Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City - Volume 1: BDCPS 2022, December 16-17, Bangkok, Thailand 9819908795, 9789819908790

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Lecture Notes on Data Engineering and Communications Technologies 167

Mohammed Atiquzzaman Neil Yen Zheng Xu   Editors

Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City— Volume 1 BDCPS 2022, December 16–17, Bangkok, Thailand

Lecture Notes on Data Engineering and Communications Technologies Volume 167

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.

Mohammed Atiquzzaman · Neil Yen · Zheng Xu Editors

Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1 BDCPS 2022, December 16–17, Bangkok, Thailand

Editors Mohammed Atiquzzaman School of Computer Science University of Oklahoma Norman, OK, USA

Neil Yen University of Aizu Fukushima, Japan

Zheng Xu Shanghai Second Polytechnic University Shanghai, China

ISSN 2367-4512 ISSN 2367-4520 (electronic) Lecture Notes on Data Engineering and Communications Technologies ISBN 978-981-99-0879-0 ISBN 978-981-99-0880-6 (eBook) https://doi.org/10.1007/978-981-99-0880-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Organization

Program Committee Chairs Mohammed Atiquzzaman, University of Oklahoma, USA Zheng Xu, Shanghai Polytechnic University, China Neil Yen, University of Aizu, Japan

Publication Chairs Deepak Kumar Jain, Chongqing University of Posts and Telecommunications, China Ranran Liu, The University of Manchester Xinzhi Wang, Shanghai University, China

Publicity Chairs Junyu Xuan, University of Technology Sydney, Australia Vijayan Sugumaran, Oakland University, USA Yu-Wei Chan, Providence University, Taiwan, China

Program Committee Members William Bradley Glisson, University of South Alabama, USA George Grispos, University of Limerick, Ireland Abdullah Azfar, KPMG Sydney, Australia Aniello Castiglione, Università di Salerno, Italy

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Organization

Wei Wang, The University of Texas at San Antonio, USA Neil Yen, University of Aizu, Japan Meng Yu, The University of Texas at San Antonio, USA Shunxiang Zhang, Anhui University of Science and Technology, China Guangli Zhu, Anhui University of Science and Technology, China Tao Liao, Anhui University of Science and Technology, China Xiaobo Yin, Anhui University of Science and Technology, China Xiangfeng Luo, Shanghai University, China Xiao Wei, Shanghai University, China Huan Du, Shanghai University, China Zhiguo Yan, Fudan University, China Rick Church, UC Santa Barbara, USA Tom Cova, University of Utah, USA Susan Cutter, University of South Carolina, USA Zhiming Ding, Beijing University of Technology, China Yong Ge, University of North Carolina at Charlotte, USA T. V. Geetha, Anna University, India Danhuai Guo, Computer Network Information Center, Chinese Academy of Sciences, China Jianping Fang, University of North Carolina at Charlotte, USA Jianhui Li, Computer Network Information Center, Chinese Academy of Sciences, China Yi Liu, Tsinghua University, China Kuien Liu, Pivotal Inc., USA Feng Lu, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, China Ricardo J. Soares Magalhaes, University of Queensland, Australia D. Manjula, Anna University, India Alan Murray, Drexel University, USA S. Murugan, Sathyabama Institute of Science and Technology, India Yasuhide Okuyama, University of Kitakyushu, Japan S. Padmavathi, Amrita University, India Latha Parameswaran, Amrita University, India S. Suresh, SRM University, India Wei Xu, Renmin University of China Chaowei Phil Yang, George Mason University, USA Enwu Yin, China CDC, USA Hengshu Zhu, Baidu Inc., China Morshed Chowdhury, Deakin University, Australia Min Hu, Shanghai University, China Gang Luo, Shanghai University, China Juan Chen, Shanghai University, China Qigang Liu, Shanghai University, China

Conference Program

December 16, 2022, Tencent Meeting 9:50–10:00

Opening ceremony

10:00–10:40

Keynote 1: Mohammed Atiquzzaman

Shaorong Sun

10:40–11:20

Keynote 2: Neil Yen

14:00–18:00

Session 1

Junyu Xuan

Session 2

Yan Sun

Session 3

Ranran Liu

Session 4

Xinzhi Wang

Session 5

Guangli Zhu

Session 6

Zhiguo Yan

Session 7

Huan Du

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Preface

With the rapid development of big data and current popular information technology, the problems include how to efficiently use systems to generate all the different kinds of new network intelligence and how to dynamically collect urban information. In this context, Internet of things and powerful computers can simulate urban operations while operating with reasonable safety regulations. However, achieving sustainable development for a new urban generation currently requires major breakthroughs to solve a series of practical problems facing cities. A smart city involves wide use of information technology for multidimensional aggregation. The development of smart cities is a new concept. Using Internet of things technology on the Internet, networking, and other advanced technology, all types of cities will use intelligent sensor placement to create object-linked information integration. Then, using intelligent analysis to integrate the collected information along with the Internet and other networking, the system can provide analyses that meet the demand for intelligent communications and decision support. This concept represents the way smart cities will think. BDCPS 2022 which is held online on December 16, 2022, which is dedicated to address the challenges in the areas of CPS, thereby presenting a consolidated view to the interested researchers in the related fields. The conference looks for significant contributions on CPS in theoretical and practical aspects. Each paper was reviewed by at least two independent experts. The conference would not have been a reality without the contributions of the authors. We sincerely thank all the authors for their valuable contributions. We would like to express our appreciation to all members of the Program Committee for their valuable efforts in the review process that helped us to guarantee the highest quality of the selected papers for the conference. We would like to acknowledge the General Chairs, Publication Chairs, Organizing Chairs, Program Committee Members, and all volunteers. Our special thanks are due also to the editors of Springer book series “Lecture Notes on Data Engineering and Communications Technologies” and editor

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Ramesh Nath Premnath, Ramamoorthy Rajangam, and Sathya Subramaniam for their assistance throughout the publication process. Norman, USA Fukushima, Japan Shanghai, China

Mohammed Atiquzzaman Neil Yen Zheng Xu

BDCPS 2022 Keynotes

Keynote 1: State-of-the-Art Survey of Artificial Intelligent Techniques for IoT Security Professor Mohammed Atiquzzaman Edith Kinney Gaylord Presidential Professor, School of Computer Science, University of Oklahoma, USA

Mohammed Atiquzzaman (Senior Member, IEEE) obtained his M.S. and Ph.D. in Electrical Engineering and Electronics from the University of Manchester (UK) in 1984 and 1987, respectively. He joined as an assistant professor in 1987 and was later promoted to senior lecturer and associate professor in 1995 and 1997, respectively. Since 2003, he has been a professor in the School of Computer Science at the University of Oklahoma. Dr. Atiquzzaman is the editor in chief of Journal of Networks and Computer Applications, co-editor in chief of Computer Communications journal and serves on the editorial boards of IEEE Communications Magazine, International Journal

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BDCPS 2022 Keynotes

on Wireless and Optical Communications, Real Time Imaging journal, Journal of Communication Systems, Communication Networks and Distributed Systems, and Journal of Sensor Networks. He co-chaired the IEEE High Performance Switching and Routing Symposium (2003) and the SPIE Quality of Service over Next Generation Data Networks conferences (2001, 2002, 2003). He was the panels co-chair of INFOCOM’05, is/has been in the program committee of many conferences such as INFOCOM, GLOBECOM, ICCCN, Local Computer Networks, and serves on the review panels at the National Science Foundation. He received the NASA Group Achievement Award for “outstanding work to further NASA Glenn Research Center’s effort in the area of Advanced Communications/Air Traffic Management’s Fiber Optic Signal Distribution for Aeronautical Communications” project. He is the coauthor of the book Performance of TCP/IP Over ATM Networks and has over 150 refereed publications, most of which can be accessed at www.cs.ou.edu/~atiq.

Keynote 2: Emerging Services in the Next-Generation Web: Human Meets Artificial Intelligence Professor Neil Yen University of Aizu, Japan, Tsuruga, Ikkimachi, Aizuwakamatsu, Fukushima 9658580, Japan

Dr. Neil Yen is an Associate Professor at the University of Aizu, Japan. He received doctorates in Human Sciences (major in Human Informatics) at Waseda University, Japan, and in Engineering (major in Computer Science) at Tamkang University, Taiwan, in March and June 2012, respectively. His doctor degree at Waseda University was funded by the Japan Society for the Promotion of Science

BDCPS 2022 Keynotes

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(JSPS) under RONPAKU program. He has actively involved himself in the international activities, including editorial works in journals and books, society services in academic conferences sponsored by IEEE/ACM, etc., and devoted himself to discover advanced and interesting research directions. He has been engaged in the interdisciplinary realms of research, and his research interests are now primarily in the scope of human-centered computing and big data.

Oral Presentation Instruction

1. Timing: A maximum of 10 minutes total, including speaking time and discussion. Please make sure your presentation is well-timed. Please keep in mind that the program is full and that the speaker after you would like their allocated time available to them. 2. You can use CD or USB flash drive (memory stick), make sure you scanned viruses in your own computer. Each speaker is required to meet her/his session chair in the corresponding session rooms 10 minutes before the session starts and copy the slide file(PPT or PDF) to the computer. 3. It is suggested that you email a copy of your presentation to your personal inbox as a backup. If for some reason the files can’t be accessed from your flash drive, you will be able to download them to the computer from your email. 4. Please note that each session room will be equipped with a LCD projector, screen, point device, microphone, and a laptop with general presentation software such as Microsoft PowerPoint and Adobe Reader. Please make sure that your files are compatible and readable with our operation system by using commonly used fronts and symbols. If you plan to use your own computer, please try the connection and make sure it works before your presentation. 5. Movies: If your PowerPoint files contain movies please make sure that they are well-formatted and connected to the main files.

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Contents

Power Grid Sensitive Information Detection Technology Based on Internet Big Data and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . Kai Cheng, Zhan Wu, Qiang Wang, Mu Ren, Xiaoyan Wei, and Weijing Yao

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Optimization Study of Wind Speed Prediction Based on Combined Model of Neural Network Optimization Algorithm . . . . . . . . . . . . . . . . . . . Xiao-Fei Li, Xiao-Yu Zhang, and Jin-Rui Wei

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Optimal Decision Tree Algorithm in Sports Video Tracking Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingxia Han

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PLC Controller-Based Automatic Control System Design for Electric System Manipulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hengchao Zhou, Yan Su, and Jinxia Li

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Development of Object Identification APP Based on YoloV2 . . . . . . . . . . . Baiming Zhao, Nan Xie, Junxiao Ge, and Weimin Chen Intelligent Defense Policy for Web Security Defense on Account of Semantic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ning Xu, Zheng Zhou, Jie Xu, Liang Dong, Wangsong Ke, Zhaoyu Zhu, Yuxuan Ye, Xiang Li, and Chao Huang

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Consistency Comparison of Machine Vision Images Based on Improved ORB Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gang Huang

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Nonlinear Multi-objective Probabilistic Optimization Based on Stochastic Simulation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoxiao Ma

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Contents

Artificial Intelligence Technology in Fault Diagnosis of Transmission Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yimeng Li, Haoran Li, and P. Rashmi

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Research on Power Supply Chain Equipment Traceability Method Based on System Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianhong Wu, Junchang Lin, and Xinghua Deng

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Artificial Intelligence Application Processing Method and System Based on Open Source Deep Learning Framework . . . . . . . . . . . . . . . . . . . Chunhua Deng and A. C. Ramachandra

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A Novel Association Rules Mining Based on Improved Fusion Particle Swarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Qing Tan and Libo Sun Comparative Analysis of Machine Translation (MT) and Computer Aided Translation (CAT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Tingting Wang and Venugopal Sridhar Dynamic Monitoring of Sea Reclamation Based on UAV Remote Sensing Technology Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Ningjun Wang and Tiantian Liu The Synthesis Model Based on Multimodal Data for Asian Giant Hornets Sighting Report Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Feiyang Wu, Shengqiang Han, Jinyi Song, Xinqing Xu, and S. Pradeep Kumar Interactive Translation System of Intelligent Fuzzy Decision Tree Algorithm (IFDTA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Junkai Wang, Wenjun Liu, and Huijuan Liu Adaptive Neural Network (NN) Coordinated Tracking Control Based on Artificial Intelligence Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Bo Lu, Yuanda Guo, Jia Song, and I. G. Naveen Early Warning of Linguistic Achievement Pattern Based on DNA-GA Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Xiaohui Wan AI Big Data Multi-dimensional Intelligent Pension Model Recommendation Algorithm Empowered by Artificial Intelligence . . . . . 169 Ming Li Artificial Intelligence Medical Construction and Data Mining Based on Cloud Computing Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Lujun Lv Visual Intelligent Recognition System Based on Visual Thinking . . . . . . . 187 Wenqiang Zhang

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Path Optimization of e-Commerce Logistics Terminal Distribution Mode Based on Dijkstra Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Shuoyuan Lin Complex SPARQL Queries Based on Ontology and RDF . . . . . . . . . . . . . . 205 Wei Guan and Yiduo Liang Prediction System Analysis of Microbial Treatment of Organic Pollution Based on Particle Swarm Optimization Algorithm . . . . . . . . . . . 215 Dewei Zhu Data Preprocessing Technology in Network Traffic Anomaly Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Xueyuan Duan, Yu Fu, and Kun Wang Intelligent Control Optimization of Sewage Treatment Process Based on Process Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Li Feng and Yuxin Li Research on Dynamic Cost Management System of Power Transmission and Transformation Project Based on 3D Technology . . . . 245 Linlin Zhang, Shanwu Huo, Jing Wang, and Qing Ren An Algorithm for Constructing Fractal Graph of Frieze Group Based on NIFS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Jiahui Zhang, Fengying Wang, Liming Du, and Haiyan Wang Application of Artificial Intelligence Technology in Distributed Privacy-Preserving Clustering Mining Algorithm . . . . . . . . . . . . . . . . . . . . . 263 Jin Zhang Simulation of a Sports Body Index Monitoring System Based on Internet of Things Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Haoran Gong and Yunhu Si Processing of Natural Language Information Hiding Algorithm Based on Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Zhenpeng Yang Color Model-Based Control Algorithm for 3D Printing of FDM Eco-friendly Art Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Diwen Hu and Hao Sun Research on Ecological Environment Early Warning System of Power Transmission and Transformation Project Based on Multi-objective Programming Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 301 Ming Lv and Junda Tong Research on Computer Information Security Technology Based on DES Data Encryption Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Zhibo Fu, Guocong Feng, and Jian Wang

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Stereoscopic Visual Effect Simulation of Film and Television Works Based on 3D Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Hongxing Qian Research on Intelligent Detection Technology of Protection Based on AC Electromagnetic Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Liang Zhang, Bing Wei, Jingyuan Chi, Han Wang, Lijun Fu, and Jian Zhang Performance Simulation and Application Research of Typical Directional Valve Based on Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Yuxi Zheng, Yaqin Tang, Zhuotao Zou, and Feng Cao Intelligent Multimedia News Communication Platform Based on Machine Learning and Data Fusion Technology . . . . . . . . . . . . . . . . . . . 345 Ying Qian Digital Economic Dispatch System Based on Improved Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Yuan Li and Xin Yu Research on Target Detection and Anti-Attack Method Based on Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Yi Wu, Jinchi Zhang, Hao Luo, Zhihao Zhong, and Xiaosheng Shi Autonomous Driving System of Intelligent Connected Vehicle Based on ASAM Standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Huiyu Xie, Pengchao Zhao, Zhibin Du, Bolin Zhou, and Jun Jiang Smart Clothing Try-on System Based on Data Analysis Algorithm . . . . . 383 Peipei Zhao, Ning Yang, and Dan Yu Design of English Automatic Translation System Based on Machine Intelligent Improved GLR Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Can Wang Financial Risk Monitoring Analysis Based on Integrated SVM Data Stream Classification Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Chunyun Yao A Virtual Culture Brand Image Design System Based on Corba/Java Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 Hongyan Yuan, Rongzeng Hou, and Weicheng Li “Digital New Infrastructure” Contributes to the Construction of New Power Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 Guang Chen, Wanting Yin, Di Wang, Yihan Zheng, and Xiaonan Gao 3D Map Modeling Technology Based on Unmanned Vehicle Environment Perception Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 Wenjun Xue, Ke Xiao, Jielun Zhao, Leiyu Wang, and Yifan Guo

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Traffic Image Classification Algorithm Based on Deep-Learning . . . . . . . 437 Yi Ren and Lanjun Cong The Application of 3D Cable Laying in Substation Based on Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Wenzhe Xu, Wenhua Tao, Shudong Lv, and Jiabing Shi Research on the Digital Measurement Method of Ship Flow Based on Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 Jianchao Xia Development of Virtual Imaging Technology for Engineering Surveying and Mapping Based on Genetic Algorithm . . . . . . . . . . . . . . . . . 465 Donghuan Qiu Path Planning Algorithm of Intelligent Unmanned Inspection Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 Ning Wang, Junren Shao, Zhenlin Huang, Tao Yang, Xing Wen, Shaosen Li, Liuqi Zhao, Jinwei Zhu, and Yuheng Zhang Multiple-Processes Integratived Management System of Construction Project Based on Ant Colony Algorithm . . . . . . . . . . . . . . 485 Lei Lei Moving Image Processing Technology and Method Based on Neural Network Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 Xinyu Liu and Yingwei Zhu Building Fall Safety Early Warning System Based on Directed Weighted Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Xinyu Zhang, Xiaoxuan Wang, and Jinmei Lin Maritime Mobile Communication System Based on Wireless Mobile Ad Hoc Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Jia Liu Cryptographic Reverse Firewalls in Cyber-Physical Systems: Preliminaries and Outreach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519 Wanda Guo Information Collection System of Learning City Based on Big Data Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529 Shifa Lu A DOA Estimation Algorithm Based on Unitary Transform . . . . . . . . . . . 539 Wenchao He, Liyan Li, and Ting Xu 3D Animation Character Simulation Technology Based on Swarm Intelligence Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 Chulei Zhang

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The Construction of Power Grid Operation Monitoring Platform Driven by High-Tech Information Technology . . . . . . . . . . . . . . . . . . . . . . . . 555 Chengfang Gao, Jinman Luo, Haobo Liang, and Xiaoji Guo Design of Tag Recommendation Algorithm in Travel APP Intelligent Guidance System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 Fei Meng Practice of Plant Factory Visualization System Based on Internet of Things Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Shaowei Sun and Dan Li Blockchain Technology Drives the Transformation and Upgrading of Audit Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 Shiya Zhou and Wenbin Liu Research on Blockchain Technology Applications for Digital Currency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 Xiaoqi Yu and Minghong Sun Construction of Fuzzy Control Evaluation System for Regional Economy Based on VIKOR Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Yan Zuo Predictive Analysis of Key Performance Indicators of Distributed Database Based on Machine Learning Algorithm . . . . . . . . . . . . . . . . . . . . . 611 Tingting Wu Design of Regional Economic Information Sharing Based on Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621 Bin Ji and Zhiheng Zhang Building Structure Optimization Based on Computer Big Data . . . . . . . . 629 Chao Li, Qiufan Chen, Huafei Huang, and Qiong Zeng Design of Aero-engine Start-Up Fuel Control System Based on Particle Swarm Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 Binhui Li, Shiyin Wang, and Chen Chen Safety Verification Technology of Urban Power Grid Under Strong Convective Weather Based on Laser Point Cloud Data . . . . . . . . . . . . . . . . 647 Weinan Fan, Hongbin Wang, Junxiang Liu, Zhong Xu, and Yong Wang Blockchain Computing Resource Allocation and Benefit Sharing Based on Artificial Intelligence Technology . . . . . . . . . . . . . . . . . . . . . . . . . . 657 Jian Liu Integrated Development of Smart City and Smart New Media . . . . . . . . . 667 Yue Luo and Yao Huang

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Intelligent Logistics Transport Prediction of Forest Products Based on BP Neural Network Learning Algorithm . . . . . . . . . . . . . . . . . . . . 677 Qian Chen, Ning Li, and Siyu Deng Distributed 3D Interior Design System Based on Intelligent VR Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687 Jianfeng Wang, Lulu Liu, and Linzi Li Application of Decision Tree Mining Algorithm in Data Model Collection of Hydraulic Engineering Equipment . . . . . . . . . . . . . . . . . . . . . . 697 Guang Yang and Xin Guo ArcGIS-Based Landscaping Management System . . . . . . . . . . . . . . . . . . . . 707 Juechao Tan and Qianying Lu Construction of English Grammatical Error Correction Algorithm Model Based on Deep Learning Technology . . . . . . . . . . . . . . . . . . . . . . . . . . 717 Jiaying Meng and Zhifan Wang Design of the University Management Information System Based on User Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725 Mingxiu Chen Design of Dynamic Monitoring System for Cold Chain Logistics Vehicles Based on Internet Digital Intelligence Technology . . . . . . . . . . . . 735 Yingling Du, Zongbao Sun, and Jinxia Li Cloud Computing Information System Security Monitoring Under Artificial Intelligence Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745 Cuijin Lao and Shen Qin Optimization of Parametric Park Landscape Design Based on Grasshopper Module Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757 Yi Fu and Chensong Wang Energy Consumption Trend Analysis Based on Energy Elastic Consumption Coefficient Method Under the Background of Carbon Dual Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765 Ding Chen, Chun Li, Weidong Zhong, Wei Liu, and Yan Yan The Development and Application of the New Art Deco Style in Poster Design Under the Perspective of Artificial Intelligence . . . . . . . . 781 Bingnan Pang and Tiantian Chen Product Feature Modeling Based on Graphics and Image Fusion . . . . . . 793 Chaoran Tong and Shi Yang Blockchain-Based Car Networking Data Privacy Security Assessment Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801 Puqing Wang and K. L. Hemalatha

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Research on Multi-modal Human–Computer Interaction Products Under the Background of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . 811 Shuo Li Frame Design Based on Machine Learning Sports Result Prediction . . . 821 Xiaodan Yang and B. P. Upendra Roy Vehicle Detection Algorithm Based on Video Image Processing in Intelligent Transportation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829 Hongliang Guan Path Planning of Autonomous Vehicles Based on Deep Learning Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837 Yangyong Liu

Power Grid Sensitive Information Detection Technology Based on Internet Big Data and Machine Learning Kai Cheng, Zhan Wu, Qiang Wang, Mu Ren, Xiaoyan Wei, and Weijing Yao

Abstract With the continuous development of China’s electric industry, the power grid system and other infrastructure have also been well integrated. In the era of big data, the analysis and processing of public opinion information based on the Internet has become the mainstream. In order to make better use of the existing BD technology to effectively identify public opinion problems and hot topics in the power grid and put forward corresponding solutions, in this paper, the machine learning-based sensitive information detection technology for power grid is deeply studied. Firstly, this paper introduces the define and characteristics of power grid sensitive information, and then studies the Internet BD technology and machine learning technology. Based on this, a power grid sensitive information detection framework is designed and tested. Finally, the experimental results show that the grid sensitive information based on the BD and machine learning basic under 3% error rate of detection system, which shows that this technology can make the user satisfied to detect grid sensitive information. Keywords Internet technology · Big Data · Machine learning · Power grid sensitive information

1 Introduction For the past few years, with the rapid development of China’s power industry, power grid construction made great achievements. However, in the era of BD, it is very important to mine and analyze information to obtain reliable and accurate public opinion information. As one of the indispensable infrastructure in power use and distribution, power grid also has a lot of problems in the rapidly growing power system [1, 2]. Due to the large amount of network information, many and miscellaneous contents, the use of data mining technology in the domain of smart grid planning K. Cheng (B) · Z. Wu · Q. Wang · M. Ren · X. Wei · W. Yao Central China Branch of State Grid Corporation of China, Wuhan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_1

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is of great significance, and it has effectively supported the construction of smart cities. In addition, with the development of artificial intelligence and the Internet and the rapid expansion of the communication industry and a series of information technology innovations, higher standards and the safe operation of power grid is required [3, 4]. In the BD environment, the amount of information in the power grid has increased dramatically. How to effectively manage these huge and complex digital mass user groups is the current research hotspot. At present, a lot of relevant work has been done at home and abroad to solve this problem. Some scholars have proposed electric system modeling on account of artificial neural net model. This method is mainly used in the cases of high feature similarity and time-varying parameter sensitivity on the collected original sample set. At the same time, it can also combine the data with other simulation information to realize the unification of intelligent analysis and processing mode [5, 6]. In the multivariable comprehensive evaluation method based on grey correlation theory, another scholar proposed the application of artificial neural network to smart grid optimization system modeling [7, 8]. Therefore, based on BD and machine learning, this paper studies the power grid sensitive information detection technology. For the sake of solve the influence of bad information in electric grid on users, this paper proposes an artificial neural network prediction techniques based on machine learning approach in BD environment. Firstly, the research background and meaning of the algorithm are introduced, relevant literature review and theoretical basis at home and abroad. Secondly, it describes and analyzes the artificial neural network and introduces its key feature extraction process and specific steps. The third layer uses rbfid (PCA) technology to realize model training and recognition, After the user history information is collected by the embedded perceptron, the deep learning method is used for data mining to predict the potential risks in the power grid.

2 Discussion on Power Grid Sensitive Information Detection Technology Based on Internet Big Fata and Machine Learning 2.1 Sensitive Information of Power Grid Power industry is the focus of national development. As the foundation of national economy, power grid has an important impact on social economy. Its safe and stable operation is related to the safety of people’s lives and property [9, 10]. Power grid information contains a large number of data, These data of different types, properties and attributes are obtained by manual collection, analysis and processing. There are a large number of important parameter information including various power system load values, such as voltage level, temperature and humidity changes, etc., which may also be triggered at some special time when there are key or interface problems

Power Grid Sensitive Information Detection Technology Based …

3

with sensitive components, resulting in faults or major accidents in the power grid, resulting in false alarms or failure to log in [11, 12]. The power BD in the power grid contains a large amount of unstructured, heterogeneous and multi-source information. Therefore, it is necessary to extract its features in the analysis process. Its sensitive information features are as follows: (1) large amount of information. The features extracted from the data need to be analyzed, and a large number of samples are discrete, discontinuous and uncertain. Therefore, it is necessary to use classification algorithm in processing. (2) The variation law of sensitivity coefficient is complex. With the passage of time and load growth, a certain number of new structures have been formed, and the differences between different types of power grids will lead to new problems or phenomena. (3) Text features. Text is a form of language expression, rather than words or symbols, which can be processed and transformed by simple representation methods. At the same time, it also has some nonlinear and time-varying characteristics. These properties provide us with the possibility to study the relationship between power grid structure changes and power system sensitivity based on large samples, and play an important role in the subsequent analysis.

2.2 Internet Data Technology The collection of public opinion data of power grid mainly uses cloud computing technology to retrieve, mine and process a large amount of information in a wide range. PageRank algorithm is needed in this process. At present, some on-line clustering methods based on boundary detection (KBE) have been implemented. There are two forms of pager: one is the service-oriented layer, and the other is to use the layered mode (i.e. b/a mode or C-M model) at the bottom to obtain data. However, these technologies have certain defects and shortcomings, such as poor generalization ability and slow response to user requirements. The date weight concept of the web page is: W = C.D/r

(1)

PageRank algorithm is an online identification method based on data set. It uses different information contained in the data set to classify users to obtain the corresponding service content under the corresponding category. It is widely used in the field of public opinion analysis of power grid. This technology realizes the process of user identification by clustering and quantifying the collected samples to get the relevant attribute values, then calculating the feature vectors and matching their feature quantities. At the same time, it can also improve the algorithms such as fuzzy processing and support vector machine (SVM) according to specific standards, so that the text classification based on data sets can be completed quickly and efficiently.

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R(u) = c



2eR(v)/N

(2)

W is the site date weight T, how long it takes a search engine to retrieve its inventory list of web pages at a time; H indicates the access period; T is founded by web spider web pages (the web spider is a specified number of days between the date of automatic Internet search and loading of the viewing file and the date of web page placement). The last d is a number whose value represents the visit period of the spider.

2.3 Machine Learning In machine learning, the core part is artificial nerve network. It combines the human brain with the animal brain. The process of realizing knowledge transmission and cognitive ability by simulating human brain’s activities such as external signal processing, recognition and evaluation is called artificial neural system (backengine) technology, which is also called computer self-organizing system or expert system design method. Among them, machine learning in the mechanical software development platform of artificial intelligence technology is used for information retrieval, storage and management. Machine learning is a process that encodes, filters and recombines the known knowledge, making it a process with high efficiency, high quality and quick access to useful information. In real life, we can use computers to deal with some complex problems. However, when some problems need to be solved or the task changes, the system will be required to timely convert the input data into standard output results to achieve the goal. At the same time, there may be large differences in results due to different algorithms. Suppose we get n samples of unknown categories X1, X2, …, Xn from sample space A, the grouping procedure is described as follows: for each example Xi (i = 1, 2, …, n), it belongs to the m a regional one of a, and Xi is only one of them. The distribution of sample meets the following formula: A1 ∪ A2 ∪ A3 . . . ∪ An = A

(3)

Ai ∩ Aj = φ(∀i /= j)

(4)

At present, the main application fields include: artificial neural network technology (RTs), clustering and so on. As a typical non-linear model, artificial neural network has high efficiency and robustness. Machine learning realizes knowledge transfer and reorganization by transforming complex problems into simple mathematical forms, so as to better solve practical problems and combine prediction and simulation methods.

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3 Experimental Process of Power Grid Sensitive Information Detection Technology Based on Internet Big Fata and Machine Learning 3.1 Technical Framework for Sensitive Information Detection of Power Grid The research in this paper is based on artificial neural network techniques, support vector machine (SVM) and object-oriented model, using the BD platform to collect a large number of samples, and extracting potentially interesting features from the collected original text images containing important information. You can see this in Fig. 1, the power grid public opinion information analysis based on BD is mainly to preprocess a lot of samples. First, the characteristics of the original samples are obtained through manual screening, statistics and descriptive statistics, and then using neural network algorithm to construct model and the classifier parameters are trained to predict potential anomalies and possible risk points.

2*TWI POWER MGT

DUAL CRC

Machine learning

Internet big data

SRAM WITH ECC

8*TIMERS 1*COUNTER 1*CAN

DUAL WATCHDOG

2*PWM TRIGGER ROUTING

2*UART SYSTEM CROSSBAR AND DMA SUBSYSTEM

WATCH-POINTS

2*SPI 3*SPORT+ACM 3*EPPI 1*USB OTG MP

DOR2/LPDDR1

ASYNC

Fig. 1 Framework diagram of power grid sensitive information exploration

G P I O

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3.2 Power Grid Sensitive Information Detection Technology Test Experiment In processing the data, i Sample sets of different types, attributes, and quantities need to be mapped to the same database and then extracted from these sample sets. Because the power grid BD contains a number of different categories of large amounts of information and a large number of different contents. Therefore, to ensure the accuracy and reliability of the analysis results, this paper uses the machine learning method to mine the features of event sequences with lower probability and calculate their correlation to study the prediction model. To ensure better describe the information of power grid, the following research objectives are proposed: analyze historical data based on BD platform, use crawler technology to obtain a large number of high-frequency text sequences and images, and extract these images as potential samples through clustering method; Realize the establishment and optimization process of automatic identification model (including artificial neural network algorithm and other intelligent methods); An artificial neuron system is built to simulate the complex environment and predict various possible risk factors (such as power and communication failures) in the power grid under different scenarios, so as to give early warning to the power grid.

4 Experimental Analysis of Power Grid Sensitive Information Detection Technology Based on Internet Big Fata and Machine Learning 4.1 Power Grid Sensitive Information Detection Performance Test Table 1 shows the experimental data of the power grid sensitive information exploration. Table 1 Power grid sensitive information exploration experiment based on BD and machine learning Test times

Number of simulated sensitive information bars

Number of probed bars

Mistest rate (%)

1

340

328

3.5

2

425

417

1.8

3

521

515

1.2

4

352

348

1.1

5

475

465

2.1

Power Grid Sensitive Information Detection Technology Based …

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Fig. 2 Grid sensitive information exploration rate on account of machine learning and BD

Artificial neural network model in this paper is used in power grid information management. By training and analyzing the data sets, it is found that there are similarities between different types of sample sets. The main aspects of the work in this paper are as follows: (1) test the sensitivity of power BD, and test which parameter values are valid through experiments. (2) In practical application, it is necessary to analyze the information flow and behavior of artificial neural networks to the power grid. The most commonly used algorithm is based on clustering. This method can classify and recognize complex, abstract, unknown or semi-structured objects, and can also select different types of samples as training sets according to specific problems to obtain the optimal results. You can see this in Fig. 2, the error rate of the grid sensitive information detection framework based on BD and machine learning is basically below 3%, which shows that this technology can make the user satisfied to detect grid sensitive information.

5 Conclusion At the same time in the rapid development of Internet, the problem of power grid security has become more and more serious, and BD analysis has become a current research hotspot. After reading and sorting out a lot of documents, the discovery excavates that sensitive public opinions such as large sample characteristics and highfrequency information in the power field have been excavated in a way. According to

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the artificial neural network technology, this paper establishes an intelligent recommendation system on the basis of the combination of clustering algorithm and clustering rules to realize user demand prediction. At the same time, the similarity function is used to map the text data to the decision tree module, and BPSK dynamic neurons are used to process the large data set of power grid.

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Optimization Study of Wind Speed Prediction Based on Combined Model of Neural Network Optimization Algorithm Xiao-Fei Li, Xiao-Yu Zhang, and Jin-Rui Wei

Abstract Short-time forecasting of wind speed has such an immense effect on boosting organizational excellence and raising market prosperity in the system of wind power plants. Significant number of models for predicting wind speed have been proposed by many scholars, which aim to improve the prediction performance. Taking the historical meteorological data of Penglai wind farm in Shandong province as an example, the wind speed data were firstly denoised by CEEMDAN, then TCN was selected to predict the denoised data, and finally the neural network was combined and optimized by using the GWO algorithm. The experimental findings demonstrate that, in comparison to all other traditional neural network models, the developed combinatorial model is unquestionably superior. Additionally, it may be applied as a successful method for designing the smart grid. Keywords Data preparation · Wind speed forecast · Combined model · Optimization method

1 Background and Literature Renewable energy sources have generated a lot of interest and excitement for study due to environmental degradation, as well as the depletion of traditional energy sources [1]. Wind energy plays an important role in carbon-free sources of energy. It might lead to the creation of an environmentally friendly energy source and is essential in the worldwide new energy industry. Furthermore, it has recently undergone X.-F. Li · X.-Y. Zhang School of Big Data Management and Application, Dalian Neusoft University of Information, Dalian 116025, Liaoning, China J.-R. Wei (B) School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_2

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surprisingly rapid development and is gaining international attention [2]. The Global Wind Energy Council claims that, the yearly market for wind energy in 2016 was 54.6 GW, with around 487 GW of installed capacity globally [3]. However, several issues with wind power have emerged that severely limit its advancement. More precisely, the quality of wind power generation and the dependability of system projections are both lowered by the stochastic and intermittent character of wind power output. Additionally, it is challenging to forecast how much electricity will be injected into the distribution system because to the constant variation in wind speed, which might lead to issues with energy transportation. In general, due to wind-related uncertainties, wind speed forecasting can help to lower the risk of wind power generation [4]. As a result, numerous academics have given the study techniques for predicting wind speed a lot of attention. Physical operations, conventional statistical operations, traditional statistical operations, spatial correlation operations, and artificial intelligence operations are the four categories into which these techniques may be divided. Physical operations primarily rely on numerical weather prediction models for predicting wind speed in subsequent periods based on physical information such as topographical data, humidity, movement, and intensity [5]. To increase wind speed predictability, the wind turbines’ estimations of wind speed are assimilated into the NWP system [6]. The wind speed prediction performance was improved by correcting the output of the NWP model using Kalman filtering technique [7]. However, physical methods are not able to handle short-term ranges and are computationally expensive and resource intensive. For predicting short-term wind speed, the statistical transport approach is better suitable, since historical data are needed to determine wind speed and adjusts the model’s parameters based on the discrepancy between actual and expected wind speed [8]. In addition, a lot of researchers have successfully predicted wind speeds using artificial intelligence techniques thanks to the quick development and broad usage of artificial intelligence computers. These techniques include FL, SVMs, and ANNs [9]. The following are some examples of artificial intelligence approaches in the literature. The accuracy of wind speed predictions was significantly increased using a unique hybrid approach based on seasonal exponential adjustment and BPNN [10]. ENN and the quadratic decomposition technique were used to create a novel prediction approach, which performed satisfactorily in multi-step wind speed predictions [11].

2 Introduction of Data Sources and Model We chose the Penglai wind farm’s ten minutes statistics on wind speed to examine the prediction ability of the newly suggested model, and each data set was separated into two groups: a training set and a test set. In particular, the first twenty days included 2880 data points used as the training sample for forecasting wind speed, and the weight values for each model were derived using the most recent five days of data from the training set. The following three days are dedicated to the test sample, which

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Table 1 Experimental sample statistical indicators Samples

Numbers

Indicator (m/s) Max

Min

Mean

Std

All

3312

20.1

0.9

7.3136

3.4331

Training samples

2880

19.1

1.1

7.0964

3.3138

Testing samples

432

16.8

0.9

8.3993

3.8501

contains 432 data points. Table 1 shows several statistical indicators for sampling wind speed data, such as minimum, maximum, mean, and standard deviation.

2.1 CEEMD The EEMD and CEEMD decomposition techniques address the problem of modal aliasing in the decomposed signal of the EMD method by reducing the modal aliasing of the EMD decomposition by adding pairs of positive and negative Gaussian white noise to the signal to be decomposed. However, the eigen mode component of the decomposed signal is always left with a certain amount of white noise, which affects subsequent signal processing and analysis. Complete Ensemble Empirical Modal Decomposition (CEEMD), often referred to as Complete EEMD with Adaptive Noise (CEEMDAN), was developed by TORRES et al. as a solution to these issues. The CEEMDAN decomposition solves the above problem in two aspects: (i) Instead of just adding the Gaussian white noise signal into the original signal, the IMF components with the auxiliary noise after the EMD decomposition are included; (ii) Unlike the CEEMDAN decomposition, which performs this after the first-order IMF components are gathered, the EEMD decomposition and CEEMD decomposition execute the overall average of the modal components created after the empirical modal decomposition. The issue of white noise transfer from high to low frequency is effectively solved by employing the CEEMDAN decomposition, which executes the overall averaging calculation after the first-order IMF component and then repeats the procedure for the residual portion.

2.2 TCN A causal, dilated 1D convolutional network of the same input and output lengths is named a Temporal Convolutional Network (TCN). It combines the capability of extracting features at low parametric number of convolutions with the ability of modeling in the time domain.

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Fig. 1 Flow chart of GWO algorithm

It has the following advantages when performing modeling: (i) Parallelism. When given a sentence, TCN can process the sentence in parallel instead of sequentially like RNN; (ii) flexible perceptual field. the size of TCN’s perceptual field is determined by the number of layers, convolutional kernel size, and dilation coefficient, which can be flexibly tailored to different characteristics of different tasks; (iii) stable gradient. the TCN is less likely to have gradient disappearance and explosion problems; (iv) lower memory. the RNN uses The RNN needs to save the information of each step, which will occupy a lot of memory, while the TCN has shared convolutional kernels inside a layer, which is much lower memory usage.

2.3 GWO The gray wolf optimization algorithm (GWO) is inspired by the predatory behavior of gray wolves and is a group intelligence heuristic. GWO was proposed by Mirjalili in 2014. The predatory character of gray wolf populations in nature served as the model for GWO. It has strong convergence performance, few parameters and easy to implement. Despite the similarity to other group intelligence algorithms for finding the best, the algorithm has a simple mathematical model and is adapted to complex problems in different domains. Figure 1 shows the flow chart of GWO algorithm.

3 Empirical Analysis of Wind Speed Forecasting The prediction efficacy of various models has been assessed using a variety of performance indicators. However, there is no common benchmark for evaluating prediction

Optimization Study of Wind Speed Prediction Based on Combined … Table 2 The four error indicators

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Measurement index

Definitions and interpretation

MAE

The average absolute inaccuracy of N predictions

RMSE

The square root of the average of error squares

MAPE

The average of N absolute percentage error

SSE

The square root of the error

model inaccuracy. As a result, many error measures are employed in this work. To avoid both positive and negative prediction errors from canceling out, the average magnitude between predicted and actual values is determined using mean absolute error (MAE) and root mean square error (RMSE). The most generally used statistic to indicate the validity and dependability of the suggested new model is mean absolute percent error (MAPE), which is a measure of absolute error average. SSE, on the other hand, is used to represent the model’s overall prediction error. The performance of the model improves as the exponent value for these four criteria decreases. Table 2 shows the definition and equation of four error indicators. In this section, we develop two distinct experiments to assess the suggested combinatorial model’s predictive capability. Experiment 1: Evaluation against several CEEMDAN models based on denoising. With the assistance of the other four CEEMDAN denoising-based models, this experiment will test the suggested innovative combination model’s effectiveness. Table 3 displays the particular forecast findings. The suggested model has the best SSE, MAPE, RMSE and MAE, and with 36.6700, 2.92 percent, 0.2564 and 0.1903, in the one-step forecast, respectively. Next, with regard to which the MAPE values were 3.17 percent, 5.37 percent, 5.58 percent, and 10.27 percent, the models with the greatest to lowest prediction accuracy are CEEMDAN-BP, CEEMDAN-ENN, CEEMDAN-LSTM, and CEEMDAN-TCN. The combined CEEMDAN-GWO-TCN model, having a MAPE value of 3.45%, provides the greatest prediction performance outcomes when the model predicts in two phases. The produced model which has a MAPE value of 4.24 percent according to the three-step projection, nonetheless, of the four CEEMDAN-based models, has the highest prediction accuracy. Among the three-step forward forecasting models, the combined CEEMDAN-GWO-TCN model continues to provide the most precise and effective forecasts. As show in Fig. 2, it is a comparison of the predicted performance of step one, step two, and step three in this experiment. Experiment 2: Model testing using various data pre-processing techniques. This experiment compares the integrated CEEMDAN-GWO-TCN model with models employing various data pretreatment techniques to see how well the wind speed time series performs as a forecasting tool. Table 4 provides the comparative outcomes. We may infer the following facts from this table.

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Table 3 Comparison between the combined model’s ability to predict outcomes and other CEEMDAN denoising-based models Model

CEEMDAN-BP

Sum of squares

Mean absolute percentage error (%)

One-step

Two-step

Three-step

44.2743

47.2522

75.6808

One-step 3.17

Two-step 3.90

Three-step 5.42

CEEMDAN-ENN

99.4013

107.0033

147.2832

5.37

6.05

7.35

CEEMDAN-LSTM

99.6408

137.9602

229.1793

5.58

7.46

10.27

277.2242

295.2666

313.4750

10.27

10.82

11.38

36.6700

44.9290

64.1497

2.92

3.45

4.24

CEEMDAN-TCN Proposed model Model

Root mean square error One-step

Two-step

Three-step

One-step

Two-step

Three-step

CEEMDAN-BP

0.3201

0.3307

0.4184

0.2371

0.2502

0.3221

CEEMDAN-ENN

0.4775

0.4959

0.5798

0.3588

0.3741

0.4369

CEEMDAN-LSTM

0.4694

0.5563

0.7169

0.3598

0.4343

0.5766

CEEMDAN-TCN

0.8011

0.8267

0.8518

0.6217

0.6430

0.6654

Proposed model

0.2564

0.3224

0.3849

0.1903

0.2401

0.2886

Mean absolute error

The suggested combined CEEMDAN-GWO-TCN model provides the maximum accuracy for the three-step wind speed forecast. With MAPE values ranging from 3.78 to 5.09% from one-step to three-step predictions, the model preprocessed using ICEEMDAN comes in second place of the other three methods for processing data. Additionally, the values for MAPE in EMD fell by 0.68 percent, 0.96 percent, and 0.61 percent, respectively, compared to SSA for the two model’s pretreatment with EMD and SSA.

4 Conclusion Wind energy is a key component of low-carbon energy technologies and has sparked a lot of attention in the scientific community. However, the development of wind power generation is severely constrained by the intermittent nature and constant variation of wind speed series. As a result, to improve the operational efficiency and financial success of wind power production systems, accurate wind speed forecasting is essential. A new combination model built on data preparation technologies., prediction algorithm and optimization algorithm are presented in this research, which introduces a new practical alternative for wind speed prediction and smart grid planning and efficiently makes use of the benefits of a single prediction model to improve and optimize wind speed prediction..

Optimization Study of Wind Speed Prediction Based on Combined …

Fig. 2 Comparison of multi-step prediction results

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Table 4 Comparison between the combined model’s ability to predict outcomes and other data pre-processing methods Model

Sum of squares

EMD-GWO-TCN

97.5358

Mean absolute percentage error (%)

One-step Two-step Three-step One-step Two-step Three-step ICEEMDAN-GWO-TCN 53.6707

105.4688 132.0605 61.3601

5.14

5.34

6.28

78.1900

3.78

4.51

5.09

104.9523 121.0303

5.82

6.38

6.89

3.02

3.57

4.43

SSA-GWO-TCN

96.9230

Proposed model

38.7300

Model

Root mean square error

44.9290

64.1497

Mean absolute error

One-step Two-step Three-step One-step Two-step Three-step 0.494

0.5517

0.3643

0.3780

0.4197

ICEEMDAN-GWO-TCN 0.3523

0.3761

0.4234

0.2677

0.2855

0.3232

SSA-GWO-TCN

0.4736

0.4928

0.5289

0.3812

0.3939

0.4187

Proposed model

0.2994

0.3224

0.3849

0.2201

0.2401

0.2886

EMD-GWO-TCN

0.4751

Acknowledgements This work was supported by DUFE202126, L20BTJ003 and SF-Y202113.

