology and computer technology have been quickly and effectively popularized. In the traditional sense of our country, the smart English translation industry started late and is in its infancy. At present, the domestic research on deep learning theory is not perfect, lack of scientificity and accuracy, and many other problems seriously affect the integration of deep learning and English and Chinese languages, and the two complement each other. Therefore, we should start from the essence and formulate more rational strategies based on the actual situation in order to improve its influence and visibility in the international society, and better achieve the goal of internationalization. Acknowledgements. This work was supported by Sichuan Federation of Social Science Associations, Project No. SC16KP026.

References 1. Lin, L., Liu, J., Zhang, X., et al.: Automatic translation of spoken English based on improved machine learning algorithm. J. Intell. Fuzzy Syst. 40(2), 2385–2395 (2021) 2. Li, W., Cao, Z., Zhu, C., et al.: Intelligent feedback cognition of greengage grade based on deep ensemble learning. Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng. 33(23), 276–283 (2017) 3. Song, G.: Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval. J. Intell. Fuzzy Syst. 40(2), 2109–2120 (2021) 4. Xu, F., Zhang, X., Xin, Z., et al.: Investigation on the chinese text sentiment analysis based on convolutional neural networks in deep learning. Comput. Mater. Continua 58(3), 697– 709 (2019) 5. Lv, H., Feng, S.: A pragmatic analysis of public signs in Chinese-English translation—— based on the example of Shaoguan national forest park. Overseas Engl. 384(20), 87–89 (2018) 6. Venkateswara, H., Chakraborty, S., Panchanathan, S.: Deep-learning systems for domain adaptation in computer vision: learning transferable feature representations. IEEE Sig. Process. Mag. 34(6), 117–129 (2017) 7. Jaegul, C., Liu, S.: Visual analytics for explainable deep learning. IEEE Comput. Graph. Appl. 38(4), 84–92 (2018) 8. Zhang, Y., Liu, Y., Zhang, H., et al.: Seismic facies analysis based on deep learning. IEEE Geosci. Remote Sens. Lett. 17, 1119–1123 (2019) 9. Zhang, C., Hu, H., Tai, Y., et al.: Trustworthy image fusion with deep learning for wireless applications. Wirel. Commun. Mob. Comput. 2021(7), 1–9 (2021) 10. Abdi, A., Shamsuddin, S.M., Hasan, S., et al.: Deep learning-based sentiment classification of evaluative text based on multi-feature fusion. Inf. Process. Manage. 56(4), 1245–1259 (2019) 11. Vijayan, S., Geethalakshmi, S.N.: A survey on crack detection using image processing techniques and deep learning algorithms. Int. J. Pure Appl. Math. 118(8), 215–219 (2018) 12. Golgooni, Z., et al.: Deep learning-based proarrhythmia analysis using field potentials recorded from human pluripotent stem cells derived cardiomyocytes. IEEE J. Transl. Eng. Health Med. 7, 1–9 (2019). https://doi.org/10.1109/JTEHM.2019.2907945

Application of Neural Network Algorithm in Robot Eye-Hand System Xiaolei Zhang1(&), Yaowu Shen1, and Junli Chen2 1 2

Guangdong Country Garden Polytechnic, Qingyuan, Guangdong, China China Australia Business College of Shanxi, Jinzhong, Shanxi, China

Abstract. With the advent of the intelligent era, intelligent industrial robots integrated with vision systems have penetrated into many fields. Especially robots with hand-eye functions play an important role in the fields of automobile manufacturing, logistics, and aviation exploration. Radial basis function (RBF) neural network has a high degree of non-linear mapping ability. This paper analyzes the structure characteristics, learning algorithm and application of RBF neural network in the robot eye-hand system, and analyzes the nonlinearity of RBF neural network. The linear approximation characteristics are theoretically verified. The purpose of this article is to study the application of neural network algorithms in robot eye-hand systems. Simulation experiments show that the designed algorithm can track the ideal position well, and can significantly reduce chattering, and improve the control performance of the manipulator system when the system itself has uncertainty and external interference. Keywords: Neural network Mobile manipulator


Eye-hand system
Hand-eye coordination


1 Introduction At present, the world’s technology development has reached a new era [1, 2]. In the 20th century, many scientists have put forward various concepts about neural networks and computer vision, but at that time these theories were limited by the hardware conditions and only stayed at the theoretical level [3, 4]. With the rapid progress of the hardware level, the artificial intelligence related technologies and theories proposed by scientists can be put into practice and promote the rise and progress of various fields such as robotics [5, 6]. Many studies have been done on the eye-hand coordination system of robots in our country. The Institute of Automation of Southeast University has conducted research on the hand-eye coordination of dual robots. The hand and eye are installed on the two robots respectively. This hand-eye coordination system can realize all-round observation and can greatly expand the perception space, which can be based on the requirements of the work. To design perceptual strategies flexibly, the capabilities of the visual feedback information system have been fully utilized. This system provides a strong support for the development and research of active vision [7]. Changchun University of Technology has implemented visual tracking in the hand-eye © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 112–120, 2022. https://doi.org/10.1007/978-3-030-89508-2_15

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collaboration system, which uses the Jacobian matrix and the relative pose of the robot terminal fixture and the target to estimate the robot’s motion trajectory [8]. Zhejiang University has conducted research on hand-eye coordination methods for industrial grasping applications. It designed a complete set of hand-eye coordination system based on ROS platform to achieve precise grasping of objects and proposed an iteratively adjusted servo method for mobile grasping. At the same time, its research uses deep learning, which not only simplifies the operation process but also the grasping accuracy has been improved [9]. Foreign research on robotic eye-hand systems is more advanced. Li W, N Lü, Dong M and others described a learning-based hand-eye coordination method for obtaining robots from monocular images. In order to learn hand-eye coordination for grasping, the research on hand-eye coordination has trained a large convolutional neural network to predict the probability that the task space movement of the grasper will lead to a successful grasp, using only the camera correction or the current robot posture alone [10]. This paper makes a comprehensive analysis of the RBF neural network algorithm, and designs a new manipulator fixed-point tracking control strategy with external interference and uncertainty: based on traditional PD control, the radial basis function neural network (RBFNN) is introduced to achieve the the compensation of the gravity term of the manipulator is used to offset the approximation error of the neural network, and its stability is analyzed.

2 Application of Neural Network Algorithm in Robot EyeHand System 2.1

Robot Hand-Eye Coordination System

The structure of the robot hand-eye coordination system is composed of four main modules. As shown in Fig. 1, the vision processing module learns the external environment and changes of the hand-eye coordination system by analyzing the images obtained by the image sensor, and observes the current posture of the robot and the target object, etc., the visual feedback module feeds back the information obtained from the visual processing module to the system. The system decision-making module determines the next action of the robot by analyzing the tasks of the robot system and the relative posture of the camera, the robot and the target object, and gives the corresponding control instructions [11, 12]. task

System decision

Visual feedback

robot

Visual processing

Fig. 1. The structure diagram of the robot hand-eye coordination system

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Visual System Design

The camera, lens, network card for image data transmission, and light source together form the vision system. The vision system and the control actuator of the six-axis robot together form the hand-eye vision system. To build a stable vision system, it is necessary to start from the technical indicators and select the appropriate system components based on the industrial field environment. (1) Camera According to technical indicators, the camera resolution is greater than 100 W, the maximum height between the camera and the measurement plane is 350 mm, and the field of view of the captured image is 180 mm  150 mm. Since the image data information of sports products needs to be collected in the hand-eye system, the camera frame rate is required to be 60 Hz after testing. In summary, the RSA1300-GM60 area scan industrial camera of Beijing Microvision Image is selected, and its performance parameters are shown in Table 1. Table 1. RS-A1300-GM60 industrial camera performance parameters Performance Resolution (H  V) Sensor model Sensor optical size Pixel size Maximum frame rate

Parameter 1280  1024 E2vEV76C560 Progressive, global shutter 1/1.8 inch 5.3 lm  5.3 lm 60 Hz

(2) Lens The lens can be approximated by a simple convex lens model. Generally, the size of the target surface of the lens should be greater than or equal to the optical size of the optical sensor. Considering the focal length of 16 mm and the image size of 2/3 inch, RICOH’s model is FL-CC1614-2M. (3) Light source In this system, the capture module of sports products has higher requirements for image quality. The smaller the difference in gray value, the greater the help to obtain feature points, and can reduce the complexity of image processing algorithms, so it is necessary to ensure that the illumination in the field of view Uniformity. The most commonly used light sources in industrial environments are area array light sources, high-angle incident light sources, coaxial light sources, and ring light sources. Since the product is reflective and the on-site installation environment is not suitable for the use of backlight, a ring light source with uniform illumination is selected. (4) Vision system installation In the vision system module, the camera is fixed on the six-axis flange of the robot by a fixture, and the Eye-in-Hand model is realized. During the installation process, the camera plane is parallel to the measurement plane, and the camera can move with the manipulator.

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With Calibrated Hand-Eye Coordination

In order to complete the dynamic scene assembly, the hand-eye coordination technology based on calibration is used, that is, hand-eye calibration technology. According to the feedback information method, hand-eye calibration is divided into position-based control and image-based control. The method based on image control generally involves the solution of the Jacobian matrix. For moving image information, the Jacobian matrix has poor adaptability and is difficult to apply in industrial scenes. Since the robot itself is controlled by a servo motor, and its movement is established by the D-H model, the feedback control of the robot is more operability and controllability. Therefore, this article chooses the hand-eye calibration method based on position control, and completes the calibration process with the help of standard calibrators. The images collected in the experiment exist under the camera coordinate system, and the robot coordinate information can be transformed into its coordinate representation under the base coordinate system through the D-H model. Since the movement of the robot is known, it can be read by the handheld manipulator. If the position of the phase coordinate system relative to the robot is known, the pixel coordinates in the phase coordinate system can be converted into the coordinates in the robot end effector coordinate system. Further transformed into the representation mode under the robot base coordinates, the robot can learn the pose information of the target point and accurately grasp the target. 2.4

RBF Neural Network Approximation

The algorithm used in this article is the Radial Basis Function Neural Network (RBFNN) algorithm. In this paper, the conventional control method and online adaptive RBF neural network control are combined, and the Lyapunov analysis method is used to prove the stability of the system. In order to estimate the function f(x), the following RBF network algorithm can be used: 

2  hj ¼ exp
x  cij
=b2j

ð1Þ

f ¼ W T hð X Þ þ g

ð2Þ

Where η is the approximation error of the network. Generally, the state of the system can be used as the input of the network, and the output of the network is: b ðX Þ ¼ W b T hð X Þ F

ð3Þ

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Neural Network Model of Mobile Manipulator

The principle of the speed control of the decomposition movement of the manipulator is to decompose the comprehensive movement of the mobile manipulator into independent controllable movements along each Cartesian coordinate axis. The most critical part is the speed inverse kinematics calculation. In this article, the above-mentioned RBF neural network will be used to approximate the kinematics model of the mobile robot to decouple. In order to approximate the kinematics model of the moving car body, take the joint rotation angle of the mobile manipulator and the rotation angle speed of the car body wheel as the output, and the posture of the manipulator and the car body as the input to establish the neural network model of the mobile manipulator. Position control, as the name suggests, is to make the joints (or claws) of the controlled manipulator reach an ideal position. As shown in Fig. 2, the difference between the ideal position and the current given value is used as the input of the controller, and then through processing, its value will be differentiated from the feedback speed and output to the speed controller.

Position control

Speed control

Amplificat ion drive

Motor

Joint

Fig. 2. The control diagram of joint position

The joint speed control of the manipulator is similar to the position control, just remove the position outer ring in Fig. 2. Manipulator speed control is generally used in target tracking tasks. 2.6

Manipulator Sliding Mode Control of RBFNN

In this paper, the neural network and sliding mode variable structure control are combined to design a continuous trajectory tracking controller, which can make the manipulator complete finer welding, spraying and other tasks. In this paper, RBFNN and sliding mode controller are serially processed, and RBFNN is used to approximate the switching gain term. The design of the aforementioned control law is to artificially set the value of the switching gain K to achieve the effective tracking of the manipulator’s trajectory under the action of a simple sliding mode controller. Aiming at the problem that sliding mode variable structure control will cause system chattering due to its inherent discontinuous switching characteristics, an online adaptive gain adjustment method is proposed, using RBF neural network to estimate the uncertain part of the manipulator system, and then approach the switching gain K.

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Design a new control law as:


b q þG b  As  K  V b q

r þ C s¼M

ð4Þ

~ ¼ Psi hðsi Þ x

ð5Þ

1 1 X2  T 1  ~i P x ~i L ¼ ST Ms þ x i¼1 2 2

ð6Þ

The adaptive law is:

Define the Lyapunov function:

Derivation: L_ ¼ ST As þ

X2

s ðDfi  ki Þ þ i¼1 i

X2  i¼1

 e Ti P1 x e i  ST V x

ð7Þ

Substituting the adaptive law into the above formula: L_ ¼ ST As þ

X2

  T s Df  x h ð s Þ  ST V i i i I i¼1

ð8Þ

There are very small positive real numbers, so that formula (8) satisfies Dfi  xT hðsi Þ  g  ci jsi j ; 0\ci \1 I

ð9Þ

Only when s = 0, L_ ¼ 0, the adaptive law gradually converges. Which leads to: lim s ¼ lim ðe_ þ keÞ ¼ 0

t!1

t!1

ð10Þ

It can be concluded from the above analysis that the designed control algorithm can ensure that the tracking error converges to zero with arbitrary accuracy.

3 Simulation Experiment 3.1

Two-Joint Manipulator Model

In this article, the simulation design of a two-degree-of-freedom planar joint manipulator is mainly carried out, and the performance of the controller is mainly verified on this model.

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The design of the control method in this paper is based on the following assumptions: (1) (2) (3) (4) 3.2

The mass of the connecting rod manipulator is concentrated on one end of the rod; The inertia tensor of the manipulator relative to the center of mass is 0; Ignore the friction between the connecting rods; Free end effector; Matlab to Establish a Manipulator Model

Because the manipulator system is very complicated, it is not easy to build with ordinary Simulink modules. The two-joint manipulator proposed in this article is one of the simulation objects of this article. This article uses the S function in MATLAB software to write the manipulator dynamics program model. According to the specific relationship characteristics of the manipulator system and the writing steps of the S function, the steps of the written S function program are as follows: (1) Initialization function (lag = 0). (2) The state variable (flag = 1) is obtained according to the internal relationship of the manipulator system. (3) Calculate the output of the system (flag = 3).

4 Simulation Experiment Analysis Take the two-joint manipulator model, the mass of the first joint is m1 = 2.04 kg, the mass of the second joint is m2 = 1 kg, the length of the first joint is L1 = 1 m, and the length of the second joint is L2 = 0.87 m. In order to show the advantages of the designed algorithm, under the same given parameters, the traditional sliding mode control is compared with RBFNNSMC, and the joint position, joint torque, and joint speed under the two control methods are compared. Draw the following conclusions through comparison: (1) Under the two control methods, the position tracking curves of the two joints of the manipulator are ideal, indicating that the sliding mode variable structure control has the advantage of being simple and easy to implement when dealing with the manipulator system. (2) is the position tracking error under the traditional sliding mode control. Joint 1 fluctuates between 0.03 and −0.04, and joint 2 fluctuates between 0.01 and −0.01. In the position tracking error under RBF neural network sliding mode control, the steady-state error of joint 1 is 0.002 * −0.006, and the steady-state error of joint 2 is 0.01 * −001. It can be concluded from the error comparison of joint 1 that the accuracy of RBF neural network sliding mode control is much higher than that of traditional sliding mode control. The two control methods have almost the same effect on the steady-state error of joint 2, but the initial error of 0.02 under RBF neural network sliding mode control is smaller than 0.03 of traditional sliding mode control. From this we can conclude that the overall performance of the

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system has been significantly improved after the RBF neural network is added; the addition of robust control items effectively compensates for the adverse effects of neural network approximation errors. (3) The speed tracking under traditional sliding mode control has very large fluctuations. The speed curve under RBF neural network sliding mode control can smoothly track the given ideal speed. (4) The chattering of the output torque under traditional sliding mode control is very large, which greatly restricts this control method in practical applications; the system after adding the RBF neural network controller can adaptively adjust the switching gain, thereby reducing the chattering of the output torque is improved. (5) The change process of the switching gain under the adjustment of the RBF neural network. The gain can be automatically adjusted in real time according to the operating conditions of the system, thereby reducing system chattering.

5 Conclusions Robots are indispensable in people’s lives. The production of humans in various industries, and even the daily life of the family in the future, cannot do without the help of robots. This article takes industrial robots as the research object and focuses on the trajectory tracking of manipulators in joint space. Advanced intelligent algorithms emerge in endlessly. Among them, neural network algorithm is a typical representative of intelligent control and bionic algorithm. With its powerful function, it is favored by researchers from all walks of life. In this paper, the RBF neural network algorithm is used throughout, combined with the PD adaptive control of gravity compensation and the sliding mode adaptive control, respectively, to design the nonlinear controller containing the RBF neural network. This article not only theoretically reasoned about the correctness of the hybrid algorithm, but also through MATLAB simulation, intuitively sees the effectiveness of the RBF neural network hybrid algorithm in the trajectory tracking control of the manipulator from the graphical results, showing good performance. Each can learn from each other’s strengths.

References 1. Cao, C.T., Do V.P, Lee, B.R.: A novel indirect calibration approach for robot positioning error compensation based on neural network and hand-eye vision. Appl. Sci. 9(9), 1940 (2019) 2. Chen, Y.S., Lin, K.L.: CUBot: Computer vision on the eye-hand coordination with a computer-using robot and its implementation. Int. J. Patt. Recogn. Artif. Intell. 32(4), 1855005.1–1855005.25 (2018) 3. Lim, Y., Park, J.M., Rhyu, S.Y., et al.: Eye-Hand Span is not an Indicator of but a strategy for proficient sight-reading in piano performance. Sci. Rep. 9(1), 17906 (2019) 4. Niu, L., Aha, L., Mattila, J., et al.: A stereoscopic eye-in-hand vision system for remote handling in ITER. Fusion Eng. Design. 146, 1790–1795 (2019)

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5. Kumar, A.S., Alavandar, S.: Control of robot manipulator error using FPDI–IQGA in neural network. J. Comput. Theoretical Nanosci. 13(3), 1740–1748 (2016) 6. Chi, G.: Application study of fuzzy ARTMAP neural network in robot servo system. J. Phy. Conf. Series, 1026(1), 012024 (2018) 7. Qian, W., Gu, J.: Design of robot visual servo controller based on neural network. J. Comput. Methods Sci. Eng. 18(2), 541–549 (2018) 8. Zhao, X., He, Y., Chen, X., et al.: Human-robot collaborative assembly based on eye-hand and a finite state machine in a virtual environment. Appl. Sci. 11(12), 5754 (2021) 9. Shaw, J., Chi, W.L.: Automatic classification of moving objects on an unknown SPEED production line with an eye-in-hand robot manipulator. J. Mar. Sci. Technol. 26(3), 387–396 (2018) 10. Li, W., Lü, N., Dong, M., et al.: Simultaneous robot-world/hand-eye calibration using dual quaternion. Jiqiren/Robot, 40(3), 301–308 (2018) 11. Alzarok, H.: A new strategy for correction of Eye-to-hand camera pose errors in dynamic environments. IOSR J. Electron. Commun. Eng. 15(5), 23–33 (2020) 12. Guo, Y., Su, P., Wu, Y., et al.: Object detection and location of robot based on Faster RCNN. Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/J. Huazhong Univ. Sci. Technol. Nat. Sci. Edn. 46(12), 55–59 (2018)

Construction Cost Optimization System Based on AHP-BP Neural Network Algorithm Ningning Zhang(&) Henan Technical College of Construction, Zhengzhou 450052, Henan, China

Abstract. The level of cost management has a direct impact on the management effect of the project process. The cost management system of construction projects can optimize cost management strategies for construction companies and further enhance their market competitiveness and market share. AHP and BP neural network have a certain degree of effect on project cost optimization, so it has good feasibility to apply them in construction engineering cost optimization. This article focuses on the research on the construction cost optimization system based on the AHP-BP neural network algorithm. This article first introduces the status quo of construction project cost management, and summarizes the existing problems. Aiming at the characteristics and requirements of construction engineering cost management, this paper proposes a construction engineering cost optimization system scheme design based on AHP-BP neural network algorithm, and elaborates the design of typical functional modules, such as system management, target cost management, and actual management cost, etc. Finally, the performance test of the system was completed, and the normal operation of the system was realized. Experimental data shows that for 400 concurrent users, the average response time is 1.67 s, and the average CPU resource usage of the system and database is 46.3% and 39.1%, respectively, which are both lower than 50%. It can be seen that the system meets the cost management needs of construction companies, with stable system functions and high operating efficiency. Keywords: Neural network management


Construction engineering
Cost control
Cost

1 Introduction In recent years, the construction industry has become increasingly competitive in a diversified market. However, due to the strong growth of the construction industry in the short term and the irregularities in the competition within the industry, the cost of construction companies has risen and the profit margin has fallen [1, 2]. Project cost management is an important part of corporate governance in the construction industry, and it is the key to achieving business goals and achieving sustainable development [3, 4]. Effective project cost management is an effective way to increase profits. Therefore, strengthening the implementation of project cost management in the construction industry can not only meet market demand, but also enhance the overall competitiveness of the enterprise [5, 6]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 121–129, 2022. https://doi.org/10.1007/978-3-030-89508-2_16

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Regarding the research of neural network algorithm and cost optimization, many scholars have carried out in-depth research. For example, Gudipati VK used the analytic hierarchy process to improve the BP neural network and established the engineering cost estimation model [7]; Wang W for the BP neural network The shortcomings of slow convergence and inaccurate predictions are improved by using genetic algorithms and self-organizing competitive learning [8]; Shen T emphasized the importance of basic work in the process of controlling costs, and put forward the steps to control costs in construction projects and important implementation methods [9]. This article focuses on the research on the construction cost optimization system based on the AHP-BP neural network algorithm. This article first introduces the status quo of construction project cost management, and summarizes the existing problems. Aiming at the characteristics and requirements of construction engineering cost management, this paper proposes a construction engineering cost optimization system scheme design based on AHP-BP neural network algorithm, and elaborates the design of typical functional modules, such as system management, target cost management, and actual management cost, etc. Finally, the performance test of the system was completed, and the normal operation of the system was realized.

2 Construction Cost Optimization System Based on AHP-BP Neural Network Algorithm 2.1

Problems in Cost Management of Construction Projects

(1) Weak awareness of cost control. During the construction process, construction personnel often fail to realize that cost management is an important prerequisite for managing target cost costs, and their awareness of cost management is weak. For example, the project manager only pays attention to the quality of the project, whether the project can be completed within the construction period, and whether the project funds are in place; the technical staff only pays attention to solving technical problems and ensuring the quality of the project; the project staff only pays attention to the safety situation and construction progress of the project. This shows that companies need to strengthen the cost control awareness of the staff. (2) Lack of a scientific cost management system. Many companies in the construction industry still have not formed a scientific cost management method system throughout the entire construction process. Lack of scientific methods and positive attitudes, project cost management is only a mere formality [10, 11]. In specific projects, there are problems of disconnection of individual links in the whole process control, lack of scientific prediction before construction, lack of dynamic accounting during construction, and lack of feedback after completion. (3) Cost management is too focused on the construction process. In the construction engineering system, the cost management system requires management and control of the entire process from the start of bidding to completion and settlement [12]. However, in actual construction, many companies did

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not implement the whole process management of the project, but focused on the construction phase, and the requirements in other phases were not strict, which led to loopholes in the cost management of the company. Each stage of a construction project will have a great impact on the final cost of the project. This current state of enterprise cost management is unscientific. The nature of the construction project determines that the construction work needs to be continuously promoted, so the cost control must not be limited to the construction process, but should be the cost planning design control and implementation during and after the completion of the construction. (4) The formalization of cost management seriously lacks explanation attachments. The implementation of the system requires the joint operation of the formal system and the assistance system to ensure the realization of the expected results. Many companies have relatively complete engineering project cost management systems, but they lack relevant supporting documents to explain the system. For example, in the process of reviewing construction contracting contracts, various departments need to review the contract, but the cost management system lacks relevant explanations on how to review and the basis for review, which makes cost management work arbitrarily and irregular. 2.2

Design of System Function

(1) System management. 1) User management module: this function can manage content and information such as user names, user accounts, and user passwords. When users conduct related business operations through the construction project cost management information system, they must first register, and only after completing the registration and passing the review can they perform related business operations. 2) Authority assignment module: user authority management is to manage the operation authority owned by the user according to the actual situation. In the construction cost management information system, the system administrator manages the authority and other information of all parties in accordance with the actual situation of the system. System administrators need to manage and update the departments and roles of users and system users in a timely manner. In the specific operation process, the user-related permissions are removed according to the user’s job requirements, and the same permission can correspond to multiple users, so that multiple users have the same permissions during the cost management operation. 3) Log management module: log management allows clearing, querying and exporting of system logs and other related management operations. System administrators can easily analyze and manage the entire system through operation log management, and can grasp the specific conditions of system user operations. (2) Target cost management. The main roles of the target cost management module include the accounting personnel of the project department and the relevant leaders of the production and operation management department. In the actual operation process, the accounting personnel of the project department operate on related information and business

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such as cost rules, contract costs, cost preparation and changes. The leaders of the production and operation management department reviewed the relevant cost management business in it. (3) Actual cost management. 1) Labor expense management: Labor expense management requires the management of labor information and labor costs. In the cost management of construction projects, labor costs are divided into labor costs in the company’s projects and labor costs in outsourcing costs. There is a great relationship between project labor cost and project schedule. Therefore, in labor cost management, the project schedule should be properly arranged. 2) Material cost management: Material cost management involves multiple aspects and is directly related to corporate procurement. Material cost management involves many aspects, not only related to material procurement, but also closely related to material applicability and material inventory. Through the module of material cost management, it is possible to realize the input and statistics of material-related information, and realize automatic material cost management. 3) Mechanical cost management: In modern construction projects, the construction and implementation of the project rely more on machinery. The mechanical cost management not only includes the cost of machinery in the fixed assets of the enterprise, but also includes the cost of machinery leasing. Through the mechanical expense management module, the management of the information directly related to the cost, such as the name, type, and depreciation rate of the machine, is realized. 4) Subcontracting cost management: After the company has successfully won the bid, most of the project construction and construction are realized through subcontracting, which means that part of the project construction is subcontracted to other units, and the resulting costs are the same. It occupies an important position in the cost of construction projects. 5) On-site funding management: On-site funding management is mainly to realize the automated management of on-site funding spent in the construction of construction projects. Specifically speaking, it starts from the construction preparations before construction, as well as all costs incurred in the organization and management of construction during the construction process. On-site expenses include not only on-site temporary facility costs, but also on-site management costs. 2.3

Cost Optimization Based on AHP-BP Neural Network Algorithm

When applying the AHP-BP neural network algorithm to calculate the weight of the construction industry engineering cost optimization effectiveness evaluation index, the key issue is to construct a hierarchical structure model, and the more critical issue is how to organize the organizational goals in the hierarchical structure model and hierarchical. Assuming that there are n evaluation objects in the evaluation system, and the index system includes m evaluation indicators, an m  n original data evaluation matrix is obtained. The specific process can be divided into the following steps:

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(1) Data standardization. There may be differences in the dimensions and units of the indicators in the evaluation system, and in order to prevent zero values from appearing in the data, when evaluating the system, the data must be standardized first, and then normalized after the standardized processing, such as the following two formulas. 1) Standardized formula: Aij ¼

xij  min xij i

max xij  min xij i

þ1

ð1Þ

i

2) Normalization processing formula: Aij Bij ¼ Pn 1 Aij

ð2Þ

3) The specific formula for solving the j-th information entropy is as follows: Wj ¼ 1  Fj

ð3Þ

Dj Vj ¼ Pm1

ð4Þ

4) Solve the weight of the jth index

1

Dj

Finally, when both the more subjective weights of the analytic hierarchy process and the more objective weights of the entropy method are obtained, the final weight is obtained by using the optimized comprehensive weight formula of the AHP-entropy method. Fj Vj Yj ¼ Pm1 1 Fj Vj

ð5Þ

Finally get the combined weight Y T ¼ ðY1 ; Y2 ; Y3 . . .ÞT.

3 Experimental Research on Construction Cost Optimization System Based on AHP-BP Neural Network Algorithm 3.1

Test Purpose

Through the differentiated operation of the test data, check the system’s ability to accurately process the data; verify the system performance indicators, focusing on stability, high efficiency, etc.

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Test Environment

Operating system: Windows XP; CPU: P42.2 GHz; Hard disk: 10G free space; Memory: 1G; 3.3

Test Tool

Use the system performance testing tool Microsoft Visual Studio Team System 2008 Test Edition to simulate concurrent users for testing. 3.4

Expected Performance

400 concurrent users, 6,000 processes concurrently, the average CPU resource usage of the system and database is less than 50%, and the response time for any browser interface to open is less than 2 s.

4 Data Analysis of Construction Project Cost Optimization System Based on AHP-BP Neural Network Algorithm 4.1

Average CPU Usage

Under the condition of multi-user concurrent and multi-process, the average CPU occupancy rate of the system application server and database server is shown in Table 1: When the number of concurrent users is 100, 150, 200, 300, 400, the average CPU occupancy rate of the system server is respectively 14.9%, 22.1%, 27.6%, 37.2%, 46.3%, the average database server CPU occupancy rate is 11.2%, 16.8%, 23.4%, 30.1%, 39.1%. Table 1. Average CPU usage Number of concurrent users 100 150 200 300 400 500 600

Number of concurrent users average system server CPU usage (%) 14.9 22.1 27.6 37.2 46.3 54.1 66.0

Average CPU usage of database server (%) 11.2 16.8 23.4 30.1 39.1 47.9 57.6

Construction Cost Optimization System Based on AHP-BP Average CPU usage of database server Number of concurrent users average system server CPU usage

Users

600 500 400 300 200 150 100

47.9 39.1 30.1 16.8 11.2 14.9 0

10

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54.1

46.3

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23.4 27.6 22.1

20

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30 Unit: % 40

50

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Fig. 1. Average CPU usage

It can be seen from Fig. 1 that for 400 concurrent users, the average CPU resource usage of the system and database is 46.3% and 39.1% respectively, both of which are lower than 50%, and the system reaches the expected performance. 4.2

Response Time

Table 2 shows the system response time under different numbers of concurrent users: when the number of concurrent users is 100, 150, 200, 300, 400, the average response time of the system is 0.42 s, 0.67 s, 1.04 s, 1.46 s, and 1.67 s respectively; the maximum response time is 0.45 s, 0.79 s, 1.16 s, 1.54, 1.81 s, respectively. Table 2. Response time Number of concurrent users Average response time (s) Maximum response time (s) 100 0.42 0.45 150 0.67 0.79 200 1.04 1.16 300 1.46 1.54 400 1.67 1.81 500 2.14 2.43 600 2.76 3.24

unit: s

4

Average response time

Maximum response time

2 0.420.45

0.670.79

1.041.16

1.461.54

1.671.81

2.43 2.14

3.24 2.76

0 100

150

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Fig. 2. Response time

400

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Looking at Fig. 2, we can find that for 400 concurrent users, the average response time is 1.67 s, and the maximum response time is 1.81 s, which is less than 2 s. Therefore, the test results of the performance of the construction engineering cost optimization system show that the system has well realized the non-functional requirements in the demand analysis and meets the cost management needs of the construction company. At the same time, the system has stable functions and high operating efficiency.

5 Conclusion Cost management and optimization are the most important key issues in construction project management. An effective cost management plan can increase the financial income of an engineering company. The level of cost control also reflects the level of corporate management. If a construction company wants to enter a highly competitive environment, the key is to pay attention to the cost management of engineering projects. Under the new normal, market growth patterns are constantly changing, and construction companies are facing increasing competition pressure. In such a difficult situation, cost management and optimization will become more and more important. Strengthening engineering project cost control is conducive to improving the company’s project management level, increasing the company’s net profit, and stabilizing the company’s core competitiveness. This article focuses on the research on the construction cost optimization system based on the AHP-BP neural network algorithm. This article first introduces the status quo of construction project cost management, and summarizes the existing problems. Aiming at the characteristics and requirements of construction engineering cost management, this paper proposes a construction engineering cost optimization system scheme design based on AHP-BP neural network algorithm, and elaborates the design of typical functional modules, such as system management, target cost management, and actual management cost, etc. Finally, the performance test of the system was completed, and the normal operation of the system was realized.

References 1. You, H.: Optimization of cost management for building construction based on large data analysis. Boletin Tecnico Tech. Bulletin 55(6), 88–94 (2017) 2. Anysz, H., Zawistowski, J.: Cost minimization of locating construction machinery park with the use of simulation and optimization algorithms. MATEC Web Conf. 196, 04088 (2017) 3. Almeida, M., Ferreira, M.: Ten questions concerning cost-effective energy and carbon emissions optimization in building renovation. Build. Environ. 143, 15–23 (2018) 4. Amusan, L., Charles, A.K., Owolabi, D.J., et al.: Multi-parameter optimization of cost entropy for reinforced concreteoffice building projects using ant colony optimization. J. Eng. Appl. Sci. 12(19), 5018–5023 (2017) 5. Elkabany, S.N., Elkordy, A.M., Sobh, H.A.: Optimization of the panels used in free-form buildings and its impact on building cost. IOP Conf. Series Mater. Sci. Eng. 974, 012010 (2020)

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6. Fa, A., Nb, A., Gmm, B., et al.: Building envelope design: multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. application to different Italian climatic zones. Energy, 174, 359–374 (2019) 7. Gudipati, V.K., Cha, E.J.: A framework for optimization of target reliability index for a building class based on aggregated cost. Struct. Safety, 81(9), 101873 (2019) 8. Wang, W., Tang, R., Li, C., et al.: A BP neural network model optimized by mind evolutionary algorithm for predicting the ocean wave heights. Ocean Eng. 162, 98–107 (2018) 9. Shen, T., Nagai, Y., Gao, C.: Design of building construction safety prediction model based on optimized BP neural network algorithm. Soft. Comput. 24(11), 7839–7850 (2019). https://doi.org/10.1007/s00500-019-03917-4 10. Sari, F., Latief, Y.: Safety cost estimation of building construction with fuzzy logic and artificial neural network. J. Phys. Conf. Series, 1803(1), 012020–10 (2021) 11. Cong, T.D, Minh, Q.N.: Estimating the construction schools cost in Ho Chi Minh City using artificial neural network. IOP Conf. Series Mater. Sci. Eng. 869(6), 062014–7 (2020) 12. Ding, X., Lu, Q.: Construction cost management strategy based on BIM technology and neural network model. J. Intell. Fuzzy Syst. (6), 1–13 (2020)

Design and Implementation of Sensitive Information Detection Algorithm Based on Deep Learning Jianchao Fang(&) School of Information Science and Engineering, Wuchang Shouyi University, Wuhan, Hubei, China

Abstract. With the continuous progress of science and technology, people get information on the Internet more and more quickly, the way is more and more convenient. People are more willing to use mobile devices, computers and other electronic products than to obtain information from paper books, newspapers and other channels. While using these electronic products, there will be many behaviors of storing or spreading bad information. Traditional OCR technology can be widely used in the detection and recognition of ordinary single background level text detection and recognition, but for the natural scene pictures with a certain Angle of the text detection and recognition effect is not good. With the development of deep learning technology, text detection and text recognition are mostly completed by deep learning architecture. In this paper, the popular EAST algorithm and CRNN algorithm are combined to detect and identify the text information in the natural scene. Keywords: Deep learning


Text detection
Character recognition

1 Introduction With the continuous innovation and development of science and technology, people have more and more access to information, and pictures have become an important way to convey information, which greatly facilitates people’s life, but also brings some adverse effects, such as the dissemination of sensitive information. Under the national government supervision, many for the spread of sensitive information in plain text form has received the good control, but some criminals will text line in the image, especially the text line rotation Angle, or the text in turn into the image, and even the text information communicated in the complex background images, to avoid sensitive information detection, and even more difficult for the government regulation. Not only that, but some people also store illegal text messages in their computers after viewing them, so that they can continue to spread when the information on the network is detected and deleted.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 130–136, 2022. https://doi.org/10.1007/978-3-030-89508-2_17

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2 Related Work 2.1

Research on Text Detection

At present, there are two categories of natural scene text detection algorithms, the first is the classical natural scene text detection method, and the second is the deep learningbased natural scene detection method [1]. Classical text detection methods of natural scenes are mainly divided into two categories: one is based on connected domain analysis, the other is based on sliding window to detect text. The literature [2] A text detection model based on MSERS is proposed, but this algorithm will produce a large number of candidate regions of noncharacters, so pruning operation is needed to remove the regions that are not text parts. The culling method is to construct an MSERS tree, and judge whether it is a text area according to the parent-child node relationship, and then cull it. The literature [3]. In this paper, we propose a method to optimize text recognition by candidate edge recombination and edge classification. In the process of edge recombination, we divide the edges of the input image into small segments by dividing and merging regions. If the surrounding edge blocks have similar stroke width and color, the edge blocks will be combined. In edge classification, the candidate boundaries are aggregated into text chains, and then the characters of text and text chains are used for chain classification. The literature [4] Matching is proposed based on CNN and depth prior network quadrilateral sliding window to detect text, the analysis of the eigenvalues of the convolution between layer and choose the candidate text box with high contact ratio, behind a Shared Monte - Carlo method is proposed to calculate more quickly and accurately quadrilateral window position, finally USES the relative return to the order of the agreement, can more accurately predict closely quadrilateral text. Above all, the text detection algorithm based on connected domain is from the bottom up, by one estimate the words together to form the final text lines based on association rules, also it is using the heuristic algorithm to build the candidate regions, but the algorithm is simple, more suitable for the background noise of images, and heuristic method cannot fully guarantee the accuracy, unable to separate the text and background well; However, the text detection method based on sliding window depends too much on the size and shape of sliding window, and its generalization performance is not strong. Among the text detection algorithms based on deep learning, they mainly include the text detection algorithm based on candidate box, the algorithm based on segmentation, the algorithm based on mixing of the two and other algorithms. Among the algorithms based on candidate boxes, the most typical ones are based on FastersRCNN, SSD and RFCN. Most are in the VGG-16 model [5] Based on the completion of. Inspired by Fasters-RCNN and CNN, literature [6] CTPN algorithm is proposed, the text detection task into multiple small scale text box detection task, it puts forward a kind of vertical anchor, which only detect the location of the vertical direction, the width of the anchor is fixed good to 16 pixels, but the literature shows that the height of the anchor can be from 11 to 283 pixels of 10 different changes, subsequent combines two-way LSTM to improve the effect of text detection. Due to the addition of two-way LSTM, CTPN has a good detection effect on horizontal text. In the segmentation based algorithm, the literature [7] A Pixellink algorithm is proposed, which realizes text

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detection through instance segmentation and extracts BBox directly from segmentation results. In the algorithm based on the mixture of the two, the literature [8] East algorithm is proposed, which is exactly the algorithm used in this project. The idea of EAST algorithm is simple. At present, deep learning is applied more and more widely, and text detection is more and more carried out by deep learning. 2.2

Research on Text Recognition

With the development of OCR technology, text recognition technology is becoming more and more mature. CRNN and CNN + Seq2Seq + Attention are the most commonly used methods for horizontal text line recognition. CRNN9 is CNN + RNN + CTC, in which CTC was originally mainly used for speech recognition applications. In CRNN, CTC provides blank to solve the problem of having no characters in some positions. The whole process of using CRNN is as follows: firstly, CNN is used to extract the image convolution features, then LSTM is used to further extract the sequence features in the image convolution features, and finally CTC is used to solve the problem of unaligned text. In the SEQ2Seq model structure of CNN + Seq2Seq + Attention method, it encodes all inputs into a uniform semantic, and then decodes them with a decoder. Each time, the output of the previous moment is used as the input of the next moment, until it encounters a stop symbol. The Attention mechanism is added to transfer the weighted state of the hidden layer of the encoder to the decoder, so as to enrich the information of the network. Text recognition in natural scenes is widely used, and it has a wide range of applications in the current instant translation of photos, image retrieval and other aspects, not only the above mentioned several algorithms, but also based on RARE [14] Network, FAN [15] Network, FACLSTM [16] Network and other text recognition algorithms.

3 Proposed Method 3.1

EAST is Introduced

Text detection is a prerequisite for subsequent text recognition. Its main purpose is to separate text information from the background in the natural scene. However, both traditional text detection algorithms and most text detection methods based on deep learning are composed of multi-stages and multi-components, which will be timeconsuming and not good enough. Therefore, researchers have proposed a fast and accurate text detection algorithm, namely EAST [8] algorithm. This algorithm has only two stages, realizes the end-to-end detection method, eliminates the intermediate stages, and can directly detect the text. The two stages of using EAST for text detection are as follows: firstly, the prediction at pixel level can be directly generated based on the full convolutional network, which can be the prediction of rotating rectangles or other quadrilaterals, excluding redundant and time-consuming intermediate steps; secondly, non-maximum suppression (NMS) is performed for screening, so as to obtain

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the final result. Therefore, it can realize multi-angle text detection and can adapt to complex scenes. This algorithm is superior to the previous algorithms in time and precision [11]. The main network model structure of EAST is shown in Fig. 1, which consists of three parts: feature extraction, feature merging and output.

Fig. 1. East network model structure

Phase of feature extraction is proposed in the literature by a trained convolution in ImageNet data set the network parameters initialization, and then based on PVANet model to extract the characteristics of the four levels, size is original, respectively, to extract the characteristics of different size chart, can meet the demand of prediction of 1 1 different size of the text. 14 18 16 32 It is well known that features extracted early can predict small text, and features extracted late can predict large text. On the characteristics of consolidation stage, first will take place on a layer of the characteristics of the input figure on the sample to double, and connecting with the current figure, and then used to reduce the number of channels and the amount of calculation, the information fusion of reoccupy will get to the next stage, finally in the last stage of the merger of the operation characteristics of figure, and feedback to the output layer.conv11 conv33 conv33 In the output stage, several features are extracted, and finally three parts are output, namely score map, RBOX and QUAD. conv11 The output shape is either RBox or Quad. The former is composed of four channels representing the distance from the center point of the pixel to the upper, right, lower and left side of the rectangle, and one channel representing the rotation Angle. h The latter is the coordinate offset representing the coordinates of the four vertices of the quadrilateral to the position of the center point of the pixel. Since each offset is composed of two numbers, it contains eight channels. Let’s talk about the loss function. First, it classifies loss. It mainly calculates loss value for the F_score output after feature fusion to determine whether the predicted RGB pixel position is in the position of the text in the image. Balance the cross entropy function is used in the literature, but the code is actually used in dice loss function, the text detection problem for this project, is kind of sample is much larger than the negative samples, samples will lead to extreme imbalance, extremely uneven, and in

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the sample training data when using dice loss effect will be better than balance of cross entropy function, so use the dice loss for classification loss calculation. The definition of Dice Loss Formula is as shown in Formula 1: Dice Loss ¼ 1 

2j X \ Y j j X j þ jY j

ð1Þ

Where, represents the intersection between X and Y, represents the number of elements in X and the number of elements in Y, respectively. Since the common elements in X and Y are repeatedly counted in the denominator, the numerator is multiplied by a coefficient.jX \ YjjXj þ jYj When calculating the loss value of RBox, the logarithmic method of taking IOU is used. 3.2

CRNN Algorithm Introduction

CRNN [9, 10] The algorithm is composed of CNN (convolutional neural network), RNN (cyclic neural network), CTC [13] (time series classification based on neural network), and its network structure is shown in Fig. 2.

Fig. 2. CRNN network structure

Among them, the convolution layer adopts the VGG model, and improves on the basis of the VGG model. Convenient to CNN’s output as input of RNN, researchers in the third and fourth largest pool of CNN, the nuclear scale from 2  2  2 to 1, the purpose is to highly by half four times, and half the width of the 2 times, because of the high in real life, most of the text lines is smaller, and long wide, this convenient identification size for elongated text information, as well as the layer without full connection layer, retained its space. RNN is the most likely character for each position, and here RNN is BI-LSTM. In other words, if you scan from left to right, the predicted words on the left will have an effect on the predicted words on the right [11, 12]. Similarly, if you scan again from right to left, the predicted words on the right will have an effect on the predicted

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characters on the left. Due to the use of RNN, character prediction is more accurate, such as the distinction between uppercase I and lowercase l is better; Secondly, in the reverse transfer of RNN, the residuals can be transmitted back to the input layer and the convolution layer connected by the input layer. Finally, the transcription layer is to join the characters and align them. For example, if the two grids obtained from RNN are the same character, then it is necessary to derepeat and finally output a complete sentence. There are many possibilities for each location of RNN output, and the transcription layer needs to convert these possibilities into a complete sentence. There are many combinations for a sentence, so the transcription layer has to choose the sentence with the most possibility.

4 The Experiment In this paper, a figure from the ICPR dataset is selected as an effect demonstration, as shown in Fig. 3. Although there are still some undetectable text positions and text with low pixel level that cannot be recognized, the overall results appear to be reliable.

Fig. 3. Demonstration of results

5 Conclusion Generally speaking, the text detection model has a certain robustness, and the recognition model is relatively reliable, which can identify certain fuzzy text. If the text is too fuzzy to be distinguished by the naked eye, CRNN cannot recognize it, which can meet the requirements of daily needs. The EAST text detection algorithm and CRNN text recognition model used in this paper can get a better result of identifying text sensitive information from pictures. There are still some shortcomings. For example, images with too large inclination angles need to be rotated so that the text can be directly detected and recognized. For long texts greater than 45°, the direct detection effect is not good, and further processing is needed to get the final results required.