References 1. Abbasi T, Abbasi SA (2012) Is the use of renewable energy sources an answer to the problems of global warming and pollution. Crit Rev Environ Sci Technol 42(2):99–154 2. Bonnet C, Hache E, Seck GS et al (2019) Who’s winning the low-carbon innovation race? An assessment of countries’ leadership in renewable energy technologies. Int Econ 160:31–42 3. Alkhalidi MA, Al-Dabbous SK, Neelamani S et al (2019) Wind energy potential at coastal and offshore locations in the state of Kuwait. Renew Energy 135:529–539 4. Yeter B, Garbatov Y (2022) Structural integrity assessment of fixed support structures for offshore wind turbines: a review. Ocean Eng 244:110271 5. Yeom J-M et al (2020) Exploring solar and wind energy resources in North Korea with COMS MI geostationary satellite data coupled with numerical weather prediction reanalysis variables. Renew Sustain Energy Rev 119:109570 6. Dupré A et al (2020) Sub-hourly forecasting of wind speed and wind energy. Renew Energy 145:2373–2379 7. Baró Pérez A et al (2020) Kalman Filter bank post-processor methodology for the weather research and forecasting model wind speed grid model output correction. Int J Sustain Energy 38(6):511–525 8. Sharma R, Singh D (2018) A review of wind power and wind speed forecasting. J Eng Res Appl 8(7):1–9 9. Brahimi T (2019) Using artificial intelligence to predict wind speed for energy application in Saudi Arabia. Energy 12(24):4669 10. Emeksiz C, Tan M (2022) Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach. Energy 238:121764 11. Hannah Jessie Rani R, Aruldoss Albert Victoire T (2019) A hybrid Elman recurrent neural network, group search optimization, and refined VMD-based framework for multi-step ahead electricity price forecasting. Soft Comput 23(18):8413–8434

Optimal Decision Tree Algorithm in Sports Video Tracking Technology Mingxia Han

Abstract For the current social development, human motion analysis is a very important direction in the field of virtual reality in recent years. The prospect of its future technological development will be directly proportional to the degree of social importance. It will have its presence in almost all important fields of life, including intelligent traffic monitoring, sports analysis, animation generation and so on. Sports video behavior sequence tracking, detection and recognition technology is one of the main research technologies in the research. One of the purposes is to automatically track and detect the behavior sequence from at least one arbitrary group of video image sequence information containing multi person behavior information, and accurately and quickly identify and analyze the behavior. Among them, motion detection and trajectory tracking belong to high-level vision problems. Based on the optimized decision tree algorithm, this paper makes relevant research on sports video tracking technology, analyzes the background significance, queries the relevant technology, constructs the experimental model, and finally obtains the feasibility of the results through the experiment. Keywords Decision Tree algorithm · Video tracking · Video image processing · Sports modeling

1 Introduction The real-time dynamic tracking and capture algorithm of moving vision target is one of the most key and core topics applied in the field of contemporary moving computer vision. Its main technology and core idea refers to the comprehensive use of modern image technology means such as computer real-time image information processing software and digital video image acquisition, analysis and processing M. Han (B) Shandong Transport Vocational College, Weifang, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_3

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system technology accurately track and capture moving real-time visual moving targets. With the increasing demand for scientific research, technical training and educational technology of athletes and the whole modern social sports workers, how can we conveniently, effectively, scientifically, quickly, timely and accurately understand and master the current Chinese athletes’ own parameters, and provide various technologies and help to solve the problems of scientific research and training of their own daily sports in the future, This will increasingly become an urgent task for the majority of amateur athletes and sports science and technology workers in China [1, 2]. The re playing task of sports video moving target detection and tracking system is to track the selected target in real time, and feed back its tracking data and trajectory data in real time, so as to analyze the target according to these data. In order to make the analysis results accurate and perfect, the sports video eye scanning detection and tracking system must have strong robustness and real-time performance. It should also be noted that if we need to prepare the analysis results of tracking target scanning, we must rely on a large amount of analysis data. However, a large amount of analysis data requires high computing power, and complex computing will affect the operation efficiency of the system. This problem has been perplexing scholars. Similarly, this problem has not been reasonably solved, resulting in a bottleneck in the research of video tracking. By integrating the application of the dynamic detection and recognition of the target points of the motion data and the motion tracking analysis system into the sports video monitoring, the system will finally be able to collect and monitor the relevant motion state information data of the human body, process and analyze the algorithm of motion related data analysis, and directly obtain the real-time motion information parameters accurate to the target human body. The successful acquisition of these data has an extremely important and positive research practical significance for improving the quality level of professional sports skills of coaches and athletes mobilized and guided by the language committee and further expanding the research direction of professional development of professional athletes’ skills. Based on these background, this paper studies the sports video tracking technology [3, 4]. The research method selected in this paper is mainly aimed at the development of video-based human motion image tracking research technology. The main work contents are as follows: firstly, it mainly introduces the research and development background and application field characteristics of video human motion image tracking data analysis technology at home and abroad, as well as the application development and trend of video human motion tracking analysis technology in the future, This paper makes a technical comparison of the common image tracking data algorithms, and expounds the methods and specific reasons for the selection in this paper. The human body is modeled based on the algorithm, and then the experimental scheme is determined, tested and analyzed. Finally, it summarizes and puts forward the next work direction and goal [5, 6].

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2 Research and Discussion of Optimized Decision Tree Algorithm in Sports Video Tracking Technology 2.1 Video Image Preprocessing Image preprocessing and moving target detection and extraction are the preparatory work and primary task in the process of target tracking. For a given image sequence, we need to remove the interference irrelevant to tracking, such as background, and detect and extract the moving target. The quality of target segmentation affects the subsequent tracking effect, and the speed of target segmentation affects the realtime performance of tracking. This chapter will describe the detection and extraction of moving targets from two aspects. Firstly, the process of Gaussian smoothing detection and denoising and noise analysis is studied for all tracked video image targets; Secondly, it introduces the most mature and popular detection technology and new data extraction and processing methods for the following video image target smoothing, which are widely used in the current image industry: inter frame difference method, optical flow method, background difference method and so on; Finally, two methods of target image detection and extraction that can be used in this paper are proposed: skin color region detection and extraction method, which binarizes the moving video image. The skin color region of non target moving image is 0 and skin color region is 1. The target in moving video image is extracted, and morphological method can also be considered to corrode or expand the target video image, So as to reduce the hole phenomenon in the segmented moving target area.

2.1.1

Image Enhancement

The research purpose of image information enhancement methods is generally to further enhance the visual information of various parts of the image structure that users are particularly interested in, such as edge, contour, contrast change, etc., and expand the subtle differences between the feature parts of objects at different levels in the image system, It provides a good mathematical basis for the analysis and extraction of advanced image information features and the research, application and popularization of many other new technology achievements of image analysis. The second important technical problem of image signal enhancement method is how to improve the image details, the degree of light and shade coordination of image information and how to sharpen the edge of image structure. Gray transformation algorithm is another important research method for image gray enhancement analysis. Its ultimate main purpose is to directly change the total gray value between a whole ten gray images or only change the local gray in some blank areas of the image with a more unified and reliable numerical method, so as to increase the visual contrast and strengthen the image interpretation and the effect of color recognition, Thus, various details in the image can be seen more easily.

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Filtering Processing

Image noise filtering algorithm is the most common and commonly used signal analysis and processing filtering method as an algorithm to eliminate all image noise. The main types of image noise commonly used to remove images are salt and pepper noise, impulse noise, Gaussian image noise filter and so on. In order to basically eliminate almost all these main image noise or fully highlight some important information features hidden in these image information or displayed in addition, it is necessary for the computer to smooth, filter or sharpen all these main image signal features at the same time. Various filter methods commonly used in filtering processing generally design three filtering signal processing methods: median filtering, mean filtering and Gaussian filtering [7, 8].

2.2 Common Algorithms of Decision Tree 2.2.1

CLS Algorithm

CLS algorithm is used to study the most primitive algorithm in a decision tree algorithm. It is used for us to study the most primitive basic algorithm in a decision tree algorithm. Therefore, it must be very helpful for us to study a CLS algorithm in any decision tree algorithm. One of the algorithm instance optimization training is also a very basic algorithm optimization training. An idea and method should be yes. After establishing the decision tree node of one of the empty algorithms, gradually add and establish some decision tree nodes of new algorithms, and then gradually improve the decision trees of the original algorithms, It is not until the end that the decision tree nodes of those new algorithms that should have been added and established can come more correctly and effectively. After the optimization training, the established algorithm instances are reclassified, and the tree building is over. From a theoretical point of view, the process described by the so-called abstraction and construction in the decision tree model itself is actually its own process. In fact, it is a hypothetical and modeling process, and it is a process of abstraction and construction, Therefore, the CLS algorithm itself is actually an abstract algorithm based on the model description of deep machine learning, which can be seen even more comprehensively. In fact, it is an abstract algorithm that describes one or only one operation and symbol process. The hypothesis of a current model is modeled by adding a decision node of the hypothesis of a new model under the condition of a decision node with the hypothesis of a new model. Then, the algorithm recursively and repeatedly calls this operator and acts on each leaf node of the tree in turn, thus finally successfully constructing a decision tree [9, 10].

Optimal Decision Tree Algorithm in Sports Video Tracking Technology

2.2.2

21

C4.5 Algorithm

C4.5 is a set of decision tree algorithm with ID3 algorithm as the main core idea. It is an upgraded version of ID3 algorithm. It will be further separated in the following. It will gradually establish the spanning tree stage and pruning tree stage of the decision tree through the two most important steps introduced below. Various practical use experiences in previous tests have also shown that the use of information gain rate function will become more flexible and robust than using only one information gain function, and it is easier and more stable to complete the test of a better branch function. C4.5 function is a branch function used to use one information gain rate function as another default branch function for test analysis and effect evaluation. The following is a formula for the information gain rate function: split (S, A) = −

m  |Si | i=1

|S|

log2

|Si | |S|

(1)

Represents the ith sample subset, s represents the training sample set, and a represents the attribute. The formula for calculating the gain ratio is: gain_ratio(S, A) =

gain(S, A) split (S, A)

(2)

Gain (s, a) indicates information gain.

2.3 Summary of Decision Tree Algorithm Since the earliest decision tree classification algorithm CLS algorithm was proposed, there have been dozens of classification algorithms. These classification algorithms have their own advantages and disadvantages in classification accuracy, model creation speed and complexity, robustness, decision tree simplicity and scalability. In order to further greatly reduce the scale of the calculation variables of the decision tree itself, and also consider making the mathematical structure of the strategy tree algorithm as clear and concise as possible, researchers recently proposed and studied another strategy algorithm that needs to be improved and studied, such as cart algorithm, which takes the minimum lokini index as the selection attribute and test attribute of the strategy. The data structure used in cart algorithm is a binary tree and a recursive split tree technology. The generated decision tree is also a binary tree, and the resulting decision tree is easier to understand. In order to remove the noise data in the training set and the problem of over fitting caused by it, a pruning strategy is proposed [11, 12].

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3 Experiments 3.1 Workflow In order to well verify the overall effectiveness of the algorithm application in this paper, we need to build such an overall framework according to the Bayesian theoretical framework and take the algorithm application into account. The algorithm process of human motion trajectory tracking and analysis based on video is also a process of establishing the three-dimensional human model and its mapping relationship model with human feature image information. In this paper, the particle filter algorithm is proposed to gradually realize the random process of tracking, and the particle filter model is used to estimate and track the time distribution. All the sample information on the current prior time distribution is randomly propagated on the time point according to the random motion model or random noise model, and the description of each sample information on each posterior distribution is obtained, Then, according to the estimated weight of the likelihood function model, the current estimated tracking distribution result is obtained for each random a posteriori time sample information step by step. In order to make each sample distribution in the tracking candidate time distribution more consistent with the real random distribution, we need to optimize according to the likelihood observation model in the stage of a priori random distribution, so as to obtain the real a priori distribution of correctable values, Because the statistical method of random analysis has no certainty and directionality, in the resampling stage, the gradient samples from the gradient samples in the a priori distribution are not generated by adding random noise directly, but from the gradient samples in the a posteriori distribution. The specific operation process is shown in Fig. 1. According to the overall flow chart of the system, the first step is to build the motion database, and then to build the model and model. After modeling, the model is optimized. Based on 3D motion database‚ at the same time, it combines human posture reconstruction and refinement technology‚ the 3D continuous motion extraction method based on the spatio-temporal model of 3D motion library can be realized.

4 Discussion Part of the data sets obtained from the experimental simulation are used as the experimental data for simulation, and the algorithms proposed in this paper are compared to obtain the data table shown in Table 1. As shown in Fig. 2, the accuracy of CODT algorithm is higher than the other two. Because CODT algorithm eliminates the centralized noise and hybrid area of samples, CODT algorithm is ideal for other decision tree algorithms. Generally speaking, the performance of CODT algorithm is higher than other algorithms to meet the design requirements of this paper.

Optimal Decision Tree Algorithm in Sports Video Tracking Technology

Motion database

23

Sports video

3D pose

3D pose

spatial-temporal model

3D motion sequence

Fig. 1 Overall technical framework

Table 1 Accuracy comparison

Sampleset name

CODT (%)

C4.5 (%)

ID3 (%)

Breast-cancer

96.31

92.21

90.35

SPECT heart

92.92

72.52

52.36

Balance

67.03

64.94

38.25

Monks

64.56

57.12

50.35

Hayes

74.32

72.37

62.12

Nursery

97.46

97.03

90.31

5 Conclusion With the rapid popularity of digital video in the field of users’ daily life, the latest research content of its related disciplines will increasingly develop into another very fast and active frontier research and hot spot in the field of contemporary multimedia art and future computer vision. The wide application of digital video processing technology involves more and more disciplines ‚ Such as digital video editing and synthesis, video editing and segmentation, video coding and compression, video coding registration processing, network video monitoring and acquisition and network monitoring. This paper is aimed at the technical characteristics of sports

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M. Han 120.00% 100.00% 80.00% 60.00% 40.00% 20.00% 0.00%

CODT

C4.5

ID3

Fig. 2 Accuracy comparison

video technology‚ and the data accuracy in the application of moving target tracking technology‚ The efficiency problem and the time complexity of related algorithms are studied, and some meaningful application results are obtained.

References 1. Kamiyama T, Kameda Y, Ohta Y et al (2017) Improvement of badminton-player tracking applying image pixel compensation. ITE Trans Media Technol Appl 5(2):36–41 2. Athanasiadis PJ (2019) On the behavior of slackline webbings under dynamic loads and the simulation of leash falls. Proc Inst Mech Eng, Part P: J Sports Eng Technol 233(1):75–85 3. Williams D, Lee N (2018) Motion controllers, sound, and music in video games: state of the art and research perspectives. In: Emotion in video game sound tracking [International series on computer entertainment and media technology], Chap 8, pp 85–103. https://doi.org/10.1007/ 978-3-319-72272-6 4. Battistone F, Petrosino A, Santopietro V (2018) Watch out: embedded video tracking with BST for unmanned aerial vehicles. J Sig Process Syst Sig Image Video Technol 90(6):891–900 5. Chithra PL (2019) Exclusive transportation system with video tracking for avoiding conflicts. Int J Appl Eng Res 10(76):433–437 6. Beato M, Jamil M, Devereux G (2017) The Reliability of technical and tactical tagging analysis conducted by a semi-automatic video-tracking system (Digital stadium) in soccer. J Human Kinetics 62(1):103–110 7. Williams D, Lee N (2018) The impact of multichannel game audio on the quality and enjoyment of player experience. In: Emotion in video game soundtracking [International series on computer entertainment and media technology], Chap 11, pp 143–163. https://doi.org/10.1007/ 978-3-319-72272-6 8. Mulimani N, Makandar A (2021) Sports video annotation and multi-target tracking using extended Gaussian mixture model. Int J Recent Technol Eng 10(1):1–6 9. Billah T, Rahman S, Ahmad MO et al (2019) Recognizing distractions for assistive driving by tracking body parts. IEEE Trans Circuits Syst Video Technol 29(4):1048–1062 10. Phu V, Tran V, Chau V et al (2017) A decision tree using ID3 algorithm for English semantic analysis. Int J Speech Technol 20(3):1–21

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11. Datta S, Dev VA et al (2017) Hybrid genetic algorithm-decision tree approach for rate constant prediction using structures of reactants and solvent for Diels-Alder reaction. Comput Chem Eng 106:690–698 12. Ayinla SO, Akinola IB (2021) An improved collaborative pruning using ant colony optimization and pessimistic technique of C5.0 decision tree algorithm. Int J Comp Sci Inf Security 18(12):111–123

PLC Controller-Based Automatic Control System Design for Electric System Manipulator Hengchao Zhou, Yan Su, and Jinxia Li

Abstract With the rapid development of China’s economy, the demand for electricity is increasing, and the contradiction between supply and demand is becoming more and more serious, the safe and stable operation of the power system plays a vital role. In the production process, many machinery and equipment need to use high-voltage electrical facilities to complete the work, a large number of electrical equipment to meet the life and industrial production, and the power system control plays a role in regulating and protecting the role of electrical equipment, so the electrical automation control system is essential. With the development of society, the requirements for intelligence and humanization are getting higher and higher. In order to improve the efficiency of power supply, reduce labor intensity and save energy, it is required to use advanced technical means to control the operation design, so as to achieve the set control purposes, PLC control system is a kind of automation controller with high reliability, flexible control, easy to expand the function. This paper is based on the PLC controller control system design of the power system manipulator mainly introduces the electrical automation control technology, the basic function of PLC controller and its role in the power system control. Keywords PLC controller · Power systems · Manipulator control system

1 Introduction With the development of modern machinery manufacturing automation, the traditional sense of human operation has not been able to meet the production process of control requirements, so the use of PLC control systems to replace the manual

H. Zhou (B) · Y. Su · J. Li Industry College of Cold Chain Logistics and Supply Chain, Shandong Institute of Commerce and Technology, Jinan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_4

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realization of industrial automation, which is a development of contemporary significance. PLC control system has a wide range of applications in the industrial field, it can quickly and accurately operate the site, and it can achieve real-time control and remote monitoring, can adapt to the environment temperature, humidity and other complex working conditions under different conditions, it can both automatically start and stop command according to the system instructions, but also to complete the sequence function robot equipment with each other, so that the production process is more secure orderly, thus realizing the requirements of industrial automation. In practice, it can also carry out remote control, real-time monitoring, etc., according to the specific situation of the robot for intelligent control, so that the production process is more automated, improving labor efficiency. However, this kind of automatic line for the factory environment and workers’ technical level is not high precision demand, low cost, and this will cause waste of resources and labor intensity and other disadvantages. With the rapid development of modern science and technology and the speed of social productivity, the production process of the control requirements must be more intelligent and automated [1].

2 PLC Controller-Based Hardware Design of Automated Control System for Power System Manipulator The automation control system of the power system manipulator is composed of control unit, drive unit, transmission mechanism and motion controller. Under the control of PLC, it realizes the automatic sorting, lifting and handling of the robotic hand, and at the same time completes the precise access to the workpiece and the positioning function through the program, and realizes the operation process of the robotic hand to grasp the material by setting the size of the solenoid valve opening and the pressure size, uses frequency converter to adjust the speed to achieve the purpose of speed regulation, and speed change according to the work needs, so as to meet the needs for control accuracy and stability in the process of different operations [2].

2.1 Collector Design The collector is an important part of the industrial automatic control system, which mainly consists of a touch displacement sensor, a photoelectric measurement circuit and a power control unit. The signal acquisition circuit gets the switching quantity and outputs it to the PLC, which is then driven by the power control unit (such as inverter, speed controller, etc.). The PLC has the advantages of compactness, lightness and flexibility, and its output signal is sent to the A/D conversion part for processing after calculation and amplification, and the working state is set under the action conditions

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through the corresponding program, so as to realize the precise tracking function and positioning function of the robot grasping the object. In industrial production, a variety of sensing elements are used to collect various environmental information, data parameters and motion trajectories, and the collected data is transmitted to the PLC, which realizes real-time status information feedback control during the action of gripping object position, movement and handling of the robot. Using the collector to collect the internal data of the electric power system manipulator, the collector of the control system selects TI7629 produced by TI, and the chip selects SD8329 series chip, which has the functions of data acquisition, storage and control. Through the basic principle and structural characteristics of PLC to achieve automatic conversion between the set value of the robot system state parameters and the program design requirements and output the corresponding signal, so that it can correctly execute the command action to achieve the various operating conditions required in the automated production process. The structure of the collector is shown in Fig. 1. According to Fig. 1, it can be seen that when designing the voltage regulator circuit of the collector, the need of the collector to supply power to the processor, a large number of interfaces and peripherals is fully considered. At the same time, in order to improve the system efficiency, it adopts modular design in the hardware

Fig. 1 Collector structure

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circuit [3]. The control system mainly consists of robot, inverter and servo drive, etc. It realizes the information exchange and data interaction operation function between human–machine interface through software. It uses PID algorithm to operate and process the input parameters and calculate the best range compared with the set value, so as to achieve the purpose of adjusting the accuracy and can automatically complete the control task.

2.2 Microcontroller Design Microcontroller is the smallest system of CPU, which consists of I/O ports and timers. the CPU receives, decodes and outputs the corresponding signals to execute the corresponding operations when working on the instructions. In the industrial production process, the microcontroller often needs to process a lot of data for it, such as robots, sensors and other devices and system connectors, as well as some other peripheral circuits. At the same time, it is also used as a whole to control the operating status of machines and equipment, and the execution unit is responsible for completing various operational tasks or connecting with computer communication interface circuits to achieve data exchange in a system called microcontroller network system, which is a complete and complex micro-control device. The system’s microcontroller model is R7FOCO14, with high precision control function, it can realize the combination of PLC and robot, through the PLC control system for real-time monitoring of the industrial control machine [4, 5]. The peripheral circuit of this microcontroller uses an AC power supply with a voltage of 6 V and a current of 1.3 A. It can convert the 6 V voltage to 4.2 V through a voltage regulator chip and control it through a relay, which achieves fast and accurate adjustment of the voltage of the high-voltage grid, thus effectively improving the safety of power system equipment operation. This design uses PLC as the upper computer. PLC is used as the control device to complete the automation transformation and intelligent upgrade of the electrical hand control system, which has the characteristics of strong anti-interference ability and flexibility; using its powerful functions to make the operation more simple, convenient and reliable, using serial communication technology to connect the PC host with the industrial computer, so as to realize the human–computer interactive control interface, thus effectively improving the system efficiency and reducing the cost.

2.3 Microprocessor Design In the industrial control process, many control systems use PLC or digital circuits to perform various operations of the system. For example, when a robot needs to be handled, it can be connected to an external sensor through the I/O port output interface of a microcontroller. Microprocessor is composed of CPU, memory and

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input channels, etc., where the CPU is responsible for executing unit instructions and sending control signals to the corresponding processing units to make them act after receiving data back. The memory is used to store the logic operation results received by the controller in RAM, so that it is convenient to calculate the robot movement speed, while the output pulse is used by the CPU to carry out a series of operations on the robot according to the control program instructions, so as to realize the whole industrial production process [6]. After the microcontroller realizes the data control, the microprocessor is used to realize the data processing. The microprocessor of the control system belongs to the 16-core processor, which realizes the automation of the industrial control process and it can operate the system remotely, making it automatic and real-time. PLC is a digital computing device composed of programming instructions, it uses microcontroller (MCU) as the core driving element to run and perform the work, connecting the switching circuit with the corresponding signal processing program through relays, thus completing the functions required for various tasks and logical information transfer and transformation work process, which realizes the functions of the PLC control system. Microprocessor structure as shown in Fig. 2. Crystal oscillation circuits and reset circuits are standard peripherals of microprocessors to automate various complex operations in the production process of industrial manipulators. When designing the control circuit, we must consider how to use PLC and microcontroller so as to realize the control requirements of industrial control equipment, testing devices and other related systems by software to program and debug the program and hardware function modules and other tasks [7].

Fig. 2 Microprocessor

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2.4 Power Supply Circuit Design The main control power supply of the PLC is provided by a capacitive rechargeable battery with an internal DC voltage regulator module, it converts AC power into DC power, which is then output through the control input circuit after rectification and filtering. Generally, the system design is done with a microcontroller using thyristor as the switching element. When the conduction and disconnection trigger, the level is lifted, and when the full shutdown of the shutdown power supply is to make the load current to the maximum, and generate the loop current or inverter process reversal phenomenon to stop the power supply to maintain steady state, etc., and then through the control switch to achieve automation, the main control power supply output of the PLC is added to the industrial frequency capacitor, when the industrial frequency disappears and will produce a new round of working state. PLC in the actual application, mainly through its internal working process to complete the robot equipment and work force simulation, conversion and other operation and protection functions, at the same time it can also be real-time detection and control of the robot action, so as to achieve the requirements of its automation control system design in the actual production life [8]. In the automatic control system of electric system manipulator designed in this paper, since the manipulator needs to complete the telescopic or forward movement, it is necessary to use PLC to control the travel of the manipulator and use relays to switch it on and off. The system in this paper uses a 16 V battery with high current to supply power to it, and PLC is used to control the switch of the manipulator. By designing the relay and contactor, travel switch and other devices, the PID adjustment algorithm is used to realize the automatic operation of the electrical equipment. The system can improve labor productivity as well as reduce the probability of accidents caused by human factors in the process of electric power production, which not only realizes the automation of the electric power production process, but also provides a safer power supply environment for people.

2.5 PLC Controller Design PLC controller is the core of industrial manipulator, which is mainly used for automation control. PLC controller has the advantages of simple structure, high reliability and good anti-interference ability, etc. It can automatically select the switch state or program operation mode to complete the control task according to the environmental changes, and PLC controller has powerful self-diagnosis function, which can quickly and accurately detect the action parameters generated by the manipulator actuator and automatically control according to the feedback information. And the PLC controller has powerful self-diagnostic function, it can quickly and accurately detect the action parameters generated by the manipulator actuator, and automatically control according to the feedback information [9].

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3 PLC Controller-Based Automatic Control System Software Design for Electric Power System Manipulator In this paper, the PLC controller is used in the automatic control system of electric power system manipulator, and the precise adjustment of the movement speed and position of the manipulator is realized through the control of PLC. Meanwhile, the STC89S52 microcontroller is used as the main controller in hardware. The software part is mainly composed of configuration king CC1602 as the core unit circuit and D/A converter, using relay analog stepping motor to drive the load to run, so as to achieve the purpose of fast start, stop and forward and reverse rotation and speed regulation, etc. The PLC is programmed to control the way to realize the linkage between the devices in the robotic automatic control system. The selection of PLC controller model selected in this paper needs to consider the number of control system input and output points, input points in more than 10, output points greater than 8, memory bytes greater than 130, according to the performance and cost of the controller, the design of the PLC controller to meet the following requirements: it can automatically identify the input points, storage and implementation of the operation; it has a high reliability it can ensure that the system uninterrupted operation during data transmission. The results are shown in Fig. 3.

4 Experimental Study In order to verify the effectiveness of the PLC controller-based automated control system for electric power system manipulator designed in this paper, we designed the PLC as the controller, programmable logic controller as the main body, and use industrial robot as the control core of electric power system manipulator control system, using PID algorithm for the speed regulation of the work-in motor. The system in this paper is experimentally compared with the traditional system, and the conclusion from the analysis and comparison is that the use of PLC as the controller and the inclusion of the robot in the system makes it highly reliable. And it can realize the intelligent automatic control of the controlled quantity of electricity. Set the experimental parameters as shown in Table 1. According to the parameters in Table 1, the comparison experiments were conducted and the results of the control time consumption experiments are shown in Table 2. According to Table 2, it can be seen that the control time consuming time of the control system proposed in this paper is much less than that of the traditional control system, and the control system can achieve better control in shorter time. The system is designed mainly according to the control system control requirements, and the PLC controller is used to realize the automatic programming of the manipulator [10].

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Fig. 3 Flow of the automated control system

Table 1 Experimental parameters

Item

Parameter

Operating voltage/V

120

Operating current/A

80

Operating system

Windows 10

Operating frequency/Hz

150

Operation language

C++

5 Conclusion In summary, in today’s industrial life, there are many jobs that rely on robots to complete. As society continues to progress and develop and automation technology becomes more and more sophisticated, the traditional use of human operators can result in a lot of wasted labor. Therefore, in order to improve this situation, it is necessary to design a simple, efficient and low-cost industrial robot that can be automatically controlled and intelligent, so that it can be better applied to industrial production, and it should also be able to deal with the emergence of unexpected

PLC Controller-Based Automatic Control System Design for Electric … Table 2 Experimental results of control time consumption

35

Number of experiments

Control time/min Traditional control system

This paper control system

1

5.25

0.96

2

5.44

0.85

3

5.28

0.88

4

5.04

0.94

5

5.86

0.84

6

5.09

0.98

7

5.43

0.87

8

5.21

0.81

9

5.69

0.83

10

5.84

0.89

situations in the production process in a timely manner, and to reduce losses to a certain extent, so that the enterprise can continue to develop steadily.

References 1. Lysek K, Gwiazda A, Herbu´s K (2019) Communication between cad systems and the PLC controller. Int J Modern Manuf Technol:11–13 2. Hulewicz A, Krawiecki Z, Dziarski K (2019) Distributed control system DCS using a PLC controller. ITM Web Conf:26–28 3. Chen L, Xiaoyue X, Zhe L et al (2021) Design and test of a multifunctional mobile manipulator control system on an experimental platform. J Phys: Conf Ser 1:1871 4. Tyutikov VV, Krasilnikyants EV, Varkov AA (2019) Robot manipulator control system with dynamic moment compensation. Autom Remote Control 1:78–80 5. Yu M, Liu GW, Kong B (2014) The design of spraying manipulator control system in mines. Appl Mech Mater:602–605 6. Krasilnikyants EV, Varkov AA, Tyutikov VV (2013) Robot manipulator control system. Autom Remote Control:74–76 7. Ru-xiong L (2011) Design and realization of 3-DOF welding manipulator control system based on motion controller. In: Proceedings of 2011 2nd international conference on advances in energy engineering (ICAEE), pp 944–949 8. Lu Y, Jiang Z, Chen C et al (2021) Energy efficiency optimization of field-oriented control for PMSM in all electric system. Sustain Energy Technol Assess:45–48 9. Neto JEA, Castro CA (2021) Optimal maintenance scheduling of transmission assets in the Brazilian electric system. J Control Autom Electr Syst 2:33–35 10. Thayumanavan P, Kaliyaperumal D, Subramaniam U et al (2020) Combined harmonic reduction and DC voltage regulation of a single DC source five-level multilevel inverter for wind electric system. Electronics 6:9–13

Development of Object Identification APP Based on YoloV2 Baiming Zhao, Nan Xie, Junxiao Ge, and Weimin Chen

Abstract As people pay more and more attention to education, many people begin to teach children to learn from infants. However, parents are busy with their work and can’t spare time to give consideration to children’s education, even in terms of language, text and image learning. This paper has developed mobile phone application software that can identify items, and the APP uses YoloV2 as the model for realizing the specific functions of the application software, and the packaging and UI design of the application software are implemented using the kivy library in Python. Kivy library can access files and cameras of Android devices, which is helpful to the realization of application software functions. After learning, designing and implementing, we successfully integrated the functions into the framework of kivy, and finally successfully produced an application that can identify objects by selecting local files or calling cameras. The APP uses YoloV2 model for learning, and uses the completed data set to identify objects. Finally, it has developed an application with the function of item identification, which has certain use value, and can continue to add or develop new functions in preschool education, bringing better application prospects and reference value. Keywords Object identification APP · YoloV2 · Kivy · Python

1 Introduction With the rapid development of modern society, people pay more and more attention to education. Many people begin to learn from infants and young children. But B. Zhao · N. Xie (B) · J. Ge College of Information Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou, Zhejiang, China e-mail: [email protected] W. Chen College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_5

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many parents are too busy with their work to give consideration to their children’s education. Not to mention the education of ordinary subjects, even the parents of language, character and image learning cannot find time to give consideration to both. At present, there are mainly two problems in the field of object recognition and target detection. One is that the fusion algorithm based on target detection ignores the correlation between features at all levels and directly fuses; second, in the landing application of the target detection system, targets in low light and dark scenes cannot be detected. However, the target detection algorithm has made an important breakthrough. There are two widely used algorithms, namely, the CNN algorithm with excellent accuracy, based on Region Proposal and two stage, and the Yolo and SSD algorithms with faster detection speed that only use convolutional neural networks and one stage [1–3]. This paper will focus on Yolo model. YoloV2 can prevent the model from over fitting and improve the speed of model convergence. In addition, YoloV2 also supports a high-resolution pre training classification network. Compared with the previous version, it can input 448 × 448 resolution, so as to improve the detection capability of the model. In practical applications, such as medicine, Yolo model can automatically identify patients’ conditions, with higher efficiency and accuracy. In addition, the Yolo model can also be used to detect the wearing of masks in real time when the epidemic is not over [4]. Based on the Yolov2 model, this paper will train an excellent target detection framework using Yolov2. At the same time, it will optimize the accuracy and efficiency of the model to make it more excellent. It will realize an app that can identify items on the Android system [12]. The object recognition function enables children to recognize and learn common items and help them learn basic items, it is an application with certain market development potential.

2 Data Acquisition and Annotation 2.1 Data Collection Whether the training of AI model can be successful is closely related to the setting of some parameters of the model, as well as the quantity and quality of data. The system uses COCO2014 dataset, Android phones and networks to collect and sort data, and finally obtains results [4–6]. (1) Data label development According to the plan, 80 labels were developed to correspond to COCO2014 related data. They are common animals and objects. Partial data labels are shown in Table 1. (2) Collection of specific pictures

Development of Object Identification APP Based on YoloV2 Table 1 Partial data labels

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Person

Bicycle

Car

Motorbike

Airplane

Bus

Train

Truck

Boat

Traffic light

Fire hydrant

Stop sign

Parking meter

Bench

Bird

Cat

Dog

Horse

Sheep

Cow

Elephant

Bear

Zebra

Giraffe

COCO dataset is a large and rich object detection, separation and caption dataset. The APP has selected the COCO2014 dataset as the specific learning dataset. In addition to the COCO data set, we also collected some pictures through the Internet. They are all 80 tagged items. In addition, there are a few photos taken by ourselves.

2.2 Data Annotation In the acquired COCO2014 dataset, the train2014 folder contains all the pictures required for training, and the label folder contains the coordinates of the corresponding items of all the pictures. In short, each picture has a corresponding txt file to mark the contents of the items in the picture, including the name and specific location of the items. Through these, the program can obtain better learning effect and save time with fewer samples.

2.3 COCO Dataset Screening The data set provides 80 categories, and the COCO data set train 2014 contains 82,783 images. I randomly selected some of them as the data required for training, and also selected the corresponding txt file to participate in learning. The rest of the pictures will be selected to participate in the test.

3 Training of Data Model 3.1 Selection of Model Parameters The official sample uses Tesla V100 calculation card to train the model. Considering the limited hardware configuration, some parameters of the model need to be modified, and considering the limited practical computing capacity of mobile terminal detection devices, the models used need to be compared and screened [5–8].

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Table 2 Training parameters to be modified Variable name

Modified value

Modification description

– Weights

The first weight training is ‘ ’, and the subsequent training is set as the previous weights file

The weight file needs to be initialized for the first training, and then further trained based on the previous results

– Cfg

Files modified based on YoloV2.yaml

Need to change the number of types to 19

– Data

Data Training picture folder

Develop the directory of training data

– Epochs

600

Once training rounds is 600

– Batch-size

16

The larger the better, set to 16 due to hardware limitations

– Img-size

640

Null

– Workers

6

The recommended setting is the same as the number of CPU cores

Through the study of official code, it is found that in the main function of the .py file, some parameters and corresponding comments are provided officially. Select some parameters that need to be modified, and set parameters for each variable according to the actual situation, as summarized in Table 2.

3.2 Training of YoloV2 Model The training of YoloV2 model can be divided into three stages. This training will select about 20,000 pictures [9–12]. In the first stage, the Darknet-19 network will be pre trained on the ImageNet classified dataset. During training, the input of the model is 224 × 224, a total of 160 cycles of training. This is for the second training input 448 × 448 Prepare. After the training, the second stage is the classification model, and the input is adjusted to 448 × 448, and then continues training. According to the classification, the accuracy of each classification is also different. In the last stage, the classification model will be modified to the detection model, and the specific process is to use three 3 × 3 × 2014 convolution layer replaces the last convolution layer, global vanpooling layer and soft max layer, and then adds a pass through layer. Finally, just use 1 × 1, and then fine tune the network on the detection data set [5–8, 10]. Part of the training process is shown in Fig. 1, including the number of iterations, total loss, average loss, learning rate, time spent, number of pictures and other information. The parameters used in training are selected, and a model that can identify most of the targets is obtained through training, and its performance is relatively stable.

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Fig. 1 YoloV2 model training process

4 UI Design and Implementation In order to collect functions and make it easier for users to use various functions, UI design and implementation are essential. UI can improve the interaction between the system and users, so as to obtain a better use experience. Because the kivy library does not support Chinese very well, English will be used in the UI.

4.1 Pre-design of Modules and Layout Considering the functions required by the program, I added 4 buttons, which are placed in different positions according to the functions. The background image is in the middle of the UI and is expected to occupy most of the position. The system page will be divided into three parts according to the design, the top view result function button, the middle welcome image and the lower get image and detection button. Functions of the button are been described as following. (1) Check result: view the results after the test is completed. (2) Load file: Read the picture from the specified position. (3) Camera: Call the camera function of Android device to take photos and save files in the specified format (4) Detection: The object identification function uses the read image as the object to identify the item and save the results

4.2 Kivy Implementation UI As a new library in recent years, the Kivy library still has various problems. During the design process, I encountered problems such as failure to obtain file paths and failure to call functions. But it also has many advantages. For example, if two buttons of the same height are not added to the horizontal layout, it will automatically split the space into two halves for the button. This space sharing is convenient when designing UI.

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Fig. 2 UI design

When implementing the function of obtaining files, I used the file selector component that comes with Kivy at the beginning. Its advantage is that it is very close to the file selection UI of Android devices, but its disadvantage is that it cannot assign the path value to other variables. In addition, when outputting a specific file path, the path will have its own parentheses. For various reasons, tkinder is used to call the system’s own file manager for file selection. When the virtual machine is not used for testing on the PC side, the file resource manager of Windows system will be called, while the file resource manager of Android system will be called. After clicking the check result button, a pop-up window will open to display the test results. The current UI design effect is shown in Fig. 2.

5 Function Design and Implementation 5.1 Pre Logic Design of Item Identification Function After designing the UI, the next step is to design the logical structure between functions. For example, before check result, it is necessary to ensure that there is an image file that has been identified, that is, it is necessary to ensure that the item identification has been performed once (once detection has been clicked) [4–6, 9]. Before item identification, there must be a picture to be identified in the system cache, that is, a load file or a photo taken with a camera is required. Next, we will describe the call relationship between the functions in order. According to their relationship, they can be divided into three pages: main page, file selector page and camera page.

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The first design is load file and camera. After loading files, you need to pop out a window and call the file resource manager of the device system, where you need to implement the load function and cancel function. In camera, you need to pop up a window and call the camera function of the device in it. After the file is successfully imported into the cache, detection is responsible for realizing the specific object identification function. The realization of the item identification function will be explained in the following 5.3. After item identification is completed, the function of check result button can realize the function of viewing results. This function will also be reflected in the form of pop-up windows. After the window pops up, kivy’s Image module will be called to display the detected image.

5.2 Design of Functional Functions In the Main.py file, you need buttons and pictures designed in the “.kv” file in the system, and many functions need to be designed to call various functions. Each page has its own corresponding function. (1) Home page design. Click the load button to call the function root show_load, show_the function of the load function is to jump to the file selector page. Click the camera button to call the function root Capture, the function is to jump to the camera page. Click the detection button to call the main function to realize the item identification function. If there is no image in the cache, a null value will be returned and an error will be reported. Click the show result button to trigger the pop-up event, and the new pop-up will automatically execute show_Result function to display the result image. (2) File selector page. After jumping to the file selector page, the main function will be called to realize the function of the file selector. The load and cancel buttons correspond to the load and cancel functions, respectively. Their functions are to read files into the cache and close the pop-up window. (3) Camera page. After jumping to the camera page, the main function will be called to call the system camera, and the Capture function will realize the photo saving function of the photographing button.

5.3 Realization of Object Identification Function After training, the files needed for identification will be placed in the root directory respectively. 80 data sets are placed in Yolo2 in order_Data folder in the txt file, the data file required for identification will be placed in Yolo2_under the model folder. There are checkpoint, index and Meta files of Yolo2, and the most important data 0–1 data file [8–10].

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After the image is read at the beginning, the size of the read image will be changed to 416 regardless of its original resolution 416 × 416. After that, the system will normalize the image and add the batch size dimension to the 0 dimension. After that, the picture will be input into the darknet19 network to get the feature map, and decoded to get the boundary box, confidence level, category probability, etc. Then the decoded regression bounding box will be filtered and drawn. After the item identification is completed, a window will pop up to prompt that the detection is complete. The final result will be output in .jpg format and stored in the root directory.

6 Conclusion In this paper, a large amount of data is obtained through on-site collection, script screening of data sets from existing data sets, and training with YoloV2 model. Finally, a model with high confidence and fast recognition speed is successfully obtained. Finally, through consulting a large number of materials, as well as learning about several existing projects and frameworks, the trained model was finally successfully encapsulated as an application. The test effect of this APP is good, which provides a certain use value for the development market of children’s item identification APP. The APP has not yet achieved the optimal result in terms of the item identification function, and in terms of the optimization of the file reading function, instead of using the replacement tkinder file selector, it uses the kivy file selector component, which gives users a more overall experience. In addition, the video recognition function can be completed by splitting every frame in the video and identifying it, which can also provide certain application reference value for the development of similar APPs. Acknowledgements This work was financially supported by the 2022 ZJWEU College Students’ Innovation and Entrepreneurship Training project.

References 1. Shi X, Cai H, Wang M, Wang G, Huang B, Xie J, Qian C (2021) Tag attention: mobile object tracing with zero appearance knowledge by vision-RFID fusion. IEEE/ACM Trans Netw 29(2):890–903. 1063-6692 2. Ma Z, Zhong H, Cheng T, Pi J, Meng F (2021) Redundant and nonbinding transmission constraints identification method combining physical and economic insights of unit commitment. IEEE Trans Power Syst 36(4):3487–3495. 0885-8950 3. Jaskolka K, Seiler J, Beyer F, Kaup A (2019) A Python-based laboratory course for image and video signal processing on embedded systems. Heliyon 5(10):2405–8440 4. Rastogi R, Jain R, Jain P, Singhal P, Garg P, Rastogi M (2020) Inference-based statistical analysis for suspicious activity detection using facial analysis. In: Computational intelligence in pattern recognition, pp 29–51

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5. Biondi E, Barnier G, Clapp RG, Picetti F, Farris S (2021) An object-oriented optimization framework for large-scale inverse problems. Comput Geosci 154(000):0098–3004 6. Mishra P, Narayan Tripathi L (2020) Characterization of two-dimensional materials from Raman spectral data. J Raman Spectrosc 51(1):37–45. 0377-0486 7. Dushepa VA, Tiahnyriadno YA, Baryshev IV (2020) Image registration comparative analysis: normalized correlation versus SIFT-based registration. Padiotexnika 000(203):191–196. 04858972 8. Abi-Mansour A (2019) PyGran: an object-oriented library for DEM simulation and analysis. Software X 9(000):168–174. 2352-7110 9. Cheatwood L, Cote P, Perygin D (2019) A python analysis of mass loss related to thermogravimetric analysis of tetrachloroethylene-methyl methacrylate copolymers. Chem Data Collect 24(000):2405–8300 10. Hori M, Fujimoto K, Hori T, Sekine H, Ueno A, Kato A, Kawai T (2020) Development of image analysis using Python: relationship between matrix ratio of composite resin. Dental Mater J 39(4):648–656. 0287-4547 11. Sreeraj M, Jestin J, Kuriakose A, Bhameesh MB, Babu AK, Kunjumon M (2020) VIZIYON: assistive handheld device for visually challenged. Proc Comp Sci 171:2486–2492. 1877-0509 12. Jiang T, Li C, Yang M, Wang Z (2022) An improved YOLOv5s algorithm for object detection with an attention mechanism. Electronics 11(16):2494. 2079-9292

Intelligent Defense Policy for Web Security Defense on Account of Semantic Analysis Ning Xu, Zheng Zhou, Jie Xu, Liang Dong, Wangsong Ke, Zhaoyu Zhu, Yuxuan Ye, Xiang Li, and Chao Huang

Abstract Software security service technology has been developed rapidly in recent years. It uses computer algorithms to analyze massive data and provide decision suggestions and information for users. Software security service technology has been widely used in product promotion, tourism security services and other fields. In professional competitions, the content of the competition should be summarized. In the summary process, the network security system should be established, and then the data of the network security system should be collected and analyzed according to the template content. This paper studies the intelligent defense strategy of Web security protection based on semantic analysis, which promotes the technical progress of Web security protection software. The test shows that the intelligent defense strategy based on semantic analysis has high performance in network security defense. Keywords Semantic analysis · Web security · Intelligent protection · Defense strategy

1 Introduction With the rapid development of Internet technology, the data information generated on the Internet every day is growing exponentially. Images, videos, audio, texts and other information are filling people’s daily life. At this time network security has become a hot topic. In this paper, for information security, industry scholars put forward the Web security protection intelligent system, professional intelligent detection of network viruses and intelligent processing of viruses, to promote the security of the network environment. The intelligent defense policy based on semantic analysis is of great significance in the field of Web security defense. N. Xu (B) · Z. Zhou · J. Xu · L. Dong · W. Ke · Z. Zhu · Y. Ye · X. Li · C. Huang State Grid Information and Communication Branch of Hubei Electric Power Co., Ltd., Wuhan, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_6

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As for semantic analysis, many scholars at home and abroad have studied it. Abroad study, Rahman M S latent semantic analysis (Latent Semantic Analysis, LSA) is the most effective way to implement multilingual document summary, the purpose of which in this paper including automatic generation of a large amount of text documents concise summary is to succinctly present the most important information. This allows the reader to get the main idea [1] without reading the whole thing. Sarrouti M proposes SemBioNLQA, a semantic biomedical question answering system with the ability to deal with natural language questions such as yes/no, statements, lists and summaries. This paper introduces the architecture and evaluation of SemBioNLQA, an end-to-end biomedical question answering system which consists of question classification, document retrieval, article retrieval and answer extraction modules. Take natural language questions as input, and output short and accurate answers and summaries as results [2]. Essel DD proposed SLSDDL algorithm significantly improved the performance of video semantic detection compared with existing algorithms. This method is robust to various video environments, proving its universality. SLSDDL optimization method is used to solve the sparse coefficient of the feature sample of the test video, and the video semantic classification results are obtained by minimizing the error between the original sample and the reconstructed sample [3]. Network security recommendation requirements come from network security system collection work [4, 5]. Generally, network security design is a one-by-one process with a lot of repetitive work, which requires a lot of manpower and time, making the collection and analysis efficiency of network security system very low [6, 7]. It is a new attempt to use software security service technology to solve network security recommendation problem in combination with network security system acquisition requirements. The intelligent defense strategy for Web security defense based on semantic analysis plays an important role in improving Web security defense.

2 Design and Exploration of Intelligent Defense Strategy for Web Security Protection on Account of Semantic Analysis 2.1 Semantic Analysis The algorithm combines semantic keyword extraction technology with recommendation technology based on association rules, and focuses on solving network virus protection and processing problems that cannot be solved by traditional recommendation algorithm [8, 9]. The algorithm is mainly divided into four parts [10, 11]. The first part is data preprocessing. The second part is semantic keyword extraction; The third part is

Intelligent Defense Policy for Web Security Defense on Account … Fig. 1 The whole algorithm flow based on semantic algorithm

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Data preprocessing

Keyword extraction

Association rule mining

Frequent item safety monitoring and handling

mining association rules. The fourth part is frequent item safety monitoring and treatment. The overall algorithm flow is as follows, as shown in Fig. 1. (1) Data preprocessing It is necessary to collect historical data, which are preprocessed from the templates used to collect data in the past. These basic data are preprocessed from the templates used to collect data in previous competitions. The original data will be stored in a file, and the template and collection intent used after each recommendation will be added to the file, and the content of the file will be increased according to the increase of the analysis sessions. The subsequent analysis process is based on the pre-processed data. (2) Keyword extraction The strategy adopted is to extract keywords from the text first, and then compare the extracted words with the existing database, so as to select the keywords that are consistent with the content of the document. Here, the forward maximum matching algorithm is used for matching, and the keyword dictionary is updated synchronously according to the requirements, which can ensure that the documents to be collected are completely matched and key words will not be missed. At the same time, the document collection is recorded every time, so that the used keywords can be directly selected and used directly or with a little modification during the collection in the future, so as to improve the efficiency.