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References 1. Zhi-cheng, B., Qing, L., Peng, C., Li-qing, G.: Text detection in natural scenes: a literature review. Chin. J. Eng. 42(11), 1433–1448 (2020) 2. Xu-Cheng, Y., et al.: Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 970–983 (2013) 3. Yu, C., Song, Y., Meng, Q., et al.: Text detection and recognition in natural scene with edge analysis. IET Comput. Vis. 9(4), 603 (2015) 4. Liu, Y.L., Jin, L.W.: Deep matching prior network: toward tighter multi-oriented text detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, 1962 (2017) 5. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recongnition. ArXiv Preprint arXiv:1409.1556 (2014) 6. Zhi, T., Huang, W., Tong, H., et al.: Detecting text in natural image with connectionist text proposal network. Eur. Conf. Comput. Vis. 9912, 56–72 (2016) 7. Dan, D., Liu, H., Li, X., et al.: PixelLink: detecting scene text via instance segmentation. ArXiv 32(1) (2018) 8. Zhou, X., Yao, C., Wen, H., et al.: East: an efficient and accurate scene text detector. In: IEEE, pp. 2642–2651 (2017) 9. Shi, B., Xiang, B., Cong, Y.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2016) 10. Zhang, X., He, K., Ren, S., et al.: Deep residual learning for pattern recognition. In: IEEE International Conference on Pattern Recognition (CVPR), 2016, arXiv Preprint, arXiv:1512. 03385 (2016) 11. Zaremba, W., Sutskever, I., Vinyals, O.: Recurring neural network regularization. EPrint arXiv, arPrint preprint arXiv: 1409.2329 (2014) 12. Shi, X., Chen, Z., Wang, H., et al.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. MIT Press, arXiv preprint arXiv: 1506.04214 (2015) 13. Graves, A., Fernandez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the International Conference on Machine Learning (2006) 14. Shi, B., Wang, X., Lyu, P., et al.: Robust scene text recognition with automatic rectification. In: IEEE, pp. 4168–4176 (2016) 15. Cheng, Z., Bai, F., Xu, Y., et al.: Focusing attention: towards accurate text recognition in natural images. In: IEEE, pp. 5076–5084 (2017) 16. Wang, Q., et al.: FACLSTM: ConvLSTM with focused attention for scene text recognition. Sci. China Inf. Sci. 63(2), 1–14 (2020)

Pruning Technology Based on Gaussian Mixture Model Mengya Sun(&) Department of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 610000, Sichuan, China [email protected]

Abstract. There are a lot of redundant parameters in the deep learning network model from the convolutional layer to the full connection layer, and the activation value of a large number of neurons approaches 0. After these neurons are removed, the same model expression ability can be shown. This situation is called over-parameterization, and the corresponding technology is called model pruning. Pruning is an important research area aimed at reducing the computational cost of neural networks. It can efficiently generate models with smaller scale, higher memory utilization, lower energy consumption, faster inference speed, and minimal loss of inference accuracy. Model pruning, as a model compression technology with a long history, has made great progress and development at present. With the deeper development of the technology, there are more requirements in the society about this new method. Generally speaking, conventional pruning methods follow fixed steps, first training a large and redundant network, and then identifying less important neurons or channels that can be deleted. There are two of traditional pruning methods technically: structured pruning and unstructured pruning. Among the two, the latter one is mainly direct pruning weight. The pruning method of this work is to first classify the weights by size through the clustering method based on GaussianMixture, then remove certain types of channels with low weights, and finally use the weights before pruning as initialization to retrain the network. Experiments on VGG-19, Resnet and Densenet show that the network can obtain basically the same or even better accuracy with fewer channels. Keywords: Neural network Weight


Pruning
Gaussianmixture clustering method


1 Introduction The way humans store information is different from flash memory cards. Not only do they store certain information, but at the same time, it will change the way neurons are connected: either disconnected, connected, or strengthened/weakened. When information enters the brain, it will be shredded by a crazy blender and scattered to various areas of the brain. The neural network used in deep learning is estimated to be a mechanical simulation of the above phenomenon. It’s just that the brain has a mechanism that can greatly adjust the network and neurons after inputting different information. Neural network has been widely used in many cases and has achieved the latest © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 137–144, 2022. https://doi.org/10.1007/978-3-030-89508-2_18

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achievements in many tasks [1, 2]. One key to improving performance is its increased depth and width, thus increasing the number of parameters. However, the scale of the network designed according to experience is too large, and it has been proven that there is significant redundancy in several deep learning models [3], and it is mainly caused by a large number of parameters in the deep neural network [4]. An over-parameterized model wastes memory and computation, and leads to severe overfitting problems [5]. Most previous efforts to improve network architectures fall into two broad categories: one focused on high-level architecture design [6, 7], and the other on pruning [8, 9]. By using Gaussianmixture clustering method to classify the weight in the matrix, the insignificant weight in the network is eliminated, thus eliminating some channels [10]. Beginning with the empirically designed network, the algorithm first finds the weight category with smaller weight by analyzing the weight density category on the large data set. The corresponding channel with the lower weight is then pruned away, while the other channels and their trained weights are retained to initialize the new model. Finally, the new model is retrained or improved according to the performance rise and fall. The new model, which has been retrained, can maintain the same accuracy using fewer channels. We trained 160 generations to achieve the convergence of the model and compared the changes of the model before and after pruning.

2 Pruning Theory Based on Gaussianmixture 2.1

CNN Pruning Theory

Fig. 1. The schematic diagram of CNN pruning theory

In Fig. 1, ni represent the number of input channels in the ith convolution layer, hi=wi is the height/width of the input factor graph. Convolution layer will input characteristic figure xi 2 Rni hi wi converted into output characteristic figure xi þ 1 2 Rni þ 1hi þ 1wi þ 1 , used as a convolution layer under the input figure. It is achieved by applying ni þ 1 3D filters Fi;j 2 Rni kk over ni input channels, one of which generates a feature graph. Each filter consists of ni 2D cores k 2 Rkk (for example, 3  3). All the filters together form a kernel matrix Fi 2 Rni ni þ 1 kk . The operand of the convolutional layer is ni þ 1 ni k2 hi þ 1 wi þ 1 . As shown in Fig. 1, when the filter Fi;j is trimmed, its corresponding feature graph xi þ 1;j will be deleted, which reduces the operation of ni k2 hi þ 1 wi þ 1 . The kernel applied to the factor map removed

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from the filter of the next convolution layer is also removed, saving additional ni þ 2 k2 hi þ 2 wi þ 2 operations (see Fig. 2). Trimming m filters of layer i will reduce the calculation cost of layer i and layer i + 1 by m=ni þ 1 .

Fig. 2. The pipeline for iteratively pruning deep neutral works

2.2

Gaussianmixture Method

Firstly, the maximum likelihood objective function of Gaussian Mixture Model (GMM) was established based on the hypothesis, and the Expectation Maximization (EM) iteration thought was used to solve the objective function. Finally, the pseudocode was arranged. (1) Assume The observation sample set y comes from the Mixture Model, which is made up of a linear combination of K Gaussian distributions [1]. This Model of probability disP tribution models: PðyjHÞ ¼ K k ak Uðyjhk Þ. The H ¼ fak ; hk jk ¼ 11; 2; :::; Kg; ak  0 P indicates the linear coefficient K k ak ¼ 1, Uðyjhk Þ said the first K Gaussian probability density function of the Model. The sample y is derived from the model with the following probability distribution: PðyjHÞ ¼

K Y



cjk

½ak Uðyj jhk Þ ; cjk ¼

k

1; Sample j comes from model k 0; else

so: PðyjHÞ ¼ Pðy; cjHÞ ¼

YN YK j¼1

k¼1

½ak Uðyj jhk Þcjk

(2) Construct the objective function X X LðHÞ ¼ logPðYjHÞ ¼ log PðY; ZjHÞ ¼ log PðYjZ; HÞPðZjHÞ ¼ Z

Z

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h XK i XN  hXK i log Uðyjh Þa log Uðy jh Þa ¼ k k k k j k¼1 j¼1 k¼1

From Jensen’s inequality:   X   ðiÞ LðHÞ  L HðiÞ ¼ P YjZ; H log Z

PðY; ZjHÞPðZjHÞ PðZjY; HðiÞ ÞPðYjHðiÞ Þ

make:     X   PðY; ZjHÞPðZjHÞ ðiÞ    B H; HðiÞ ¼ L HðiÞ þ P YjZ; H log  Z P ZjY; HðiÞ P YjHðiÞ   Hði þ 1Þ ¼ argmax B H; HðiÞ H 8 9 <  =  X   PðY; ZjHÞPðZjHÞ argmax ðiÞ     ¼H L HðiÞ þ P YjZ; H log Z : P ZjY; HðiÞ P YjHðiÞ ; n   X   ðiÞ ¼ argmax L HðiÞ þ P YjZ; H ½log PðYjZ; HÞ þ log PðZjHÞ H Z    io  log P ZjY; HðiÞ  log P YjHðiÞ ¼ argmax H

nX

¼ argmax H

  o ðiÞ P ZjY; H log PðYjZ; HÞPðZjHÞ Z

nX

  o ðiÞ P ZjY; H log PðY; ZjHÞ Z

¼ argmax fEz ½log PðY; ZjHÞg H   ðiÞ ¼ argmax Q H; H H

(3) Solve the objective function PðY; ZjHÞ ¼

K Y

(

k¼1

Write:

PN j¼1

cjk ¼ nk ,

ankk

" N Y j¼1

PN j¼1

!#cjk )! P 1 ðyi  lk ÞT 1 k ðyi  lk Þ  d P 1 exp 2 ð2pÞ2 j kj2

nk ¼ N

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141

logPðy; cjHÞ ¼

8 K < X> k¼1

> :

 T P  39 1   = 1 y j  lk y j  lk > k d X 2 6 7 cjk 4 logð2pÞ2  log K  5 > 2 ; 2

nk log ak þ

N X j¼1

  Q H; HðiÞ ¼ 8

K > < X k¼1

> :

 T P  39 1   = 1 yj  l k y j  lk > k d X 2 6 7 2  log cc  logð2pÞ k    4 5 jk > 2 ; 2

nk log ak þ

N X j¼1

Write:

N N N   P P P cc E cjk ¼ cc cjk ¼ nk ¼ jk , 所以 jk j¼1

j¼1

j¼1

  ðiÞ ðiÞ cc jk ¼ E cjk ¼ Eðcjk jyj ; H Þ ¼ Pðcjk ¼ 1jyj ; H Þ ¼  P T 1 ðyi lk Þ k ðyi lk Þ 1 ak d P 1 exp  2 ð2pÞ2 j kj 2  T P1 PK ðyi lk Þ k ðyi lk Þ 1 1 exp  d P l¼1 al 2 kj 2 ð2pÞ2 j The optimal values of l and r2 are gotten through the set of partial derivatives to 0. When the constrained optimization problem is solved, the optimal value of a is obtained after Lagurange is transformed into an unconstrained problem and the partial derivative is 0.   @Q H; HðiÞ @lk   @Q H; HðiÞ @r2k

max ak Q

PN N   c 1X jk yj j¼1 c cc ¼ 2 l k ¼ PN jk lk  yj ¼ 0 ! c rk j¼1 c jk j¼1 c

¼

N X

cc jk

j¼1

1 þ 2r2k

N X j¼1

 2 yj  lk cc jk cc ¼ 0 ! r2k ¼ jk 2 r4k  2 PN c c y  l k j j¼1 jk PN c jk j¼1 c

K K   X X H; HðiÞ ; s:t: ak ¼ 1 ! max n loga ; s:t: ak ¼ 1 ! k k ak k¼1

k¼1

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Lðak ; kÞ ¼

K X

K X

nk logak þ k

k¼1

! ak  1

!

k¼1

@Lðak ; kÞ ¼ @ak @

PK k¼1

nk logak þ k @ak

 PK k¼1

ak  1

 ! N ¼ k ! abk ¼

nk ¼ N

PN

(4) Algorithm. Input: sample y ¼ fyj jj ¼ 1; 2; :::; Ng. ð0Þ

Initialization parameter Hð0Þ ¼ fHk jk ¼ 1; 2; :::; Kg. b = {H ck | k = 1,2,…,K}. Output: GMM Optimal fitting parameters H 1. Initialization Hk and Q n o ð0Þ ð0Þ ð0Þ i ¼ 0 : Hð0Þ ¼ ak ; lk ; rk jk ¼ 1; 2; :::; K     Qð0Þ H; Hð0Þ ¼ 1; Qð1Þ H; Hð1Þ ¼ 0

  2. While Qði þ 1Þ  QðiÞ   e ðiÞ ak ðiÞ

cjk ¼

 1 qffiffiffiffiffiffiffiffiffiffi ði Þ2

exp 

ðyj lðkiÞ Þ

2prk

2



ði Þ2

2rk

 2 ðyj lðkiÞ Þ ðiÞ 1 q ffiffiffiffiffiffiffiffiffiffi exp  a k¼1 k ði Þ2

PK

2rk

ði Þ2

2prk

d ði þ 1Þ lk ¼

ðd i þ 1Þ2 ¼ rk

PN c ðiÞ j¼1 cjk yj PN c ðiÞ j¼1 cjk

 2 PN c ðiÞ ðiÞ j¼1 cjk yj  lk

ði þ 1Þ ak

PN c ðiÞ j¼1 cjk ¼

PN c ðiÞ j¼1 cjk N

j¼1

N

cc jk

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Q ð iÞ

143

8 0  2 1 9 ! ði þ 1Þ > K > K N = < X y  l X X j k 1 c c C ðiÞ ði þ 1 Þ ðiÞ B ði þ 1Þ cjk logak cjk @logð2pÞ2 logak ¼ þ  A 2 ði þ 1 Þ > > ; 2r j¼1 k¼1 : j¼1 k

3 Experimental Results In this paper, we use Intel i5 processor, Windows 10 operating system to carry out experiments, and use NVIDIA GeForce GTX 1650 GPU for computing acceleration. The network structure is based on PyTorch architecture, and the data sets used are CIFAR100 and CIFAR10. VGG-19, RESNET and Densenet were then tested and the results were recorded, with the percentage of parameter drop and FLOPS drop counted. (See Tables 1 and 2).

Table 1. Test on CIFAR-100 Test error (%) VGG-19 27.16–28.26 DenseNet 27.45–27.69 ResNet 25.07–26.57

Parameters pruned (%) FLOPs pruned (%) 73.8 34.8 68.1 57.8 50.0 54.0

Table 2. Test on CIFAR-10 Test error (%) VGG-19 6.41–6.66 DenseNet 6.81–6.82 ResNet 6.01–6.71

Parameters pruned (%) FLOPs pruned (%) 89.7 52.7 76.1 69.7 77.6 70.6

4 Conclusion In tasks such as compression, pruning has been a great success. However, most of the existing models make manual trade-off based on the weight size. GaussianMixture clustering algorithm for weight classification, and then automatic trade-off, has achieved better experimental results, significantly improve the efficiency of the network, at the same time the model after pruning, generally will bring precision loss, so in pruning at the same time also need to consider the recovery of precision.

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References 1. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classifification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097– 1105 (2012) 2. Graves, A., Schmidhuber, J.: Framewise phoneme classifification with bidirectional lstm and other neural network architectures. Neural Netw. 18(5), 602–610 (2005) 3. Denil, M., Shakibi, B., Dinh, L., Ranzato, M., de Freitas, N.: Predicting parameters in deep learning (2013). https://arxiv.org/abs/1306.0543 4. Hu, H., Peng, R., Tai, Y.W., et al.: Network Trimming: A Data-Driven Neuron Pruning Approach Towards Efficient Deep Architectures (2016) 5. Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning. Trained Quant. Huffman Coding Fiber 56(4), 3–7 (2015) 6. Lin, M., Chen, Q., Yan, S.: Network in network (2013). https://arxiv.org/abs/1312.4400 7. Szegedy, C., et al.: Going deeper with convolutions (2014). https://arxiv.org/abs/1409.4842 8. Hanson, S.J., Pratt, L.: Advances in Neural Information Processing Systems 1, pp. 177–185. Morgan Kaufmann Publishers Inc., San Francisco (1989) 9. Hassibi, B., Stork, D.G.: Second order derivatives for network pruning: optimal brain surgeon. In: Advances in Neural Information Processing Systems 5, [NIPS Conference], pp. 164–171. Morgan Kaufmann Publishers Inc., San Francisco (1993) 10. Li, H., Kadav, A., Durdanovic, I., et al.: Pruning Filters for Efficient ConvNets (2016)

Analysis of College Students’ Behavior Based on Machine Learning and Big Data Technology Siyu Ning(&) Sanya Aviation and Tourism College, Sanya, Hainan, China [email protected]

Abstract. With the continuous progress of society, information technology has developed rapidly, and the digital age has quietly arrived. The computer development science and Internet technology has continuously produced massive amounts of data, which not only contains countless wealth, but also constantly challenge traditional cognition. In order to better dig out the mysteries and find the rules, it is possible to analyze and master the endless information, knowledge and wisdom hidden behind the massive data. Big data technology came into being, and obtained in all aspects. In recent years, campus data has received more and more attention from college student management staff. They hope that with machine learning and big data technology-related theories, they can analyze the various types of data generated by students during school to find the law of student growth. Discover hidden dangers in time and deal with them properly, formulate a more scientific and humanized management plan for students, and truly teach students in accordance with their aptitude. This article analyzes the characteristics of college student behavior data, and analyzes the use of machine learning and big data in college student behavior data analysis. Keywords: Machine learning behavior
Behavior analysis


Big data technology
Colleges
Student

1 Introduction Traditional student behavior management mainly relies on experience and path. Today’s big data application can actively grasp the characteristics and laws of campus student behavior, to make judgments and predictions, so as to realize the school’s “front-end management model innovation”. Therefore, mining the actual value of teaching management education, teaching and learning, and developing a large database early warning management platform for student campus behavior can improve teaching quality, manage student daily behavior, student safety control, psychological counseling, etc. [1]. Processing and analysis of educational big data, assisting school management macro decision-making, assisting teaching production safety management and control, including: financial credit risk, network behavior, consumer behavior, absenteeism, absenteeism, lack of credits, etc. A series of early warnings, in-depth exploration of student psychological problems, early warning there are problems with paying attention to students [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 145–151, 2022. https://doi.org/10.1007/978-3-030-89508-2_19

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2 Overview of Mechanical Learning Machine learning technology is the AI core and computer intelligence foundation. In fact, as early as when computer science was founded [1]. Turing, the originator of computer science, opened the door to AI for humankind. “If it could deceive a human into believing that it was human.” Means “when a computer can successfully deceive humans and make them feel that it is a person, it can be called AI.” If you want to truly realize computer AI, you must relying on machine learning technology, only machines with the same learning capabilities as humans can be called AI [2]. Multidisciplinary cross through discipline, mainly involves statistics, convex analysis, approximation theory, probability theory, and the theory of algorithm complexity science [2]. The main research is to allow computers to acquire new skills or knowledge by studying how computers learn and simulate human learning behaviors, and to improve their performance, as shown in Fig. 1.

Fig. 1. Machine learning

3 Introduction to Big Data Technology People’s daily lives have produced a large variety of data. These data are very complex and huge. There is an urgent need for big data technology to manage and mine valuable information. There are various definitions of big data, and the more authoritative one is given by McKinsey Consulting: Big data refers to data sets that are beyond the ability of conventional database tools to obtain, store, manage and analyze, and have massive data four characteristics: scale, rapid data flow, diverse data types, and low value density [2]. At present, as a valuable information asset, big data is developing rapidly around the world and has attracted great attention from all occupations. Through effective management and big data analysis and extracting its value, it can provide high-quality services to the industry and realize extremely huge economic and social value. In informatization process, colleges have produced a large amount of student-related data, forming a huge data resource [3]. Obviously, the big data technology application and

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the information integration in education and teaching management will have a major on “smart campus” construction.

4 Collection of Student Behavior Data in Colleges At present, various universities have built learning management, educational administration management, financial management and other systems around smart campuses. Each application system has a lot of unstructured, semi-structured or structured historical data information [3]. The processing and processing of these big data can be produce very good application effect. The data collection of specific student behavior is shown in Fig. 2.

Fig. 2. Data collection of student behavior

4.1

Collect Daily Data

Collect some data of college students’ daily life, including network communication data, student life consumption data, student and financial data, etc., to realize functions such as identification, conversion, transmission and management of massive data. The establishment of the big data platform adopts virtual servers to realize independent processing and analysis of various data [4]. 4.2

Extract Mining Data

Data extraction and mining include combing, extracting, and converting incomplete and noisy data of different types. It is a very important step before big data processing [4]. The Hive tool in Hadoop big data processing technology can be used as a data

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extraction tool, HBase as a data storage library, and Map Reduce as a data processing technology, with a behavior target of 70%, as shown in Fig. 3.

Fig. 3. Data extraction and mining

4.3

Data Summary

Data cleaning and aggregation, including the cleaning of various complex and disordered data from different sources, is the final step to complete the original collection of big data. There are lot of big data sources, which are not necessarily the target data required by the system. In order to avoid certain data from affecting the analysis results during the data analysis process, they need to be cleaned and de-dried, so that future analysis results will be more accurate [5].

5 The Big Data Application in the Analysis of Student Behavior Data in Colleges 5.1

The Core Technology of Student Behavior Data Analysis

(1) Machine learning It implements self-improvement through classifier or algorithm training on a large amount of known data, finds a decision function, quickly and accurately predicts unknown samples, and gradually realize the work of machines instead of humans [5]. Commonly used machine learning algorithms include decision trees, Kmeans, SVM, and Bayes classifiers. (2) Big data technology Traditional technology is difficult to realize real-time analysis and processing of massive data. Big data technology is urgently needed to coordinate the relationship between various parts to realize data analysis and mining, as shown in Fig. 4. As an open source general parallel framework, spark makes better use of memory

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and is more suitable for algorithms that require repeated iterations such as data mining and machine learning than Hadoop [6].

Fig. 4. Mechanical learning big data technology analysis student behavior

(3) Public opinion analysis technology Students’ comments on certain events are ubiquitous, and these comments are subjective and emotional. Use big data technology to analyze their opinions and comments, and judge the attitudes and emotional nature of the critics [7]. At present, the analysis of this type of text can be carried out from three aspects: words, sentences and texts. Initially, through word analysis, the part of speech emotion is judged, and then gradually through the form and characteristics of the sentence, combined with certain algorithms, the nature of the article is judged.

5.2

Applying Big Data to Analyze College Student Behavior Data

There are many student behavior data, and big data can be used to analysis from the following aspects: (1) Analysis of learning behavior Using big data to analyze the course selection and examination data of school students, on the one hand, establish a model template for students with excellent performance, study their learning and life behavior patterns, and widely promote and apply them to other students to improve teachers the level of teaching and academic performance of students [7]. On the other hand, the monitoring of students with fluctuating grades has aroused the alertness of teachers and administrators, found out the reasons for the changes, and prescribed the right medicine [8].

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(2) Analysis of big data in student employment behavior Through big data analysis of students’ comprehensive evaluation data and employment feedback information, through the analysis of students’ employment situation and comprehensive analysis of school performance, summarize the advantages and disadvantages of teaching, for students of different majors, focus on increasing investment in key points that affect students’ growth and employment, vigorously develop student social practice, and improve teaching quality and employment rate [8], as shown in Fig. 5.

Fig. 5. Analysis of big data on machine learning in student employment behavior

(3) Analyze consumption behavior Analyze students’ canteen consumption data, commissary consumption data, and water and electricity consumption data through big data, dig out students’ frequent entry and exit locations, draw student’s daily hot activity areas, and summarize the rules of students’ spare-time life [9]. (4) Moral behavior analysis Use big data to analyze students’ library credit scores, teacher-student evaluations, award information, social practice records, etc., analyze students’ ideological ethics and code of conduct, and improve ideological education for some students [10]. (5) Psychological behavior analysis Analyze students’ daily consumption data, teacher teaching feedback information, and counselor feedback information through big data, and focus on the abnormal student dynamics such as withdrawn, extreme, delusion, etc., and take effective measures to protect students’ mental health development of. For example, a student who is relatively withdrawn for a certain period leads to poor classroom performance and poor grades [10]. This can be pre-warned by analyzing teaching and consumption data, shown as Fig. 6.

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Fig. 6. Psychological behavior analysis of machine learning big data

6 Conclusion The development of campus informatization and the application of machine learning big data technology, the use of machine learning big data to analyze student behaviors and provide support for teachers to teach students in accordance with their aptitude can improve the work efficiency and service quality of college student management staff.

References 1. Zhang, J., Liu, W., Yang, B.: Analysis of the big data application analysis in discipline construction. J. High. Educ. 11(05), 1–5 (2019) 2. Yang, Y., Shi, C., Su, L.: A probe into the educational management of college students in the big data era. Road Talent 11(6), 115–118 (2019) 3. Yuan, X.M.: Research on university student education management based on big data. China Adult Educ. 4(24), 34–36 (2018) 4. Huang, L., Xue, S.: Analysis of the status quo of machine learning technology development and international competition. Modern Intell. 9(10), 165–168 (2019) 5. Zhang, J.Y.: Machine true intelligence and current machine learning technology. New Educ. Era Electron. Magaz. 12(11), 91–94 (2019) 6. Liu, W.J., Li, B.Q.: Application research of internet of things, big data analysis and machine learning technology in disaster preparedness. Microelectron. Comput. 7(12), 55–58 (2018) 7. Lin, H.Y.: Machine learning and its key technologies in the context of big data. Comput. Fans 11(06), 119–122 (2018) 8. Wang, H., Xu, C.: Research on the big data application in colleges. China Collect. Econ. 11 (17), 134–135 (2014) 9. Wang, H.: Analysis of the intelligent big data application in the employment information system of college graduates. Enterp. Technol. Develop. 34(02), 82–84 (2015) 10. Li, L., Peng, C.L.: Discussion on the situational awareness and management scheme of network behavior based on college students. Netw. Secur. Technol. Appl. 13(09), 80–83 (2019)

Adaptive Sliding Mode Control of Crawler Robot Based on Fuzzy Neural Network Zhengtao Li1(&) and Xiaoxia Liu2 1

Anhui Vocational College of Electronics and Information Technology, Bengbu, Anhui, China [email protected] 2 Anhui Medical College, Hefei, Anhui, China

Abstract. For the nonlinear system with structural instability and parameter uncertainty, this paper combines neural network theory and sliding mode control based on fuzzy neural network and designs a controller for the control problem of uncertain nonlinear system. This paper first establishes the kinematic model of crawler robot, based on adaptive sliding mode control theory, equivalent controller and switching controller based on fuzzy neural network theory to ensure the global stability and convergence of the controller. Simulation s demonstrate that it are highly adaptive and robust to the s system, a nonlinear system of crawler robot, using neural networks. And has the characteristics of rapid response and strong tracking performance. Keywords: Crawler robot network


Sliding mode control (SMC)
Fuzzy neural

1 Introduction As a typical strong non-linear system, the crawler robot has a variety of unforeseen external interference and fuselage elastic deformation [1]. For these problems, sliding mode control (Sliding mode control, SMC) of the system parameters, which is widely used in the crawler robot control field, but SMC switching in different control logic, resulting in the actual sliding mode difficult to occur on the switching surface, causing system shaking [2]. Therefore, combine other theories to suppress Douvibration. The saturation function replaces the symbolic functions in the literature [3, 4] and the problem of the boundary layer achieving the shake-vibration weakening of the robot is designed near the sliding modes. The problem of designing the time integral action of higher order sliding modes in the literature [5]. The literature combines fuzzy control and sliding mode control to achieve less shaking. For the crawler robot kinematic model constructed in this paper, Combining fuzzy neural network theory and adaptive sliding mode control design controller (Fuzzy Neural network sliding mode control, NSMC), Based on the theory of sliding mode control, Design the equivalent controller moves along the sliding surface built in this paper. To reduce interference from the external environment, using fuzzy neural network theory to construct equivalent control of adaptive law and eliminate system shaking, ensure the control system realizes a stable tracking and control. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 152–160, 2022. https://doi.org/10.1007/978-3-030-89508-2_20

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2 Track-Type Robot Kinematic Model Was Established The platform studied in this paper is based on an independently developed crawler robot, whose driving track is shown in Fig. 1. The drive system mainly consists of drive wheels, driven wheels, induction wheels and tracks. Two DC servo motors drive two tracks respectively, which can have the speed and position state of the robot tracks adjusted by adjusting the input voltage of the servo motor [6].

Fig. 1. Schematic diagram of the crawler robot exercise trajectory

In Fig. 1: B is the drive wheel spacing, the angle h between the driving speed direction and the x axis, P is the connection intersection between the ideal track and the ideal track geometric center point A, and the angle u between the tangent and x axis at the P point of the ideal track. Defining the turning radius R of the t and its center of mass O at the instantaneous A point at some point [7]: R¼

Bðtl þ tr Þ 2ð tl  tr Þ

ð1Þ

Formula: tl and tr is the line speed of the left and right wheels relative to the ground; The two-drive wheel center speed is: tc ¼

t l  tr 2

ð2Þ

At a certain moment Dt ! 0, judging from the geometric relationship and ideal path analysis of the crawler robot in Fig. 1, the differential form of the robot azimuth h and its central position d is: tl  tr 2 h_ ¼ ¼ Dt R R

ð3Þ

tl þ tr d_ ¼ sin h ¼ tc sin h 2

ð4Þ

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According to the DC servo motor characteristics: tr ðsÞ ¼

kn U r ðsÞ 1 þ Tm s

ð5Þ

tl ðsÞ ¼

kn U l ðsÞ 1 þ Tm s

ð6Þ

In the formula: Tm is time constant; kn Drive gain for the robot transmission system; tr ðsÞtl ðsÞ is Lplace transformation of tr tl , respectively, Ul ðsÞ,Ur ðsÞ is Lplace transformation of armature voltage of left and right servo motors; formula (5) minus formula (6) is: DtðsÞ ¼

kn DUðsÞ 1 þ Tm

ð7Þ

Formula (7) Laplace is available: Dt_ ðtÞ ¼ 

1 kn DtðtÞ þ DUðtÞ Tm Tm

ð8Þ

The motor parameters kn ; Tm are motor parameters, and the influence of external interference signal is D, Formula (8) can be change to: Dt_ ¼ 

1 kn þ Dkn Dt þ uþD Tm þ DTm Tm þ DTm

ð9Þ

Expand and with (9) Taylor to: Dt_ ¼ 


 1 DTm kn Dkn DTm kn Dt þ 2 Dt þ uþ  uþD Tm Tm Tm þ DTm Tm T2m

ð10Þ

The formula (3) requires: 2 h ¼ Dt_ R

ð11Þ

Substitute formula (10) to formula (11), in _ €h ¼  h þ 2kn u þ 2n Tm RTm R

ð12Þ

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3 Control Unit Design 3.1

Fuzzy Neural Network

The fuzzy neural network controller structure diagram is shown in the figure. A fourlayer network based on the Mamdani model is adopted. The first layer is the input layer, each node is connected to the input vector xi , and the number of nodes is the dimension of the vector xi . The second layer is the fuzzy layer, which completes the fuzzy input through the fuzzy membership function and obtains the corresponding fuzzy membership. Because the Gaussian function is well smooth, this paper uses it as a fuzzy neural network function, and its membership function is " 

xi  cij gij ¼ exp  2r2ij

2 # j ¼ 1; 2; . . .; u; i ¼ 1; 2; . . .; r:

ð13Þ

In the formula, gij is the i Gaussian function of the fifth j; cij is the i Gaussian function center of the fifth j; rij is the i Gaussian function height of the fifth j; k is the input vector dimension; n is the number of neurons. Its membership value output is: "

 2 # r X xi  cij nj ¼ exp  j ¼ 1; 2; . . .; u; 2r2ij i¼1

ð14Þ

The third layer is the rule calculation layer. Each node represents a fuzzy rule, using the scale of the membership as a fuzzy rule fj = Wj * Wj. 3.2

Design the Sliding Mode Control Law

Nonlinear system formula (12) for crawler robots established above, order,m ¼ 2n=R, _ m ; g ¼ 2kn =ðRTm Þ, formula (12) is expressed as: f ðh; tÞ ¼ h=T €h ¼ f ðh; tÞ þ gu þ m

ð15Þ

The desired output of the controller is hd , the tangential angle at P at the ideal path tangent h. The real-time output direction angle of the controller tracks the tangential angle of the track, while making the robot center position deviation tend to 0, thus achieving the purpose of smooth tracking the ideal path d [8]. Make the angle tracking error: eðtÞ ¼ h  hd . Adaptive sliding mode variable structure control is a new control strategy to solve the problem of parameter uncertainty or time-varying parameter system control, to enhance the good overperformance and robustness of the control process [9]. Define the integral slip modes:

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  Zt sðtÞ ¼ h_ ðtÞ  €hd  k1 e_ ðtÞ  k2 eðtÞ dt

ð16Þ

0

Among which, the sum of k1 ; k2 is not a zero normal number. When the sliding mode control is ideal, that is sðtÞ ! 0, the tracking error eðtÞ will be close to zero. Convert the formula (18) guidance to: s_ ðtÞ ¼ €hðtÞ  h_ d þ k1 e_ ðtÞ þ k2 eðtÞ

ð17Þ

By placing formula (17) into formula (19), the control law in the sliding mode state is:   u ðtÞ ¼ g1 gsD ðtÞ  f ðh; tÞ þ €hd ðtÞ  k1 e_ ðtÞ  k2 eðtÞ

ð18Þ

In formula: g is normal number, sD ðtÞ is the algebraic distance between the state and the boundary layer, / is the thickness of the boundary layer. Substitute formula (20) into formula (17), and then substitute the result into formula (19) to: s_ ðtÞ þ gsD ðtÞ ¼ 0

ð19Þ

When jsj [ / and t ! 1, then sD ðtÞ ! 0, the size of eðtÞ ! 0 neighborhood is related to the value /. Due to non-deterministic factors such as inertia, delay and external noise to change the intermediate parameters f ðh; tÞ and g in formula (20), so it is difficult to achieve accurate tracking control of the output calculated according to formula (20). Based on the fuzzy neural network theory approximation u ðtÞ, with the sum of sðtÞ; s_ ðtÞ as the input amount, the control law ue ¼ /T nðxÞ is designed according to the formula (16) derived above, using the adaptive law formula (22) [10]. U_ ¼ rsD ðtÞnðxÞ

ð20Þ

r is normal number in the formula. Control the difference usw between compensation u and u ðtÞ by switching. usw ¼ EðtÞsgnðsD ðtÞÞ

ð21Þ

Defined adaptive law EðtÞ is: E_ ðtÞ ¼ g2 jsD ðtÞj g2 is normal number in the formula.

ð22Þ

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The total control law of adaptive sliding mode based on neural network is: ue ¼ ue þ usw

ð23Þ

4 Simulation The control theory proposed in this paper uses MATLAB /Simulink for simulation research and establishes a simulation model [11]. Simulation parameters are selected as Table 1:

Table 1. Simulation parameters in simulation model Motor time parameters Motor drive gain Wheel radius Wheel spacing kn ¼ 0:4 V/s r = 0.12 m B = 0.6 m Tm ¼ 0:21 s

The initial value w and E is set to 0.1, and the track robot driving speed is 3 m/s. The simulation sampling frequency is 100 Hz, fuzzy neural network controller input.½x1 ; x2  ¼ ½s; s_ . The angle tracking curve and path tracking curve of the simulation object are y ¼ sinx, its starting state ½xð0Þ yð0Þ ¼ ½ 0 0 , angular starting state error ½h; d  ¼ ½p=6; 0, ideal trajectory starting state ½xð0Þ yð0Þ ¼ ½ 0 0 , starting state of crawler robot ½xð0Þ yð0Þ ¼ ½02:5, and starting state error ½ e1 ð0Þ; e2 ð0Þ ¼ ½0; 2:5. Initial value of sliding mode control k1 ¼ 5; k2 ¼ 12; g ¼ 0:3, the response termination time is 30 s, adopts adaptive rhythm (22) and control law (23), and the control flow block diagram is shown in Fig. 2.

Fig. 2. Flow chart of control process

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Fig. 3. NSMC Control curve

Fig. 4. SMC Control curve

Fig. 5. Angle tracking deviation curve

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Fig. 6. Path following deviation curve

Compared to Figs. 3 and 4, the NSMC control output curve is smooth and can compensate for parameters according to switching control, with the characteristics of real-time adjustment of parameters, its shaking amplitude is relatively small because of the strong resistance of adaptive law in the control system to external environmental interference. The analysis of Fig. 5 shows that at the initial moment, due to the large initial deviation between the robot initial position and the ideal trajectory, the NSMC in the starting phase. It proves that the new control method has the ability of angle tracking and external interference. As can be seen from Fig. 6, in terms of path tracking, after the simulation time of about 3.6 s, both controllers can have fast response performance, good dynamic performance and no superharmonic steady state error, but the NSMC control error in the initial state is small relative to SMC, the output is smooth, and the resulting error tends to 0. This is because the SMC output in the figure is calculated by formula (19) with poor interference and parameter perturbation ability, so the medium shake amplitude in the control curve is large. The equation of state controlled by NSMC is based on the fuzzy neural network theory to approximate the initial value of the optimal parameters of the input fuzzy network and modify the prediction model, reducing the output error and making the dynamic performance and rapid response.

5 Conclusion In this paper for crawler robot nonlinear system and neural network approximation, approximate the nonlinear function, improve the controller accuracy, combine the sliding mode control, reduce the system tracking error, play a robust control role. Simsimulation theorem show that the dynamic response, adaptability and stability are better than the traditional SMC control, which can realize the path tracking control of crawler robot.

References 1. Jwo, D., Lai, C.: Unscented kalman filter with nonlinear dynamic process modeling for GPS navigation. GPS Solutions 9(12), 249–260 (2008)

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2. Low, C.B., Wang, D.: GPS-based tracking control for a car-like wheeled mobile robot with skidding and slipping. IEEE Trans. Mechatron. 13(4), 480–484 (2008) 3. Park, C.G., Kim, K., Kang, W.Y.: UKF based in-flight alignment using low cost IMU. AIAA Guidance, Navigation, and Control Conference, pp.2637–2648 (2006) 4. Zhou, B., Dai, X., Han, J.: Online modelling and tracking control of mobile robots with slippage in outdoor environments. Robot 33(3), 265–272 (2011) 5. Jun, J., Wuwei, C., Jixian, W., et al.: Automated guided vehicle self-adaptive variable structure control based on genetic algorithm. Trans. Chin. Soc. Agricult. Mach. 39(3), 114– 118 (2008) 6. Jun, J., Wuwei, C., Jixian, W., et al.: Automatic guided vehicle variable structure control based on genetic algorithm and least aquare-support vector machine. J. Syst. Simulat. 20 (14), 3777–3781 (2008) 7. Jiao, J., Jiang, C., Jin, R., et al.: Adaptive internal model control for agricultural robot steering system. Trans. Chinese Soc. Agric. Mach. 42(10), 186–191 (2011) 8. Juan, C., Lideng, P., Liulin, C.: A filter’s time parameter self-adjusting method based on the internal model control for time-delay system. J. Syst. Simulat. 18(6), 1630–1633 (2006) 9. Liu, K.: Gain adaptive internal model control of time delay systems based on Pade approximation. J. Wuhan Univ. Eng. Sci. 34(4), 93–95 (2001) 10. Isashi, H., Nakagaw, T.: Recent trends in sheet metals and their formability in manufacturing automotive panels. J. Mater. Process. Technol. 46(4), 455–487 (1994) 11. Stuart, P.K.: Application and forming of higher strength steel. J. Mater. Process. Technol. 46 (4), 443–454 (1994)

Application of Machine Learning Algorithms in Financial Market Risk Prediction Yunfei Cao(&) Sichuan University, Chengdu, Sichuan, China [email protected] Abstract. The financial industry has a fairly direct impact on our country’s economic development, and financial institutions are paying more and more attention to risk management. In order to reduce the risk rate, it is particularly important to predict the risk. Therefore, the prediction of financial market risks has always been an important direction of financial industry research. The purpose of this article is to explore the application of machine learning algorithms in financial market risk prediction. This paper analyzes the main characteristics of the financial market, explores the significance of financial market risk prediction, establishes a financial market risk prediction system based on machine learning algorithms, and explains the process of financial market risk prediction. Finally, experiments are carried out to verify the effectiveness of the financial market forecasting model proposed in this paper. Experimental data shows that the prediction success rate of the prediction model proposed in this study in the test set is 95.64%, which is only 0.54% away from the original 95.10% in the training set. It is a relatively successful prediction model. Keywords: Risk prediction market


Machine learning
Financial risk
Financial

1 Introduction In recent years, with the rapid development of computer technology and Internet technology, various companies have stored a large amount of financial information, and big data technology and machine learning algorithms have emerged [1, 2]. As an important core of the modern economy, the contemporary financial industry has a fairly direct impact on the economy [3, 4]. Among them, the risk prediction and prevention of the financial market is an important part of the research on risk management of the financial industry [5, 6]. How to use machine learning algorithms to analyze and model financial information to establish a complete financial market risk prediction system has become a new opportunity for risk management in the financial industry [7, 8]. Regarding the research on financial market risk prediction, many scholars at home and abroad have conducted in-depth discussions on it. For example, Zhao D studied the key factors of risk measurement from the perspective of quantitative analysis, namely the tail probability of financial asset return distribution and portfolio profit and loss distribution [9]; Yeh HY pointed out that if the price of financial products fluctuates greatly or the volatility jumps, the asset value may change significantly, or the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 161–168, 2022. https://doi.org/10.1007/978-3-030-89508-2_21

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investment portfolio contains financial derivatives, etc., and the VaR value may not be Appropriate risk measurement indicators [10]; starting from the characteristics of financial forecasting, Qi Q introduced the wavelet clustering algorithm and FCM clustering algorithm to the chaotic time local model [11]. It can be seen that most scholars only study a single economic period or financial institution, and relatively few scholars study economic conditions in multiple periods or financial markets. This paper takes the application of machine learning algorithms in financial market risk prediction as the research purpose. Taking financial stock market risk prediction as the research object, combined with machine learning algorithms, the main characteristics of the financial market are first analyzed, and the significance of using machine learning algorithms to predict financial market risks is discussed. Then, a financial market risk prediction system based on machine learning algorithms is established to explain the process of financial market risk prediction. Finally, through experiments, the model proposed in this paper is compared with the other three models, and the validity of the prediction model proposed in this paper is verified.

2 Application of Machine Learning Algorithms in Financial Market Risk Prediction 2.1

Main Characteristics of Financial Markets and Financial Market Risks

(1) Market segmentation The composition of the financial market itself is very complex, so there are multiple classification methods according to the specific types of credit instruments, such as bonds, accounts, foreign exchange, gold, and stock exchanges. For example, the money market refers to the short-term (within one year) capital market; the capital market refers to the long-term (more than one year) capital market, whose main function is to provide long-term funds for governments, enterprises and individuals to realize currency resources. The issuance of stocks is an important part of the financial market. The stock market refers to the venue for stock issuance and trading activities. On the one hand, it provides a convenient trading platform for listed companies to realize monetary capital financing. On the other hand, the free trading of stocks and stocks, and real-time price changes can provide a lot of information for the business choices of listed companies. (2) The continuous increase of financial innovations or derivatives Driven by government deregulation and science and technology, innovative activities to circumvent the management system are gradually unfolding, and a new trend of innovative thinking has emerged. Generally speaking, the purpose of financial derivatives is to decompose various risks in the core financial market, but in fact it is a product that leads to further increases in risks [12]. But because it is an effective risk management tool, it is welcomed by many financial companies. (3) Uncertainty of benefits or costs Generally speaking, financial market risk is considered to be the source of other types of risks and plays an important role in many types of financial risk analysis.

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As asset prices fluctuate to varying degrees due to uncertain factors such as information asymmetry, investors or institutions will encounter fluctuations in investment returns or price fluctuations in the process of investment and financing. 2.2

Significance of Using Machine Learning Algorithms for Financial Market Risk Prediction

In order to expand the scale of business and reduce the risks caused by market risks, the financial industry adopts a market risk assessment model based on machine learning algorithms. In the current era of big data, the financial industry collects a large amount of data such as exchange rate changes and stock price fluctuations that urgently need to be processed, capture hidden features related to market risks, and establish a predictive classification model of future market risks as a basis for decision-making in the development of the current financial industry. Based on this, the Internet financial platform adopts a financial market risk prediction model based on machine learning algorithms, which can expand the business scale of the financial industry and promote the development of our country’s financial sector. 2.3

Establishment of the Structure of the Financial Market Risk Prediction System Based on Machine Learning Algorithms

(1) Determination of index parameters Different risks brought by financial markets can be measured by various economic and financial parameters. Therefore, the most critical point of the financial market risk assessment system is how to obtain early warning indicators. There are many factors that affect the financial market, and the importance of these different factors depends on the level of the financial market, the level of development of the financial system structure, and the level of difference in government-related economic policies. Therefore, from the perspective of risk analysis, the selected indicator parameters are also very different. (2) Early warning limit When predicting financial risks, two issues need to be determined: one is the selection of early warning indicators; the other is the measurement model for determining the level of early warning. Therefore, it is necessary to clarify the evaluation criteria of the risk level in the financial market risk management, and use this as the basis for deciding whether to take measures to maintain financial stability. (3) Data processing Financial market risk prediction information data comes from financial monitoring. Through financial monitoring activities, multiple high-value regulatory signals are obtained, and then quantitative models are used to analyze and evaluate the monitoring results. Next, look for information that affects the financial performance of the financial industry in the future and the feedback signals obtained from the evaluation, sort or edit the searched signals, calculate the change or increase rate of each parameter, and edit the corresponding time series.

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(4) Light indicator Different light signals are used to represent the security status of different sectors in the financial market, which can intuitively display different types of financial market risk predictions and the situation. According to the different conditions of different financial risks, different score ranges are given. The higher the score, the better the financial status; the lower the score reflects the higher the risk and the greater the stability of the financial market. (5) Obtain the final evaluation The various risks generated by finance are unclear and cannot be described with certainty. Its formation is based on the influence of many factors, and it cannot be refined. However, the establishment of various financial risks requires various Parameters can be expressed with specific data, which is also a key factor in the risk prediction system. 2.4

Related Models

(1) CovaR model Given a financial institution with a rate of return of rti , and a confidence level of p, then VaRiip can be expressed as:
 Pr rti  VaRiip ¼ 1  p

ð1Þ

In the formula, VaRiip is usually a negative value, but in practical applications, VaRiip is generally expressed as a positive value.