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(3) Mining association rules By using the divide-and-conquer method, the data set is scanned first, and then the item sets in it are associated to generate a frequent pattern tree imitation. Then it is divided into several conditional libraries, each of which is related to the item set of length 1. Finally, it is mined according to specific conditions. (4) Frequent item safety monitoring and treatment Mining the mined data set according to the association rules in Step (3), the system monitors and protects the data set. First, analyze which data is secure or not; Then, for the insecure data set, the system analyzes the insecure modules. Finally, the insecure module is automatically scanned for network viruses, and finally the network viruses are cleared.

2.2 Intelligent Defense Policies for Web Security Defense Based on Semantic Analysis (1) Security intelligent detection to identify and analyze Web attacks There are thousands of network viruses invading and entering Web sites every day [12, 13]. If the network virus is not cleaned up in time, it will cause great harm to the website, and even make the website break down. The Web security protection system designed in this paper will first of all on the website, the network security intelligent detection identification and analysis, scan and find any existing network virus threats, in order to eliminate the network virus interference and infringement on the website. The Web security protection system designed in this paper adopts semantic analysis algorithm to conduct semantic analysis on foreign network intrusions and block suspicious behaviors or objects. At the same time for the possible existence of Web attack capture, and professional network virus treatment, to eliminate the harm. (2) Web security protection is enabled In Web network, a large number of suspicious network viruses exist on websites at any time [14]. In view of this phenomenon, the website system has opened a 24-h security protection policy to provide 24-h security defense services for the daily use of the website. Web security protection usually carries out intelligent detection on all the data entering and leaving the website, and timely finds the network and the virus in the website. This kind of protection is a large area, the whole cycle of network security protection behavior, just like a huge space–time network, to scan and explore the virus. (3) Network virus recognition and processing If a suspicious network virus is detected during intelligent detection, the Web security protection system processes the virus. This system uses semantic analysis algorithm, through the intelligent analysis of network virus, so as to find the virus body of

Intelligent Defense Policy for Web Security Defense on Account …

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network virus. The system found the network virus, the first network virus analysis, analysis of the danger of the virus, the potential threat of the virus, may cause serious consequences. Then the algorithm module is used to clear the network virus. Finally, network protection against possible Web attacks and virus removal techniques are provided.

3 Explore the Effect of Intelligent Defense Strategy for Web Security Defense on Account of Semantic Analysis The first step of SVD (Ingular Value Decomposition) is to replace the text set with an M × N row matrix A, which is the network incoming and outgoing data, as follows: A = (ai j )m×n

(1)

where text feature words are represented by line vectors, and the corresponding quantity is M. Text is represented by column vectors, and the corresponding quantity is n; aij represents the occurrence of the ith feature word in the JTH text. In the process of generating local semantic space, sigmoid() function is used to optimize its 0–1 jump curve: s(ri j ) =

1 1 + e−a(ri j +b)

(2)

where when a approaches ∞ and b approaches ∞, s(r ij ) = 1 indicates that all texts are selected and added to the local area.

3.1 Algorithm Flow First, all the texts in the training set are used to train SVM classifier. Secondly, SVM classifier is used to calculate correlation parameters between all texts and this class, namely tS ij ; Finally, the corresponding values are determined, and the corresponding texts are selected from large to small, which are successively added to the local area to generate the local semantic space.

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4 Investigation and Research on Intelligent Defense Strategy of Web Security Protection on Account of Semantic Analysis Web intelligent detection module is mainly to realize a user interface for friendly interaction between users and application software. On the basis of realizing the content of each function module of the Web security protection system, the user interface function is realized to facilitate the use of users. The recommendation result display module is developed based on the MFC framework, and the specific development environment is as follows: Processor: Intel(R)Core(TM)[email protected] GHz. Memory: 8 g. Operating system: Window 7. Development language: C++. Development tool: VisualStudio2013. Matlab SVMtollbox is used to realize the experimental function of SVM classifier. (1) Comparison of Web security protection and network virus processing performance between Semantic analysis and LLSA, LSA, LSA + SVM, LSA + improved SVM, etc. In the test experiment, the characteristic dimension parameters were selected as 150, 250, 450, 700 and 1 000 successively. Lsas, Lsas + SVM, LSA + improved SVM, LLSA and Semantic analysis (Semantic analysis) micro-average micro-F1 values were compared, in order to show the effect of network virus processing. Verify the test experiments and classify the results. As shown in Table 1, among the six algorithms, Semantic analysis (SA) had the highest micro-average micro-F1 value when the feature dimension was 150, 250, 450, 700 and 1000, indicating that Semantic analysis (SA) was very excellent. As can be seen from Fig. 2, the micro-average micro-F1 value of Semantic analysis (SA) algorithm is higher than other algorithms, and the performance of Web security protection and network virus processing is very efficient. Table 1 Comparison of micro-F1 values of various algorithms LSA

LSA + SVM

LSA + Improved SVM

LLSA

Semantic analysis (SA)

150

0.717

0.719

0.731

0.732

0.728

250

0.763

0.782

0.808

0.823

0.831

450

0.821

0.834

0.846

0.857

0.878

700

0.829

0.837

0.872

0.875

0.884

1000

0.825

0.853

0.877

0.881

0.895

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Fig. 2 Comparison of micro-F1 values of various algorithms

The data test shows that the intelligent defense strategy of Web security protection based on semantic analysis promotes the progress of Web security protection and is beneficial to the improvement of network virus intelligent processing technology.

5 Conclusion After comprehensive research status at home and abroad about the security system, this paper has analyzed the inapplicability of traditional security protection algorithm in the field of security, and then semantic analysis and association rules algorithm are combined to form a recommendation algorithm suitable for network security system, which will be applied in the practical application to improve the efficiency of the network security system analysis. It is of great practical significance to solve the slow speed problem of establishing network security system manually. The intelligent defense policy for Web security defense based on semantic analysis effectively improves the technical level of intelligent defense for Web security defense.

References 1. Rahman MS (2020) ELSA: a multilingual document summarization algorithm based on frequent item sets and latent semantic analysis. Comput Rev 61(5):184–184 2. Sarrouti M, Said O (2020) SemBioNLQA: a semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions. Artif Intell Med 102(Jan):101767.1–101767.16 3. Essel DD, Benuwa BB, Ghansah B (2021) Video semantic analysis: the sparsity based localitysensitive discriminative dictionary learning factor. Int J Comp Vis Image Process 11(2):1–21

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4. Das A, Mandal J, Danial Z et al (2020) A novel approach for automatic Bengali question answering system using semantic similarity analysis. Int J Speech Technol 23(4):873–884 5. Jorge-Botana G, Ricardo et al (2020) Bridging the theoretical gap between semantic representation models without the pressure of a ranking: some lessons learnt from LSA. Cogn Process 21(1):1–21 6. Irkhin IA, Vorontsov KV (2022) Convergence of the algorithm of additive regularization of topic models. Proc Steklov Inst Math 315(Suppl 1):S128–S139 7. Potaraev VV, Serebryanaya LV (2020) Automatic generation of semantic network for question answering. Doklady BGUIR 18(4):44–52 8. Huetle-Figueroa J, Perez-Tellez F, Pinto D (2020) Measuring semantic similarity of documents with weighted cosine and fuzzy logic. J Intell Fuzzy Syst 39(3):1–16 9. Kermani FZ, Sadeghi F, Eslami E (2020) Solving the twitter sentiment analysis problem based on a machine learning-based approach. Evol Intel 13(6):1–18 10. Mansour M, Davidson P, Stepanov O et al (2021) Towards semantic SLAM: 3D position and velocity estimation by fusing image semantic information with camera motion parameters for traffic scene analysis. Remote Sens 13(3):388–388 11. Chahal P, Singh M (2021) An efficient approach for ranking of semantic web documents by computing semantic similarity and using HCS clustering. Int J Semiotics Vis Rhetoric 5(1):45–56 12. Reddy GP, Geetha MK, Sureshanand M et al (2021) A shared blockchain intelligent public protection service framework. J Phys: Conf Ser 1964(4):042024–042024 13. Guliyev HB, Tomin NV, Ibrahimov FS (2020) Methods of intelligent protection from asymmetrical conditions in electric networks. E3S Web Conf 209(13):07004 14. Kaur G, Rai D (2021) Captcha: a tool for web security. Int J Tech Res Sci 6(1):16–20

Consistency Comparison of Machine Vision Images Based on Improved ORB Algorithm Gang Huang

Abstract With the rapid development of the computer field, machine vision recognition technology is often used to process image recognition and analysis. Machine vision is a key technology in the field of artificial intelligence, which can greatly improve the cognitive ability and perception ability of robots. Identifying and converting images into simple text information can focus on the important content of the image and make relevant expressions and statements about the content in the image. In this paper, the improved ORB algorithm is used to compare and study the consistency of machine vision images. Machine vision technology has played a great role in improving detection efficiency and reducing labor costs. The machine vision image processing method is the basis for the application of machine vision technology. and key. This paper aims to study the consistency of visual images and improve the consistency of machine vision image comparison. As an image matching feature point algorithm, ORB algorithm has good image fusion adaptability, which is of great help to improve the accuracy of machine vision image recognition and image registration. The final results of the research show that the extraction times of the three algorithms in the third extraction result are 2.459 s, 1.686 s, and 1.458 s, respectively. It can be seen in turn that the improved ORB algorithm used in this paper can significantly improve the processing time of visual images. ORB The algorithm is better than Hough transform and RLD algorithm. Keywords Machine vision · Artificial intelligence · Image consistency · Image matching

G. Huang (B) Science and Technology Department, Chongqing Vocational College of Transportation, Chongqing 402260, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_7

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1 Introduction In recent years, artificial intelligence technology has been involved in various fields, among which the main goal of machine vision technology is to replace human eyes with computers. The technology is achieved through three steps: image capture of the target, image analysis and comprehensive recognition of the target object. Machine vision is mainly composed of vision, which consists of knowledge part, image editing part and recognition part [1]. The optical sensing unit is used to receive image information of the object to be measured. The main purpose of the image processing unit is to process the intensity distribution, brightness, color and other information of the measured object to obtain the relevant features of the target. Machine vision technology can not only simulate the visual function of the human eye, but it can also undertake some tasks that the human eye cannot. In recent years, many researchers have studied the consistency comparison of machine vision images based on the improved ORB algorithm, and achieved good results. For example, Zhao believes that machine vision technology mainly performs a series of processing on the required images, and removes the interference information and irrelevant information, so as to highlight the important information needed [2]. Zhang believes that the so-called image processing is to digitally operate and process the acquired resources, segment the important information from the image, and obtain the final image through the steps of graphic description and graphic feature extraction [3]. At present, domestic and foreign scholars have carried out a lot of research on the consistency comparison of machine vision images. These previous theoretical and experimental results provide a theoretical basis for the research in this paper. Based on the improved ORB algorithm, this paper analyzes the consistency comparison of machine vision images. The text information in the video or image may reflect the important content of the image itself to a certain extent and briefly describe the content displayed in the image. For example, news video headlines can describe what happened, and the time and place can reflect the generality of the content. If these target areas can be automatically detected and detected, and these areas are segmented from the original image, through OCR identification and analysis, the machine can automatically understand and store, transmit, display.

2 Related Theoretical Overview and Research 2.1 Related Content of Visual Image Processing Visual image processing involves a variety of disciplines and is used in many industries, including disciplines such as computing, mathematics, signal and information coding. The main research contents include:

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(1) Image enhancement and restoration Image enhancement and restoration have the same processing method, and the operation methods of both are the same. The advantages of both are that they can improve the image clarity in image extraction, and image enhancement can perform appropriate operations on specific parts according to the needs of the operator., extract the key parts of the target image features, and image restoration is to restore the missing image, and restore the image to be restored to the initial state through a series of operations [4, 5]. (2) Image segmentation Image segmentation is a necessary step in the image editing process. Many methods have been studied, but until now, there is no general method that can be applied to all types of images. Therefore, the research goal of image segmentation method is still one of the hot spots in the field of image processing. At present, most of the methods for editing subtitles in a video are to block the subtitle lines or mosaic the original subtitles, which will affect the video display effect. In industrial production lines, the collected visual images of products may be lost or blurred [6]. Occlusion affects product judgment and can negatively impact real-time machine vision inspection; therefore, please preserve the integrity of the product’s possible application value. (3) Pattern recognition The subtitles in the visual image are added and compressed, and pattern recognition is required. The solidified subtitles affect the reuse of the video. For example, if the fixed subtitle information on the video can be removed, and the part of the video that is excluded from the text can be restored, then more extensive use of video information [7]. For example, when evaluating video images from other areas, it may be necessary to add subtitles in another language to the movie. If it is just placed above the original subtitles, the video display effect of this image will be significantly reduced, which is not conducive to video reuse. At this time, it should be the original subtitles are automatically detected and restored to the original background before adding text in other languages.

2.2 Classification of Visual Image Comparison Methods (1) Area-based method Region-based visual image alignment methods assume that subtitles have similar colors or grayscales, use color image clustering or segmentation techniques to divide linked regions of the image and borrow linked subtitle elements, with the help of some heuristic knowledge (such as size, aspect ratio, etc.) zoom out and look at the resolution to get the text area. Because part of the background may be the same as the subtitle region, this method is not suitable for locating subtitle regions on complex backgrounds [8, 9]. Visual image comparison uses the connected domain method.

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When preprocessing text detection, a local color quantization method is proposed to convert color images into 256 color images. In 256-color mode, each color in the image is grouped by color. To quantify the method obtains the candidate domain field by computing the minimum outline rectangle of each linked sector and obtains the candidate text region using heuristic text attributes, validates each candidate text region and finally determines the subtitle region. (2) Edge-based methods Due to the high contrast between the touch of text in the image and the color or brightness of the background and text area, it consists of many closely spaced characters, making the area where the text is located has richer edge information than other areas. Edge-based methods are designed according to this feature of text. The general procedure is as follows: First, with the help of a certain edge detection operator, edge detection is performed in the image, and then the edge image or edge map is morphologically aggregated. Just like sorting to get candidate text regions, then use the following advanced heuristic rules to remove noise regions or interfere with the final verification of encrypted regions to get text regions [10]. The visual image comparison first uses the improved sobel operator to extract image edge information, uses the improved sobel operator to locally adjust the threshold method to divide the edge image into binary images, and uses horizontal and vertical binary images. Realtime projection analysis finally determines the location of text regions from coarse to fine, and can also pinpoint the location of multiple languages. (3) Texture-based method The normal layout and strokes of the text make the text area look slightly different from the background. Specific texture attributes, texture-based methods use textual texture attributes to identify a pixel or an image. If the block belongs to the text area, use Gabon filtering, Gaussian filtering, ripple, spatial variation, etc. to get the text. The classifier crosses texture information to determine whether a pixel block is a text block. Visual image alignment has gone through the steps of texture segmentation and text filtering [11, 12]. In order to detect texts of different scales, the images are inserted into a pyramid structure, and the specific vector of the local energy of each pixel can be obtained by the K-means method. The grouping of these feature vectors can be used to distinguish textual and non-textual regions in an image, and texture-based methods can derive image textual properties of different resolutions, different sizes, different languages, and different fonts.

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3 Experiment and Research 3.1 Experimental Method (1) Improved ORB algorithm The improved ORB algorithm is a typical linear filter. Its main idea is to replace the value of each pixel in the image corresponding to the template with the average value of the gray value of the pixels in each neighborhood under the template. The “sharp” change in the grayscale of the graph, the formula is: n n   1 f (i + k, j + l) p(i, j) = 2 n k=−n k=−n

(1)

Among them, the pixel of a certain point on the input image is f(i, j), and the average gray value of its neighborhood is used as the output of the pixel, If the value is p(i, j), then the size of the neighborhood is n × n. The straight line detection formula is: ρ =x cos θ + y sin θ

(2)

Among them, ρ represents the vertical distance from the origin to the line, and θ represents the angle between the vertical line of the line and the positive direction of the x-axis. If the two-dimensional space determined by the parameters ρ and θ is (ρ, θ), then the image space (x, y), the Hough transform of any straight line corresponds to a point on the space (ρ, θ).

3.2 Experimental Requirements This experiment is designed based on the improved ORB algorithm for the consistency comparison analysis of machine vision images. The text in the image is an important symbol of the machine vision detection technology. The text area in the visual image is very important as the target area of interest for the sensor. Therefore, the first task to be completed is to automatically locate the text location, the text in the image is neatly arranged, the font size is consistent, the text itself is obviously different from the background, and the visible edge outline can be used to extract the image text. This paper summarizes the existing machine vision image comparison algorithms, proposes an improved ORB algorithm in this paper, and finally completes the comparison of consistency in images.

60 Table 1 Comparative analysis table of extraction time under different algorithms

G. Huang Extraction times

Hough transform (s)

RLD algorithm (s)

ORB algorithm (s)

One

2.511

1.651

1.335

Two

2.452

1.684

1.393

Three

2.459

1.686

1.458

Four

2.487

1.671

1.446

Five

2.407

1.699

1.365

4 Analysis and Discussion 4.1 Comparative Analysis of Extraction Time Under Different Algorithms In this experiment, in order to verify the image extraction speed of the improved ORB algorithm, Hough transform and RLD algorithm, the images with added noise were extracted and identified. The extraction time results of the three algorithms under the same conditions are shown in Table 1. It can be seen from Fig. 1 that the extraction times of the ORB algorithm, Hough transform and RLD algorithm in the first extraction result are 2.511 s, 1.651 s and 1.335 s respectively, and the extraction times of the three algorithms in the second extraction result are 2.452 s, 1.684 s and 1.393 s, the extraction times of the three algorithms in the third extraction result are 2.459 s, 1.686 s and 1.458 s respectively. It can be seen in turn that the improved ORB algorithm used in this paper can significantly improve the processing time of visual images. ORB The algorithm is better than Hough transform and RLD algorithm.

4.2 Consistency Analysis of Visual Image Processing Under Three Algorithms In this experiment, in order to verify the consistency of the improved ORB algorithm with the Hough transform and RLD algorithms, the images with added noise were extracted and identified separately. The visual image processing consistency results of the three algorithms under the same conditions are shown in the following figure. As shown in Fig. 2, it can be seen from the experimental data that the extraction consistency of ORB algorithm, Hough transform and RLD algorithm in the first extraction result are 81.2%, 85.6% and 93.6% respectively. The extraction consistency of the algorithm is 82.5%, 87.4% and 94.5%, and the extraction consistency of the three algorithms in the third extraction result is 85.1%, 86.6% and 97.2%. It can be seen that the ORB algorithm used in this paper is used for visual image extraction. The consistency is higher than the other two algorithms.

Consistency Comparison of Machine Vision Images Based on Improved …

Hough transform(s)

RLD algorithm(s)

61

ORB algorithm(s)

3

Experimental data

2.5

2.511

2

1.684

1.651

1.393

1.335

1.5

2.487

2.459

2.452

2.407

1.686 1.458

1.671 1.446

1.699

Three

Four

Five

1.365

1 0.5 0 One

Two

Experimental variables Fig. 1 Comparative analysis table of extraction time under different algorithms

Experimental data

ORB algorithm(%)

RLD algorithm(%)

Hough transform(%)

Five

95.4 90.1 83.3

Four

96.2 88.4 80.2

Three

86.6 85.1

97.2

Two

94.5 87.4 82.5

One

93.6 85.6 81.2 0

20

40

60

80

100

120

Experimental variables Fig. 2 Consistency analysis diagram of visual image processing under three algorithms

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5 Conclusion In this paper, the improved ORB algorithm is used to design the consistency comparison of machine vision images, and relevant experimental simulation analysis is carried out. Under the improved ORB algorithm, the extraction time of machine vision images is faster, and the accuracy and consistency are higher. have certain feasibility. As a modern technology, machine vision images are mainly used in industrial fields, and the basic theories applied to vision system image processing, image understanding, pattern recognition and other technologies are the same. Therefore, the machine vision image processing method is the basis for the application of machine vision technology, and the optical image processing technology is gradually moving to other fields. The penetration and utilization of this industry by other industries is inevitable. This paper studies the improved ORB algorithm using the processing method in machine vision to complete the detection and processing of the target area. The text in the optical image data is a specific target object area. The target area The realization of automatic placement and processing is beneficial to the realization of robot intelligence, and the region rendering process can be used to retrieve missing or missing information regions in visual images. Perceiving the integrity of objects in an image has implications.

References 1. Xiaokang R, Danling C, Jie R et al (2020) Research on augmented reality method based on improved ORB algorithm. J Phys: Conf Ser 1453:24–25 2. Zhao E, Li L, Song M et al (2019) Research on image registration algorithm and its application in photovoltaic images. IEEE J Photovoltaics (99):1–12 3. Zhang H, Zheng G, Fu H (2020) Research on image feature point matching based on ORB and RANSAC algorithm. J Phys: Conf Ser 1651(1):187–190 4. Zhang LX (2019) Research on similarity measurement of video motion images based on improved genetic algorithm in paper industry. Paper Asia 2(1):135–138 5. Zhang L, Wei L, Shen P et al (2018) Semantic SLAM based on object detection and improved octomap. IEEE Access:1 6. Dai Y (2018) Design of interactive intelligent assistant translation platform based on multistrategies. Clust Comput 24:1–7 7. Shewell C, Medina-Quero J, Espinilla M et al (2017) Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor. Int J Commun Syst 30(5):e3223.1–e3223.15 8. Li LJ, Zhou YA (2017) An application oriented self-adaptive SLAM system for mobile robots. 28(5):316–326 9. (2018) Research on precise calibration algorithm of image control point based on phase consistency principle. Geomatics Sci Technol 6(4):281–287 10. Zhang J, Piao Y (2018) Research on stereo matching algorithm based on improved steady-state matching probability. J Phys: Conf Ser 1004(1):29–30 11. Zhang J, Xiong F, Duan Z (2020) Research on resource scheduling of cloud computing based on improved genetic algorithm. J Electron Res Appl 4(2):35–49 12. Lin T (2021) Research of personalized recommendation technology based on knowledge graphs. Appl Sci 11:1–1

Nonlinear Multi-objective Probabilistic Optimization Based on Stochastic Simulation Algorithm Xiaoxiao Ma

Abstract At present, multi-optimization problems have been widely used in environmental resource allocation, robotics research, aviation, urban construction, transportation and many other fields. These are closely related to our daily activities. The purpose of this paper is to study nonlinear multi-objective probabilistic optimization based on stochastic simulation algorithms. Taking the WEDM-HS process as the application object, a prediction reliability optimization method based on Gaussian process regression and an interactive alternative decision-making method are proposed. Optimization experiments and candidate solution sets show that the adjustable reliability coefficient can reduce the prediction variance of the solution and obtain a more reliable solution. The mean population prediction variance dropped by 28% for the procedure that considered robust optimization. Keywords Stochastic simulation · Nonlinear multi-objective · Probabilistic optimization · Process modeling

1 Introduction In daily life, people often encounter optimization problems [1]. Among them, when the optimization problem has only one objective function, it is called a singleobjective optimization problem; when the optimization problem has multiple functions and needs to be improved at the same time, the problem is called a multiobjective optimization problem. In the real world, optimization problems usually take many forms [2]. With the rapid development of science and technology, people will face more and more problems, which makes the multi-task problem a very hot research problem [3]. X. Ma (B) School of Transportation, Chongqing Vocational College of Transportation, Chongqing 402247, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_8

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At present, the stochastic simulation algorithm has been highly concerned by engineering scholars, and the theoretical analysis of the stochastic simulation algorithm has gradually become a research hotspot of mathematicians [4]. But Ghosh developed the Block Search Stochastic Simulation Algorithm (BlSSSA). BlSSSA is fast in modeling not only weakly coupled networks, but also strongly coupled and rigid networks. Compare its performance to other existing Algorithms to compare networks [5]. Choi proposes an efficient sorting and selection algorithm for stochastic simulation models. The proposed algorithm evaluates uncertainty based on hypothesis testing to assess whether the observed best design is truly optimal. It then conservatively allocates additional simulation resources to reduce uncertainty through intuitive allocation rules at each iteration of the sequential process. This conservative assignment provides the algorithm with high robustness to noise [6]. It is of practical significance to study nonlinear multi-objective probabilistic optimization based on stochastic simulation algorithm [7]. This paper outlines the benefits and importance of studying nonlinear multiobjective probabilistic optimization based on stochastic simulation algorithms. Each simulation algorithm is described in detail. Simulation algorithms can be divided into two categories: stochastic simulation algorithms and deterministic simulation algorithms. In the stochastic simulation algorithm, the actual algorithm, the accelerated algorithm and the hybrid algorithm are divided and compared according to the process and application completion. At the same time, the defects of the linear selection method and the multi-objective nonlinear programming problem algorithm are studied, the nonlinear process is modeled by GPR.

2 Research on Nonlinear Multi-objective Probabilistic Optimization Based on Stochastic Simulation Algorithm 2.1 Randomness Simulation Algorithm In a reaction system, reactions occur due to positive interactions between substances of different species. The collisions between molecules are irregular, which makes the whole process of change random [8]. The first step was the first stochastic simulation of the regression system, an important discovery that laid a solid foundation for the development of future stochastic simulation algorithms. The first reaction method examines the appearance of the reaction in the system and the changes in the number of molecules of each species from a microscopic perspective, and accurately and truly reflects the random changes of the system and the mutation of the intermolecular voltage [9]. Stochastic time algorithm, suitable for multiple systems (such as linear systems, arbitrary systems), is an accurate simulation algorithm system response. For some physical systems with hundreds of reactions, this means that hundreds of numbers are randomly popped at each statistical step, reducing the computational performance of the algorithm [10].

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In many practical systems, due to the difference in the amount of voltage function, some reactions occur frequently, and some reactions rarely occur simultaneously. If precise algorithms are used to automate these systems, it will take a long time to practice slow results [11]. As a function of the simulation method, the higher the probability of P reversal, the higher the density corresponding to w, so the probability density is significantly reduced, so that slower motion may occur earlier in the simulation, reducing time [12]. In the probability weighted response method, the magnitude corresponds to the weight of W. While the magnitude may increase for larger W, the magnitude also increases. Therefore, when using the probability response method, the choice of W must take into account both the normal simulation and the simulation speed, so that the variance error can be determined.

2.2 Defects of Linear Selection Method A scientific and reasonable selection method should conform to two principles: the principle of equal probability selection in the inferior solution set and the non-inferior solution set; and the preferential selection principle of the non-inferior solution set relative to the inferior solution set. The traditional linear selection method obviously cannot satisfy the above two principles. Because according to the linear selection method, the probability of each individual in the inferior solution set A and the non-inferior solution set B being selected is different, so the selection probability of the latter individual may be very different from the selection probability of the former individual, which does not meet the non-inferiority. The principle of equal probability selection in the solution set and the inferior solution set, the selection probability of some individuals ranked at the bottom in the non-inferior solution set B and the selection probability of some individuals in the inferior solution set A are very small, which does not meet the non-inferior solution set. The absolute preference principle of inferior solution set to inferior solution set. The alternative method also has a big disadvantage, that is, it only considers the high position of Pareto and the indifference order of the individual, but does not consider the dispersion of the individual, so it is easy to develop a variety of Pareto optimal solutions, that is, it is difficult to generate distributions. Better Pareto optimal solution.

2.3 Multi-objective Nonlinear Programming Problem Algorithm The characteristics of multi-objective programming problems: first: multi-objective (including two or more objective functions); second: contradictory (that is, if you try to increase the index value of one objective through a certain scheme, it may reduce

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the value of another objective. value); third: the goals are incommensurable (that is, there is generally no unified measurement standard between goals, and generally they cannot be directly compared). Algorithms for solving multi-objective programming problems can be roughly divided into two categories: direct algorithms and indirect algorithms: direct algorithms solve multi-objective programming itself, directly find its effective solution or seek the overall (or partial) effectiveness of multi-objectives. Goal planning problem. It mainly includes univariate multi-objective programming method, linear multiobjective programming method and optimal order method when the feasible region is a finite set. The common methods of direct algorithm are: hierarchical sequence method, key objective method, grouping sequence method, center method, feasible direction method, interactive programming method, etc.; while indirect algorithm is to put the multi-objective programming problem into a certain problem according to the actual background of the problem. In a sense, it is transformed into a singleobjective programming problem: to solve, but in the process of transformation, it is often ignored, the loss of information is large, and the efficiency is very low (that is, the decision maker has less choice); the common methods of indirect algorithms are: main objective method, linear weighted sum method, ideal point method, multiplication and division method, power function method, etc.

2.4 Modeling Nonlinear Processes Using GPR Although the Gaussian noise hypothesis GPR works well under the assumption of other distributions (uniform distribution), if the sample data tends to have more outliers, its prediction will have a slightly larger error. In this case, a robust Gaussian process regression model using distributional assumptions may have better results. The disadvantage of the Gaussian process regression method: the time complexity of the training process is O(n3), the memory required to store the kernel matrix is O(n2), and the time for prediction computation on the test dataset is O(n2) Complexity D(n), where n is the number of samples. Since only a relatively sparse training set is involved in the WEDM optimization process and the training process is offline, this computational complexity is not a serious problem in this problem. Sparse Gaussian Process Regression can be used if large samples are involved.

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3 Investigation and Research on Nonlinear Multi-objective Probabilistic Optimization Based on Stochastic Simulation Algorithm 3.1 Experimental Setup The experiments in this paper are completed on the DK7732C2WEDM-HS machine tool. The maximum average processing current capacity of the machine tool is 6A; the variation range of its processing pulse width is from 1 to 64 µs; the variation range of the duty cycle is from 1 to 32. The maximum MRR is 120 mm-/min; when the MRR works at 20 mm-/min, the maximum surface finish quality SR can be lower than 2.5 µm. The experimental parameters are as follows: molybdenum wire with a diameter of 0.17 mm is used as the working electrode; chromium alloy Cr12 is used as the workpiece, and the thickness is 40 mm; the slit width is 0.19 mm; the output voltage of the power supply is kept at 100 V; the wire speed of the electrode wire is 10 m/s The working fluid is DX-3 saponification fluid. To demonstrate the effectiveness of the proposed method in EDM process modeling, we used the EDM dataset, the sample system obtained in the EDM process optimization was mainly used to show the material removal rate and electrode penetration rate by peak current, pulse rate and efficiency nonlinear relationship between. The EDM dataset includes 78 experimental samples. In the modeling experiment, it was randomly divided into 62 training sample sets, 7 validation sample sets and 9 test sample sets.

3.2 Multiple Optimization Objective Functions of WEDM-HS Process In the optimization of the WEDM-HS process, the optimization goal is to maximize MRR (best machining efficiency) and minimize SR (best machining effect). These goals are inherently conflicting. We express the two objective functions in the minimized form: Obj1 =

1 + λ1 σ ·(M R R) yˆ ·(M R R)

Obj2 = yˆ ·(S R) +λ2 σ ·(S R)

(1) (2)

At this time, because the target value is not cross-compared in the definition of Pareto optimal solution set and the non-dominant sorting of NSGA-II, that is, Objl(X1) is compared with Obji(X2) or Obj2(X1) is compared with Obj2(X2) is compared to Obj2(X) instead of Objl(X).

68 Table 1 MSE comparison of GPRs with SVMs, RVMs and BPNN on the test set after training

X. Ma Modeling method

WEDM-HS

EDM

BPNN

8.33

7.32

SVR

2.21

1.89

RVM

1.76

1.66

GPR

1.65

1.52

4 Analysis and Research of Nonlinear Multi-objective Probabilistic Optimization Based on Stochastic Simulation Algorithm 4.1 WEDM-HS and EDM Modeling The comparison of modeling methods using GPR with other methods on the two datasets is shown in Table 1. It should be noted in the table that because the model response is not normalized to [− l, 1], the MSE will change with the expectations of different responses, so the mean value of each response is given in this table; the network structure of BPNN in the table uses recommended parameters. Compared with GPR, SVR and RVM, the modeling method using BPNN is difficult to adjust to meet the requirements of high precision due to too many parameters, while the modeling accuracy of GPR is highly competitive compared with SVR and RVM, as shown in Fig. 1.

4.2 WEDM-HS Optimization Results Decision In the optimization process, a probability measure of the uncertainty of the prediction model: variance is added to the objective function, so that reliable optimization can be achieved only by adjusting the coefficient λ in the objective function. The coefficient λ is used to characterize the reliability of the regression model, that is, to characterize the quality of the sample data set. Since the search process tends to look for those chromosomes that minimize the response and variance due to the prediction reliability of changes in different input features, the mean of the population prediction variance of a process that considers reliable optimization compared to a process that does not consider reliable optimization down 28%. For the larger Pareto optimal solution set, after selecting a suitable solution from the above solution set, the solution set of the class to which the solution belongs can be used for the alternative solution set for extended selection: repeat the process of clustering and selection. The comparison of whether the five solutions closest to the cluster center in the reliability optimization results are considered, it can be seen that a relatively conservative solution set is obtained in the reliable optimization process based on the interpretability of the regression model, and the optimized response is

Nonlinear Multi-objective Probabilistic Optimization Based …

WEDM-HS

69

EDM

9 8

Modeling accuracy

7 6 5 4 3 2 1 0 BPNN

SVR RVM Modeling method

GPR

Fig. 1 MSE comparison of GPRs with SVMs, RVMs and BPNN on the test set after training

more efficient. Narrow margins but higher reliability (lower variance mean). After the above solution is rounded, the cutting experiment is carried out. The comparison between WEDM-HS machine tool cutting results and predicted values is shown in Fig. 2.

5 Conclusion Since multi-objective optimization problems are technically representative and widely used, the study of multi-correction methods has attracted extensive attention. In this paper, there is still a lack of a rigorous and systematic quantitative method based on probabilistic significance for the performance evaluation of stochastic simulation algorithms. Taking a certain form of random distribution as a mathematical model to describe and process the results of multiple optimizations of the algorithm, taking the goodness-of-fit test and maximum likelihood estimation as the theoretical basis, and using the mean integral as the means, a set of probability-based methods is developed., A method that can quantitatively compare and evaluate the performance of different stochastic optimization algorithms, and has carried out a preliminary application of the method.

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9

7.87

Unreliable

8

Reliable and excellent

Results and predictions

7 6 5 4 3

1.65

2

0.67

1

0.32

0 (MRR)

Abs Err (SR) Alternative solution set

Abs Err

Fig. 2 Alternative solution set for WEDM-HS

References 1. Yuan Y (2018) Non-negativity preserving numerical algorithms for problems in mathematical finance. Appl Math 9(3):313–335 2. Liu L, Shi L, Zhao G (2019) Simulation optimization on complex job shop scheduling with non-identical job sizes. Asia-Pacific J Oper Res 36(5):1950026.1–1950026.26 3. Trigilio AD, Marien YW, Steenberge P et al (2020) Gillespie-driven kinetic Monte Carlo algorithms to model events for bulk or solution (bio)chemical systems containing elemental and distributed species. Indus Eng Chem Res 59(41):18357–18386 4. Ng A, Siegmund F, Deb K (2018) Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement. J Syst Inf Technol 20(4):489–512 5. Ghosh D, De RK (2021) Block search stochastic simulation algorithm (BlSSSA): a fast stochastic simulation algorithm for modeling large biochemical networks. IEEE/ACM Trans Comput Biol Bioinform (99):1 6. Choi SH, Kim TG (2018) Efficient ranking and selection for stochastic simulation model based on hypothesis test. IEEE Trans Syst Man Cybern 48(9):1555–1565 7. Srg A, Hj B, Ivna C (2020) A multiobjective stochastic simulation optimization algorithm. Eur J Oper Res 284(1):212–226 8. Clement EJ, Schulze TT, Soliman GA et al (2020) Stochastic simulation of cellular metabolism. IEEE Access (99):1 9. Choi SH, Kim TG (2018) Optimal subset selection of stochastic model using statistical hypothesis test. IEEE Trans Syst Man Cybern Syst 48(4):557–564 10. Korneev AM, Sukhanov AV (2018) Investigation of accuracy and speed of convergence of algorithms of stochastic optimization of functions on a multidimensional space. Vestnik Astrakhan State Tech Univ Ser Manage Comp Sci Inform 3:26–37

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11. Albert M, Ruch C, Frazzoli E (2019) Imbalance in mobility-on-demand systems: a stochastic model and distributed control approach. ACM Trans Spatial Alg Syst 5(2):1–22 12. Kar S, Majumder S, Constales D et al (2019) A comparative study of multi objective optimization algorithms for a cellular automata model. Revista Mexicana de Ingeniería Química 19(1):299–311

Artificial Intelligence Technology in Fault Diagnosis of Transmission Network Yimeng Li, Haoran Li, and P. Rashmi

Abstract In recent years, with the rapid advancement of science and technology such as artificial intelligence (AI) in the power model, transmission network (TN) technology has also been greatly improved. At the same time, the fault diagnosis (FD) and maintenance of the TN are becoming more and more difficult. The manual FD method is often time-consuming and laborious, and the fault cannot be diagnosed and removed quickly and accurately. It is urgent to find a fault that can accurately and quickly diagnose the fault of the TN and positioning method. Therefore, this article applies AI technology to the FD of the TN for research. This article first analyzes the functional requirements and performance requirements of the TN FD model, and then designs the model from the aspects of fault data collection, processing, fault location and prediction. So that verify the accuracy of the model for FD, this paper designs related experiments. The experiment shows that the training accuracy, verification accuracy, and test accuracy of the model for the TN fault test are as high as 99.82, 97.44, and 97.15%. This shows that the model can screen out faults more accurately and has a good ability to identify unknown fault types. Keywords Power transmission model · Fault location · FD · Fault identification

1 Introduction In recent years, various fields have explored and experimented with AI technology. It has a very good problem-solving effect on various complex problems, especially nonlinear problems, and it has a wide scope of applications [1, 2]. The TN occupies Y. Li · H. Li (B) State Grid Jibei Electric Power Company Limited Skills Training Center, Baoding Technical College of Electric Power, Baoding, Hebei, China e-mail: [email protected] P. Rashmi Inventuriz Labs Private Limited, Bangalore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_9

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an important position in the power signal model [3]. Due to the increase of the transmission voltage level, the transmission scope of the TN has been expanded, and the transmission distance has become longer. When a fault occurs, if the fault cannot be diagnosed and removed quickly and accurately, it will cause many harms [4, 5]. Regarding the research of TN FD, many scholars have launched a multi-angle discussion on it [6, 7]. For example, Xu constructed a power grid FD model based on topological primitive information fusion [8]; Wang conducts research and analysis on power grid FD methods [9]; Azzolin explored the electrical and topological driving factors of TN fault dynamics [10]. This article combines AI technology to study the FD method of the TN, which is conducive to quickly identifying and removing faults, ensuring power safety and restoring power supply, so this research has important practical significance. This paper first analyzes the functional requirements and performance requirements of the TN FD model based on the intelligent neural network, and then designs the model’s data collection structure, fault data processing, fault location and analysis structure. Finally, the progressive test of the model verifies that the model has good diagnostic accuracy and can realize rapid identification and diagnosis of TN faults [11, 12].

2 Application Research of AI Technology in FD of TN 2.1 Design of the FD Model for the TN Based on AI 2.1.1

Analysis of Model Function Requirements

(1) TN data index management function In the process of data mining analysis and FD of the TN, various evaluation indicators must be managed. Therefore, it is necessary to create a TN fault data indicator management function on the model, which can be voltage and current limits, temperature and other indicators to improve the ability to analyze the fault indicators of the TN. (2) Power TN failure index data management function After the establishment of the TN failure index database, it is necessary to load relevant data according to the type and target of the failure analysis. And according to the data to calculate the relevant indicators to obtain the indicator data, and then based on the indicator data for the mining and analysis of the fault data, so it is also necessary to create a TN fault data management function. (3) Data mining and analysis function of power TN fault information In the design, it is necessary to build a TN fault information data mining function, with the help of data mining algorithms to realize the fault mining analysis of the

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TN, so that realize the automatic analysis of fault types and realize the fault location function of the TN. (4) Power TN failure prediction and analysis function It is necessary to realize the fault prediction of some transmission lines based on the existing data, so that find the early fault information and deal with it promptly, so that avoid the occurrence of major power supply accidents due to the failure of the transmission line.

2.1.2

Analysis of Model Performance Requirements

(1) Real-time The diagnosis speed of the TN FD model requires real-time performance. The longer the fault exists, the less conducive to the stability of the TN, and a long-term power outage will cause economic losses. Therefore, there are strict requirements on the timeliness of FD, and the earlier the fault area is required to be removed, the better. This is an important performance requirement for the design of FD model. (2) Versatility Versatility requires that for a distribution network with complex line connections and more equipment, when its network structure changes, the model can continue to be used, and the TN can still be quickly diagnosed without modification or minor modifications. If the model is not applicable to the FD method of the TN, it has no practical value. (3) Fault tolerance The model must first ensure the accuracy of the FD of the TN. If the diagnosis accuracy is not high, it cannot be put into practical use. Incorrect diagnosis results will even increase the unnecessary workload of maintenance personnel, lengthen the power outage time and enlarge the power outage area. Therefore, when there is a lack of fault information and distortion in the TN, the model must be able to perform accurate and effective diagnosis to ensure the safe and stable operation of the power grid.

2.2 Design of the FD Model for the TN 2.2.1

Collection of Fault Data on the TN

Use the SCADA model (data acquisition and monitoring control model) to collect the fault data of the TN. The model includes many submodels such as workstations, network servers, databases, etc., so that realize online monitoring of various data of

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the transmission grid’s operating status: power, temperature, voltage, and fault trip information. Through monitoring, the model can not only detect accident information in time, generate corresponding data messages, but also directly transmit to the data processing part, which improves work efficiency and also provides data backup for future TN maintenance.

2.2.2

Processing of Fault Data in the TN

The transmission line fault index data management function is mainly to provide users with a defined index data source and data processing method. The model program will automatically go to the location specified by the data source to retrieve the index data, and preprocess the transmission network data according to the data processing rules.

2.2.3

Fault Location of TN

The model can locate the fault area of the TN. The main process is: First, the entire TN is partitioned, and each feeder and its connected components are regarded as an area; Then, read the fault current flowing through the circuit breaker in each area of the SCADA model, and roughly judge the suspected fault area based on the fault current of the circuit breaker; Next, construct node inference rules according to the principle of fault location, and establish corresponding fault location models containing distributed TN by the node inference rule structural unit for the partitions that are suspected to have failed; Finally, according to the direction of the fault current of the circuit breaker, initial values are assigned to the node neurons, and an inference algorithm is performed on the established partition fault location model. Because there is a certain mathematical relationship between the fault distance of the transmission line and the natural frequency of the traveling wave and its speed. According to the characteristics of the DC capacitor boundary in the VSC-HVDC symmetrical unipolar operation mode, the calculation method of the fault distance is shown in formula (1): d=

v 2 f1

(1)

In formula (1): f 1 represents the extracted natural frequency, and d represents the distance to the fault. This paper uses the IMF component HVCR (High Frequency Variance Contribution Rate) to distinguish the fault area. The specific method is shown in formulas (2), (3), (4):

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]2 [ N N  1  2 1 |ci (kΔt)| Di = ci (kΔt) N k=1 N k=1 Di M i = ∑n i=1

Di

H V C R% = Mi × 100

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(2)

(3) (4)

In the formula, Di represents the variance of the i-th IMF component, Δt is the sampling interval, and ci represents the coefficient of the i-th IMF component. The HVCR method is calculated by M i representing the high-frequency components. This method can be used to analyze the distribution characteristics of the signal in different frequency bands to locate the fault area.

2.2.4

Failure Prediction Analysis

When the TN fails, the model starts relevant algorithm analysis. The algorithm automatically extracts the voltage and current data of the line in a periodic manner from the data warehouse, and then performs feature extraction and correlation analysis on it. Analyze the correlation between the characteristic data of the voltage and current generated on the line in the same period with the equipment and other line data, and obtain the equipment and other lines with the highest correlation with the faulty line. These equipment and lines are very likely to be the point of failure, and troubleshooting can be quickly performed through the point of failure. At the same time, setting time parameters of the model and analyzing data with time series can form predictive confidence results related to time. By analyzing the trend of related faults and the confidence value of lines and equipment, it is possible to predict the related faults on the transmission line.

3 Experimental Research on the Application of AI Technology in the FD of Power TN 3.1 Experimental Environment A simulation experiment was carried out on an Intel Lenovo computer, with a main frequency of 3.5 GHz, 8G RAM, IT hard disk, 17CORE processor, and Eclipse environment for the simulation environment.

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3.2 Experimental Data Six different types of fault indicators in the power grid were randomly selected for experimental analysis. Based on these six fault indicators, random simulations were performed to form 5000 simulated fault indicator data and import them into the MySQL3 database. Then compare the efficiency of ant colony algorithm and memetic algorithm for fault detection in the MyEclipse environment.

3.3 Experimental Process First, 5000 pieces of fault data are selected from the database for analysis, which contains 6 types of fault indicators. So that verify the effectiveness of the model and exclude other influences in the simulation, the data was normalized and feature data extracted before the experiment. Then, the ant colony algorithm and the memetic algorithm are used to compare and analyze the FD situation to verify the feasibility of the model in the FD analysis of the TN.

4 Application Experiment Analysis of AI Technology in FD of TN 4.1 FD Results of Different Algorithms After sorting out the data obtained from the experiment, the FD results under different algorithms are shown in Table 1: The diagnosis time of this model is 5.72 s, the number of iterations is 68, the FD accuracy rate is 96.4%; the fault of the ant colony algorithm the diagnosis time is 8.37 s, the number of iterations is 164, and the FD accuracy rate is 76.1%; the diagnosis time of the memetic algorithm is 9.16 s, the number of iterations is 223, and the FD accuracy rate is 52.6%. It can be found from Fig. 1 that the AI algorithm has obvious advantages in terms of overall diagnosis time, number of iterations, and accuracy. The TN diagnosis Table 1 Statistical table of FD results of different algorithms

Algorithm name

Diagnosis time (s)

AI

5.72

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8.34

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Memetic algorithm

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Fig. 1 Statistical table of FD results of different algorithms

model using intelligent neural network can shorten the diagnosis time and increase the diagnosis rate.

4.2 FD Accuracy The 5000 pieces of fault index data are classified and tested, including 1000 pieces of data in the training set and the verification set, and 3000 pieces of fault data in the test set. The model was tested 8 times, and the experimental results are shown in Table 2: Observing Fig. 2, we can find that the training accuracy, verification accuracy, and test accuracy of the model for the TN fault test are as high as 99.82, 97.44, and Table 2 Model FD accuracy (%)

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96.13

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95.79

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94.80

97.15

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95.41

96.23

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97.15%. The model can filter out the faults more accurately, has a better ability to identify unknown fault types, and achieves rapid and accurate FD.