(2) Mean-CoVaR model 8 > > < > > :

Subject to

M in a þ b VaRtj P Pn xi ¼ 1; ni¼1 xi rt ¼ Rp  rexp Pi¼1 n  j i i¼1 ri xi ¼ a þ b ri þ et i ¼ 1; . . .n; t ¼ 1; 0  xi  1

ð2Þ

In the formula, xi is the weight of the investment in the i-th asset, Rp is the expected return rate of the asset portfolio P, a and b are the estimated coefficients in the quantile regression, et is the residual term in the quantile regression, and rexp is the required rate of return of asset portfolio P, rt is the average daily rate of return of asset i, rij is the rate of return of asset i at t, rij is the rate of return of the market at t, and VaRtj is the rate of return of the market at a significant level. The VaR value under s%, n is the number of underlying assets in the portfolio P.

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3 Experimental Design 3.1

Selection of Sample Data

In order to better explain, verify and analyze the effectiveness of the proposed financial market forecasting method, this experiment will use open source real data sets to verify the effectiveness of the forecasting method proposed in this article. This data set is the desensitized asset return related data from the branch of China H Commercial Bank. Among them, the sample training data set contains 2000 sample data, and the test data set contains 1700 sample data. 3.2

Data Preprocessing

In order to ensure the accuracy of the prediction results, first process the collected asset income data of the bank. For the training set and validation set, if the data is missing as Null (NaN) information, this experiment uses * or 0 as a substitute. This experiment collected a total of 16 key characteristics related to the key characteristics of the financial market. Among these features, the completeness of some data features is very lacking. Therefore, these useless features are discarded before prediction to prevent interference with the prediction results. 3.3

Evaluation Index of Model Prediction Effect

After all models are established, the mean square error (MSE), mean absolute error (MAE), HMSE adjusted for heteroscedasticity of MSE, and mean absolute percentage error (MAPE) are used to measure the prediction effect of the model proposed by this research. The definitions of the four statistical indicators are: MSE ¼

1 Xn ð x  xi Þ 2 i¼1 i n

1 Xn jx  ^xi j i¼1 i n   1 Xn xi  ^xi 2 HMSE ¼ i¼1 n xi   1 Xn xi  ^xi  MAPE ¼  i¼1  x n i MAE ¼

ð3Þ ð4Þ ð5Þ ð6Þ

In the formula, xi represents the true value, ^xi represents the predicted value, and n represents the number of volatility prediction windows.

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4 Analysis of Experimental Results 4.1

Comparison of Prediction Effects

Based on the EGARCH model, GELM model, EGARCH + EFKOS-GELM model proposed in related research, and the model based on the machine learning algorithm proposed in this paper, the data set is predicted, and the results are shown in Table 1: The prediction proposed by this research, the MSE value, MAE value, HMSE value and MAPE value of the model are 0.0002, 0.0117, 0.0023, 0.0412, respectively. Table 1. Comparison of prediction effects of different models MSE EGARCH model 0.0349 GELM model 0.0009 EGARCH + GELM model 0.0004 This research 0.0002

MAE 0.1702 0.0300 0.0195 0.0117

HMSE 0.4230 0.0107 0.0046 0.0023

MAPE 0.5606 0.1045 0.0677 0.0412

It can be seen from Fig. 1 that under all four indicators, the prediction error of the model proposed in this paper is smaller than that of the other three models. The mixed model proposed in this paper has the smallest prediction error and the highest prediction accuracy. The combination of neural network and traditional prediction methods can improve the prediction accuracy of the model.

MAE

HMSE

MAPE

Number

MSE

EGARCH model

GELM model

EGARCH+GELM model Model

this research

Fig. 1. Comparison of prediction effects of different models

4.2

Forecast Accuracy

Comparing the prediction accuracy of the three models on Bank H’s return on assets, the results are shown in Table 2. The prediction accuracy of the training set of the

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EGARCH model, the GELM model, and the EGARCH + GELM model are 91.23%, 90.16%, and 91.26, respectively. %; the prediction accuracy of the test set is 93.14%, 92.16%, and 93.37% respectively. Table 2. Forecast accuracy of different models (%)

EGARCH model GELM model EGARCH + GELM model This research

Training set prediction accuracy 91.23 90.16 91.26

Test set prediction accuracy 93.14 92.16 93.37

Gap value 1.91 2 2.11

95.10

95.64

0.54

Test set prediction accuracy

Training set prediction accuracy

this research

Model

EGARCH+GELM model

GELM model

EGARCH model 84

86

88

90 92 Unit: %

94

96

98

Fig. 2. Forecast accuracy of different models (%)

It can be seen from Fig. 2 that the prediction success rate of the prediction model proposed in this study is 95.64% in the test set, which is only 0.54% away from the original 95.10% in the training set. It is a relatively successful prediction model.

5 Conclusion In a society of economic globalization, the financial markets of various countries are gradually merging, and the mutual connection continues to myth. The market is driving capital flows and accelerating the efficiency of resource allocation. At the same time, it also brings greater risks to the financial markets of various countries. This paper proposes a financial market risk prediction model based on machine learning algorithms, and conducts an experimental analysis on the prediction model, and verifies the effectiveness of the model proposed in this paper.

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References 1. Huang, X.: Macroeconomic news announcements, systemic risk, financial market volatility, and jumps. J. Futur. Mark. 38(5), 513–534 (2018) 2. Carolin, P., Emil, S., Adi, S.: Financial market risk perceptions and the macroeconomy*. Q. J. Econ. 3, 3 (2020) 3. Jacobs, P.D., Cohen, M.L., Keenan, P.: Risk adjustment, reinsurance improved financial outcomes for individual market insurers with the highest claims. Health Aff. 36(4), 755 (2017) 4. Guijarro, F., Moya-Clemente, I., Saleemi, J.: Liquidity risk and investors’ mood: linking the financial market liquidity to sentiment analysis through Twitter in the S&P500 index. Sustainability 11(24), 7048 (2019) 5. David, C., Sergei, S., Schill, M.J.: Market and regional segmentation and risk premia in the first era of financial globalization. Rev. Finan. Stud. 10, 4063–4098 (2018) 6. Junttila, J., Pesonen, J., Raatikainen, J.: Commodity market based hedging against stock market risk in times of financial crisis: the case of crude oil and gold. J. Int. Finan. Market. Inst. Money 56, 255–280 (2018) 7. Wang, M.: Financial risk prediction model of international trade enterprises of marine transportation. J. Coastal Res. 103(sp1), 173 (2020) 8. Song, Y., Peng, Y.: A MCDM-based evaluation approach for imbalanced classification methods in financial risk prediction. IEEE Access 7, 84897–84906 (2019) 9. Zhao, D., Ding, J., Chai, S.: Systemic financial risk prediction using least squares support vector machines. Mod. Phys. Lett. B 32(17), 1850183 (2018) 10. Yeh, H.Y., Yeh, Y.C., Shen, D.B.: Word vector models approach to text regression of financial risk prediction. Symmetry 12(1), 89 (2020) 11. Qi, Q.: Study on financial risk prediction of enterprises based on logistic regression. J. Comput. Method. Sci. Eng. 3, 1–7 (2021) 12. Jia, L., Li, S., Zhu, X.: Hydrological layered dialysis research on supply chain financial risk prediction under big data scenario. Discret. Dyn. Nat. Soc. 2018(31), 1–9 (2018)

The Application of Big Data Analysis Technology in the Research of English Online Learning Platform Jun Li(&) School of General Education, Nantong Institute of Technology, Nantong 226006, Jiangsu, China

Abstract. English is becoming more and more important in our daily life, and learning English also happens at any time, especially now that high-tech products emerge in endlessly. It is very convenient to use learning English on mobile devices such as mobile phones, ipads and other products. Therefore, online learning English has become extremely popular. As a result, there are countless platforms for learning English on online platforms, but the learning content they provide is extremely uniform. All adults and students, no matter what their learning goals are, every learning content they see should be the same, so there will be problems, only what is the result of each same learning content? Especially adults and adolescents, the learning goals are relatively clear, the needs of work or academic exploration and research, etc., a single learning content is completely untargeted for them, and the learning time of adults is relatively short, and the impact of such learning can be imagined. This paper focuses on the application of big data analysis technology in the research of English online learning platform. First, it has a general understanding of the online English platform based on relevant materials, and then on the basis of the problems that appear in the English online learning platform, and then summarize the application of big data analysis technology in the research of English online learning platform, and finally design the data mining system of English online learning platform, test it according to the designed system, and obtain the test results. It is shown that although the system still has its shortcomings, in general, the number of people with better evaluations of the system’s performance accounted for more than 32%, and generally more than 30%. Keywords: Big data analysis mining


Online English
Learning platform
Data

1 Introductions For our country, the educational research and learning time of English teaching and cultural courses is relatively short [1, 2]. Compared with foreign countries, the starting time for learning English online is also very short [3, 4]. There are still many shortcomings in this part of the domestic data management. What we face is that the overall quality of an online English learning platform is low [5, 6], and the application of data mining technology in online learning platforms also makes this problem available. It is © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 169–176, 2022. https://doi.org/10.1007/978-3-030-89508-2_22

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a very good solution, so the research on the application of data mining and analysis technology in the online learning platform is also very important [7, 8]. In the research of big data analysis technology in the English online learning platform, the current researches include: cluster analysis, decision tree classification method, the registration information of students when they log on to the website, and the analysis of the performance of each subject, so as to classify students, discover students’ interests, use the classification results to group different students scientifically, form an online learning exchange group, and learn more in exchange learning [9]. Use correlation analysis technology to analyze the web browsing logs of students in the process of online learning, including content structure, mining of usage records, discover effective, novel, and potentially useful information, and predict which web pages students will browse in the future, but this is mostly theoretical Sexual research [10]. Some researchers have conducted research on the problems of English online learning and pointed out that online courses lack unified norms and standards, and there is no special development team for courses [11]. There are no professional researchers and developers for this course in China. Many countries have established standards for online learning courses. Online independent learners can design and develop the content they need to learn according to this standard, but my country has not yet established a standard. The courses of many network research institutes can only transfer the content of our traditional classroom teaching to the Internet, and some people’s learning content is even the presentation made by the teacher in their face-toface classroom. There is no dedicated teacher team to develop online courses, and of course there is no certain development process [12]. This article focuses on the application of big data analysis technology in the research of English online learning platform, and has made a general understanding of online English learning platform based on relevant literature. Then, on the basis of it, it summarizes the main problems of English online learning, then analyzes and summarizes the application of big data analysis technology in English online learning platform, and finally designs a data mining system based on English online learning platform.

2 Research on Big Data Analysis Technology and English Online Learning Platform 2.1

Problems in English Online Learning

(1) There is a lack of analysis of students’ initial abilities, and the classification of courses also lacks a scientific basis. Many courses have set clear standards and levels. For example, courses like English are divided into three levels: high, medium and low. For underage students, what level of education courses should they master? Many online learning platforms do not have a scientific education category as a basis for assessment. For example, online learning courses use a scientific and common standard-cefr to classify levels. This is very scientific, but the platform does not have a clear method to determine what level or what a learner belongs to courses, and what standards they want to use. The dividing

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method is that online teachers artificially divide the student level test results of each type of course at each level according to their own teaching experience. For example, 10 points or less belong to the entry level. Such classification standards are very unscientific and cannot be used. It cannot really reflect the English proficiency of students. (2) The learning content of the same course is independent of each other The courses provided by many online learning platforms are relatively independent. For example, they are provided to students by chapters, and the correlation between chapters is rarely considered. For example, online learning courses are divided into four parts: listening, speaking, reading, and writing. Each level of the course includes these four parts, and the content of each part is independent of each other and has no connection. Students should study each part while studying. No matter what kind of English skills they want to improve, the listening, speaking, reading and writing of English will definitely affect each other and have a certain connection. So what kind of connection is there? We can focus on cultivating certain skills of students, and other skills will be significantly improved. These problems are not tested on the EF online learning platform. Their learning content is independent of each other, separated from each other, and not connected. (3) The learning content is single and fixed, and learners cannot customize it individually The courses provided by many online learning platforms are fixed, so learners learn the same content, regardless of their initial ability, knowledge background, and learning purpose, they can only learn the same content as a single, without the right to choose. 2.2

Application of Big Data Analysis Technology in English Online Learning Platform

(1) Clustering processing learners First, classify according to the learning level of different English students, which must be applied to modern cluster analysis algorithms. Before students enter the learning platform, their actual knowledge and English proficiency must be collected for them according to the final reflected results, and different English proficiency levels must be determined, and then English teachers will be arranged to assign different learning groups and learning content to their English learning proficiency. Through practical research, we found that through this grouping method, students can more clearly identify their own English learning level, and teachers on the online English learning platform can also personalize students according to their specific conditions, and the effect of learning English is better. (2) Correlation analysis learning content We believe that the application of data mining technology on the English network platform can roughly divide the process of English learning into four segments, namely, reading, listening, writing, speaking, etc., and then through the application of relevant analysis methods, it can respond to different paragraphs. Through

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the correlation analysis of different paragraphs, the rules for the correlation of different content between different paragraphs can be obtained, and then the level of online learning can be judged. Teachers can conduct targeted teaching work for students according to the problems reflected on the learning platform. (3) Development and application of online platform content organization tools The application of the algorithm of association rules contained in the mining results from data mining and cluster analysis can help online English teachers develop automated learning content according to the actual teaching needs in teaching. The development tools and this tool require teachers to integrate the actual learning situation of learners. So that it will finally appear on the English learning platform should include a modification function, which is useful for different students’ learning levels, develop different learning content from learning problems.

3 Data Mining System Design Based on English Online Learning Platform 3.1

The Overall Design of System Functions

According to the requirements of system functions, the author describes the system structure of the system, which mainly includes four levels: content library layer, student data layer, content organization layer and content display layer. Figure 1 is the architecture diagram of the content organization system designed in this article.

Content display layer

Content organization layer

Student data layer

Content library layer

Fig. 1. System function total design

3.2

Content Organization Layer

The content library layer is the database of the system, which contains four tables, namely the content library of the listening part, the content library of the speaking part, the content library of the reading part and the content library of the writing part.

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173

The Realization of the System

(1) Development mode The system adopts B/S architecture for design and development. The B/S (browser/server) structure is called the browser/server structure, which is based on a three-tier structure, namely, the browser, the Web server, and the database server. The browser is the interface between the system and the user, and is responsible for transmitting user requests and displaying the content that the user wants. The web server is responsible for retrieving the data that the user wants from the database and accepting the data returned by the database for sorting. The database server provides data for the application. (2) Development tools Java is very suitable for the Internet or corporate network environment, so it has become one of the most popular and important programming languages on the Internet. Compared with C++, Java has deleted many unused features, including those whose advantages outweigh the disadvantages; simple, object-oriented, distributed, structurally neutral, portability, high performance, interpretability, reliability, safety, multithreading, dynamics and other advantages. Clients of any processor are allowed to run and stream on the Internet, so this article chooses Java as the development tool. 3.4

Database Design

(1) Collect data This paper randomly selects 1000 students’ ability test data from the online learning platform for complex analysis. The level test is divided into four parts: listening, speaking, reading and writing. The score for each unit is calculated on a 100-point scale. At the same time, this article extracts more valuable data from the basic data of these 1,000 students as the basis for the analysis of cluster mining results, including age, gender, occupation, position, reason for learning, etc. Since this article mainly studies online learning, the learning objects are all adults. The age limit for selected subjects is 20 years old and above. The value ranges of occupations, positions, and learning reasons are all based on the online English learning platform. (2) Data preprocessing According to the in-depth research of English teachers on the online platform, adult learners want to learn complete English. They divide the built-in English into six levels: entry level, elementary, intermediate, intermediate, advanced, advanced and competent. Therefore, this article sets the grouping categories to six, representing the six English levels listed above: entry level, elementary, intermediate, intermediate, advanced, and competent.

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4 System Inspection 4.1

Experimental Design

The subjects selected for the trial are randomly selected employees of an English platform, a total of 82 people, after the use of the data mining system based on the English online learning platform. After the trial, the questionnaire was issued and asked to fill it out. A total of 82 copies were distributed and 82 copies were recovered, of which 80 were valid questionnaires. 4.2

Data Processing

(1) When performing correlation analysis on the collected data, the data must be classified and sorted. This will not only increase the utilization rate of the data, but also promote cross-data analysis. Therefore, the main consideration is the completeness and accuracy of the data. First of all, about data integrity. When the questionnaire is delivered to the sample subject for completion and collection, some sample items are arbitrarily completed, or their selection cannot be completed, which will cause some data sorting problems, but because the retrieved data accounts for the majority, so the deleted data means the need to delete the lost data. Secondly, the precision and accuracy of the data, when conducting an audit, the main consideration is to check whether these data are inconsistent with other choices, or the principle that conflicts with them should be selectively removed but as much as possible should be retained. (2) The main meaning of a correlation relationship in the objective correlation analysis method is to generally refer to a certain relationship between various objective phenomena, but they are not strictly corresponding to each other in quantity. There are two main forms of determining the relevant properties of objective phenomena here: qualitative analysis and quantitative analysis. The main purpose of qualitative analysis is to rely on the scientific theoretical knowledge and practical experience of the researcher to accurately judge whether there are correlations between various objective phenomena. Or what kind of factor, the subjectivity of this analysis method is relatively strong. Among them, the commonly used calculation formula is expressed as: P S^ 2xy ðx  xÞðy  yÞ=n ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r¼ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiqP SxSy ðy  yÞ^ 2=n Rðx  xÞ^ 2=n

ð1Þ

P P P n xy  x y ffi r ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffi P P P P n x^ 2  ð xÞ^ 2 ðn y^ 2  ð yÞ^ 2Þ

ð2Þ

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175

Analysis of Experimental Results

Through the questionnaire survey, the data mining system based on the English online learning platform designed in this paper is tested and the rationality of the system is tested. The survey results are shown in Table 1: Table 1. Data mining system based on English online learning platform

Good General Not good Do not know

Experimental interface effects 46% 32% 10% 12%

percentage

60%

System response time 34% 45% 9% 12%

Facilitate project management 48% 30% 13% 9%

Content design 32% 47% 12% 9%

Experimental interface effects. System response time Facilitate project management Content design

40%

20%

0% Good

General evaluation Not good

Do not know

Fig. 2. Data mining system based on English online learning platform

It can be seen from Fig. 2 that although the system still has its shortcomings, in general, the number of people with better evaluations of the system’s performance accounted for more than 32%, and generally more than 30%.

5 Conclusions By continuously deepening the application of cloud computing technology in the education field, platform-based education reform will bring huge resources. By analyzing the large amount of data on the platform, we can obtain useful resources for different students to learn, and continuously improve the operation of the platform. Combining with the problems of students in mathematics and science learning at the basic education stage, if they have insufficient understanding of previous knowledge points, it will affect current or future learning, and most students cannot effectively control the deficiencies and make up for the vacancies in time, but with extensive help the applied data mining technology can provide effective assistance for teaching and learning, analyze the online completion of student homework, and fill the gap in traditional education.

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References 1. Veryaeva, K., Solovyeva, O.: The influence of gamification and platform affordances on user engagement in online learning. Int. J. Dist. Educ. Technol. 19(1), 1–17 (2021) 2. Baker, R., Wang, F., Zhenjun, M.A., et al.: Effectiveness of an online language learning platform in China. J. Interact. Learn. Res. 29(1), 5–24 (2018) 3. Liu, J.: Study on the autonomous learning of college English based on online learning platform. Int. J. Smart Home 10(2), 165–174 (2016) 4. Albashtawi, A.H., Bataineh, K.: The effectiveness of google classroom among EFL students in Jordan: an innovative teaching and learning online platform. Int. J. Emerg. Technol. Learn. (iJET) 15(11), 78–89 (2020) 5. Wang, P., Qiao, S.: Emerging applications of blockchain technology on a virtual platform for English teaching and learning. Wirel. Commun. Mob. Comput. 2020(2), 1–10 (2020) 6. Cheng, Y.: Cultivation of college students’ autonomous English learning ability in IT environment based on project-based learning platform. Rev. Facult. Ingenier. 32(9), 100– 105 (2017) 7. Husaini, R.: Student’s response in online learning process: a case study of English education students. JETLe (J. English Lang. Teach. Learn.) 2(1), 16–22 (2020) 8. Rakhmanina, L., Martina, F., Halolo, F.B., et al.: Students’ perception on online English learning during Covid-19 pandemic era. Silamp. Bisa J. Penelit. Pendidik. Bahasa Indonesia Daerah dan Asing 3(2), 428–439 (2021) 9. Emelyanova, O.A.: Organization of self-study of English in a non-language specialized university based on an online platform. Sci. School 5, 126–134 (2020) 10. Al-Ajmi, Z.: The impact of the virtual programs in promoting English language learning in the context of middle east college. Arab World English J. 2(2), 400–415 (2021) 11. Sukri, S., Yunus, M.M.: Revitalising English teacher education through blended learning: a boon or bane? Int. J. Eng. Technol. 7(4.21), 97–101 (2018) 12. Ali, S.M., Harun, H., Mahir, N.A., et al.: Meeting the demands of the 21st century English language learning through PBL-LcCRAFT. Gema Online J. Lang. Stud. 18(2), 255–266 (2018)

Application of Decision Tree ID3 Algorithm in Tax Policy Document Recognition Chao Pang(&) Faculty of Economics, Yunnan University of Finance and Economics, Kunming 650000, Yunnan, China [email protected]

Abstract. In recent years, the tax system has vigorously promoted office automation, and more and more data needs to be processed. This puts forward higher requirements for the classification of massive data and the output of tax knowledge, but the current tax system is still in the simple data search stage. The processing of official documents in the taxation system is the core of office automation and the main method for issuing tax policies. Since the operation of the tax system, a large amount of official document data has been generated, which contains unknown and possibly useful tax information. Taxpayers must keep abreast of and implement the latest tax policies through formal document processing. Therefore, how to quickly and effectively identify tax policies is a major challenge facing current tax information. This article explores and studies the decision tree ID3 algorithm in the recognition of tax policy official documents. It has a general understanding of the text recognition process on the basis of literature data, and then proposes the application advantages of the decision tree ID3 algorithm on the basis of it, which provides the following experiments. The idea is that according to the tax policy document recognition experiment based on the decision tree ID3 algorithm, the actual tax policy samples are 43, and the decision tree ID3 algorithm has identified 41, with an accuracy of 97%. In order to further verify the superiority of the algorithm, on the basis of comparison with the other two algorithms, the experimental results show that the classification accuracy of WNB, SWNB and decision tree ID3 are compared. It is not difficult to find that the classification of official documents in the field. Decision tree ID3 has achieved good classification results, and its performance is significantly better than WNB classification algorithm and SWNB classification algorithm. Keywords: Id3 algorithm
Tax policy
Document recognition
Decision tree

1 Introduction The automatic identification of official document tax policy documents is based on official document mining technology, which is the process of formally discovering official document knowledge and official document automatic classification [1, 2]. Text mining is a combination of artificial intelligence, machine learning, natural language processing, data mining and automatic word processing. Classification is a means of grouping the same things together and distinguishing different things according to the “similarity” and “features” of things [3, 4]. As a branch of data mining, text mining and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 177–184, 2022. https://doi.org/10.1007/978-3-030-89508-2_23

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text classification have gradually become more and more important research fields. ID3 decision tree algorithm classification algorithm is a learning algorithm based on probability. It has a solid foundation in mathematical theory and can synthesize prior information and sample data [5, 6]. Its performance is comparable to algorithms such as Bayesian and neural networks. It is a typical representative of text classifier and data mining technology, based on data mining to predict the possibility of participation [7, 8]. In the study of the decision tree ID3 algorithm in the identification of tax policy documents, for the decision tree ID3 algorithm, some researchers focused on the improvement of the two existing ID3 algorithms, and analyzed their respective advantages and disadvantages [9]. On this basis, he proposed an MPID3 scheme to improve the ID3 algorithm, explained its implementation mechanism and provided some application codes, and then verified MPID3 from the perspective of theory, experiment and practical application. MPID3 is better than the classic ID3 algorithm [10]. There are existing methods for improving classification time and classification accuracy, and some application codes are also given. For formal document recognition, some researchers have proposed a language-independent classification model based on machine learning [11]. Subsequently, the research on Chinese text classification technology developed rapidly, from the initial output of words, phrases or N-grams, parts of speech, punctuation and other lexical features, to the present, Chinese natural language understanding technology, especially with the Chinese word segmentation technology with the maturity of machine learning, the research on Chinese text classification has made many progress [12]. This article focuses on the decision tree ID3 algorithm in tax policy document recognition. Based on the literature research method, the process of text recognition is summarized, and the advantage of the decision tree ID3 algorithm is analyzed. It provides ideas for the following experiments. Based on the decision tree ID3 algorithm, the tax policy document identification experiment, and the relevant conclusions are drawn.

2 Decision Tree Id3 Algorithm and Tax Policy Document Recognition Research 2.1

The Basic Process of Text Classification

(1) Define the category set and construct the corpus. In the process of corpus construction, the problem of corpus collection must be considered. The corpus has limited capacity, high requirements for information timeliness, and heavy construction workload. Therefore, the collected corpus information should be representative of the maximum. (2) Preprocessing of the corpus. Preprocessing includes word segmentation, text formatting and text modeling. The corpus must be preprocessed before entering the system. The preprocessing is mainly to create a general processing template from the highlighted corpus prototype according to a custom template format, and to normalize the source corpus.

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(3) Feature selection and derivation of feature set. The text text obtained in the preprocessing stage cannot be directly used for classification. Before classification, the characteristics of each category must be determined. The text usually reflects a specific theme or concept. Categories are abstractions and summaries of these concepts. Selection is a text sorting technique. (4) Train and create a classifier. The classifier is usually an optimized model defined according to certain evaluation criteria, and training is to determine the relevant parameters of the model through the training set. The mathematical basis of evaluation standards is divided into three types: rule-based, probability-based and discrete function. According to this, classifiers can be divided into three categories: rule-based classifiers, probability classifiers and discrete function classifiers. (5) Classification calculation and output category. Mainly through classification operation to determine the category of the newly input text and output. 2.2

The Advantages of the Decision Tree Id3 Algorithm in Identifying Tax Policy Documents

(1) The decision tree structure is simple, the created rules are easier to understand, and the decision tree creation process is also very intuitive. (2) The decision tree algorithm has a small amount of calculation and a high model efficiency, which is more suitable for a large training sample set. (3) The decision tree does not need to be biased against the nature of the data, and does not need to know too much basic knowledge about the learning process. (4) Decision tree is suitable for discrete data, but when there are many attribute values, the result may be low. If the number of branches is limited, the result of the decision tree is better. (5) The output of the decision tree includes the classification of features. Since the test attributes are selected according to the maximum gain of information when the decision tree is created, the relative importance of the attributes can be approximated in the decision tree. 2.3

Decision Tree Id3 Algorithm

(1) For a suitable decision tree on E, the classification probability of any instance is the same as the probability of positive and negative examples in E; (2) The amount of information required for a decision tree to make accurate category judgments for an instance is: I ðp; nÞ ¼ 

p p n n log  log pþn 2 pþn pþn 2pþn

ð1Þ

Assume that there is an attribute A in a training sample set. 4 contains q attribute values, namely {v1, v2, v3, …, vq}. Then these q attribute values will divide E into q sub-sets {E1, E2, …, En.}. Suppose that in a subset E, the numbers of positive and negative examples are p and n, respectively; E; the expected information value required

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is I(p;,n;), and attribute A is used as the expected entropy of the classification attribute. Calculate with formula (2): E ð AÞ ¼

X q pi þ ni I ð pi ; ni Þ i¼1 p þ n

ð2Þ

Then we can get the information gain with A as the classification attribute: gatnð AÞ ¼ I ðp; nÞ  E ð AÞ

ð3Þ

One of the main basis for selecting classification attributes in the d3 algorithm is to determine the size of the attribute gain. The id3 algorithm can select the attribute with the largest gain value as the classification node in the decision tree. For any given training sample data set, i (p, n) is constant, so id3 selects an attribute with the smallest expected entropy e as each node to be classified. For each current node of the decision tree, a gain value must be calculated upward recursively, and compared with the above nodes, it is possible to produce a complete decision tree. The id3 algorithm has been widely concerned and used because it is easy to be understood and used by people.

3 Tax Policy Document Identification Experiment Based on Decision Tree Id3 Algorithm 3.1

The Purpose of the Experiment

First, verify the feasibility of the decision tree ID3 algorithm in tax policy document identification through experiments, and then compare it with other algorithms to verify the superiority of the decision tree ID3 algorithm. 3.2

Selection of Official Document Samples

Select the sample in this municipal affairs system. The sample is generally divided into two categories: taxation policy and non-taxation policy. Taxation policy accounts for 1/4 of the total number of samples. The sample data is selected according to the rules of high cohesion and low coupling. 3.3

Official Document Classifier Parameters

(1) Attribute subset weight The principle of calculating the weight of feature subsets is to group attribute documents and divide text attributes into key attribute subsets, interpolation subsets, and regular attribute subsets. Calculate the weights of key sample feature subsets, rule feature subsets, and interference feature subsets according to related formulas. In the text preprocessing step, the weights are calculated and the weight list of feature subgroups is created at the same time. (2) The prior probability of entry

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1) By scanning all training samples, measuring the number of entries of various training types and the total number of entries in various training sets, a statistical table is formed. 2) Learn probability parameters and calculate the previous probability of all items. 3) Create a priori entry probability table. 3.4

Preprocessing of Official Document Data

Use office OLE technology to convert official documents into PDF copies. This method is: use OLE technology and Word automation, use the OLE automation object of the word automation server, and complete the data exchange between the user program MSWORD and the PDF document through the server. Then, using SHELL technology, call the XPDF software package to convert the PDF copy into a text document. Finally, the data cleaning was completed and 2 official documents were produced: a PDF copy and a text copy. 3.5

Classification of Official Documents

The basic process of formal document classification is: first call the probability table and the attribute weight table. Then, according to the weight of the attribute subset, the decision tree ID3 algorithm is used to calculate the tax policy category and non-tax policy category of the document text to be recognized, and the final ranking is performed. The document to be classified belongs to the category with the highest reverse probability value. 3.6

Document Data Integration

(1) Data preprocessing stage The best way to integrate official document data is to create self-descriptive information and make copies of the official document data in PDF and text formats. The self-describing information is reflected in the name of the copied file. Due to the large amount of official document data, a unified index file can be established to speed up the retrieval speed for further processing. (2) Tax policy identification stage Copy and organize copies of the PDF documents corresponding to the tax policy text recognized in the classified document library for browsing or as the source of the text retrieval system.

4 Analysis of Experimental Results 4.1

Results of Classification of Official Document Test Data

Text sorting is essentially a mapping process. The closer the text sorting result is to the manual sorting result, the higher the sorting accuracy. After preliminary analysis of the data, in all 1324 data samples, the classification is based on all samples into two

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categories: tax policy and non-tax policy. Among them, there are 287 tax policy samples and 1037 tax policy samples. This article uses the first 1047 pieces of data as training samples, and the last 277 pieces of data as test samples to verify the classification results. Table 1 shows the results of the decision tree ID3 algorithm to classify part of the official document test data. Table 1. The results of the classification of some official document test data Tax policy Non-tax policy total Accuracy Tax policy 38 5 43 97.67% Non-tax policy 3 227 230 99.13% Total 41 232 273 98.4%

Amount

Tax policy

Non-tax policy 227 230

43

38

273

total 232

41

5

3

Tax policy

category

Non-tax policy

total

Fig. 1. The results of the classification of some official document test data

It can be seen from Fig. 1 that the actual tax policy samples are 43, and the decision tree ID3 algorithm has identified 41, with an accuracy of 97%. 4.2

Comparison of Algorithms

In order to further test the superiority of the decision tree ID3 algorithm, compare it with the classification results of the other two algorithms. The two algorithms are the naive Bayes classification algorithm and the selective weighted naive Bayes. The experimental results are shown in Table 2. Table 2. Algorithm comparison results WNB SWNB Decision tree ID3 Tax policy 48.89% 68.89% 86.66% Non-tax policy 95.68% 97.41% 98.27% Complete works 72.95% 83.15% 92.47%

Application of Decision Tree ID3 Algorithm in Tax Policy

Tax policy

Non-tax policy

183

Complete works

120.00% percentage

100.00% 80.00% 60.00% 40.00% 20.00% 0.00%

WNB

SWNB

Decision tree ID3

Algorithm

Fig. 2. Algorithm comparison results

It can be seen from Fig. 2 that the classification accuracy of WNB, SWNB and Decision tree ID3 is compared. It is not difficult to find that in the field of official document text classification, Decision tree ID3 has achieved good classification results, and its performance is significantly better than WNB classification algorithm and SWNB classification algorithm.

5 Conclusion The national tax system actively promotes the implementation of the official document processing system, which generates a large amount of text data. How to obtain knowledge from these Chinese information is a major challenge facing the current tax system science and information technology. Office automation ODPS system is the core of government informatization and the main way to liberalize taxation policies. Tax officers manually select tax policies from a large number of official documents, collect and implement the latest policies. However, official tax policy documents are only a small part of the many official documents managed by the official document processing system. They only rely on manual identification of tax policies and do not have corresponding text clustering elements to complete text classification, which brings about time lag and tax policy impact.

References 1. Phu, V.N., Tran, V.T.N., Chau, V.T.N., Dat, N.D., Duy, K.L.D.: A decision tree using ID3 algorithm for English semantic analysis. Int. J. Speech Technol. 20(3), 593–613 (2017) 2. Dai, Q.Y., Zhang, C.P., Wu, H.: Research of decision tree classification algorithm in data mining. Int. J. Datab. Theory Appl. 9(5), 1–8 (2016) 3. Zhang, S., Zhenjie, H., Ye, L., Zheng, Y., et al.: [Application of logistic regression and decision tree analysis in prediction of acute myocardial infarction events]. Zhejiang da xue xue bao. Yi xue ban = J. Zhejiang Univ. Med. Sci. 48(6), 594–602 (2019)

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4. Zhu, L.: Retraction notice: application research of decision tree algorithm in sports grade analysis. Open Mech. Eng. J. 10(1), 135 (2016) 5. Maingi, N.N., Lukandu, I.A., Mwau, M.: Inter-county comparative analysis of ID3 decision tree algorithms for disease symptom burden classification and diagnosis. Int. J. Sci. Res. (IJSR) 8(5), 83–89 (2019) 6. Wang, H., Jin, H., Xue, F.: Research on improvement of the decision tree algorithm based on information gain. Rev. Facult. Ingenier. 32(2), 126–136 (2017) 7. Devasenapathy, K., Duraisamy, S.: Evaluating the performance of teaching assistant using decision tree ID3 algorithm. Int. J. Comput. Appl. 164(7), 23–27 (2017) 8. Fathima, E., Anithaa, S.: An overview of medical image classification using decision tree algorithm and association rule classifier. Int. J. Pure Appl. Math. 119(12), 7535–7540 (2018) 9. Hammed, J.S.: An implementation of decision tree algorithm augmented with regression analysis for fraud detection in credit card. Int. J. Comput. Sci. Inf. Secur. 18(2), 79–88 (2020) 10. Lin, W.C., Feng, Y.: An automated system for document recognition. Eng. Appl. Artif. Intell. 2(2), 120–130 (2016) 11. Xingrong, S.: Research on time series data mining algorithm based on Bayesian node incremental decision tree. Clust. Comput. 22(4), 10361–10370 (2017) 12. Li, G., Wang, F.: Research on art innovation teaching platform based on data mining algorithm. Clust. Comput. 22(6), 13867–13872 (2018)

The Value Embodiment of VR Interactive Technology in Product Design Yuwen Ma, Hong Chen, and Huayun Gao(&) Department of Product Design, Dalian Polytechnic University, Dalian, Liaoning, China

Abstract. Today’s society is already a technology-based society, and people cannot do without the help of technology in their lives and work. With the continuous development of VR interactive technology, VR interactive technology has penetrated into many areas of society. Similarly, the field of product design is inevitably affected by VR interactive technology. The wide application of VR interactive technology in the design field has comprehensively improved the expressiveness of design. In this context, this article studies the value of VR interactive technology in product design. This article first analyzes the development status of VR interactive technology in the field of product design, and the role of VR interactive technology in product design; then, this article studies the limitations of traditional product design under VR interactive technology, and analyzes in detail the value of VR interactive technology in product design. Finally, this article conducted a questionnaire survey of 50 domestic enterprises that have applied VR interactive technology. The results of the survey show that the application of VR interactive technology has greatly helped the improvement of enterprise efficiency, and the enterprises are also very satisfied with VR interactive technology. Keywords: VR interactive technology
Product design
Value manifestation
Questionnaire

1 Introduction The advent of the VR era accelerates the globalization process of the world and breaks geographical restrictions, every company faces a vast global market [1]. For the field of product design and manufacturing, VR technology brings not only opportunities, but also more intense competition and challenges. In the face of these, what companies need to do is to reform the design and production process, and to maximize the design of the product, in order to gain a place in the highly competitive international market [2]. As an important link in product development and production, product design is largely affected by technical conditions. The current VR technology has had a profound impact on various industries in society, and this impact will inevitably enter product design. In the field of development, it has caused a major change in design methods and design ideas [3]. Chinese scholar Gao Kaihui conducted a comprehensive analysis and interpretation of the design strategies, existing problems and development trends in the field of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 185–192, 2022. https://doi.org/10.1007/978-3-030-89508-2_24

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product design under VR technology. On the whole, our country’s product design research based on VR technology is still in its infancy, but its development potential is strong and it is the primary development direction of our country’s future VR technology [4]. You Xuemin pointed out that VR technology is a relatively new and active technology that has emerged in the process of high-tech development. The interactivity, immersion and other characteristics shown in the technical field not only bring users an immersive sensory experience, but also highlight the many possibilities of its combination with other fields [5]. Yan Bao pointed out that the use of VR interactive technology can improve the efficiency of product modeling design. By applying virtual technology to product modeling design, product modeling and human-computer interaction effects can be evaluated and improved, so as to shorten the development cycle and reduce the production cost of the product [6]. With the help of the VR interactive system, the speed of information exchange has been greatly increased, and the channels for obtaining and publishing information are also more diversified. This allows products to be well monitored in all aspects of design, and designers can use VR interactive technology for closer cooperation [7, 8]. Through the internet and design software in VR interactive technology, an interactive platform is provided for manufacturers and consumers to realize: 1. Modern design methods replace traditional design methods. 2. Good communication between designers and designers to create more exquisitely designed products. 3. Consumers directly participate in the product development process, production decision-making, and obtain their own satisfactory products. 4, the virtual market platform responds quickly to the individual needs of customers in the production enterprises, improves the innovation ability and output level of the manufacturing industry, and reduces the impact of blindness in production. 5. Order production and sales, close to or achieve the goal of zero inventory. 6. Focusing on the trend of updating and upgrading traditional manufacturing industry is in line with the broad prospects for the development of the information society.

2 The Value Embodiment of VR Interactive Technology in Product Design 2.1

The Main Research Content of VR Interactive Technology

The difference between VR interactive technology and other interactive technologies is that the user’s interaction with the virtual environment is not based on traditional mouse and keyboard-style operations, nor is it interacted on flat devices such as touch screens, but is based on the user’s interaction in the three-dimensional space. For example, the head rotation is used to control the movement of the perspective, the hand movement is used to realize the operation of virtual objects, and the feet are freely moved to realize the roaming in the virtual environment and so on [9]. Based on the interaction of computer vision, the color and depth images of the scene are recorded through the camera, and the recorded image sequence or video is analyzed and processed, and the human target is separated from the irrelevant background, and its model and motion characteristics are obtained. The tracking identification information of the

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action corresponds to the virtual reality environment, which provides the basis for further interactive behavior [10]. Wearable device-based interaction means real-time monitoring of the angle changes of the user’s joints through wearable devices such as data gloves or exoskeleton robots, so as to capture user actions and realize the interaction between humans and the virtual reality environment. The interaction based on the transmission platform can realize the user’s position movement in the virtual environment. The main equipment includes a one-way treadmill and an omnidirectional transmission platform. Both of them use auxiliary equipment to limit the user to the specified range of the device. In the case of no displacement on the ground, the user’s foot movement drives the conveyor belt or roller of the device to rotate, and these rotation data will be mapped to the user’s real-time movement in the virtual world to realize the interaction between the user and the virtual world. 2.2

Limitations of Traditional Product Design Under VR Interactive Technology

First of all, the traditional product design process is the use of drawing design drawing tools, and everyone will naturally choose to use various brushes, erasers, tools, plus paints and various media tools to make design drawings [11, 12]. In view of the different design themes and selected design styles that you want to express, there are unique requirements in the use of tools, and they vary infinitely due to different techniques, but the final design works are unique. Secondly, the drawing of design drawings, generally after the product design concept is drawn up, begins to draw three views of the shape of the device, which are front view, top view and side view. In the decoration of design renderings, hand-painted is generally traditionally used, and the tools used include hook line pens, brush line pens, painted paints, palettes and so on. In terms of color control and accurate expression, an excellent designer must go through long-term repeated experiments and practice, and the color modulation is also susceptible to inaccurate expression due to the limitations of various conditions and the influence of some external factors. The traditional design drawings are generally limited to the twodimensional stage, or in a flat stage. If the design plan needs to be modified for some reasons, then it is repeated to the initial stage, and everything has to be restarted from scratch, but the VR interactive technology solves this important problem, and you can modify the command in the simulation software. In terms of technique, the technique of a general product designer is a complete set of technical methods summed up after countless repeated practice and research in daily life. This technique includes: how to improve the modeling ability of things, how to coordinate the relationship between the overall structure, how to use a variety of different painting tools to deal with each different link in the work, and at the same time master scientific methods. 2.3

The Significance of This Study

With the development of society, in order to meet the needs of people’s lives, different types and styles of daily necessities have emerged. In the modern era when material life is relatively sufficient, people have higher and higher requirements for daily necessities, and different countries have different demands for daily necessities. People’s concept of

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daily necessities is not only practical, but a combination of practical and artistic. The significance of the research in this article lies in: 1. Use VR interactive technology to simulate and predict daily products to enrich the design methods of daily products. 2. Direct communication with designers, learn from each other’s weaknesses, teamwork, communication between designers and consumers, and understand the different individual needs of consumers. 3. Develop the network virtual market, provide a broader market for daily products, and save the manpower, material resources and cost of the exhibition venue. At present, whether it is products, activities or skills and other fields, participants have a deeper understanding of the needs of daily products. This change based on the needs of participants has formed a strong impact on design concepts. This article hopes to summarize and summarize the theoretical research on the design methods of daily-use products through the analysis of the perceptual level between man and machine, and the connection between aesthetic culture and daily-use products.

3 The Value of VR Interactive Technology in Product Design 3.1

The Predictability of VR Interactive Technology for Product Design Drawings

With the help of VR interactive technology, products can get realistic visual simulation effects in the styling design stage. In this process, designers use the powerful interactive performance of VR technology to quickly modify the product based on user feedback and reproduce the modified results in a timely manner. Through this simulation process, designers and engineers can discover the problems in the design in the early stage of product development, and solve them in the early stage. To a certain extent, the repetitive work in the design process is avoided, and the design efficiency is greatly improved. The introduction of computer-aided design has made product design form a new parallel network process model, and the links between product design links have become closer. The simultaneous working method of multiple design links ensures the integrity and systemicity of the design process. Computer-aided design can also be involved in different stages in different ways, and the work results of each stage can be connected in series, and the advantages of computers in information management and data processing can be used to liberate designers from complicated transactional work. Since products can exhibit strong irregular characteristics, the conceptual design of products or components requires high efficiency in modeling and supports multiple transformations. Therefore, it is necessary to study new theories and new methods of geometric modeling to support the conceptual design of products. 3.2

Information Exchange Platform Built by VR Interactive Technology

Through VR interactive technology and VR interactive equipment, a VR interactive platform is provided for manufacturing enterprises and consumers. Let consumers directly participate in the product development process, production decision-making, and obtain their own satisfactory products. Manufacturers respond quickly to the individual needs of customers, improve the innovation capability and output level of

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the manufacturing industry, reduce blindness in production, and approach or achieve the goal of zero inventory. Using VR interactive technology to build a virtualized, interactive standardized development environment is an interactive platform that enables designers and customers to communicate with each other and standardized interactively. Through the operation of the network platform, the Internet is used to directly connect the production enterprises and potential customers, and create new products through the needs of customers. The enterprise puts the basic framework of the product to be launched on the Internet, and the customer consults, revises, and even changes the design through the Internet, and then provides the completed requirements to the enterprise and places an order for customization. The enterprise produces according to the customized contract, and provides it to the user after the output. Realize the generalization and standardization of the platform, and finally establish a standardized platform for all production enterprises and the entire process of product production and sales. Fundamentally speaking, the blindness of enterprise production is caused by the high cost of obtaining information. Using VR interactive technology to communicate closely with consumers can eliminate blindness in production, improve the process of product design, reduce the necessity of commercial existence, and improve production efficiency and product profitability. 3.3

VR Interactive Technology to Build a Product Virtual Market

Compared with the physical market, the virtual market appeared much later, but its development speed is amazing. Generally speaking, its advantages are: First, in the virtual market, consumers are no longer restricted by space and passively accept the products introduced by the producers, but can actively search for transaction objects and products in the network. Second, the virtual market can be a true all-weather market. The third virtual market is a one-to-one, interactive market between producers and consumers. They can directly communicate information and exchange products. Producers can also provide goods according to the needs of consumers. Precisely because of its advantages, relying on the virtual market can obtain more space for product promotion. Compared with the physical market, it reduces transaction costs. Transactions in the physical market require a relative venue space. A large number of sales products and goods are displayed in this venue space, which consumes a lot of transportation time and labor. However, in the virtual market, producers can save space and manpower, reduce many intermediate links, and reduce costs. Secondly, compared with the physical market, the advertising and publicity costs in the virtual market are much lower, and the speed and scope of information dissemination are unmatched by the physical market. In terms of promotion, the limitations of the physical market are relatively large. The physical market is limited to dealing with consumers in one place, while the popularity of the virtual market is very extensive. You only need to enter the URL, move the mouse and keyboard, no matter where you are. You can find suitable products. Compared with the physical market, the virtual market can also bring together the market prices of many daily-use ceramic products in different regions, and reflect the price changes at different times at any time. The search function provided by the Internet enables consumers to quickly obtain information about the products they need and compare prices. This undoubtedly reduces consumer transaction costs caused by search costs.

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4 Questionnaire This article takes 50 domestic companies as the research object, and these companies have begun to use VR interactive technology for product design and manufacturing. This article conducts related surveys on these companies in the form of a questionnaire survey. The content of the survey is the impact of VR interactive technology on the efficiency of the enterprise, and the degree of satisfaction of the enterprise with the VR interactive technology. After the investigation, the data is processed and analyzed. The formulas used in the data processing process are: S2 ¼

ðM  x1 Þ2 þ ðM  x2 Þ2 þ ðM  x3 Þ2 þ


þ ðM  xn Þ2 n ðx þ aÞn ¼

4.1

ð1Þ

n X n ð Þxk ank k k¼0

ð2Þ

The Impact of VR Interactive Technology on Corporate Efficiency

Table 1. The impact of VR interactive technology on corporate efficiency

changes in efficiency

Changes in efficiency Number of companies Proportion of enterprises Significant efficiency gains 32 64% Small efficiency gains 13 26% No change in efficiency 4 8% Efficiency has decreased 1 2%

the impact of VR interactive technology on corporate efficiency Significant efficiency gains Small efficiency 13 gains No change in 4 efficiency Efficiency has 1 decreased number of companies

32

Fig. 1. The impact of VR interactive technology on corporate efficiency

It can be seen from Table 1 and Fig. 1 that a survey of the impact of VR interactive technology on corporate efficiency was conducted on 50 companies. Among them, 32 companies have significantly improved their efficiency, accounting for 64%. There are 13 companies that have a small improvement in efficiency, accounting for 26%. There are 4 companies that have no change in efficiency, accounting for 8%. There is a

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decline in efficiency of one company, accounting for 2%. An in-depth investigation was conducted on companies whose efficiency had declined, and it was found that the efficiency decline was due to an imperfect corporate management system, which had nothing to do with the application of VR interactive technology. Therefore, on the whole, the application of VR interactive technology is still helpful to the production efficiency of enterprise products. 4.2

Enterprise’s Satisfaction with VR Interactive Technology Table 2. Satisfaction of enterprises with VR interactive technology Satisfaction level Number of companies Proportion of enterprises Very satisfied 27 54% Quite satisfied 15 30% Generally satisfied 6 12% Dissatisfied 2 4%

4%

enterprise's satisfaction with VR technology

12%

Very satisfied Quite satisfied

30%

54%

Generally satisfied Dissatisfied

Fig. 2. How satisfied companies are with VR interactive technology

It can be seen from Table 2 and Fig. 2 that 50 companies have been surveyed on the degree of satisfaction with VR interactive technology. Among them, 27 companies are very satisfied, accounting for 54%. 15 companies are relatively satisfied, accounting for 30%. Six companies are generally satisfied, accounting for 12%. Two companies were dissatisfied, accounting for 4%. An in-depth investigation was conducted on dissatisfied companies, and it was found that the two companies were not very proficient in the use of VR interactive technology and lacked corresponding guidance, so they gave dissatisfactory evaluations. Generally speaking, enterprises are quite satisfied with the application of VR interactive technology.

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5 Conclusions Product design in life is one of the important components of human culture. Humans have designed many products, and design ideas are often imprisoned by the products they design. In the VR era, most people are often still immersed in traditional product design thinking. Only by using innovative design to form the negation of old things can people be awakened and liberated from the shackles of the old model. Although the research of VR interactive technology is not very mature, the current VR interactive equipment can not meet all the designers’ requirements for product design, but VR interactive technology is still one of the important technologies in the future. The application of VR interactive technology in product design can realize the complete digitization of the product, because a virtual product is a computer model that can have all the information and functions of the product and is close to reality. It is foreseeable that in the future product design, VR interactive technology will gradually be applied to all links. From the appearance of products, to the development of new varieties, to product design management, and advertising roaming, VR interactive technology is bound to exert greater advantages.

References 1. Zhang, M., Huang, H.: Research on product design evaluation system based on VR interactive technology. Modern Electron. Technol. 042(019), 173–177 (2019) 2. Liu, B., Yuan, S.: Research on voice interaction design in digital museum based on VR. Intell. Comput. Appl. 9(03), 239–243 (2019) 3. Wang, Z., Tao, R.: Research on VR interaction based on design psychology. Digital Design 009(002), 279 (2020) 4. Gao, K.: Research on liling underglaze multicolored ceramic cultural creative product design based on VR technology. Ceramic Sci. Art 54(531(03)), 20–23 (2020) 5. You, X.: Research on the fusion of virtual reality technology and product design. Art Technol. 032(009), 169 (2019) 6. Yan, B.: Industrial product modeling design based on virtual reality technology. Modern Electron. Technol. 42(530(03)), 192–194 (2019) 7. Chen, Y.: Digital campus somatosensory interaction system design based on Unity3D and VR technology. J. Changchun Inst. Technol. 020(003), 67–71 (2019) 8. Li, Y.: Research on industrial product packaging engineering design based on VR and AR technology. Automat. Instrument. 244(02), 195–198 (2020) 9. Wu, L.: Analysis of the interactive design of animation based on virtual reality technology. Inf. Technol. 43(332(07)), 133-136+140 (2019) 10. Pan, Y.: Research on the application of VR digital virtual simulation teaching in architectural space design. Explorat. High. Educ. 209(09), 138–138 (2020) 11. Luo, J.: Research on interactive design of virtual dinosaur museum based on VR technology. Comput. Knowl. Technol. 16(13), 263–265 (2020) 12. Li, N.: Research on the effectiveness of virtual reality technology in product design. Western Leather 41(465(24)), 76 (2019)

Innovation of Economic Business Model Based on Particle Swarm Optimization Algorithm Shaohua Zhao(&) Henan College of Industry and Information Technology, Jiaozuo, Henan, China Abstract. With the advancement of technology, changes in demand and intensified competition, business models have gradually become the focus of attention of enterprises. Business model innovation has also brought unprecedented challenges to enterprises and has become an inevitable choice for enterprises. In this context, this paper studies the innovation of economic business model based on particle swarm optimization algorithm. This article first studies the theoretical knowledge and research status of business models, and then introduces a NK model method for business model innovation. Next, this paper proposes a business model particle swarm optimization algorithm based on the particle swarm optimization algorithm, and establishes a model hypothesis for a business model particle swarm optimization algorithm. Finally, this paper conducts an experimental analysis on the model, and derives the law of business model evolution based on the experimental results. Keywords: Particle swarm optimization algorithm model
Law of evolution


Business model
NK

1 Introduction With the intensification of competition among enterprises, the innovation of economic business models has become the focus of modern business. The innovation of economic business models requires companies to constantly understand themselves and the external environment to adapt to changes. Just like the natural selection theory in biology, the innovation of economic business models is a process of continuous progress over time [1, 2]. In the context of humankind’s continuous promotion of economic growth, business models continue to repeat the process of birth, rise and fall, and collapse. Some people argue that entrepreneurs come first, and they believe that entrepreneurs are the driving force of economic and social development and the source of power for social development. Entrepreneurs are the soul and core of an enterprise, and a key factor in determining enterprise performance. Entrepreneurs are the intermediate link in coordinating various relationships inside and outside the enterprise. Entrepreneur behavior is innovative behavior, this innovative behavior can be divided into five situations: creating new products, adopting new methods, opening up new markets, acquiring new resources, and realizing new ways [3]. Since the 1990s, business models have received widespread attention and become a hot topic at the moment. Especially in the “new economy” era, the business model seems to be a magic wand with the function of turning gold [4, 5]. Some companies have created business legends one after another. Their business models are being © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 193–201, 2022. https://doi.org/10.1007/978-3-030-89508-2_25

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discussed by the business community. Some companies are trying to follow suit, and some companies are seriously reflecting [6]. On the one hand, the business community generally agrees that the business model determines the company’s fate. A successful company needs a successful business model. No matter how they explain success to the outside world, the business model must be secretive and is a key factor in its success. On the other hand, the business model is usually an independent department that has attracted wide attention from researchers, and the research on business models at home and abroad is showing a gradual upward trend. Nowadays, business model has become one of the most popular terms, often appearing in academic journals, newspapers and even people’s daily conversations. Chinese scholar Luo Zuohan puts forward the connotation of business model innovation of startups, comparatively analyzes the characteristics of business model innovation of startups, and proposes an integrated analysis framework for business model innovation of startups, providing a certain reference for startups to carry out business model innovation And refer to [7]. Wu Songqiang designed a business model based on the top-level elements of the three business models: value positioning, value creation and delivery, and value realization, and verified the feasibility of the framework, hoping to propose practical and feasible solutions for the sustainable development of the company from the perspective of business model innovation Recommendation [8]. Li Hengna pointed out that from the proposal of the “new economy”, to the emergence of “Internet +” big data, and then to the 5G era. The accelerated change of information technology continues to promote the progress of the new economic era, and the market will adapt more flexibly to the matching mode of resources. Based on the integration of the background of the new era, compared with traditional industries, emerging industries can effectively use Internet technology to break traditional monopolies and ease the current domestic employment pressure. However, the integrated emerging industries have a smaller coverage, and the business model still uses the traditional type, and is still in a state of barbaric growth [9].

2 Innovation of Economic Business Model Based on Particle Swarm Optimization Algorithm 2.1

Mechanism Analysis of Business Model Evolution

The evolution of an enterprise’s business model is based on the practice of innovative behavior. Conventions are concepts derived from the framework of evolutionary theory [10]. Its meaning is like “inertia” in physics, and in evolution theory it refers to the permanent characteristics of an organism and determines the possible behavior of the organism. This characteristic is widespread both in the biological world and in the economic system. In the transformation of the objective world by human beings, different natural persons always have different mindsets [11, 12]. The meaning of a convention does not mean that it remains unchanged in the evolution of business models. Companies often need to constantly learn and innovate to find a convention that can survive in the existing environment. At this time, the new convention becomes another way to dominate the behavior of the next decision point of the enterprise. The

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complex system characteristics of the business model determine that innovation needs to improve and reform its complex system components and elements, which requires continuous experimentation, adjustment and learning. Sometimes, the cost of jumping from one track to another is extremely high or even impossible. This shows that the direction of business model search is limited by the company’s existing accumulation capabilities, and limited by factors such as existing products and processes. The existence of conventions shows the limited flexibility in the process of business model innovation. Like the concept of “path dependence” widely used in technological innovation research, today’s choices are affected by historical factors, and the choices people make in the past determine their present Possible options. Once you enter a certain path, whether it is good or bad, the current choice may depend on this path. 2.2

Significance of Research on Business Model Evolution

So far, because business models have not yet formed an authoritative unified understanding and system in theoretical research, many studies have generally stayed at the level of simple descriptions of business models or case studies of business models, lacking a complete and in-depth structural system. The mesoscopic business model theory not only provides a set of methods and frameworks for the analysis of business models, but also lays a logical foundation for its scientific research. At present, the simulation research on the mesoscopic business model is mainly based on the research of the static complexity structure of the business model. Although the impact of different business model structures on innovation strategies has been systematically analyzed, the complex adaptation of business models to the characteristics of the system determines that it is incomplete to study the impact of static complexity structures on business model innovation. From the perspective of management practice, the business model of an enterprise is always constantly improving and evolving in a changing business environment. Its innovation is essentially a process of dynamic evolution. Therefore, it is necessary to conduct a systematic theoretical study on the evolution of the business model, and to propose Guiding conclusions on this issue. Although the issue of corporate business models has attracted the attention of some domestic and foreign scholars, the quantitative research on the evolution of business models, domestic and foreign related studies are still not involved. Based on the particle swarm optimization algorithm, combined with the research results of the complexity of the business model, this paper will introduce the dynamic evolution of the business model into the research and analysis framework, and further study the evolution mechanism of the business model through the statistical analysis of the model results. In the process of evolution, the influence of convention, cognitive learning and social learning on evolution, and then summarize the general laws and strategies followed by the analysis of the dynamic process of business model innovation. The research of this article has obtained some new theoretical results, and pointed out the problems that need further research, which is of great significance. 2.3

NK Model Method for Business Model Innovation

The NK model abstractly presents the complexity of the system through two simple parameters N and K. It describes the complex system as a system composed of N elements

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(n = 1, 2 … N). The relationship between elements and elements is represented by K. The K value represents the number of association relationships between elements. When K = 0, K takes the minimum value, that is, there is no superordinate relationship between elements, the complexity of the system is the lowest, the topographic map changes smoothly and there is only one mountain. When K = N−1, K takes the maximum value, and there is a superordinate relationship between all elements. At this time, the complexity of the system is the highest, the topographic map becomes steep, and there are many mountains. Therefore, the K value is directly used as a parameter of system complexity. Its value range is 0  K  N−1. This assumption simplifies the operating parameters to achieve the purpose of research and use on the basis of ensuring the prototype of the model. When the system scale is determined, the value of N is fixed. By changing the value of K, it is possible to study the characteristics of a system of a specific scale under different levels of complexity. The NK model system scale of the business model is 3, that is, N = 3. In practice, each component contains several possible states. The abstract simplified here is only taking 0 or 1. The upper relationship between the model components is expressed by the K value. There are three possible K values for each component, namely 0 or 1 or 2. The formulas used in the NK model are: W ðc; s; pÞ ¼

 1 wc ðcÞ þ ws ðsÞ þ wp ð pÞ 3

ð1Þ

3 Business Model Evolution Model Based on Particle Swarm Optimization Algorithm 3.1

Overview of Particle Swarm Optimization Algorithm

To study the evolution of business models, it is necessary to establish a business model population space based on the model description of individual business models, and to examine the innovation process of business models on a time scale. Therefore, the model will resort to evolutionary algorithms-particle swarm optimization algorithms. In the optimization problem, a solution of the problem is like a “bird” searching for food in the area, and the process of finding the optimal solution in the solution space is similar to the problem of birds searching for food. All possible solutions to the problem are called “particles”, and each particle contains two attribute information: one is position information, which represents the relative position of the solution in the solution space. The second is speed information, which indicates the direction and distance of the particle’s flight. The particle swarm optimization algorithm has been widely concerned and studied in the engineering field and social science research due to its simple and effective characteristics. The formulas in the particle swarm optimization algorithm are: Sigðvi ðt þ 1ÞÞ ¼

1 1 þ expðvi ðt þ 1ÞÞ

ð2Þ

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Business Model Particle Swarm Optimization Algorithm Description

According to the structure of the mesoscopic business model, when the particle swarm optimization algorithm is used to describe the business model innovation, the individual business model of each enterprise is visualized as a particle flying in an hdimensional space. h represents the number of evaluation indicators for the business model system. For the NK model of the business model, h is 3. For the generalized NK model of the business model, h is 4. The position of the particle in the flight space depends on its evaluation index value in each dimension space, so the quality of the particle position information determines the level of the fitness value of the business model type. The evolution of the business model is the same as the innovation process. It is visualized as the process of constantly trying and error searching for the peaks on the topographic map. It is a continuous process of quantitative change and transformation. It is manifested in the continuous flight of particles in three-dimensional space, changing and adjusting themselves in the particle space position. At the beginning of each iteration of particle flight, the behavior of particles adjusting their position in space according to their speed can be regarded as the behavior of enterprises innovating and evolving according to the conventions of their business model innovation behavior. 3.3

Model Assumptions of Business Model Particle Swarm Optimization Algorithm

Aiming at the business model evolution model based on the NK model and the generalized NK model, the particle swarm optimization algorithm needs to be set and corrected for the problem itself in application. It should be noted that this article studies the generalized NK model. Compared with the evolutionary model based on the NK model, the difference in algorithm between the two is only in the description of the particle flight space structure and the calculation of fitness. The first is the setting of the initial population. Root the speed equation of the particle swarm algorithm. The speed of the algorithm in the first iteration is generated by a random number generator. At the same time, the algorithm has not been iterated initially, so it is assumed that the particle itself has experienced the optimal value and the initial the value is the same. Secondly, in practice, the process of business model evolution by enterprises is rational. In other words, companies will not actively accept an innovative choice that is inferior to the current business model. Therefore, in the course of evolution, the company will not change the current business model status until a better business model emerges. Finally, the termination condition of the algorithm can be set according to the actual situation of the problem. It is not difficult to understand that there is a life cycle for both the product and the enterprise itself. Similarly, the cycle of entering a certain type of industry cluster in a certain state to carry out business model evolution is not eternal. It is interrupted or transitioned by many factors such as the environment and competitors. Therefore, it is advisable to artificially set the particle fitness that remains unchanged after 15 iterations as the final state of the model’s business model evolution.

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4 Analysis of Business Model Evolution Law Based on Particle Swarm Optimization Algorithm 4.1

The Evolution Results of the NK Model and the Generalized NK Model When the Composition Does not Change

Table 1. The evolution results of the NK model and the generalized NK model when the composition is unchanged Relevance K Average fitness of NK model Average fitness of generalized NK model 1000 0.666 0.639 2000 0.679 0.652 3000 0.691 0.648 4000 0.688 0.665 5000 0.706 0.662

the evolution results of the NK model and generalized NK model when the composition does not change

average fitness

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Generalized NK model 0.691

0.679 0.666 0.652

0.648

2000

3000 relevance K

0.639

1000

0.706

0.688 0.665

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4000

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Fig. 1. The evolution results of the NK model and the generalized NK model when the composition does not change

According to Table 1 and Fig. 1, it can be seen that when the complexity of the business model is the greatest, the result of the evolution in the enterprise group is the best, and it also shows that as the scale effect of the business model increases, the fitness value shows a linear increase trend. This result not only affirmed the results of the NK model, but also reached a more general conclusion: in the evolution of business models, it is beneficial to gradually increase the scale effect of multiple sources. At the same time, under the same scale, the relationship between uniform components and reducing their structural effects are also conducive to the progress of the evolution process. In summary, one of the laws of business model evolution: In the evolution of

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business models, the scale of complexity increases or remains unchanged, and the evenly distributed components and the interaction relationship between the components can innovate the current business model. 4.2

The Evolution Results of the NK Model and the Generalized NK Model When the Composition Changes

According to Table 2 and Fig. 2, it can be seen that the changes of the two are consistent, showing that for all complex models of business models, the fitness of the business model is monotonically decreasing. Therefore, it can be summarized as the second law of business model evolution: the business model evolution process, the result of continuously increasing the intensity of independent innovation and imitating innovation is unfavorable. In the process of innovation, enterprises increase the proportion of independent innovation and imitative innovation at the same time, but in the end they may not be able to get the expected results. This is mainly manifested in the limited resources of enterprises. When increasing efforts to carry out independent innovation and imitative innovation of business models at the same time, it is often overlooked that business model innovation requires the support of various internal resources of the enterprise. Companies ignore the allocation of their own resources and blindly innovate business models, even if they eventually evolve to the best business models in the industry, the result will still lead to the failure of innovation. Therefore, while emphasizing business model innovation, companies should realize that the pace of business model innovation is not constant, but rather periodic. To coordinate with the development of the enterprise itself, only the best business model is truly suitable for the enterprise itself.

Table 2. The evolution results of the NK model and the generalized NK model when the composition changes Relevance K Average fitness of NK model Average fitness of generalized NK model 1000 0.668 0.641 2000 0.662 0.639 3000 0.659 0.638 4000 0.655 0.635 5000 0.653 0.633

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the evolution results of the NK model and the generalized NK model when the composition changes

average fitness

0.668

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1000

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0.638

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0.653

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Fig. 2. The evolution results of the NK model and the generalized NK model when the composition changes

5 Conclusions Based on the in-depth analysis of the complexity of the business model, this paper aims at the dynamic problem of business model innovation, and adds particle swarm optimization algorithm based on the research of the NK model method, placing the business model innovation of a single enterprise into the business model innovation evolution of the enterprise group, further analyze the general laws that companies follow in the evolution of business models. In order to study the generality, this paper establishes a business model particle swarm optimization algorithm model based on the NK model and the generalized NK model at the same time, analyzes the influence of the complex structure of the business model on the evolution of the business model, and discusses the model results obtained under different parameter inputs. In the process of business model evolution, the influence of innovation conventions, imitative innovation and independent innovation on evolution is discussed, and then a general summary law is drawn.

References 1. Wang, Z., Ge, B., Gong, J., et al.: Visual analysis of foreign business model innovation research based on CiteSpace. Technoeconomics 39(386(02)):10–16 (2020) 2. Xia, Q., Huang, J.: Research on business model innovation of spin-off companies—based on the dual dynamic balance perspective of embedding and de-embedding. Econ. Manage. Res. 40(04), 110–125 (2019) 3. Wang, L.: Research on business model innovation based on contextual use-“Lemei Intelligence” case. Sci. Res. Manage. 41(293(03)), 176–184 (2020) 4. Wang, B., Hao, X., Liu, L.: Research on business model innovation of strategic emerging industries-environmental uncertainty and organizational learning matching perspective. Soft Sci. 34(250(10)), 53–58 (2020)

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5. Yan, R., Jiang, G.: Research on the innovation of long-term rental apartment business model based on competitive advantage. Oper. Manage. 430(04), 66–70 (2020) 6. Li, N., Wang, X., Wang, D., et al.: Research on business model innovation under the sharing economy—taking shared space as an example. Think Tank Times 184(16), 67–68 (2019) 7. Luo, Z., Tang, Y.: Overview and prospects of research on business model innovation of new ventures—an integrated analysis framework. Sci. Technol. Manage. Res. 39(002), 209–216 (2019) 8. Wu, S., Lu, Y., Huang, P.: Research on business model innovation of technology-based small and micro enterprises based on big data. Sci. Manage. Res. 37(233(06)), 95–101 (2019) 9. Li, H., Li, W., Zhang, S., Wang, L.: Research on business model innovation of convergent emerging industries in the new economic era. Mod. Bus. 584(31), 22–25 (2020) 10. Wang, X., Li, Y., Song, Z.: Research on the innovation of the business model of the vehiclecargo matching platform in the same city. J. Shandong Inst. Bus. Technol. 34(158(03)), 37– 46 (2020) 11. Peng, S.: Research on Guangdong foreign trade enterprise business model innovation under the background of sharing economy. Chin. Mark. 1035(08), 180–183 (2020) 12. Zhou, L., Guo, D., Yu, K.: Research on the business model innovation of Chinese unicorn enterprises under the background of “Internet+”. Foreign Econ. Relat. Trade 298(04), 95–99 +159 (2019)

University Public Resource Management System Based on DBSCAN Algorithm Yu Guo(&) Sanya Aviation and Tourism College, Sanya, Hainan Province, China

Abstract. With the rapid development and widespread popularity of mobile Internet and mobile terminal equipment worldwide, more and more traditional industries have been challenged unprecedentedly, and various industries pay more attention to the importance of data mining technology in the construction of their information systems. The purpose of this article is to study the university public resource management system based on DBSCAN algorithm. Based on the shortcomings of the DBSCAN algorithm, the Greedy DBSCAN algorithm is proposed and used to design the university public resource management system. Finally, the system was tested, and the test results showed that there were 8 bugs in the code development of the system, there were no bugs in the demand design and development, the development defect rate was 8.9%, and the design defect rate was 0. After the bug is found, the bug is repaired in time, and the defect rate reaches 0. Therefore, the system designed in this paper is successfully developed and the overall quality meets the requirements. Keywords: DBSCAN algorithm Multi-density clustering


Public resources
Management system


1 Introduction Machine learning has gradually emerged in the commercial applications of data mining, has produced impressive and excellent results, has important commercial value, and has gradually become an important solution for data mining [1, 2]. Clustering algorithm is an important technology in the field of machine learning. It is deeply used in a wide range of data mining scenarios, such as product recommendation, numerical prediction, pattern recognition and other scenarios. Therefore, the research on clustering algorithm has important value, but also has high application value [3, 4]. Many scholars at home and abroad have conducted research on the research on the university public resource management system based on the DBSCAN algorithm. The research on educational resource sharing technology abroad is relatively early. As pioneers, developed countries such as the United States, Canada, and Australia have done a lot of work in educational resource sharing, and many related products have been widely used [5, 6]. In September 2002, the Massachusetts Institute of Technology (MIT) announced the Open Course Ware (OCW) project, promising to make more than 2,000 courses covering various MIT majors into online courseware within a few years. Put it on the network, so that learners from all over the world can use it to realize the complete open sharing of all MIT courses [7]. The research on education information © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 202–210, 2022. https://doi.org/10.1007/978-3-030-89508-2_26

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resource sharing in our country mainly focuses on the integration and intercommunication of resources between university libraries, but lacks the construction of teaching resource sharing [8]. Although there are many related researches on the public resource management system of universities, no solution has been given to solve some other problems. Therefore, it is very necessary to strengthen the research on it. This paper uses the improved Greedy DBSCAN algorithm to design the university public resource management system. This article mainly introduces the goal of the system design, the function of the system, and the architecture design of the university public resource management system. Finally, the system designed in this paper is tested, and the test results show that the system basically meets the quality requirements.

2 University Public Resource Management System Based on DBSCAN Algorithm 2.1

DBSCAN Algorithm

Although some scholars have improved the DBSCAN algorithm, the result is not very satisfactory. The improved algorithm has many input parameters and is difficult to determine, which cannot achieve the final desired effect [9, 10]. In this paper, the greedy algorithm is added to the basis of the DBSCAN algorithm, so that the DBSCAN algorithm is no longer sensitive to the input parameters, and it is unable to improve the problem of clustering multi-density data sets. The idea of improving the DBSCAN algorithm in this paper is to use the idea of greedy algorithm, starting from the first initial solution of the problem, and then looking for the optimal solution step by step. In this paper, the detection and recognition of noise points are placed after the formation of circular clusters with a direct density, and the relative density is used for detection: (1) Using the average distance between points as a measure of relative density. The average distance calculation formula is as follows: distance ¼

1X d ðQi ; Qk neaest Þ Q2C k

ð1Þ

Among them, Qi represents a data object in the circular cluster C, Qk nearestðiÞ represents the k nearest points to the point Qi , and d(∙) represents the standard Euclidean distance between the points. The smaller the average distance in formula (1), the higher the density around the point, and vice versa, the lower the density. (2) Using the average distance between points as a measure of relative density. When the point Q satisfies the formula (2), it is an outlier noise point:

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distance

avg \\e

ð2Þ

Finally, it is verified that the Greedy-based improved DBSCAN algorithm proposed in this paper not only has the advantages of the density clustering algorithm, but also has a good clustering effect on noisy data sets. The most important thing is that the user only needs to input one parameter, and the algorithm also It is relatively simple and has high execution efficiency [11, 12]. 2.2

University Public Resource Management System Based on Greedy DBSCAN Algorithm

(1) Design principles In the early stage of research, the public resource management system of colleges and universities should follow certain principles to design. The following principles run through the entire research work: 1) The principle of integrity The development of the system adopts the strategy of ``top-down, subdivision''. At the beginning of development, an overall plan is made, starting with requirements and decomposing layer by layer to achieve the system design goals. 2) Modular design principle The functional requirements of the university public resource management system can be dynamically changed according to the needs of the business. The office strategies of different companies are very different. To adapt to different application requirements, the system must have good flexibility. (2) The goal of the construction of the university’s public resource management system. In the process of system construction, from a technical point of view, the following goals should be achieved: 1) Ease of use The system must have a simple, clear, soft, beautiful, generous, unified style interface, simple and convenient operation, and conform to user habits. Optimizing the system can effectively reduce user waiting time and avoid excessive response time due to excessive data volume. 2) Security System security mainly includes five aspects: login security, user management, monitoring and auditing, password management, and data security settings. (3) Introduction to the functions of the university public resource management system. 1) User management: The system administrator has higher authority than ordinary users, and is mainly responsible for user management, resource management and training plan management. When performing user management, the system administrator can add, delete and modify the information of ordinary users; when carrying out resource management, the system administrator is mainly responsible for the addition, deletion and modification of teaching resources; when carrying out

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3)

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announcement management, the system administrator is mainly responsible for the addition, deletion and modification of system announcements. Resource management System administrators can classify resources, add, modify, or delete resources, and can also choose to classify the resources uploaded by themselves. Resources are classified according to majors, audience grades, and source schools. The audience grades reflect the knowledge level of the subjects to be studied by the demanders. For example, the basic courses are divided according to the grades of the audience, helping learners to quickly find resources that meet their own needs. Training plan management As one of the most important components of the teaching resource database platform, it may require the professional person in charge to formulate the training plan for each student of the major. Each item of the training plan contains the teacher, the courses in the plan, and the specific details of the course (class hours, credits, course type). Through the specific analysis of the needs of the training learning module in the chapter of needs analysis, it is clear that the training learning module functions include user training data query and view submodules, training record viewing submodules, and data download submodules. Organization type maintenance Organization type maintenance defines the organizational existence form of product users, which is an important attribute of the organization; organization type maintenance is to add new organization types in the system, and to query and maintain (modify, delete) existing organization types at the same time. Organizational perspective management In order to meet the needs of various businesses in colleges and universities, it is necessary to establish a business organization perspective based on the human resource organization perspective, including the basic education perspective, the school safety engineering perspective, the student funding perspective, etc.: The organization perspective management is to add new organizational perspectives and maintain (Modify, delete) Existing organizational perspective. Application system integration

The application system integration function module is mainly for the basic information maintenance interface of each management information system, and manages and controls the user authentication authorization range of various management information systems of the Ministry of Education. The administrator can perform application system maintenance, application system authorization and synchronization in this function module. (4) Architecture design of university public resource management system. When the server side and the mobile side have communication requirements, for example, users need to log in to the system and operate related functions according to their own needs. When working based on the system, the mobile device client is required to have a wifi network, a 3G network, a 4G network or 5G networks. Through these networks, the request content initiated by the user through the client is digitized and sent to the device. As the request is transmitted to the web server of the public

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resource management system based on the Internet network, the server and the client are in this series of processes. The connection between the terminals is realized on the basis of communication. Based on the above, the network structure of the university’s public resource management system is shown in Fig. 1.

Database server Management side

Mobile terminal

web server

Fig. 1. Network structure of university public resource management system

The university public resource management system mainly realizes the functions of collecting mobile data and analyzing business classification. This article divides the mobile data analysis system into three layers for implementation: UI layer, data analysis layer and data layer. The system software architecture is shown in Fig. 2. University Public Resource Management System UI layer Administrator interface

Data analysis layer Functional module

Data layer File system

Database

Fig. 2. System software architecture

The UI layer is an interface for administrators, providing administrators with boundary-based functional operations;

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The data analysis layer contains several functional modules, which are responsible for the specific meaning of the functions. The UI layer calls the functional modules of the data analysis layer to obtain the data that needs to be displayed on the interface, or to update the data of the data layer; The data layer includes a file system that stores the captured mobile Internet data and a back-end database of the data analysis system, providing data storage and query functions.

3 Realization of the University Public Resource Management System Based on Greedy DBSCAN Algorithm 3.1

System Development Environment

The realization environment of the university public resource management system based on Greedy DBSCAN algorithm has software environment and hardware environment. Among them, the software environment mainly includes the system operating platform, development language, and operating system. The hardware environment mainly includes client hardware configuration and server. The operating environment of the university public resource management system is as follows: The server adopts a Web server configured with Tomcat and a database server with MySQL version. Web server: Tomcat Database server: MySQL Server operating system: Windows Server Client operating system: Android Development language: Java language Development platform: Eclipse 3.2

Realization of System Functions (1) (2) (3) (4)

User registration and login System resource management Resource classification module Training and learning modules

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4 Test of University Public Resource Management System Based on Greedy DBSCAN Algorithm 4.1

Background Login Test Case

This part of the test cases mainly tests whether the background login function is realized. During the background login process, various test cases need to be input, as shown in Table 1. Table 1. Back-end login test case table Function description Use case purpose Preconditions Input/action Do not enter username and password Enter only the login password Enter only the login user name Enter the wrong username and password Enter username and password

Background login Normally use the administrator login function Connect to the web server normally Desired output Actual situation System prompts for username In line with expectations System prompts for username In line with expectations System prompts for a password In line with expectations Prompt that the user name or In line with password is wrong expectations Successful landing In line with expectations

It can be seen from Table 1 that the background login function of the system is normal. Only when the user name and password are entered correctly will the system prompt login success, otherwise different error prompts will be displayed. 4.2

Test Process Analysis

In response to project requirements, the university public resource management system based on the Greedy DBSCAN algorithm was incrementally developed; mainly for the incremental part of the test, as well as the basic function divergence test, the total number of questionnaires is small, showing a state of convergence. Result analysis: Judging from the test results, the version quality still meets the requirements, as shown in Table 2. It can be seen from Table 2 that there are 8 bugs in code development, no bugs in requirement design and development, development defect rate is 8.9%, and design defect rate is also 0. After finding a bug, fix the bug in time. Although there are some bugs, in general, the development of the system is relatively successful, and the overall quality meets the requirements.

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Table 2. Test trend analysis table Total number of use cases System

286

Code development bug 8

Requirements design bug

Development defect rate

0

8.90%

Design defect rate 0

5 Conclusions In the era of big data, human electronic interaction behaviors are transformed into strips of data. These data contain great value. Machine learning has shown excellent results in data mining and has gradually become the main technology of data mining. Clustering algorithm is an important technology of unsupervised learning. This paper comprehensively analyzes the problems that exist in the public resource management of colleges and universities, such as focusing on construction and neglecting management, information islands, multi-head management, and unclear powers and responsibilities. The improved Greedy DBSCAN algorithm is used to design the college public resource management system, and the combination of management and decentralization is implemented. Prioritize efficiency, and differentiate management; focus on service-oriented, incentive system construction, stimulate the vitality of management, and make up for the deficiencies of the original research. Finally, the system designed in this paper is tested, and the test results show that the system basically meets the quality requirements.

References 1. Shukur, H.M., Zeebaree, S., Zebari, R.R., et al.: Design and implementation of electronic enterprise university human resource management system. J. Phys.: Conf. Ser. 1804(1), 012058 (2021) 2. Jeoung, O.C., Choi, H.G., Lee, Y.W., et al.: The Effects of high performance human resource management system on organizational commitment in public institution. Korean Rev. Corp. Manag. 10(1), 133–151 (2019) 3. Yuslim, S.S., Sulistio, H.: Soft system methodology and human resource management in designing public park. Int. J. Livable Space 4(2), 67 (2019) 4. Battaglio, P.: The future of public human resource management. Public Pers. Manag. 49(4), 499–502 (2020) 5. Ingold, K., Tosun, J.: Special issue “Public policy analysis of integrated water resource management”. Water 12(9), 2321 (2020) 6. Abdulraheem, A.S., Zeeba Ree, S., Abdulazeez, A.M.: Design and implementation of electronic human resource management system for Duhok Polytechnic University. Tech. Rep. Kansai Univ. 62(4), 1407–1420 (2020) 7. Taryono, Wulandari, D.Y.: Management strategy of plastic waste in the Cimandiri RiverSukabumi, West Java. IOP Conf. Ser.: Earth Environ. Sci. 744(1), 012089 (2021)

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8. Nan, Y.: Design and construction of new human resource management system for rural public service in contemporary China: based on configuration framework. Agro Food Ind. Hi Tech 28(1), 2364–2368 (2017) 9. Seitio-Kgokgwe, O.S., Gauld, R., Hill, P.C., et al.: Understanding human resource management practices in Botswana’s public health sector. J. Heal. Organ. Manag. 30(8), 1284–1300 (2016) 10. Such, B., Ritzhaupt, A., Thompson, G.: Migrating learning management systems: a case of a large public university. Admin. Issues J. 7(2), 6 (2017) 11. Sayl, K.N., Muhammad, N.S., Yaseen, Z.M., El-shafie, A.: Estimation the physical variables of rainwater harvesting system using integrated GIS-based remote sensing approach. Water Resour. Manag. 30(9), 3299–3313 (2016). https://doi.org/10.1007/s11269-016-1350-6 12. Kim, W., Yue, L., Chang, G.L.: Advanced traffic management system: integrated multicriterion system for assessing detour decisions during nonrecurrent freeway congestion. Transp. Res. Rec. 2324(1), 91–100 (2018)

Influence and Mechanism of Welding Residual Stress of 16MnR with Machine Learning Pengju Zhang, Wenqian Bai, Yu Wu, and Jingqing Chen(&) Institute of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China [email protected]

Abstract. In order to evaluate the effect of laser shock strengthening (LSP) technology on the residual stress and stress corrosion resistance of welded joints, different laser shock strengthening tests were carried out on 16MnR steel plate, and the stress corrosion resistance of joints before and after laser shock strengthening was studied.The results show that the maximum plastic deformation layer of 475 lm and compressive stress distribution of −593 MPa can be introduced on the surface of 16MnR steel plate after laser shock peening. After the surface treatment of the 16MnR steel welded joints by the optimized LSP process, the residual tensile stress distribution on the welded joint surface can be effectively reduced. Under the condition of 3.5% NaCl, the slow variable speed stress corrosion test results of gap samples after laser impact show that the stress corrosion performance of 16MnR steel welded joints is improved. The research content shows that LSP can eliminate the residual stress on the surface of welded joints and improve the stress corrosion performance of materials. Reasonable evaluation of aroma is also of great reference value. Keywords: Laser shock peening
Laser-MAG hybrid welding
Weld joints
Residual stress
Stress corrosion resistance

1 Introduction 16MnR steel is a high-strength low-alloy structural steel and high-temperature pressure vessel, which is widely used in the manufacture of transportation machinery and building structures. This material has low content of C and Mn and good welding adaptability [1]. In particular, the fire resistance and corrosion resistance, wear resistance, stress corrosion crack resistance and fatigue strength [2–5] LSP are improved. Compared with the previous strengthening process, the impact pressure and impact energy are improved, and the treatment process can protect the environment more effectively. The effect of Chu et al. [6] LSP treatment on the surface microstructure and properties of low carbon steel was studied. It was found that LSP treatment increased the hardness of the material surface by 80%, and the high error density and residual stress of the material surface were 100 lM. the effect of the first LSP treatment on the nano hardness and residual stress of ANSI304 stainless steel surface is studied [7]. LSP significantly improves the nano hardness near the material surface, and the material © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 211–220, 2022. https://doi.org/10.1007/978-3-030-89508-2_27

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surface is 900 l Introducing m-grade plastic deformation layer, the maximum pressure stress on the material surface is distributed at –300 mpa, Li et al. [8]. The effect of laser power density on 316L stainless steel welded joint is studied. The distribution of regional residual stress gradually changed to compressive residual stress. Wang et al. Carried out LSP strengthening treatment on the laser welded joint. After LSP treatment, the microhardness of 7075-te6 aluminum alloy in the welding gap area was increased from 152 hv to 175 hv, and LSP strengthening treatment was carried out. The maximum residual pressure stress in the area passing through the welding line is − 200MPa. In this paper, 16MnR steel is analyzed. Considering the surface deformation and microhardness depth distribution before and after LSP hardening, the best LSP method is selected to eliminate the welding stress of the steel plate. The development of residual stress distribution before and after LSP processing was verified by 16MnR welded joint and delayed corrosion test. The stress corrosion resistance of LSP welded joint of 16 mw laser MAG steel was tested, and the corrosion resistance of the welded joint was analyzed. Connector 16 MW.

2 Materials and Methods This study is 150  seventy-five  10 mm3 (length)  Narrow width  Thickness) 16MnR steel plate and jm56 wire with gs1.2 diameter are represented as welding materials, Table 1 shows the chemical composition of 16MnR steel and wire, the shielding gas of arc torch is the mixture of carbon dioxide and argon (20% CO2 + 80% AR), and laser Mg composite welding is a single Y with root spacing of 0.8 mm. The V-shaped inclined plane butt joint is welded by hybrid laser and MAG method. Two welding methods are adopted, including base weldinsg and cover welding. The size and welding configuration of the welding port are shown in Fig. 1. Table 2 shows the laserMAG hybrid welding process parameters, where V1 is the welding speed, V2 is the wire feed speed, P is the laser power, I and U are the welding current and voltage, respectively. The main parameters of the LSP test equipment are as follows: laser beam wavelength 1064 nm, repetition frequency 5 Hz, pulse width 15 ns. The absorbing layer and confining layer used in the test are black tape and distilled water, respectively. Figure 2a is a schematic diagram of the LSP treatment path of the 16MnR base metal surface with different impact energy, impact times, spot diameter and overlapping rate. After welding, the optimized LSP process is used to relieve stress on the welded joint, and the path is shown in Fig. 2b. Table 1. Chemical composition of base material and filling material (wt%) Material C Si Mn S P Cr Ni Cu Fe 16MnR 0.16 0.34 1.43 0.011 0.011 0.06 0.07 – Balance JM56 0.07 0.91 1.49 0.015 0.01 0.017 0.01 0.01 Balance

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Fig. 1. Schematic diagram of welding groove and bead layout

Table 2. Welding parameters Welding pass V1/(mm
s−1) V2/(m
min−1) P/kW I/A U/V Root 17 12 3.5 286 29.6 Cap 6 13.5 1 257 27.8

The test also divides the sample depth into 4 intervals. l When the hardness test is carried out, each position is measured with 5 sets of data, the average value is slowly deformed with a fine hardness value, and the tensile sample is extracted from the vertical bead, and the processing size is slowly deformed as shown in Fig. 3 (1.6 mm thick), and the tensile speed of the corrosion test is 10−5 / s. This environment was air and 3.5% NaCl solution, and the environmental temperature was 20−25 °C, and the SEM structure was observed with the SEM quantum FEG 250 scan mirror to observe the fine structure of the slow tension breakage, such as the blue test point of Fig. 2.

Fig. 2. Schematic diagram of LSP and residual stress path (a) 16MnR plate, (b) 16MnR welded joint

Fig. 3. Schematic diagram of slow strain tensile sample

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3 Results and analysis 3.1

Surface Plastic Deformation Measurement in the LSP Area

Figure 4 shows the depth of surface deformation caused by different spot sizes, impact times, overlapping rate and impact energy respectively. Set the impact energy as 20 J, the overlapping rate as 30%, the number of impacts is 2 times. As can be seen from the Fig. 4, when the spot diameter is 3 mm, 4 mm and 5 mm, the deformation caused by LSP on the material surface is 3.91 lm, 1.64 lm and 0.61 lm, respectively. It shows that under the same conditions, the smaller the spot diameter, the more concentrated the laser energy density. The impact energy was 20 J, the overlap rate was 70%, and the point diameter was 3 mm. The effect of impact times on surface deformation was observed. When the impact times are 1,2,3, the surface deformation of the material is 2.93lm. 5.73 lm and 8.1 lm respectively. As the number increases, the degree of deformation increases accordingly, but the amplitude decreases relatively. This is because the hardening phenomenon on the material surface occurs, the more the number of impacts, the more obvious the phenomenon. The test also divides the sample depth into 4 intervals. l The processing size is shown in Fig. 3 (thickness 1.6 mm), and the tensile speed of the slow deformation corrosion experiment is 10−5 / s, and the environment is air and 3.5% NaCl solution, respectively, and the environmental temperature is 20−25 °C., and the blue test point in the direction of Fig. 2.

Fig. 4. Surface deformation induced by different LSP processes

3.2

Measurement of Plastic Deformation in the Depth Direction

Hardness is an index to measure the degree of work hardening of materials. In this paper, the plastic deformation depth of LSP was measured by microhardness. Figure 5 shows the microhardness distribution of 16MnR base metal in the depth direction under different LSP processes, in which the green line is the average microhardness value of non-impact material (matrix area) measured by the test, and the value is 159 HV. It can be seen from Fig. 5a that when the spot diameter is 3 mm, 4 mm, and 5 mm, the plastic deformation influence layer caused by LSP on the material surface is 240 lm, 160 lm and 116 lm, respectively. When the spot diameter of 4 mm is compared with that of 5 mm, the plastic deformation depth increased by 37.93%, and when the spot

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diameter of 3 mm is compared with that of 4 mm, the plastic deformation depth increased by 50%. It can be seen that under the given conditions of other processes, the use of a smaller spot diameter can cause a deeper layer of plastic deformation. As shown in Fig. 5b, because the LSP process impact samples have different overlap rates, the overlap area may be impacted multiple times. A larger overlap rate must introduce a deeper plastic deformation layer to the material surface, and the overlap rates are 30% and 50%. The plastic deformation depth of 70% is 240 respectively. lm, 292 lm, 342 lm. The degree of deformation of the 16MnR material, as shown in Fig. 6, increased in the different machining process conditions, as the number of impact increased, the work hardening occurred on the surface of the material, and the degree of deformation decreased somewhat, and thus the positional error density of the material surface increased.

Fig. 5. Distributions of microhardness along the depth direction after different LSP processes. (a) Spot diameter, (b) Overlapping rate

Fig. 6. Variation of plastic deformation depth after different LSP processes

After being processed by LSP, the dislocation density of the surface layer of the material has increased significantly, and a large number of dislocations have increased in value, resulting in high-density dislocation clusters and dislocation entanglement, which makes the surface of the material work hardening. In order to better understand the effect of LSP treatment on the plastic deformation of the surface of 16MnR material, it was subjected to LSP treatment with an impact energy of 8 J, a spot

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diameter of 5 mm and a single impact. It can be seen from the Fig. 7 (a) and (c) that compared with the cross section of the untreated sample, the grain size of the LSP treated sample does not change significantly within a certain area from the impact surface. However, by comparing the local dislocation Figs. 7(b) and (d) before and after the LSP, more dislocation accumulates in the grains of the surface layer of the material after LSP, that is, the accumulation of plastic deformation occurs, resulting in the work-hardening of the material surface, which increases the microhardness of the material. The depth of plastic deformation observed by EBSD is less than the actual value.