5 Conclusion Long-term exposure of power transmission lines to the outdoors is a part of the power model with a high incidence of failures, which directly affects the safe operation of power. Today, the development of AI technology provides a technical guarantee for timely detection of TN failures and effective countermeasures. This paper mainly takes the FD of the TN as the research object, has studied the fault analysis and prediction methods of the intelligent neural network in depth, and designed a FD model. Finally, the comparison and analysis of experiments have verified that the model is accurate and reliable, and can effectively diagnose TN faults.

References 1. Ma B, Nie S, Ji M et al (2020) Research and analysis of sports training real-time monitoring system based on mobile artificial intelligence terminal. Wirel Commun Mob Comput 2020(6):1–10 2. Nandish BM, Pushparajesh V (2021) Review of internet of things: distributed power in smart grid. IOP Conf Ser: Mater Sci Eng 1055(1):012139 (8pp)

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3. Muhammad G, Alhussein M (2021) Convergence of artificial intelligence and Internet of Things in smart healthcare: a case study of voice pathology detection. IEEE Access (99):1 4. Srinivasa Rao TC, Tulasi Ram SS, Subrahmanyam JBV (2022) Neural network with adaptive evolutionary learning and cascaded support vector machine for fault localization and diagnosis in power distribution system. Evol Intel 15(2):1171–1182 5. Yazidi ME, Himdi M, Daniel JP (2018) Transmission line analysis of nonlinear slot coupled microstrip antenna. Electron Lett 28(15):1406–1408 6. Himdi M, Daniel JP (2018) Analysis of printed linear slot antenna using lossy transmission line model. Electron Lett 28(6):598–601 7. Makeev MO, Osipkov AS, Filyaev AA et al (2022) Transparent radio-shielding materials based on multilayer and mesh structures. J Commun Technol Electron 67(11):1411–1418 8. Jrgensen AA, Kong D, Henriksen MR et al (2022) Petabit-per-second data transmission using a chip-scale microcomb ring resonator source. Nat Photonics 16(11):798–802 9. Wang S, Zhao D (2017) Research review and prospects for power grid FD. Dianli Xitong Zidonghua/Autom Electric Power Models 41(19):164–175 10. Azzolin A, Duenas-Osorio L, Cadini F et al (2018) Electrical and topological drivers of the cascading failure dynamics in power TNs. Reliab Eng Model Safety 175(July):196–206 11. Silva R, Kurokawa S (2018) Model of three-phase transmission line with the theory of modal decomposition implied. Energy Power Eng 05(4):1139–1146 12. Pathirikkat G, Balimidi M, Maddikara J et al (2018) Remote monitoring model for real time detection and classification of transmission line faults in a power grid using PMU measurements. Protect Control Modern Power Models 3(1):16

Research on Power Supply Chain Equipment Traceability Method Based on System Blockchain Jianhong Wu, Junchang Lin, and Xinghua Deng

Abstract The life cycle management of power supply chain equipment is the most effective means to supervise equipment quality. And supply chain traceability is an effective way to solve supply chain equipment life cycle management. In order to solve the problem that the current traceability methods do not consider the correlation between equipment or the low traceability efficiency, it is proposed in this paper that an equipment traceability method in power supply chain based on blockchain. Firstly, the traceability structure for complex equipment is designed, which can effectively describe the association relationship and the relationship circulation between equipments. Then an equipment traceability method based on traceability structure and equipment index tree is proposed. Finally, the effectiveness of this method is verified by simulation experiments. Keywords Blockchain · Supply chain · Traceability · Power equipment · Life cycle

1 Introduction Supply chain refers to an integral functional network chain structure that starts from supporting parts to produce intermediate products and final products [1], and finally delivers products to consumers by sales network, connecting suppliers, manufacturers, distributors and end users [2, 3]. It can be seen that a product, from the initial procurement and processing of spare parts to the final use, needs to go through multiple circulation links. In order to ensure the quality of the final product, it is necessary to manage the whole life cycle of the supply chain equipment, especially in the power supply chain [4]. J. Wu · J. Lin (B) · X. Deng China Southern Power Grid Materials Co. Ltd., Guangzhou, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_10

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Equipment traceability in power supply chain is an effective way to solve the full life cycle management of supply chain equipment. The so-called traceability is an information chain that records the circulation process of the equipment [5]. It can be connected to equipment production, inspection, supervision, procurement, distribution and use and other links [6]. At the same time, it can carry out forward and reverse tracking management of the equipment to realize the source can be queried, the whereabouts can be traced, and ensure the quality and safety of the equipment [7]. The equipment in the supply chain usually involves multiple manufacturers, and the same manufacturer involves multiple departments, which brings difficulties to equipment information sharing and information security across manufacturers and departments [2, 8]. In 2008, Satoshi Nakamoto proposed the concept of blockchain [9], it immediately received widespread attention. Blockchain is a distributed ledger technology with immutability, multi-party participation, openness and transparency [10]. The characteristics of blockchain can effectively solve the problems faced by traditional traceability methods, so it has been widely used in supply chain traceability in recent years. However, the current blockchain-based supply chain traceability method does not consider the correlation relationship between equipment, and the traceability efficiency is low. For complex equipment systems, there are often correlation between equipment, and the number is large. To this end, this paper proposes a full life cycle management method for complex equipment in supply chain. Firstly, the traceability structure for complex equipment is designed, including the data structure describing equipment and the data structure describing equipment circulation information. Then the traceability method based on traceability structure and equipment index tree is designed. Finally, the effectiveness of the proposed method is verified by simulation.

2 This Method of the Paper The equipment traceability method based on block chain of power supply chain includes two parts: one is the data structure for complex equipment traceability; the other is using the designed traceability structure to efficiently retrieve the traceability information to realize the whole life cycle management of the equipment.

2.1 Traceability Structure for Complex Equipment In order to trace the full life cycle of the equipment. It is designed in this paper that the data structure shown in Table 1 to describe the supply chain equipment. Since the equipment will constantly circulation in the supply chain, it is not enough to describe the basic information shown in Table 1, and also needs to record

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Table 1 The data structure of the equipment Variable

Description

E P T _I D

Equipment identifier

E P T _T ype

Equipment type

E P T _PublicK ey, E P T _Pri vateK ey Public or private key issued by the equipment, used to ensure the authenticity and uniqueness of the equipment E P T _Content

Description of the equipment

E P T _Curr ent State

The current state of the equipment

E P T _Owner

Equipment owner/custodian

E P T _Last T x H ash

Recent transaction/circulation of the equipment

E P T _Contract List

Equipment-associated contract

Related_E P T _List

Other equipment associated with the equipment

E P T _Relation_List

Type of association (such as inclusion, composition, dependency, etc.)

the circulation information of the equipment. So the data structure shown in Table 2 is designed. In Table 2, variables T x_I nput and T x_Out put are established for the data structure of equipment circulation. T x_I nput =< E P T _I D_list, Latest_T x_I D_List >, E P T _I D_list represents other equipment ID associated with the equipment in circulation. Latest_T x_I D_List represents the corresponding last circulation of information. Table 2 Data structure of equipment circulation Variable

Description

T x_I D

Identifier of the equipment circulation

T x_T ype

Equipment circulation type

E P T _Fr om List

Provider of equipment

E P T _T oList

Receiver of the equipment

T x_E P T

Circulation equipment (structure shown in Table 1)

Pr eT x H ash

The previous circulation of the circulation equipment

E P T _State

Equipment status change information before and after circulation

E P T _O peration

The operation of the circulation on the equipment

T x_signatur eList

A list of circulating multi-party signatures

T x_I nput

Other equipment associated with the circulation equipment

T x_Out put

Operation and status of other equipment associated with the circulation equipment

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T x_Out put =< E P T _I D_List, E P T _State_list, E P T _O peration_List >, E P T _I D_List represents other equipment ID associated with the equipment in circulation. E P T _State_list represents the states of other equipment. E P T _O peration_List represents the operation of the equipment. When the equipment circulations from one stage of the supply chain to another, the associated equipment information is updated accordingly. Based on the above two structures, associations between equipment and between equipment circulations can be established, as shown in Fig. 1. According to the variables Pr eT x H ash in the data structure of the equipment circulation information, the circulation chain of the same equipment can be established. In Fig. 1, the equipment 02 has experienced two consecutive circulations (04 and 12). On the other hand, according to variables T x_I nput and T x_Out put in the equipment circulation data structure, the correlation relationship between the equipment can be established. Take the circulation of equipment 02 and 04 in Fig. 1 as an example, this circulation needs related equipment 03 and 05. After the equipment 02 completed circulation 04, the status of the equipment 03 and 05 is also updated (e.g., the equipment 03 and 05 are part of the equipment 02, when the equipment 02 transferred, the equipment 03 and 05 should also be transferred).

Equipment Circulation 04

Equipment Circulation 12

Tx_EPT(Equipment 02) Tx_Input Tx_Output

Tx_EPT(Equipment 02) PreTxHash Tx_Input Tx_Output

Iuput

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EPT_ID_List (Equipment 03 05)

EPT_ID_List (Equipment 03 05)

EPT_ID_List (Equipment 03 05)

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Fig. 1 Schematic diagram of the equipment association

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2.2 Trace-Method Based on Traceability Structure and Equipment Index Tree Based on the data structure proposed in Sect. 2.1, the information of equipment in the supply chain can be queried to realize the life-cycle management of equipment. This paper uses Merkle Patricia Tree to organize equipment information, which is called the equipment index tree. The branch path of the tree represents the equipment encoding, and the leaf node of the tree records the equipment information. When a equipment circulations in the supply chain, the equipment circulation data structure T x is generated. Take the following steps to update the equipment index tree in this paper: The network node receives the equipment circulation information T x, and then obtains the last circulation information E P T _Last T x H ash of the equipment according to the circulation of the equipment T x_E P T . Do the following operation according to the value of E P T _Last T x H ash: If it is not empty, locate to the block including E P T _Last T x H ash. From the block, we can obtain the hash values needed to build Merkle Tree. Then compare the calculated value Merkle Root with the Merkle Root in the block header. If the two are equal, it indicates that the circulation is true and effective. The variable Pr eT x H ash of the equipment circulation information T x is assigned as E P T _Last T x H ash. If E P T _Last T x H ash is empty, it means that the circulation is the first circulation of the equipment, and the variable Pr eT x H ash of T x is assigned empty. Assign the variable value E P T _Last T x H ash of the transferred equipment T x_E P T as T x. As shown in Fig. 2, the corresponding branch path is first found in the equipment index tree according to the equipment code, and the terminal leaf node records the equipment information. Then finds the corresponding circulation record in the blockchain according to the variables E P T _Last T x H ash of the equipment information. then the previous circulation can be found according to the circulation record Pr eT x H ash. Finally obtains the complete circulation process of the equipment in the supply chain.

3 Experimental Results This paper builds a prototype system based on the Ethereum platform to verify the effectiveness. The experimental environment of this paper is: Intel processor, 8 core, main frequency 3.6 GHz; memory DDR43200MHz32Gb; 2 TB solid state disk.

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BlockHeader

BlockHeader

BlockHeader

Tx0

Tx3

Tx6

Tx4

Tx7

Tx1 Tx2

PreTxHash

Tx5

PreTxHash

Equipment coding

Tx8 EPT_LatestTxHash

Fig. 2 Full life cycle information traceability process of the equipment

3.1 Data Transmission Efficiency Analysis For the Ethereum platform, the time of data writing can be expressed as t = t 1 + t 2 . Where t 1 is the target block producing time in the blockchain, which is determined by the blockchain platform itself; t 2 is the time when a new block is generated and broadcast to the whole network, including the transmission time and verification time of the block in the network. Therefore, the value t 2 that should be lowered in order to reduce the data write time. Obviously, the smaller the block capacity, the smaller the transmission time and the validation time. But the block is too small and the single transmission time is very short, it takes many times to transmit all the information. In the paper, the transmission time is recorded as t t . Different rations of t t and t 2 were tested respectively, so t 1 /t 2 is 50 and 70, and 90% successively, the resulting transmission efficiency curve is shown in Fig. 3. Therefore, the transmission cost is large when the block size is below 9000 KB, and it is small when the block size is above 9000 KB. Therefore, the optimal block size should be 9000 KB.

3.2 Analysis of Data Circulation Efficiency In the paper, the number of power equipment simulated is 5000, the total number of circulations is 50,000. This paper conducted multiple tests. Each test randomly selected a different number of equipment and the corresponding different number of total circulation times, and then retrieved the circulation information of each equipment in the supply chain. In this paper, it selected the Ethereum traversal query method for comparison. The specific test results are shown in Fig. 4 (the red line is the paper’s method, and the blue line is the Ethereum traversal query method). It can be seen that when the circulation number is small, this method has obvious advantages (The speed of tracing information query is 1–2 orders of magnitude higher). This is because Ethereum

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Transmi ssion Effici ency(s/100kb)

tt/t2=50% tt/t2=70% tt/t2=90%

Block Size(10Kmb)

Fig. 3 Block size and transmission efficiency curves

traversal query method needs to traverse all blocks, and the paper’s method only needs to retrieve the relevant blocks according to the index value in the equipment data structure and equipment circulation data structure. When the number of circulations increases, the time required by Ethereum to traverse the query method increases slowly, while the time consumed by this method increases rapidly. Because when the circulation records are evenly distributed across all blocks and increase, the number of blocks required by this method increases.

4 Summary The centralized device traceability method is faced with the problem of device information tampering. Meanwhile, the existing equipment traceability method based on block chain does not consider the association between equipment in the complex equipment system of power. To address these issues, it is proposed in this paper that a blockchain-based power supply chain equipment traceability method. Firstly, the data structure of the equipment and the data structure of the equipment circulation are designed, and the relation between the equipment and the equipment circulation is described. Then, through experimental analysis, the optimal block size for efficient transmission of traceability information is determined, and a equipment traceability

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Fig. 4 Time-consuming comparison of traceability information query

method based on traceability structure and equipment index tree is proposed. Finally, the simulation experiment verifies that the proposed method has higher traceability information retrieval efficiency compared with Ethereum traversal query method. Acknowledgements This work was supported by the research and application project of Southern Power Grid Materials Co., Ltd blockchain technology in the field of supply chain (ZBKJXM20200544).

References 1. Dutta P, Choi TM, Somani S et al (2020) Blockchain technology in supply chain operations: applications, challenges and research opportunities. Transp Res Part E: Logis Transp Rev 142:102067 2. Hussain M, Javed W, Hakeem O et al (2021) Blockchain-based IoT devices in supply chain management: a systematic literature review. Sustainability 13(24):13646 3. Kouhizadeh M, Saberi S, Sarkis J (2021) Blockchain technology and the sustainable supply chain: theoretically exploring adoption barriers. Int J Prod Econ 231:107831 4. Shang H, Lei M, Ma H et al (2017) Research on big data application planning method of power supply chain. China Electric Power 50(6):69–74 5. Esmaeilian B, Sarkis J, Lewis K et al (2020) Blockchain for the future of sustainable supply chain management in Industry 4.0. Resour Conserv Recycl 163:105064 6. Wamba SF, Queiroz MM (2020) Blockchain in the operations and supply chain management: benefits, challenges and future research opportunities. Int J Inf Manage 52:102064 7. Xu D, Gong J (2021) Research on third-party electronic data deposit certificate method based on blockchain technology. Microcomp Appl 10

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8. Moosavi J, Naeni LM, Fathollahi-Fard AM et al (2021) Blockchain in supply chain management: a review, bibliometric, and network analysis. Environ Sci Poll Res:1–15 9. Nakamoto S. Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review, 2008: 21260. 10. Koens T, Poll E. Assessing interoperability solutions for distributed ledgers. Pervas Mobile Comput 59:101079–101079

Artificial Intelligence Application Processing Method and System Based on Open Source Deep Learning Framework Chunhua Deng and A. C. Ramachandra

Abstract In the field of artificial intelligence, deep learning is a very important research topic. At present, there are a large number of machine intelligence algorithms for processing and analyzing problems, solving complex data mining and other key technologies and related application requirements, as well as deep model processing methods to realize the huge knowledge value and potential commercial value and developability of massive data. And other characteristic information content. This paper mainly uses the time series analysis method and the experimental method to carry out the correlation research on the face recognition system, and compares and analyzes the open source deep learning framework to study its processing in artificial intelligence applications. Experimental results show that when the threshold is 0.74 and the side face angle is 0, the accuracy of the face recognition system is more credible. Keywords Deep learning · Open source framework · Artificial intelligence applications · Processing methods

1 Introduction With the development of artificial intelligence, its application range is becoming wider and wider, and it has its presence in various fields. Artificial intelligence technology has penetrated into many aspects of our lives. The current computer processing power is mainly realized by simulating human intelligence. But because C. Deng (B) Jiangxi University of Applied Science, Nanchang, Jiangxi, China e-mail: [email protected] A. C. Ramachandra Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_11

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of its complexity and hugeness. Therefore, how to improve the operating speed and accuracy of the system has become a current research hotspot. There are still scholars who study artificial intelligence applications of open source deep learning frameworks. For example, Caropreso introduced the development of deep learning, introduced several popular deep learning programming frameworks, and believed that deep learning will accelerate the development of artificial intelligence, and intelligent technology promotes social progress [1]. Drozdowicz said that deep learning is the core technology of artificial intelligence development. With the revolutionary development of big data, hardware computing capabilities and deep neural network optimization algorithms, deep learning has achieved the best results that are outstanding or even better than human effects in the research fields of computer vision, speech recognition, and natural language processing [2]. According to the intelligent characteristics of the deep learning model, Whatmough proposed a deep learning defect detection model based on the Caffe framework [3]. Therefore, this article learns artificial intelligence technology and processing system from the open source deep learning framework, which is conducive to the development and application of artificial intelligence technology. This article first studies the related theories of neural network technology and deep learning. Secondly, it studies the intelligent framework system based on the open source deep learning framework. Then the processing method and system of artificial intelligence application are described. Finally, the face recognition system using open source deep learning is tested experimentally, and the data results are obtained.

2 The Processing Method and System of Artificial Intelligence Applications Based on the Open Source Deep Learning Framework 2.1 Neural Networks and Deep Learning Machine learning is the core of artificial intelligence. Its purpose is to allow the machine to learn learning rules from a large amount of training data, and then identify new data or new samples in the future. It is widely used in text recovery, picture/video recovery. Supervised learning is a classic learning method in machine learning. Supervised learning is our previous classification. If we want to classify several types of images, in the training phase we input an image, get a vector and compare the result with the previously labeled training example. If the result is different from the expected result, continue to change the adjustable parameters until the classification result reaches the expected effect. The difference between unsupervised learning and supervised learning is that there is no training supervised information and labeled training samples during the training process, so the model cannot know whether the recognition result is correct during the training process,

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and the model has to be recombined to self-learning related data directly useful information for modeling [4, 5]. Data collection and processing. In the process of acquiring big data, a multi-source parallel matrix algorithm can be used to convert massive amounts of original information into predictable and effective text. Based on the deep learning framework, it has a good ability to solve the characteristics of user behavior analysis, machine learning, and decision tree models. At the same time, it can also transform a large number of complex problems into simple problems to solve and realize intelligent calculation [6, 7]. Neural network is a nonlinear system modeling method, it can transform complex problems into a simple, easy to implement, and has been widely used in practical applications. Genetic algorithm is currently one of the most effective and commonly used intelligent technologies to solve the problem of multiple types of environmental information fusion and processing. The basis of neural network is its selflearning function, which can process a large amount of data, and realize information processing and knowledge discovery through the interconnection between artificial neurons and the brain [8, 9]. Logistic regression, as a simple classification algorithm, is inextricably linked with neural networks. It integrates input data into a logical function, and then uses the output of the function to predict the probability of various events. s = q 0 + q 1 a1 + q 2 a2

(1)

Logistic regression makes a linear combination of the inputs a1 , a2 , and the result of the addition is passed to the sigmoid function of the excitation function: t = f (s) =

1 1 + k −s

(2)

When the value of s is less than 0, t will gradually tend to 0, when it is greater than 0, t will gradually tend to 1, and when s = 0, t = 0.5. The final result forms a two-class classifier according to whether the value of s is positive or negative. As one of the most widely used neural network models, BP neural network also has certain limitations. As the number of network layers increases, the BP neural network may converge to a local minimum, resulting in less and less ideal training results. The overall learning speed is slow. As the signal is transmitted layer by layer, the error correction signal during back propagation becomes smaller and smaller [10, 11]. Convolutional neural network uses its unique weight sharing mechanism. The neurons in each layer are connected to the small area of the previous layer to calculate the weights, which can reduce network training parameters and make the network model simpler, especially when training multi-dimensional images. It has unique advantages. The specific process of using deep learning training is as follows: unsupervised bottom-up learning. Compared with traditional neural networks, the process

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of learning machine functions is an unsupervised learning process. Training uses uncalibrated samples to train the lower layer of the hidden layer corresponding to the previous layer. Some parameters have better functions than the input, and the previous output is used as the input for the next layer. The development of deep learning technology is very rapid. Each framework has its own characteristics and advantages, and plays an important role in language, image and search. As a completely open source framework, Caffe has the following characteristics: Use cuda for accelerated calculations. It is convenient to switch between GPU and CPU modes. The code is readable, even if the code is not well understood, the network model can be improved well. The official tool set and hands-on tutorial are more complete. You can use visualization methods to view training data and models.

2.2 Intelligent Framework System Based on Open Source Deep Learning Framework Artificial intelligence applications. The human brain is a special function. It can recognize the knowledge stored in the human brain and convert it into corresponding language codes to generate relevant task files. At the same time, the relevant learning resource information and other auxiliary materials and other data can be obtained through the neural network. The intelligent framework plays a very important role in the computer system. The intelligent system based on the open source deep learning framework is mainly composed of two parts: the user side and the server side. Functional module includes three sub-categories: data acquisition, knowledge discovery and mining, which are different types of application areas. Among them, it can be divided into object-oriented model and rule-oriented model. Different target groups need to be processed hierarchically in order to better achieve their goals. The function-driven layer is mainly to divide the various requirements in the system into several small units that are easy to implement. The intelligent system based on the open source deep learning framework is based on artificial intelligence, through data mining and analysis, so as to achieve the purpose of obtaining useful information, controlling input and output, and decisionmaking. Based on the application of large database technology. The large database contains a large amount of user data and related knowledge bases to realize the combination of personalized recommendation system and artificial neural network. When categorizing large amounts of complex texts, clustering algorithms can be used to transform them into multi-dimensional model sets oriented to specific target tasks. By extracting and analyzing data features. The application system based on the open source deep learning framework mainly includes the following major functional modules. Data mining, through the analysis of a large number of users, to obtain a large number of original features, so as to achieve multi-dimensional description, and use it as a prediction of the future development trend of unknown things. At the

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same time, using machine learning technology, neural network and other methods to deal with this information, there are many uncertain factors, nonlinear relationships and some regular problems. Intelligent control and decision support system: Based on the deep learning framework, a model that can comprehensively analyze artificial intelligence applications is constructed. Common open source deep learning frameworks are: Caffe. Caffe from the University of California, Berkeley is very common. Theano has derived a large number of deep learning Python packages, the most famous of which are Blocks and Keras. It has been ten years since Torch was born, but because Facebook opened up many deep learning modules and extensions for Torch last year, its progress is very smooth. Another feature of Torch is the use of the less popular programming language Lua. Brainstorm is a promising deep learning software package from IDSIA, a Swiss artificial intelligence laboratory that can handle hundreds of ultra-deep neural network layers. There are two manifestations of intelligent applications based on the open source deep learning framework, namely deep development and non-technical design. For a topic system, it has many structural features, such as topics, models, etc. For an ordinary user, it can also contain multiple data sources of different types. The intelligent application system based on the deep learning framework is composed of open source databases, artificial intelligence and other related technologies, which together provide services for the realization of user needs.

2.3 Processing Methods and Systems for Artificial Intelligence Applications The problems that the application of artificial intelligence needs to solve are: insufficient machine learning capabilities. When intelligent algorithms process data, there will be a lot of redundant information, rather than being able to perform accurate calculations in the true sense. Intelligent algorithms cannot meet the requirements for system control performance in complex environments. Traditional technology cannot adapt to the characteristics of large capacity, high computing speed and huge storage capacity. Artificial intelligence is an application of computer language programming technology (AI) that cannot simulate the functions of the human brain. Machine learning methods have shortcomings in solving practical problems. Multi-source parallel. In the development of artificial intelligence applications, different types of information need to be processed, such as data, images, and semantics. There are multiple sources when performing intelligent analysis. Therefore, it is necessary to adopt a variety of ways to obtain the original information. Algorithm is automatically selected. Artificial intelligence applications need to adopt corresponding strategies to meet the needs of users according to different stages and specific conditions. Improving the practicability of artificial intelligence applications to make computer technology widely used, we first need a good environment. Under

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this premise, it is optimized and transformed. The second is to apply artificial intelligence to specific fields. Finally, it is through the use of new algorithms to solve existing problems and the use of neural networks and other methods to deal with difficulties or errors in the relationship between complex systems and nonlinear data. The artificial intelligence is controlled to make it have a certain degree of intelligence. For some non-computer programs that do not require human participation, they can be realized through automatic analysis and calculation.

3 The Face Recognition Training Experiment Test of the Open Source Deep Learning Framework 3.1 System Design The entire system includes a registration information module, a detection module, and a recognition module. Users only need to register their facial information and register their names in the early stage. After personal information registration is completed, if the camera captures their facial images, the system will automatically recognize their name, identity, and Output corresponding information. As an attendance system, this system uses the name information of all teachers and students in the laboratory building as IDs, which is simple to operate, and the recognition results are efficient and stable.

3.2 Experimental Environment (a) Software Environment. This system is based on the Visual Studio 2018 programming environment under the windows 10 operating system, with the OpenCV9 visual function library, MySQL database, and the recognition system interface written by MFC. (b) Hardware environment. This system uses a six-point infrared touch all-in-one machine. The specific hardware information is as follows: Processor: Intel(R) Core(TM) i7-4799 K [email protected] GHz. Motherboard: Mini-ITX H81. Network card: Gigabit network card. Graphics: Integrated graphics, basic frequency 350 MHz, maximum dynamic frequency 1150 MHz. Hard Disk: 1 T.

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Table 1 System test effect at different thresholds Threshold

0.65

0.68

0.71

0.74

Recognition time

0.7

1

1.3

2

Semblance

0.65

0.68

0.73

0.76

3.3 System Design Framework The design framework of the whole system is as follows: First, upload the photo information collection, make the teacher and student photos in the laboratory into an adult face database, and convert the face database into a face feature database through feature training. The front end uses the video stream obtained by the camera to detect whether there is a human face in each frame of the image. If there is a face, mark the face frame and extract the features of the face, use the features of the face to be recognized and the feature database to perform feature matching, calculate the cosine distance of the two vectors, and finally output the recognition result.

4 Experimental Test Results 4.1 Analysis of Face Recognition Results Under Different Thresholds According to the experimental tests conducted in this article, the standards are different from the actual test results under different thresholds, as shown in Table 1: As shown in Fig. 1, we can see that as the similarity threshold increases, the time it takes for the system to recognize face information gradually becomes longer, but the threshold is prone to erroneous recognition results when the threshold is low.

4.2 System Test Results Under Different Side Face Angles Different side face angles also have an impact on face recognition. Among them, this article selects the data results that can identify the image for description, as shown in Table 2. As shown in Fig. 2, we can see that the recognition effect of the system is the best when the face is 0° (i.e. the front face), and the angle of the side face gradually increases. If it is as large as 30°, the recognition speed of the system will slow down, and the recognition effect will be affected accordingly.

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0.65 2.5

0.68

0.71 2

Data

2 1.5 1

0.74

1.3 1

0.68

0.7

0.65

0.76 0.73

0.5 0 Recognition time

Semblance

Standard Fig. 1 System test effect at different thresholds Table 2 System test effect at different side face angles Side face angle

0

10º

20º

30º

Recognition time

0.6

0.9

1.2

1.5

Semblance

0.99

0.92

0.85

0.78

30º

20º

10º

0

0.78 0.85

Semblance

Test

0.92 0.99

1.5 1.2

Recognition time

0.9 0.6 0

0.5

Unit

Fig. 2 System test effect at different side face angles

1

1.5

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5 Conclusion In the deep learning framework, we mainly use data mining and analysis to better process massive amounts of information. Artificial neural networks can effectively identify and analyze complex information when dealing with problems, but they also have certain shortcomings. Based on the artificial intelligence processing of the open source deep learning framework, a large amount of data is pre-trained, and its laws are analyzed in combination with the problems generated by users in the process. Applying it to the face recognition intelligent system can calculate and detect more effectively. Acknowledgements Jiangxi Province Educational Science “14th Five-Year Plan” Project (21YB286).

References 1. Caropreso R, Fernandes R, Osorio D et al (2019) An open-source framework for smart meters: data communication and security traffic analysis. IEEE Trans Indus Electron 66(2):1638–1647 2. Drozdowicz J (2021) The open-source framework for 3D synthetic aperture radar simulation. IEEE Access 99:1 3. Whatmough P, Donato M, Ko G et al (2020) CHIPKIT: an agile, reusable open-source framework for rapid test chip development. IEEE Micro 40(4):32–40 4. Eroglu O, Boyd DR, Kurum M (2020) The signals of opportunity coherent bistatic scattering simulator: a free, open source simulator framework. IEEE Geosci Remote Sens Mag 99:1 5. Oughton EJ, Katsaros K, Entezami F et al (2019) An open-source techno-economic assessment framework for 5G deployment. IEEE Access 99:1 6. Mahajan A, Singh M, Mansotra V (2018) An open source threat detection engine with visualization framework to uncover threats from offline PCAP files. Natl J Syst Inf Technol 11(2):85–98 7. Choi D (2020) Development of open-source motor controller framework for robotic applications. IEEE Access 99:1 8. Mokhati F, Marir T, Silem A et al (2019) NorJADE: an open source JADE-based framework for programming normative multi-agent systems. Int J Open Source Softw Process 10(2):1–20 9. Qasim I, Anwar MW, Azam F et al (2020) A model-driven mobile HMI framework (MMHF) for industrial control systems. IEEE Access 99:1 10. Anonymous (2018) The development of artificial intelligence needs to be wary of deep learning frameworks. Softw Integr Circ 000(002):92–94 11. Ventura M, Veiga J, Coheur L et al (2020) The B-Subtle framework: tailoring subtitles to your needs. Lang Resour Eval 54(4):1143–1159

A Novel Association Rules Mining Based on Improved Fusion Particle Swarm Optimization Algorithm Qing Tan and Libo Sun

Abstract Association rule learning is to mine the correlation between items and objects from certain data or other information carriers, and the results can reveal the hidden association patterns in the data. This paper firstly analyzes the advantages and disadvantages of the traditional particle swarm optimization algorithm, and finds the existing problems. Aiming at these problems, this paper improves the particle swarm optimization algorithm based on the random search theory of complex groups. In this paper, the improved fusion particle swarm optimization algorithm is used in the association rule mining model, and more effective rules can be obtained. Experiments show that the improved fusion particle swarm optimization algorithm proposed in this paper is feasible and efficient in mining association rules. Keywords Fusion model · Particle swarm optimization · Association rules · Apriori algorithm · Frequent item set

1 Introduction At present, the related research on association rule mining is relatively mature, generally using precise algorithm or intelligent algorithm to solve it, such as Apriori algorithm, FP-growth (frequent pattern growth), Eclat algorithm, etc. are typical accurate algorithms [1]. The particle swarm optimization algorithm randomly selects a group of particles as the initial particle swarm in the implementation process, each particle is a possible solution, and the algorithm finds the optimal solution through iteration [2]. During Q. Tan (B) School of Information Technology, Luoyang Normal University, Luoyang, Henan, China e-mail: [email protected] L. Sun Binghamton University, State University of New York, Binghamton, USA © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_12

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the algorithm iteration, the particles update themselves based on two extreme values, the first extreme value. This paper starts from the structural analysis (that is, using the dependency structure of “local functional dependency” in the database schema normalization to analyze that the frequent occurrence of redundant information will lead to redundant information. The association rule mining is processed into frequent items and corresponding association rules), and no new evaluation measures are introduced, respectively, based on the main attributes in the database schema design and the influence of new attributes in the frequent (K + 1) item set on the generated association rules. Use MapReduce to set parameters and update synaptic weights, then improve the energy function in the initial algorithm, align it with the standard energy function, and use the memristive value to represent the weight, amplify the bias and weight, and form association rules Mining models. Although the above models have achieved relatively satisfactory research results at this stage. In this paper, the method of connecting first and then pruning is used to process the selected candidate sets to obtain frequent itemsets. This method requires two scans of the global transaction database [3]. Since the traditional PSO algorithm was proposed, many scholars have studied from the perspectives of parameters, convergence, motion trajectory, topology structure, etc., and made improvements based on its shortcomings to improve the performance of the algorithm. A PSO algorithm with a shrinkage factor χ is proposed to avoid the “explosion” or “random walk” that occurs when the particle velocity and position grow to infinity, and to ensure the convergence of the algorithm while expanding the exploration range of the particle. Many foreign researchers have proposed improved measures to generate the same candidate set without scanning the transaction database multiple times [4]. Although the computational efficiency of these algorithms has been improved, the efficiency of the algorithm can be significantly improved only if the parameter setting values in the Apriori algorithm framework are improved at the same time, that is, the minimum confidence and minimum support are relatively large, or some data constraints are set. Aiming at dynamic data, this paper proposes an incremental association mining algorithm based on AP-CAN. On the basis of introducing the AP clustering algorithm, the algorithm optimizes the data by changing the sorting method of the data volume, and adds a hash function-based algorithm. The auxiliary storage structure improves the item header table.

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2 Analysis of Improved Fusion Particle Swarm Optimization Algorithm The PSO algorithm mainly uses two methods to encode the rules when mining association rules: the first method is the Pittsburgh approach, which focuses on the characteristics of all rules, and each particle is represented as a set of rules [5]. Therefore, this method has a large amount of calculation and poor portability, and is more suitable for solving the problem of mining classification rules; the second method is the Michigan approach, which focuses on the quality of a single rule. Each particle is represented as a rule, and only some of the rules in the data need to be encoded. Association rules refer to the correlation rule characteristics of two or more variables. The data in the database is usually not isolated, but there is some kind of relationship. Generate association rules from these high-frequency names. Using noisy frequent itemsets to find association rules, high-confidence rules can theoretically be determined, but in practice only very high-frequency rules are retained, while a large number of medium-frequency rules are discarded. An algorithm that can adaptively set support thresholds and extract high-confidence rules is proposed. The algorithm sets multiple support thresholds, that is, sets a specific minimum support threshold for the true support of each item in each item, extract rules that reflect the nature of the item itself, and use random truncation and uniform partitioning to reduce the dimensionality of the dataset. Definition 1 Given a relational schema R, X and Y are subsets of the attribute set U, and functional dependency is a proposition of the form X → Y, as long as r is the current relation of R, for any two tuples t and s in r, all have t[X] = s[X] implying t[Y] = s[Y], then FD X → Y is said to hold in relational schema R. γˆ (n) (m|m ) = γˆ (n) (m, M) = ϕw (m − 1)x(m ˆ − 1, M) +

M ∑

K

(η) [

z(m, s) − Ψw (m, s)γˆ (η) (m, s)

]

(1)

s=1

where in Eq. (1), m is the interest degree standard based on the idea of difference, z is introduced, and the interest degree is defined from the difference which helps users to choose more meaningful association rules among many rules and saves a lot of time for analyzing rules [6]. In order to better mine association rules effectively. Therefore, the algorithm is described as follows: Input: DB original data set, Lk is the item set in DB, db is the new data set, s is the degree threshold. Output: frequent itemsets L' k in DB + db.

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Convention: W is the item set of the original k, C is the candidate set for the new k, L' k is the frequent itemset in DB, and P is the infrequent itemset. Step 1: The specific row and column compression process has been introduced in the association rule mining algorithm based on compressed matrix; Step 2: The attribute set of the relational schema R is U, and X is a subset of U; Step 3: Its function is to limit the absolute value of the degree of interest in the range of [0, 1], fconf (X → Y)-fsup(Y) is expressed as the difference between the probability of the occurrence of the consequent Y under the influence of the antecedent X and the probability of its occurrence. Definition 2 Adjacent datasets. For the two data sets D1 and D2, if D1 − D2 1 = 1 is satisfied, then the data sets D1 and D2 are adjacent data sets. If X → U holds on R, then X is said to be a superkeyword of R. If X → U holds on R, but X1 → U does not hold for any proper subset X1 of X, then X is said to be a candidate key on R. The AP-CAN tree scale is small, there are only 7 nodes in the tree, and the method of first clustering and then sorting according to the amount of data is significantly smaller than the tree scale obtained by the traditional sorting method [7]. The improved AP-CAN tree is different from the traditional FP tree and has the following advantages: the improved AP-CAN tree only needs to scan the database once to construct the tree, while the FP tree needs to scan the database twice, saving The time required to scan the database improves the efficiency of subsequent mining. The greater the difference between the confidence of the rule X → Y and the support of the consequent Y, which is more interesting [8]. The interest degree criterion based on the idea of difference is mainly to avoid the high-confidence rule directly generated by the consequent Y with high support degree. A rule is composed of two different itemsets, represented as X is called the premise, and Y is called the conclusion. For example, a rule in a supermarket {butter, bread}{milk} means that if butter and bread have been purchased at the same time, then milk will also be purchased. Some useful rules are selected from all possible rules. The CAN-tree algorithm uses the method of first tree building and then mining to find the frequent itemsets in the transaction [9]. The tree building process: (1) Design an item header table for the tree according to a specific sequence (generally including lexicographical order, alphabetical order, etc.); (2) to create a root node root of the tree; (3) Globally scan the database, sort the items of each transaction according to the item header table, and perform the node insertion operation of the tree for each item x after sorting.

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3 Association Rules Mining Based on Improved Fusion Particle Swarm Optimization Algorithm Based on the Snort intrusion detection system, this paper conducts data mining on its massive log to dependence on the scale of the data set. The second step executes the loop part, which is mainly realized by two functions: AprioriGen and InitLK [10]. The FP-growth algorithm uses the pattern growth mining method, and proposes a FP-tree structure to highly compress the data [11]. Too much, resulting in a lot of memory overhead, resulting in a significant drop in algorithm efficiency. Therefore, the FP-growth algorithm is suitable for low-dimensional Boolean association rule mining. Its value and minimum support are the same. Confidence affects the generation of rules, but not the generation of itemsets. The number of rules generated depends on the value of the minimum confidence set. Definition 3 If the relational schema R is 1NF, and each non-primary attribute is fully functionally dependent on the candidate key, then R is said to be a schema of 2NF (SecondNormalForm). If every relational schema in the database schema is 2NF, then the database schema is called a 2NF database schema. The general algorithm truncates long transactions in order to reduce the global sensitivity. Considering that transaction truncation will cause certain information loss, this paper designs an association rule algorithm based on the dual condition mechanism to segment long transactions, namely the TS-ARM algorithm. The TSARM algorithm consists of two parts, namely data preprocessing and association rule mining. Data preprocessing is the process of generating new data sets after dividing long transactions. W (Y, k) =

∑ i j ∈Y

wj +

k−q ∑

wr j

(2)

j=1

In formula (2), support(Y, k) is the number of occurrences of transactions contained in ARi and ARj in the database, and support(ARi) is the number of occurrences of transactions contained in ARi in the database. If the rules ARi and ARj do not have a similar relationship, then Simi[i, j] = 0. This evaluation standard comprehensively considers the similarity of the probability distributions of the support degrees of the rules ARi and ARj, and can dig out more effective rules by screening the mined similar rules. The CAN-tree stores all the data in the transaction set. When the number of transaction sets or support changes, the tree does not need to scan the original transaction set, but the large amount of stored data makes the construction of the CAN-tree too large and occupies the system memory. The Eclat algorithm uses the depth-first mining method. Different from the FPgrowth and Apriori algorithms, this algorithm converts the data into a vertical format

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and only needs to scan the database once, which greatly reduces the I/O consumption and has high operating efficiency. However, the vertical data format relies heavily on memory. In large-scale data sets, the frequency of items appears higher, the frequent itemsets constructed are too large, and the processing of intersection operations needs to consume a lot of memory, which reduces the efficiency of the algorithm. The concept of association rule mining is specifically described as follows: an item set I = {i1, i2, …, in} consists of n different items, D = {t1, t2, …, tm} is called a data set, where tk, k ∈ [1, m] is called a transaction. A property of the Apriori algorithm is used in the pruning step, that is: the subset of the maximum frequent itemset must also be the maximum frequent itemset, which can effectively Improve the accuracy of correlated alarm data mining. The process of Apriori mining the largest frequent item set, in which the support degree is set to 2, TID is the transaction identifier, and items is the specific item contained in the transaction.

4 Experiment and Analysis In order to verify the validity of the mining method, the main frequency of the running environment host CPU is 2.5 GHz, the memory capacity of 4 GB is used in the experiment, the Windows 10 operating system is used, the C++ language programming code, and the data structure is defined in C++ STL Standard Template Library. This part mainly includes two operations: remove irrelevant data items. The database contains many alarm items in the Snort alarm log. Since each item contains a large amount of irrelevant data, in order to improve the efficiency of the system, before the correlation analysis is performed, the irrelevant data items are eliminated and the hidden data items are preserved. Export data according to the Apriori algorithm. According to the input settings of Apriori, two parts of data are fetched according to requirements: a candidate set and an item set, and stored in ArrayList format, the size of the array can be flexibly set, and elements can be dynamically increased or decreased. Each particle is associated with the globally optimal particle. In addition to the well-known topological structure, some scholars have proposed other topological structures, and proposed a hierarchical particle swarm optimization (HPSO) algorithm based on tree topology. For the above reasons, most of them involve PSO algorithm to solve the association rule mining problem The Michigan method is used in the literature, which can more effectively mine high-quality prediction rules and rare rules. Taking the Michigan method as an example, assuming that there are N items in the database, each item contains two parts VN1 and VN2, the value of VN1 indicates whether the item appears in the rule, and the value of VN2 indicates that the item is a rule antecedent or rules. Under the condition that the minimum support is 0.02, 0.04, 0.06, 0.08, 0.10, the mining time of the three algorithms, the FP-growth algorithm needs a lot of sorting

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Fig. 1 Comparison of FPSO and BPSO algorithms in association rules mining

work when the amount of data increases, while the CAN-tree algorithm requires a lot of sorting work. A large number of infrequent items are retained, and a large number of new nodes are added, which also takes part of the time. Therefore, the mining time of the FP-growth algorithm and the CAN-tree, the mining efficiency of the AP-CAN algorithm is more significant. However, it can be seen from Fig. 1 that the running time of the algorithm in this paper is always smaller than that of the BPSO algorithm, and this advantage becomes more and more obvious as the scale becomes larger, so it fully shows that the algorithm in this paper is efficient under the premise of changing the population size. C Find frequent items in W, add them to L’k, and add infrequent items to P. Prune C to remove infrequent entries of C in db. Scan the db and calculate the C support in the DB. Frequent terms are added to L’1. The frequent items in the above process are all added to P, the transactions containing the items in P are removed, and then L’1 is returned. The statement to generate association rules is: R = AssociationRules(L, c), where input L is the output item of the statement to generate frequent itemsets, c is the minimum confidence set in advance, and the format is double; the output result R is the obtained Association rules, stored in List format; the function AssociationRules is used to search all non-empty subsets of each largest itemset A. As a classic association rule mining algorithm, the core idea of Apriori algorithm is to generate frequent itemsets through a layer-by-layer search of candidate sets, which effectively reduces the scanning time for infrequent items. Assuming that the dataset contains n items and the itemset length is k, which requires a lot of memory and computation time, as is shown by formula (3). P(x) = √

) ( (x − μ)(x − μ)T exp − 2σ 2 2π σ 2 1

(3)

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In formula (3), P(x) is an represents the amplitude of the time series data planning model. For a continuous time series data, the distributed time series data structure analysis is mainly carried out. The model mainly realizes two functions: ➀ Association rule mining function. By mining the borrowing history of readers, some valuable association rules can be found, and hidden rules can be summarized. ➁ Personalized service function. Implement the association rules obtained in the previous step into the personalized recommendation service of the specific smart library. The platform adopts B/S mode, and the server operating system selects Windows Server 2008. The development environment uses VisualStudio2010, and the development language is C#. Reader Data storage tool select SQLServer2008 database. In the experiment, the GRBPSO algorithm is compared with the BPSO algorithm from the number of iterations. The parameters of the algorithm are set as the population size K = 80, the inertia factor ω = 0.2, the learning factor C1 = C2 = 1, and the maximum particle velocity is 2. The minimum support in the algorithm is set to 0.3, the minimum confidence (min_conf) is set to 0.6, and the minimum lift (lift) is set to 4. The Apriori algorithm and FP-growth algorithm based on the Hadoop MapReduce model, the Eclat algorithm based on the Spark RDD framework, etc., while the PSO algorithm has essential parallelism, each particle is an independent individual, and its fitness value calculation and motion process are all different. are parallel. Therefore, designing a parallelized PSO algorithm to more effectively deal with the mining of association rules in massive data is a topic worthy of in-depth study.

5 Conclusion Experiments show that the algorithm has higher mining efficiency and faster convergence speed. The time complexity of the HUIM-IPSO algorithm is the same as that of the traditional PSO algorithm. Assuming that the number of particles in the PSO algorithm is m, the number of iterations is N, the operation time required for each iteration of each particle is T, and the total operation of the algorithm is The time is m × N × T. When mining frequent itemsets, the algorithm in this paper selects the initial particle swarm probabilistically, so the probability that the particle position is 0 becomes larger during the preprocessing process, resulting in poor mining results. But on the whole, the algorithm in this paper is feasible to mine frequent itemsets in big data, and the mining results are also considerable.