(a)

(c)

(b)

20μm

20μm

(d)

20μm

20μm

Fig. 7. (a) IPF diagram before LSP, (b) local dislocation diagram before LSP, (c) IPF diagram after LSP, (d) local dislocation diagram after LSP

3.3

Residual Stress Distribution in LSP Area

The measured residual stresses in the 16MnR river model material after different LSP processes are shown in Fig. 8. Because the test is strengthened by overlapping multiple LSP processes, all LSP paths measure the residual stress as X-ray according to the X direction of Fig. 2aX. As shown in Fig. 8a, the overall variation law of residual stress distribution is consistent with the experimental results in Sect. 3.2 and Sect. 3.3 above. Taking into account the effect of spot size on the LSP residual stress field. 3 mm, 4 m and 5 mm spot diameters are selected to impact strengthen the material surface. The residual stresses in the X direction of the material surface are −397 mpa, −293 mpa and −209 mpa respectively, which can reduce the speckle diameter, The stress value of residual pressure is effectively increased. Therefore, after selecting the speckle diameter, the LSP strengthening effect is significantly improved. 1. After 2 and 3 times of impact, the peak residual pressure on the material surface is 316 MPa, −485 MPa and −593 mpa respectively. Therefore, as the number of impacts increases, the maximum residual compressive stress on the surface increases, the increase in surface residual stress gradually decreases, and the residual compressive stress on the surface after repeated impacts reaches saturation. The reflection of shock wave after repeated shock shows that the surface residual pressure stress reaches saturation state. As the residual pressure stress level decreases, as shown in Fig. 8C, when the overlap rate is 30%, 50% and 70%, the compressive residual stress of the material surface is −397 MPa, and the 30% connection rate is 444 MPa and −4485 MPa. the residual stress value of 50%

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connection rate increases by 11.84%, and the residual stress value of 70% connection rate increases by 9.23% compared with that of 50%. The rate of change of surface residual stresses decreases with increasing connection rate. The overall residual stress tends to increase as the impact energy increases. the maximum residual compressive stress is −3388 mpa for 8J and −5515 mpa for 14J. the maximum residual compressive stress is −593 mpa for 20J. During LSP strengthening, as the impact energy increases, the energy transferred to the material increases and the interaction with the material increases. forming a large residual pressure stress distribution.using 14J impact energy compared with 8J impact energy, the residual compressive stress is increased by 32.73%, and using 20J impact energy compared with 14J impact energy, the stress is increased by 15.15%. As shown in Fig. 8, the number of impacts increases and the level of residual compressive stresses on the surface increases, but the increase range keeps decreasing, which is caused by the work hardening effect of the material.

Fig. 8. Residual stress distributions of material surface after different LSP processes. (a) spot diameter, (b) impact times, (c) overlapping rate, (d) impact energy

3.4

Effect of LSP on Residual Stress of 16MnR Welded Joint

The welding results are shown in Fig. 9 and the welds are very well formed.

Fig. 9. Macro-morphology of welding bead. (a) Weld surface; (b) Weld root

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As shown in Fig. 10, after hybrid welding the 16MnR high-intensity laser MAG shown in Fig. 2B, This paper uses the results of the residual stress measurement path for welded joints with relatively small transverse tensile stresses in the heat affected zone. The maximum tensile stress is 351 mpa, as shown in Fig. 10B. Tensile stresses are mainly concentrated at the edge of the weld and in the heat affected zone. The maximum vertical residual stress appears near the weld, which is 530 mpa. Away from the heat affected area, the tensile stress decreases gradually. The total stress level of vertical residual tensile stress is higher than that of horizontal residual tensile stress, and the plastic deformation accumulation in the vertical and weld directions is greater than that in the transverse direction. Therefore, there is a large vertical residual stress in the work.Because the 16MnR steel with laser-MAG hybrid welding technology has a large residual stress after welding, and the LSP process with a overlapping rate of 70% can make the residual stress field distribution of the welded joint more uniform. In consideration of the above research results comprehensively, the LSP process with laser impact energy of 20 J, spot diameter of 3 mm, coverage rate of 70% and impact for three times was selected in this paper to conduct stress relief treatment on the 16MnR steel welded joint 30 mm from the weld center. As shown in Fig. 10, after the stress relief treatment of the joint through the above LSP process, the welded joint is transformed from tensile stress to pressure stress. The maximum transverse residual stress is −4482 mpa and the longitudinal residual stress is −203 mpalsp. The residual compressive stress distribution formed in the welded joint improves the residual compressive stress after welding.

Fig. 10. Residual stress of welded joint before and after LSP. (a) transverse stress, (b) longitudinal stress

3.5

Stress Corrosion Resistance Test Results and Tensile Fracture Analysis

As shown in Fig. 11(a), the macroscopic morphology of 16MnR specimens before and after LSP treatment in air and 3.5% NaCl solution after slow deformation tensile test, as shown in Fig. 11B, the stress/deformation curves after tensile test are shown in Table 3, the tensile strength and tensile rate of 16MnR connector samples without LSP treatment in air were 569.3 mpa and 18.44%, the tensile strength and tensile rate in a 3.5% NaCl solution of 16MnR connector or non-LSP treated samples were 541.1 mpa and

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17.50%, respectively, with a stress corrosion susceptibility factor ISSRT of 0.0571. The tensile strength and elongation of LSP treated 16MnR steel welded joint samples in air were 572.9 MPa and 19.19%, the tensile strength and elongation of LSP-treated joint specimens in 3.5% NaCl solution were 547.9 MPa and 18.25%, respectively, with a calculated stress corrosion sensitivity factor ISSRT of 0.0512. By comparison, it was found that the tensile strength and elongation of 16MnR welded joint specimens without LSP treatment in 3.5% NaCl solution elongation were significantly lower, and the LSP-treated joint specimens had significantly lower tensile strength and elongation in 3.5% NaCl solution, with smaller decreases in tensile strength and elongation, resulting in the corrosion sensitivity coefficients of the LSP-treated joint specimens being smaller than those of the untreated joint specimens. The smaller decrease in corrosion susceptibility coefficient for the LSP strengthened specimens compared to the untreated joint specimens was mainly due to the fact that only one side of the joint specimen was strengthened with LSP in this test. The results show that the LSP treatment improved the stress corrosion resistance of the welded joints of 16MnR steel.

Fig. 11. Tensile specimens of 16MnR welded joints in air and 3.5% NaCl solution before and after LSP. (a) Tensile fracture position, (b) tensile curve

Table 3. Slow strain tensile properties of 16MnR steel joint specimens before and after LSP Specimen number Without LSP, Air Without LSP, 3.5%NaCl LSP, Air LSP, 3.5%NaCl

Tensile strength/MPa Deformation/mm 569.3 2.95 541.1 2.8 572.9 3.07 547.9 2.92

Elongation/% 18.44 17.50 19.19 18.25

ISSRT – 0.0571 – 0.0512

LSP treatment shows better toughness and LSP treatment increases the degree of plastic deformation of materials, and LSP treatment is the main cause of material toughness. The residual stress of the welded joint promotes stress corrosion and cracking, and the pressure stress introduced by the LSP on the material surface cancels the partial welding residual stress and improves the stress corrosion performance of the joint [4].

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4 Conclusions (1) The effects of laser shock forming process parameters (point diameter, impact times, connection speed and laser pulse energy) on the surface deformation, deep microhardness distribution and residual stress field of 16MnR steel plate were studied. The results show that the surface 475 of 16MnR steel treated by LSP. l The maximum plastic deformation layer of M is formed, and the pressure stress distribution is −593 mpa. (2) After the compound welding of 16MnR high-intensity laser MAG, the welded joint forms a high tensile stress distribution in the vertical and horizontal directions, which is specifically reflected in the tensile stress concentration stress distribution in the heat affected area. The maximum transverse residual tensile stress is 351 mpa and the longitudinal residual tensile stress is 530 mpa. (3) After LSP stress relief, the residual tensile stress of 16MnR steel welded joint decreased significantly. The maximum transverse residual pressure stress was −4482 mpa and the maximum vertical residual pressure stress was −203 mpa, which effectively eliminated the welding residual tensile stress. (4) The corrosion sensitivity coefficient of the 16MnR welded joint specimens after LSP treatment is lower than that of the untreated joint specimens, which indicates that LSP treatment improves the stress corrosion resistance.

References 1. Guo, L., Zhang, T.H., Xu, R.P., et al.: Properties Effect of 16MnR steel weld joint by different welding methods. J. Adv. Mater. Res. 690, 2639–2642 (2013) 2. Lu, J.Z., Qi, H., Luo, K.Y., et al.: Corrosion behaviour of AISI 304 stainless steel subjected to massive laser shock peening impacts with different pulse energies. J. Corros. Sci. 80, 53–59 (2014) 3. Rubio-González, C., Ocaña, J.L., Gomez-Rosas, G., et al.: Effect of laser shock processing on fatigue crack growth and fracture toughness of 6061–T6 aluminum alloy. J. Mater. Sci. Eng. A 386(1–2), 291–295 (2004) 4. Lu, J.Z., Luo, K.Y., Yang, D.K., et al.: Effects of laser peening on stress corrosion cracking (SCC) of ANSI 304 austenitic stainless steel. J. Corros. Sci. 60(06), 145–152 (2012) 5. Liao, Y.L., Suslov, S., Ye, C., et al.: The mechanisms of thermal engineered laser shock peening for enhanced fatigue performance. J. Acta Materialia 60(13–14), 4997–5009 (2012) 6. Li, Y., Li, Y.H., Wang, X.D., et al.: Effect of nanosecond pulse laser shock peening on the microstructure and performance of welded joint of 316L stainless steel. M. Adv. Mater. Process. 978, 113–125 (2018) 7. Wang, L.Q., Hu, Y.N., Che, Z.G., et al.: On the fatigue performance of laser shock processed fusion welded 7075 Alalloy. J. Acta Aeronautica et Astronautica Sinica 40, 1000–6893 (2020) 8. Peyre, P., Chaieb, I., Braham, C.: FEM calculation of residual stresses induced by laser shock processing in stainless steels. J. Modell. Simul. Mater. Sci. Eng. 15(3), 205 (2007) 9. Lu, J.Z., Luo, K.Y., Zhang, Y.K., et al.: Grain refinement of LY2 aluminum alloy induced by ultra-high plastic strain during multiple laser shock processing impacts. J. Acta Materialia 58 (11), 3984–3994 (2010)

Intelligent Clothing Recommendation Design System Based on RFID Technology AiQing Tang(&) Shandong University of Technology, Zibo 255000, China

Abstract. Apparel design is a very complex process involving science and technology, and the use of interactive evolution in industrial design is growing every day. In this article, we describe and provide examples of interactive apparel evolutionary design methods. Users can choose and evaluate different clothing styles according to their emotional preferences, and evolutionary computation allows them to obtain designs that match their internal expectations. Keywords: Interactive genetic algorithm
Clothing design
Individualization

1 Introduction Interactive evolutionary computation (IGA) is an evolutionary computation method in which a human evaluates a fitness function, of which interactive genetic algorithm (IGA) is the most widely used. The IEC/IgA method has not only the advantages of traditional evolutionary algorithms, but also a mechanism for interaction with the decision maker. It has the dual property of interactive system adaptation, which makes it suitable for solving problems that are inherently complex. The disadvantage of IgA is also that the user has to evaluate each individual in the interaction. The large number of individuals and the long evolutionary time contribute to user fatigue. In particular, if a generation consists of four very different personality types, having to evaluate the relative strengths and weaknesses of each can be a source of great psychological stress and fatigue. To solve the problem of user fatigue, this paper proposes using the best individual in each generation of genetic operation as the central value, or data center, node of the hidden layer of the radial basis function network, and using a similar distance value and K-means method to obtain the kernel Gaussian function decomposition constant and output weighting coefficients obtained. In this paper, we implement a garment design system based on this algorithm and show its efficiency [1].

2 Interactive Genetic Algorithm 2.1

Principle of Interactive Genetic Algorithm

The basic flow of an interactive genetic algorithm is illustrated in Fig. 1 and comprises the following steps: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 221–226, 2022. https://doi.org/10.1007/978-3-030-89508-2_28

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① Population parameter setting; ② Binary code; ③ The initial population was generated; ④ Decoding, users evaluate the evolutionary individuals; ⑤ Judge whether the user has a satisfied individual, if so, output the optimal solution, and the algorithm ends; If not, the new species group was generated by genetic operation and transferred to 3.

Fig. 1. Flow chart of interactive genetic algorithm

2.2

Features of Interactive Genetic Algorithms

Interactive genetic algorithms contain both the features embedded in traditional genetic algorithms and have their own unique advantages: ① Cognitive limitations of the user: probably because the fitted values of the individuals evolved are determined by the user, the individuals selected and the scores generated by the application algorithm are self-aware, each individual has different preferences and different individuals may have very different results for the same individual. ② Small population size and less iterations: for users, each generation of new population needs to carry out fitness evaluation operation, frequent human-computer interaction always can not get the expected results, which will cause user fatigue, so it requires small population size and less iterations. ③ The optimal solution is not unique: everyone has different preferences, so the results are different. In addition, there are two kinds of users, one is clear about the inner needs, the other is vague about the evaluation object. They all have the possibility of multiple preferences and are satisfied with multiple feasible solutions in the optimal solution set [2].

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3 Element Design Kim et al. investigated and analysed the feasibility of applying IgA to apparel product research and design and proposed an IgA-based clothing design system to support actual clothing design. According to the research idea of Kim et al. we divided the suit into three parts, namely the skirt, sleeves and collar. Each part of the garment is designed with different corresponding styles (four styles are used here) and twodimensional graphics are used to represent these garment styles. The patterns of these different parts of the garment can correspond to different colours (in this case four colours) and these 2D pattern models are stored in a database. After this processing, the garment can be described by six variables representing the colours of the skirt, sleeves and collar, neckline and neck. Here, each variable can take four values between 1, 2, 3, 4, which form the parameter space of the garment [3]. It is inevitable that the clothing styles generated by the separation of these six random variables may not yield satisfactory clothing style results, therefore, traditional genetic algorithms (GA) and global search patterns have previously been used for optimisation, which is a feasible approach. However, since it is not possible to express the adaptation function, this paper proposes an interactive genetic algorithm (IGA) that solves the style evaluation problem by interacting with the user to obtain the adaptation function. Styles and colours correspond to the values of these variables, divided into a series of garments. For each user, the system outputs the evaluation accordingly. To facilitate the processing of the clothing patterns, the entire clothing set (system features) is also referred to as an individual or personal form as shown in Fig. 2.

Fig. 2. Individual expression type of clothing

The clothing design system of this paper is based on the coding method, and the fitness function of the genetic algorithm is approximated through artificial neural network.

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4 Using Neural Network to Approach Fitness Function 4.1

Radial Basis Neural Function Network

The radial neural network (RBFNN) has the advantage of nonlinear access ability, effective effect, network structure, weight and output line shape, fast learning speed, model identification, function access, signal processing, etc. Widely applied to the field of system modeling and control, the radial function neural network structure used herein is shown as an input layer as shown in Fig. 3. The output is only one node, including the hiding layer and the output layer, the degree of adaptation of the GA, the fitness range is between 0 and 1, the hiding layer consists of a radial basis function, and the center and width are related to all the hidden layer nodes. The base function of the radial direction is the Goth function. [4].

Fig. 3. Topological structure of radial Gauss function network

Network input and output can be seen as a mapping relationship yðXÞ ¼ w0 þ

n X i¼1

4.2

wi ui ðxÞ ¼ w0 þ

n X i¼1

wi expð

kX  Ci k2 Þ 2r2i

ð1Þ

Solution Process and Algorithm of Hidden Node Data Center.

You must check the hidden node data center before using the RBF network. Each radial function data center, C, and expansion constant 8; Output a weight correction source. After the preceding two conditions have been determined, the RBF may be a linear equation group from the input to the output, then weight learning may be trained in the manner of supervisory learning, but the most concise method is calculated by direct least multiplication (LMS), and the system is directly calculated with minimal multiplication. All.

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The evaluator first evaluates the eight individuals who have appeared (such as Fig. 4), evaluates the next generation by genetic evolution, records the best individual of this generation, repeats the user to feel tired, and is the best dog recorded in all generations. Based on the k-means algorithm, an object is used as an initial set center of a radial basis function network.

Fig. 4. Interface of clothing design system based on radial basis network and interactive genetic algorithm

4.3

GA decoding

The evaluator can set a threshold value by himself, and the system is required to display the corresponding styles of all individuals whose real stress is greater than the threshold value. The evaluator will discard the styles that are not approved, select the style with high satisfaction, or ask the system to search for further. In this process, the user may modify the self adaptive value of the search result so that the RBF neural network functions as a self adaptive function of feedback correction, the system has the ability to learn continuously and can further meet the satisfaction of the evaluator.

5 Conclusion With the improvement of economic level and the development of science and technology, people are increasingly urgent for personalized goods and customized technology. Interactive evolutionary design has gradually entered people’s vision, and its unique algorithm mechanism fully meets the clothing independent design. This design method of non pure logic theory is a bottom-up process. In the process of interactive design, the initial population required by design is tried to be solved. The whole design feasible region is retrieved by large-scale evolutionary derivation of genetic operation

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to obtain the optimal solution that meets the user’s preference. But based on the characteristics of the algorithm, the final product combination forms are various, and the traditional model has some defects in convergence speed and user evaluation. Therefore, the algorithm needs to introduce logical methods, namely, adding appropriate guidance and constraints, so as to effectively combine interactive genetic algorithm to promote the marketing upgrading of the garment industry and improve the competitiveness of the clothing industry and the added value of the products.

References 1. Gao, J.: Principles and Simulation Examples of Artificial Neural Network M, pp. 56–76. China Machine Press, Beijing (2003) 2. Hu, J., Chen, E., Wang, S., et al. Improvement of convergence and user evaluation quality in interactive genetic algorithm J. Univ. Sci. Technol. China, 32(2), 210–216 (2002) 3. Gong, D., Hao, G., Zhou, Y., et al.: Hierarchical interactive evolutionary computation and its application. J. Control Decision 19(10), 1117–1120 (2004) 4. Zhang, X., Yu, J., Jiang, L., et al.: Computer aided generation of paper cut image. J. Acta Comput. Aided Design Graphics 17(6), 1378–1380 (2005)

Analysis and Optimization of Tourism Landscape Pattern Based on GIS Siqi Wang(&) Yunnan Open University, Yunnan 650000, China

Abstract. Landscape spatial pattern analysis is one of the core issues in landscape ecology, which is helpful to explore the relationship between landscape pattern and ecological process. With the accelerating process of urbanization, the city scale is expanding, and the urban form and structure are constantly changing; Urbanization profoundly changes the natural landscape of an area, affects the ecological process of the city, and then changes the whole landscape pattern. The impact of urban spatial sprawl caused by urbanization on the function of urban ecosystem and its ecological effect is one of the key problems that need to be solved urgently in urban development. Based on GIS, this paper studies the landscape pattern of urban tourism, aiming at reasonable planning and construction of urban green space landscape pattern and improving urban ecological environment. Keywords: GIS


Tourism landscape
Pattern analysis

1 Introduction Since the beginning of the 21st century, tourism has become one of the fastest growing industries, and also a powerful growth point of domestic and international economy. According to statistics, at the beginning of this century, the number of international tourists in the world has reached 698 million, while the international tourism revenue has reached 476 billion US dollars; By 2012, the number of tourists has reached 1.035 billion, and the tourism revenue has increased to US $1075 billion. In China, with the further increase of urban and rural residents’ income and the rapid improvement of living standards, tourism demand is growing rapidly. Domestic tourism revenue and overseas tourism revenue were 36.077 billion yuan and 5.953 billion yuan respectively, up 22.5% and 10.5% year on year. It is estimated that by 2020, China’s total domestic tourism consumption will reach 5.5 trillion yuan, the number of trips per capita will reach 4.5 times per year, and the proportion of added value of tourism in GDP will exceed 5% “. The progress of science promotes the development of society and facilitates people’s daily life, but at the same time, it also increases the pressure of human survival and reduces the opportunities and time for people to contact with nature. The reasonable planning and construction of urban green space can become an opportunity for people to return to nature, realize the dream of contacting and embracing nature, which is of great benefit to people’s physical and mental health. On the other hand, urban green space system can purify the air, conserve water, regulate the microclimate, so as © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 227–233, 2022. https://doi.org/10.1007/978-3-030-89508-2_29

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to achieve the goal of improving the environment and protecting nature. Therefore, this paper uses GIS technology and the principle of landscape ecology to analyze and study the urban green patches, aiming to find out the problems existing in the urban green space layout of the city, and make reasonable optimization, so as to improve the utilization rate of urban land, establish the ecological security framework of urban tourism landscape, and solve the contradictions and conflicts among the economy, population, land and environment of Qingcheng city, Improve the management and maintenance level of urban green space, so as to promote the sustainable development of the city [1].

2 GIS Technology GIS Technology (Geographic Information Systems) is the product of interdisciplinary. It is based on geospatial, uses the method of geographic model analysis, and provides a variety of spatial and dynamic geographic information in real time. It is a computer technology system for geographic research and geographic decision-making. Its basic function is to convert the tabular data (whether it comes from database, spreadsheet file or directly input in the program) into geographic graphic display, and then browse, operate and analyze the displayed results. The display range can range from intercontinental maps to very detailed block maps, and the real objects include population, sales, transportation routes and other contents. 2.1

Current Situation of GIS Technology

Geographic Information System (GIS) is a computer system used to input, store, query, analyze and display geospatial data. At the beginning of this century, geographic information technology, biotechnology and nanotechnology have been listed as the three most important emerging technologies in the new century by the United States. In the field of information, the emerging Internet of things applications are closely related to GIS, and in the popular field of cloud technology, one of the best practices is GIS cloud. Under the background of the rapid development of GIS, the application scope of GIS in China is more and more extensive, involving fields from traditional land, environment, public security to communication, finance, commerce and other industries. With the update of IT technology, GIS application mode is also rapidly diversified. Rich client makes WebGIS have a better customer experience: new mobile intelligent technology makes IOS, Android, Windows Phone and other mobile phones and tablets also have GIS capabilities. These changes bring more attention to GIS application, and also bring more choices for GIS users [2]. 2.2

Application of GIS in Urban Landscape Ecology Research

GIS technology can efficiently use, store a large number of data and establish database; It can determine the type and size of patches and establish spatial ecosystem model; Being able to analyze and deduce ecological process is widely used in urban landscape ecological research, and highly valued. The progress of science and technology

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promotes the comprehensive and high-speed development of GIS technology, which effectively speeds up the planning and design of urban green landscape. In line with the principle of “human text” and “harmony between human and nature”, the ecological research on urban landscape is no longer simply focused on the natural landscape, but focuses on the interaction between human and natural landscape. At the same time, combining remote sensing (RS) and global positioning system (GPS), as shown in Fig. 1, each landscape unit is strictly analyzed, It increases the scientificity and feasibility of the research results.

Fig. 1. GIS application

3 Landscape Pattern Analysis and Optimization 3.1

Landscape Pattern

Landscape pattern, mainly refers to its spatial pattern, is one of the focuses of landscape ecology research. Landscape elements with different sizes and shapes are randomly arranged and combined in space to form landscape pattern. For example, different types of patches can be distributed with models, even or clustered patterns in space. This result is generated by the effect of different ecological processes on different scales and also reflects the landscape heterogeneity. Traditional ecology studies scale, spatial pattern and ecological process separately, and seldom combines them. The results of the study are always unsatisfactory. The main reason why landscape ecology can be distinguished from other ecological disciplines is that landscape ecology regards these three as a whole, and understands and analyzes their interaction and influence. Although the study of spatial pattern can strengthen the cognition and understanding of ecological process, the development of landscape ecology has not reached the ideal level. The extension of landscape ecology from pattern to process is still an important problem to be solved urgently. Landscape pattern can be divided into five categories:

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uniform distribution pattern, agglomeration distribution pattern, linear distribution pattern, parallel distribution pattern and specific combination or spatial connection. The analysis of landscape pattern mostly adopts two-dimensional plane, and threedimensional landscape spatial pattern model is rare. At present, the research on spatial heterogeneity has always been the focus of landscape pattern research. Most traditional methods use spatial segmentation analysis, but there are still many shortcomings in this method, so it is necessary to continue to develop new methods to make up for it [3]. 3.2

Basic Principles of Urban Landscape Pattern Optimization

(1) Sustainability principles. Landscape is the most suitable spatial scale for planning and managing the sustainable development environment. Urban landscape planning should be based on the current situation and long-term consideration, not only benefits the contemporary people, but also benefits the sustainable development of the city. According to the basic principles of ecological economics and circular economy, following the natural law and social economic basis, scientific planning, rational utilization, active protection and practical results are observed, the development and utilization of resources are changed, and the guarantee ability of ecological environment to social and economic development can be enhanced. We must pay attention to the carrying capacity of environment on human activities and correctly measure the impact of human activities on the environment. (2) Overall optimization principle. Landscape is a whole with certain structure and function composed of a series of ecosystems. Landscape planning and design should consider landscape as a whole unit to achieve the best state. Based on natural landform and ecological landscape, the integrity of the structure and function of the ecosystems at all levels shall be fully considered, the stable flow relationship between energy flow, logistics and information flow between ecological networks shall be maintained, the ability of ecosystem self-maintenance and self-regulation shall be fully exerted, and the economic, social development and people's living needs shall be considered. (3) The principles of diversity and heterogeneity. Diversity leads to stability. Diversity principle has two meanings. Firstly, the diversity of landscape elements, namely, corridor and patch form are various, and the size and size patches are combined, and the wide and narrow corridors are combined, and concentration and dispersion are combined; Secondly, species diversity. Although the scattered green space landscape in the city can play a role in biodiversity protection, due to the limited area, the relationship between them is loose, which limits the species and migration of organisms. In urban landscape planning, green corridor should be built between urban green space and natural environment outside the city, forming a complete urban green space landscape system, introducing nature into cities, and improving the diversity and stability of urban ecosystem. The maintenance of landscape spatial heterogeneity is also an important aspect of urban landscape planning. As a highly artificial landscape, the city has monotonous

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structure and single function, which seriously affects the stability of urban ecosystem and the ability of self recovery to the disturbance [4].

4 Analysis of Tourism Landscape Ecological Network 4.1

Basic Points of Green Space Ecological Network

(1) Node. Node is the key to constitute the urban ecological network, and its role varies with the area. Medium and large green patches play a key role in the urban ecological environment, which can effectively improve the urban microclimate and protect the species diversity. In short, medium and large patches are indispensable in the process of urban construction. Small and medium-sized green patches are also very important, which can increase the landscape heterogeneity, enrich the landscape types, supplement the urban ecological environment model, and is also an important way to enrich and optimize the ecological network of urban green space. (2) Ecological corridor. Considering social, cultural and other factors, different levels of belt landscape corridors are planned to form ecological corridors. Ecological corridor is the basic framework of urban ecological network, which is mainly composed of water body, road green space and so on. The urban ecological network plays an important role in connecting landscape patches and giving full play to the role of patches. As the highest density belt landscape, river and road green space is the basis of urban ecological corridor, so it is very important to plan and construct River and road reasonably, and it also has high economic and ecological value. 4.2

Analysis Index of Green Space Ecological Network

Urban landscape network has special structural characteristics, such as network density, network connectivity and network circuit. Through the study of the simplified graph composed of nodes and links, the urban greening ecological network can be divided into three parts b Index, a index and c The index reflects the structure of the ecological network and its connection and function. Among them, a b,c The index and cost measure reflect the abstract property of the network and the length of the corridor respectively, so as to reflect the effectiveness of the network. (1)

a-index is the degree of network closure, which reflects the degree of loop appearing in the network and reflects the diversity of material and energy flow routes. The formula is as follows: a¼

(2)

L  V þ1 2V  5

ð1Þ

b The index reflects the ratio of the number of network nodes to the number of corridors, and represents the line point rate. It can reflect the average number of

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connections in each grid and the accessibility of the ecological network. The formula is as follows: c¼

4.3

L 2ðV  2Þ

ð2Þ

Technical Route of Landscape Pattern Optimization Based on GIS

The technical route of landscape pattern optimization based on GIS is shown in Fig. 2.

Fig. 2. Technology roadmap

5 Conclusion The purpose of analyzing and optimizing the tourism landscape pattern based on GIS is to reveal the inherent regularity of urban tourism, find out the distribution of comparative advantages of tourism in the region, make full use of advantages and avoid disadvantages, so as to provide scientific basis for regulating the internal tourism flow of the city, formulating and implementing the medium and long-term tourism development strategy, and promoting the healthy and sustainable development of urban tourism.

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Acknowledgements. Supported by the research fund of Yunnan Provincial Department of Education(2019J1150). Project name: A study of landscape pattern around Erhai Lake in Dali from the perspective of tourism attraction.

References 1. Li, T.: Urban landscape heterogeneity and its maintenance J. Ecol. 17(1), 70–72 (1998) 2. Zhang, M.: Landscape pattern and evolution of fragile ecological environment in Yulin area, J. Geogr. Res. 19(1), 30–36 (2000) 3. Tao, Z., Huimin, L., et al.: Study on spatial pattern of forest landscape in Yuhang City during urbanization. J. Fudan Univ. Nat. Sci. Edn. 41(1), 83–88 (2002) 4. Analysis on spatial autocorrelation characteristics of landscape pattern of rapid urbanization in Longhua District of Shenzhen. J. Peking Univ. Nat. Sci. Edn. 36(6), 824–831 (2000)

Hidden Markov Model for Oral Training System Jian-Gong1,2(&) 1

Henan University of Animal Husbandry and Economy, Zhengzhou 450000, Henan, China 2 Philippine Christian University, Manila, Philippines

Abstract. With more and more Chinese people paying attention to English learning, correspondingly, there are more and more English learning software. However, most of the software do not have good oral pronunciation scoring and feedback function. In the process of oral English learning, especially compared with non-native English learners, good learning feedback is particularly important. This has become a bottleneck in the development of intelligent English learning software. The study of speech signals and speech recognition is an important component of research in the field of speech signal processing, which is also a branch of pattern recognition. It involves psychology, linguistics, signal processing, computer science and other related fields, and even human body language. It aims to realize the natural language communication between human and machine. The application prospect of speech recognition is very broad, and it has been widely used in home appliance control, telephone system, dictator and other related fields. Keywords: English learning assessment


Speech recognition
Hidden Markov
Oral

1 Introduction As the most widely used language in the world, English has become an indispensable tool in people’s daily work and life. At the same time, the relevant training institutions and learning tools have sprung up. According to incomplete statistics, by 2011, the number of English learners in China has reached 460million. However, only about 14.2% of the learners have reached the normal level of oral English deviation. The improvement of spoken English has gradually become an important problem in the process of Chinese learning. The main reasons are as follows: 1. there are quite different pronunciation ways between English and Chinese. The native language environment of Chinese is Chinese, which is inevitable when learning English - some pronunciation errors which are not easily detected in their own aspects. If they are not corrected in time in the early stage of learning, they will gradually become non-standard spoken English such as “Chinese English”.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 234–240, 2022. https://doi.org/10.1007/978-3-030-89508-2_30

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2. there is a lack of excellent oral teachers in domestic schools. Even in schools in some medium-sized cities. Traditional multimedia courses can only be taught unilaterally. For the reasons of teaching plan, teachers seldom adopt different oral teaching schemes for different students’ situations. The effect of this teaching method is limited. 3. lack of time and environment for oral practice. Language is a way of communication, and the most important is pronunciation and practice. However, in traditional English learning, people often spend a lot of time on English reading and writing, but in oral pronunciation, there is no chance and time guarantee for practicing [1]. At present, most of the English learning software in the market has been focused on improving the ability of reading and writing. A few of the spoken English pronunciation learning software functions are relatively single, only simple pronunciation follow-up and other repetitive operations can be carried out, lack of effective evaluation feedback, training effect is not ideal.

2 Basic Principles of Speech Recognition The current intelligent spoken English learning system needs to provide the functions of identifying the user’s pronunciation content, comparing with the expert pronunciation and correcting errors. The basis of all these functions is speech recognition. The accuracy of speech recognition and the robustness of recognition algorithm will directly determine the overall performance of the learning system. Most speech recognition systems today are based on the principle of pattern matching. According to this principle, unknown speech samples are successively compared with a reference sample of known speech, and the best match with the reference sample is used as the recognition result. The entire recognition process consists of the following steps: speech signal preprocessing, feature parameter extraction, reference model training, pattern matching (recognition), rule judgment, output recognition results (as shown in Fig. 1).

Fig. 1. Schematic diagram of speech recognition

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Digital Analog Signal

An audio signal is a one-dimensional analog signal whose amplitude changes continuously with time. It is only through digital processing that the signal can be analyzed by a computer. In other words, a digital audio signal is a prerequisite for digital processing. The processing of a digital audio signal consists of sampling and quantization. Through these two processes, a digital signal with discrete time and amplitude is obtained. 2.2

Time Dependence Processing

Speech signals are produced in close association with the movement of the sound organ, usually as a non-smooth signal. This physical movement is much slower than the speed of sound propagation. Therefore, it is usually assumed that the speech signal is temporally stable, i.e., its spectral characteristics can be approximated as constant for 10–20 ms. This time-dependent process typically uses a sequence of finite-length windows {w(m)} to intercept segments of the speech signal for analysis, allowing the signal to be analyzed around a certain time. Its general formula is as follows [2]: Qn ¼

Xx m¼1

T ½xðmÞ wðn  mÞ

ð1Þ

Where T½ represents an operation, ½xðmÞ is the input signal sequence. 2.3

Voice Endpoint Detection

Correct endpoint detection is the key to all automatic speech recognition (ASR) systems. Studies show that more than half of all recognition errors are caused by unreliable endpoint detection. Endpoint detection, as the name implies, is the detection of the beginning and end points of speech. Two methods are used: multi-threshold front detection and dual-threshold front detection. For real-time feature extraction, a dualthreshold endpoint detection algorithm is usually used. This is because, although the front edge error is smaller in the former case, the time delay is larger, making it unsuitable for real-time control. The problem of multi-threshold edge detection can be solved by using the time-domain parameters e (short-time energy) and Z (short-time excess factor zero) of the speech signal to determine the endpoint. Short-time energy: The energy of a speech signal varies greatly with time. In general, the energy of a muddy segment will be much higher than the energy of a pure segment. This is why it is useful for pure tone segment recognition applications using pure tone segments. For a signal {x(n)}, the short-term energy is defined as follows: En ¼

X1 m¼1

½X ðnÞ  wðn  mÞ2

ð2Þ

The value of the short-term average rate of exceeding zero is the number of times the signal passes through zero per frame. For discrete speech signals, the short-term average rate of exceeding zero is, in fact, the number of character changes in the sample point.

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The digital model of speech signal shows that audio signal is the output of linear short-time invariant system excited by quasi periodic pulse or random white noise. Therefore, speech signal can be regarded as convolution of channel impulse response and glottic excitation signal. However, in the speech signal processing, it is the core problem that we usually encounter to obtain the impulse response and the glottal excitation by the voice signal. To overcome this, it is found that the signal is convoluted by the source of excitation and the frequency of the channel, and thus the cepstrum feature is established. It can separate the two by using the properties of the components of its convolution by using the signal as appropriate homomorphic filtering (Fig. 2).

Fig. 2. Cepstrum processing of speech signal

3 System Design The goal and characteristic of the system is to effectively evaluate the quality of learners’ pronunciation and detect the pronunciation errors through speech recognition technology. The system uses the phoneme recognizer of Sphinx, the open source engine of Carnegie Mellon University. At the same time, the recognition rate of the recognition engine in large vocabulary and continuous pronunciation recognition is very good. 3.1

System Design Objectives

The system designed in this paper can help learners in the following three aspects: providing learners with correct pronunciation, scoring and evaluating the quality of learning pronunciation, correcting mistakes in pronunciation practice and giving suggestions and feedback. Therefore, the main ideas of design are as follows: Provide waveform display. Let learners have an intuitive feeling of their pronunciation. Provide corrective feedback. Learners can point out improper pronunciation at any time in oral practice. Intensive use of scoring system can correct pronunciation and improve oral English ability. This kind of correction is repeated constantly, which can help learners to improve in time, so as to avoid mistakes becoming habits and difficult to correct. So for users, giving appropriate training score will be more helpful to learners’ oral English learning [3]. System prompt and self correction are equally important. Because the students have enough independent thinking ability, the system hopes to combine the two characteristics of students’ independent thinking and system prompt correction, so that students’ English learning can be carried out under the guidance and practice of the system, and students’ subjective initiative can be brought into full play.

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The main modules of the speech recognition system are feature extraction, phoneme recognition, phoneme association, speech evaluation, and error detection. Finally, the system provides effective feedback to the student through grading and critical comments. This approach helps students understand pronunciation errors more clearly and correct them purposefully, leading to improved spoken English. The speech recognition module is shown in Fig. 3.

Fig. 3. Schematic diagram of speech recognition module

3.2

System Functions and Requirements

External features. Viewing window. Example library view: reading of given examples and learning strategies. Information display: display of sentences and diacritics, display of monosyllabic notes and corrections, display of rhyme notes and corrections for sentences, reading of corrections. User management interface: display of user log, display of study files, analysis of error-prone phonemes, analysis of common errors. The system is able to recognize English learners with strong Chinese accents [4]. The system developed in this paper is programmed in C++ and runs on a Windows platform. A small part of the system’s algorithms is written in MATLAB. The specific requirements are as follows. 1) It requires little memory. 2) Fewer restrictions on the operating system. 3) Scalability and ease of use of the system itself should be considered when implementing a VC platform-based spoken English learning system based on HMMs. 4) For a given pronunciation of the user, the system can give a corresponding score, which can eventually be fed back to the user to help them learn English.

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4 Scoring Design The process of voice scoring can be described as follows: 1. Extract the speech feature parameters (MFCC, Mel frequency cepstral coefficients) from standard sentences and test sentences. 2. Viterbi decoding is used for forced alignment to cut out every consonant and vowel. This part needs to use speaker independent core of English speech recognition. 3. Pick up the score factors of each consonant and vowel, including volume, pitch, length, etc., as well as the previous MFCC. 4. Each scoring factor is scored, and then weighted average is used to get the final scoring result. The factors of these scores are as follows: 1) Timbre: the content of speech and the accuracy of pronunciation. The score of this part is usually obtained by calculating the probability value of acoustic models and ranking with similar sounds, rather than directly comparing with standard pronunciation. This is because the example of standard pronunciation may cause the user’s expected error. 2) Tone: compare the similarity between the pitch curve of each syllable and the target pronunciation. If it is Chinese, tone recognition should be added in order to determine whether the user’s pronunciation conforms to the Chinese tone and tone sandhi rules. 3) Pitch length: compare the similarity with the target sentence by the pronunciation length of each syllable. 4) Intensity: through the pronunciation volume of each syllable, to compare the similarity with the target sentence.

5 Conclusion The research topic of this paper is speech recognition technology in oral English learning. This paper studies the related speech recognition theory and signal processing technology. This paper systematically integrates and applies the related technologies, briefly introduces the historical development of speech recognition technology, the background of English learning and the research status at home and abroad, and expounds the basic theory and some key speech processing technologies involved in speech recognition system. By using hidden Markov model (HMM), we design and apply HMM modeling, Viterbi decoding and speech evaluation algorithm. We have completed the segmentation of sentences in the corpus of oral training system and the basic unit of English pronunciation of English learners. Based on the research of recognition algorithm, the functions of waveform display, speech recognition and score feedback are realized. To provide English learners with feasible pronunciation information and correct pronunciation errors. It can improve learners’ oral English learning level to a certain extent.

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Acknowledgements. 1. Project of Henan humanities and social science in 2020 (2020-ZZJH182): Study on Flipped Class based Wechat in College English Teaching\014278. 2. School level Project of Research and innovation Fund 2020: A case study of Publicity on Chinese culture based on CGTN and Bilibili.

References 1. Zhao, Q.:. Feature extraction of cough sounds and its application in identity recognition D. Nanjing Univ. Posts Telecommun. (2012) 2. Cheng, X., Ma, Y., Liu, C., et al.: Research on heart sound identification technology. J. Chin. Sci. (f) Inf. Sci. (42) (2012) 3. Wu, W.: Research on intelligent evaluation system of College English following test based on fuzzy theory. J. Audio Visual Foreign Lang. Teach. 4, 33–38 (2012) 4. Huang, J.: Analysis of chinglish from chinglish to english J. Modern Commun. (8) (2011)

Multi-system Platform Cooperative Electronic System Mingyan Peng(&) and Yan Yu Weifang Vocational College of Engineering, Weifang District, Qingzhou City 262500, Shandong, China

Abstract. With the continuous progress of science and technology, it provides more possibilities for the development of electronic music. It is possible to make rational use of the cooperation of different software and hardware platforms to jointly complete the creation and practice of electronic music. This paper provides theoretical and practical analysis and support for the construction of multi system platform collaborative electronic music system by combing the relevant elements. Keywords: Computer aided system
English vocabulary query tedious
Struts
Query module
JSP page


Page

1 Introduction The development of electronic computer technology provides technical support for digital electronic music creation. With powerful hardware processing ability and flexible software design, electronic music creation presents rich ways and means, which greatly promotes the development of electronic music. However, due to the constraints of communication between systems and the interface standards of software and hardware, software and hardware engineers and electronic musicians are committed to developing more powerful software and hardware to meet the growing demand of electronic music system. This is also a unique phenomenon in electronic music, that is, the hardware and software systems are more and more complex, the development and design are more and more difficult, the price is more and more expensive, and the flexibility of the system is relatively low. With the development of hardware manufacturing technology, network communication technology, multimedia technology, interface standardization and other related technologies, more and more hardware systems can work together and operate in a modular way; With the development and standardization of operating systems, it is easier for different operating systems to communicate and control each other, and the obstacles in data sharing are gradually disappearing; Different systems and software can communicate with each other easily by using standard interface protocol, and can realize low delay data transmission and cooperative operation; At the same time, due to the development of wireless communication technology and mobile computing technology, different types of software and hardware can carry out standardized signal transmission and control to achieve richer effects [1]. This paper analyzes the software © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 241–247, 2022. https://doi.org/10.1007/978-3-030-89508-2_31

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and hardware system, creation platform and other aspects, in order to find out the possibility of using different software and hardware systems to build electronic music creation platform conveniently and quickly.

2 Multi System Platform 2.1

Principle of Multi System Coexistence

After the power on self-test of the system is passed, the BIOS reads the main boot record of the hard disk into the memory, detects the partition status, and looks for the boot record of the active partition. Finally, the boot record loads the operating system installed on the hard disk to complete the startup of the computer. When the operating system is DOS/Windows 9x, the boot is completed by I0. Sys (or ibmbio. Com) file, which is located in the root directory of the boot partition. When the installed operating system is Windows NT/2000/XP, the NTLDR program calls the boor. Ini initialization file to control the boot. 2.2

Methods of Multi System Coexistence

(1) Use multiple physical hard disks. Different operating systems are installed on different hard disks, and boot order is set from BIOS to complete the coexistence of multiple systems; The disadvantage is that it needs additional physical equipment support, and the number of systems and hard disks is the same, which increases the cost. Every time you start, different systems need to be set in BIOS, which is inconvenient. (2) Using the guidance mechanism of the system itself. This method is most commonly used. Different systems are installed in a certain order, and the boot menu will be automatically generated. The advantages of this method are simple implementation, convenient startup and selection, and no need to add additional physical equipment; The disadvantage is that the compatibility is poor, the files between the systems are crossed, and the boot files of each system exist in the hard disk. The root directory of the same active partition, modifying, upgrading and uninstalling an operating system will affect other systems, which is not convenient for later management. (3) By creating a virtual machine. Some tools are used to divide a part of space and memory from the hard disk, create several virtual machines under the existing system, and then install new operating systems in the virtual machine to run different operating systems at the same time; The disadvantage is to have enough memory to run at the same time, the original system should be stable, the creation process is complex, and the later management is cumbersome. (4) Install multi system tool software. The software modifies the boot code of the first sector of the main partition to realize and manage the coexistence of multiple systems. It can automatically manage the system boot record and system boot configuration file. The advantage is that it is equivalent to the multi hard disk mode, without additional hardware support, good compatibility, independent

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operating systems, and the later modification, upgrade and uninstall of each system does not affect other systems, It is easy to use. It can set the menu password and control the users of each system. It has good security and can install two sets of Windows 98 systems in different languages; The disadvantage is that the installation process is complex. 2.3

Why Research and Discussion on Multi System Platform

Before discussing how to build an electronic music creation system based on multi system platform, we first analyze a basic problem, that is, what are the advantages and disadvantages of using cross system platform for creation? What needs to be clear is that the multi system platform here refers to a composite creation system that uses multiple different hardware systems and software systems to make full use of the advantages of different software and hardware systems through communication devices and protocols to jointly complete a complex task [2]. The main reasons for discussing the cooperation of multi system platforms are as follows. (1) Application programs developed for different hardware systems and software systems usually give full play to the functions of software and hardware in algorithm design to obtain the best operation efficiency, and give full play to the advantages of specific algorithms and functions in specific systems to obtain the best operation efficiency and stability. (2) Reasonable use of the combination of different software and hardware systems can reduce the repeated purchase of software and hardware, reduce the cost of software, and obtain more flexible system combination through reasonable combination. For software developers, it can reduce the waste of resources caused by repeated development of the same software under different operating systems. (3) The use of multi system cooperation can reduce the difficulty of software system and application development, enable software and hardware developers to focus more on the development of specific functional modules, provide a more stable and reliable operating environment, and provide a more friendly application expansion and upgrade environment. Of course, in this process, protocol standards and predetermined framework are particularly important.

3 Factors Affecting the Construction of Multi System Platform Collaborative Electronic Music System When using different software and hardware systems to build electronic music creation system, it will be affected and restricted by many factors. Understanding the relevant factors will help to make rational use of various existing software and hardware resources, and make a reasonable combination of them, so as to achieve a balance between efficiency and cost, ease of use and stability. The following introduces the main factors that affect the construction of multi system platform writing electronic music creation system.

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Hardware System

Any hardware system supports a limited instruction system and can perform limited functions. No matter how powerful the computing power is, the computing power of any hardware system is also limited; Due to different hardware systems, the same operating system and software may not work well on different hardware systems. In the past, in order to be more compatible with different hardware systems, many manufacturers need to develop software for different hardware systems when developing the same operating system and application software, which increases the repetitive work in software development. At the same time, due to the different hardware, it may cause the same kind of software. With the development of technology and economy, the CPU used in personal computers is gradually concentrated in the CPU system represented by Intel and AMD, and the difference between the designed and manufactured personal computers is getting smaller and smaller. In the past, there was a big difference in hardware between PC and MAC, but today, as the CPU used is getting closer and closer, the hardware system design is becoming more and more similar, and the difference between MAC and PC is gradually decreasing. This provides a better opportunity to build a more efficient electronic music creation system. Of course, due to the relatively closed integrated hardware system design of MAC, it will still show more excellent efficiency and stability in many electronic music applications. 3.2

Operating System

Different operating systems can run on different hardware platforms. A reasonable choice of operating system will give full play to the ability of specific hardware system, and provide a good and stable system software platform for the operation of electronic music software. With the popularity of mobile platform, more and more electronic music software has been transplanted to the mobile platform. There are many different electronic music applications on IOS and Android systems, as well as some apps that can be used as creative AIDS. Compared with the traditional personal computer, the tablet computer similar to iPad is more intuitive and convenient in operation, and the use of multi touch technology can achieve richer control of hands. At the same time, the use of a variety of sensors provided by the tablet PC can become an important controller in electronic music creation. At the same time, because of the unity and intuition of IOS system and Android system platform, it is very convenient to install different apps to achieve different functions. At present, more and more electronic musicians and modern artists use tablet computers to create their works, and use tablet computers to control and perform in creation and performance. Using virtual machine technology, we can simulate other operating system environment in a specific operating system, so that we can easily realize the function of multi system cooperation on a single hardware system. For the application software that needs to run on different operating systems, we can also realize mutual cooperation through virtual machine technology with the support of powerful hardware. Choosing an appropriate operating system will provide a better creation experience for electronic music creation [3].

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4 Method of Constructing Multi System Platform Cooperative Electronic Music System In the actual process of building a multi system platform collaborative electronic music creation and performance system, there will be great changes according to the software and hardware environment, work types, performance methods, etc., so there is no fixed method to realize the construction of the system, which requires electronic musicians and audio engineers to design and build reasonably by using the technology they have mastered, In order to obtain the most reasonable effect [4]. In order to realize the complex multi-media control and processing, only relying on a single computer software and hardware system can not effectively achieve the required functions, it needs to use a variety of different media processing technologies and software and hardware support. Therefore, in practical design, the system is designed as a basic form of multi system interconnection. As shown in Fig. 1, in the actual design, the system is mainly composed of three parts: sound processing part, video processing part and sensor control part, which are implemented by one MAC, two PCs and - block Arduino MCU respectively.

Fig. 1. System connection diagram

4.1

Sound Processing

Due to the need to calculate the content of each channel in the eight channels in real time according to the position of visitors, the traditional audio processing software cannot be used directly. In this example, the Mac version of Max/MSP is used to complete the processing and calculation of multi-channel audio signal. Because the

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amount of high-quality audio signal data of eight channels is large, an independent Mac computer is used to complete the function. 4.2

Video Processing

In the aspect of video processing, it mainly consists of two parts: one is to generate images according to the position and movement speed of the visitors, which are projected to the ground in real time by multiple projectors; the other is to trigger the controllable fog glass dynamic display window at the end of the corridor for video triggering and playing. Because the real-time projection to the ground dynamic image needs to be adjusted frequently and generated by real-time operation, and at the same time, it also needs to control multiple projectors, and the calculation of dynamic image is large. In order to ensure the calculation effect, an independent PC with multi port output professional graphics card is used. The demonstration of ground projection effect is shown in Fig. 2. Another PC is used to control the trigger and play of the video in the display window, so as to ensure no interference and the best stability. The dynamic video processing part is mainly implemented by Windows version VV software, which can generate and render video in real time according to the transmitted 0sc signal.

Fig. 2. Demonstration of ground projection effect

4.3

Sensor Control Part

In the selection of sensors, considering the ease of use and stability of the sensor, the infrared sensor is mainly used in the initial stage of system design. In order to control the audio and video information by using the analog electrical signal generated by infrared sensor, the programmable Arduino single chip microcomputer is used to control. After the analog signal is sampled and converted into digital signal, it is transmitted to the computer through the standard interface. According to the control data requirements of audio and video processing software, it is converted into the

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specified format data specification. Using Arduino microcontroller, through its flexible programmable function, to avoid the repeated use of more personal computer systems, SCM deployment is more convenient, and more stable in specific areas, efficiency is also higher.

5 Conclusion The coexistence of multiple operating systems has solved the problem of single operating system in the current computer teaching experimental environment, and electronic music has developed to a new historical stage, which is no longer just a simple processing of sound by a computer. Modern electronic music creation needs the support of computer technology, network technology and new media equipment. Only by using digital technology and combining different art forms can we complete the new electronic music form with diversity, multi-level and multi genre. Acknowledgements. Research on the development and inheritance of Zhucheng School Guqin.

References 1. Li, Y.: Development of electronic music at home and abroad: professor Wu Yuebei's symposium. J. Tianjin Cons. Music (1) (2014) 2. Yao, Z.: The influence of new technology media on music creation in the digital era. J. Nanjing Art College Music Perf. (2) (2008) 3. Xiao, H.: Theoretical Basis of Electronic Music. Southwest Normal University Press, Chongqing (2014) 4. Li, X.: Transformation and development of electronic music in the new era. Yuefu Xinsheng (J. Shenyang Cons. Music) (2) (2011)

Design and Implementation of Children's Cognitive Education Software Based on IOS Platform Yan Yu(&) and Mingyan Peng Weifang Vocational College of Engineering, Weifang District, Qingzhou City 262500, Shandong Province, China

Abstract. Children's music cognitive education plays an important role in the process of children's growth and development. In recent years, with the development of science and technology, the emergence of smart phones, pad and other electronic products that integrate intelligent systems makes music cognition more intuitive and easy to use. How to develop excellent children's music cognitive education products has become an urgent task. Starting from the analysis of the characteristics of children's music growth, this paper refines a set of cognitive characteristics of children's sensitivity to music in its wisdom growth, emotional growth, social growth, perceptual growth, physiological growth, aesthetic growth and creative growth. A set of music cognitive assistant education software based on IOS mobile platform is designed, which can accompany the healthy emotion and mental growth of preschool children. So as to systematically cultivate children's creativity, perception, coordination, concentration and observation. Keywords: IOS


Children's music
Music cognition

1 Introduction With the rapid improvement of material level, parents pay more and more attention to children's education, and there is a lot of room for the development of children's education market. As an enlightenment education, music is a core component of children's all-round development, and also an important way to develop children's intelligence quotient, emotional quotient, aesthetic quality and associative thinking ability. Children's psychology has proved that music cognition plays an important role in children's brain development, cognitive development, emotional development, creativity development, physical development and temperament perception. However, due to children's music cognitive characteristics are abstract, not specific, and children's thinking ability is not perfect. Therefore, in the traditional music cognitive education mode, due to the lack of external rich educational resources and the lack of personalized teaching, some children, especially preschool children, have more or less difficulties in understanding music cognition. At present, there are few kinds of multimedia education software products for children, and the form is single. There is no consideration to design educational software © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 248–254, 2022. https://doi.org/10.1007/978-3-030-89508-2_32

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from children's music cognitive characteristics and cognitive characteristics. Combined with the characteristics of preschool children's music cognition, this paper mainly studies and designs a set of interactive children's music cognition assistant education software based on IOS platform, which can accompany preschool children's individual healthy emotion and mental growth. Preschool children's stage education has the characteristics of life, game, directness and potential. Preschool children's music cognitive education should pay attention to the cultivation of children's interest. Compared with the traditional preschool children's music cognitive mode, the mode of i0s mobile platform shows great advantages in the development of children's music cognitive assistant education software. Using the form of mobile platform can greatly improve children's interest in learning, so as to realize children's autonomous learning [1]. According to preschool children's hearing, vision, touch and other physiological and psychological characteristics, this paper constructs a new system of children's music cognitive learning theory to adapt to the cognitive characteristics of 3–6-year-old children. Based on the pre-school children's cognitive level, according to their special physiological and psychological development stage characteristics, we design a stepby-step ladder for them to overcome difficulties and feel the joy of success.

2 Analysis of Children’s Music Cognitive Education 2.1

Analysis of the Design of Music Cognitive Teaching Mode for Preschool Children

In practice, the education software of mobile platform appears as a form of auxiliary teaching in daily classroom, which has injected new vitality and vitality into children education. It greatly improves the teaching effect, and promotes the development of children's mental cognition. However, the object-oriented software of children music cognitive aids is the children of preschool age. Therefore, we should start from the needs of users to better meet the characteristics of music cognitive development of preschool children. This paper analyzes the characteristics of music cognitive learning of preschool children in detail, and then analyzes the music cognitive characteristics of children from analysis to conclusion, and tries to find the starting point to meet the needs of children, and provides theoretical and basis for the latter software design. 2.2

Stage of Children’s Music Cognitive Psychology Development

From ignorance at birth to having certain cognitive ability and thought, psychological development changes with age, and reading style and understanding of things also change accordingly. There is no unified theory about how to divide the age stage of children's psychological development scientifically. Psychologist Piaget takes intelligence or thinking level as the standard. He thinks that the development of children's thinking has both stages and continuity.

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The Status of Children’s Music Cognition

When children enter the stage of 6–9 years old, their physiological development characteristics are that the brain structure and function are developed significantly, and the nervous system is further improved. This period is an important stage for children to increase knowledge. Their perception (including hearing, vision, etc.) has been developed rapidly. They have their own aesthetic and judgment on music, and have distinguished and appreciated the music style, The children in this section have significantly improved their musical cognition for children aged 0–5. In Cao Li's music education, this paper expounds that the children aged 6–7 have been more accurate in singing pitch, and understand that the music with tonality is better than the pile of non tunes. 7–8 years old has the ability to appreciate the Concord. 8–9 years old can sense two melodies. 2.4

Characteristics of Music Cognitive Learning of Preschool Children

Preschool children generally refer to children from 3 to 6 years old. The development of thinking ability of preschool children provides psychological support for music cognitive education and learning, mainly reflected in the abstract and logical development of thinking. But at the same time, the low level of development also limits their study. In this period, because they have not yet complete thinking ability, many things must be expressed by specific objects, and they must adopt many ways to be understood by them. The conventional ways are operation, exploration and so on. In addition, for software designers, we should fully understand the characteristics of children's music cognitive learning before designing, so as to design an auxiliary education software that conforms to children's cognitive sound [2].

3 Overall Scheme Design of the System Software design generally includes four elements: planning, program, music and sound effect. However, as an auxiliary education software, this system should have the requirements of teaching content in addition to the conventional design requirements. The general process of preschool education software development includes analysis, design, development, application and evaluation. After the user's trial and evaluation, when the deficiencies are found, they can propose modifications, and finally form a complete product. Firstly, the teaching system design and analysis, the user's learning objectives, learners and learning content analysis, to guide the planning of the whole system design process, to ensure that the education software itself. The whole design workflow, centered on the design of teaching resources, carries out the design of the elements in the learning environment of preschool children's auxiliary education software to ensure the playfulness of preschool children's education software [3]. Figure 1 shows the software system design framework.

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Fig. 1. Software system design framework

The system technology development is based on IOS platform. The system architecture of the platform adopts hierarchical architecture. The software system adopts MVC mode (model, view and controller). MVC mode sets the objects in the application program into three roles: model role, view role and controller role. Figure 2 shows the system architecture.

Fig. 2. System architecture diagram

After running the system, the animation will be displayed and then directly enter the main menu. In the main menu interface, users can select one of the four options and then enter the corresponding chapter content and select different function modules.

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Each module should design a button that can help the user return to the main interface. If the user wants to exit the system, he or she can choose the home key on the iPhone to exit, and the system can manage various resources in each scenario.

4 Music Implementation of IOS Platform 4.1

Sound Effect Design

The sound effect of sound design is defined by the manufacturer of scene reality, atmosphere, or dramatic news. Sound effect system is a very important part of software design. Its role is self-evident. Children's cognition of music can not be replaced by appropriate music and sound effect in setting off the atmosphere, So it plays an important role in game software design. All kinds of suitable sound effects must be designed in this system. This system needs to be able to play music, can interact with the user to guide the operation of the voice help system, clapping page and other simulation effect sound. The music format of this project is MP3. IOS platform supports various mainstream audio formats, such as wav. MP3, AAC, Amir, etc. In this software system, the sound effects include background music and special effects, and the files in wav and MP3 format are used in the design process. Figure 3 shows the flow chart of sound effect design.

Fig. 3. Sound effect design flow chart

4.2

How to Load Sound Files

This software system uses the built-in cocos2d audio engine to load the sound file. The specific implementation is to add a single instance object class simple audio engine in the cocosdenshion of. Cocos2d to use this function.

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Fig. 4. Sound Loaded class diagram

As shown in Fig. 4, sound loading class diagram, simpleaudioengine class, setmusic class and setsound class: music system class in this project is realized through the above three categories, which can better assist children to understand music. Both setmusic and setsound courses are conducted in the sound environment of simpl eaudioengine. The simpleaudioengine class provides a series of methods to control sound, such as loading and playing background music, sound effects, volume settings, etc. The setmusic class uses the bool variable sound to determine whether the system is playing music, and initsetting is used to load music. The changemusic method controls the music playback. The setsound class uses Boolean variables to determine whether or not to play audio of sound. Initsetting is loaded by effective method. Changesound method controls audio effects [4].

5 Conclusion Children's music cognitive assistant education software based on mobile platform is easy to use, which can help parents educate their children, make children's learning process more pleasant, improve children's interest in active learning, parents less worry, children more happy. If parents can reasonably use the auxiliary education software and control the continuous use time, then the auxiliary education software will really become an important way for children's learning. In addition, children's auxiliary education software can be used at any time and place and is cheap, which is also a favorable condition to promote its development. Therefore, the development prospect of children's auxiliary education software is very good, and it is a subject worthy of study and research.

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References 1. Chen, H.: On the relationship between children's cognitive development and children's social development, literature and education materials (2009) 2. Guang, Z.: Introduction to Music Education Psychology. Shanghai Qingle Press, Shanghai (2003) 3. Zhang, L.: Design and Development of Interactive Teaching Software for Preschool Children Mathematics. Huazhong Normal University, Shanghai (2012) 4. Liu, J.: Design and Implementation of a Game Based on IOS Platform. University of Electronic Technology, Guilin (2012)

Hardware in the Loop Verification System for Collision Avoidance Algorithm of Intelligent Electric Vehicle Jianglin Lu(&) Chongqing Vocational College of Transportation, Chongqing 402247, China

Abstract. In order to realize the side active safety collision avoidance control of four-wheel independent drive electric vehicle, the safety distance model of vehicle side lane change is established, and the corresponding side controller is designed according to the requirements of collision avoidance. The optimal feedback matrix of lateral lane change is solved, and the feed-forward compensation strategy based on input compensation is used to control and track the yaw angle of the system, so as to ensure that the system can accurately follow the desired yaw angle. Finally, the effectiveness of the active safety distance model and the vehicle collision avoidance controller are verified by experiments, and the requirements of vehicle safety and stability are achieved. Keywords: Intelligent electric vehicle
Avoid collision
Hardware in loop test

1 Introduction Looking back 20 years ago, automobile is still a luxury in China and even in the world. With the continuous improvement of economic level and per capita living standard in various countries in the world, automobile has become an important means of transportation. According to the National Security Council statistics, 18680 people died in road traffic accidents in the United States in the first half of 2017 alone, about 1% lower than in 2016, but 8% higher than in 2015. In China, there were about 8.643 million traffic accidents in 2016, an increase of 659000 compared with 2015, with an increase of 16.5%. These accidents caused 63093 deaths and 226430 injuries, directly resulting in a loss of 1.21 billion yuan. Moreover, from January to April 2017, the number of traffic accidents of higher level nationwide increased by 12.2% compared with the same period in 2016, The death toll increased by 16.2%. It has to be admitted that in recent years, the death rate of traffic accidents is still high, and life safety is still seriously threatened, so the passive safety technology alone can not meet the requirements of the times. Vehicle active collision avoidance safety technology mainly includes active steering collision avoidance and active braking (AEB) collision avoidance measures, which belong to advanced driver assistant system (ADAS). The former is to control the steering system to perform collision avoidance, while the latter is to control the braking system to perform collision avoidance. Generally speaking, whether considering the driver’s characteristics or objective safety, braking is prior to steering, and in contrast, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 255–262, 2022. https://doi.org/10.1007/978-3-030-89508-2_33

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the frequency of braking is higher than steering, but there will be some cases, In contrast, the longitudinal collision avoidance distance required by braking is greater than that required by steering. To sum up, single active braking collision avoidance can not meet the demand of vehicle active safety, active steering collision avoidance is equally important for the development of vehicle active safety technology, and the combination of vertical and horizontal collision avoidance mode is more in line with the development demand of vehicle. Therefore, the vertical and horizontal collision avoidance studied in this paper is more important to improve the vehicle active safety [1].

2 Overall Design Framework of Simulation 2.1

Simulation Design Idea of Longitudinal Collision Avoidance System

The main function of the vehicle longitudinal active collision avoidance control system is that it can obtain the vehicle condition information around the vehicle through the external environment sensor. The active braking system mainly obtains the longitudinal road, driving, pedestrian and obstacle information, mainly including (front vehicle speed, real-time distance between the vehicle and the front vehicle, front vehicle acceleration, road adhesion coefficient and other information), According to all kinds of information obtained, the risk assessment model is used to judge the movement state of the car in front and whether there are potential safety hazards at the current moment. In case of emergency, if the driver fails to control the car effectively due to inattention, the control system will interfere with the driver’s control of the car at the critical moment, take over the car actively, and control the brake system actuator to avoid collision [2]. In the longitudinal collision avoidance simulation part, CarSim dynamics software platform and matlabgsimulink simulation software platform are mainly used. The 27 degree of freedom vehicle model of CarSim is used to approximate the real vehicle, and the active braking controller is built with Simulink module. Through the interactive interface between CarSim and Simulink, the control effect of active braking system is verified by observing dynamics variables, The longitudinal active collision avoidance control system is shown in Fig. 1.

Fig. 1. Design idea of longitudinal active collision avoidance control system

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Simulation Design Idea of Lateral Collision Avoidance System

The main function of the vehicle lateral active collision avoidance control system is that it can obtain the vehicle condition information around the vehicle through the external environment sensor, including the distance between the vehicle and the vehicle in front. According to the pre trajectory planning method and risk assessment model, it can judge the dangerous situation of the vehicle in real time, and control the steering wheel when the real-time distance trigger controller is used, Tracking the desired path, so as to achieve the function of active steering and collision avoidance. The lateral collision avoidance simulation part adopts the same co simulation method as the active braking collision avoidance part, and verifies the control effect of the active steering system by observing the dynamic variables. The design idea of the lateral active collision avoidance control system is shown in Fig. 2.

Fig. 2. Design idea of lateral active collision avoidance control system

3 Overall Design Framework of Test Bench 3.1

Design of Test Bench for Longitudinal Collision Avoidance System

In order to apply the longitudinal collision avoidance control system to practice, a real brake system test bench is designed in this paper. The test bench is required to be able to control the pressurization of the master cylinder in the braking process, and control the pressure change in the pressurization and decompression circuit of each wheel cylinder by the opening of the solenoid valve, so as to know the pressure of the master cylinder and each wheel cylinder in real time. Therefore, the frame design of the testbed is mainly divided into four parts. The first part is the main cylinder pressurization control system. Considering the pressurization rate, pressurization force and the stability of the pressurization process, this paper plans to use the electric cylinder as the pressurization control system outside the piston pump, with the servo motor as the main actuator and the rolling column screw as the main output mode, It can make the system run smoothly in the process of pressurization, and the control accuracy reaches a high level.

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The second part is the driver. In this paper, HCU with ESP function is selected as the driving mechanism of the actuator, and ESC driving board is designed as the driving part of HCU motor pump and 12 solenoid valves. The third part is the bottom controlled object, namely each wheel cylinder. In this paper, each wheel cylinder assembly of a certain model is selected as the actuator. In order to know the real-time pressure change of each wheel cylinder, the pressure sensor needs to be placed in the booster pipeline of each wheel cylinder, and the friction plate in the actuator is fixed. The real braking effect is simulated through the linear relationship between pressure and braking torque. The fourth part is the real-time hardware system, mainly PXI and dSPACE, which provides signal acquisition and output functions for the bottom actuator of HIL test bench [3]. The overall design of longitudinal active collision avoidance system test bench is shown in Fig. 3.

Fig. 3. Overall design of test bench for longitudinal active collision avoidance system

3.2

Test Bench Design of Lateral Collision Avoidance System

In order to further verify the collision avoidance effect of the lateral active collision avoidance control system, a steering test bench is designed by using the real steering gear of a passenger car. The test bench can realize the input and output of the steering wheel angle. For the input of the steering wheel angle, the g36 controller with the effect of angle loop control is used to control the steering wheel angle, The bogie test bench is mainly composed of four parts. The first part is the steering actuator, that is, the steering gear of a passenger car. The steering gear can show part of the steering process of a real car. The second part is the motor control part. Because the steering gear of a passenger car adopts the brush motor, this paper uses the g36 controller with the brush motor drive function to drive the motor, and tracks the steering wheel angle through the g36 internal angle ring.

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The third part is the angle sensor of Bosch, whose inner ring rotates with the steering wheel through the steering coil column, so as to measure the angle of the steering wheel and transmit the signal through CAN bus. The fourth part is the real-time hardware system, mainly PXI and dSPACE, which provides signal acquisition and output functions for the bottom actuator of HIL test bench. The test bench of lateral active collision avoidance system is shown in Fig. 4.

Fig. 4. Overall design of test bench for longitudinal active collision avoidance system

4 Hardware in the Loop (HIL) Test In order to further verify the feasibility of the active collision avoidance control system and the control effect of the controller in the real vehicle environment, the hardware in the loop (HIL) test is designed to analyze its robustness and anti-interference characteristics. However, due to the large reverse resistance of the electric cylinder itself and the difficulty in calibrating the steady-state pressure value under different conditions, the control effect of the braking pressure has not yet been achieved, and the hardware in the loop test of the longitudinal collision avoidance control system can not be carried out. This paper only does the HIL test on the steering test bench to verify the real control effect of the transverse active collision avoidance system [1]. This paper takes LabVIEW as the software platform, PXI as the hardware platform to load the vehicle model (namely CarSim model), simulates the vehicle part, takes Matlab/Simulink as the software platform, micro Autobox I as the hardware platform to load the controller model, simulates the controller part, and communicates with each other through can bus. The specific HIL block diagram is shown in Fig. 5.

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Fig. 5. HIL block diagram of lateral active collision avoidance

4.1

LabVIEW Model

LabVIEW model is mainly used as the loading platform of CarSim software. It interconnects the interface between CarSim control and LabVIEW model, and communicates with the outside world through PXI hardware platform and can line. Therefore, LabVIEW model mainly includes two parts, as shown in Fig. 6. The first part is the loading part of CarSim, which loads CarSim into real-time system (RT) through timing cycle; The second part is the communication part of can signal.

Fig. 6. Rear panel of vehicle loading and communication program based on LabVIEW

4.2

Analysis and Summary of Test Results of Lateral Collision Avoidance System

(1) When l = 0.3: under this condition, the vehicle speed is 20 m/s, the self driving vehicle is the limit backward collision condition for the sudden stationary target obstacle, and the preview time of preview mode is 1.2 s. The test results are shown in Figs. 7 and 8.

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Fig. 7. l = 0.3 Lateral displacement curve under working condition

Fig. 8. l = 0.3 Steering wheel angle curve under working condition

(2) Conclusion and analysis: According to the hardware in the loop test of the road with u = 0.3 adhesion coefficient, it can be seen that the PID controller based on preview can well deal with the real steering system and achieve the control effect of simulation. It can be seen from the lateral displacement curve that the tracking effect of lateral displacement is very good no matter what kind of road with adhesion coefficient, which also shows the preview mechanism similar to the driver’s; It can be seen from the steering wheel angle curve that the real steering wheel angle lags behind the expected steering wheel angle to a certain extent, which is related to the communication and mechanical hysteresis. Moreover, due to the friction of the system itself, the bottom corner ring has the problems of “top cutting” and small angle difficult to control, The controller designed in this paper can still make the system achieve good collision avoidance effect and show good robustness; From the stability coefficient, we can see that in the process of collision avoidance control of the system, the stability coefficient of the vehicle body has been kept in a very small range, which shows that the stability of the vehicle is very good and the collision avoidance effect is remarkable.

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5 Conclusion The feasibility of the longitudinal active collision avoidance control system is verified by CO simulation. The simulation results show that the longitudinal active collision avoidance control system can control the vehicle longitudinal braking distance well in emergency; The feasibility of the lateral active collision avoidance controller is verified by CO simulation and hardware in the loop test. The test results show that the lateral active collision avoidance controller can not only complete the collision avoidance task in simulation, but also control the real actuator. Acknowledgements. Chongqing Vocational College of Transportation. Project Name: Research on Intelligent Vehicle Control Method based on (No.: CJKJ202007).

References 1. Wang, P., Yu, G., Wang, Y., Wang, D.: Vehicle vehicle cooperative active collision avoidance algorithm based on sliding mode control. J. Beijing Univ. Aeronaut. Astronaut. 02 (2014) 2. Lengxue: Research on vehicle anti lock braking system based on sliding mode variable structure. Harbin University of Technology (2009) 3. Fu, S.: Research on ABS based on intelligent sliding mode control. Shanxi University (2013)

Multiple Evaluation System of Cloud Computing Quality Shuang Qiu(&) Hubei Universityof Medicine, Shiyan 442000, China

Abstract. In order to improve the quantitative performance appraisal mechanism in the existing innovation and Entrepreneurship Talent Management System, a research scheme based on data mining technology is proposed. The combination of decision tree algorithm and cluster analysis is applied to the quantitative performance appraisal system, so as to explore the relationship between the appraisal results and various factors. Kmeans clustering algorithm is used to evaluate and analyze the team members, which is roughly divided into four levels in the form of classification rules. According to the evaluation level and the core attributes of entrepreneurial team, the detailed final individual quantitative assessment score table is generated by using the decision tree algorithm. Taking the actual data of an entrepreneurial team as the sample to test, analyze and verify, the test results show that the proposed scheme has better accuracy, and provides strong decision support for talent team management. Keywords: Data mining
Evaluation index
Performance evaluation
Quantitative performance
K-means clustering
Decision tree algorithm

1 Introduction With the rapid development and large-scale popularization of computer technology, information collection and analysis has become a key problem in the development process of major enterprises and institutions. The 21st century has entered the era of big data. With the application of various computer-aided technologies such as office automation, information equipment and database software, massive data information has been produced. However, how to efficiently analyze and process these rapidly expanding data, and provide decision-making services and technical support for the business development of the Department, has become a difficult problem to be solved by the process supervision and control system, especially the innovation and entrepreneurship team management [1]. Data mining is an interdisciplinary subject that appeared in the 1990s, involving research results from database technology, knowledge engineering, probability and statistics, pattern recognition, neural network, visualization technology and other fields. The essential goal of data mining is to extract the hidden and valuable information and relationships from a large number of noisy, incomplete, fuzzy and random data. At present, the application of data mining in quantitative performance appraisal management system has become a hot research direction. Literature 5 proposes a human resource assessment system based on data mining. Literature 6 using the Apriori © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 263–272, 2022. https://doi.org/10.1007/978-3-030-89508-2_34

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algorithm of data mining association rules to comprehensively analyze the students’ scores, not only can we know the students’ mastery of knowledge, but also can explore the internal relationship between courses. (7) data mining technology is applied to mine and integrate the information with potential value of enterprises and relevant information, so as to obtain more valuable information for evaluating enterprises and use project assessment to improve efficiency. Through the above research and analysis, it is found that the existing performance appraisal methods based on data mining all adopt single decision tree or association rule analysis, and the selection of member attributes involved in performance appraisal is not accurate [2]. Therefore, this paper proposes to apply decision tree algorithm and cluster analysis to quantitative performance appraisal system, in order to reveal the valuable information hidden behind the performance appraisal. Firstly, K-means clustering algorithm is used to evaluate and analyze the team members, which are roughly divided into four levels in the form of classification rules. Then, I3 decision tree algorithm is used to generate the final individual quantitative assessment score table according to the evaluation level and the core attributes of entrepreneurial team. Taking the actual data of an entrepreneurial team as the sample to test, analyze and verify, the test results show that the proposed scheme has good clustering accuracy and evaluation accuracy, which provides strong technical support for decision-making management and improves the work efficiency of innovation and entrepreneurship team management.

2 Data Mining Definition Data mining brings together research results from machine learning, pattern recognition, database statistics, artificial intelligence and other fields. The large-scale popularization of computer produces massive data. Data mining processes and analyzes massive data by integrating the technical achievements of the above disciplines. Data mining is the key step of knowledge discovery process, as shown in Fig. 1.

Fig. 1. Data mining knowledge discovery diagram

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A large amount of business information is digitized and key information is collected, preprocessed and transformed, as well as reasonable model selection, from which valuable hidden associated information can be extracted to assist management decision-making. Data mining can effectively improve business competitiveness and team operation efficiency. Through data mining technology, we can find two unrelated data, but at the same time, it is related to other third-party data, so as to indirectly establish a hidden connection through the network, so as to facilitate the transmission and analysis of information. The research goal of this paper is to build a quantitative performance evaluation mechanism of innovation and entrepreneurship team based on data mining technology, so as to explore the relationship between the evaluation results and members’ work-related factors.

3 Research on Quantitative Performance Appraisal Method Based on Data Mining 3.1

Analysis of Assessment Index

For the innovation and Entrepreneurship Talent Management System, the performance evaluation indicators are shown in Fig. 2, including achievement indicators, daily evaluation indicators and individual evaluation indicators [3].

Fig. 2. Performance appraisal index system

3.2

Performance Appraisal Grade Evaluation Based on K-means Clustering

As a distance based partition clustering algorithm, K-means clustering algorithm has the advantages of simple structure, high efficiency and wide application range. K-means clustering algorithm is generally optimized by the objective function shown in Eq. (1): E¼

K X X  x  mj 2 j¼1 x2Cj

Where e is the clustering criterion function and K is the total number of clusters.

ð1Þ

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Quantitative Performance Evaluation Based on ID3 Decision Tree Algorithm

The key of ID3 decision tree algorithm is to calculate the information gain and entropy according to the idea of recursion. The initial entropy is calculated as follows: SðIÞ ¼

c X Ni Ni ð Þ log2 ð Þ N N i¼1

ð2Þ

In order to get more accurate evaluation results, seven core attributes are set in the performance appraisal database to build ID3 decision tree.

4 Test Results and Simulation Analysis 4.1

Test Configuration

The experimental hardware environment parameters are: Windows 7 operating system, CPU is i7 processor, 4 GB memory. The test data comes from the actual historical data of an entrepreneurial team in recent two years. The team is divided into four project groups, with a total of 38 people. 4.2

Result Analysis

K-means clustering algorithm and I3 decision tree algorithm are used to calculate the performance evaluation scores of all members in a group [4]. The results show that the personal performance evaluation score is consistent with the actual personal performance evaluation results, and the accuracy of the data reaches 92%, which can meet the actual application needs in accuracy. In addition, through the data mining method, the efficiency of quantitative performance appraisal has been greatly improved, which has verified the advanced nature and effectiveness of the method [5]. We use 5000 students to simulate our algorithm, and the results show that with the increase of time, the quality and effect of teaching is also increasing, which also shows the effectiveness of our algorithm which is shown in Fig. 3.

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Fig. 3. Algorithm verification

5 Theoretical Basis and Concept Definition 5.1

The Theory of Team Building

Team building theory is generally divided into: cooperative competition theory, team member participation theory and constructive conflict theory [6]. In college students’ innovation and entrepreneurship team, team members come from different family backgrounds and discipline backgrounds, and are good at different professional fields. According to the theory of cooperative competition, a team should have the team goals that the team members jointly identify. If a team does not have a common goal, the team members only consider their personal performance, which is easy to lead to the phenomenon of fighting separately, and they will ignore other team members. The team as a whole has no cohesion. When the team is in a competitive state, the team members try to maximize their own interests, Knowledge and resources will be blocked or even attacked and destroyed, resulting in poor team performance. Therefore, the team should first establish a common goal, and guide team members to work hard to achieve the common goal. Give full play to their own professional advantages, share the information and resources of the team, team members share their areas of expertise, cooperate with each other, and strive to get a higher level of performance. Team member participation theory can further stimulate the initiative and enthusiasm of team members by guiding team members to participate in and interact with decisions concerning their own interests. When team members participate in team decision-making and management, they will have a sense of team ownership. They are more likely to identify with the team goals set by their own participation, and they are more active in implementing decisions. The theory of construction conflict shows that team building should focus on the formation of cooperative relationship among team members. In the process of team project, team members may have different views on the same problem due to different backgrounds. Reasonable team conflict will make good preparation for the formation of high-quality decision. When the cooperative relationship is really formed, the team members will take the common goal of the team as the core, exchange

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views frankly, discuss valuable elements, fully communicate and reach consensus. Therefore, through the treatment of constructive conflict, team members have more recognition of team goals, and the team relationship is more consolidated [7]. 5.2

The Theory of Team Management

Team management theory is the concentrated embodiment of human thought in western management theory [8]. This theory is mainly based on the understanding of human nature. Since the beginning of the 20th century, there have been four major changes in the western organization management theory based on human nature: the hypothesis of “economic man”, “social man”, “self actualized man” and “complex man”. The essence of team management is the process that team managers gather their team members together through management methods, establish common team goals, guide team members to cooperate with each other, make the original loose sand members form team cohesion and become an integral team, make full use of limited human resources, and produce much higher than individual work performance. The main viewpoints of team management theory are as follows: first, team members should have different professional backgrounds and play different roles in team activities to ensure the effective operation of team projects. Secondly, team leaders are influenced by many factors when they are in team management. Third, there are different types of team leadership. Fourth, the effective operation of the team needs to have interrelated conditions to maintain. Fifthly, the success of team work depends on the degree of social identity and social performance. Sixth, in the process of team management, the team leader should authorize the members to a certain extent. When members have the right to participate in decision-making, their sense of identity with team goals will be strengthened, and their decision-making execution will also be improved. Seventh, the respect between team leaders and members is the key to the success of team management. Eighth, cultivate the team’s innovative spirit [9]. 5.3

Group Psychology and Group Behavior Theory

People have the basic psychological needs of socialization. The members of College Students’ innovation and entrepreneurship team form a group for various reasons, and have a common team goal. This goal enables members to gather together [10]. In the process of team cooperation, the overall performance is far higher than the performance level obtained by individual struggle, which not only realizes the team goal, but also realizes the goal pursued by individual. The team integrates all members, determines the common goal of the team through consultation, and guides the members to work hard for it, so as to generate team cohesion in the process of the team project, enhance the team awareness of the members, and make the team members work together and help each other to achieve the common goal; under the role of team cohesion, the team members fully communicate and exchange in the team activities, Enhance the harmonious relationship between team members, and create a good team atmosphere. Team atmosphere has a reverse effect on team cohesion, which helps to improve the overall work efficiency of the team, and further stimulates the enthusiasm and initiative of team members in the process of activities, so as to promote the production of high team performance [11].

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6 Research Conclusions and Suggestions 6.1

Members of Innovation and Entrepreneurship Team Lack Practical Experience, but They Have High Interest in Innovation and Entrepreneurship

When organizing the basic information of team members, it was found that more than half of the respondents in the college students’ innovation and entrepreneurship competition had only one experience, while the number of participants three or more times was relatively rare. The statistical analysis of the reasons of the members shows that most of the participants participate in the competition with strong interest in innovation and entrepreneurship, and the situation of receiving the encouragement of the team members is relatively small. It can be seen that although college students lack practical experience, they are still enthusiastic about innovation and entrepreneurship [12]. We can find that colleges and universities provide support for college students’ innovation and entrepreneurship team from different aspects, such as school publicity, instructors and team internal characteristics. In the connection of instructors, it is found that most of the teams have fixed instructors or innovation and entrepreneurship trainers; and most of these instructors have practical entrepreneurial experience, which can make up for the lack of team cooperation and actual entrepreneurial experience of college students to a large extent, So as to better improve the team performance of innovation and entrepreneurship team [13]. The effective improvement of College Students’ innovation and entrepreneurship team performance can stimulate college students’ entrepreneurial passion and improve their entrepreneurial willingness. The purpose of the university students’ innovation and entrepreneurship competition is to stimulate the university students’ innovation and entrepreneurship intention, so the team project audit is relatively loose in the early stage of the competition, and gradually strict in the later stage. According to the final score distribution of the team, 11.6%, 15.6%, 548%, 13.9% and 4.1% of the total team did not receive awards, department level, school level, provincial level and national level awards, respectively. Through the analysis of team performance and entrepreneurial intention, we can find that college students’ innovation and entrepreneurship competition has a positive impact on College Students’ entrepreneurial intention [14]. 6.2

Countermeasures and Suggestions

This study analyzes the main factors affecting the performance of College Students’ innovation and entrepreneurship team. In the actual management and construction tasks of the team, how to improve the performance of College Students’ innovation and entrepreneurship team, so as to stimulate college students’ entrepreneurial intention, is an important issue that university leaders, scientific research managers and team leaders must face. This section concludes the previous research work, and gives feasible and operational suggestions for the reference of the builders and managers of College Students’ innovation and entrepreneurship team [15].

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First of all, the team leader should make clear the motivation of the members to participate in the college students’ innovation and entrepreneurship team. College Students’ innovation and entrepreneurship team members are generally divided into three categories: the first type is interest oriented, with clear goals and strong execution, and will actively enrich the team’s capital reserves from various aspects. The second is the competition utilitarian type, the participation of the team is only to obtain the corresponding credits, competition experience or awards. Although such members may not be interested in the direction of the team project, their strong purpose will bring efficient execution. The third type is the human persuasion type, which is generally because the team leader finds out and persuades the team leader to participate in the team without the intention of taking the initiative. Secondly, through the analysis of member information, the project tasks are arranged. Through communication with members, the team leader can collect as much information as possible, analyze the personal goals of members and the corresponding abilities required by the project. After mastering the above information, according to the principle of “differential treatment”, targeted arrangements should be made to promote the integration of individual goals and team goals, so as to maximize the realization of project goals while ensuring the stability of members’ participation in the project. Taking the interest oriented members as an example, the team should empower them to make their own team work plans and arrange their work, so that they can feel greater personal responsibility in the process of team work, stimulate their autonomy to participate in team work, encourage them to persist in research interest, and further cultivate the subjective consciousness and initiative consciousness of the interest oriented members. Finally, implement the evaluation and set the goal. In team work, specific and clear goals make team members have a stronger sense of commitment. Team members’ commitment to goals will affect team performance. Therefore, the goal setting should be in line with the requirements of all parties involved in the process of achieving the team goal [16]. The clear division of the team goal is that the team members can timely understand the stages of the project, as well as the tasks of each stage and the standards to be achieved. This combination of specific team goals and project-oriented team norms can enhance the effectiveness of team members to complete team tasks and achieve team goals.

7 Conclusion In this paper, the decision tree algorithm and cluster analysis are applied to the quantitative performance appraisal system. Firstly, K-means clustering algorithm is used to evaluate and analyze the team members, which are roughly divided into four levels in the form of classification rules. Then, ID3 decision tree algorithm is used to generate the final individual quantitative assessment score table according to the evaluation level and the core attributes of entrepreneurial team. The actual test results show that the proposed scheme has good clustering accuracy and evaluation accuracy, which has a certain reference significance for quantitative performance appraisal system.

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Limitations of research tools. This study was conducted from different levels. In addition to the basic information, the addition of the three scales results in a long questionnaire, which makes the subjects feel tired and affects the quality of the test. In order to reduce the number of invalid questionnaires, I use the paper version of all the questionnaires, and send them to each team one by one, and explain the reasons with the subjects, and ask them to fill in carefully. This is of great help to the quality of questionnaire filling, but it still can not avoid the phenomenon that individual subjects do not want to do or give up halfway, which has a certain impact on the recovery rate and efficiency of the questionnaire. In the future questionnaire design, we should pay more attention to the time and patience of the subjects, and reduce the number of questions without affecting the test results. In terms of research methods, due to objective reasons, some members of the team are not convenient to interview, and privacy issues may also cause resistance. The lack of guidance in the interview and the lack of in-depth analysis lead to defects in research methods.

References 1. Xinming, P.: Analysis of human resource assessment system based on data mining. Enterp. Reform Manage. 18, 57–58 (2016) 2. Mengzhao, W., Chong, Z.: Realization of information system in engineering construction field. Inf. Technol. 5, 206–209 (2014) 3. Jing, Y., Gong, L.L., Yang, Z.C., et al.: Data forwarding control strategy for opportunistic networks with fuzzy control. Syst. Eng. Electron. Technol. 38(2), 392–399 (2016) 4. Li, Z., Zang, L., Shujuan, T., et al.: Clustering network data collection method based on hybrid compressed sensing. Comput. Res. Dev. 54(3), 493–501 (2017) 5. Bilin, X.: An empirical study on the interaction between formal and informal organizations based on group dynamics. Nankai Econ. Res. 04, 21–27 (2005) 6. Wang, L., Ge, J.: The formal limit of organization and informal organization. Zhejiang Soc. Sci. 04, 56-61+127 (2009) 7. Guo, X., Shi, S., Wen, L.: Incubation and cultivation of college Students’ innovative team: a study of Chinese youth. 11, 106-108 (2008) 8. Futian, S.: The current situation, problems and suggestions of college students’ participation in “innovation and entrepreneurship.” Macroecon. Manage. 01, 67–71 (2018) 9. Jing, Y., Xianpeng, T.: Development status and path selection of innovation and entrepreneurship education in colleges and universities under the background of new normal – Based on the investigation and analysis of eight colleges and universities in the “Yangtze River Delta region.” Mod. Educ. Manage. 06, 35–41 (2018) 10. Wen, Z., Chen, Y.: The learning process of the entrepreneurial competition team: organizational ideas. In: Proceedings of the Symposium on the Practice of Creativity. Taipei: Chengchi University, pp. 608–631 (2003) 11. Yang, Y., Zhongde, H., Yu, M.: Thinking and policy suggestions on the construction of scientific and technological innovation team in colleges and universities. Ding R & D Manage. 26(002) 129–132 (2014) 12. Qi, X., Qi, E., Shi, Z.: Cross level influence of organizational structure characteristics on product innovation team performance: an empirical study based on Chinese manufacturing enterprises. Sci. Sci. Technol. Manage. 34(003), 162–169 (2013)

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13. Ping, L., Weiming, Z.: Management and Countermeasures of university innovation team construction. Heilongjiang High. Educ. Res. 8, 86–88 (2010) 14. Yao, K., Xiaoming, C.: Study on the interaction between formal organizations and informal organizations from the perspective of externality. J. Fudan. 55(06), 143-150+180 (2013) 15. Yang, F.: The influence mechanism of top management team leadership behavior on team performance: a case study. J. Manage. 04, 504–516 (2018) 16. Jingjie, Z., Defu, S.: Target deviation and correction strategy of quantitative design of administrative performance appraisal index. Soc. Sci. Front 08, 260–264 (2018)

Network Governance Prediction Based on Artificial Intelligence and Algorithm Recommendation Xiaoying Ruan(&) and Hongfu Chen Law School of Minjiang University, Foreign Language College of Minjiang University, Fuzhou 350001, Fujian, China

Abstract. In recent years, China has entered the era of rapid development of network information, network communication mode is more diversified, professional and intelligent, leading to the governance of its communication order needs to face great challenges and development opportunities. As one of the ways of network communication, algorithmic recommendation can achieve accurate delivery to users and solve the problem of information flooding, but it also brings some problems. For example, some enterprises spread vulgar and harmful content in order to attract attention and search hot, which is a very severe challenge to the controllers of network transmission order. In this regard, this paper first introduces the algorithm recommendation, analyzes the problems faced by the network propagation of algorithm recommendation, and puts forward the corresponding countermeasures. Keywords: Algorithm recommendation Government


Network
Communication order


1 Introduction In recent years, with the continuous improvement of Internet Science and technology, a large number of information including microblog, short video, blog and other channels are published on the Internet every day. The traditional search technology can not meet the needs of users for information discovery. The main reason is that many users can not use appropriate keywords to describe what they need, Or they can’t describe the unknown information they are interested in. Therefore, recommendation engine came into being to break the shortcomings of traditional search technology, so that network users can get meaningful information faster. At present, in the network communication environment, algorithm recommendation has developed into a leading way for major enterprises to emphasize users. Through algorithm recommendation, we can grasp the areas in line with the public interest, so as to obtain higher exposure and bring more economic benefits to enterprises. However, the non-public nature of algorithm recommendation makes it more difficult to supervise, and even some enterprises and news aggregation media have fallen into the “wrong way” in the use of algorithm recommendation, resulting in the wrong value

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 273–279, 2022. https://doi.org/10.1007/978-3-030-89508-2_35

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orientation. Thus, it is urgent to strengthen the governance order of algorithm recommendation in network propagation [1].

2 Types of Algorithm Recommendations The so-called algorithm recommendation can also be called personalized recommendation algorithm. Based on the interests and behavior characteristics of network users, we can infer and deepen what users may like through statistics, so as to recommend the information or goods to users, help users quickly find the goods they need in the information in the sea, and strengthen the sticky relationship between users and the network, At the same time, it also strengthens the intelligence of the business platform and promotes the sales and sales of goods. Usually, algorithm recommendation is mainly based on hot information machine products, user information, user history browsing records, social relations and so on. At present, the mainstream recommendation system mainly includes association rule-based recommendation algorithm, content-based recommendation algorithm and collaborative filtering algorithm. Recommendation algorithm is the core of personalized recommendation system, which directly determines the quality of recommendation system [2]. 2.1

Association Rule Algorithm

Association rule is a very popular and widely used method for discovering the association between variables in massive databases. Recommendation based on association rules is based on the items that have been purchased, and then find out the relevance of the items, and recommend other items to users, so that users can use the e-commerce platform. The disadvantage is that there is the problem of over recommendation of popular goods. Rakesh Agrawal introduced association rules to discover the rules between products in large-scale purchase data of supermarket system. For example, the rule {mutton, radish} ! {green onion} indicates that if a customer buys mutton and radish, he is likely to buy green onion. Such information can be used as the basis of marketing decisions in shopping malls and supermarkets, such as price promotion and product placement. In addition, association rules are widely used in Web mining, intrusion detection and bioinformatics personalized recommendation. 2.2

Content Based Recommendation

The content-based recommendation method selects other objects with similar features from the recommended objects based on the user’s previous preference records according to the objects that the user has selected. This recommendation strategy first extracts the content features of the recommended objects and matches the user’s interests and preferences in the user model. The recommended objects with high matching degree can be recommended to users as the recommendation results. Generally, information recommendation applications are common, but the disadvantage is that the accuracy of information extraction is very high.