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References 1. Lv L, Wu Z, Zhang L, Gupta BB, Tian Z (2022) An edge-AI based forecasting approach for improving smart microgrid efficiency. IEEE Trans Industr Inf 3:1–1 2. Blackwell T, Kennedy J (2018) Impact of communication topology in particle swarm optimization. IEEE Trans Evol Comput 23:689–702 3. Xu H, Fan G, Song Y (2022) Novel key indicators selection method of financial fraud prediction model based on machine learning hybrid mode. Mob Inf Syst 2022 4. Jadoon RN, Awan AA, Khan MA, Zhou W, Malik AN (2020) PACR: position-aware protocol for connectivity restoration in mobile sensor networks. Wirel Commun Mob Comput 2020 5. Zhang L, Tang S, Lv L (2020) An finite iterative algorithm for solving periodic Sylvester bimatrix equations. J Franklin Inst 357(15):10757–10772 6. Moslehi F, Haeri A, Martínez-Álvarez F (2020) A novel hybrid GA-PSO framework for mining quantitative association rules. Soft Comput 24(6):4645–4666 7. Xu H, Fan G, Song Y (2022) Application analysis of the machine learning fusion model in building a financial fraud prediction model. Secur Commun Netw 2022 8. Telikani A, Gandomi AH, Shahbahrami A (2020) A survey of evolutionary computation for association rule mining. Inf Sci 524:318–352 9. Zhang L et al (2021) Weather radar echo prediction method based on convolution neural network and long short-term memory networks for sustainable e-agriculture. J Clean Prod 298(2021):126776 10. Gueye I, Dioum I, Diop I, Keita KW, Ndiaye P, Diallo M, Farssi SM (2020) Performance of hybrid RF/FSO cooperative systems based on quasicyclic LDPC codes and space-coupled LDPC codes. Wirel Commun Mob Comput 2020 11. Khalil A, Minallah N, Awan MA, Khan HU, Khan AS, Rehman AU (2020) On the performance of wireless video communication using iterative joint source channel decoding and transmitter diversity gain technique. Wirel Commun Mobile Comput 2020

Comparative Analysis of Machine Translation (MT) and Computer Aided Translation (CAT) Tingting Wang and Venugopal Sridhar

Abstract Since the emergence of MT, its fast translation speed and declining cost have been favored by the whole society. In order to make MT more useful, computational linguists have been committed to improving the accuracy of MT. By discussing the advantages and disadvantages of computer-aided translation and the advantages and disadvantages of MT, this paper compares the changes of translation accuracy, recall rate and word reasoning ability under different sentence lengths under different translation modes of MT, computer-aided translation and human translation through the test of machine frame model algorithm. The results show that in the length range of 0–10, the translation accuracy of the three translation models has reached more than 85%, which shows that the additional non top-level information of the encoder has an obvious gain for the model in translating shorter sentences; In the length range of 10–20, the translation accuracy does not decrease significantly; In the range of 30–40, the accuracy of sentence translation begins to decline significantly, especially in MT. With the increase of sentence length, the accuracy decreases to 70% in the range of 40–50; The decline of computer-aided translation is not obvious, and the accuracy is 79% in the length of 40–50; The accuracy of human translation is the most stable, maintained at more than 85%; There is little difference in the recall rate, which has reached more than 85 points, but the score of word reasoning is slightly different. The highest score is MT, which has reached 88 points, which fully proves the effectiveness of MT. Based on the above experimental test results, it can be seen that MT and computer-aided translation have their own advantages, and there is little difference in the overall level. For the translation accuracy, computer-aided translation combines human translation on the basis of MT, and computer-aided translation has more advantages than MT. T. Wang (B) Changchun University of Finance and Economics, Changchun, Jilin, China e-mail: [email protected] V. Sridhar Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_13

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Keywords Machine translation · Computer translation · Auxiliary translation · Translation comparison

1 Introduction With the increasing proportion of translation in daily life, computer-aided translation, as a convenient tool, has become a form of translation that people are used to. However, because people don’t know much about this aspect, they often can’t make rational use of computer-aided translation. Through my own practical experience, the author explains how to realize the quality control of computer-aided translation from the aspects of term preparation and correction, the utilization of memory and matching functions, the updating of memory bank, the proofreading and modification of translated translation and so on. It is hoped that through this article, more people will have some understanding of computer-aided translation and realize the various conveniences it can bring to translators; It is also hoped that people will realize that computer-aided translation has the advantages that manual translation does not have, but the convenience it brings is based on the translator’s own translation quality. Even if there are more unified terms and more corpus resources, only the translator himself has the power good translation skills. Many scholars at home and abroad have studied the comparative analysis of MT and computer-aided translation. Alarifia introduces an optimized cognitive assisted statistical MT process to reduce these difficulties. This process uses supervised machine learning technology for natural language processing, and aims to translate phrases with higher accuracy than other support vector machines, linear regression, decision trees, naive Bayes and k-nearest neighbor MT technologies. Semantic operations are used to check the collected messages. These messages are processed in the network, and the results are stored in memory to obtain accurate translation [1]. Dtorretrosa proposes a black box approach that does not require access to the internal working mechanism of the bilingual resources used to generate recommendations: this new approach allows new sources of bilingual information to be included almost seamlessly. It is the first time to compare the glass box method and the black box method by automatically evaluating the translation tasks between related and unrelated languages. It shows that in our setting, using these two methods can save 20–50% of keystrokes, and the performance of the black box method is better than that of the glass box method. In one fifth of the six cases operated under similar conditions, the performance of the black box method is better than that of the glass box method [2]. This paper analyzes the advantages and disadvantages of MT and computer-aided translation, and compares them. Through the machine frame model algorithm, it compares its accuracy and word reasoning ability, which can comprehensively use a large amount of data analysis and calculation ability, so as to achieve a good translation effect. Moreover, with its remarkable effect, it also promotes the development of hardware that provides computing power. The comparative analysis of MT and

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machine aided translation can not only solve the urgent needs of today’s society, but also have more practical significance. It also promotes the development of computer science and computing power, and has important research value and social value [3, 4].

2 Comparison Between MT and CAT CAT refers to the use of computer as an auxiliary tool to help people translate. Different from MT, computer-aided translation does not directly let the machine translate, but let the translator participate in the whole translation process. Through the use of this tool, the translation efficiency is greatly improved compared with the past. CAT stores previous translated texts by establishing a memory bank.

2.1 Advantages and Disadvantages of Quality Control in CAT System Nowadays, people have more and more needs for translation. The traditional manual translation is sometimes difficult to deal with such a huge translation task, let alone ensure the quality of translation in the translation process. However, computer-aided translation can overcome the defects of manual work, give full play to its advantages and provide convenience in the process of quality control. Compared with traditional manual translation, computer-aided translation has the following three advantages: First, in the environment of computer-aided translation system, the unity of terms can be guaranteed because of the existence of translation memory function. At the beginning of translation, the translator establishes a term base, which is checked and imported into the translator’s respective software. In the whole project, the terms seen by different translators are the same translation standard, so there is no difference in terms caused by different word selection by translators. It’s not just the consistency of terms. Because there are too many translated texts, there will always be similar or even the same sentence segments; The translator can ensure the unity of translation style according to the reference text given by the memory bank, so as to ensure the quality of translation [5, 6]. Second, in the environment of computer-aided translation system, the software often has the function of spell checking, so the spelling errors of translators are avoided to a great extent and the error rate is reduced. Third, in the system environment of computer-aided translation, the processing of format will no longer bother translators, and the quality control of computeraided translation will be compiled by software. In addition, since they are no longer confined to traditional paper texts, the sharing of information between translators will also be very convenient, not affected by the factor of distance; In addition, the unified

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standards formulated by translation have greatly improved the efficiency and effect of team translation, thus improving the level of translation quality control. Although computer-aided translation system can bring great convenience to the translation process, it should be recognized that computer-aided translation still has defects in translation quality control [7, 8]. Because computer-aided translation software divides sentences into sentence segments, which reduces the integrity of the article. Translators can only translate in the order of separate sentence segments. However, the structure of English and Chinese is different, so it is impossible for the translator to adjust the order of segmented sentences in the process of translation, which limits the translator’s subjectivity and creativity in the process of translation and will reduce the level of translation quality. Nowadays, more and more translators begin to use computer-aided translation software. CAT system can indeed give play to many advantages that can not be reflected in the traditional way, and provide a new idea and working method for translators; However, it should be objectively recognized that although computeraided translation technology will continue to improve, the quality of translation depends on the quality of translators in the final analysis. Only when translators have high translation literacy and use computer-aided translation, can they give full play to its convenience and ensure the quality of translation. In the computer-aided translation environment, the document is no longer in paper form, but presents the original text and translation in the form of electronic manuscript, so that the translator can complete the translation. Due to the different presentation methods, the translator can use software to present the original text and the translation on the same page in the process of translation, so that the translator does not have to look through the paper manuscript and check the quality of the translation as before. In the computer-aided translation environment, the translation process is closely linked. From the understanding of basic concepts and the determination of terms to the translation of later articles and the proofreading of translation, each link has a unified standard, which not only ensures the consistency of translation, but also makes the team cooperation more rigorous, reduces a lot of unnecessary labor, and greatly improves the process of quality control [9, 10].

2.2 Advantages and Disadvantages of MT MT has gradually become the preferred tool for people to translate foreign languages. Just paste and copy, and tens of thousands of words have been translated in a few seconds. As a result, many people are terrified. Will those who study translation be eliminated in the future? This is not the case. Although the speed and accuracy of MT can not be ignored. Therefore, it is not difficult to see that future translators not only need to understand translation, but also need to be able to use tools. MT has advantages in the use of information texts. At the lexical level, the problems of MT include lexical redundancy and singular and plural mistranslation; At the syntactic

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level, MT mainly has problems such as subject mistranslation, logical relationship confusion and tense mistranslation [11, 12]. Based on neural network, MT adopts optimization algorithm to directly obtain translation resources with high probability matching from parallel corpus, and generate translation according to the grammar rules of the target language. Therefore, there will be many errors in the translation results, which need to be edited and sorted out by the translator. More often, MT focuses on the translation of single words and sentences, but lacks the connection between context and text. Therefore, in the process of post-translational editing, translators need to consider more from the perspective of syntax and text.

3 Frame Model Algorithm of MT The log linear model can be used to model directly. Under the framework of maximum entropy model, it is assumed that E and F are target language and source language sentences, K1 (E, f), K (E, f), Kn (E, f) are n features (n = 1, 2, …. n) on E and f respectively, and there is a model parameter in each feature function γn . The translation algorithm is shown in formula (1):   N exp n=1 γn kn (e, f )   Pr(h| f ) = pγ (h| f ) =  N ', f ) exp γ k (e ' n n e n

(1)

For a given F, its best translation e can be expressed by formula (2): eˆ 1L

 N   max  L J L J = arg max Pr(e1 |e1 ) = arg L γn kn (e1 , f 1 ) e1 n

(2)

4 Testing and Analysis of Different Translation Modes For sentence sets with different lengths, different models are used for translation, including MT, computer-aided translation and manual translation. The logarithmic linear model—the framework model algorithm of MT is used to compare the changes of translation accuracy of the three translations under different sentence lengths. The test results are shown in Table 1 and Fig. 1: It can be seen from the histogram that the translation accuracy of the three translation models has reached more than 85% in the length range of 0–10 according to the scores of sentence sets with different lengths, which shows that the additional non top-level information of the encoder has a significant gain for the model in translating

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Table 1 Translation accuracy of sentences with different lengths under different translation modes 0–10

10–20

20–30

30–40

40–50

MT

87%

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70%

65%

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90%

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84%

77%

69%

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88%

87%

82%

78%

100% 90% 80%

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70% 60% 50% 40% 30% 20% 10% 0% 0-10

.10-20

20-30

30-40

40-50

Translation accuracy of sentences with different lengths under different translation modes MT

CAT

human translation

Fig. 1 Translation accuracy of sentences with different lengths under different translation modes

shorter sentences; In the length range of 10–20, the translation accuracy does not decrease significantly; In the range of 30–40, the accuracy of sentence translation begins to decline significantly, especially in MT. With the increase of sentence length, the accuracy decreases to 70% in the range of 40–50; The decline of computer-aided translation is not obvious, and the accuracy is 79% in the length of 40–50; The accuracy of human translation is the most stable, maintained at more than 85%. With the continuous increase of sentence length, the overall translation accuracy of all models is declining, which is in line with people’s general cognition. The longer the sentence length, the more complex the possible dependencies in the sentence, so the difficulty of translation will rise. Word reasoning task is used to test the ability of word vectors output by the model to infer from each other according to semantics. Next, measure the word reasoning score and recall rate in MT, computer-aided translation and manual translation modes. The test results are shown in Fig. 2: As can be seen from the above figure, the recall rates of MT, computer-aided translation and manual translation modes are almost the same, reaching more than

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MT CAT human translation Word reasoning score and recall rate of different translation modes Word reasoning

recall

Fig. 2 Word reasoning score and recall rate of different translation modes

85 points, but the score of word reasoning is slightly different. The highest score is MT, reaching 88 points, which fully proves the effectiveness of MT. Based on the above experimental test results, it can be seen that MT and computer-aided translation have their own advantages, and there is little difference in the overall level. For the translation accuracy, computer-aided translation combines human translation on the basis of MT, and computer-aided translation has more advantages than MT.

5 Conclusions With the development of one belt, one road and the deepening of exchanges and cooperation among countries, the demand for translation has been increasing. Relying solely on human translation can no longer meet this demand, so more and more people have done research on MT and computer-aided translation. Combined with computer-aided translation tools, reasonably calculate and allocate the translation quantity, strictly control the translation quality, and make the whole project flow. Therefore, the requirements of the translation market for translators will be higher and higher. In addition to the basic skills, translators also have to master technologies such as information acquisition, text editing, translation project management and localization. It can be said that only translators who fully master various technologies will not be afraid of the challenge of human intelligence and will not be eliminated in the torrent of science and technology. This paper analyzes and compares MT and computer-aided translation, but due to the limited technology at present, the experimental data are not accurate enough, and the research on translation mode needs to be further improved.

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Acknowledgements Social Science Research Project of the Education Department of Jilin Province: A study of Japanese female writers Pseudo-manchukuo “Returning to Experience Literature” (JJKH20221259SK).

References 1. Alarifi A, Alwadain A (2020) An optimized cognitive-assisted MT approach for natural language processing. Computing 102(3):605–622 2. Torregrosa D, Pérez-Ortiz JA, Forcada ML (2017) Comparative human and automatic evaluation of glass-box and black-box approaches to interactive translation prediction. Prague Bull Math Linguist 108(1):97–108 3. Guarin DL, Yunusova Y, Taati B et al (2020) Toward an automatic system for computer-aided assessment in facial palsy. Facial Plast Surg Aesthetic Med 22(1):42–49 4. Goeroeg A (2017) Perspectives. Multilingual Comput Technol 28(1):22–23 5. Wu H (2021) Multimedia interaction-based computer-aided translation technology in applied English teaching. Mob Inf Syst 2021(5):1–10 6. Devale PR, Patil SH (2018) MT need of the time. Int J Comput Sci Eng 6(1):402–404 7. Tariq Z (2020) Remanufacturing aided upgrading of universal testing machine through sustainability assessment modeling. Curr J Appl Sci Technol 39(25):49–84 8. Ajmi C, Zapata J, Elferchichi S et al (2020) Deep learning technology for weld defects classification based on transfer learning and activation features. Adv Mater Sci Eng 2020(1):1–16 9. Imam AT, Alnsour AJ (2019) The use of natural language processing approach for converting pseudo code to C# Code. J Intell Syst 29(1):1388–1407 10. Munoz-Guijosa JM, Lantada AD, Otero JE et al (2019) Using smartphones physical interfaces in engineering education: experiences in promoting student motivation and learning. Int J Eng Educ 35(5):1290–1305 11. Handzel Z, Gajer M (2019) Wybrane Zagadnienia Przekadu Wspomaganego Przez Komputer. Elektronika 60(12):56–58 12. Gu S, Li X (2021) Optimization of computer-aided English translation teaching based on network teaching platform. Comput-Aided Des Appl 19(S1):151–160

Dynamic Monitoring of Sea Reclamation Based on UAV Remote Sensing Technology Monitoring System Ningjun Wang and Tiantian Liu

Abstract The drone is controlled by the on-board computer, so that the drone can replace the artificial travel to the reclamation area and surrounding sea areas, the remote sensing technology in the monitoring system is used to replace the operator to dynamically monitor the sea environment and climate change in the area where the reclamation project is implemented and the surrounding area, and to collect relevant data detected by the monitoring system. In this experiment, data statistics are made on the changes in the coastline of the six bays around a certain sea area, the changes in the area of the bay, the sea usage of some industries, and the connection between the surrounding islands and the land, conduct dynamic monitoring of the reclamation project in this sea area, and analyze the impact of reclamation on the surrounding sea area. Final results showed that reclamation has some influence on the coastline of the Gulf and the Gulf area, the coastline has increased, reducing the Gulf area; the proportion of the use of sea ports, maritime industries, tourism, farming and other industries in the reclamation of respectively 75%, 9%, 3%, 2%, the surrounding sea reclamation to bring some economic benefits; and reclamation to the surrounding islands also caused a corresponding impact, with the reduction in the bay area, more and more islands connected to the mainland, there have been 445 since the island reclamation project lost its independence. Keywords UAV · Remote sensing technology · Reclamation · Dynamic monitoring

N. Wang (B) · T. Liu Panzhihua University, Panzhihua, Sichuan, China e-mail: [email protected] T. Liu Nueva Ecija University of Science and Technology, Cabanatuan City, Philippines © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_14

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1 Introduction 1.1 Background Meaning In the case of land shortage in coastal cities, reclamation is an important way for people to expand their living and living space [1]. Appropriate and orderly reclamation can extend the construction and economic development of coastal cities to a certain extent and reduce cultivated land pressure effectively promotes investment, adapts to marine industrial structure and urban construction, and is of great significance to the management and restoration of the marine and adjacent areas and coastal ecological environment. However, excessive reclamation disorder will cause serious damage to national interests, and likely to cause many environmental problems, so very necessary for the implementation of dynamic reclamation monitoring tools. Our country’s reclamation projects in the coastal areas can alleviate the shortage of land resources in the coastal areas, but due to various reasons, there are a large number of idle phenomena, which form the stock resources of reclamation [2].

1.2 Related Work At present, many scholars have conducted relevant research on dynamic monitoring of reclamation. Vorovencii [3] uses satellite to monitor the change of coastline during reclamation, which effectively avoids over-reclamation. Lim [4] research can monitor the seawater quality in real time in the reclamation project, which can strictly control the seawater quality in the sea area around the reclamation. Nkongolo [5] research shows that the reclamation project will cause serious damage to the soil in the surrounding sea area, and the soil microbial community in the surrounding sea area should be monitored dynamically to ensure the soil quality. Ayad [6] proposed an Internet of Things node for marine environment data acquisition and dynamic monitoring to monitor the marine environment, but the operation steps of this method are too complicated. Dhar [7] uses marine remote sensing classified data to quantitatively monitor the reclamation process, so as to accurately control the reclamation process. There are some shortcomings in the above research. Based on this, this paper makes some improvements to the above research.

1.3 Innovation of This Article Based on the research results and practical experience of previous scholars, this paper studies the dynamic monitoring of sea reclamation based on the UAV remote sensing technology monitoring system, and has made certain innovations in technology and experiment. The innovations of this article are mainly reflected in the following

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aspects: (1) I checked the relevant research and literature of many scholars, and designed the experiment of this article based on the research results of previous scholars; (2) The use of drone remote sensing technology monitoring system to collect data related to sea reclamation has improved the efficiency of the experiment; (3) The dynamic monitoring of sea reclamation has important practical significance and research value in the aspects of society, economy, technology and environmental protection.

2 Dynamic Monitoring of Sea Reclamation Based on UAV Remote Sensing Technology Monitoring System 2.1 UAV Unmanned aerial vehicle (UAV) is an unmanned aircraft that uses radio remote control and autonomous program control devices, or is completely or intermittently operated autonomously by an on-board computer [8]. Early applications of drones include cultural heritage and archaeology, mainly for the recording and modeling of monuments, buildings and landscapes. Compared with ordinary manned aircraft, unmanned aircraft are small in size, low in cost, easy to use, and require low environmental conditions when performing tasks, and can safely and effectively perform high-risk tasks. It makes it possible to obtain high spatiotemporal resolution images within a region.

2.2 Remote Sensing Technology Remote sensing technology is a technical method that uses the physical characteristics of the electromagnetic spectrum to detect and study objects from a distance without direct contact with the target. It has significantly improved people’s vision, provided humans with an advanced technical means to explore the mysteries of the earth [9]. The development of remote sensing technology provides effective technical means for human beings to understand the living environment and use natural resources, however, different sensors have different simultaneous interpretation characteristics for the same scene, and the amount of image data collected from different imaging sensors is huge every day, such as multi-source remote sensing image, multi-spectral image, color image, infrared image, in order to process and utilize these data efficiently and integratedly, multiple source images of the same scene should be recorded and merged [10]. High-resolution remote sensing technology has brought new changes to dynamic monitoring projects in various fields. With the continuous development of aviation technology and the continuous improvement of the performance of remote sensing observation systems, remote sensing technology

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has reached a new height, and the world is competing to research and develop highresolution remote sensing satellites. Remote sensing technology has also been widely used in animal husbandry, aquaculture, military, geography, ocean and other fields, which has promoted the continuous development of science and technology in our country.

2.3 Reclamation Reclamation refers to the construction of dikes on the beach or shallow sea to isolate the external sea water, and drain and drain the water from the dikes to turn the sea into land. It provides a place for the development of agriculture, industry, transportation, foreign trade, etc. Reclamation can create new islands in independent shallow seas. The implementation of reclamation projects will have a greater impact on the marine ecological environment, and the interaction between multiple factors constitutes a type of complex system.

2.4 Dynamic Monitoring Dynamic monitoring refers to the application of data from multiple platforms, multiple times, multiple regions and multiple sources to monitor and detect temporal and spatial changes in earth resources and the environment. The dynamic monitoring of the sea area where the reclamation project is implemented is an effective method for the marine management department to strengthen supervision, regulate the use of the sea and construction of the project, and protect the marine resources and environment.

3 Reclamation Dynamic Monitoring Related Experiments 3.1 Reclamation Related Data Collection UAV dynamic remote sensing technology is used to detect and survey the reclamation and surrounding areas, and use the monitoring system to collect relevant data on the reclamation and surrounding areas, and present the reclamation and surrounding areas in the form of pictures or images. By counting the changes in the coastline around a certain sea area and the changes in the area of the bay, analyze the impact of reclamation on the surrounding bays of the sea area.

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3.2 Construction of a Suitability Evaluation Model for Reclamation Establish relevant evaluation models for the relevant indicators of the reclamation based on the UAV remote sensing technology monitoring system, and use linear weighting to construct different rating indicators the calculation formula is: M=

n 

(Wi × Ui )

(1)

i=1

M =C×

n 

(Wi × Ui )

(2)

i=1

M is the index of related indicators of reclamation,W i is the weight of each Wi = 1, U i is the index indicator of the indicator layer to the target layer, and value of the index layer, C is the limiting factor, and the value is 0 or 1. This experiment uses the extreme value exchange algorithm to standardize various numerical indicators. There are three situations: forward conversion, reverse conversion, and proper conversion. In terms of an index for the forward and reclamation affect a positive correlation, the 100 point scale, each index is calculated as: F=

x − xmin × 100 xmax − xmin

(3)

Type conversion and reverse metrics for reclamation without being affected inversely proportional to that of each index is calculated as follows: F

xmax − x × 100 xmax − xmin

(4)

In terms of the right type at the index value defines the best quality waters, larger or smaller than the defined value, the quality of the waters to the advantage or disadvantage of development are, each index is calculated as:  F=

100(x − xmin )/(xbest − xmin ) 100(xmax − x)/(xmax − xbest )

(5)

In the above formula, x is the actual value of an index, x max is the maximum value of the index, x min is the minimum value of the index, x best is the optimal value of the index.

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Table 1 Coastline length data table Bay

2014

2015

2016

2017

2018

2019

A bay

281.4

277.9

282

287.4

290.4

288.9

B bay

361.7

365.4

358.6

360.1

364.4

367.7

C bay

258.2

264.1

260.5

258

264.3

269.8

D bay

400.6

406.6

400.4

395.5

400.3

407.1

E bay

452.1

455

450.7

456.3

460.7

465.8

F bay

197.3

201

207.6

212.4

215.3

220

4 Dynamic Monitoring of Reclamation 4.1 Changes in the Coastline of the Bay Around a Certain Sea Area Through the drone remote sensing technology monitoring system, statistics of the coastline length and coastline changes of A bay, B bay, C bay, D bay, E bay and F bay around a certain sea area from 2014 to 2019, analyze the impact of the reclamation project on the surrounding bays of the sea area. The coastline length data of the six bays from 2014 to 2019 are shown in Table 1, unit kilometers. According to the data in Table 1, we can see that the coastline of the bay around the sea area changes every year. Where F Gulf coastline is growing year by year, and the rest of the Gulf coastline annual growth also decreased, but by 2019, when the lengths of the Gulf coastline is longer than the length of the coastline in 2014, when the. From 2014 to 2019, the coastline of Bay A has increased by 7.5 km, the coastline of Bay B has increased by 6 km, the coastline of Bay C has increased by 11.6 km, the coastline of Bay D has increased by 11.5 km, the coastline of Bay E has increased by 13.7 km, and the coastline of Bay F has increased by 22.7 km.

4.2 Changes in the Area of the Bay Around a Certain Sea Area According to the space monitoring capability of the UAV remote sensing technology monitoring system, the area of bays A, B, C, D, E, and F in a certain sea area after the reclamation project is implemented, statistics of the changes in the area of these six bays from 2015 to 2019, the final result is shown in Fig. 1, unit: 103 km2 . According to the data in Fig. 1, we can see that the area of each bay in the sea area in 2019 has decreased compared to 2015. The area of Bay A decreased by 0.2 * 103 km2 , the area of Bay B decreased by 0.5 * 103 km2 , the area of Bay C decreased by 0.5 * 103 km2 , the area of Bay D decreased by 1.3 * 103 km2 , and the area of Bay E decreased by 1 * 103 km2 , the area of F bay is reduced by 0.3 * 103 km2 .

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Change in bay area A bay

B bay

C bay

D bay

E bay

F bay

35 30

Area

25

4.5

4.3

4.4

4

6

4.2

5.8

5.5

5.2

5

5.5

5.4

4.8

4.4

5.2

4.7

4.5

4.3

4.3

4.9

5

4.7

5.1

4.6

4.4

4.5

2016

2017 Year

2018

2019

20 5.7 15 4.8 10 5.2 5 4.7 0 2015

Fig. 1 Map of the area of the bay

4.3 Sea Usage of Some Industries in Reclamation Projects Sea reclamation has a certain role in promoting economic development, and reasonable planning of reclamation projects can bring good economic benefits to the sea area and surrounding areas. It has provided valuable land resources for many sectors of the national economy, formed a large-scale grain and cotton production base, and a marine and freshwater agricultural base. The research and statistics of the sea area in the port, coastal industries, tourism facilities, sewage discharge and aquaculture industry and other aspects of the sea use situation, the final statistical results are shown in Fig. 2. According to the data in Fig. 2, we can see that the sea area has the highest proportion of sea reclamation for port construction, reaching 75%, in the coastal industry, the proportion of sea reclamation is 9%, the proportion of sea reclamation in the construction of tourist facilities is 3%, and the proportion of sewage discharge is 11%, the lowest proportion of sea reclamation in the aquaculture industry is 2%.

4.4 Impact of Reclamation on Islands At present, due to the implementation of the reclamation project, many islands have been affected by the reclamation and have been connected to the land. After the reclamation project is implemented, the surrounding areas of A bay, B bay, C bay, D bay, E bay and F bay are The number of land-connected islands and independent islands is analyzed and the impact of reclamation on islands is analyzed. The final result is shown in Fig. 3.

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Sea usage 2% 11% 3% port linhai Industry

9%

tourist facility sewage disposal Farming 75%

Fig. 2 The proportion of sea usage in some areas

Bay quantity statistics Connected islands

Independent island

250 198

Quantity

200

174

50

117

229

179 142

150 100

181

210

total

153

124 104

86 88 64

75

74

68

76

D bay

E bay

F bay

0 A bay

B bay

C bay Bay

Fig. 3 Statistics of the number of connected islands and independent islands

According to the data in Fig. 3, we can see that the reclamation project has caused a certain impact on the islands surrounding the sea area. The number of land-connected islands in Bay A is 88, Bay B has 64, Bay C has 75, Bay D has 74, Bay E has 68, and Bay F has 76. Although the total number of islands in Bay A is the smallest, the number of islands connected to the land around it is the largest after reclamation.

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5 Conclusions In this study, the monitoring system based on UAV remote sensing technology is used to monitor the coastal line change, bay area change, industrial use of sea around the Bay, and the connection between islands and land around the bay. The final monitoring results show that reclamation has a certain impact on the coastline, area and islands around the bay, the implementation of reclamation project shortens the length of the coastline and the area of the Bay, and causes 445 islands around the bay to lose their independence and connect with the land. Some industries around the Bay occupy more sea area in reclamation, which brings economic benefits to the surrounding sea area to a certain extent, and promotes the economic development of the sea area and the surrounding sea area. Acknowledgements 2021 University-level project of Panzhihua University.

References 1. Raslan AM, Riad PH, Hagras MA (2020) 1D hydraulic modelling of Bahr El-Baqar new channel for northwest Sinai reclamation project, Egypt. Ain Shams Eng J 11(4):971–982 2. Bisaro A, de Bel M, Hinkel J (2020) Leveraging public adaptation finance through urban land reclamation: cases from Germany, the Netherlands and the Maldives. Clim Change 160(4):671– 689 3. Vorovencii I (2021) Changes detected in the extent of surface mining and reclamation using multitemporal Landsat imagery: a case study of Jiu Valley, Romania. Environ Monit Assess 193(1):1–24 4. Lim K, Evans PJ, Utter J (2020) Dynamic monitoring and proactive fouling management in a pilot scale gas-sparged anaerobic membrane bioreactor. Environ Sci: Water Res Technol 6(10):2914–2925 5. Nkongolo KK, Narendrula-Kotha R (2020) Advances in monitoring soil microbial community dynamic and function. J Appl Genet 61(2):249–263 6. Ayad AR, Khalifa AK, ElFawy M (2021) Holistic assessment of the Egyptian national land reclamation project from the view of IWRM. J Environ Treat Tech 9(4):776–787 7. Dhar A, Naeth MA, Jennings PD (2020) Geothermal energy resources: potential environmental impact and land reclamation. Environ Rev 28(4):415–427 8. Elkhrachy I (2021) Accuracy assessment of low-cost Unmanned Aerial Vehicle (UAV) photogrammetry. Alex Eng J 60(6):5579–5590 9. Adam M, Rass CA, Storch M (2022) Conflicted landscapes: the Kall Trail. Monitoring transformations of a Second World War heritage site using UAV-lidar remote sensing and ground truthing. Antiquity 96(386):494–499 10. Giordan D, Adams MS, Aicardi I (2020) The use of unmanned aerial vehicles (UAVs) for engineering geology applications. Bull Eng Geol Env 79(7):3437–3481

The Synthesis Model Based on Multimodal Data for Asian Giant Hornets Sighting Report Recognition Feiyang Wu, Shengqiang Han, Jinyi Song, Xinqing Xu, and S. Pradeep Kumar

Abstract Asian giant hornets (Vespa mandarinia), the largest species of hornet in the world, are the predator of honeybee populations and agricultural pests. In light of the enormous damage to local ecological balance, we obtain the experienced linear regression model by multiple linear stepwise regression. Meanwhile, the government has created the helplines and a website so that it’s convenient for people to report sightings of these hornets. Aim at the problem of the classification of the Asia giant hornet accurately, the paper presents a novel ensemble model composed of image recognition and text classification. By the joint decision function, the model integrates the prediction value of the vision transform, LSTM and BP neural network to predict the hornet category finally. Specially, taking into account that complex background and small proportion of the target area in image data, the vision transform comprises accretion onto automatic pruning based on reinforcement learning. The proposed model has been trained and validated on the available data and the accuracy rate is over 88.9%. Keywords Linear regression · Reinforcement · Vision transform · Classification

F. Wu (B) · S. Han · J. Song · X. Xu Business School, Shandong Normal University, Jinan, Shandong, China e-mail: [email protected] S. Pradeep Kumar Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_15

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1 Introduction 1.1 Background of the Asian Giant Hornet Problem The main background of this question is that the large hornet was originally distributed in Asian Japan, Taiwan and other regions, but recently it appeared in the United States, and even recently [1] (around August 2020) a nest of Asian giant hornet was discovered. The larvae of the Asian giant hornet mainly feed on insect larvae such as caterpillars, and the adults mainly eat mature fruit or tree sap. From the case of the American white moth invasion, we can analogize that the Asia giant hornet will not only cause serious damage to the healthy growth of forest trees, fruit trees, flowers and crops in my country [2] but also cause major economic and ecological losses [3]. So a series of related research on the Asian giant hornet is very necessary and has application significance.

1.2 Modeling Plan Aiming at the problem of the spread and identification of the Asia giant hornet, we plan to establish a linear regression model and an ensemble classification model. We use a joint decision function to integrate three different algorithms. The BP neural network fully considers the spatial and temporal density of the big hornet’s survival [4]; the Vision Transform [5–10] extracts image features and outputs the predicted values of the Asia giant hornet image category; the LSTM classifier classifies the text information [11, 12].

2 Multiple Linear Stepwise Regression 2.1 Problem Analysis Theoretically, the flight of bees in any direction is random, but insects have the advantages to avoid harm. Due to the influence of factors, such as distance to food, climate, the probability of flying in each direction is not equal. We should fit a multiple linear regression model to comprehensively consider various factors.

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Table 1 The distance of coordinated and real x1

1

2

3

4

5

6

7

Coordinate distance

4.01

2.97

122.60

7.28

9.27

11.64

37.17

Real distance

1.35

1.36

3.61

3.43

8.67

8.60

25.42

x1

8

9

10

11

12

13

14

Coordinate distance

2.86

5.76

15.38

14.73

14.61

15.38

15.39

Real distance

1.75

5.90

0.01

0.44

0.43

0.01

0.01

2.2 Data Processing We might as well ignore the unequal distance of latitude and longitude, thinking that the longitude and latitude between each degree are equidistant. The most of between the sighting reports is less than 22, and the location is  1 the distance  1 I , I . We use the form of XY two-dimensional coordinates to express Longitude Latitude n n the propagation trend, as shown in Table 1. The actual distance of each point from its nearest emergence point (km). The mortality rate of Asia giant hornet is related to many complex factors. Every time there will be about 35 new queens appearing, exponential growth in form 35t (t is the number Years). It determined that pest can spread up to 30 km at a time. The temperature factor is not only related to latitude, but also related to complex factors such as topography and vegetation coverage. We propose the influencing factors x1 (distance from the origin), x2 (The distance to the mountains). Therefore, Y = σ0 + σ1 x 1 + σ2 x 2 + ε

(1)

σi is the regression parameter, xi is the explanatory variable, Y indicates latitude, and ε is the random error term.

2.3 Results and Analysis The significance of T and F is both less than 0.05. The degree of correlation is high, and the spearman correlation coefficient is very low. The p-value is greater than the significance α = 0.05, so the rank correlation coefficient is not correlated, and there is no heteroskedasticity (see Tables 2 and 3). Table 2 Model summary Model

R

R2

Adjusted R2

Standard deviation error

1

0.788a

0.622

0.553

5.3041697

a Estimate:

(constant), x2 x1

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Table 3 Analysis of the number of variables Model

Sum of squares

df

Mean square

F

Significance

Regression

508.426

2

254.213

9.036

0.005b

Residual

309.476

11

28.134

Total

817.903

13

a Dependent

variable: y, b Estimate: (constant) x2 , x1

From the equation, it gradually deviates to the fourth quadrant as x becomes larger. At the same time, if the first batch of Asia giant hornets is recorded as the 0th generation, and each cycle is one year. Therefore, the spreading trend of Asia giant hornet conforms to the straight line y = −0.15x2 , spreading to low latitude areas close to mountains and far from humans, as shown in Figs. 1 and 2. The accuracy of the prediction is the confidence interval that the actual distance of each unit in the big map is larger. We can take a rough empirical estimation interval (y − 1.96 sigema, y + 1.96 sigema, the sigema = 5.85, and (y − 11.45, y + 11.45). Fig. 1 The distribution of the sample

Fig. 2 The tendency of the spreading

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Table 4 Relevant Average Pearson correlation coefficient Significance

1.000

−0.705

X2

−0.705

1.000

Y

N

Table 5 Variable entered/removed a

Standard deviation

Y

0.002

X2

0.002

Y

14

14

X2

14

14

1

x2 b

Enter

a. Estimate: (constant) x2 , x1 b. All required variables have been entered

The diffusion trend error will not be greater than or less than about 15 km (see Tables 4, 5, 6 and 7).

3 A Ensemble Model for Classification 3.1 Problem Analysis Analyzing the possibility of true Asia giant hornet report by the provided dataset files. To extract information effectively, a model that can accurately classify the text and image datasets should be proposed. Finally, the multi-modal information should be integrated using the integration idea to obtain accurate classification results.

3.2 Data Processing 3.2.1

Sample Image Data Pre-processing

After comparison and confirmation by professional and technical personnel, the dataset contains 14 images of Asia giant hornet and 3195 images of other wasps. In order to increase the proportion of effective information in training the image, six pre-processing algorithm model are used to pre-process the sample dataset, as shown in Fig. 3. For the raw dataset, six new datasets are generated according to the preprocessing method designed in Table 8. The normalization process is a mandatory size unification process for sample images with inconsistent sizes.

(constant), x2 ,

variable: y

5.853692

deviation error

R2

0.455

Standard

Adujusted

b Dependent

0.497

0.705a

1

a Estimate:

R2

R

Model

Table 6 Model summary b change

0.497

R2 11.869

F change

Change Statistics 1

df1 12

df2

0.005

Significant F change 2.673

Durbin-waston

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Table 7 Relevant/removed a Spearman’s rho

e

e

X2

1.000

0.200

N

14

14

Correlation coefficient

0.200

1.000

Correlation coefficient Significance

X2

0.493

Significance

0.493

N

14

14

(a) Fuzzy enhancement (b) White filled background image (c) Affine transformation Fig. 3 Image results from different pre-processing

Table 8 Six pre-processing algorithm model Pre-processing methods SLIC Background removal Average pixel padding with shorter edges

Model 1 √

Model 2 √

Model 3 √

Model 5 √

Model 6 √ √

√

√

Interpolation reconstruction Morphological processing Fuzzy enhancement Affine transformation Normalization process

√

√

√

√

√ √

√ √ √

√

√ √

White filled background

3.2.2

Model 4 √

√

√ √

√

√

√

Text Data Pre-processing

The detection Date, Lab Status, Latitude, Longitude, and Note are mainly used as experimental datasets in the data. Submission Date has nothing to do with the activity of the Asia giant hornet. it has little influence on the prediction of the hornet classification and it cannot be used as the classification feature of the hornet; GlobalID as a primary key, it serves as an identification and is not a classification feature; Notes and Lab Comments are used as text data in text classifiers.

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Fig. 4 The structure of integrated classification model

After the above operation, we can obtain the dataset, which can be used for the BP neural network classifier based on the three features of Detection Date, Latitude and Longitude.

3.3 Model Building For multi-modal data, we propose an ensemble classification model. The ViT is used to classify the pre-processed image dataset. At the same time, we use the BP neural network to classify the time and space data in the text data. For comments provided by the person submitting the report, we use LSTM for classification. We use BP neural network classifier, ViT and LSTM to learn the feature data or images in the train dataset as well as test and evaluate the feature data or images in the test dataset. The system structure of the integrated classification model is shown in Fig. 4. Due to LSTM classification the data learned by the device is comments provided by the person submitting the report, which has strong subjectivity characteristics, resulting in poor recognition of samples with the correct label as positive by the LSTM classifier. Based on the recognition performance of the three base classifier algorithms on the Asia giant hornet, the simultaneous decision function of the ensemble model is set as:  1, i f a = 1 or b = 0 or c = 0 d= (2) 0, i f a = 0 or b = 0 where: d is the output of the ensemble model; a, b, c is the output of the BP neural network classifier, the ViT, the LSTM text classifier. Output 0 represents the predicted classification is yes, output 1 represents the predicted classification is not.

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Fig. 5 a True classification, b predicted classification

4 Results and Analysis By observing the fitting curve of the integrated network model on the train dataset, the final image recognition accuracy of the integrated network model can reach more than 88.9%, as shown in Fig. 5. The Vision Transform parameters are set as follows: the initial weight of the network is extracted from a Gaussian distribution with a standard deviation of 0.01 and a mean of 0; the training phase uses an asynchronous stochastic gradient descent with a momentum term of 0.9, and sets the initial learning of the weight parameters The learning rate is 0.01.The BP neural network accuracy reaches 87.13%. Because the model only uses the three features of Detection Date, Latitude, and Longitude and ignores the impact of other features on the classification results, the prediction results are not very effective. The results are as follows: The accuracy of the model on the test dataset is 79.22%. The classifier is not functional because it ignores the influence of other features. It must be combined with the results of the other two classifiers to be more convincing.

5 Conclusion Based on the prediction of the linear regression model, we believe that the hornet will spread along the southeast direction, which is consistent with the basic habit of the hornet living in the middle altitude, the temperature should not be too low, and the vegetation cover. The prediction accuracy does not exceed about 15 km of the position determined by the linear prediction model, so the prediction result is basically correct. For multimodal data, we propose an integrated classification model. According to the performance of the classifier in the train dataset and the test dataset, the joint decision function is set to make the output of the three base classifiers determine the final output of the integrated model. The final image recognition accuracy of the

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integrated network model can reach more than 88.9%. After using the callback function to adjust the learning rate dynamically, the model with the best generalization ability can be obtained more efficiently.

References 1. Kamioka T, Suzuki HC, Ugajin A, Yamaguchi Y, Nishimura M, Sasaki T, Ono M, Kawata M (2022) Genes associated with hot defensive bee ball in the Japanese honeybee, Apis cerana japonica. BMC Ecol Evol 22(1) 2. Mattila HR, Kernen HG, Otis GW, Nguyen LTP, Pham HD, Knight OM, Phan NT (2021) Giant hornet (Vespa soror) attacks trigger frenetic antipredator signalling in honeybee (Apis cerana) colonies. Royal Soc Open Sci 8(11) 3. First live Asian giant hornet of ‘21 found. Southwest Farm Press, 2021 4. Sadeghi B (2000) A BP-neural network predictor model for plastic injection molding process. J Mater Process Technol 103(3):411–416 5. Dosovitskiy A, Beyer L, Kolesnikov A et al (2020) An image is worth 16 × 16 words: transformers for image recognition at scale 6. Gong H, Deng X, Liu J, Huang J (2022) Quantitative loosening detection of threaded fasteners using vision-based deep learning and geometric imaging theory. Autom Constr 133 7. Wu H, Zhong B, Li H, Love P, Pan X, Zhao N (2021) Combining computer vision with semantic reasoning for on-site safety management in construction. J Build Eng 42 8. Chen H, Zhang D, Gui R, Pu F, Cao M, Xu X (2022) 3D pavement data decomposition and texture level evaluation based on step extraction and pavement-transformer. Measurement 188 9. Naveen S, Ram Kiran MSS, Indupriya M, Manikanta TV, Sudeep PV (2021) Transformer models for enhancing AttnGAN based text to image generation. Image Vision Comput 10. Machine Learning; Researchers from Batman University Report Details of New Studies and Findings in the Area of Machine Learning (Detection of Unregistered Electric Distribution Transformers In Agricultural Fields With the Aid of Sentinel-1 Sar Images By Machine...). Agriculture Week, 2020 11. Greff K, Srivastava RK, Koutník J et al (2016) LSTM: a search space odyssey. IEEE Trans Neur Netw Learn Syst 28(10):2222–2232 12. Praveen Kumar Y, Suguna R (2021) Classification of image and text data using deep learningbased LSTM model. TS 38(6)

Interactive Translation System of Intelligent Fuzzy Decision Tree Algorithm (IFDTA) Junkai Wang, Wenjun Liu, and Huijuan Liu

Abstract Interactive machine translation is a technology that uses machine translation and human–computer interaction to improve the translation efficiency between natural languages. Although the current interactive machine translation has achieved some success, there are still some weaknesses. Based on this, this paper introduces the IFDTA into the interactive translation (IT) system, discusses the interactive machine translation method based on word graph and the application of the IFDTA in the IT system, overcomes the weakness of the current interactive machine translation, so as to improve the ability of the IT system and reduce the cost of user translation; The experimental results show that the translation efficiency of the system implemented in this paper is significantly improved on different corpora. Taking Parliamentary records as an example, the average translation time of the decision tree algorithm is 78.34 s, which is 22.83% shorter than the traditional IT. The addition of phrase translation item table can provide translators with more references of word translation items, effectively reduce the translation time delayed due to unknown words in the translation process and improve the translation efficiency. Keywords Intelligent algorithm · Fuzzy decision tree algorithm · Interactive translation · Translation system

J. Wang School of Information Science and Engineering, Linyi University, Linyi 276005, Shandong, China W. Liu (B) School of Foreign Languages, Linyi University, Linyi 276005, Shandong, China e-mail: [email protected] H. Liu Center for International Education, Philippine Christian University, 1004, Manila, Philippines © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_16

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1 Introduction The traditional interactive machine translation adopts the interactive framework from left to right. In the interactive framework, given a sentence in the source language to be translated, the user can complete the translation or correct the error from left to right. The system provides completion suggestions for subsequent translation according to the correct translation prefix confirmed by the user. Although the left to right interaction framework has been successful, there is a potential weakness in the framework, that is, it is difficult to directly correct the key translation errors at the end of the sentence. Under the above background, this chapter proposes a new interactive machine translation framework, in which the IFDTA is introduced: select a key translation error and correct the translation error. The user operation is not limited to the frame from left to right. You can select phrases at any position of the sentence and correct their translation, so as to improve the efficiency of human–computer interaction. Many scholars at home and abroad have studied the application of IFDTA in IT system. Nancyp proposes a new intrusion detection system, which uses intelligent decision tree classification algorithm to detect known and unknown types of attacks. The algorithm selects the best number of features from the data set. By extending the decision tree algorithm, an intelligent fuzzy temporal decision tree algorithm is proposed to effectively detect intruders [1]. Bakhshipoura has developed a computer vision system based on fuzzy decision tree (DT) for the classification of common Iranian tea. The images of different kinds of tea were taken by CCD camera, and the membership function and rules of fuzzy system were set by rep tree structure. The results show that the fuzzy logic system based on DT can effectively classify different quality categories of tea according to the characteristics of image extraction [2]. This paper mainly introduces the interactive machine translation method. The selection correction framework is an iterative IT framework. Under this framework, the IFDTA is introduced. In order to improve the efficiency of IT and reduce the number of user interaction. Based on this framework, we propose an IFDTA for IT; Based on the translation interaction framework, aiming at the phrase ordering error of the translation system, the IT framework is extended and the interaction method of limiting translation fragments is introduced; So that the machine translation system can naturally use the information provided by the user for decoding and obtain better translation results [3, 4].

2 Application of IFDTA in IT System Translation model is the most important part of interactive machine translation decoder. To train a translation model, we must first extract the bilingual word alignment information of the source language and the target language from the training

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corpus. Word alignment represents the translation between bilinguals. In this paper, the IFDTA is applied to the IT system. The extracted alignment information is the result of one-way word alignment. After obtaining the one-way word alignment information, the two-way word alignment information is obtained by using grow diag final. Word alignment is the basis of phrase model extraction. After obtaining word alignment information, what to do is to extract bilingual phrases from bilingual corpus containing word alignment information and calculate phrase probability estimation, so as to obtain phrase table. In the interactive machine translation method, translators use linguistic knowledge to guide the translation system, participate in and influence the generation of target translation. The purpose of this paper is to realize an efficient interactive machine translation system and improve work efficiency while meeting the requirements of translators for translation quality. The interactive machine translation system consists of a complete machine translation system and an interactive platform. The system provides the machine translation for the translator before translation. The translator understands and analyzes the source language according to the linguistic knowledge, and selects and confirms the reference translation; If there are errors in the translation, the translator can modify it directly. The system decodes it again according to the modified content of the translator and searches for the translation that meets the needs [5].

2.1 Interactive Machine Translation Method Based on Word Graph Interactive machine translation technology has been continuously updated and developed since it was proposed, and a variety of important methods have been formed. Word graph, i.e. word hypothesis graph, is a directed acyclic graph composed of all possible translations corresponding to the source language, which is composed of nodes and edges. Multiple nodes in the graph represent different translation assumptions, and the edges between nodes represent all possible words in the target language. The translation probability calculated according to the language model and translation model is the weight on each edge [6, 7]. Figure 1 shows a complete translation example based on word graph. At the beginning of translation, the system is the source language “what did you just say?” Load all possible target translations for each word in the sentence and generate the word map of the sentence. The translator accepts or modifies the translation during the interaction with the system. The system expands the nodes in the word map and searches the path again according to the translator’s operation, and selects the path with the highest score in the matching path as the new target translation. The translation ends when all translation options are covered. In the search process based on word graph, the system needs to use heuristic function to calculate the score of expanded nodes and nodes to be expanded, and

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Fig. 1 An example of interactive machine translation based on word graph

select the translation path with the highest score [8]. In the interactive machine translation method based on word map, the system does not need to regenerate a new word map according to the translator’s feedback every time, but only needs to generate a word map for the source language at the beginning of translation, and every interaction after that is carried out on this word map. Word graph is easy to obtain and only needs to be generated once, so the search efficiency realized by word graph in interactive machine translation is very high. At the same time, the calculation results of each time in this method can be directly used in subsequent calculation, so the calculation cost is very small.

2.2 Application of IFDTA in IT System The decoding problem in statistical machine translation is a NP complete problem. Each word in the source language will correspond to multiple target translations. With the increase of the length of the source language, the number of word nodes will increase, the number of target translations will increase exponentially, and the word graph will become larger and larger. At this time, if the word graph is not pruned, the translation efficiency of sentences will be seriously affected. However, excessive pruning will lead to sparse word graph nodes, The suffix translation cannot be searched. This paper attempts to apply the IFDTA to the pruning process, use the language model of the target translation to predict a translation word with the highest probability, and then use the translation to complete the current prefix. This method can recover the words deleted in the pruning process and better improve the performance of the interactive machine translation system [9, 10].

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Phrase Based Machine Translation Model

The earliest statistical machine translation adopts the word based model. The input and output of this method are limited to one word, and the translation between words is isolated. Taking a single word as the translation unit is not in line with the characteristics of natural language. According to this characteristic, the translation probability between source language and target language phrases is used to replace the translation probability of a single word, and the traditional word based method is also included. The phrase based translation model regards the training corpus as a fragment composed of multiple continuous phrases, extracts the phrases of the source language and the target language using the word alignment information, and gives all possible segmentation situations of sentences with the equal probability distribution. If the target language allows reordering, the decision tree algorithm can be introduced to order the target translation. There are many kinds of training methods for the parameters in the model. At present, the mainstream is to use heuristic methods to learn the parameters from the word aligned bilingual corpus. In this paper, the IFDTA is applied to the IT system.

2.2.2

Phrase Based Machine Translation Search Strategy

The multi stack based search algorithm regards all translation options as a set of partial translation assumptions, which are the target language prefixes calculated jointly by the translation model and the language model. In the decoding process, the search space is regarded as many large stacks to store the translation assumptions of the source language with the same number of translations [11]. Search speed is a very important problem in interactive machine translation. In interactive machine translation, the prefix entered by the user is an important constraint affecting the search. If only monotonic search is used to pursue speed, the translation prefix will not match. In order to solve the above problems and ensure the search efficiency, the combination of monotonic search and non monotonic search strategy is adopted in interactive machine translation, Firstly, non monotonic search is used to match the prefix fed back by the translator, and then monotonic search is used to complete the search of translation suffix, so as to improve the search efficiency. The phrase model-based interactive machine translation method uses the strategy of re decoding every interaction as an interactive method, which can retain the translation assumptions in the search process and solve the problem that the abandoned path in the above two methods cannot be recovered. Although this method will slightly affect the efficiency, it is helpful to generate better translations, At the same time, with the rapid update of computer hardware equipment, the problem of decoding speed has also been solved [12].

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Fig. 2 Decision tree model diagram generated by fuzzy ID3 algorithm

3 Analysis of Fuzzy Decision Tree Algorithm Let ┌i ∈ E(X ) (1 ≤ i ≤ m) be a fuzzy subset on X, and its cardinality measure M(┌i ) > 1, , the directed tree T is a fuzzy decision tree, and all nodes in the tree belong to e (x); For node a, the calculation is as shown in formula (1): β AH =

M( A ∩ H ) = M(A)

∑ x∈X μ A

∑

(x)Ʌμ H (x)

x∈X μ A

(x)

(1)

where M represents cardinality measurement and authenticity β Used to terminate the generation of the tree. For non leaf node s, there are n optional attributes {A1, A2, …, an}, among which the attribute AR (1) ≤ K ≤ n has Mr semantic values of {T1R, T2R, …, tmrr} Mr, and the classification attribute an + 1 has m values of {T1N + 1, T2N + 1, …, TMN + 1}. For non leaf node s, the calculation is as shown in formula (2): I (Tir ) = −

m ∑

pirj log( pirj )

(2)

j=1

∑m r where pi = M(S ∩ Tir )/ i=1 M(S ∩ Tir ) represents the proportion of the ith attribute value in ar. Figure 2 shows the decision tree model.

4 Experimental Test and Analysis This paper tests the traditional IT and the IT of the IFDTA, and carries out the comparative experiment of manual evaluation on different corpora to verify the improved effect of the IFDTA introduced in this paper on the traditional methods, and analyzes

Interactive Translation System of Intelligent Fuzzy Decision Tree … Table 1 Time comparison table of different ITs Traditional IT IT of decision tree algorithm

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Parliamentary records

Law

Journalism

102.92

102.37

92.17

78.34

83.02

84.28

120 100

Time (s)

80 60 40 20 0 Parliamentary records

law

Journalism

Time of different interactive translation Traditional interactive translation Interactive translation of decision tree algorithm

Fig. 3 Time of different IT

and summarizes the experimental results according to different evaluation indicators. The average translation time of the translator’s Cross translation experiment on different corpora is shown in Table 1 and Fig. 3. The experimental results show that the translation efficiency of the system implemented in this paper is significantly improved on different corpora. Taking Parliamentary records as an example, the average translation time of the decision tree algorithm is 78.34 s, which is 22.83% shorter than the traditional IT, indicating that the translation efficiency of this method is greatly improved compared with the traditional method. The translation time of using the same system on different corpora is different, because the characteristics of different kinds of corpora are different; The addition of phrase translation item table can provide translators with more references to word translation items, effectively reduce the translation time delayed due to unknown words in the translation process, and improve the translation efficiency.

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5 Conclusions In today’s increasingly frequent global communication, people’s communication inevitably requires mutual translation of various languages. Due to the high cost and low efficiency of human translation, it is difficult to meet the needs of largescale translation, which makes people’s demand for automatic translation technology increase day by day. The development of machine translation technology is the general trend. Based on this, this paper applies the IFDTA to the IT system. Although the results have been achieved, there are still some points worthy of further research: under the current IT framework, the IT model of IFDTA proposed in this paper can simplify the user’s operation into a single operation, and the current performance has reached an acceptable level, but its model ability still has great room for improvement. We can use larger data scale and more features, Better model to further improve the model capability.

References 1. Nancy P, Muthurajkumar S, Ganapathy S et al (2020) Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks. IET Commun 14(5):888–895 2. Bakhshipour A, Zareiforoush H, Bagheri I (2020) Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features. J Food Meas Charact 14(3):1402–1416 3. Teekaraman D, Sendhilkumar S, Mahalakshmi GS (2020) Semantic provenance based trustworthy users classification on book-based social network using fuzzy decision tree. Int J Uncertain Fuzziness Knowl-Based Syst 28(1):47–77 4. Bian F, Wang X (2020) School enterprise cooperation mechanism based on improved decision tree algorithm. J Intell Fuzzy Syst 40(13):1–11 5. John SN, Adewale AA, Ndujiuba CN et al (2019) A neuro-fuzzy model for intelligent last mile routing. Int J Civil Eng Technol 10(1):2341–2356 6. Randhawa P, Shanthagiri V, Kumar A (2020) Violent activity recognition by E-textile sensors based on machine learning methods. J Intell Fuzzy Syst 39(6):8115–8123 7. Elavarasan D, Durai R (2020) Reinforced XGBoost machine learning model for sustainable intelligent agrarian applications. J Intell Fuzzy Syst 39(5):7605–7620 8. Wumaier H, Gao J, Zhou J (2020) Short-term forecasting method for dynamic traffic flow based on stochastic forest algorithm. J Intell Fuzzy Syst 39(8):1–13 9. Dhanalakshmi R, Devi TS (2020) Adaptive cognitive intelligence in analyzing employee feedback using LSTM. J Intell Fuzzy Syst 39(6):8069–8078 10. Kumar M, Reddy MR (2021) A C4.5 decision tree algorithm with MRMR features selection based recommendation system for tourists. Psychology (Savannah, Ga.) 58(1):3640–3643 11. Dhanjal AS, Singh W (2022) An automatic machine translation system for multi-lingual speech to Indian sign language. Multimedia Tools Appl 81(3):4283–4321 12. Meikle G (2020) ScreenPlay: a topic-theory-inspired interactive system. Organised Sound 25(1):89–105

Adaptive Neural Network (NN) Coordinated Tracking Control Based on Artificial Intelligence Algorithm Bo Lu, Yuanda Guo, Jia Song, and I. G. Naveen

Abstract The research of NN adaptive algorithm and detail factors has carried out a lot of academic exploration in the field of control. The known linear or partially linear system models can be solved by the algorithm of control theory. However, in the actual process, there must be external interference and the system has unknown parameters, which should be considered in the design of control strategy. In this paper, the adaptive NN coordinated tracking control based on artificial intelligence algorithm is studied, and the NN control, adaptive control and NN adaptive algorithm are discussed and analyzed; Taking the variable speed fan as the research object, the power tracking problem of the unit below the rated wind speed is studied, and a high-precision and fast nonlinear power tracking control is designed. A NN adaptive proportional tracking control method is designed to solve the power tracking control problem of the system with model uncertainty, external interference and unknown ideal power trajectory, and the model construction of unknown ideal power trajectory is proposed; Through control simulation and analysis, the wind farm output power tracking curve is tested. The results show that the proposed control method before and after optimization can achieve good trajectory tracking effect, make the NN play a better learning and estimation ability, better tracking effect and less error. Keywords Artificial intelligence algorithm · Adaptive · Neural network · Coordinated tracking

B. Lu (B) · Y. Guo · J. Song Hao Jing College of Shaanxi University of Science and Technology, Xi’an, Shaanxi, China e-mail: [email protected] I. G. Naveen Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_17

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1 Introduction NN algorithm does not rely on too much parameter design and can approach any unknown and uncertain part. It has been applied in many fields, including high-tech fields such as artificial intelligence and unmanned driving. This paper introduces one kind of RBF NN structure, puts forward the control strategy based on NN adaptive algorithm, further studies the influence of the number of neurons and the selection of activation function on the control effect in the design of network internal structure, introduces the algorithm of neuron self growth, and puts forward a multi-level grouping network structure design idea. The research on adaptive NN coordinated tracking control based on artificial intelligence algorithm has been analyzed by many scholars at home and abroad. P. Trand proposes an adaptive learning solution based on CNN model. This method automatically updates the recognition model according to the online training data directly accumulated by the system, and retrains the recognition model. The purpose of this solution is to upgrade the model to a more adaptive new model in order to achieve higher accuracy. The experimental results show that this method has higher accuracy when the model is self-learning over time [1]. M. Fouzia designed a nonlinear system based on fuzzy NN control. Through supervised training, the reference trajectory is executed by the flexible joint manipulator. The adaptive neuro fuzzy inference system is used to identify the structure of the controller network, and the new parameters and weight coefficients are automatically adjusted to reduce the position tracking error [2]. Taking the wind farm as the control object, this paper proposes adaptive power tracking control and robust adaptive power tracking control based on NN to control the power output of the wind farm, so that the output power of the wind farm can just meet the power demand of users. The two control algorithms proposed in this paper have the advantages of simple structure, small amount of calculation, easy implementation, and proportional structure. They can automatically adjust the proportional parameters according to the changes of the external environment, and can still maintain the effectiveness of the algorithm in the case of system model uncertainty and external interference. The simulation results show that the proposed controller can realize stable power tracking of wind farm [3, 4].

2 Research on Adaptive NN Coordinated Tracking Control 2.1 Adaptive NN Coordinated Tracking Control 2.1.1

NN Control

The artificial computer network simulates the brain nervous system from the function and makes it have some functions of brain NN, such as learning, control and so on.

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Functionally, the connection modes of NNs are mainly divided into feedforward NNs and feedback NNs. Feedforward NN mainly realizes function mapping function, function approximation and pattern recognition.

2.1.2

Adaptive Control

In many practical projects, the dynamic model of the controlled object is difficult to determine. Even if the model structure is determined at a certain time, the system is inevitably subject to external interference. After the conditions change, the dynamic parameters or model of the system will change, such as missile control, electric traction, ship route control and flight control. When the system parameters and even the model structure change, the conventional controller is no longer applicable. Therefore, a special controller that can automatically capture the changes of parameters, namely adaptive controller, should be designed. The adaptive control system is shown in Fig. 1. It is composed of adjustable system, adaptive mechanism, IP measurement (performance index measurement) and performance index comparison and decision-making system. These components work together to realize the corresponding functions of the adaptive controller. Many advantages of adaptive control make it successfully applied in many practical systems [5].

Fig. 1 Adaptive control structure diagram

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2.2 NN Adaptive Algorithm NN is to explore a system with self-learning ability, memory storage, information aggregation and intelligent processing like human brain by imitating the working mode of human brain nervous system. The model mainly includes the modeling and connection of neurons. At present, BP network, radial basis function (RBF) network and CG network are common in dozens of network models. RBF NN algorithm has strong self-learning, self-organization and self-tuning functions, and can realize the network approximation of any uncertain part. In RBF NN, the direction of parameter propagation is unified, there is no reverse or repeated propagation, and the learning method is flexible, so the network can quickly process a large amount of data at the same time. The adaptive process is to continuously adjust the output by adjusting the adaptive law, which is described by a specific data model, which is called adaptive algorithm. It is mainly aimed at the system that cannot be completely determined, and the system parameters are unknown or random. In the process of control, there is little known about the model or disturbance. Constantly adjust the model according to the law of system operation to realize the online identification of parameters, and the model will be close to the reality. Such a control process has the adaptability of continuous adjustment, and has a wider application range compared with the conventional feedback system [6, 7]. The control algorithm in this paper is based on RBF NN. The input signal is converted to the hidden layer through nonlinear transformation, and the weight connection is implemented from the hidden layer to the output layer. The unknown and uncertain part of the system is approximated by NN algorithm to deal with the nonlinear part. The algorithm combines the traditional proportional control and uses the network to adjust in a small range to realize the purpose of parameter selfregulation and tuning. In the hidden layer, neurons realize the transformation of nonlinear factors through learning and network connection. Therefore, the number of neurons in the network structure and the selection of activation function are also part of the factors considered in the algorithm design [8].

2.3 Basic Principle of RBF NN NN is composed of multiple neurons. According to the differences of connection modes, there are many ways to classify computers through the network. RBF network is a hierarchical network structure, which is a forward feedback network composed of input layer, hidden layer and linear output layer. Its network structure is shown in Fig. 2. In the hidden layer of RBF network, the activation function is used as the mapping relationship, and the radial basis function is commonly used. Radial basis function is a nonlinear function with symmetric center and radial symmetry and attenuation.

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Fig. 2 Structure diagram of RBF NN

The most commonly used is Gaussian function with local response characteristics [9, 10]. RBF can approach any unknown and uncertain part infinitely. It is excellent in nonlinear, uncertain and real-time systems, and improves the self-regulation ability, accuracy and robustness of the system.

2.4 Research on Detailed Factors of Network Internal Structure According to the design of NN adaptive algorithm, the internal structure of the network is mainly connected through the transformation relationship among neurons, activation functions and weights. NN has a wide range of applications, mostly focusing on the network structure, parameter propagation direction and data training. It is easy to ignore the research of some detailed factors. The number of neurons is mostly selected according to the empirical value, but the requirements for the system are different. In the design of NN structure, the computer warp element and activation function are important factors in the design of NN structure. The selection or change of the number of neurons will indirectly affect the control effect and ultimately affect the overall control accuracy. Therefore, this chapter focuses on the selection of the number of neurons and activation function, and optimizes the processing of NN structure details to achieve better control effect [11, 12].

3 RBF NN Algorithm In RBF network, the radial basis functions in the hidden layer are mostly: 

−(ai − ci )s (ai − ci ) ki = ecp 2di2

 (1)

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⎡

c11 · · ·   ⎢ . . where c = ci j = ⎣ .. . .

⎤ c1m .. ⎥ is the coordinate vector from the j-th node of the . ⎦

cn1 · · · cnm hidden layer to the center point of the function. D = [D1, D2, … DM] SRBF, DJ represents the baseband width of the jth neuron. I = 1, 2 … n, j = 1, 2 … m. The network connection weight is: v = [v1 , v2 , ...vm ]T

(2)

y(t) = v T k = v1 k1 + v2 k2 + · · · vm km

(3)

RBF network output is:

RBF is a local approximation network, and the network structure is relatively simple and clear. Compared with the global approximation network, changing a variable parameter or threshold in the network has less impact on the whole network, and the learning convergence speed is faster. Therefore, in this paper, RBF NN algorithm is selected to design the controller.

4 Design and Verification of NN Adaptive Tracking Control Strategy In the control method proposed in this paper, the upper bound of the nonlinear uncertain vector function of the system is estimated by using NN technology. Compared with directly approaching the nonlinear vector function, the amount of calculation of this processing method is much reduced. Finally, this paper improves the proposed controller by using the limited Lyapunov function, controls the input of the NN unit in a compact set, reduces the tracking error, and makes the NN play a better learning and estimation ability in the operation of the wind power system.

4.1 Control Simulation and Analysis In order to verify the effectiveness of the designed NN adaptive control algorithm, the proposed controller is simulated and verified based on MATLAB software. The simulation is simple. The power tracking of five variable-speed fans in the wind farm is simulated. The tracking data of wind farm output power is shown in Table 1 (Fig. 3). It can be seen from the data above that the control methods before and after optimization proposed in this chapter can achieve good trajectory tracking effect, and the tracking effect of the optimized control method (NN adaptive method based

11.18

13.54

13.55

NN adaptive method

NN adaptive method based on BRF

Ideal power orbit

0

6.76

7.24

4.84

2

Table 1 Wind farm output power tracking data sheet 6.82 6.89 13.34

4

10.34

10.53

12.64

6

7.11

6.37

7.52

8

7.63

7.32

8.13

10

8.48

8.46

8.47

12

8.58

8.49

16.34

14

8.99

9.42

13.58

16

7.78

7.68

8.53

18

14.23

13.85

13.63

20

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156 18 16 14 12 10 8 6 4 2 0 0

2

4

6

8

10

12

14

16

18

20

Wind farm output power tracking curve Neural network adaptive method Neural network adaptive method based on BRF Ideal power orbit

Fig. 3 Wind farm output power tracking curve

on BLF) is better than that before optimization (NN adaptive method), because the input of NN can be limited to a tight set by using limited Lyapunov function, The NN has better learning and estimation ability, better tracking effect and less error.

5 Conclusions The control strategy designed in this paper takes the wind farm as the control object, adjusts the output power of each wind turbine in the wind farm, and finally makes the output power of the whole wind farm track the power demand signal from the user stably and accurately. However, in order to be closer to the reality, the multi-objective power distribution strategy should be considered, and the algorithm of dynamically adjusting the allocated power ratio still needs further research; Due to the changeable and complex system, the unknown ideal trajectory tracking control is still in the research stage; Making full use of intelligent control technology, nonlinear control theory and dynamic system theory, combined with various control methods and algorithms such as self-learning iterative control and genetic algorithm, developing and studying intelligent and efficient control methods and designing intelligent and low-cost controllers is a main research direction in the field of application control in the future.

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References 1. Tran DP, Hoang VD (2019) adaptive learning based on tracking and reldentifying objects using convolutional NN. Neural Process Lett 50(1):263–282 2. Fouzia M, Khenfer N, Boukezzoula NE (2020) Robust adaptive tracking control of manipulator arms with fuzzy NNs. Eng Technol Appl Sci Res 10(4):6131–6141 3. Boussana A, Galy O, Gallais DL et al (2020) Relationships between lung volume and respiratory muscle performance in triathletes. Medicina Dello Sport; Rivista di Fisiopatologia Dello Sport 73(3):405–417 4. Gupta S, Thakur S, Gupta A (2022) Optimized hybrid machine learning approach for smartphone based diabetic retinopathy detection. Multimedia Tools Appl 81(10):14475–14501 5. Graewingholt A, Rossi PG (2021) Retrospective analysis of the effect on interval cancer rate of adding an artificial intelligence algorithm to the reading process for two-dimensional full-field digital mammography. J Med Screen 28(3):369–371 6. Esfandyari S, Rafe V (2021) Correction to: GALP: a hybrid artificial intelligence algorithm for generating covering array. Soft Comput 25(11):1–1 7. Kumar CS, Kandaswamy A, Ramanathan RP (2020) Artificial NN based approach for diagnosis of respiratory system using model based parameters of maximum expiratory flow-volume curve. Int J Biomed Soft Comput Human Sci Official J Biomed Fuzzy Syst Assoc 8(1):15–20 8. Morozov SP, Chernyaeva GN, Bazhin AV et al (2020) Validation of diagnostic accuracy of anartificial intelligence algorithm for detecting multiple sclerosis in a city polyclinic setting. Diagn Radiol Radiother 11(2):58–65 9. Leiter R, Santus E, Jin Z et al (2020) An artificial intelligence algorithm to identify documented symptoms in patients with heart failure who received cardiac resynchronization therapy (GP757). J Pain Symptom Manage 60(1):279–280 10. Torres MP, Castillo F (2019) Artificial intelligence algorithm for autonomous movement of a smart wheelchair. Res J Appl Sci 13(9):552–558 11. Saikumar K, Rajesh V (2020) Coronary blockage of artery for heart diagnosis with DT artificial intelligence algorithm. Int J Res Pharm Sci 11(1):471–479 12. Gerges M, Eng H, Chhina H et al (2020) Modernization of bone age assessment: comparing the accuracy and reliability of an artificial intelligence algorithm and shorthand bone age to Greulich and Pyle. Skeletal Radiol 49(9):1449–1457

Early Warning of Linguistic Achievement Pattern Based on DNA-GA Algorithm Xiaohui Wan

Abstract With the continuous improvement and development of China’s education system, the education management system has been applied to all colleges and universities in China. In recent years, it undertakes the management and storage of educational information in Colleges and universities, and produces a large number of educational data information. This paper summarizes the basic principle of DNA-GA algorithm, analyzes the advantages of DNA-GA algorithm, discusses the difficulties of DNA-GA algorithm, puts forward the language ability test scale, and tests the linguistic performance of college students. The results show that the current situation of 50 college students Take a linguistic test is above the medium level, and the overall performance of academic performance is normal distribution. Keywords DNA-GA algorithm · Linguistics · Score pattern analysis and early warning · K-means clustering algorithm

1 Introduction From the information age to the big data age, earth shaking changes have taken place in all walks of life. From educational informatization to educational computability, big data technology has brought dawn to education, and the traditional experiential teaching is developing to accurate teaching. With the continuous progress of science and technology, many experts have studied the analysis and early warning of language achievement model. For example, Fang W.C., Yeh H.H.C., Luo B.R. developed a mobile task-based language teaching application to provide language and task support. English achievement tests include vocabulary, grammar and conversational understanding to determine whether the technical framework has improved the learning effect of the course. This paper puts X. Wan (B) School of Foreign Languages, Ili Normal University, Yining 835000, Xinjiang, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_18

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forward the Enlightenment of designing mobile learning in EFL environment to strengthen Task-based Teaching [1]. N.A.A. Vondrová, J.N. Ovotná, R. Havlíková mainly studies how the order, context, position of unknown transformation and the sentence length of digital data affect pupils’ performance and reasoning on additive words. The students were divided into four groups with the same ability. Each group solved different word problems and used them for quantitative interpretation of the data. Students’ solutions were also analyzed qualitatively [2]. Jung Y.J., Crossley S., McNamara D., explored whether language features can predict the level of second language writing in the writing task of the University of Michigan English language assessment team (MELAB). This study is of great significance for defining the writing level of different achievement levels in second language academic writing and improving the existing MELAB scoring scale and rater training practice [3]. Although the research results of language achievement pattern analysis and early warning are fruitful, there are still deficiencies in the research of language achievement pattern analysis and early warning based on DNA genetic algorithm. In order to study the language achievement pattern analysis and early warning based on DNA GA algorithm, this paper studies the DNA GA algorithm and language achievement pattern analysis and early warning, and finds the K-means clustering algorithm. The results show that DNA GA algorithm is helpful to the analysis and early warning of language achievement patterns.

2 Method 2.1 DNA-GA Algorithm (1) Basic principle of DNA-GA algorithm. The essence of DNA-GA algorithm is an efficient, parallel and global search method. Genetic algorithm and computing have natural similarities, but the implementation methods are different. NDA GA algorithm, also known as base complementary pairing method, encodes information, that is, for all possible results of mathematical problems, the data objects of the original problem are mapped into DNA molecular chains according to certain rules and encoded with different DNA sequences. It carries biological genetic information [3]. It is not only the decisive molecule in active cells, but also the central molecule of DNA computing. DNA has an amazing structure, which determines the coding and self replication of the two most important functional proteins in DNA. DNA molecules carry all the genetic information of biological species and can be transcribed into RNA and then translated into proteins. (2) Advantages of DNA-GA algorithm The DNA GA algorithm is cleverly designed to achieve the fastest optimization under the requirement of minimization. DNA GA algorithm is the best algorithm in static priority scheduling algorithm, that is, when any static priority algorithm

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can produce feasible scheduling, the algorithm can also be used. Assign priority to tasks according to their cycle [4]. The priority of a task is inversely proportional to its cycle, that is, a task with a smaller cycle can be assigned a higher priority. At the same time, tasks with higher priority can preempt tasks with lower priority. If the priority of a task is described as a function of its rate, the result is a monotonically increasing function, so it is called DNA GA algorithm. DNA GA algorithm usually adopts closed-loop structure that is, continuously sampling the system output during operation, and automatically adjusting the scheduling components by using the feedback information to adapt to the changes of the environment [5]. (3) Difficulties of NDA GA algorithm Difficulties of DNA calculation: at present, DNA calculation is mainly carried out through biochemical experiments, and the calculation accuracy has reached a high level. However, if DNA computing is used to solve mathematical problems and obtain accurate solutions, there are still some difficulties. The difficulty is that no matter what method is used, there will always be some errors in the extraction process. Therefore, the current DNA Computing mainly exists in the biological experimental operation level, and is not widely used in the industrial field. In the calculation process, genetic algorithm uses chromosomes to represent individuals, and continuously optimizes individuals through genetic operation [6]. This idea is very similar to DNA computing, but genetic algorithm itself only uses 0,1 binary form to represent individuals, which can not express rich genetic information. Inspired by the background of DNA computational biology, they tried to introduce the rich genetic information of DNA into genetic algorithm and proposed DNA genetic algorithm. The algorithm operates on individuals in the population based on DNA coding. The algorithm establishes a genetic information model at the molecular level, which better simulates the genetic mechanism of organisms and enriches the expression mechanism of genetic information [7]. On the one hand, the impact of biotechnology errors on DNA computing is inevitable. In biochemical reactions, errors will inevitably occur, and these errors may spread with the progress of the reaction. For example, if the reaction conditions are not well controlled, the hybridization process will be chaotic, and then mismatched hybridization will occur; During the operation of the test tube, the DNA strand will stick to the test tube wall, resulting in inaccurate calculation results. On the other hand, DNA computing can only stay above the level of biological experimental operation. This is an essential extraction operation in DNA computing. No matter what method is used to extract the result chain, it will produce a certain error [8].

2.2 Linguistic Achievement Test Scale This test cannot be used in areas and groups without hearing equipment or with imperfect hearing equipment. In addition, in the process of answer evaluation, due to the need to evaluate the answers of listening materials, there are many difficulties and

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external uncontrollable factors, which are easy to cause errors in the test results, thus affecting the reliability and effectiveness of the research conclusions. Non adaptive scheduling is usually an open-loop structure, that is, on the premise of predicting the accurate information related to real-time tasks and scheduling, the strategies and parameters of scheduling components are determined before the system starts running, and they are no longer adjusted according to the feedback information during the system running [9]. This test cannot be used in areas and groups without hearing equipment or with imperfect hearing equipment. In addition, in the process of answer evaluation, due to the need to evaluate the answers of listening materials, there are many difficulties and external uncontrollable factors, which are easy to cause errors in the test results, thus affecting the reliability and effectiveness of the research conclusions. Non adaptive scheduling is usually an open-loop structure, that is, on the premise of predicting the accurate information related to real-time tasks and scheduling, the strategies and parameters of scheduling components are determined before the system starts running, and they are no longer adjusted according to the feedback information during the system running [9].

2.3 K-Means Clustering Algorithm Let µ j be the average value of C j cluster, then the square error between each data point in the cluster and the previously randomly selected cluster center point is defined as Eq. (1): J (ck ) =



||xi − µ j ||2

(1)

xi∈cj

The expression of the sum of squares of all clusters is Eq. (2): J (C) =

k  

||xi − µ j ||2

(2)

j=1 xi∈cj

The ultimate goal of the algorithm is to minimize the value in the above formula [10]. If J gets the minimum value, the corresponding µ j is the global best clustering center, which means that the clustering segmentation effect is the best at this time. Through this complementary pairing relationship, genetic algorithm is more conducive to the design of new operators [11]. According to the above coding method, the minimization optimization problem can be described as Eq. (3): 

M inf(x1 , x2 , ..., xn ) xmin i ≤ xi ≤ xmax i , i = 1, 2, ..., n

(3)

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where xi is an integer string variable with a quaternion length of L, f (x1 , x2 , ...xn ) represents the objective function, and (xmin i , xmax i ) corresponds to the value range of each control variable [12].

3 Experience 3.1 Object Extraction Based on the analysis of the system requirements, the functions required by the student examination information collection and early warning system are designed. It mainly solves what kind of functional structure to design, and provides more effective and accurate teaching suggestions for teachers’ teaching and students’ learning by collecting and analyzing students’ examination information. The core function module of the system is the performance early warning management module. The key function of this function module is based on other function modules. The referenced grade data information can be divided into grade segments according to the evaluation category to form an early warning Student object. The performance early warning objectives that can be achieved through this function include strengthening the early warning students’ learning of corresponding subjects. The functions of this module are divided into early warning operation management, early warning condition management and early warning query management. Alert condition management is used to set corresponding alert parameters. Including the setting of alarm object and the selection of alarm mode. These are the preliminary preparations for performance early warning. Alert operation management is to send alert information to alert objects. In order to provide more effective effect, the alarm level is divided into level 1 alarm and level 2 alarm. Debug the developed student examination information collection and early warning system, check the page link and function realization, and improve the system on this basis. Then conduct a preliminary review on a small scale to test whether the system design and development process is reasonable and feasible. What the system needs to do is to make a detailed analysis of students’ examination completion and daily homework, score and distinguish right from wrong for each small problem done by each student, so as to obtain each student’s mastery of the knowledge points corresponding to each small problem, give early warning to the knowledge points not mastered by students, and make adaptive remedies, Provide students with learning materials corresponding to knowledge points.

3.2 Experimental Analysis The first step is to select 50 students who take the linguistics test in a university in Wuhan. The second step is to test the language ability of 50 students. Each group of

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students first understand the type of test questions before the test. All explanations about the type of test questions are in Chinese. After everyone knows, start testing each component for 45 min. Then collect answer sheets. The third step is to collect the IELTS standardized language test scores of these students. The fourth step is to select a certain number of valid answers and compare the language level with the score of the standardized language test. The fifth step is to select 45 students to conduct a questionnaire survey to understand their personal views on how linguistics affects their English academic performance. After collecting relevant data, use spss24. 0 will use 0 software for data analysis, and comprehensively analyze and discuss the data in combination with relevant theories. The processing process of this architecture is: the client sends the access request to the web server through the web browser, the web server parses the access request, submits the parsed SQL syntax command to the database server, the database system analyzes and processes the SQL syntax command, and returns the processing result to the web server, and then the web server returns the information required by the user to the client in the form of web page. B/S architecture divides the whole system into three layers: interaction layer, service layer and data access layer. Compared with C/S structure, B/S structure separates the transaction logic module from the client, reducing the pressure on the client. The client only needs to send and receive information for users, and provide an interactive interface to present information. The client itself does not process data. The core part of the system is located in the service layer, and the data request, processing, result return and dynamic page generation are completed by the service layer. In this program execution mode, ASP files, ASA files, DLL files and other execution files are placed on the web server, and the database and database backup files are placed on the database server. However, it is also ready to establish a series of connection with the database and server, and realize the effect of database access and management at any time.

4 Discussion 4.1 College Students’ Linguistic Achievement Test In order to obtain the total score distribution of English language proficiency test for all subjects, as shown in Table 1, the scores of most subjects are mainly concentrated between 110 and 120. According to the histogram distribution, no extreme data are found, and most of the data are within three standard variances. It can be seen from the above that the frequency of College Students’ English test score of 80 is 5, the frequency of English test score of 100 is 10, the frequency of English test score of 110 is 15, and the frequency of English test score of 120 is 20. The specific presentation results are shown in Fig. 1. As can be seen from the above figure, the number of people in each score segment is displayed in the form of histogram, and it is found that the subjects’ language scores

Early Warning of Linguistic Achievement Pattern Based on DNA-GA … Table 1 Linguistics can describe statistical results

Frequency

165

Total score

5

80

10

100

15

110

20

120

140 120

total score

100 80 60 40 20 0 5

10

15

20

frequency Total score

Fig. 1 Linguistics can describe statistical results

are normally distributed. To sum up, the 50 college students who participated in the MLAT score test belong to the upper middle level. The overall academic achievement score is normally distributed. There are significant differences in the total score of subjects’ academic performance, and the total score of academic performance is at the upper middle level.

4.2 Parameter Setting Taking the screenshot of the variation function in this section and the average value of the convergence function in this section as an example. For test function F1, the comparison of convergence speed between NDNA GA algorithm and basic DNA GA algorithm is shown in Table 2. Table 2 Comparison of convergence speed between improved DNA-GA and basic DNA-GA

F(x)

Number of iterations

NDNA-GA

3

342

Basic DNA GA

6

425

X. Wan 7

450

6

400 350

f(x)

5

300

4

250

3

200 150

2

100 1

number of iterations

166

50

0

0 NDNA-GA

Basic DNA GA

type F(x)

Number of iterations

Fig. 2 Comparison of convergence speed between improved DNA-GA and basic DNA-GA

It can be seen from the above that the number of iterations of the convergence speed of NDNA-GA algorithm is 342, and the number of iterations of the convergence speed of basic DNA-GA algorithm is 425. The specific presentation results are shown in Fig. 2. It can be seen that the convergence speed of NDNA genetic algorithm is slightly faster than that of DNA genetic algorithm. The improved DNA genetic algorithm has good stability. By changing the genetic operator in DNA genetic algorithm, the convergence speed of the algorithm can be accelerated and the optimization ability of the algorithm can be improved.

5 Conclusion With the continuous popularization of computers, more and more examination data are stored and processed by computers in the field of examination. On the one hand, due to the reduction of manual processing, the storage space is reduced. On the other hand, the convenience and security of storage are improved. This paper compares the convergence speed of NDNA genetic algorithm and DNA genetic algorithm. The results show that by changing the genetic operator in DNA genetic algorithm, the convergence speed can be accelerated and the optimization ability can be improved. Acknowledgements Fund program: The Belt and Road Development Research Institute of Yili Normal University Key Project of Opening Project: “The Belt and Road” Project title: Research on college students’ Ability to tell Chinese stories well in English from a cross-cultural perspective. Project Number: YDYL2021ZD004

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References 1. Fang WC, Yeh HC, Luo BR et al (2021) Effects of mobile-supported task-based language teaching on EFL students’ linguistic achievement and conversational interaction. ReCALL 33(1):71–87 2. Vondrová N, Ovotná JN, Havlíková R (2019) The influence of situational information on pupils’ achievement in additive word problems with several states and transformations. ZDM 51(1):183–197 3. Jung YJ, Crossley S, Mcnamara D (2019) Predicting second language writing proficiency in learner texts using computational tools. J Asia TEFL 16(1):37–52 4. Huang P, Zou Z, Xia XG et al (2020) Multichannel sea clutter modeling for spaceborne early warning radar and clutter suppression performance analysis. IEEE Trans Geosci Remote Sens PP(99):1–18 5. Wu KJ, Tseng ML, Lim MK et al (2019) Causal sustainable resource management model using a hierarchical structure and linguistic preferences. J Clean Prod 229(AUG.20):640–651 6. Malaj L (2020) Summary strategies for literary texts in English. Stud Logic Gramm Rhetoric 65(1):7–20 7. Wang J, Cui E, Liu K et al (2020) Referring expression comprehension model with matching detection and linguistic feedback. IET Comput Vision 14(8):625–633 8. Choudhary P, Nain N (2020) CALAM: model-based compilation and linguistic statistical analysis of Urdu corpus. S¯adhan¯a 45(1):1–10 9. Caymaz B, Aydin A (2021) The effect of common knowledge construction model-based instruction on 7th grade students’ academic achievement and their views about the nature of science in the electrical energy unit at schools of different socio-economic levels. Int J Sci Math Educ 19(2):233–265 10. Aladhadh S, Zhang X, Sanderson M (2019) Location impact on source and linguistic features for information credibility of social media. Online Inf Rev 43(1):89–112 11. Arega NG, Heard WN, Tran N et al (2021) Zinc-finger-protein-based microfluidic electrophoretic mobility reversal assay for quantitative double-stranded DNA analysis. BioChip J 15(4):381–395 12. Sosa-Acosta JR, Iriarte-Mesa C, Ortega GA et al (2020) DNA–Iron oxide nanoparticles conjugates: functional magnetic nanoplatforms in biomedical applications. Top Curr Chem 378(1):1–29

AI Big Data Multi-dimensional Intelligent Pension Model Recommendation Algorithm Empowered by Artificial Intelligence Ming Li

Abstract The application of artificial intelligence technology has brought about great changes. Artificial intelligence technology has facilitated a change in the understanding of elderly care service models, elderly care centers and elderly care management. This paper aims to study the recommendation algorithm of AI big data multidimensional intelligent elderly care model empowered by artificial intelligence. This paper describes the integration of elderly care services from the aspects of elderly care service content and work quality. By collecting and analyzing key customer information on the elderly care service platform, empowering behavioral information and shaping the image of elderly users. The user chart depicts the individual needs and preferences of elderly customers, and on this basis, combined with the proposed algorithm, elderly customers can be recommended elderly care services that meet their needs and preferences. Experiments have shown that the recommendation accuracy of the algorithm constructed in this paper is as high as 90%, and the number of users who have been taught within 1 h is more than 90%, which is easy to operate. Keywords Artificial intelligence · Big data · Intelligent pension · Recommendation algorithm

1 Introduction In recent years, with the development of society, the problem of population aging has also attracted more and more attention from the government and researchers. At the same time, a new generation of information technology represented by technologies such as artificial intelligence has been used to solve this problem, and researchers have also proposed the concept of smart aging. In addition, nursing homes have also M. Li (B) Jilin Agricultural Science and Technology University, Jilin, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_19

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begun to pursue integration with artificial intelligence, making full use of national development units and advanced technologies to achieve self-improvement, selfdevelopment and optimization of personal data, such as introducing smart products and services to the elderly population. While taking care of the elderly, provide effective and flexible elderly care services and understand elderly care services in a timely manner [1, 2]. Research of AI big data multi-dimensional intelligent elderly care model recommendation algorithm empowered by artificial intelligence, many scholars have studied it and achieved good results. Designed and implemented a hospital information management system in the field of electronic medical care, built a health care information resource platform, and provided health care information services for the elderly at home [3]; Aghaeipoor [4] is under the development trend of the combination of smart medical care and smart care, discussed the advantages, disadvantages and future development of the current main smart old-age care models in China. This paper describes the integration of elderly care services from the aspects of elderly care service content and work quality. By collecting and analyzing key customer information on the elderly care service platform, empowering behavioral information and shaping the image of elderly users. The user graph depicts the individual needs and preferences of elderly clients, and on this basis, combined with recommendation algorithms, elderly clients can provide preventive aged care services that meet their needs and preferences.

2 Research on the Recommendation Algorithm of AI Big Data Multi-dimensional Intelligent Pension Model Empowered by Artificial Intelligence 2.1 The Application Status of Artificial Intelligence in the Field of Elderly Care Services Artificial intelligence technology has penetrated into all fields of geriatric care, constantly promoting the intelligent transformation of geriatric medicine. At this stage, the elderly care industry is characterized by precise connection of supply and demand, intelligent service management, intelligent service products, and platformbased integration of resources. The application of artificial intelligence in nursing services includes the following points [5, 6]. (1) Intelligent reform of elderly care service model With the changes in the socio-economic situation, the elderly care service model in China is also evolving, from the home care model that has stood for thousands of years to the socially responsible elder care model, which is emerging. This model is generally widely accepted by the entire community. The intelligent age-classified care model based on a large database effectively brings together age-classified work

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resources, improves the enthusiasm of adults through technology, and provides equal rights and abilities for the elderly. At the same time, it can effectively meet the retirement needs of the elderly and improve the ability of the elderly to cope with social risks. China has gradually explored a variety of geriatric expert nursing service models, such as smart home care services, specialized health care services, professional institutional care services, and community care services. (2) Intelligent transformation of the elderly care service industry With the transformation of the home-based elderly care system and the increase in nursing demand, the supply level and capacity of traditional nursing home services have slowly declined after seasonal development, and there are many challenges in terms of performance and quality. Extraterritorial countries have chosen to develop specialized care centers as a way to address the above dilemma. A synergistic solution to meet the personal care needs of the elderly. Now, my country’s most advanced nursing home service center based on the new potential of artificial intelligence mobile has become an important factor in solving the differences in different nursing needs and providing adult nursing services.

2.2 Functional Analysis of the Intelligent Transformation of Elderly Care Services (1) Improve the efficiency of elderly care services In the process of traditional family care and institutional care services, due to the structural contradictions in the provision of care services and the imperfect supervision and management mechanism of care services, it is difficult for work efficiency and care service quality to meet the growing demand. Providing high-quality care for older people requires elder care. With the help of artificial intelligence technology, nursing staff can connect all aspects of the nursing industry chain, and technologies such as big data and cloud computing also ensure that nursing staff can meet the needs of the elderly in a timely manner [7, 8]. (2) Precise match between supply and demand of elderly care services With the help of big data, cloud computing, Internet of Things and other technologies, it is possible to carry out effective statistics and big data analysis on the adult care service market, timely and accurately understand my important information and practices, and finally identify the needs of care services, supply of care products and retirement Adequate compliance between facilities. Big data pension management mode recognition accurately collects elderly work data, regularly analyzes questionnaires, provides regular services and regular collaborative management, and provides personalized elderly care services for the elderly [9, 10]. (3) Reduce the cost of human resources in elderly care services

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Aging level in China has created a huge demand for the number of nursing service talents and the labor cost of combining medical care and nursing services. As a result, the number of personnel engaged in nursing services at this stage is insufficient and the situation of lack of nursing personnel will soon be outstanding, replaced by smart aged care tools.

2.3 Algorithm Selection This paper mainly uses the domain-based social recommendation algorithm, which mines the friend relationship between users from the social relationship, analyzes the set of items that friends are interested in from the user’s historical data, and recommends it to the target user. The implementation process is as follows [11, 12]: (1) Calculate user item interest degree: use social relationship to calculate user item interest degree; (2) Generate recommendation list: select the top N items according to the degree of interest to generate a recommendation list. User u’s interest in item i can be obtained I nt(u, i ) by formula (1). 

I nt(u, i ) =

Wu,v pv,i

(1)

v∈ f n u

Among them, fnu is the set of friends of user u in the social relationship. pv, i = 1 if user v is interested in item i, otherwise pv, i = 0. The value of W u, v is determined by the familiarity of user u and user v fam(u, v) and the similarity of interest simint (u, v) is calculated from the number of common friends and the number of items that the two users share in common, respectively. The calculation formulas are shown in formulas (2) and (3). f am(u, v) =

| f nu ∩ f nv | | f nu ∪ f nv |

(2)

|Iu ∩ Iv | |Iu ∪ Iv |

(3)

sim int (u, v) =

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3 Research and Design Experiment of AI Big Data Multi-dimensional Intelligent Pension Model Recommendation Algorithm Empowered by Artificial Intelligence 3.1 System Implementation The pension service recommendation system designed in this paper is implemented by J2EE technology architecture, the platform is B/S structure, the system development uses Eclipse tools, the database uses MySql, the middleware uses Tomcat, the cache uses Memcahed, and the development environment, deployment environment and data server operation All systems are Windows7, and SVN is used for code management. System background development adopts CERP3X integration framework, including Spring framework and Hibernate framework, configure bean file for each service class (BO class), realize instantiation through dependency injection, configure hbm mapping file for each data entity class, and use BaseDAO The operations in the class implement data persistence, and the combination of the two reduces the coupling between system classes. The front desk uses Html, Js and Jquery technologies, and uses css files to standardize Html styles. The front-end and back-end interaction adopts Dwr technology.

3.2 System Test Design The system function test adopts the black-box test method. From the user’s point of view, the system is accessed through the browser to test whether the relevant functions are normal. Taking the recommendation function of similar elderly care services as an example, test cases are designed for functional testing. System security test: The system grants different menu access rights according to different login roles. Prevent different roles from performing operations beyond their privileges. At the same time, the system sets the session time for each logged-in user. When the time arrives, the user is required to log in to the system again, which ensures the security of the system. Adaptability test of the system: The system adopts B/S architecture, elderly customers and their families can access the system through browsers, and they can be displayed normally in mainstream browsers IE, Chrome, Firefox and different zoom ratios, indicating that the system has good adaptability.