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Collaborative Filtering Recommendation Algorithm

The collaborative filtering algorithm based on users finds users’ preferences for goods or content through their historical behavior data, calculates the relationship between users according to different users’ attitudes and preferences for the same goods or content, and recommends goods among users with the same preferences, as shown in Fig. 1.

Fig. 1. Struts frame structure

Recommend favorite items to users according to similar items. The user based collaborative filtering algorithm mainly includes two steps. (1) The similarity between users is calculated to get the similarity list. (2) It is to find the products that the user does not have among the users with the highest similarity by querying the similarity set [3]. Step (1) there are many methods to calculate the similarity. Of course, readers can also define the design similarity rules. Here we give the calculation method of similarity between users in general. Suppose there are user u and user V, where n (U) is the collection of goods of interest obtained by analyzing user U’s historical behavior, and n (V) is the collection of goods with purchase intention obtained by analyzing user V’s historical behavior. Then, we can simply calculate the interest similarity of u and V through the following Jaccard formula: Wuv ¼

jNðuÞ \ NðvÞj jNðuÞ [ NðvÞj

ð1Þ

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Or through cosine similarity calculation: jNðuÞ \ NðvÞj Wuv ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffipffiffiffiffiffiffiffiffiffiffiffiffi jNðuÞj jNðvÞj

ð2Þ

Suppose that user A’s interest list is {b, c}, and user C’s interest list is {a, b, d}, we can calculate the similarity between user a and user C as follows: jfb; cg \ fa; b; d gj WAC ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffipffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi jfb; cgj jfa; b; d gj

ð2Þ

3 The Problems of Network Propagation of Algorithm Recommendation At this stage, with the application of algorithm recommendation in network communication, click through rate and relevance are no longer the only criteria. Although the intelligence of algorithm recommendation brings us rich and diverse network information, it is easy to set up the site and values due to algorithm recommendation to a large extent, which leads to many portal websites boasting of “only doing content Porter”, In order to pursue the flow and cater to the audience, we recommend vulgar and pornographic network content to users without bottom line; There are also some short videos and live broadcast platforms that contain violent elements and Yiyang’s drunken lifestyle. After they are released through algorithm recommendation technology, they attract tens of millions of fans and spread the wrong value orientation. Even if the relevant departments ban the illegal anchors and platforms, they still can’t stop them; There are also some bad phenomena, such as the excessive pursuit of economic benefits by Internet enterprises, ignoring social benefits, and recommending fake websites to “buy hot search”, which have caused extremely negative effects on the society. At the same time, the non-public nature of algorithm recommendation also brings great difficulties for government regulation, and Internet companies have not really fulfilled their social responsibilities [4].

4 The Governance Strategy of Algorithm Recommendation in Network Communication Order The rapid development of the Internet has promoted profound changes in the way of network communication. In the face of the drawbacks of algorithm recommendation, we should take active measures to strengthen the governance of network communication order, strengthen the construction of network content, and make all media communication develop healthily on the track of the rule of law, so as to create a good

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atmosphere for the Internet and make a correct value orientation for the audience, So as to ensure the “clear sky” of the network world. In this regard, the author proposes the following four countermeasures for the drawbacks of algorithm recommendation. 4.1

Build the Management System of Algorithm Recommendation

In order to better safeguard the national political security, cultural security and ideological security, strengthen the governance of cyberspace, and strengthen the construction of network content in the all media era, government departments are duty bound. Government departments should adhere to the principle of being responsible for the society and the people, clarify the goals and tasks of network security governance from the perspective of national network security strategy, adhere to the combination of online and offline, and build a management system of algorithm recommendation. First, strengthen the governance of cyberspace in accordance with the law, establish the management and cooperation mechanism of comprehensive governance of network content, and divide and cooperate the governance of network content through management and accountability; Secondly, improve the means of technical supervision, maintain keen insight, strengthen the management of algorithm recommendation by means of scientific supervision and technical governance, set the “red line” standard of network content by the competent department, do a good job in the keynote and value proposition of network content quality, and filter and clean up the network unsafe information by means of artificial intelligence, On the one hand, we should carry out network security education for network platform operators to enhance their sense of social responsibility; on the other hand, we should strengthen positive online publicity to cultivate a positive and healthy network culture for Internet users. 4.2

Establishing the Legislative Standard of Algorithm Recommendation

In order to purify the content of algorithm recommendation network information, the government regulatory department should actively establish the legislative standard of algorithm recommendation. Those algorithms that infringe on the interests of network users and the public rights and interests should be investigated in accordance with the law. Through laws and regulations, the content, judgment standard and intervention means of algorithm recommendation should be regulated by law, especially for social customs and social activities Personal privacy and human rights protection should be included in the legal regulation of algorithm recommendation, so as to provide legal basis for network content management. Network platform operators should establish a network content regulation system at the production end, formulate standards and regulations for quality requirements, strictly control the content quality at the source of production, strengthen the main responsibility of platform enterprises, and let the platform take the social responsibility of content governance. Network platform operators can enhance netizens’ self-discipline and improve netizens’ awareness of civilized access to the Internet by formulating access rights, rules, instructions and technical restrictions.

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Perfect and Improve the Existing Algorithm

The reason why algorithmic recommendation network propagation technology can be used by criminals is that algorithmic recommendation also has its algorithmic loopholes and is not perfect. In this regard, with the continuous progress of science and technology, the existing algorithm recommendation technology should be constantly improved. On the one hand, from the technical level to improve, the media can learn from the experience of foreign aggregation media to improve the algorithm recommendation, but also constantly summarize the experience of domestic media to make up for the algorithm, so as to lay a good foundation for improving the algorithm recommendation communication technology. On the other hand, in order to improve the content security management, in order to manage the daily massive network information and prevent the occurrence of bad information, the media needs to adopt the method of “first audit, second inspection and third severe punishment” to strengthen the security management of network content. “First audit” means to strictly review the reputation value of network content publishers, and regularly audit the publishers “Two checks” refers to strengthening the random sampling of the content released by the platform through the combination of machine and manual work to ensure the content security. “Three severe punishments” refers to severely punishing those who violate the network security according to the degree of network violation, The violators can be punished by warning, deleting posts, sealing and banning for a limited period of time and indefinitely, which shall be recorded and regarded as the key review object in the regular safety management. 4.4

Strengthen the Supervision and Management of Algorithm Recommendation

In order to better strengthen the supervision and management of algorithm recommendation, purify the network information environment, and create a correct value orientation for Internet users, the competent departments of national and local network content governance and the third-party evaluation institutions need to work together to build a cyberspace ecological evaluation index system. Among them, the government’s Internet content regulatory department is responsible for customizing, modifying and leading the implementation of institutional documents for the governance and evaluation of Internet content; Then entrust a third-party organization to build and improve the evaluation index system of network content governance. Through the use of the evaluation results to supervise and manage the network communication mode recommended by the algorithm, so as to better restrict the network platform operators to fulfill their social responsibilities, spread benign network information content, and present healthy and positive value orientation to users. At the same time, the government’s Internet content regulatory authorities should also manage the third-party assessment agencies, which should adhere to the principles of fairness and impartiality, scientifically play the authority and professionalism of the assessment agencies, strengthen their network supervision, provide feedback for the

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regulatory authorities on the main body of network content, and lay the foundation for the governance of the regulatory authorities’ network order.

5 Conclusion In the all media era, the governance of network communication content is facing great challenges. We should dialectically treat the communication function of algorithm recommendation, reasonably use algorithm recommendation, and improve the economic development of our own enterprises. At the same time, we also need to rectify the “misguided” algorithm recommendation. First of all, the government should actively build the governance of network order, and severely punish all violations of the communication order and involving the forbidden zone of pornography, gambling and drugs; Secondly, from the perspective of market fairness and justice, we should strengthen the establishment of legislative procedures for the governance of network communication order, so as to make algorithm recommendation governance more institutionalized and legalized; Secondly, it is necessary to constantly improve the algorithm recommendation to make up for the network loopholes and improve the network security management; Finally, we should strengthen the supervision of algorithm recommendation to better purify the network environment for the audience, and do a good job in the correct value orientation.

References 1. Chen, C., Song, Y.: Media strategy in the age of algorithm: personalized news and its controversy. News Writ. 08, 29–30 (2019) 2. Wu, Y., Zhao, L., Zhao, Y.: User relationship based interest point recommendation algorithm. Surveying Spat. Geogr. Inf. 07, 55 (2019) 3. Xu, C., Meng, F.R., Yuan, G., Li, Y., Liu, X.: Recommendation algorithm of interest points based on location influence. Comput. Appl. 07, 25–27 (2019) 4. Sun, J.: Algorithm recommendation should be guided by values. China Bus. Times. 07, 14–16 (2019)

Big Data Analytics for IoT Security

Application of 3D Image Technology in the 3-Dimensional Reconstruction of Impressionist Oil Painting Art Nan Gao(&) and Liya Fu School of Digital Art and Design, Dalian Neusoft University of Information, Dalian 116000, Liaoning, China Abstract. In this era of rapid development of science and technology, people are less and less satisfied with text as a source of information. With the development and expansion of computer application, image has become an important source of information for people, and digital image processing technology has exerted a profound influence on the field of artistic creation, among which oil painting creation has also been challenged unprecedentally. This paper mainly studies the application of 3D image technology in 3D reconstruction of Impressionist oil painting. Based on point cloud(PB) three-dimensional reconstruction technique was studied. Experimental results show that the improved coarse PB registration algorithm improves the registration accuracy compared with other coarse registration algorithms, and the registration effect is good, which proves the effectiveness of the algorithm. At the same time, in this paper, 3 d reconstruction technology process in terms of the reconstruction of PB model, the effect is outstanding. Keywords: 3D Image
PB reconstruction reconstruction algorithm


3D reconstruction
Poisson

1 Introduction Today's rapid development of society, has been the information of the society, not only more and more information recorded in the computer, and all aspects of the society has been gradually infiltrated by the computer's various technologies, computer technology can be said to be pervasive. With the development of science and technology, the two fields of image and art have more combined points, the traditional concept of painting is experiencing great challenges. More rich and novel images not only bring artists a very convenient way to create, but also inspire many more innovative ideas. With the development of computer technology, image processing technology is becoming more and more perfect, the original only in the hands of professionals to do some simple operations, developed to now more and more powerful, the use of more and more convenient. The invention of this technology makes the pictures more and more diverse, and thus helps the oil painting to find many novel creative perspectives, and to some extent speeds up the process of creation. Nowadays, in the process of oil painting creation, the integration of 3D image technology is not only an innovation in the history of oil painting technology creation, but also makes the creation concept more diversified and diversified [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 283–290, 2022. https://doi.org/10.1007/978-3-030-89508-2_36

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In the 1950s, digital oil painting was invented and developed rapidly in the United States. Subsequently, a German saw the development and application prospects and commercial prospects of digital oil painting, so he introduced the technology of digital oil painting from the United States to his own country. After vigorously promoting it in Hamburg, he slowly won great success [2]. And with the development of digital oil painting, it has become very popular in European countries, such as France, Germany, Italy and other countries. Generations of Americans have been influenced by digital oil painting, which has become almost crazy in the eyes of Europeans and Americans [3]. At present, digital oil painting is a very innovative product in China. However, it has been popular for more than 60 years in the United States and continues to maintain a popular momentum. It has also been popular for more than 20 years in Asian countries such as Japan and South Korea [4]. This paper takes the current optimal PB reconstruction network as the research starting point to improve and perfect, so as to obtain better 3D reconstruction results.

2 Three-Dimensional Reconstruction of Oil Painting Based on Three-Dimensional Image Technology 2.1

Representation of a Three-Dimensional Object

In the three-dimensional space, there are four forms of representation of the threedimensional model: Depth image, Voxel, spatial PB and Mesh [5]. The four representation modes have their own characteristics and defects, and each representation mode has its own value and significance, and has different uses in different application scenarios. Therefore, it is necessary to choose the appropriate representation form in three-dimensional space according to the research content. Depth image is a three-dimensional representation of depth of field information. Different from ordinary two-dimensional images, the data stored in depth image is not pixel points, but depth value, and its depth value is the expression of distance [6]. The distance expressed is the vertical distance between the camera position and the position the camera can reach on the image surface. Volume elements, called voxels for short, are conceptually similar to pixels. A pixel is the smallest unit of a two-dimensional image, and a voxel is the smallest unit of a three-dimensional solid. A voxel 3D model is a three-dimensional representation with very low resolution. It does not have position information itself and can be obtained mainly through the spatial relative relationship with the positions of other voxels. PB, the point set in three-dimensional space is called PB. For three-dimensional representation like PB, the data organization structure is simple and the analysis is convenient, but it also has unique properties. PBs are the most widely used threedimensional representations. PBs can be obtained by 3D scanner and can also be realized by image reconstruction [7]. Grid contains three-dimensional representations of points, edges and surfaces. Different from PB, grid not only contains spatial coordinate points, but also includes the connection relationship between adjacent points in the space. Connection points and points form surfaces, namely grids, which is quite complicated to represent in computer language [8].

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To sum up, compared with depth map, PB form can represent a complete threedimensional body. Compared with voxels, PBs have higher resolution and can describe 3D model details. Compared with grid, PB data is simple in form and convenient for processing and analysis. 2.2

Three-Dimensional Laser Scanning Technology

3D laser scanning technology is another new surveying and mapping technology following the Global Positioning System (GPS), and has now become one of the important means to obtain spatial data [9]. 3 d laser scanning technology is a fully automatic, high precision three-dimensional scanning technology, it overcomes the limitations of traditional single point measurement, with high efficiency, accurate, convenient and unique advantages, can be continuous, automatic and rapid treatment of the scanned object surface to get a lot of three-dimensional coordinate information of the object under test, that is, PBs [10]. There are many advantages in constructing 3D solid model of objects by 3D laser scanning technology. For example, the data acquisition speed is fast, which can scan hundreds of thousands of data points per second; High scanning accuracy, data accuracy can reach millimeter level; Strong initiative, can work all day long; no lighting, because it is infrared laser scanning, can be operated in the dark conditions: full digital features, conducive to information transmission, processing, processing, output expression; the integration of image information and azimuth information is realized [11, 12]. (1) 3D measurement: 3D measurement and reconstruction of 3D model for irregular or complex geometry. (2) Property preservation and redevelopment; the surface information of cultural relics is scanned by 3D laser scanning technology, and the texture and geometric features of cultural relics are recorded, which are retained in the computer in the form of PB data, so as to facilitate the construction of 3D models and provide data support for the protection and restoration of cultural relics. (3) 3D modeling; At present, 3D modeling involves a wide range of scientific fields. This process can realize real scene replication, and it is an inevitable trend of academic research to store and process physical objects digitally with computers. 2.3

3D Reconstruction Algorithm

(1) PB drop sampling In this paper, the PB drop sampling method was added before the PB 3D reconstruction. On the one hand, PB sampling can reduce the amount of PB data and improve the efficiency. On the other hand, even if the amount of PB data is reduced, the whole shape of PB will not be affected. At present, the sampling methods of PB drop include random down sampling method, average grid method and non-uniform grid sampling method. The average grid method can not only reduce the amount of PB data, but also preserve the shape of PB well, which is beneficial to the 3D reconstruction of PB. Although the random subsampling method is simple and convenient, each data point of the PB after descending

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sampling is not representative. Although the non-uniform mesh sampling method can retain the shape of the PB, it is not as good as the average mesh method. Through the comparative analysis of the above three methods, this paper selects the average grid method to sample the PB. (2) Poisson reconstruction algorithm The key of Poisson reconstruction algorithm is to accurately calculate the index function of sampling point S. In general, the indicator function on the surface of the model rarely changes in any position, and in general, it can be regarded as a constant. Therefore, the indicator function of the model surface, the data point in the model space and its normal vector will be one-to-one opposite. Therefore, the gradient of the indicator function on the model surface is approximately zero at the spatial position scattered on the model surface. Therefore, the normal vector of the function is indicated by the model surface to replace the normal vector of some space elements within the allowable range. r ¼ ðX

M !

Z

F Þðq0 Þ ¼

hM

~ p ðq0 Þ! F N @MðPÞd p

ð1Þ

Of course, you can't directly compute the surface integral, because you don't know the surface geometry of the entity. However, the set of inputs with directed points provides accurate enough information to approximate the integral of the discrete sum. You solve the Poisson problem and you form a vector field, and you can compute the function X. But you can't integrate a vector field. So Gaussian operator is used to construct Poisson's equation in order to find the best least squares approximation solution. F o ðqÞ ¼ Fð

qo
c 1 Þ o
w 0  w3

ð2Þ

o
c represents the center of node 0, and o
w represents the width of node 0. Equation Fo is used to qualify. Equation Fo needs to meet the following requirements: The equation Fo can accurately express the vector field linearly. The equation Fo can express the matrix of implicit function effectively and efficiently. Equation Fo can efficiently evaluate the indicated function near the surface of the model. The reconstruction process of the object model surface by the poisson surface reconstruction algorithm is the regular stitching of the triangular surfaces, and the surface model formed after stitching is the result of the Poisson surface reconstruction algorithm.

3 Simulation Experiment Based on 3D PB Reconstruction 3.1

Experimental Environment

The experimental materials of the 3D PB model simulation experiment in this paper are taken from the open data sources of the Computer Graphics Laboratory of Stanford University. Based on the Matlab 2016 platform, this paper conducts simulation

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experiments on Windows 10 64-bit system to verify the improved PB coarse registration algorithm and 3D reconstruction technology. 3.2

PB Registration Experiment

In this paper, the improved PB coarse registration algorithm and the 4PCS coarse registration algorithm, the PB coarse registration algorithm based on the PFH feature descriptor operator and the PB coarse registration algorithm based on the FPFH feature descriptor operator are carried out in terms of the registration rate. In order to compare, the registration accuracy and the registration effect. 3.3

3D Reconstruction Experiment

Based on the results of PB registration obtained by the coarse registration algorithm and other algorithms in this paper, the average grid method is used for down sampling, bilateral filtering, and the PB is finally reconstructed. In order to better analyze the three-dimensional reconstruction effect display, 3D software was selected to display the reconstructed PB model effect.

4 Simulation of the Experimental Results 4.1

Point-Cloud Registration Experiment 14

4PCS

PFH

FPFH

Our method

Registration error

12 10 8 6 4 2 0 60

50 Overlap ratio 40

30

Fig. 1. Comparison of registration errors of different algorithms

As shown in Fig. 1, available in PB overlap ratio is high, in this paper, the coarse registration algorithm with 4 PCS, the coarse registration algorithm based on PFH character description operator, the coarse registration algorithm based on FPFH character description operator registration error are very few, as the PB overlap ratio is more and more small, coarse registration algorithm is better than the other three algorithms on the registration error is more, the registration accuracy is obviously better than the other three algorithms.

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Registration time(s)

80

PFH

FPFH

Our method

60 40 20 0 60

50 Overlap ratio 40

30

Fig. 2. Comparison of registration time of different algorithms

As shown in Fig. 2, when the overlap ratio of PB is high, the registration rate of the coarse registration algorithm in this paper is faster than that of 4PCS algorithm, PFH based rough registration algorithm and FPFH based rough registration operator. However, as the overlap ratio of PB becomes smaller and smaller, the calculation quantity becomes larger. In this paper, the rough registration algorithm takes longer time than the 4PCS algorithm. 4.2

Three-Dimensional Reconstruction Experiments Table 1. Image reconstruction data Method 1 Method 1 Our method Number of PB 2267 1925 3417 Elapsed time(s) 49.5 40.3 29.6

As shown in Table 1, the number of PBs in Method 1 is 2267, and the computation time is 49.5 s. The number of PBs in Method 2 is 1925, and the computation time is 40.3 s. The number of PBs of the method in this paper is 3417, and the computation time is 29.6 s. It can be seen that the proposed method has excellent performance in 3D reconstruction. In Method 1 and Method 2, SIFT and SURF are used to calculate the matching data. Due to their excellent robustness, the requirements for the camera environment are relatively low. In the experimental conditions of this paper, the images are all obtained under the condition of relatively small changes in illumination and scale, and it can be considered that objects in the three-dimensional space have a small displacement. Therefore, even in the low frequency part of the image, relatively dense matching data can still be obtained, which reduces the void effect to a certain extent.

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5 Conclusions In this rapidly changing Internet information age, 3D reconstruction technology has been widely used in medicine, virtual reality, biological engineering, art and other fields. This paper introduces the concept of PB registration and the basic theory of the mathematical basis of PB registration. Based on the deep analysis and research of the PB coarse registration algorithm, an improved PB coarse registration algorithm is proposed. Experiments show that the improved coarse PB registration algorithm has a certain improvement in the registration accuracy, and can provide a good initial location for ICP fine matching. Meanwhile, Poisson reconstruction algorithm has a good effect on the 3D reconstruction of PB model. Therefore, the improved PB coarse registration algorithm has a good registration effect, and its 3D reconstruction technology process has a prominent effect on the reconstruction model. Acknowledgment. This work was supported by Humanities and Social Sciences Research of the Education Department of Liaoning Province Project ``Chinese Contemporary Oil Painting Based on the Mission of Cultural Communication and Research on Innovation and Convergence of Digital Media'' (SYDR202010).

References 1. Hua, K.-L., Ho, T.-T., Jangtjik, K.-A., Chen, Y.-J., Yeh, M.-C.: Artist-based painting classification using Markov random fields with convolution neural network. Multimedia Tools Appl. 79(17–18), 12635–12658 (2020). https://doi.org/10.1007/s11042-019-08547-4 2. Okaichi, N., Watanabe, H., Sasaki, H., et al.: Integral three-dimensional display with high image quality using multiple flat-panel displays. Electron. Imaging 2017(5), 74–79 (2017) 3. Fernandez, M.A., et al.: Painting the pacific: a comparative analysis of the lightfastness of watercolors made from indigenous plants in the pacific region. J. Health Disparities Res. Pract. 12(4), 23–23 (2018) 4. Gao, L.: Research on the application of digital art in traditional painting. Boletin Tecnico/Tech. Bull. 55(11), 145–150 (2017) 5. Nazmitdinov, R.G., Robledo, L.M., Ring, P., et al.: Representation of three-dimensional rotations in oscillator basis sets. Nucl. Phys. A 596(1), 53–66 (2016) 6. Erez, F., Tzvi, G., Ilan, S., et al.: Three-dimensional representations of objects in dorsal cortex are dissociable from those in ventral cortex. Cerebral Cortex 27(1), 422–434 (2017) 7. Ruiu, J., Caumon, G., Viseur, S.: Modeling channel forms and related sedimentary objects using a boundary representation based on non-uniform rational b-splines. Math. Geosci. 48 (3), 259–284 (2015). https://doi.org/10.1007/s11004-015-9629-3 8. Plotnick, D., Marston, T.M.: Three-dimensional image reconstruction of objects using synthetic aperture sonar. J. Acoust. Soc. Am. 140(4), 3347 (2016) 9. Zhang, Y., Hou, H., Han, Y., et al.: Application progress of three-dimensional laser scanning technology in medical surface mapping. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 33(2), 373–377 (2016) 10. Zeraatkar, M., Khalili, K., Foorginejad, A.: High-precision laser scanning system for threedimensional modeling of saffron flower: 3D modeling of saffron flower. J. Food Process Eng. 39(6), 553–563 (2016). https://doi.org/10.1111/jfpe.12248

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11. Ohno, K., Date, H., Kanai, S.: Study on real-time PB superimposition on camera image to assist environmental three-dimensional laser scanning. Int. J. Autom. Technol. 15(3), 324– 333 (2021) 12. Chao, M., Chiu, H.J., Lu, C.W., et al.: Using three-dimensional laser scanning for monitoring a long-span arch bridge launch. Proc. Inst. Civil Eng. Bridge Eng. 172(BE3), 204–216 (2019)

Application of 3D Technology in Garment Design Template Kaichen Zhang(&) School of Design, Xianyang Normal University, Xianyang, Shaanxi, China

Abstract. The emergence of 3D technology has a great impact on people’s production and life. In the virtual environment, consumers can browse a large amount of information to quickly search for products. At the same time, because of its unique visual impact and tactile enjoyment, clothing is closer to people. This new form brings a new round of challenges to enterprises, that is, how to implement the design concept into the actual process, how to achieve sustainable development as the goal of innovative thinking application and technology research, so as to make 3D technology gain an advantage in the market competition. This paper will study the optimization and innovation of fashion design template from the perspective of 3D technology, and use the relevant algorithms in 3D technology to realize the multi-dimensional realization of 3D fashion design. The process uses the simulation experiment method to test the recognition probability of human features. The test results show that 3D technology can effectively improve the efficiency of clothing design template in the process of identifying human body feature data, and can greatly promote the development of clothing design industry. Keywords: 3D technology template


Garment design
Design template
Garment

1 Introduction In order to promote China’s economic development, we must take science, technology and education and culture as the top priority of “clothing, food, housing and transportation”. The development of garment industry is also an important factor to promote China's economic development, and the development of garment industry also has a great influence on the economic development. The garment industry needs a high-tech force similar to 3D technology to drive the entire garment industry [1, 2]. Over the past few years, 3D clothing has appeared in many fashion design competitions and fashion weeks. More and more undergraduate and graduate students are beginning to be trying to use the 3D design as a new technology. For example, the 3D model and 3D craft skirt produced by HK Mencisha Group are the perfect combination of technology and fashion, with a strong sense of 3D space. In general, the model is usually planar two-dimensional pressure, while 3D models are based on threedimensional techniques; this design approach is unique, more surprising and special and is highly valued [3, 4].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 291–299, 2022. https://doi.org/10.1007/978-3-030-89508-2_37

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Clothing innovation design is a way for designers to show their talents, clothing innovation lies in form innovation, structural technology breakthroughs, art decoration creation, and the innovation of new products and new services. The main innovation point of this article is the clothing parametric design in 3D technology, the clothing design style in 3D technology is unique, not only has a strong sense of threedimensional space, but also can choose a variety of metals, plastics, clothing, and use parametric design methods to make the clothing structure more diversified. In this technological innovation, the garment design takes on a new look, enhances the value of the whole clothing industry, and reflects the avant-garde charm of high-tech to the garment industry [5, 6].

2 3D Technology in the Design Basis of Clothing Design Template 2.1

Definition of 3D Technology

3D technology is a kind of virtual technology based on 3D space technology. 3D technology has many advantages in personalized garment prototype design. The introduction of three-dimensional technology has provided more innovative opportunities for the field of modern garment design, and has gradually become a hot spot in the field of modern clothing design. 3D technology is a very advanced and flexible manufacturing technology. With the development of science and technology, in addition to coating technology, 3D technology is also constantly developing. Addition machining is the most traditional three-dimensional machining technology. Now the material reduction method has been extended to cut and polish a piece of material, and finally form the desired object shape. However, the surface of the object in the material addition manufacturing technology is relatively rough, which needs polishing and other finishing. The processing method of the material reduction technology is more accurate and the material selection is larger. The 3D methods and advantages of technology are summarized as follows: integrated product molding, rapid technology, unlimited design space, less production waste, accurate manufacturing modeling and multi-purpose one machine [7, 8]. 2.2

3D Technical Design Requirements

Business processes (1) Design conception Determine the division of spatial functions and the overall style according to the design requirements. Then according to the characteristics of the household type to make a detailed design, in order to ensure the unity of style and meet the use of the function on the basis of as delicate as possible. (2) 3D modeling Model the designed elements through 3DsMax software, and the resulting virtual model needs to save the STL code file. (3) Layer by layer scanning slicing and virtual display

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The previously stored STL code file is imported and scanned layer by layer, and the virtual form of the 3D technology object can be observed in the computer through the 3 D technology computer software Repetier-Host. (4) Set 3D technical parameters After obtaining the form of the virtual model, it is necessary to analyze its modeling structure and accuracy requirements, and determine different parameters settings according to different 3D technical models. When the configuration parameters are modified in the upper position computer software Repetier-Host of 3D technology machine, the variables that mainly affect the physical model production mainly include nozzle temperature, default heat bed temperature, layer height, filling density, 3D technology speed, and so on. (5) Solid model grinding After the 3D technology obtains the physical model, the physical model needs reprocessed. The supporting structure needs to be removed, the surface needs to be polished and polished, if necessary for better preservation. Functional requirements Through the application of this paper, 3D technology carries on the entity production to the custom clothing design element. The target user of this paper is the person who has the demand for individualized clothing design when carrying on the clothing design, the ideal design of the user is often to make some meticulous design or decoration suitable for his own clothing model separately, which can greatly reflect the self-value and taste. However, it is often counterproductive, the early design is very perfect, but the later construction stage is a continuous emergence of problems. The making of the same product as the original design, even more can not be determined at all. Over time, due to the difficulty of production, the design thinking of users is greatly constrained, and priority is given to the feasibility of the design rather than the design itself. The 3D technology introduced in this paper can help users to display the original design perfectly when making design elements; it can help users design and make all kinds of elements of clothing themselves; it can help users greatly reduce the material cost of making clothing when designing cloth; it can help users shorten the construction period greatly when making customized clothing design elements. It can help users liberate their minds at design time and let them focus on the design itself [9, 10]. 2.3

Application Technology and Algorithm Principle of 3D Technology in Fashion Design Template

Clothing templates attribute conversion Whether it is manual cutting, then read by the digitizer, or the clothing template designed by the CAD system, it only has two-dimensional attributes and can not be directly in the3DDisplayed on the virtual human platform. In order to realize the virtual garment template modeling, it is necessary to carry out the three-dimensional processing of the two-dimensional garment template, that is, the attribute conversion of the garment template. The so-called clothing template attribute, that is, describes the clothing template shape, geometric structure information, such as: Clothing template name, dart position, edge starting point, end point coordinate, edge name and so on [11, 12].

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After attribute conversion, the clothing template has a series of information about positioning and stitching, and prepares for the modeling of clothing template. Construction of middleware virtual capsid In the real dress effect, the final shape of the clothes should conform to the human body, so it is conceived to construct the middleware virtual clothing casing, support the clothing model, and establish the clothing model model. The specific practice is as follows: After the virtual platform is generated, add a certain amount based on its data and a certain amount to generate “capsid”. Then, the “capsid” is adjusted according to the two-dimensional clothing template attribute, and eventually forms a 3D clothing template model. “capsid” comes from the platform, so the capsid also predetermined the corresponding layer of the platform. The capsid has 64 layers, the 0th layer is the baseline (thigh root), the hip, the abdomen, the waist, the lower chest, the 35th, the 42 upper chests, and the 49 shoulder, the 56 neck, and the 63 is the upper neck line. These data are the basis for clothing template modeling. The Calculation of Mapping Positioning Lines and Clothing Model Modeling Algorithm.

Fig. 1. Program flow diagram

With the help of middleware “capsid”, the virtual garment template modeling is realized. Due to the clothing model, the model is affected by the2DThe template is strictly restricted, so corresponding to the same set of templates, the obtained clothing template model is consistent in two-dimensional attributes; however, in different sizes of the platform, the three-dimensional attributes of the clothing template model will be different. Therefore, the clothing template must be modeled separately based on the human platform. Because the capsid has the corresponding relationship with the human platform, it can be modeled based on the capsid. The corresponding relationship between the template and the capsid is the basis of the idea of vertical cutting, so for different clothing templates on the same person's stage, the positioning line should be calculated separately. By the same token, the same clothing template on the stage of

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different people should also calculate the positioning line separately. The calculation of the positioning line is to determine the corresponding position of the garment template model on the human platform with the help of the capsid. In the past, garment template modeling was taken as an example. First of all, the neck point positioning line, shoulder point positioning line, armhole positioning line and so on should be determined. After the positioning line is determined, the point set mapping of the neckline and armhole is carried out. After the control point set is obtained, the front garment template model is obtained by the surface slice method. Below, the neck point location line algorithm and neckline mapping algorithm are listed respectively, and the modeling process is described in detail. Example of positioning line algorithm: neck point positioning line calculation. Figure 1 is a flowchart for calculating the neck point positioning line. (1) Get the width of the collar on the supplied 2 D clothing sample at L. (2) Calculate 1/2 of the spline length of layers 51 to 56 on the capsid. Because the neckline width on the two-dimensional clothing template map to the casing is half the length of the sample. Let 1/2 of the spline length of layer i be L1, layer i − 1 be L2. (3) When the L is between L1 and L2, if the L is closer to that of the L1, the neck positioning line is in the i layer; if the L is closer to the L2, neck positioning line is in the i − 1 layer. (4) If such L1 and L2, are not found, use the layer 57 spline as the neck point alignment. Big Data Association Rules Big data association rules are the basis for supporting the normal operation of the 3D technology. Therefore, let A = B be an association rule whose support refers to the number of transactions containing both A and B as the percentage of all transactions, namely U ð A [ BÞ probability. Calculation method like formula (1): Support ð A ) BÞ ¼ U ð A [ BÞ

ð1Þ

Let W = X be an association rule whose confidence refers to the percentage of transactions containing both W and X as the percentage of transactions containing only X, the conditional UðW=XÞ probability. Calculation method such as formula (2): SupportðW ) XÞ ¼ UðW=XÞ ¼

UðW=XÞ UðXÞ

ð2Þ

The execution steps of the algorithm are described as follows: (1) The user can set the minimum support and minimum confidence of the algorithm. (2) Finds out all the frequent item sets. The transaction set is determined from the original given data, the alternate set. The items in this set with greater support than the smallest support are the elements in the complex item set, and multiple scans can get frequent item sets. (3) In the execution of the algorithm, first read all the data items, obtain the support of a candidate 1-item set C1, and then find out the set L 1, of the frequent item set 1items through the obtained frequent set with the set of 2-items.

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(4) Repeat scan the given database to obtain support for the candidate 2-item set C2, and the 2-item set L2, obtain the candidate 3-item set C3 by a combination of frequent 2-item set L2. (5) Repeat the above methods, scan the database, get a higher level collection of frequent items, according to optimize the data. Repeat the condition that a new candidate frequency set is no longer combined.

3 Implementation of 3D Technology in Garment Design Template 3.1

Interaction of Clothing Template

The purpose of clothing interaction is to model and evaluate clothing through the interactive behavior of users. Mainly using the related technology of computer graphics, using the interface provided by GUI to provide interactive tools for users. In the process of clothing interactive modeling, users can guide modeling by inputting design parameters. In the clothing evaluation, users can evaluate the fit degree of clothing and the appearance evaluation on the network platform through the corresponding operation. The clothing virtual product design system mainly carries on the modeling, the simulation evaluation and so on related work. There are the following functional modules: The main contents are as follows: (1) Interactive modeling: the relevant parameters of the model are obtained by interactive means, and the model of human platform and clothing model is established. After modeling, the related model is transformed into network model. (2) Degree of fit evaluation: through quantitative means, users have a continuous evaluation of the effect of virtual sample clothing on the human platform. (3) Appearance evaluation: through 360° rotation, the user can have a comprehensive understanding of the overall appearance effect of the virtual sample garment. 3.2

Operating Environment of the Clothing Model Platform

The entire system is built on the Windows2000 operating environment based on OpenGL technology using the VC++ 6.0 development tool. The hardware configuration of the host is: Pentium4 CPU, 256M memory, NVIDIA GeForce2MX 100/200 graphics card. 3.3

Application of Partial Decoration of Garment Design Template

Due to the limitation of 3D materials, most of the materials that can be used are not as soft, breathable and comfortable as clothing fabrics, and have poor practicability. If you will the 3D technology can be used as decoration in clothing part, which can not only ensure the comfort of clothing wearing part, but also enrich the diversity of clothing. In this series of four sets of clothing, the design inspiration comes from life, mainly

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clothing practicability, respectively. The 3D decoration part is designed in the collar, chest, shoulder and crotch. In the sketch of the four sets of clothing, select the first set of clothing modeling. The structural concept of knotting buckle is redesigned to enhance the threedimensional sense of knotting. On the premise of ensuring the practicability of clothing, part of the decoration of clothing is realized by 3D technology. As can be seen from the design sketch, the collar half circle is a continuous clasp shape. In the design, it is emphasized that the shape of neck buckle fits the structure of human body, and the curvature of neck varies with the Radian of neck. At the same time, the clothing profile is required to conform to the requirements. The 3D artistic and aesthetic characteristics of clothing, the interconnection and segmentation of each part of the cloth is completely different from the material. The 3D shape of the collar is connected with each other and complements each other to achieve a harmonious and unified visual effect. Based on 3D difference between material and fabric, in the design color, the color of clothing fabric is relatively reduced saturation, the contrast is weakened, more choose the same color system, and choose hard fabric, can be compared with the 3D collar decoration is coordinated. Thus, the whole series of clothing has the characteristics of material change and color harmony in the formation and unity of the whole series of clothing.

4 Experimental Analysis of 3D Technology in the Design of Garment Design Template 4.1

Human Body Image Analysis

According to the proportional relationship of all parts of the human body, the segmentation of the 3D point cloud human body model data of the various parts of the human body was obtained. The body ratio here refers to the length ratio of the human body. The ratio of human size position, height and height refers to the human ratio relationship used by men and women in the identification of human characteristic points based on 3D point cloud data as a reference, as shown in Table 1. Table 1. Human body size, position, height and height ratio Gender Height Eye Neck Shoulder Armpit Chest Waist Hip Crotch Female 1.60 0.93 0.86 0.81 0.75 0.72 0.63 0.53 0.47 Male 1.80 0.94 0.85 0.82 0.80 0.60 0.75 0.55 0.46

4.2

Analysis of Simulation Experiments

This simulation experiment selects representative 3D scanning human model from 3D scanning data provided by China Institute of Standardization. The 3D scan human model is characterized by different ages and genders. Then the algorithm is applied to identify the selected 3D scanning human model and mark the specific location of each feature. Finally, we obtain the intersection of the cutting plane of the 3D scan at the characteristic

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relative position, and then fit the curve with polynomial-based least squares. This experiment is based on matlab programming, because the experiment is trained on the front and side of the human body, different training models have different effect and recognition probability of human characteristics. When the probability of recognition for a part of the body is less than 50%, the network is not considered to identify 50%. Table 2 the basic data of 45-year-old men in the south of the Yangtze River, fat and 1617 mm, 71.6 kg, are representative in middle-aged people. Simulation results show that all the characteristic lines of the key human parts are effectively identified. According to the relative error of the measurement and benchmark value in Table 2, the relative error between buttock circumference and circumference and benchmark value is large, and the deviation of other characteristics is small, and the algorithm is effectively verified to some extent. Table 2. Results of the circumference curve fitting calculation Position

Benchmark (mm)

Height Neck circumference Chest circumference Waist circumference Hip circumference

Absolute error (mm) / twelve

Relative error (%) / 0.36

8.5

2.3

942.3

942.3

0

0

1052

1050

two

0.43

Results of Garment Template Parameters

Identification probability

4.3

1617 Our hundred and seventy seven 1045.2

Measurement data (mm) in this paper / Four hundred and sixty five 1036.7

Frontage

94.00% 93.00%

92.74%

92.00% 91.23% 91.00%

92.45% 91.25%

92% 91.45%

Profile 92.89% 91%

92.65%

91.36%

92.36% 91.78%

92.12%

90%

90.00% 89.00% 88.00% After the neck

Neck

Shoulders Underarm Body

Chest

Loin

Fig. 2. Characteristic recognition effect and identification probability

Haunch

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It can be seen from Fig. 2 that the probability of human characteristics is very high. The measurement of the body include the back neck, neck, shoulder, armpit, chest, waist and buttocks. The measured angles are divided into front and side, with their recognition probability on average between 91.28% and 92.78%.

5 Conclusion With the rapid development of computer graphics and artificial intelligence, the transformation of digital clothing design to 3D is accelerated. 3D digital clothing design will be the general trend of the future development of the clothing industry. Aiming at the key technology in 3D digital clothing design, the latest research results of deep learning technology at home and abroad, and combining mature graphic image processing and computer geometry theory.

References 1. Yang, Y., Gu, Y., Shao, L., et al.: Application of computer-aided design and 3D printing template in mandibular angloplasty. J. Huazhong Univ. Sci. Technol. (Med. Edn.), 46(003), 317–321 (2017) 2. Hae, K.E.: A study on 3D printing technology application in automobile design-focused on scenario based on 3D printing technology development. J. Korean Soc. Des. Cult. 23(1), 87– 100 (2017) 3. Wang, G.: The application of 3D printing technology in garment design. Jiangsu Text. 4, 70–71, 77 (2018) 4. Jia, Q., Tian, K.: 3D personalized human modeling and deformation technology for garment CAD. Comput. Aided Des. Appl. 18(S3), 23–33 (2020) 5. Yun, J.B.: Technology trends and case studies for application of AR coloring in fashion design education. J. Korean Soc. Fashion Des. 18(4), 117–129 (2018) 6. Huang is expected, Jally: Application of laser 3D printing technology in product design. Laser Mag. 39(008), 93–95 (2018) 7. Hao, F.: Application of digital information integration and 3D technology in urban landscape environment design. Boletin Tecnico/Tech. Bull. 55(4), 551–558 (2017) 8. Yang, J., Yang, S.: Application of 3D reality technology combined with CAD in animation modeling design. Comput. Aided Des. Appl. 18(S3), 164–175 (2020) 9. Liu, G.: Research on the application of 3D digital technology in traditional arts and crafts design. Paper Asia 2(1), 180–185 (2019) 10. Liu, F., Lu, Z.: Design and application of garment templates based on technology features. Wool Text. J. 45(12), 52–55 (2017) 11. Liang, L.: Research on the development and application of 3D printing technology in product design. Revista de la Facultad de Ingenieria 32(9), 400–405 (2017) 12. Wu, J., Chen, J., Yang, L., et al.: Application of 3D acquisition design technology in HCX gas storage project*. J. Geosci. Environ. Protect. 9(5), 176–182 (2021)

Application of BIM Technology in Civil Engineering Under the Background of Big Data Jinlan Tan, Dongping Hu(&), Huijuan Zhang, and Jun Duan School of Civil Engineering, Chongqing Metropolitan College of Science and Technology, Chongqing, China

Abstract. The social development in the new era takes the technological advancement to drive productivity as the main rhythm, especially the development of computer technology represented by information technology and Internet technology, which has enabled various industries to have new achievements in the field of innovation. This article combines the application of BIM technology in the context of big data, discusses the development of civil engineering projects in the new era and specific practical strategies, and lays a solid theoretical foundation for the advancement of actual businesses. Keywords: Big Data


BIM
Civil Engineering

1 Introduction In the context of big data, the speed of scientific and technological development is getting faster and faster. The ever-changing social background not only brings more help to economic development, but also makes various new technologies widely used in the construction industry, becoming its important Driving force. However, as the scale of various projects becomes larger and the actual demand becomes higher, the overall style becomes more and more complicated. Construction engineering has gradually become a complex system involving multiple aspects, multiple fields and multiple personnel. There are a lot of contradictions among design units, construction units, construction units and customers. In addition, in the era of big data, due to the rapid increase in the overall data volume, all aspects of construction engineering have higher requirements in terms of information transmission, requiring relevant personnel to adapt to it in terms of technology, so traditional Corresponding reforms should be made to various problems existing in the model, and new technologies should be introduced to improve overall efficiency.

2 Overview of BIM Applications 2.1

BIM Technology Connotation

BIM is the abbreviation of the English word for building information modeling. It is a new technology that has emerged in recent years. It was first proposed by Clark, an © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 300–306, 2022. https://doi.org/10.1007/978-3-030-89508-2_38

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American management scholar, in the 1970s. In the field of theoretical research, it combines virtual computers to design and simulate architectural practices. The system built (see Fig. 1). Especially with the emergence and popularization of new software such as CAD, the application and development of new technologies have been promoted to a large extent. In 2007, related industries in the United States made new specifications for the storage format and standards of BIM data, which made its application widely promoted [1].

Fig. 1. China’s big data industry scale

BIM technology is a type of integrated multi-mode data model system, which plays a continuous assistance to the functional and physical characteristics of the building function. It is based on standard information and knowledge sharing and provides a reliable basis with the aid of decision-making throughout the life cycle system. BIM can not only solve the incomprehensive problem of two-dimensional drawing data information in the traditional mode, but also integrate all kinds of information to make intelligent assistance, and apply it intelligently in all aspects of engineering construction [2]. BIM also differs in digital expression. Compared with traditional 3D renderings, it can bring diversified design and interaction to the overall construction from multiple attributes. The use of BIM also attaches great importance to the visualization and quantitative analysis of data resources, construction management, and implementation effects. It is very beneficial to the accumulation of engineering experience, especially in the context of the rapid increase in data. It can play an important role. 2.2

BIM Technical Characteristics

The specific use characteristics of BIM can be roughly divided into the following aspects in terms of technology. First, the simulation of technology. With the aid of multi-dimensional modeling, BIM has relatively good simulation effects, and can simulate high-point environments, such as construction conditions and disaster protection. Exploring the actual value of the application effect with the assistance of the situation, and then estimating the control and monitoring, assisting the relevant personnel to do the detailed content, and

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also very helpful for the progress control. In addition, BIM technology can also provide relatively intuitive effects for the overall project construction. Especially for the simulated evacuation of some emergencies, it has a good auxiliary value to ensure the safety of the property in the disaster. This is a good solution to the problem of information isolation in the traditional mode. Therefore, it is recognized and promoted by the industry. Second, the diversity of technology. Due to the relatively large number of businesses involved in construction projects under the traditional model, problems frequently occur due to insufficient organization and interaction efficiency between departments [3]. The use of BIM technology can lead the overall development from a technical perspective, and directly solve the problems of construction units, design units, and construction units in technical details, organizational communication, and personnel management. Especially with the assistance of architectural model 3D display and information sharing, all parties can make corresponding analysis and discussion on the details, to a large extent avoid different opinions caused by independent work. Third, the technical expertise. BIM technology can ensure the elimination of communication barriers between different professions with the aid of the distributed architecture design of the same platform. On the basis of synchronization work, try to ensure the symmetry and consistency of information, which is an effect that cannot be achieved by traditional models. Fourth, the visibility of technology. With the aid of visualization, BIM can convert all kinds of three-dimensional graphics in the mode of new drawings, so that it can directly understand its structure in the specific data expression drawing process, and assist the advancement of the project. Compared with the traditional model that relies solely on human imagination to complete related businesses, the overall error probability will be greatly reduced, and corresponding visual assistance can also be given.