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4 Research and Experimental Analysis of AI Big Data Multi-dimensional Intelligent Pension Model Recommendation Algorithm Empowered by Artificial Intelligence 4.1 Recommender System Accuracy This paper simulates user recommendation based on the AI big data multidimensional intelligent pension model recommendation algorithm under the artificial intelligence empowerment constructed in this paper and the traditional algorithm. The higher it is, the higher the recommendation accuracy is. The experimental data are shown in Table 1. As can be seen from Fig. 1, the recommendation accuracy of the algorithm constructed in this paper is much higher than that of the traditional recommendation algorithm. The traditional recommendation algorithm recommends to users, and the number of users who are interested and clicked is only about 60%, while the recommendation constructed in this paper is recommended. The number of users who click on the content recommended by the algorithm accounts for 90%, which meets the needs of the algorithm recommendation system. Table 1 Accuracy of recommendations for users for the two algorithms 100

200

500

1000

Traditional algorithm

62

57

66

63

The algorithm in this paper

91

92

95

91

traditional algorithm 100

The algorithm in this paper 95

92

91

91

90 80

per cent

70

62

60

66

63

57

50 40 30 20 10 0 100

200

500

number of users

Fig. 1 Accuracy of recommendations for users for the two algorithms

1000

AI Big Data Multi-dimensional Intelligent Pension Model … Table 2 The number of elderly users in the two places who learned the operating system under teaching at different times

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30 min

30–60 min

60–90 min

Area1

72

19

6

Area2

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7

30min

30-60min

60-90min

60

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area

Area2

Area1

0

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number of users

Fig. 2 The number of elderly users in the two places who learned the operating system under teaching at different times

4.2 System Performance Test This paper tests the performance of the algorithm system constructed in this paper, mainly for the security, adaptability and operability of the system. Because the recommended algorithm in this paper mainly faces elderly users, the operability of this paper is emphasized test. It is mainly aimed at how long the elderly users can freely operate the system under the condition of operating instructions. The test is mainly carried out on a total of 200 users in the two places. The experimental data is shown in Table 2. It can be seen from Fig. 2 that the vast majority of elderly users can learn to operate within 30 min, and 90% of users can learn to operate within 60 min. Such data proves that the system constructed in this paper is easy to operate and conforms to need for aged care services.

5 Conclusions Based on the background of old-age services, this paper aims at the problems of the large-scale emergence of old-age service resources and the continuous emergence

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of old-age needs through in-depth research and analysis of the current shortage of old-age services in my country. Elderly care services tend to be individualized, and it is difficult for elderly clients and their families to quickly obtain appropriate elderly care services. On the basis of the above research, the recommendation algorithm is studied. Through experimental comparison, the algorithm has improved in terms of running speed and recommendation accuracy. It also uses user portraits to improve the traditional matrix factorization algorithm, adding user neighbor model and shortterm interest model to the traditional algorithm. Through experimental analysis, the algorithm Improved recommendation accuracy and sparsity resistance.

References 1. Alwetaishi M, Shamseldin A (2021) The use of artificial intelligence (AI) and big-data to improve energy consumption in existing buildings. IOP Conf Ser Mater Sci Eng 1148(1):6, 012001 2. Kedra J, Radstake T, Pandit A et al (2019) Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations. RMD Open 5(2):e001004 3. Wu B, Qiu W, Huang W et al (2022) A multi-source information fusion evaluation method for the tunneling collapse disaster based on the artificial intelligence deformation prediction. Arab J Sci Eng 47(4):5053–5071 4. Aghaeipoor F, Javidi MM, Fernandez A (2021) IFC-BD: an interpretable fuzzy classifier for boosting explainable artificial intelligence in big data. IEEE Trans Fuzzy Syst PP(99):1–1 5. Yoshisaki D et al (2020) Proposal of intelligent polishing system by artificial intelligence using neural networks. J Japan Soc Precis Eng 86(1):80–86 6. Swanson G (2019) Non-Autonomous artificial intelligence programs and products liability: how new AI products challenge existing liability models and pose new financial burdens. Seattle Univ Law Rev 42(3):11–11 7. Yang T, Zhang L, Kim T et al (2021) A large-scale comparison of artificial intelligence and data mining (AI&DM) techniques in simulating reservoir releases over the upper Colorado region. J Hydrol 602(6):126723 8. Wang KJ, Asrini LJ (2022) Deep learning-based automatic optical inspection system empowered by online multivariate autocorrelated process control. Int J Adv Manuf Technol 120(9–10):6143–6162 9. Wu ZY, Ismail M, Serpedin E et al (2021) Artificial intelligence for smart resource management in multi-user mobile heterogeneous RF-light networks. IEEE Wirel Commun PP(99):1–7 10. Barja-Martinez S, Aragüés-Pealba M, Munné-Collado N et al (2021) Artificial intelligence techniques for enabling big data services in distribution networks: a review. Renew Sustain Energy Rev 150(January 2016):111459 11. Zhang X, Huang T, Wu B et al (2021) Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples. Front Mech Eng 16(2):340–352 12. Khosravi A, Syri S, Pabon J et al (2019) Energy modeling of a solar dish/Stirling by artificial intelligence approach. Energy Conv Manage 199(Nov):112021.1–112021.20

Artificial Intelligence Medical Construction and Data Mining Based on Cloud Computing Technology Lujun Lv

Abstract Informatization is a modern trend of advanced productive forces, and medical informatization has become a hot spot in the field of communication and medicine. With the continuous iteration of computer technology and the scope of computer applications continue to expand, the development of intelligent terminals and the dissemination of medical information has begun. Therefore, how to quickly and effectively solve the problems brought about by large-scale medical data research is a very important issue. The potential of big data analytics applications is improving the value of healthcare. As an open source framework under the Apache Foundation, Apache Hadoop was originally one of many open source implementations of Map Reduce with a focus on addressing the massive scale supported index Web crawler. For this purpose, its execution architecture is adjusted to ensure a strong fault tolerance for a large number of data-intensive computing. One reason Hadoop is so popular is that it provides a platform for engineers and researchers in an organization to access immediate and virtually unlimited amounts of computing resources and corporate databases. The paper mainly uses qualitative analysis method, and expounds the opportunities and challenges brought by cloud computing to medical construction and data mining, as well as the reasons for the success of Hadoop, and the importance of association rule mining algorithm in the field of medical cloud computing and clinical diagnosis. The analysis of the feasibility of traditional and optimized algorithms. Keywords Cloud computing · Medical construction · Big data · Data mining

L. Lv (B) Jilin Agricultural Science and Technology University, Jilin, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_20

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1 Introduction Cloud computing is the most important means to obtain a large amount of data. Cloud data storage, sharing and extraction technology can be an environmentally effective way to solve large-scale, rapid and variable data storage, data analysis and computing. In the context of China’s modernization, education, transportation, medical care and other industries that are closely related to people’s well-being, if we use cloud computing with big data-driven technological innovation to solve these problems, we will achieve the rapid development of these industries as [1]. Cloud computing started very early and developed very fast at home and abroad. Western countries have developed new products and standards, and the latest research results, such as intelligent medical systems, HL7 and other modern systems. In the United States, cloud computer systems are very effective and widely used. This system has been widely used in various hospitals. FALL Acte Plus system is common in Germany. It enables all German hospitals to provide medical services, helping German hospitals share medical information and cooperate with each other to establish a unified foundation and be widely used. Distributed medical resources have become a comprehensive medical resource, shared by both. Cloud services can provide software, hardware, infrastructure and network services, which will help solve the situation of fragmentation of treatment information and help the overall industry. Based on this, cloud computing-based data mining methods can more effectively meet the computing needs of complex, unstructured and massive medical data. Currently, many advances have been made in health data processing and analysis, including diabetes data analysis, cancer etiology research, clinical data analysis of cardiovascular diseases, and certification of health evaluation models [2].

2 Proposed Method 2.1 The Concept of Cloud Computing The narrow definition of cloud computing means that vendors can create data centers or supercomputers through distributed and virtual computing, rent them out to customers, and provide storage or computing facilities, “cloud computing” means that a service provider provides different types of online services to different customers by creating servers. The definition of many concepts is incomplete, but it can be seen from the above definition that cloud computing is computing space, service space and resource sharing [3]. It can provide certain dynamic virtual localization through virtual technology according to the needs of users.

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Fig. 1 Key support technologies of cloud computing

2.2 Key Technologies of Cloud Computing In today’s computer information, virtualization technology and distributed storage management technology has become the two main key technology of cloud computing mode, the perfect key technology to complete the dynamic elastic management of cloud resource pool, at the same time can provide huge amounts of data resources storage, flexible resources, processing massive amounts of data computing and distribution ability [4]. As shown in Fig. 1, The large number of servers is due to the massive use of cloud computing platforms becomes possible which running hundreds of different applications and distributed across hundreds of different locations to work together. Distributed parallel programming technology: In a cloud computing system, the processing of massive data requires not only powerful storage and computing power, but also accurate, high-speed and accurate computing [5].

2.3 Overview of Medical Informatization Medical service is an important part of social security, which is related to people’s life, health and quality of life, and has a special status and role in social life and public management. With information technology, medical services and quality have been greatly improved. With the development of medical informatization, —medical informatics (Medical Informatics), a new medical field based on information technology, has been spawned. Some foreign scholars define medical informatics as a science of systematic processing of data, information and knowledge in medical and health care [6]. With the deepening of research, the application of medical informatics has penetrated into the electronic treatment record system, information system, electronic treatment system, assisted medical decision-making and other fields, promoting the development of medical informatization and medical service innovation.

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2.4 Big Data Data age is the most attention is to be able to get a lot of data after processing behind the potential value and expectations for the future, big data has huge amounts of data storage, the core of the key is the project for data information analysis and processing, and using virtual technology such as cloud computing for high quality and efficient processing to ensure the real value of data. The purpose of building the big data medical construction system is to use information technology to promote the development of medical information in China, and to provide theoretical support and technical solution for the regional medical information construction in China [10]. Bayes’s theorem is formulated as follows: P(Bi )P( A|Bi ) ) ( ) ( j = 1 P Bj P A|Bj

P( Bi |A)= ∑n

(1)

where P(A|B) is the possibility of A occurring in the case of B occurrence. A1, … An is a complete event group, namely: n 

( ) Ai = Ω, Ai Aj = ∅, P A21 > 0

(2)

i=1

When there are more than two variables, Bayes’s theorem still holds, such as for example: P( A|B,C)=

P(A)P( B|A)P( C|A,B) P(B)P( C|B)

(3)

2.5 Hadoop Platform Now, big data design processing system is divided into two directions, centralized computing and distributed computing. Centralized computing obtains big data processing capabilities through mainframes, which are often expensive and for stand-alone systems, the scale of data processing always bottlenecks. Currently, the commonly used distributed big data processing platforms are Hadoop, Spark, and Storm, which are open-source software and are freely available. The three have different characteristics and ways of data processing. The Hadoop platform is the best choice for the data generated by the medical information system that we need to process.

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3 Experiments 3.1 Experimental Subjects As the current medical industry technical pillar, this paper analysis the good application prospects of cloud computing and greater development advantages to the medical and health industry. In this context, the paper expounds the relevant technical knowledge, such as “cloud computing”, “data mining”, “big data” and “cloud computing” core technology theoretical knowledge, analyzes the advantages of various cloud computing and data mining technology, how it affects the medical and health industry; through the research status of data mining at home and abroad, deeply understand the current medical construction urgently breakthrough optimized algorithm to realize data mining, thus introducing the Hadoop platform. Hadoop platform has its unique advantages and can provide effective means for medical construction and data mining.

3.2 Experimental Methods The paper mainly adopts text research method, qualitative analysis and comparative analysis, etc., by reading a large number of excellent text around the world, expounds the “big data” and “as cloud computing technology” basic concepts, collected the research results of data mining technology at home and abroad, the paper to carry out the follow-up research work has played a key reference role. By introducing Hadoop, the traditional Apriori algorithm, 0.2 of interest threshold and 0.35 at different minimum support thresholds.

4 Discussion 4.1 Research on Medical Data Association Rule Algorithm Based on Hadoop Given the huge amount of medical data, traditional data extraction methods seem to be overburdened. Improving a better data mining algorithm from the traditional data mining method has also become the research direction of researchers. At the same time, the rise of cloud computing technology has also brought new choices to big data processing. Storage is only the first step in processing huge amounts of medical data. In daily life, patients are sick to the column will be accompanied by complications, and complications are often ignored in diagnosis.

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Hadoop Core Architecture

It is mainly developed based on the Java language, but other languages such as C++, Python, and PHP can be developed, so related jobs can be done across platforms. Apache Hadoop’s initial design [7]. focused on running large-scale Map Reduce jobs to handle network grabs. For increasingly diverse companies, Hadoop has become a pioneer in data and computing.

4.1.2

Apriori Algorithm and Association Rule Algorithm

As shown in Fig. 2, The Apriori algorithm is the first to propose support-based ligation and pruning techniques that control the growth of candidate sets. Association rule algorithm data mining is to discover hidden information and interesting patterns in the database. It consists of two subproblems: finding sets of frequent items based on some predefined thresholds; and generating association rules that satisfy confidential constraints. The basic search strategy of association rule mining can be divided into two categories: candidate generation and pattern growth [8].

4.2 Improved Algorithm Simulation Traditional medical data statistical methods are powerless in the face of huge medical data, For medical data, most data have non-canonical, non-standard expressions. it is now simulated with data mining training data accidents data. Simulation hardware environment is: CPU is Inter (R) Core (TM) i3-4250 dual-core 1.3 Hz, 4 GB of memory, operating system Win7 64 bit. The algorithm is written in the Java language, and the development environment is jdkl.7.0_79. First, we test the improved algorithm in time against the classical Apriori algorithm with the same support. As shown in Table 1, the table shows that with the same support, the two algorithms have basically the same mining time, and the data mining method is to automatically collect the data by using the corresponding massive data processing algorithm. It is pointed out that analyzing the complex medical data can expand and supplement the data mining methods [9]. Figure 3 shows the traditional Apriori algorithm, interest threshold of 0.2 and 0.35 at different minimum support threshold min_supp. With the same minimum support threshold, the improved algorithm has fewer association rules, and the number of association rules is inversely proportional to the minimum interest threshold of 0.1, the improved algorithm filters out 50.7% of 0.2 and 0.35 for 67.3% of users. Therefore, in practical application, the relevant experts set interest threshold according to their own experience judgment, and directly filter out irrelevant users and uninterested users. The algorithm has strong pertinence, optimizes the usability of interested

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Fig. 2 Flow diagram of the Apriori algorithm Table 1 Comparison of mining time between improved Apriori algorithm and classical algorithm(s) Support threshold

Confidence threshold

The classical Apriori algorithm

0.1

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1200000 1000000 800000 600000 400000 200000 0 0.1

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Fig. 3 Comparison of the number of mining association rules between improved Apriori algorithm and improved classical algorithm

users, eliminates irrelevant users from occupying platform resources, and saves a lot of labor cost and time cost for the use of the platform [10]. By combining the open source cloud computing framework Hadoop with the data processing technology, the Hadoop-based medical data storage technology is proposed, and the Apriori algorithm is improved and transplanted to the Hadoop platform, realizing the Hadoop-based medical data analysis. Finally, we show that both the storage method and the improved Apriori algorithm have a good performance improvement.

5 Conclusions Nowadays, “Internet+” has been widely used in various industries. Hospitals are an important part of the society, hospitals should accelerate the progress to the information society, and the medical system itself is to serve the people. With the spread of information, hospitals need to timely update their own medical databases. The hospital that constitutes the core of the digital control system must establish an internal information system. Today, the medical system has become an important pillar of social development. As a part of the reform of the national health plan, information technology has become the trend and mainstream of social development.

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In the development of the therapeutic system„ cloud computing is a very fast developing technology. At present, many hospitals are developing cloud computing platforms to promote industrial informatization. With the progress of the world medical technology and the rapid advancement of medical equipment, medical informatization begins to spread in the cloud, and the society also develops to the direction of informatization.

References 1. Sun H, Chen SP, Xu LP (2018) Research on cloud computing modeling based on fusion difference method and self-adaptive threshold segmentation. Int J Patt Recogn Artif Intell 32(6):1859010.1–1859010.15 2. Sun H, Chen SP et al (2018) Research on cloud computing modeling based on fusion difference method and self-adaptive threshold segmentation. Int J Patt Recogn Artif Intell 32(6):1859010.1 3. Sun Y (2021) Cloud edge computing for socialization robot based on intelligent data envelopment. Comput Electr Eng 92(6):107136 4. Bhargavi K, Babu BS, Pitt J (2020) Performance modeling of load balancing techniques in cloud: some of the recent competitive swarm artificial intelligence-based. J Intell Syst 30(1):40– 58 5. Yin Y (2020) Research on ideological and political evaluation model of university students based on data mining artificial intelligence technology. J Intell Fuzzy Syst 40(6):1–10 6. Li W, Jiang B, Zhao W (2020) Obstetric imaging diagnostic platform based on cloud computing technology under the background of smart medical big data and deep learning. IEEE Access PP(99):1–1 7. Lv X, Li M (2021) Application and research of the intelligent management system based on internet of things technology in the era of big data. Mob Inf Syst 2021(16):1–6 8. Gomathi N, Karlekar NP (2019) Ontology and hybrid optimization based SVNN for privacy preserved medical data classification in cloud. Int J Artif Intell Tools 28(3):1950009 9. Tuli K, Kaur A, Sharma M (2019) A survey on comparison of performance analysis on a cloud-based big data framework. Int J Distrib Artif Intell 11(2):41–52 10. Wang M (2017) Artificial intelligence hypermedia teaching based on cloud technology. Revista de la Facultad de Ingenieria 32(12):986–993

Visual Intelligent Recognition System Based on Visual Thinking Wenqiang Zhang

Abstract With globalization, technological growth and the rapid popularization of the Internet, the marketing atmosphere faced by enterprises is undergoing tremendous changes. For the relevant marketing enterprises, the actual efficiency of information response will have a significant impact on the comprehensive competitiveness. Therefore, this paper studies and analyzes the enterprise marketing strategy (MS), combination optimization system based on data analysis algorithm (DAA); This paper analyzes the application of data analysis technology in Combinatorial Marketing and the mining of marketing data, puts forward the optimization strategy of Combinatorial Marketing for the optimal project solution, and discusses the construction of marketing mix; Finally, taking enterprise a as an example, the accuracy of the data mining technology of the DAA proposed in this paper is compared with that of the traditional algorithm for the analysis of marketing factors, as well as the data comparison of the monthly average value of business income, cost expenditure and promotion profit of enterprise a under different marketing models. The results show that the accuracy of the DAA proposed in this paper is much higher than that of the traditional algorithm. After the optimization of the MS combination of enterprise a, The average monthly income and marketing business of the enterprise are much higher than the profits under the traditional marketing methods in the past, which verifies the accuracy of the data mining technology analysis algorithm. It can also be seen that the DAA proposed in this paper is very helpful to the enterprise marketing and plays an important role in the long-term stable development of the enterprise. Keywords Data analysis algorithm · Enterprise marketing · Marketing strategy · Optimization system

W. Zhang (B) School of Economics and Management, Tsinghua University, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_21

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1 Introduction In the dynamic market competition, marketing activities can improve the brand image and market share, but enterprises spend huge costs on advertising and promotion, which will cause consumers’ attention to product prices and erosion of brand assets. Therefore, in marketing activities, it is necessary to consider the interaction between the elements of marketing activities, and consider the trade-offs in planning marketing mix strategies by analyzing the interaction between marketing activities. Therefore, this paper studies and analyzes the enterprise MS combination optimization system based on DAA. Many scholars at home and abroad have studied and analyzed the enterprise MS combination optimization system based on DAA. Rajanmp proposes an algorithm based on iterative method, which has a parameter selection strategy to solve the ridge regression problem, avoids such restrictions, and automatically calculates parameters and best fit model. The effectiveness of the algorithm is illustrated by an example and compared with the standard method. The experimental data analysis clearly shows the superiority of the algorithm [1]. Sharmap analyzed the MS and expenditure of coronavirus pandemic, discussed and tested the impact of 2019 coronavirus disease on digital marketing, social media marketing, e-mail marketing, 4P marketing, advertising and search engines; It also discusses how enterprises can change their marketing strategies and take different directions while reducing marketing expenditure [2]. Based on the traditional MS, this paper proposes an enterprise MS combination optimization system based on DAA. Through the data mining technology algorithm, the management model obtained through effective evaluation and analysis, weather change and stable ARMA part of the information form a whole and perfect prediction and evaluation system, improve various indicators, make the prediction results consistent with the real situation of the industry, and improve the accuracy of the industry prediction, Try to improve the company’s market estimation system. On this basis, the enterprise MS combination optimization system is proposed. Through in-depth data mining, we can estimate the development of the industry, more reasonably guide the company to more accurately grasp the development direction of the market, and make the company’s market decisions more predictable [3, 4].

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2 Enterprise MS Combination Optimization System Based on DAA 2.1 Application of Data Analysis Technology in Portfolio Marketing The system of data analysis technology is huge and complex. The basic technologies include data collection, data preprocessing, distributed storage, NoSQL database, data warehouse, machine learning, data mining, visualization and other technical categories and different technical levels. This paper mainly discusses the data mining technology. The significance of data mining technology lies in discovering the internal relationship of scattered data information, so as to respond to the needs of different users and give accurate future forecasts. Based on this, the company can formulate more effective and relatively low-cost product design and planning, so as to maximize the company’s profits and finally achieve the “1 + 1 > 2” effect [5, 6].

2.1.1

Marketing Data Mining

Determine the purpose: first of all, determine the target customer groups for selling products. In the process of data mining, finding and determining the purpose of data mining is an important part. Although the results of data mining can not be predicted in advance, the purpose and goal of data mining can be determined in advance. The data mining process with a clear purpose will be clear and effective. Therefore, before mining, it is necessary to understand the company’s purpose and needs [7]. Data preparation: after relevant data is transferred to the center in terms of customers and services, the data mining center will carry out supporting preparation activities, which also covers four sub steps: data integration. It may contact various data sources and require effective combination of various data and storage in the corresponding storage system, so as to facilitate relevant data mining operations. Data selection. In the database of the data mining center, the data of all customers are stored, the information related to the target in all aspects is retrieved, and then the relevant information that meets the mining needs is selected. Data preprocessing. Analyze the specific quality status to prepare for subsequent analysis. At the same time, judge the corresponding class requiring mining activities, and verify whether the determined data can meet the target requirements. Data transformation. In this step, the data will be adjusted to fit the mining mode, that is, relevant import operations will be carried out to evolve into the corresponding analysis model [8, 9]. The establishment of an analysis model, which is built with reference to the algorithm, and the construction of a supporting model that fits the algorithm, is the core element to achieve comprehensive operation. Mining operation: carry out mining around the actually obtained relevant data. Select the algorithm and import the model to carry out by yourself. However, there is

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no general data and algorithm, so it is necessary to build a differentiated model with reference to the actual situation, and select the most ideal scheme after comparison.

2.2 Construction of “4P”, “4C” and “4E” Interactive Marketing Mix 4P marketing theory holds that the key to organizing marketing activities is to develop appropriate products, market prices that match products, channels that can reach consumers widely and promotional activities that attract consumers’ attention, so that products or services can succeed in the target market. 4P guides product marketing practice from the perspective of enterprise product thinking. The 4C marketing theory starts with consumer demand and emphasizes that customer first is to meet customer demand as much as possible; The second is to optimize the manufacturing cost within the enterprise and strive to reduce the selling price to win more consumers; Moreover, fully consider the convenience of customers to purchase products, and widely distribute channels or stores; Finally, two-way communication with consumers to improve the stickiness of consumers. 4C is more a marketing activity from the perspective of consumer demand [10]. Based on the characteristics of industrial products, this paper selects the 4E marketing theory of dingxingliang, an industrial products marketing expert, to provide a project as the core of operation and pay more attention to the value of products rather than just the price. Industrial products have high technical requirements and complex application scenarios. The transmission of product prices is based on short channels, that is, shortcut. The trust of industrial products marketing is the key. Due to the long decision-making process and high product application risk, it wins the trust of customers, It can reduce customers’ doubts. MES products are customized industrial products. For customized chemical products, the products are customized, and the customized products are the project itself [11]. Therefore, combined with the traditional marketing theories of 4P and 4C, a marketing mix based on the interaction between 4P, 4C and 4E is constructed.

2.3 Combined Marketing Optimization Strategy for Optimal Project Solution Industrial products marketing is different from consumer goods. Consumer goods mainly meet the needs of individuals, while industrial products operate around the project and need to meet the requirements of the project. Standard industrial products focus on product functions, while customized industrial products emphasize the need to solve project problems. In terms of product introduction evaluation, standard industrial products have a series of national or industrial standards, and the evaluation

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standards are relatively clear. Product price, quality, functional characteristics, aftersales service and other indicators can be quickly selected through market competition. However, customized industrial products. From the perspective of product promotion, the product promotion of industrial products is a three-dimensional marketing method. Because the product purchase decision is decided by the enterprise project team, the object of the product promotion scheme is the project decision-making team. Therefore, the enterprise’s solution is displayed by demonstrating the product functions and establishing a simulated production line to promote mutual understanding, so as to eliminate customers’ doubts about the product performance and reduce the risk caused by project failure [12]. From the perspective of customer communication, enterprises pay attention to business efficiency, which also needs to be considered when communicating with customers. When visiting customers for the sale of industrial products, they pay attention to “having a purpose when going out and a result when going in”, which emphasizes the effect of communication. When communicating with customers, enterprises are required to be effective and stable. Effective communication means that the purpose of communication is to solve doubts, while stability means that the communicators should be fixed as much as possible, rather than changing new faces every time, Customer adaptation will be more and more difficult, affecting the customer’s communication experience. Adopt face-to-face, stable and effective communication, have special project pre marketing and marketing personnel to follow up the project needs, and arrange special project managers to promote the project and accelerate the project development after the project is determined.

3 Data Mining Algorithm According to the theory of density estimation, any continuous data distribution can be approximately expressed by data mining Gaussian mixture model. Each cluster of data is often regarded as a multi-dimensional Gaussian distribution. The distribution can pass through the center of the Gaussian distribution γ R and  covariance r , r = 1 K means; A is the d-dimensional column vector, γ r. Column center vector of dimension. For each data record a in the dataset, calculate that a belongs to cluster r = 1 Probability of K: vr' (a), vr' (a)

vr' · pr (a|γr' ,

= k

l i=1 vi

·

l

r )  l pi (a|γi , li

)

(1)

Update hybrid model parameters: r  l+1

 =

a∈l

vrl (a)(a − γrl+1 )(a − γrl+1 )  , r = 1, ...k l a∈l vr (a)

(2)

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It is worth noting that in the process of algorithm steps, in order to calculate the family of the calculated data points, each record of the original data set needs to be scanned. The amount of calculation in this step is relatively large. The number of iterations of the algorithm is affected by the initial condition parameters and the data distribution. Generally speaking, the number of iterations is unpredictable, but the algorithm tends to converge. It is not only suitable for spherical Gaussian clustering, but also for other non spherical data clustering to estimate the data distribution.

4 Experimental Test Analysis In order to verify the effectiveness of the enterprise MS combination optimization system based on the DAA proposed in this paper, taking enterprise a as an example, firstly, the accuracy of the data mining technology of the DAA proposed in this paper and the traditional algorithm for marketing factor analysis are tested and compared. The test results are shown in Table 1 and Fig. 1. It can be seen from the above chart that the accuracy of the DAA proposed in this paper for the analysis of factors in all aspects of enterprise marketing has reached 80%, which is much higher than the traditional algorithm. The accuracy of the project demand analysis has reached 90%, and the accuracy of the product pricing analysis has also reached 87%. The accuracy of the analysis algorithm of data mining technology is verified. It can also be seen that the DAA proposed in this paper is very helpful for enterprise marketing. Next, taking enterprise a as an example, this paper makes a comparative analysis between the monthly average value of operating revenue, cost expenditure and promotion profit of enterprise a in the past few years under the traditional marketing method and the enterprise MS combination optimization system marketing method based on DAA in the past two years. The results are shown in Fig. 2. As can be seen from Fig. 2, after the optimization of MS combination, the monthly average income and marketing business of enterprise a are much higher than the profits under the traditional marketing methods in the past, of which the monthly average income is nearly 100,000 yuan higher than that in the past; As for the cost of the enterprise, it has been eased after the optimization of the MS portfolio, saving nearly 50,000 yuan per month. It can be concluded that the MS combination optimization system under the DAA proposed in this paper has played an important Table 1 Comparison table of accuracy rate of marketing factor analysis Fixed price

Product channel

Brand positioning

Customer demand

Project requirements

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fixed price

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customer demand

Project requirements

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traditional algorithm

Fig. 1 Marketing factor analysis accuracy of different algorithms

500000

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450000 400000 350000 300000 250000 200000 150000 100000 50000 0

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Cost expenditure

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Profitability of enterprise a under different marketing strategies Traditional marketing methods Marketing strategy combination optimization system Fig. 2 Profitability of enterprise a under different marketing strategies

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role in the long-term stable development of enterprises; DAA the analysis strategy of precision marketing is indispensable to the tracking and winning of enterprise projects.

5 Conclusions The enterprise MS combination optimization system under the numerical control analysis algorithm proposed in this paper has achieved good results, but there are still some problems to be further studied: due to the limited relevant data resources, the industry data involved in the research is inevitably not complete and timely in statistical analysis, and the understanding of relevant concepts and the application of analysis tools are not proficient enough, and the analysis of existing data may not be comprehensive and in-depth, The results obtained naturally show that there are omissions and deficiencies, resulting in the existing strategy formulation being limited to shallow exploration and attempt. In the follow-up study and work, it is also necessary to carry out further research on the innovation of marketing strategies and design more ideal strategies.

References 1. Rajan MP (2022) An efficient ridge regression algorithm with parameter estimation for data analysis in machine learning. SN Comput Sci 3(2):1–16 2. Sharma P (2020) Impact of COVID-19 on MS and expenditure. Int J Adv Res 8(9):1475–1478 3. Jalal S, Ali B (2018) The impact of MS on customer satisfaction for E-learning: a marketing strategies model approach. Int J Comput Sci Inf Secur 16(10):95–102 4. Brazeau MD, Guillerme T, Smith MR (2019) An algorithm for morphological phylogenetic analysis with inapplicable data. Syst Biol 68(4):619–631 5. Svobodová L (2021) Sensitization potential of medical devices detected by in vitro and in vivo methods. Altex 38(3):419–430 6. Burhan GF, Mansur A (2021) MS planning based on customer value. PROZIMA (Prod Optimization Manuf Syst Eng) 4(2):29–40 7. Kochikov IV, Lagutin YS, Lagutina AA et al (2020) Raising the accuracy of monitoring the optical coating deposition by application of a nonlocal algorithm of data analysis. Sibirskii Zhurnal Industrial Noi Matematiki 23(2):93–105 8. Lee WY, Liu F (2018) Analysis of a dynamic premium strategy: from theoretical and marketing perspectives. J Ind Manage Optimization 14(4):1545–1564 9. Kalpana C (2020) Health care data analysis through machine learning meta heuristic algorithm. J Adv Res Dynam Control Syst 12(7):196–201 10. Kochikov IV, Lagutin IS, Lagutina AA et al (2020) Raising the accuracy of monitoring the optical coating deposition by application of a nonlocal algorithm of data analysis. J Appl Ind Math 14(2):330–339 11. Digris AV, Shishkov VS, Novikov EG et al (2019) Correction to: influence of weighting factors on the operation of a linear algorithm for analysis of data from frequency-domain measurements of fluorescence damping times. J Appl Spectrosc 86(5):963–963 12. Shulga M (2020) Assessment of the prospects of enterprise development based on the level of competitiveness. Mod Econ 22(1):125–130

Path Optimization of e-Commerce Logistics Terminal Distribution Mode Based on Dijkstra Algorithm Shuoyuan Lin

Abstract In recent years, with the continuous development of the Internet and the increasing popularity and progress of smart phones, e-commerce has become familiar and used by everyone. The rapid development of e-commerce has had a great impact on the business model and business philosophy of the entire society, and has become a new economic development point in my country, providing conditions for the rapid development of e-commerce. This paper aims to study the optimization method of ecommerce logistics distribution based on Dijkstra algorithm. By analyzing the obstacles faced by the e-commerce industry in the development of terminal distribution areas, combined with the local characteristics and the current situation of online shopping, this paper establishes a distribution optimization mathematical model based on Dijkstra’s algorithm, the ultimate goal of solving the problem is to reduce the cost of the company. Solve the practical problems of terminal distribution. This paper makes full use of the research method combining theory and practice to study the logistics distribution of e-commerce logistics in terminal distribution. On this basis, the hybrid tag search algorithm is used to study the distribution method of e-commerce. Experiments show that the algorithm path optimization in this paper can be reduced by about 10 km compared to the empirical path, and the best terminal distribution mode is logistics cabinet distribution. Keywords Dijkstra algorithm · e-Commerce logistics · Terminal delivery · Route optimization

1 Introduction With the continuous growth of business volume and income, the logistics industry is facing new demands for intelligence, efficiency, informatization, networking and S. Lin (B) South China University of Technology, Guangzhou, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_22

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flexibility. At present, e-commerce logistics generally have problems such as low-cost competition, innovative operation, backward assembly and transportation methods, unexpected distribution, loss of flexibility and damage, which hinder the development of logistics and even e-commerce. The logistics industry has long since improved terminal delivery services. Become a hot topic [1, 2]. In the research on route optimization of e-commerce logistics terminal distribution mode based on Dijkstra algorithm, many scholars have studied it and achieved good results. For example, the research on how Ma [3] can win in the “last mile” of ecommerce is ongoing, and the main problem of e-commerce development is not to obtain more customer orders, but how to quickly deliver products to customers, that is, a comparative analysis of the “last mile” e-commerce logistics and distribution methods. Younesi [4] through Research on the current terminal logistics distribution situation, analyze the problems and weak links in various places, and the terminal logistics situation of “Internet + community” is cooperating to solve the “last mile” distribution problem. Based on the relevant research results of domestic and foreign scholars, this paper introduces the current situation of e-commerce logistics distribution in urban areas in my country, and analyzes the existing problems by using the method of network content analysis. This paper classifies the three main distribution locations of urban logistics terminals, and uses Dijkstra’s algorithm to study the factors that affect the choice of distribution locations. An evaluation index system for distribution location selection was established, and multi-attribute determination methods such as network analysis method, entropy value method, and complete solution method were used to construct urban distribution location model e-results and the constant interval of impact analysis and evaluation index density on distribution mode selection.

2 Research on Path Optimization of e-Commerce Logistics Terminal Distribution Mode Based on Dijkstra Algorithm 2.1 Overview of Mode Evolution Compared with the traditional distribution mode, the logistics department or logistics distribution company of the manufacturing enterprise divides a certain area into several distribution service areas, sets up distribution points, and divides the distribution points into multiple distribution lines according to the business volume and mileage. A route is a way of arranging a delivery person to use a vehicle to deliver the goods to the customer. This traditional distribution model is centered on the enterprise and aimed at promotion, or is called the “one-stop mode of production, supply and sales”. In this context, the emphasis is on transportation and logistics. Because this model is encountering the constraints of enterprise-centric thinking,

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the bottleneck of lagging technology upgrades, and the barriers built by information islands, it has been declining [5, 6]. With the development of market demand, logistics distribution has entered the era of supply chain management. At this time, people’s concept has strengthened “logistics as the center”, which is a “transition mode” that has evolved from a traditional model to a new model. The third-party professional logistics and distribution companies (centers) for distribution have been established one after another. Such companies often form a supply chain relationship with manufacturing companies and shopping malls (stores). It is a vertical chain relationship and emphasizes the efficiency of goods delivery. This is also the current terminal distribution. The main delivery method taken. Under the support of Internet of Things technology and supply chain management ideas, terminal distribution is evolving into a new model. It is necessary to change the concepts of “enterprise-centered” and “logistics-centered” into “customer-centered” and creative With service as the goal, a new distribution model is formed through collaborative decision-making and agile operation. This new model not only draws on the advantages of the vertical supply chain from suppliers to manufacturers to logistics distributors to consumers, but also increases the relationship with horizontal e-commerce platform sellers, third-party financial settlement providers and other big data platforms. Contacts between Information Providers. The first form of this model is the collaborative distribution model; the second form is the agile distribution model; the third form is the creative distribution model. These three forms are also the three stages of the new model of terminal distribution based on the Internet of Things technology. Collaborative distribution is the basic stage, agile distribution is the intermediate stage, and creative distribution is the advanced stage.

2.2 Improvement Idea of Dijkstra Algorithm In the existing classical clustering algorithms, the distance measurement algorithms involved are basically based on the Euclidean distance or the Harmanton distance, which measures the distance between data objects based on the attributes of the straight line between two points in space. Obtained, the measurement of the similarity between the data will have a certain effect. However, in practical problems, the distance between two points is often restricted by some objective factors such as urban planning or road design. Even if the two points are within reach, the actual distance is far greater than the straight-line distance. According to this practical need, a model should be established to represent the measurement method or formula of the actual distance between two points. This paper introduces the geodesic distance (Geodesic Distance, also called Dijkstra distance), the calculation of this distance can not only obtain the shortest actual distance between two points but also maintain the surface characteristics between two points [7, 8]. In the actual logistics industry, the process of goods distribution is not a single point and a single source. A logistics company often involves multiple distribution

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centers, and within the service scope of a distribution center, there are often multiple customers and multiple delivery man. How to reasonably divide the scope of local services, the selection of the address of the local distribution center, the optimization of the route from the logistics company to each local distribution center, and the optimization of the delivery routes of the deliverymen of each distribution center for customers are all issues that must be considered. In order to divide the service range, this paper uses the K-means clustering algorithm in data mining, and improves the similarity measure distance of the algorithm with Dijkstra distance, and proposes an improved K-means clustering algorithm based on Dijkstra distance. -DK-means. The introduction of Dijkstra distance is in line with our usual thinking mode of considering single-point and single-source problems. The journey between two places should not only have the shortest path, but also keep the surface characteristics between the two places unchanged. Therefore, it is reasonable to introduce the improved clustering algorithm based on Dijkstra distance into the logistics industry.

2.3 Analysis of Distribution Route Optimization Algorithm Nowadays, the scale of logistics is gradually expanding, and the logistics system is becoming more and more complex. When using this algorithm to establish the corresponding expression, if the actual situation is fully considered, it will often be restricted by many conditions, and the solution process will be more complicated, even impossible. Find the optimal solution. It can be seen that the exact algorithm is only suitable for small-scale VRP problems with relatively simple constraints, and considering its large amount of computation and low solution efficiency, it is necessary to seek a faster and more efficient approximate algorithm to replace the exact algorithm for optimization [9].

2.4 Decision Making Using the Topsis Method (1) Solve the standardized weighting matrix There are differences in the dimension and magnitude of the multi-index attributes. According to the TOPSIS method, the index value is first standardized to obtain the standardized matrix Z ij , and the xij = wi j · z i j standardized weighting matrix is constructed according to the formula X i j . (2) Determine the ideal solution x + and the negative ideal solution x − Ideal solution: } { } { x + = max xi j | j = 1, 2, . . . , m = x1+ , x2+ , . . . , xm+

(1)

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Negative ideal solution: } { } { x − = min xi j | j = 1, 2, . . . , m = x1− , x2− , . . . , xm−

(2)

(3) Distance calculation between the evaluation object and the ideal solution and negative ideal solution Distance to ideal solution: ┌ |∑ )2 | m ( + xi j − x +j di = √

(3)

j=1

Distance to negative ideal solution: ┌ |∑ )2 | m ( − xi j − x −j di = √

(4)

j=1

(4) Calculation of the degree of closeness of each scheme to the ideal solution Gi =

di+

di− + di−

(5)

Arrange the evaluation objects in descending order of Gi value, and select the optimal urban community e-commerce logistics terminal allocation. The sending mode is the mode corresponding to the maximum Gi value [10, 11].

3 Research and Design Experiment of Route Optimization of e-Commerce Logistics Terminal Distribution Mode Based on Dijkstra Algorithm 3.1 Mode Selection The problem of choosing the distribution mode of the e-commerce terminal in urban communities is based on the decision-making goal of choosing the optimal distribution mode. Through the analysis of the current situation of the distribution of e-commerce logistics terminals in different urban communities, the evaluation index system for the selection of distribution mode is constructed. According to the evaluation results, a comprehensive decision is made to find the optimal distribution mode for the system target [12].

200 Table 1 Comparison of this path optimization scheme with the traditional empirical path

S. Lin Optimize the path

Experience path

Algorithm optimization path

Number of vehicles

4

3

The slope factor is not considered

326.32

315.45

Consider the slope factor

326.3

315.5

3.2 Experimental Design This paper conducts experiments on the terminal delivery mode and route optimization in this paper. The first is to conduct experiments on the route optimization effect of this paper, and compare the distance between the algorithm-optimized route and the driver’s daily experience route. The second is to select the terminal delivery mode. The closeness of the three modes, select the most suitable terminal delivery method.

4 Experiment Analysis on Path Optimization of e-Commerce Logistics Terminal Distribution Mode Based on Dijkstra Algorithm 4.1 Optimized Delivery Comparison In this paper, an algorithm is used to optimize a certain delivery route. Under normal circumstances, drivers tend to arrange the route based on experience, that is, prioritize the stations that are closer and not in the same direction. Table 1 is available for comparison with the empirical path of a daily day. It can be seen from Fig. 1 that the total transportation distance of the route optimized by the algorithm is 10.8–10.87 km less than the driver’s experience route, thus reducing the transportation cost and vehicle travel time corresponding to 10.8–10.87 km, and improving the driving time. The distribution efficiency and optimization effect are remarkable.

4.2 Distribution Mode Selection Decision and Analysis This paper selects three terminal distribution modes, namely delivery-to-home mode, consignment collection mode, and self-pick-up cabinet mode. The evaluation objects

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Fig. 1 Comparison of distribution optimization

Table 2 Close degree of the three terminal distribution methods

Delivery to household mode

Entrusted collection mode

Self-lift cabinet mode

City 1

0.49256

0.47467

0.58631

City 2

0.47931

0.46154

0.59742

are arranged in descending order according to the Gi value. The closer the solution distance is, the better the distribution mode is. The sorting results are shown in Table 2. As can be seen from Fig. 2, from the sorting results of the basic distribution modes of the three urban community e-commerce logistics terminals, the self-pickup cabinet mode is the most close, and the self-pick-up cabinet mode is the most suitable for e-commerce in the terminal distribution. The result of this decision is reasonable, mainly for the following three reasons: (1) From the perspective of the community and community consumers, the overall weight of the evaluation indicators is mainly due to the cooperation ability of surrounding commercial formats, and delivery on time. Availability, delivery service capability, and community management agency opinions are several evaluation indicators, which show that the choice of terminal delivery mode is primarily to match the service needs of the community and community consumers. Judging from the actual situation of terminal delivery, the community is relatively large, and its residents are mostly office workers, the

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Fig. 2 Comparison of the effects of the distribution model

delivery service is not standardized, the cooperation ability of surrounding commercial formats is not strong, and the pick-up time of the self-pick-up mode is flexible and the service is convenient. The characteristics just match the terminal distribution, and to a certain extent, it regulates the community logistics distribution behavior, which is beneficial to the public security and management of the community, and can bring a certain rental income, which is in line with the interests of community management agencies. (2) From the perspective of logistics companies, the original e-commerce mainly adopts the delivery-to-home model. In the actual delivery process, there are indeed high customer complaint rates, low on-time delivery rates, and poor customer satisfaction. Case. E-commerce companies in this region have begun to shift to the terminal distribution mode mainly based on the self-pick-up cabinet mode. After a period of use, compared with the original delivery-to-home mode, the self-pickup cabinet mode has reduced the cost of distribution operations, and it is generally reflected that it can be It can better meet customer needs, reduce repeated delivery, save logistics and distribution resources, improve distribution efficiency, and alleviate the problem of “embolism” in the “last mile of logistics” for e-commerce. (3) From the perspective of the development of terminal logistics and distribution, the convenience, safety and flexibility of the self-delivery cabinet mode are more in line with the future development trend of e-commerce logistics terminal distribution in urban communities. The business efficiency of commercial logistics enterprises has a significant role in promoting.

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5 Conclusions Since the distribution process is the main link of the logistics cost, how to effectively plan the distribution link, improve the distribution efficiency, and reduce the distribution cost has become the main problem faced by the logistics industry. This paper proposes a method based on Dijkstra’s algorithm to optimize the shortest path in a network, by making all nodes that did not find the shortest path simultaneously see the path for which the shortest path is known. In this way, the node passing through the shortest path can be found, the number of marked symbols is greatly reduced, the time complexity is reduced to O(n), and the optimization process is improved. The simulation test results of programming test data using the advantages of Matlab processing table matrix show that the improved algorithm greatly reduces the execution time, improves the processing efficiency, and provides an effective method for large-scale network analysis. Focusing on the problems existing in the location selection and distribution of e-commerce logistics terminals in urban areas, combined with the case analysis of e-commerce logistics distribution location selection, combined with the characteristics of regional and regional distribution needs, this paper puts forward these suggestions, hoping to further improve the capacity of the urban environment. Better promote e-commerce logistics enterprises to provide efficient logistics services, tap the “third profit source” of logistics, and improve community governance capabilities.