Fig. 2. Features of BIM technology

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Fifth, technology can be optimized. Optimizability is also not guaranteed by traditional technology (see Fig. 2). Due to the high systemicity of construction engineering as a whole, there is no way to work alone and to meet the design of business goals, which will be restricted by various conditions, such as environment, weather and technology. Therefore, in the construction phase, it is necessary to make multiple changes based on the actual situation, which will make the project need to be continuously optimized. BIM information has relatively timely sharing and simulation characteristics. Therefore, while organizing professionals to optimize and improve the project, they can also get rid of the constraints brought about by the traditional assembly line model, realize fast and efficient processing, make timely optimization, and improve business quality and actual quality. In terms of optimizability, it can be combined with design institutes and other units to give different drawings, make timely optimization and simulation during the design process, provide designers with relevant design collision reports through simulation, and give specific problems. The corresponding solution strategies and suggestions [4]. 2.3

BIM Technology Construction

In addition to the content mentioned above, three-dimensional construction design such as BIM technology is also a very important link. In the past traditional business construction models, most of them were two-dimensional models. The main reason was that designers used CAD to make graphic design drawings, and they could only adopt two-dimensional methods to make corresponding presentations, so there were no details for many details. Obtaining the method intuitively, especially the hidden problems in it, will cause the overall construction efficiency to be greatly reduced. However, new actual construction business operators and related units have relatively high demand for 3D technology, but they are struggling to find a suitable starting point. The use of BIM technology provides an opportunity for access. Under the design of BIM auxiliary functions, each unit and each technical link can make timely matching. The engineering cost engineer needs to fully combine the actual situation to make a corresponding basic model, meet the various standards required by the profession, and meet the actual needs of data. On the basis of organizational design, each major should add its own content and make detailed designs. In the end, there should be a building information system manager to make corresponding tests on the overall model, and put it into actual use when there are no problems to make assistance and actual value.

3 Application of BIM Technology in Civil Engineering 3.1

Application of BIM Technology in the Design Stage

BIM technology plays a very important role in the design stage of civil engineering, especially its unique data exchange value and diversity, and other characteristics can help it efficiently contact various departments and transfer relevant data to each other. In the design stage, the commonly used BIM tools have good relevance. For example, in the process of the construction of the Crussel Bridge in foreign countries, the design

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software adopted by the relevant design company made a preliminary 3D structure analysis of the overall bridge structure. On the basis of concrete stress and fracture analysis and testing, the main structure is drawn in related software, and the close correlation of information and data is captured [5]. With the inseparable assistance, Auto CAD and math CAD have done a good job of corresponding mathematical analysis, not only giving a general conceptual diagram structure diagram, but also making refined data items involved. After obtaining sound and complete drawings and cost, it laid a solid and reliable technical foundation for the subsequent construction business development. 3.2

Application of BIM Technology in the Construction Phase

The simulation and visibility of BIM technology in the construction phase will play a very important subsidiary value in simulation technology and construction control. For example, in the construction example of the Crussel Bridge mentioned above, the 3D model adopted by the relevant workers helped the actual participants to understand the concept and technical details before the construction started [6]. Therefore, at the beginning of the construction, all parties reached an agreement to make the details involved match the expected goals, which can be seen as its advantage. BIM can also manipulate the construction progress in multiple directions and sections to ensure that the expected and actual conditions are consistent. With the help of the open model, the staff can also understand the progress of the project more intuitively. In the specific construction stage, because many temporary structures need to be erected, it will interfere with the initial design under the traditional business model. BIM technology can simulate the construction of temporary structures, assist in the demolition and erection of parts, and then assist in speeding up the construction progress and reducing the cost. Conflict and collision experiments between various structures can also be carried out with the help of BIM technology. Under the premise of information technology-assisted simulation, preliminary conclusions are obtained, and then auxiliary strategies are proposed. 3.3

Application of BIM Technology in the Operation and Maintenance Phase

Most In the operation and maintenance stage, there are relatively many characteristics and categories of construction projects. Therefore, in the operation and maintenance management of the project, there are big differences in the measures taken and the concepts held. Even for different projects of the same company, there are many variables. The application of BIM can ensure that it can be promoted and developed in a relatively stable manner during the operation stage, and it can operate efficiently on the platform [7]. Taking the construction of H International Airport in East China as an example, BIM technology was adopted in the operation and maintenance process, and the size, location, and structure of each building were specified in detail, which realized the unified and integrated management of massive data and related operation and maintenance. The personnel can combine the distribution of the status and the summary, collection, and summary of the actual data in a timely manner, and automatically

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draw the corresponding analysis conclusions, invoking orderly management in real time, and full sharing, which improves the efficiency of overall operation and maintenance [8, 9]. Compared with the traditional manual inspection, the data discussion finally put forward corresponding operation and maintenance suggestions. The overall time saving rate is over 95%, and the cost saving is nearly 80%. It can be seen that the introduction of the platform brings practical help to the overall operation and maintenance work. 3.4

Application of BIM Technology in Personnel Management

The actual role of BIM technology in personnel management cannot be ignored. Compared with the traditional mode, the chaotic management mode for the construction party, the construction party, the management party, and the personnel of all parties has become a management mode, which can realize one-stop management on the platform. For the corresponding project builders, the actual project participants and relevant stakeholders can assign platform accounts, log in independently to realize the active report of the business and the online holding of related meetings, and all kinds of data can be shared and summarized in time [10]. And then bring practical help to actual business decision-making and related problem solving. Related practitioners also need to pay attention to combining the actual operation of the platform to build a professional team to assist. The construction party and management party who have a practical understanding of the overall project also need to have corresponding supervisors and information technology personnel to do a good job in operation and maintenance. Only in this way can the overall platform operation be sufficiently efficient and improve business efficiency in terms of technology guidance. In addition, it should also ensure that company employees master the relevant information technology, especially the ideas and concepts of information development. Only in this way can they keep up with the pace of the times. Improve the depth of understanding of new technologies, which is also very beneficial for the in-depth application of BIM technology in actual projects.

4 Conclusion With the rapid development of the social economy and the rapid introduction of various new technologies, the help that BIM technology brings to the development of the construction engineering industry cannot be ignored. This is also the inevitable direction of future business level promotion and overall industrial upgrading. Therefore, design practitioners can also fully incorporate the actual situation of their own business to introduce related technologies, improve business efficiency, and ensure business quality.

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References 1. Mercedes, S., et al.: A building information modeling approach to integrate geomatic data for the documentation and preservation of cultural heritage. Remote Sens. 12(24), 4028 (2020) 2. Bracht, M.K., Melo, A.P., Lamberts, R.: A metamodel for building information modelingbuilding energy modeling integration in early design stage. Autom. Construct. 121, 103422 (2021) 3. Nita, S.W., Fitri, S.R.: Blockchain-based implementation of building information modeling information using hyperledger composer. Sustainability 13(1), 321 (2020) 4. Lu, K., Jiang, X., Yu, J., Tam Vivian, W.Y., Martin, S.: Integration of life cycle assessment and life cycle cost using building information modeling: a critical review. J. Clean. Prod. 285 (2021) 5. Pan, Y., Zhang, L.: Roles of artificial intelligence in construction engineering and management: a critical review and future trends. Autom. Construct. 122, 103517 (2021) 6. Sepasgozar Samad, M.E., et al.: Lean practices using building information modeling (BIM) and digital twinning for sustainable construction. Sustainability, 13(1), 161 (2020) 7. Lee, B., Choi, H., Min, B., Lee, D.: Applicability of formwork automation design software for aluminum formwork. Appl. Sci. 10(24), 9029 (2020) 8. Aldred, N., Baal, L., Broda, G., Trumble, S., Mahmoud, Q.H.: Design and implementation of a blockchain-based consent management system (2019) 9. Theien, S., Hper, J., Wimmer, R., Meins-Becker, A., Lambertz, M.: Suggestions for the technical integration of life cycle assessment data sets of KOBAUDAT into building information modeling and industry foundation classes (2020) 10. Shannon, H., Christopher, M.C.: Rise of the machines: artificial intelligence and the clinical laboratory. J. Appl. Lab. Med. (2021)

Using Information Technology to Analyze the Impact of Digital Technology on the Innovation Performance of Manufacturing Enterprises Fan Wu(&) School of Economics and Management, Lanzhou University of Technology, Lanzhou, Gansu, China

Abstract. With the advent of the information age, we need to grasp the new trend of economic and social development, seize the opportunity brought by the new round of scientific and Technological Industrial Revolution, use digital technology to promote the digital transformation of manufacturing industry, accelerate the integrated development of industrial chain, value chain, supply chain and innovation chain, optimize industrial ecology, promote industrial organization innovation, and improve enterprise innovation performance. Big data, industrial Internet, artificial intelligence and other digital technologies can directly drive the digital transformation and innovation of manufacturing enterprises, accelerate the R&D and application of digital technology, promote the digital transformation of industries and industrial organizations, build an industrial chain cluster ecosystem, enhance the competitive advantage of enterprises, and enhance the modernization level and independent controllability of industrial chain supply chain, To improve the innovation performance of manufacturing industry. This paper investigates the impact of the development of digital technology in manufacturing industry on the innovation performance of Chinese enterprises theoretically and empirically. The results show that: on the basis of controlling other variables, manufacturing digital transformation can significantly promote innovation performance. Under the background of the accelerated integration of digital technology and enterprise innovation in the information age, the research conclusions have important implications for Chinese manufacturing industry to further enhance the international competitiveness of Chinese enterprises by choosing digital strategy and embedding R&D innovation network. Keywords: Digital technology transformation


Innovation performance
Digital

1 Introduction 2020 Against the background of global economic downturn, China’s GDP reached 101.6 trillion yuan, an increase of 2.3% over the previous year. It is the only major economy in the world to achieve positive economic growth. At the same time, at the G20 summit in the same year, China stressed the need to accelerate the healthy © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 307–313, 2022. https://doi.org/10.1007/978-3-030-89508-2_39

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development of digital technology. It can be seen that the development of digital technology plays an irreplaceable role in China economic growth. In the dynamic and complex competitive environment, the survival and development of enterprises are facing more and more pressure. Innovation has become a key factor for enterprises to achieve and maintain their own competitive advantage. With the rapid development of the Internet, big data, cloud computing and the advent of the information age, digital transformation, as the first productivity of digital technology development, plays an important role in boosting enterprise innovation performance and promoting industrial upgrading. More and more enterprises begin to implement digital transformation, in order to obtain resources, solve the problems encountered in innovation, and realize the improvement of innovation performance. Therefore, how to apply digital transformation to improve innovation performance needs to be solved. There are few literatures on whether the digital transformation can improve the innovation performance of manufacturing enterprises, and the research conclusions are biased. Some studies suggest that there is a positive correlation between digital transformation and innovation performance of manufacturing enterprises. Ferreira, Fernandes and Ferreira (2019) conducted a questionnaire survey on 938 Portuguese companies from different industries by telephone, and found that digital transformation can improve the innovation ability and performance of enterprises. [1] The transformation of new generation information technology can improve the innovation performance of traditional industries. Others believe that absorptive capacity can significantly affect the independent innovation ability of enterprises. However, some studies have found that the impact of digital transformation on innovation performance of manufacturing enterprises is not significant. Wu Lina et al. analyzed the performance of 68 listed companies before and after digital transformation in 1999, and found no significant change. Internet plus has studied the impact of Internet plus on traditional industries. Although some enterprises have implemented the strategy of “Internet plus”, the contribution of “Internet +” to the performance of traditional industries has not yet been revealed. The possible reason for the above differences is that the latter has not been analyzed from the perspective of absorptive capacity because of its short inspection time. In view of this reason, it is necessary to re demonstrate the impact of digital transformation on the innovation performance of manufacturing enterprises. In order to further explore the relationship between digital transformation and enterprise performance, this paper selects 16 types of manufacturing industries from 2005 to 2015 as research samples, uses fixed effect model to analyze the impact of digital transformation on enterprise innovation performance, and puts forward suggestions to improve enterprise digital transformation ability and innovation ability.

2 Theoretical Analysis and Research Hypothesis In the information age, big data, Internet, cloud computing and other digital technologies continue to emerge, and are gradually applied to enterprise management. Digital transformation is to transform the traditional manufacturing industry in an allround, all angle and all chain way by using the new application of Internet technology, improve the total factor productivity, release the amplification, superposition and

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multiplication effect of digital technology on the development of manufacturing industry, so as to realize the transformation and upgrading of manufacturing industry and promote the high-quality development of manufacturing industry. Based on the existing literature, enterprises can improve their performance [2] and competitive advantage by using digital technology. [3] According to the resource-based view, enterprise innovation needs a lot of resources, which are valuable, unique and non reproducible. [4] However, it is difficult to carry out innovation activities only by relying on its internal resources, [5] and it needs to rely on the external environment to obtain the resources needed for innovation. First of all, the editability of digital technology helps enterprises adapt to the new environment quickly, [6] acquire resources in time, reorganize and utilize these tangible or intangible resources, transform them into unique resources and apply them to innovation activities, so as to improve innovation performance. [7] Secondly, the relevance characteristics of digital technology help enterprises to communicate with other enterprises, [8] expand the scope of enterprises to obtain resources, [9] make them have more diversified resources, and help enterprises to identify unused valuable resources. These unique and valuable resources can help enterprises find more innovation opportunities and produce new products or services, Improve its innovation performance. [10] Thirdly, the scalability of digital technology is helpful for enterprises to identify resource needs, quickly search valuable resources related to enterprise innovation at the lowest cost, improve the efficiency of resource allocation and innovation performance. Finally, the openness of digital technology realizes the visualization of data and enhances the transparency of information resources among enterprises. [11] Enterprises can select enterprises to obtain relevant resources according to their own innovation needs, realize more effective information resource processing, reduce the innovation risk caused by information asymmetry, so as to help enterprises find more valuable innovation resources and improve innovation performance. Therefore, this paper puts forward the following hypothesis: the development of digital technology has a significant positive effect on enterprise innovation performance.

3 Research Design 3.1

Research Sample Selection

This paper selects 16 industries in China from 2005 to 2015 as the research objects, including food, beverage and tobacco manufacturing industries; Textile, leather and clothing manufacturing industry; Wood manufacturing industry; Paper making and printing industry; Petroleum and coal processing industry; Chemical products industry; Fiber, rubber and plastic manufacturing industry; Nonmetallic mineral products industry; Base metal products industry; Metal products industry; computer industry; Electrical machinery and equipment manufacturing industry; Machinery and equipment manufacturing industry; Automobile manufacturing industry; Other transportation industries; Other manufacturing industries. Since the data provided by WIOD database

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is only updated to 2015, and the data provided by TIVA database is from 2005, this paper empirically uses the panel data of relevant variables of various industries from 2005 to 2015 for 11 consecutive years, and finally obtains the balanced panel data composed of 160 observations through lag processing of the explained variables. 3.2

Variable Definition and Measurement

Explained variable. The explained variable of this paper is enterprise innovation performance. The existing literature is mainly measured by the number of patent applications, the number of patent citations and the sales of new products. By referring to the existing literature, considering the availability of data and the lag of R & D input to output, this paper uses the number of patent applications in T + 1 period to measure the explained variable of enterprise innovation performance. The data comes from China Statistical Yearbook. Explanatory Variables. The explanatory variable of this paper is the development degree of digital technology. This paper uses “the proportion of added value from ICT industry” to measure the degree of digital technology development of manufacturing enterprises. Ferreira et al. (2019) only uses the new digital process to measure whether the enterprise carries on the digital transformation, ignoring the research on the degree of enterprise digital transformation. The process of digital transformation of enterprises is actually the process from reliable, real-time and continuous exchange of information with the help of ICT to the gradual realization of “digital twin” The index “the proportion of added value from ICT industry” objectively and truly reflects the degree of digital transformation of enterprises, and the data comes from China Statistical Yearbook. Control Variables. The control variables of this paper include three enterprise characteristics, namely enterprise size, government support and foreign direct investment. Large scale enterprises may have more strength and willingness to purchase large-scale R & D equipment and employ highly educated employees for R & D, so as to improve innovation performance. Therefore, enterprise scale is an important control variable. This paper measures enterprise scale by the ratio of the number of industries and the total amount of industry funds. Government support is measured by the proportion of government funds in the amount of industry financing. It is measured by the logarithm of the number of foreign enterprises established in China. The technology spillover effect and competition effect of FDI often provide opportunities for the transformation and upgrading of host country enterprises. With the help of technology, management, process and other advantageous resources brought by FDI, the host country can promote the structural transformation, optimization and upgrading of the local manufacturing industry, and then improve the innovation performance. Therefore, the regression coefficient of this variable is expected to be positive.

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Empirical Research

Table 1. Definition and description of variables Variable type Explained variable Variable type Explanatory variable Control variable

Variable name Enterprise innovation performance Variable name Digital transformation

Symbol ROE

Meaning and explanation Number of patent applications

Symbol DT

Industry scale Government support

SIZE GOV

Foreign direct investment

FDI

Meaning and explanation The proportion of added value of ICT Industry Number of industries/total capital The funding comes from the government Number of foreign funded factories

Table 2. Descriptive statistics of variables Variable name Mean value Standard deviation Minimum value Maximum ROE 7.622 1.556 4.430 11.095 DT 1.816 0.314 0.720 2.343 SIZE 2.610 0.863 1.029 4.825 GOV 3.436 0.754 2.086 5.758 FDI 7.899 0.993 5.075 9.642

With the help of stata16 software, the industry panel data was used for regression analysis (see Tables 1 and 2). Based on the Eq. (1) model, the mixed regression of digitalization and enterprise performance is carried out. Table 3 shows the comparison of the test results of individual fixed effect model and individual random effect model. From the significance of the test results, the individual fixed effect model is better than the individual random effect model; In the results of Hausman test, P = 0.000 of the individual fixed effect model, which rejects the original hypothesis, that is, the individual fixed effect model is better than the individual random effect model, so this paper chooses the individual fixed effect model. According to Table 3, whether it is a fixed effect model or a random effect model, the digital transformation variable (DT) is significant, that is, H1 hypothesis holds: digital transformation has a significant positive effect on enterprise innovation performance. ROE it ¼ ai þ b1 SIZE it þ b2 GOV it þ b3 FDI it þ b4 DT it þ eit

ð1Þ

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F. Wu Table 3. Benchmark regression results (1) (2) VARIABLES OLS FE DT 0.560*** 0.372** (2.92) (2.43) SIZE 1.764*** 2.372*** (20.75) (24.15) FDI 1.407*** 0.143 (18.61) (0.77) GOV 0.242*** 0.070 (2.84) (0.54) Constant −10.046*** −0.740 (−12.70) (−0.42) Observations 160 160 R-squared 0.766 0.856 Number of id 16 Robust t-statistics in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1

(3) RE 0.511*** (2.90) 2.422*** (22.93) 1.059*** (7.10) 0.059 (0.45) −8.330*** (−5.58) 160 16

4 Conclusions In the information age, the development of digital technology has a significant positive impact on the innovation performance of manufacturing industry. Enterprises should attach importance to the ability of scientific and technological innovation and strengthen the ability of absorbing foreign knowledge. The essence of innovation is to seize high-tech talents. In the future, the competition of enterprises will be the competition of scientific research talents. Only by seizing high-tech talents can enterprises create higher quality products and have core competitiveness. In the process of digital technology development, the size of enterprises has a significant positive impact on the innovation performance of manufacturing industry, while the impact of government support and foreign direct investment on the innovation performance of manufacturing industry is not significant. Enterprises should pay attention to the relationship between digital technology development and enterprise innovation performance. The government should encourage enterprises to carry out digital mode innovation and promote digital transformation and upgrading by integrating into the industrial chain ecosystem. In the era of digital economy, enterprises need to actively adopt digital technology to carry out organizational innovation, process innovation and business model innovation, master core technology, form core competence, actively integrate into the chain group structure based on digital ecology, and promote the development of digital technology with the help of digital technology of ecosystem.

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References 1. Ferreira, J.J.M., Fernandes, C.I., Ferreira, F.A.F.: To be or not to be digital, that is the question: firm innovation and performance. J. Bus. Res. 101, 583–590 (2019) 2. Li, Y., Dai, J., Cui, L.: The impact of digital technologies on economic and environmental performance in the context of industry 4.0: a moderated mediation model. Int. J. Prod. Econ. (2020) 3. AUTIOE: Strategic entrepreneurial internationalization: a normative framework. Strat. Entrep. J. 11(3), 211–227 (2017) 4. Grant, R.M.: Toward a knowledge-based theory of the firm. Strateg. Manag. J. 17(52), 109– 122 (1996) 5. Wang, J., Wu, X.: Network location, product innovation strategy and innovation performance: a case study of Chinese manufacturing enterprises. Econ. Manage. Res. 41 (1), 131–144 (2020) 6. Huang, J., Liu, M., et al.: Growing on steroids: rapidly scaling the user base of digital ventures through digital innovation. MIS Q. 41(1), 301314 (2017) 7. Priem, R.L., Butler, J.E.: Is the resource-based “view” a useful perspective for strategic management research. Acad. Manag. Rev. 26(1), 22–40 (2001) 8. Amit, R., Han, X.: Value creation through novel resource configurations in a digitally enabled world: novel resource configurations in a digitally enabled world. Strateg. Entrep. J. 11(3), 228–242 (2017) 9. Li, C., Yang, Y., Lu, S., et al.: Review and Prospect of the research on the impact of digital technology on entrepreneurial activities. Sci. Sci. Res. 37(10), 1816–1824 (2019) 10. Choi, Y.K., Shepherd, D.A.: Entrepreneurs & apos; decisions to exploit opportunities. J. Manag. 30(3), 377–395 (2004) 11. Smith, C., Smith, J.B., Shaw, E.: Embracing digital networks:entrepreneurs & apos; social capital online. J. Bus. Ventur. 32(1), 18–34 (2017)

An Enterprise Marketing Channel Optimization Strategy in the Context of “Internet + ” Jingjing Qiu(&) Wuhan Guanggu Vocational College, Wuhan, Hubei, China [email protected]

Abstract. With the continuous improvement of the development level of social modernization and the continuous deepening of economic system reforms, China has officially entered the Internet era. The Internet, through the combination of related advanced information technologies such as computers, has greatly subverted the traditional marketing model, changed the marketing business model and the behavior and habits of retail customers, leading to major changes in marketing channels. This paper briefly described the conflict between e-commerce operation and traditional channels in the context of Internet information technology, analyzed the necessity and trend of integrating marketing channels, and explored effective ways to optimize marketing channels in the Internet environment, in order to promote comprehensive integration of marketing channels,improve the development level of e-commerce operation, maximize the positive role of enterprises’ integrated marketing, enhance enterprises’ economic benefits, and effectively realize their sustainable development. Keywords: Internet + Marketing strategy


E-commerce operation
Marketing channels


1 Introduction At present, China’s economy is in an important stage of development and transformation, and the market pressure is relatively high. If enterprises want to achieve sustainable development in such a fiercely competitive environment, they must reform traditional marketing channels based on their actual development capabilities and market needs. In the context of the continuous improvement of the development level of computer technology, the rapid development of the Internet and the strengthening of Internet applications have had a huge impact on the original marketing operations. The pace of transformation and upgrading of traditional enterprises has accelerated, and the marketing behaviors of enterprises have developed to the Internet. Marketing channels in the context of Internet have greater operational advantages compared to the original marketing methods, which can conform to marketing needs, ensure the effectiveness of marketing work, and effectively enhance the overall strength of an enterprise's market operation. Therefore, optimizing marketing channels has great research value for the survival and development of enterprises. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 314–321, 2022. https://doi.org/10.1007/978-3-030-89508-2_40

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2 Conflict Between E-commerce Marketing Channels and Traditional Marketing Channels in the Context of Internet In the operation of e-commerce in the Internet environment, the changes in marketing models mainly reflect the changes in channels. In traditional marketing channels, the realization of the flow of goods or service products from producers to consumers is the entire flow process. The process includes various intermediaries that promote their flow, direct channels or indirect channels, which will increase the difficulty of management, channel maintenance cost, waste of resources, etc. In the marketing channel of the e-commerce environment, producers use the Internet to display their products and service information, and provide a complete set of links for direct communication with customers and payment and product delivery. Marketing channels in the ecommerce environment include two types of direct network marketing channels and indirect network marketing channels. Marketing channels in the e-commerce environment have a huge impact on the characteristics of one-way, static information and commodity flow of traditional channels through Internet information technology, which has changed the organizational structure of traditional channels, thereby shortening the circulation process, reducing the number of institutional personnel, and achieving organizational flatness, etc. Therefore, the changes brought about by online channels are inconsistent with traditional channels, which makes the two channels inevitably produce certain conflicts.

3 The Necessity and Trend of Effective Integration of Marketing Channels in the Context of Internet In the Internet environment, there are conflicts between e-commerce marketing channels and traditional marketing channels. Therefore, strengthening the integration of marketing channels has gradually become a key direction for marketing development. The integration of marketing channels meets the needs of market economic development and guarantees the basic capabilities of corporate marketing operations. During the business development period, manufacturers and distributors have formed a relatively close relationship of economic interests. Each member in marketing channels has its own operating rules. At the same time, manufacturers are dependent on various marketing members for their operations. When the product has production quality, it is attributed to the members of the marketing channel, which will cause conflicts between the manufacturer and the marketer. In the operating environment of e-commerce, higher requirements are placed on integrated marketing channels and channel members. By integrating marketing channels, avoiding market power and responsibility issues, and ensuring that each member enterprise in the marketing channels is in a healthy state of operation [1]. Secondly, due to the relatively large domestic area, the economic development capacity of various regions is unbalanced, and the product types show differences, enterprises are extremely susceptible to interference. The operation of ecommerce has had a certain impact on corporate marketing, which has caused a change

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in the form of marketing, highlighting the importance of the integration of marketing channels. Combining with the development of corporate competitiveness, when companies integrate marketing channels, they can improve the overall marketing quality of the marketing market. While integrating channels, they can effectively broaden the scope of marketing and facilitate companies to obtain higher economic benefits. At the same time, with the rapid development of the Internet, traditional marketing channels have been subverted, and advanced technical means, scientific theories and methods are used to control the internal elements of the marketing channel system, as well as the marketing channel system and various external elements (such as products, prices, brands and consumers) will inevitably make the entire marketing channel system more optimized and effectively realize the sustainable development of enterprises.

4 Optimizing Countermeasures of Marketing Channels in the Context of Internet 4.1

Optimizing Supply Chain and Dealer Management

In the Internet environment, enterprises rationally integrate traditional and new ecommerce marketing channels to help them enhance their marketing innovation capabilities and sales performance. When conducting integrated marketing channels, enterprises should complete channel integration from multiple latitudes and multiple perspectives. First, integrating supply chain resources. In the traditional marketing system, product supply chain processes mainly include: source organization, logistics, distribution, and retail. In the integration of marketing channels, enterprises should pay attention to adopting advanced technology and information management system, grasping product information in real time, reducing product inventory, paying attention to the effectiveness of resource utilization, and actively exploring and developing various online sales channels in order to form a marketing that adapts to the new era. The integration mechanism of channel development completes the integrated application for distribution. Secondly, integrating distributors. When integrating distributors, it is also the more critical content of integrated marketing channels. During the integration period, enterprises should rationally complete the introduction of distributors in the e-commerce marketing promotion channels, and complete the integration of the two marketing promotion paths of e-commerce and traditional marketing, so as to form an online and offline cooperation system and a community of marketing interests. 4.2

Optimizing Marketing Plans

In the context of Internet, enterprises can focus on marketing effects from multiple perspectives, so as to implement integrated marketing channels in an orderly manner, and formulate multiple marketing plans based on their own actual conditions, market rules, and user needs. Enterprises should rationally use sales methods to demonstrate the positive effects of integrated marketing channels. On the basis of improved channel management, product manufacturers should make overall plans for product

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management and distribution. Consumers can adapt to the integration of external and objective factors. During the period of optimizing the marketing system, enterprises should pay attention to the effectiveness of marketing and publicity. During the period of marketing and publicity, they should rationally guide consumers to shop in order to improve the effectiveness of sales channel integration. For example, enterprises adopt experiential marketing methods. E-commerce platforms use informatization visual display effects and product transportation services to facilitate consumers to collect product experience equipment in a timely manner, form an effective interaction between enterprises and consumers, and enrich marketing plans [2]. 4.3

Analyzing Consumer Information

(1) Analyzing and collecting consumption habits. In the Internet environment, people’s consumption patterns have undergone tremendous changes. At present, the number of Internet users in China, especially the number of mobile Internet users (see Fig. 1), is showing explosive growth. At present, the number of Internet users in China is over 90%, showing explosive growth. At the same time, the main consumer groups of domestic netizens are those born in the 80s and 90s. The base of such groups has exceeded 400 million, and they are gradually relying on the online marketing environment for products. Therefore, in the early stage of designing a marketing strategy, a combination of online and offline methods should be adopted. Enterprises should fully use technical information technology and big data. They should efficiently use data mining related technologies, reasonable analyze the behavioral characteristics and consumption habits of the target consumer group, and accurate determine the target consumer group, and comprehensively set the marketing direction. They should inject fresh blood into corporate marketing operation with more flexible marketing methods.

Fig. 1. The number of internet users in China and the number of mobile internet users (unit: 100 million people)

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(2) Analyzing the behavior characteristics of various consumer groups. When the analysis of consumption characteristics of various groups is completed, enterprises should integrate the characteristics, obtain the common characteristics of consumption of various groups by data mining technology, such as attaching importance to highquality product quality, efficient product transportation capabilities, and friendly distribution services. They should set consume common information as the direction of the overall marketing development, in order to help the comprehensive improvement of their competitiveness, and conform to the needs of various groups for enterprises’ marketing work, so as to obtain higher economic benefits [3]. 4.4

Constructing an Inventory Information Integration Management System

When enterprises operate in the Internet environment, the use and effective integration of inventory information is completed in an orderly manner, which improves the level of information management. Enterprises or third-party platforms, with the help of information technology, use commodity data management systems to provide consumers with online shopping malls, which has greatly increased the speed of commodity transactions. Consumers can log in to the company’s official website, thirdparty platforms or related shopping apps and search by entering the key word. They can accurately and quickly search for the products they need, select the products they want, and order products online. At the initial stage of corporate marketing, a certain amount of network publicity funds needs to be invested to further promote marketing with the brand effect [4]. Secondly, in the integrated marketing system of multiple distribution, retail, e-commerce, etc., an integrated management system of product inventory information should be established to facilitate the distribution department to effectively obtain product inventory status and complete product delivery according to the order of the order. Integrated inventory management can successfully circumvent the problem of unavailable delivery of orders placed by users. 4.5

Improve the Flatness of the Organizational Structure

There are many intermediate links in traditional marketing channels. Taking Midea air conditioners as an example (see Fig. 2). Products can reach consumers through intermediate links such as branch companies, large wholesalers, shopping malls, or retail. The process is also affected by various objective factors or environmental constraints. With the in-depth development of the Internet, the diversification of ways and methods of the application and network marketing, the geographical advantage occupied by middlemen has been replaced by the virtuality and cross-temporal nature of the Internet, which simplified complex distribution relationships [5]. Therefore, based on the integration of supplier resources, a flat marketing system is gradually formed to reduce the cost of goods and provide consumers with more price concessions. In the flat marketing system, product production units, as a first-level marketing organization, can establish direct cooperative relations with retailers in the region. In the secondary marketing market, enterprises can design distribution organizations, salesmen, etc., and establish product supply relationships with retailers and specialty stores [6]. In this kind

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of marketing system, middlemen can be effectively eliminated, channel distribution costs can be optimized, and the effectiveness of supplier's marketing integration management to retailers can be improved, so that suppliers can retrieve the retailer’s product feedback in a timely manner to enhance the supplier’s marketing capabilities. In this way, suppliers can reasonably control the retail pricing of products and successfully avoid the problem of fleeing goods. When an enterprise is running a direct supply and distribution system, it needs to conduct resource integration management for product transportation and product storage.

Fig. 2. Midea air conditioner dealer model

4.6

Optimizing Social Marketing Resources and Increasing Product Audience

The orderly development of retail has increased the speed of the formation of integrated marketing channels [7]. Large shopping plazas and supermarkets are all representatives of integrated marketing channels, with a relatively mature professional and traditional marketing system. In the production and circulation of products, large-scale marketing channel integration enterprises, combined with their own strong marketing strength, enhance the innovation of online marketing. Compared with large-scale shopping malls, the retail industry lacks certain marketing advantages in terms of marketing costs and marketing publicity. In this case, large-scale shopping malls have a large number of social resources and diversified marketing channels. Therefore, when the distributor channel is integrated, the operating viability of the marketing channel can be significantly enhanced. Product manufacturers have effectively integrated the social marketing resources of large-scale shopping malls, increased product marketing coverage, and facilitated the production and distribution parties to achieve a win-win marketing development goal. In a highly competitive market system, most enterprises carry out price competition activities in order to seize more resources in the market. Such marketing activities have

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had a certain impact on product manufacturers. During the orderly development of marketing, the orderly combination of upstream and downstream units should be strengthened to form a resource complementation mechanism between enterprises, so as to enhance the competitiveness of the overall industry with a complete industry chain. In this context, enterprise alliances centered on the integration of marketing channels have emerged. In the system of integrated development of marketing channels, it can improve the unity of supply chain planning, strengthen the information exchange between upstream and downstream partners, improve the openness of inventory management, reduce capital waste, clarify the direction of market development, and help the healthy operation of the supply chain. 4.7

Reasonably Managing the Customer Base of Channel Members

In the process of marketing channel integration, for channel members in the integration system, by improving their network operation experience, guiding them to carry out customer management in an orderly manner, and providing appropriate incentive mechanisms, the business revenue and the number of customer groups can be maintained to the greatest extent [8]. Therefore, during the period of integrating marketing channels, the construction of corporate culture should be completed in an orderly manner, in line with the brand effect, to enhance customers’ trust in the enterprise's development capabilities, so that it can form brand loyalty, and facilitate the establishment of long-term cooperative relations between the enterprise and customers. At the same time, in the follow-up marketing development of the enterprise, the construction of customer loyalty should be strengthened. For mature customers, one-to-one marketing connection form should be adopted. For long-term cooperative customers, matching specialists should be allocated and customer files should be established to ensure the effectiveness of customer maintenance and obtain stable economic benefits.

5 Conclusion To sum up, in the context of the Internet era, the development environment of enterprises has undergone tremendous changes. In the process of enterprises’ development, reasonable optimization and integration of marketing channels should be combined with the operation status of enterprises, and try to start from the overall perspective of the enterprises. When marketing and supply chain resources are integrated, enterprises should build an inventory information integration management system, strengthen marketing channel management, etc., to maximize the advantages of marketing resources, and orderly carry out various marketing tasks to enhance the enterprise’s market competitiveness, so as to provide continuous power for the survival and development of enterprises.

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References 1. Zhang, C.: Integration analysis of enterprise marketing channels based on e-commerce environment. Consume Guide 7, 62–64 (2019) 2. Deng, Q.: Analysis of the integration of corporate marketing channels in the e-commerce environment. Enterprise Sci. Technol. Dev. 10, 246–247 (2019) 3. Bai, J.: A preliminary exploration of the integration of enterprise marketing channels in the context of e-commerce. China Bus. Trade 13, 23–24 (2018) 4. Chen, Y.: Based on the analysis of network marketing strategies for agricultural products in the context of e-commerce. Contemp. Econ. 5, 169–171 (2020) 5. Zhang, K., Yan, L., Li, L.: Explore the transformation of marketing strategy in the era of network economy. Bus. Mag. 4, 59–60 (2021) 6. Zhou, Y.: Analysis of the transformation of marketing strategies in the era of network economy. Consume Guide 1, 63–64 (2021) 7. Meiji, Y.: Thoughts about the transformation of marketing strategy in the era of network economy. Consume Guide 1, 66–68 (2021) 8. Bo, L., He, M.: Network communication and marketing strategies for products created in the “Internet +” environment. Assets Finan. Admin. Inst. 2, 112–114 (2021)

Painting Art Style Rendering System Based on Information Intelligent Technology Tao Zhang(&) Dalian Neusoft University of Information, Dalian, Liaoning, China

Abstract. For a long time, the art of painting has been very mysterious to most ordinary people. Throughout the ages, countless artists have devoted their entire lives to paintings. Many masters have formed their own unique painting styles. With the rapid development of computer intelligence technology, ordinary people can also pry into the mystery of artistic style. Based on this, the purpose of this article is to study the painting art style rendering system based on information intelligence technology. This article first summarizes the basic theory of information intelligence technology, and then extends the core technology of information intelligence. Combining with the current situation of rendering the artistic style of painting in our country, it analyzes the existing problems and shortcomings. On this basis, it uses the core technology of information intelligence to supplement and improve it, and further research and analyze the painting art style rendering system. This article systematically expounds the functional module design, demand design and database design of the painting art style rendering system based on information intelligence technology. And use comparative analysis method, field investigation method and other research forms to carry out experimental research on the style rendering system. Studies have shown that the painting style rendering system based on information intelligence technology is superior to traditional painting style rendering methods in many aspects, especially in terms of rendering efficiency more than 20% higher, which fully reflects the art of painting based on information intelligence technology studied in this article. The excellent performance of the style rendering system and the problems of the traditional moral painting style rendering methods need to be resolved urgently. Keywords: Information intelligent technology research
Analysis and design


Painting art style
System

1 Introduction Considering that the use of computer technology in the field of art education is still lacking [1, 2]. In the teaching process of some professional courses, the use of modern computer technology to complete the dissemination of information, increase the amount of information acquired by students in the learning process, and help students find appropriate information resources in a timely manner is a problem worthy of research [3, 4]. It is for the above reasons that this article attempts to build a painting art style rendering system based on information intelligence technology [5, 6]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Macintyre et al. (Eds.): SPIoT 2021, LNDECT 97, pp. 322–330, 2022. https://doi.org/10.1007/978-3-030-89508-2_41

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Regarding the rendering of painting art style, in China, the application of style rendering has a long history. From the painted pottery of primitive society to the feudal dynasties of the past, the use of each style rendering tells the symbolic meaning and application scenarios that the age has given it, but in the scope of science. There is no systematic summary within [7, 8]. In modern style rendering research, design or image research experts in different fields have completed the definition of style rendering in their respective fields, including style rendering in image design, style rendering in architectural design, and web design [9, 10]. This article aims to improve the painting ability of college painting art students, and aims at the painting art style rendering system based on information intelligence technology. By comparing and analyzing the traditional painting art style rendering and the painting art style rendering system based on information intelligence technology, in order to judge the feasibility of the content studied in this article.

2 Application Research of Painting Art Style Rendering System Based on Information Intelligence Technology 2.1

Analysis of Rendering System Requirements

(1) System design principles The design principles of the style rendering system mainly include two aspects, one is the user-oriented aspect, and the other is the system development and deployment operation aspect. For ordinary users, the first principle of this system is stability, easy operation, and good experience. Stability is mainly reflected in the quality of the system code. To ensure the robustness, reliability and safety of the code, the system should have good error handling capabilities. When an error occurs, it should report the error in time and handle the error event. It cannot be caused by the error. The entire system was paralyzed. Easy to get started mainly requires the design of the front-end interface of the system to be user-friendly, the icons and buttons and other components should be clear and clear, and positive feedback should be given to each user’s operation. A good experience is mainly to ensure that the time for the system to render pictures cannot be too long, and users cannot wait for a long time. This requires the running time of the background rendering module to be as short as possible [11, 12]. For system development and deployment and operation, the system should design each module reasonably, and reduce the degree of coupling between modules and reduce the dependencies between modules. If you want to add new business functions in the future, the existing code and frame design should not be changed, just add the corresponding function or module. In terms of system deployment, as the price of cloud servers, especially dedicated GPU servers, is too expensive, in order to save costs, the system should ensure that the hardware configuration required for normal operation cannot be too high.

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Analysis of the Overall System Architecture and Planning

The implementation of the system is based on B/S design, and users can access the system by opening a browser. The system is divided into two parts: front-end design and back-end architecture. The front-end part directly contacts with users, is responsible for showing system functions to users, and sends requests to the back-end of the system. The backend mainly processes user requests and then returns the processing results. The entire system can be divided into two parts. The first part is the user-oriented front-end interface. In addition to the design of the front-end interface, it needs to be simple and beautiful, it also needs to have the ability to interact with the system backend and return results visualization. Another aspect is the background part of the system, which can be divided into rendering module, business logic function module and URL processing module. 1) Rendering module This part is embedded in the background of the rendering system. The rendering module is based on the TensorF1ow deep learning framework, which mainly implements the rendering functions of different painting art styles. In order to meet the different needs of more users, the rendering module should be pre-trained in a variety of different styles for users to choose. 2) Business logic module The business logic function module in the background is mainly responsible for the processing of various requests and the scheduling of the process pool. This module not only checks the legitimacy of various request operations, but also reasonably schedules rendering requests to the process pool for execution to avoid causing the user’s rendering requests to be unreturned for a long time. 3) URL processing module The URL processing module is located between the front-end request and the business logic function module. It is mainly responsible for judging the legitimacy of various link requests and forwarding the correct request to the function in the corresponding business logic function module. 2.3

Analysis of System Business Requirements

The system is designed based on the B/S architecture, and the main difficulties are focused on the realization of the rendering module and ensuring the high concurrency and non-blocking communication characteristics of the system. Regarding the design of the style rendering module, this part includes an understanding of the several style transfers networks currently proposed. On this basis, the Tensorflow framework is used for coding implementation, and the styles of the works of painters of various genres are extracted in advance. We use the train 2014 training set to train the built network. This training set contains more than 80,000 pictures. Put them in VGGNet to train and calculate the feature maps, and then calculate the content loss and style loss respectively, and add them by weighting, and finally minimize the loss function to get the best result.

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Construction of Color Knowledge Base

The color knowledge base is the central point in the realization of the entire color learning system, and functions and functions interact and influence each other through the color knowledge base. 1) Knowledge base system The knowledge base system is a collection of a series of related information that collects and organizes the original color information and color knowledge information, and classifies and saves it according to a certain method. 2) The structure and function of the color knowledge base The knowledge base is a structured, easy-to-operate and easy-to-use systematic knowledge group. The color knowledge base aims to provide learners with as comprehensive color knowledge as possible in the field of color application, so that learners can understand the mystery of color application by learning the application meaning of color in different backgrounds. Color application is often related to customs, countries, many factors such as region are related. Based on the functional design of the color learning system, as the semantic foundation of color learning, it further classifies and organizes color elements, color-related knowledge and color application cases, and establishes a color book question bank, attribute database and case database. 3) Entry of color knowledge In the painting art style rendering system, users can view color-related knowledge. When encountering new problems, users can ask questions in the system, wait for others to answer, and lock the best answer, and indicate the respondent's account when answering the question. Information to ensure the quality of the answers submitted and to mobilize the enthusiasm of users to submit answers. 2.5

Implementation of Artistic Style Rendering Based on Deep Learning

(1) Style transfer network The definition of style is a bit more complicated. Here we need to introduce the Gram matrix. The definition of Gram matrix is a matrix composed of two inner products between any m vectors in an n-dimensional space. The inner product of two vectors is the operation of multiplying the two vectors and summing them. The final loss function is defined as follows: Ltotal ðp; a; xÞ ¼ aLcontent ðp; xÞ þ bLstyle ða; xÞ

ð1Þ

Where a and b represent the weight of content and style loss, respectively. If the content image needs to be highlighted in the final generated image, a larger weight is given, and if the style image needs to be highlighted, a larger weight is given to b. (2) Generate network The style transfer network structure is divided into two parts, namely the generation network Image Transform Net and the loss network Loss Network. The input of the generation network is a picture, and the final output after network calculation

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is also a picture. The style of this picture has been converted. The generative network is a convolutional neural network in structure, which includes a convolutional layer, a residual layer, and a deconvolutional layer, and does not include any pooling operation. It can be seen from the structure of the figure that the first three layers of the generating network are down-sampled, the middle part is connected by five residual blocks, and the end part is used to increase the picture size by up-sampling in order to keep the size of the output picture the same as the input. In order to keep the pixel value of the output image of the last layer of the generated network between [0,255], a Tanh function is used to compress the output value. At the beginning of the generation network, all weight parameters are random. As the network is continuously iteratively trained, the weight values and bias values of all layers in the generation network will eventually be determined. (3) Specific implementation based on Tensor flow Tensoflow provides us with a very convenient function API, we can use the framework to quickly build a fast style migration network. Generate network-There are three convolutional layers at the beginning, which are realized by the custom function _ conv_ layer, in which the tf nn. conv2d function provided by Tensorflow is mainly used to realize the convolution operation. Then there are five residual modules, implemented by a custom function _ _residual_ _block. In order to ensure that the input image size is consistent with the output, the next three deconvolution operations are implemented by the custom function _ conv_ transpose_ layer, which mainly calls the tf.n.conv2d_ transpose function in the framework. The last of the generated network is a Tanh activation function, in order to map the output value to (0,255). The code to build the generation network is as follows: 1 def net(image): 2 conv1 = conv_ layer(image, 32,9, 1) 3 conv2 =. conv_ layer( conv1, 64, 3, 2) 4 conv3 =. conv layer(conv2, 128,3,2) 5 resid1 =. resi dual_ bock