References 1. Ho HC, Soebandrija K (2021) Hoshin Kanri’s strategic planning methodology through Dijkstra algorithm within industrial engineering and stakeholder perspectives. IOP Conf Ser Mater Sci Eng 1115(1):012038 2. Chen Y, Ding Z, Zhang M et al (2021) Metasurface parameter optimization of Fano resonance based on a BP-PSO algorithm. Appl Opt 60(29):9200–9204 3. Ma J, Zheng H, Zhao J et al (2021) An islanding detection and prevention method based on path query of distribution network topology graph. IEEE Trans Sustain Energy PP(99):1–1 4. Younesi HN, Shariati MM, Zerehdaran S et al (2019) Using quantile regression for fitting lactation curve in dairy cows. J Dairy Res 86(1):1–6 5. Ko D, Ha SY, Jin S et al (2022) Convergence analysis of the discrete consensus-based optimization algorithm with random batch interactions and heterogeneous noises. Math Models Methods Appl Sci 32(06):1071–1107 6. Saha S, Zaman PB, Tusar MIH et al (2022) Multi-objective genetic algorithm (MOGA) based optimization of high-pressure coolant assisted hard turning of 42CrMo4 steel. Int J Interact Des Manuf (IJIDeM) 16(3):1253–1272 7. Sabiha AD, Kamel MA, Said E et al (2022) Real-time path planning for autonomous vehicle based on teaching–learning-based optimization. Intel Serv Robot 15(3):381–398 8. Liao K, Huang G, Zheng Y et al (2022) Pedestrian reidentification based on multiscale convolution feature fusion. SIViP 16(6):1691–1699 9. Pham LH, Dinh BH, Nguyen TT (2022) Optimal power flow for an integrated wind-solarhydro-thermal power system considering uncertainty of wind speed and solar radiation. Neural Comput Appl 34(13):10655–10689

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10. Karthick R, Senthilselvi A, Meenalochini P et al (2022) Design and analysis of linear phase finite impulse response filter using water strider optimization algorithm in FPGA. Circ Syst Sig Process 41(9):5254–5282 11. Khalfaoui K, Kerkouche EH, Boudjedaa T et al (2022) Optimized search for complex protocols based on entanglement detection. Quantum Inf Process 21(6):1–28 12. Schulz JD (2019) E-commerce is driving surge in ‘final-mile’ deliveries, but who does it best? Logist Manag Distrib Rep 58(7):14–14

Complex SPARQL Queries Based on Ontology and RDF Wei Guan and Yiduo Liang

Abstract In order to explore the methods of complex SPARQL query based on ontology and RDF, this paper first constructs a computer ontology and formally represents it. After that, the RDF dataset is modeled and described serially using RDF/XML. Finally, the query application of complex SPARQL is carried out, including: simple query, multiple matching query, filter conditional query and optional query. Keywords Ontology · RDF · Complex SPARQL queries

1 Introduction The current web model mainly supports browsing and searching of textual contents. With the massive increase in web information, the model can no longer accommodate the exchange and processing of massive amounts of information. The semantic web needs new models to support unified access to web information sources and services and intelligent applications with standard mechanisms to exchange data and handle different data semantics. RDF is a data model of web resource objects and their relationships with simple semantics, which can have XML encoding. RDF is a resource description language standard recommended by the W3C organization and is widely used in Semantic SPARQL (Simple Protocol and RDF Query Language). SPARQL is a query language and data access protocol for RDF data model, which is a candidate recommendation specified by W3C standard. From a lifecycle perspective, the SPARQL specification is stable enough, and several SPARQL query engines are W. Guan (B) Research and Training Center, Dalian Neusoft University of Information, Dalian, Liaoning, China e-mail: [email protected] Y. Liang School of Software, Dalian University of Foreign Languages, Dalian, Liaoning, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_23

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available, which means that SPARQL queries can be applied to practical development rather than just theoretical research. SPARQL is a query language standard proposed by W3C for RDF data, and has been widely accepted. At present, scholars have achieved preliminary research results in query language, query processing and query optimization. Karim et al. [1] proposed a storage schema and ontology independent SPARQL to HiveQL translation method. Li et al. [2] proposed a knowledge graph construction method for formal specification of SOFL. Buron et al. [3] designed an RDF integration system (RIS) to implement ontology-based RDF integration of heterogeneous data. Yu et al. [4] give a method for designing and implementing semantic gateways based on SSN ontology. Urkude et al. [5] proposed an integrated ontology for green IoT-based agriculture (AgriOn). Wei et al. [6] proposed a method for UML consistency checking using SPARQL queries. Katti et al. [7] describe the bi-directional conversion process of MES source code and ontology. Kiselev et al. [8] illustrate the personalization and Semantic Web technologies and standards used to build a large-scale open online course platform. Rajabi et al. [9] illustrate the idea of using SPARQL attribute paths to achieve knowledge discovery using the case of disease datasets as an example. Alrehaili et al. [10] proposed an ontology-based intelligent system approach to automate higher education activities. Therefore, the content of this paper focuses on the SPARQL query language and its application to RDF queries. Firstly, a simple computer ontology was constructed. After that, based on this computer ontology, we constructed a computer RDF dataset. Finally, we performed a series of queries, such as simple query, multiple matching query, filter conditional query, optional query, on the computer RDF dataset in Jena environment, and gave the corresponding query results.

2 Ontology Modeling and Representation 2.1 Ontology Modeling The structure of an ontology is a five-tuple O = {C, R, Hc, Rel, Ao}. Here C and R are two disjoint sets, where: The elements in C are called concepts. The elements in R are called relations. Hc denotes the concept hierarchy, i.e., the categorical relations among concepts. Rel denotes the noncategorical relations among concepts and Ao denotes the ontology axioms. In this paper, we take computer ontology as an example to describe the process of ontology construction. Computer consists of hardware and software, and there are many kinds of hardware and software, so Hardware and Software is “partOf” relationship with Computer, and Processor, HardDriver, Memory, Screen are all subclasses of Hardware. Similarly, Operate System and Java are subclasses of Software. At the

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same time, this paper provides the concept of manufacturer (Supplier), and the relationship between Supplier and Computer is the relationship of supplying and being supplied, and this relationship is a custom relationship.

2.2 Ontology Representation The representation language of ontology is a linguistic tool for representing ontologies with different degrees of formalization, and the higher the degree of formalization, the more it facilitates automatic machine processing. The role of ontology representation language includes: Firstly, provide modeling primitives for ontology construction. Secondly, provide tools for transforming ontologies from natural language expressions to machine-readable logical expressions. Thirdly, provide a standard machine-readable format for importing and exporting ontologies between different systems. Ontology, as the basis for communication, interoperability, and systems engineering, must be carefully designed. Some useful ontology building guidelines have been summarized based on existing successful and failed building experiences to guide the ontology building process. The ontology development process is usually iterative, i.e., an initial ontology framework is given first, followed by additional details in a process of continuous modification and refinement of the ontology. The ontology building process is divided into seven steps: defining the domain and scope of the ontology, considering reuse of existing ontologies, listing important terms in the ontology, defining classes and class inheritance, defining properties and relationships, defining restrictions on properties, and building instances. Protégé software is an ontology editing and knowledge acquisition software developed by Stanford University based on the Java language, which is open source software and is mainly used for the creation of ontologies in the Semantic Web and is a common development tool for ontology construction in the Semantic Web. Protégé provides the creation of ontology concept classes, relations, attributes and their instances, and shields the specific ontology description language, so that users only need to create domain ontology models at the concept level. Using Protégé to build the above computer ontology, the results are shown in Fig. 1.

3 RDF Conversion and Serialization 3.1 RDF Modeling RDF (Resource Description Framework) is a data description framework developed by the W3C organization based on the extensible markup language XML. RDF is able to define concepts and relationships between concepts, describing

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Fig. 1 Class hierarchy and partial attributes

machine-understandable information and knowledge. It provides a semantic model that can be used to describe arbitrary resources and their types on the web, providing a common representation framework for online resource description, solving the semantic heterogeneity problem, and realizing a metadata solution for data integration. According to the above computer ontology model, a computer instance data can be described by different attributes and relationships. The instances involved and their attributes and relationships are as follows. (1) The attributes and values included in the instance Computer_001 are: the computer name (CNAME) is Computer_001, the type (CTYPE) is Laptop, the brand (CBRAND) is ASUS, the price (CPRICE) is $6000, the color (CCOLOR) is Red, and the provider (Supllier) is Company_001. (2) The instance Company_001 includes the following attributes and values: company name (COMNAME) is Dalian ASUS Com, company address (COMADDRESS) is Dalian High-Tech Industrial Zone. (3) The instance Software_001 includes the following attributes and values: software name (SOFTNAME) is Windows, and software version (SOFTVVERSION) is Win10. (4) The instance OS_001 includes the following properties and values: the memory page size (Pagesize) is 16 M. (5) The instance Software_001 is a part of the instance Computer_001. (6) The instance Software_001 is a kind of the instance OS_001.

3.2 RDF Serialization RDF documents are written in XML language and can facilitate interoperability between metadata by creating a bridge between semantic agreements and syntactic encoding through structured conventions explicitly defined based on XML syntax. The conceptual model of RDF is an RDF Graph, and the XML syntax used by RDF is called RDF/XML. The RDF/XML syntax provided by RDF is used to write and

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Fig. 2 RDF/XML representation of ontology

exchange RDF graphs. Unlike the abbreviated notation of RDF, the triadic representation, RDF/XML is the normative syntax for writing RDF. By using XML syntax, RDF information can be easily exchanged between computers that use different types of operating systems and application languages. Therefore, RDF is the best choice to solve the problem of computer knowledge representation and can describe metadata well. Referring to the ontology model, the content of the RDF/XML document is shown in Fig. 2 as follows.

4 Complex SPARQL Queries In the previous section, an RDF dataset file computer.rdf on computer ontology has been constructed. Based on this RDF dataset, the following query application is carried out using SPARQL statements.

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Fig. 3 SPARQL statement of simple query

Fig. 4 Results of simple query

4.1 Simple Query Query the names of all computer instances in the RDF dataset. The SPARQL query statement is as following in Fig. 3. The query results are shown in Fig. 4.

4.2 Multi-Match Query Query all computer instances and their manufacturers. The SPARQL query statement is as following in Fig. 5. The query results are shown in Fig. 6.

Fig. 5 SPARQL statement of multi-match query

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Fig. 6 Results of multi-match query

Fig. 7 SPARQL statement of filter conditional query

Fig. 8 Results of filter conditional query

4.3 Filter Conditional Query Queries all computers whose color is Red. The SPARQL query statement is as following in Fig. 7. The query results are shown in Fig. 8.

4.4 Optional Query Query the computer with operating system (OS) software, and if the computer has java software, then the version of java software is displayed. The SPARQL query statement is as following in Fig. 9. The query results are shown in Fig. 10.

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Fig. 9 SPARQL statement of optional query

Fig. 10 Results of optional query

5 Conclusions The deficiencies of the traditional web model in terms of resource representation and other aspects have led to the great development of the Semantic Web model. As a result, the web is filled with a large amount of RDF data and many resources are gradually represented in RDF. The SPARQL query language is an RDF-based query language. In this paper, the usage of SPARQL query language and its semantics are introduced in detail, a computer ontology is constructed, a computer RDF dataset is built, and some complex query applications are carried out using SPARQL query language, which basically meet the practical needs. The next step in this paper will explore the use of SPARQL to implement federated queries across multiple heterogeneous RDF data management systems. Acknowledgements This work was supported by the Social Science Planning Fund Project of Liaoning Province “Research on the Realization Path and Guarantee Mechanism of Government Data Opening in the Context of Big Data” (Grant No. L19CTQ002).

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References 1. Karim N, Latif K, Anwar Z et al (2015) Storage schema and ontology-independent SPARQL to HiveQL translation. J Supercomputing 71(7):2694–2719 2. Li J, Liu S, Liu A et al (2022) Knowledge graph construction for SOFL formal specifications. Int J Softw Eng Knowl Eng 32(04):605–644 3. Buron M, Goasdoué F, Manolescu I et al (2020) Obi-Wan: ontology-based RDF integration of heterogeneous data. Proc VLDB Endowment 13(12):2933–2936 4. Yu BH, Wang H, Dong XP et al (2021) Design and implementation of a semantic gateway based on SSN ontology. Procedia Comput Sci 183(6):432–439 5. Urkude G, Pandey M (2020) AgriOn: a comprehensive ontology for green IoT based agriculture. J Green Eng 10(9):7078–7101 6. Wei B, Sun J (2021) Leveraging SPARQL queries for UML consistency checking. Int J Softw Eng Knowl Eng 31(4):635–654 7. Katti B, Plociennik C, Ruskowski M et al (2020) Bidirectional transformation of MES source code and ontologies. Procedia Manuf 42:197–204 8. Kiselev B, Yakutenko V (2020) An overview of massive open online course platforms: personalization and semantic web technologies and standards. Procedia Comput Sci 2020(169):373– 379 9. Rajabi E, Sanchez-Alonso S (2021) Knowledge discovery using SPARQL property path: the case of disease data set. J Inf Sci 47(5):677–687 10. Alrehaili NA, Aslam MA, Alahmadi DH et al (2021) Ontology-based smart system to automate higher education activities. Complexity 9:1–20

Prediction System Analysis of Microbial Treatment of Organic Pollution Based on Particle Swarm Optimization Algorithm Dewei Zhu

Abstract With the rapid development of chemical, electronic and agricultural industries, a large number of organic micro pollutants are discharged into the water environment. These organic micro pollutants not only cause water environment pollution, but also pose a potential threat to aquatic organisms and human health through the food chain. Therefore, it is extremely important for the treatment and prediction of organic pollutants in environmental water. In this paper, particle swarm optimization algorithm (PSOA) is proposed and applied to the prediction system (PS) of microbial treatment of organic pollution. The basic framework of PSOA is discussed and analyzed; The degradation of chlorinated nitrobenzene compounds by P. chrysosporium microorganisms was investigated by testing the degradation of organic pollutants by adding exogenous microbial agents in different seasons under field conditions; The experimental results showed that the recovery was 76.9–85.3% and RSD was 0.15–2.35%. It is verified that the PSOA proposed in this paper has high precision and good accuracy when applied to the PS of microbial treatment of organic pollution. Keywords Particle swarm optimization algorithm · Microbial treatment · Organic pollution · Prediction system

1 Introduction Water is the source of all things, the prerequisite for the survival and reproduction of human beings and other creatures, and the necessary foundation for the stable development of economy and society. With the rapid development of economy, water D. Zhu (B) College of Marine and Bio-Engineering, Yancheng Teachers University, Yancheng 224007, China e-mail: [email protected] Jiangsu Province Engineering Research Center of Agricultural Breeding Pollution Control and Resource, Yancheng Teachers University, Yancheng 224007, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_24

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quality has become the focus of the people. A large number of organic pollutants enter the environmental water body through various ways, which can not only provide a large number of nutrients for microbial decomposition and utilization, but also aggravate the eutrophication of the water body, resulting in the death of aquatic organisms due to hypoxia; Moreover, due to the toxic effects of most organic pollutants, they are difficult to degrade in the environment and pose a serious threat to human survival and health. Therefore, the development of practical and effective PS for the treatment of organic pollutants has become the top priority of research; In this paper, PSOA is proposed and applied to the PS of microbial treatment of organic pollution. Many scholars at home and abroad have studied and analyzed the application of PSOA in the PS of microbial treatment of organic pollution. Kumar s said that microbial fuel cell (MFC) technology provides a suitable alternative for energy positive wastewater treatment, and allows synchronous wastewater treatment, bioelectricity production and resource recovery through electrochemical bioremediation mediated by electroactive microorganisms; A variety of bio based processes can be performed in the same bioreactor, including biochemical and chemical oxygen demand removal, nitrification, denitrification, sulfate removal and heavy metal removal [1]. Ajdad H. proposed to apply particle swarm optimization (PSO) method to the optical geometry optimization of linear Fresnel mirror solar concentrator (LFR). The optical radiation behavior of the system is modeled based on ray tracing Monte Carlo algorithm, which calculates the optical performance and the radiation energy collected by the absorption tube. Compared with the results of the deterministic method, the effectiveness of the established PSOA is verified, and it is proved that the method can solve the optimization problems with a large number of decision parameters and complex objective functions [2]. The intelligent optimization algorithm based on evolutionary computation has attracted more and more attention in academic circles, because the intelligent optimization algorithm can solve a large number of practical optimization problems that can not be solved by traditional mathematical methods. After a lot of research and attempts, intelligent optimization algorithm has been successfully applied to many fields of scientific research and production practice and achieved remarkable benefits. In this paper, the PS of microbial treatment of organic pollution is discussed and analyzed. Based on the traditional technology, PSOA is proposed and applied to the PS. The rationality of this method is verified by experiments [3, 4].

2 Application of PSOA in PS of Microbial Treatment of Organic Pollution With the gradual emergence of the advantages of intelligent algorithms, a large number of heuristic evolutionary optimization algorithms have emerged, and the proposal of a classical algorithm is often accompanied by the emergence of a large number of improved algorithms based on the algorithm, in order to improve its

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optimization search performance and more effectively solve a specific optimization problem. There are many ways to improve the algorithm, including optimizing the algorithm parameters, changing the population search strategy and so on. However, the optimization ability of an optimization algorithm is limited after all. Therefore, it has become a hot topic to combine different optimization algorithms to form a hybrid optimization algorithm in some way [5]. In this paper, particle swarm optimization (PSO) algorithm is proposed to study and analyze the PS of microbial treatment of organic pollution. Particle swarm optimization (PSO) is a classical heuristic optimization algorithm. Because of its simple structure, strong search ability, good universality and robustness, it has been popular among researchers. It can be seen in almost all kinds of optimization problems. Particle swarm optimization (PSO) is a population intelligent optimization algorithm, which is inspired by the principle of birds searching for food sources.

2.1 Microbial Treatment of Organic Pollution With the promotion and participation of enzymes, microorganisms in wetlands use organic matter, especially dissolved organic matter, as carbon source to complete their own metabolic activities. Microorganisms can synthesize the phosphorus in the system into their own phosphorus compounds through their own normal assimilation. Phosphorus accumulating bacteria in the system can complete phosphorus removal through excessive phosphorus uptake [6]. The abuse of antibiotics, pesticides and pesticides in people’s life and production has led to the emergence of these synthetic, toxic and refractory organic substances in polluted water. Some microorganisms can secrete reductase to reduce Hg2+ to elemental mercury, 3+ Fe to Fe2+ , Mn4+ to Mn3+ , S6+ to S2− , etc. the reduction products can interact with other metal ions. Fe2+ can reduce Cr6+ to Cr3+ , S2− can form HgS precipitation with Hg2+ . Fwukowa isolated Pseudomonas K62 from soil and found that it can decompose effective demethylation and convert highly toxic organic mercury into elemental mercury. Some heavy metals form chelate products with organic agents, which makes it more difficult to detoxify heavy metals. Oxygen is an important electron acceptor, which plays an important role in the process of microbial degradation of pollutants. In the process of bioremediation of groundwater polluted by organic matter, if aerobic conditions can be maintained, the degradation rate of organic matter can be greatly increased and the remediation time can be shortened. Therefore, the oxygen supply mode or system of the biological reaction wall has an important impact on the function of the biological reaction wall [7, 8].

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2.2 Application of PSOA in Microbial Treatment of Organic Pollution PS Water pollution control system planning is a complex multivariable and multiobjective problem. When predicting the efficiency of microbial treatment of organic pollution, it is necessary to meet the water demand, water resources protection and other indicators, reasonably allocate water sources, and achieve better organic pollution treatment effect, which also involves a large number of political, economic and technical factors [9]. When particle swarm optimization (PSO) is used to predict the effectiveness of microbial treatment, it is necessary to make the particle swarm approach the optimal front-end. The optimal solution evaluation strategy can be used to obtain the optimal solution set of multi-objective predictive optimization problems. The principle of the optimal solution evaluation strategy can be simply explained by a double objective minimization optimization problem. In the optimal solution evaluation and prediction strategy, firstly, the global optimal value and individual extreme value of each objective function in the multiobjective optimization problem are calculated, and then the weighted average is used to calculate the average value of the global optimal value of each objective function. In order to achieve a more balanced distribution of the final solution, the outlier search strategy replaces the best found by the current particle with an outlier when guiding the particle to search for the optimal solution, Take the replaced outliers as the global optimal value of particles [10]. Prediction steps and process of microbial treatment of organic pollution: set the particle population size and maximum iteration times, set the particle target fitness ranking first in each objective function fitness as the global optimal value, and the initial position of the particle is the individual extreme value of the particle itself; The mean value of the global optimal value of the multi-objective optimization problem is obtained by using the optimal solution evaluation strategy, and then the mean value of the global optimal value is replaced by outliers; Evaluate the fitness of particles, compare the fitness of the current optimal particle of the objective function with its previous fitness, and update the target fitness of the better particle to the current global optimal value; The CLS results are used to update the positions of these particles and predict the treatment efficiency. If the standard is met, the currently found optimal solution is output; Reduce the search space and randomly generate new particles in the reduced search space [11, 12].

3 PSOA According to the research of biologists, the foraging of birds has a strong guidance. Each individual in the flock will exchange information with other individuals to obtain the general direction of the food source. Through the information exchange

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between individuals and groups, the whole population will gather around the food source in a very short time. Each particle in PSOA is equivalent to a bird in the population. It has two important parameters, namely, position and velocity. The position coordinates of particles in the multi-dimensional solution space correspond to several decision variables of the problem to be optimized. The algorithm realizes the optimization operation through the movement of particles in the solution space. The velocity vector of each particle is linearly superimposed by three independent vectors, that is, each particle uses its own experience and population experience to guide the search.

3.1 Mathematical Description of PSOA When PSO algorithm solves a problem, it will first give a group of solutions randomly, and then find the optimal solution through iteration. The mathematical description of PSO algorithm is as follows. Assuming that the size of a particle population is m and the search space is e-dimensional, then after t iteration steps: position  of the ith particle is expressed as, ai (t) =  1The2 current ai , ai , . . . , aei , . . . , aEi ,   The current velocity of the ith particle is vi (t) = v1i , v2i , . . . , vei , . . . , vEi , The coordinate position (T) and velocity (T) of the ith particle are adjusted according to formulas (1) and (2) at time t + 1: 

v i (t + 1) = v i (t) + c1 s1 ( f 1 (t) − a i (t)) + c2 s2 ( f g (t) − a i (t)) a i (t + 1) = a i (t) + v i (t + 1)  e vi = vmax , vie > vmax vie = −vmax , vie < −vmax

(1)

(2)

According to formula (1), the velocity change of the ith particle in the population is mainly determined by three factors. The first factor is the particle’s previous iteration speed vi (t), vi (t) makes the particle have velocity memory, can balance the local and global search, and expands the scope of particle search until the global optimal solution is found. The second factor is the optimal position f i (t) experienced by particles, which is also the “individual cognitive part” of particles. While enhancing the local search ability of particles, it also enables itself to have sufficient global search ability to avoid local optimization. The third factor is the position f g (t) of the best particle in the whole population, which reflects the ability of the particle to learn from the population. Under the joint action of the above three factors, the particle uses the mechanism of historical information and information sharing to constantly adjust its position until it finds the optimal solution to the problem. In Eq. (1), T represents the number of iterations, and the learning factors C1 and C2 control the influence of the “individual cognition” part and “social cognition”

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part of the particle on the particle speed respectively. Because the learning factor controls the flying step of particles, it is also called velocity factor.

3.2 Basic Framework of PSOA As shown in Fig. 1, the basic framework diagram of PSOA is a general framework diagram, which can also be used for the basic process of similar improved PSOA.

3.2.1

Detailed Flow of PSOA

Set the number of iterations t = 0, and initialize the random position and speed of the particle swarm; Update learning samples. Compare the fitness value of each particle ai (t) with the fitness value of the individual’s best position f i (t). If the fitness Fig. 1 Basic flow chart of PSOA

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value of ai (t) is better than the fitness value of f i (t), the fitness value of f i (t) will be updated to the fitness value of ai (t). At the same time, the fitness value of each particle ai (t) is compared with that of the global best position f g (t). If the fitness value of ai (t) is better than that of f g (t), then f g (t) is taken as the fitness value of the global best position f g (t); Judge whether the number of iterations reaches the termination condition. Otherwise, after the number of iterations t + 1, cycle to step (2).

4 Experimental Test Analysis Chlorinated phenols and chlorinated nitrobenzene compounds are typical refractory organic pollutants. They are widely used in pesticide, medicine, synthetic materials, chemical industry and other industries. They cause serious harm to the environment and are important sources of environmental pollution. In order to verify the rationality of the PSOA proposed in this paper applied to the microbial treatment of organic pollution PS, the following experiments are made in this paper. Because pentachlorophenol (PCP) and chloronitrobenzene are the representatives of this kind of compounds, and the degradation of PCP in laboratory soil has been reported frequently, and field experiments have hardly been carried out. Therefore, this paper focuses on the degradation of PCP by adding exogenous microbial agent P. chrysosporium in different seasons of field conditions. The degradation of chloronitrobenzene compounds by P. chrysosporium was also investigated.

4.1 Materials and Methods The test field was selected in No. 12 pollution-free vegetable greenhouse (50 m) of agricultural demonstration park in city a × 60 m. Divide the field into 160 cm * 80 cm test blocks, loosen the soil of the tillage layer (the loosening depth is about 15 cm), and level it for standby; P. chrysosporium was used as the inoculant, and the sifted rice straw powder was used as the substrate; Pentachlorophenol PCP, 2nitrochlorobenzene, 3-nitrochlorobenzene, 4-nitrochlorobenzene and nitrobenzene were selected as test drugs. Treatment of pentachlorophenol degradation test: set up two repeated test plots in different seasons, dissolve PCP with appropriate amount of ethanol and spray it evenly into each test plot to make the PCP concentration in each test plot reach about 200 ppm. At the same time, add rice straw powder according to the ratio of soil to substrate of 200:1, add 600 g of dry P. chrysosporium and mix it well. Take intensive sampling at multiple points every week in each season.

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Table 1 Basic physical and chemical properties of soil Soil type

PH value

Organic matter

Total nitrogen

Total potassium

Total phosphorus

Fluvo aquic soil

5.96

21.5

1.53

3.68

0.9

4.2 Degradation Test Treatment of Mixed Organic Pollutants Nitrobenzene, four chlorinated nitrobenzene and PCP were selected as the target pollutants for the degradation of xanthosporium to predict the comprehensive degradation ability of xanthosporium to a variety of pollutants. Three test plots were set up, two of which were repeated. After a certain amount of the above compounds were weighed and mixed, a proper amount of ethanol was used to dissolve and evenly spray them into each test plot, so that the concentration of mixed organic matter in each test plot reached about 150 ppm. At the same time, rice straw powder was added according to the ratio of soil to substrate of 200:1, and 600 g of dry P. chrysosporium was added and fully mixed. Samples were taken once a week, Determine the degradation of mixed pollutants by Xanthomonas. The basic properties of soil samples and the basic physical and chemical properties of soil in the test field are shown in Table 1.

4.3 Precision and Recovery Test Take a certain amount of soil from the test field, screen and sterilize it, and divide it into 5 parts, 10 g each. Add PCP and nitrobenzene compounds to each soil respectively, and make the theoretical concentration of PCP and nitrobenzene compounds in each soil reach 30 ppm. After one week in the laboratory, repeat the determination according to the above method for determining PCP and the chromatographic conditions for Determining Nitrobenzene Compounds. The theoretical values and measured results are shown in Table 2 and Fig. 2. In the degradation system, temperature has an important effect on the degradation of organic pollutants. There are two reasons: first, increasing the temperature can improve the solubility of organic pollutants, increase the availability of microorganisms, and accelerate the degradation of organic pollutants. Second, the impact on microbial growth and reproduction. The temperature suitable for microbial growth and reproduction will significantly increase the number of microorganisms. However, in the field, with the change of air temperature in different seasons, the soil temperature and humidity change accordingly. The man-made controllable conditions are limited, and the degradation situation is not that the higher the temperature, the better. There are many uncertain factors. According to Table 2, the recovery is 76.9–85.3% and RSD is 0.15–2.35%. It shows that the PSOA proposed in this paper has high

0.15

3.21

85.3

Average (PPM)

RSD (%)

Recovery rate (%) 76.9

30 23.0877

30

25.6

Theoretical value (ppm)

Nitrobenzene

Pentachlorophenol

Component name

Table 2 Comparison table of precision and recovery

85.3

1.33

25.5787

30

2-chloronitrobenzene

80.5

2.35

24.1428

30

3-chloronitrobenzene

78.6

1.64

23.5673

30

4-chloronitrobenzene

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35

90

30

80 70

25

60

20

50

15

40 30

10

20

5 0

10 Pentachlorophenol

nitrobenzene

2-chloronitrobenzene 3-chloronitrobenzene 4-chloronitrobenzene

Recovery rate and RSD (%)

Average theoretical value

224

0

Recovery and precision of the method Theoretical value ppm

Average (PPM)

RSD %

Recovery rate (%)

Fig. 2 Recovery and precision of the method

precision and accuracy when applied to the PS of microbial treatment of organic pollution.

5 Conclusions Particle swarm optimization (PSO) is a new intelligent optimization algorithm. Its development and research have attracted extensive attention of scholars. Although academic researchers have made many improvements in the past decade, this paper has made some research and Exploration on the PSOA and its application in the microbial treatment of organic pollution PS, and achieved some results, but there are still many problems to be further studied. Particle swarm optimization (PSO) is inspired by biological population, and its mathematical theoretical foundation is very weak. In order to better apply the theory to guide the algorithm in practice, we should further study the quantitative relationship between the number of iterations of the algorithm and the convergence effect; Moreover, the parameter selection and convergence performance of PSO are related to the particle trajectory and the topology of particle distribution. Therefore, the quantitative analysis of particle trajectory and the improvement of the topology of particle distribution also need further research. Acknowledgements (1) Supported by Jiangsu Provincial Research Foundation for Basic Research, China (Grant No. BK20201063). (2) Supported by the Opening Project of Jiangsu Province Engineering Research Center of Agricultural Breeding Pollution Control and Resource (Grant No. 2021ABPCR006).

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References 1. Kumar SS, Kumar V, Malyan SK et al (2019) Microbial fuel cells (MFCs) for bioelectrochemical treatment of different wastewater streams. Fuel 254(OCT.15):115526.1–115526.17 2. Ajdad H, Baba YF, Mers AA et al (2019) PSOA for optical-geometric optimization of linear fresnel solar concentrators. Renew Energy 130(JAN.):992–1001 3. Burman I, Sinha A (2020) Performance evaluation and organic mass balance for treatment of high strength wastewater by anaerobic hybrid membrane bioreactor. Environ Progress Sustain Energy 39(2):e13311.1–e13311.10 4. Jaiswal D, Pandey J (2019) An ecological response index for simultaneous prediction of eutrophication and metal pollution in large rivers. Water Res 161(SEP.15):423–438 5. Tao Q, Sang H, Guo H et al (2021) Improved PSOA for AGV path planning. IEEE Access PP(99):1–1 6. Kuo RJ, Nugroho Y, Zulvia FE (2019) Application of PSOA for adjusting project contingencies and response strategies under budgetary constraints. Comput Ind Eng 135(SEP.):254–264 7. Kumari R, Gupta N, Kumar N (2020) Cumulative histogram based dynamic PSOA for image segmentation. Indian J Comput Sci Eng 11(5):557–567 8. Cipriani E, Fusco G, Patella SM et al (2020) A PSOA for the solution of the transit network design problem. Smart Cities 3(2):541–554 9. Mahmoodabadi MJ (2020) A novel PSOA for prediction of crude oil prices. Int J Mech Control 21(2):21–27 10. Romulo A (2021) The impact of high-pressure processing treatment on microbial inactivation of seafood—a review. Food Res 5(2):38–44 11. Yamane T, Yoshida N, Sugioka M (2021) Estimation of total energy requirement for sewage treatment by a microbial fuel cell with a one-meter air-cathode assuming Michaelis-Menten COD degradation. RSC Adv 11(33):20036–20045 12. Bento M (2021) A hybrid PSOA for the wide-area damping control design. IEEE Trans Ind Inf PP(99):1–1

Data Preprocessing Technology in Network Traffic Anomaly Detection Xueyuan Duan, Yu Fu, and Kun Wang

Abstract Computer network plays a very important role in today’s world. With the development of network technology and the increasing popularity of network topology, network management is facing huge challenges. Among them, finding network traffic anomalies is one of the most important tasks in network monitoring, and is an important technology in network intrusion, security monitoring, and performance maintenance. This paper aims to study data preprocessing techniques in network traffic anomaly detection. This paper firstly introduces the network traffic measurement characteristics completely, and then selects and applies the data preprocessing algorithm, constructs a complete data preprocessing framework, and conducts experiments for the abnormal detection of network multi-dimensional characteristic traffic and the abnormal detection of unit-level characteristic traffic. The superiority of the data preprocessing technology in this paper, by reducing the data, determines the data preprocessing threshold of this paper to ensure that the data preprocessing is maximized. Experiments show that the data preprocessing technology constructed in this paper has about 20% more data filtering than traditional X. Duan · Y. Fu (B) · K. Wang Department of Information Security, Naval University of Engineering, Wuhan 430033, China e-mail: [email protected] X. Duan e-mail: [email protected] K. Wang e-mail: [email protected] X. Duan College of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China Henan Key Laboratory of Analysis and Applications of Education Big Data, Xinyang 464000, China K. Wang School of Mathematics and Information Engineering, Xinyang Vocational and Technical College, Xinyang 464000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_25

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data filtering, which shortens the amount of detection data. The threshold is around 0.6, and data preprocessing can maximize the reduction of data without destroying the integrity of the attacked data. Keywords Network traffic anomaly detection · Network intrusion detection · Data preprocessing · Data mining

1 Introduction Most cyberattacks are primarily due to the automation and manipulation of attack tools, and with the explosion of IoT devices, large-scale and nuclear attacks are highly likely in the future. The country has also promoted the process of improving the quality of citizens through the Internet. It can be seen that this is a very good process of accumulating network knowledge and improving the level of network security. To create a new, secure network environment, researching effective network security methods is faster than ever. Given the growing number of intrusion methods and techniques, custom security lines such as user credentials and firewalls may not fully cover the entire cybersecurity space. Therefore, the network security anomaly detection system, such as the second-level security line of defense, can of course be combined with the first-level security line to improve the network security level [1, 2]. In the research of data preprocessing technology in network traffic anomaly detection, many scholars have studied it and achieved good results. For example, Zhang pointed out that only 1 Gbps continuous network data flow will pose a major data challenge for deepening data packets [3]. Tang recommends using the fast drive method. The algorithm first examines the probability gap of the connection success rate between the host and the normal host, and realizes the host performance of the station view by writing a mathematical model to test each industry [4]. This paper firstly introduces the network traffic measurement characteristics completely, and then selects and applies the data preprocessing algorithm, constructs a complete data preprocessing framework, and conducts experiments for the abnormal detection of network multi-dimensional characteristic traffic and the abnormal detection of unit-level characteristic traffic. The superiority of the data preprocessing technology in this paper, by reducing the data, determines the data preprocessing threshold of this paper to ensure that the data preprocessing is maximized.

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2 Research on Data Preprocessing Technology in Network Traffic Anomaly Detection 2.1 Network Traffic Measurement Characteristics At present, in the flow-based network traffic anomaly detection, the commonly used traffic measurement features mainly include count value and entropy value. The following describes the application of count value, entropy value and other traffic measurement features in network traffic anomaly detection. (1) Network traffic anomaly detection based on count value In network traffic anomaly detection, the count value is a commonly used traffic measurement feature, not only the total number of flows, the total number of bytes, the total number of packets, the number of bytes of a single flow, the number of packets of a single flow in a certain period of time It can be used as a traffic measurement feature, and the number of flow features is often used as a traffic measurement feature, including the total number of source IP addresses, the number of identical source IP addresses, the total number of destination IP addresses, the number of identical destination IP addresses, the total number of The number of source port numbers, the same number of source port numbers… can all be used as traffic measurement features [5, 6]. The common method of network traffic anomaly detection based on count value is: first determine and extract the traffic measurement characteristic data to be used, and then use a specific detection algorithm to detect all the traffic measurement characteristic data. The specific detection algorithm can either use a simple and easy-to-use detection algorithm based on a constant threshold or a constant rate of change, or a prediction-based detection algorithm. In addition to these simple detection algorithms, classification algorithms can also be used to divide all traffic measurement feature data into two categories: normal and abnormal, or normal and multiple abnormal, or clustering algorithms can be used without prior classification. For specific categories, the traffic measurement feature data is aggregated into different categories according to distance or density, and then the normal category and the abnormal category are distinguished according to the specific situation. The advantages of network traffic anomaly detection based on count values are direct, simple and convenient, and it is very suitable for application in distributed computing frameworks. Therefore, it is a good direction for improvement to study more detailed and targeted flow measurement characteristics of count value, and the improvement of flow measurement characteristics will also greatly improve the ability to detect abnormal flow [7, 8]. (2) Entropy-based network traffic anomaly detection

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Entropy is a commonly used traffic measurement feature in network traffic anomaly detection. The proposal and application of entropy is a process of continuous development. The general method of network traffic anomaly detection based on entropy mainly includes four steps: traffic feature extraction, entropy value calculation, anomaly judgment, and anomaly classification. The basic process of network traffic anomaly detection based on entropy is to extract traffic features from traffic data to obtain a traffic feature set, then calculate the entropy value as a measure of traffic according to the traffic feature set, and finally use the entropy value to judge the traffic abnormality and determine the traffic abnormality. Classification. The above four steps are described below. The first step is to extract traffic features and form a traffic feature set. The usual practice is: firstly divide the network traffic into time slices, and divide the network traffic into multiple time slices of equal length according to the start and end times of the flow; then extract the flow characteristics in each time slice, and classify the flow characteristics according to The type is classified, for example, source IP address, source port number, destination IP address, destination port number, etc. are often used, so that one or more sample spaces of flow characteristics can be generated for each time slice. The second step is to calculate the entropy value to obtain the entropy value corresponding to each sample space. The specific process is: first combine the same flow features, and obtain the number of occurrences of each flow feature in the sample space, then obtain the occurrence probability of the flow features in the sample space, and finally obtain each flow feature according to the entropy value formula One or more entropy values corresponding to the set. Currently commonly used entropy are Shannon entropy, Tsallis entropy, Renyi entropy, maximum entropy, relative entropy, etc. The third step is to judge the abnormal flow based on the entropy value. In the process of traffic anomaly detection based on entropy, two stages of training and detection are usually used, and the optimal detection parameters are obtained through training. In the detection stage, the detection parameters obtained by training are used to detect abnormal network traffic. Among them, the training stage mainly includes entropy value calculation, abnormality judgment, abnormality verification and other steps, as follows: first, the entropy value calculation is performed on the traffic data marked with abnormality, and then the initial detection parameters are set to perform abnormality judgment on the obtained entropy value. Verify the abnormal judgment result. If the detection result does not meet the requirements or the termination conditions, continue to adjust the detection parameters to make another abnormal judgment on the above entropy value, and continue to cycle this process; if the detection results meet the expected requirements or termination conditions, determine the final detection parameters and train Process ends. In the detection stage, the entropy value of the detected traffic data is calculated, and then the parameters obtained by training are used for detection. In abnormal judgment, a fixed threshold or a fixed entropy rate of change is usually used as a metric for judgment. The entropy value that exceeds the threshold or the rate of change of entropy value is abnormal entropy value, and the time slice corresponding to the abnormal entropy value is abnormal time slice.

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The fourth step is anomaly classification. Commonly used methods include supervised classification methods and unsupervised clustering methods. In some cases, anomaly classification and anomaly detection are performed simultaneously. The basic principle of the supervised classification algorithm is to predefine the types of network traffic anomalies, and then classify the detected traffic anomalies into the corresponding abnormal types according to the preset abnormal characteristics. Taking Shannon entropy as an example, its basic principle is to form a corresponding relationship between multi-dimensional entropy value changes and abnormal network traffic, and then classify according to the corresponding relationship. For example, when a DoS occurs in a time slice, the entropy value corresponding to the source IP address, the entropy value corresponding to the destination IP address, and the entropy value corresponding to the destination port number will be greatly reduced, and then this change in the entropy value can be reduced. As a classification basis for DoS. According to this principle, the classification of common network traffic anomalies can be obtained. In network traffic anomaly classification, unsupervised clustering algorithms, such as FKMeans, do not implement setting anomaly types, but cluster all such entropy values into one or more according to the distance of points formed by multi-dimensional entropy values. class, and finally determine which are normal classes, which are abnormal classes and the types of abnormal classes [9, 10].

2.2 Algorithm Selection The greater the importance of the attribute, the greater the weight, and the weight formula is defined as follows: qi = rc (D) − rc−i (D)/

n ∑ [ ] rc (D) − rc−1 (D)

(1)

i=1

Through the above analysis, the distance formula of clustering can be given. xi and xj are the dimension values j of the sample points i and j, and the distance between the two sample points adopts the improved Euclidean distance: di j =

n / ∑ ( )2 qi x i − x j

(2)

i=1

The distance formula defined above solves the problem of attribute redundancy and weight. The purpose of hierarchical clustering of data samples is to divide various anomalies of the network [11, 12].

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3 Research and Design Experiment of Data Preprocessing Technology in Network Traffic Anomaly Detection 3.1 Building a Data Preprocessing Framework In this paper, the method of attribute reduction and weight calculation of rough set theory is used in hierarchical clustering to deal with attribute redundancy. Rough set theory does not need to provide a priori information other than data when processing data. This method can calculate the importance of each attribute can also be reduced and redundant attributes can be removed, which reduces storage space and improves computing efficiency. Since the basis of rough set analysis is discretized data, and the data in the network is a variety of types of data based on protocols, time and network traffic, the data contains numerical attributes and symbolic attributes, and the numerical type includes discrete and continuous type, so continuous data must be effectively and accurately discretized in the data preprocessing stage. Although people have carried out extensive research on the discretization problem, the discretization problem is not a completely general research topic. In fact, different Each field has its own data characteristics, and it is most appropriate to use different discretization methods according to the characteristics of the data. For example, in the quantization problem in image compression, the information entropy after quantization is required to be the smallest, and the rough set theory is used to analyze the decision table. There is no requirement for information entropy.

3.2 Experimental Design This paper will design an experiment based on the anomaly detection method in this paper. The experiment is divided into two schemes, anomaly detection based on the combination of multiple feature dimensions of network traffic, and anomaly detection based on a single feature dimension of network traffic. During the experiment, it will replay 20. The anomalous network traffic was used for experimental testing. The superiority of this method is verified by comparison with other methods.

4 Experimental Analysis of Data Preprocessing Technology in Network Traffic Anomaly Detection 4.1 Data Filtering Aiming at the anomaly detection of multi-feature dimension combination of team network traffic and the anomaly detection of single-feature dimension, this paper

Data Preprocessing Technology in Network Traffic Anomaly Detection

percent

Table 1 Two data preprocessing methods handle the amount of data

233

Traditional filtering

The method of this paper

Multiple features

71

55

Single feature

84

62

90 80 70 60 50 40 30 20 10 0 multiple features

single feature

abnormal traditional filtering

The method of this paper

Fig. 1 The remaining data volume of multi-feature dimension and single-feature dimension under different data processing

performs traditional data filtering and the data preprocessing method constructed in this paper to filter the data volume, the experimental results are shown in Table 1. It can be clearly seen from Fig. 1 that the data preprocessing algorithm constructed in this paper can filter more normal data than traditional data, and the overall data volume is reduced by about 20%. The time required for detection can be greatly shortened without affecting the detection rate.

4.2 Data Threshold This paper uses laboratory data to conduct experiments to test the performance of our designed filtering model. The main purpose is to explore the processing threshold of this paper, which can achieve a higher data size reduction without reducing the integrity of the attack data. This paper conducts experiments on two sets of experimental data of 60 M and 100 M respectively, the experimental results are shown in Table 2. Seen from Fig. 2, when the threshold is set to 0.6, the amount of data is reduced by about 80% to avoid accidental deletion of intrusive data. In useful applications, the limit is set to 0.6, which not only reduces the size of the data, but also retains a relatively complete attack data.

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Table 2 Remaining data volume under different thresholds 0.95

0.9

0.8

0.6

60 M

47.4

41.23

32.67

11.34

100 M

81.2

76.17

68.23

24.12

0.95

0.9

90 80

the amount data

70 60 50 40 30 20 10 0 0.8

0.6

threshold 60M

100M

Fig. 2 The remaining data volume of two sets of data under different threshold filtering

5 Conclusions Based on a comprehensive discussion of traffic anomaly detection, this paper introduces the data acquisition and pre-data modules in detail, and conducts some in-depth data-level research before implementation. The data contained in the data source is of great value. Accurate data affects the accuracy and efficiency of mining. A 2D modeling based on dislocation and anomaly detection is proposed, which reduces the data volume of large data miners and improves the efficiency and effectiveness of design exploration. In the design based on anomaly detection filters, a similar analysis, a factor, is proposed to differentiate the exact data. Data preparation is a very complex and tedious task, and this article explores only a small part of the many areas that were not considered due to time constraints. Acknowledgement The National Key Research and Development Program of China (No.2018YFB0804104).

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References 1. Mao Y, Mao J, Situ J et al (2020) Research on active emergency repair technology of distribution network based on large power data. IOP Conf Ser Earth Environ Sci 440(3):032086 (6pp) 2. Guo L (2020) Research on anomaly detection in massive multimedia data transmission network based on improved PSO algorithm. IEEE Access (99):1–1 3. Zhang R, Guo MN, Wang YZ et al (2019) Research on CO2 detection system in refrigerated compartment of agricultural products based on TDLAS technology. Proc CIRP 83(C):429–433 4. Tang Z, Chen Z, Bao Y et al (2019) Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Struct Control Health Monitor 26(1):e2296.1–e2296.22 5. Bestagini P, Lombardi F, Lualdi M et al (2020) Landmine detection using autoencoders on multipolarization GPR volumetric data. IEEE Trans Geosci Remote Sens 99:1–14 6. Ahmed M (2019) Intelligent big data summarization for rare anomaly detection. IEEE Access 99:1–1 7. Guezzaz A, Asimi Y, Azrour M et al (2021) Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection. Big Data Mining Anal 4(1):18–24 8. Kurniabudi, Stiawan D, Darmawijoyo et al (2020) CICIDS-2017 dataset feature analysis with information gain for anomaly detection. IEEE Access 99:1–1 9. Yin C, Zhang S, Wang J et al (2020) Anomaly detection based on convolutional recurrent autoencoder for IoT time series. IEEE Trans Syst Man Cyber Syst 99:1–11 10. Lis A (2021) An anomaly detection method for rotating machinery monitoring based on the most representative data. J Vibroeng 23(4):16 11. Li T, Chen L (2019) Space event detection method based on cluster analysis of satellite historical orbital data. Acta astronautica 160(JUL):414–420 12. Pei Z, Gan G (2020) Research on P2P Botnet traffic identification technology based on neural network. IOP Conf Ser Earth Environ Sci 428(1):012011 (6pp)

Intelligent Control Optimization of Sewage Treatment Process Based on Process Neural Network Li Feng and Yuxin Li

Abstract With the continuous development of the market economy and the improvement of people’s living standards, water pollution has become one of the main problems. Water resources are the basis for human survival. Due to the acceleration of industrial development, the discharge of domestic sewage and industrial wastewater has increased year by year, and the difficulty of sewage treatment has increased. In order to promote the sustainable development of urbanization and social economy in my country and alleviate the problem of water shortage, the construction of sewage treatment projects will ensure the sustainable utilization of urban water resources and urban water treatment, and is an important means to realize the sustainable utilization of urban water resources. Therefore, it is very important to study the management strategies of urban sewage treatment process. This paper aims to study the intelligent control optimization of sewage treatment process based on the process neural network. Based on the analysis of the time-varying characteristics and action mechanism of the process neural network and the influencing factors in the sewage treatment control, the sewage treatment process is designed. In the actual control process, the variation range of the offline analysis value is large, and the process neural network is optimized. Finally, to verify the feasibility and effectiveness of the proposed method, the dissolved oxygen concentration was controlled based on the BSM1 model. The experimental results show that the method proposed in this paper can also make the DO concentration track its set value control, and its performance is better than PID control and MPC method, and has better control performance and stability. Keywords Process neural network · Sewage treatment · Intelligent control · Process optimization L. Feng (B) Ningxia Institute of Technology, Shizuishan 753000, China e-mail: [email protected] Y. Li Ningxia Hualei Construction Supervision Co., Ltd, Shizuishan 753000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_26

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1 Introduction With the continuous advancement of reform and opening up, the scale and mode of domestic provinces and local towns have continued to develop, and the national economy is in a stable and prosperous state. Due to the rapid growth of the industry under the conditions of a market economy, many pollution problems have also appeared [1, 2]. In particular, the treatment of industrial wastewater and domestic wastewater has seriously affected the ecological environment and is one of the main problems to be solved in current environmental protection. In recent years, the national central and state governments have proposed many solutions to this problem, the basic purpose of which is to improve the quality of human life, thereby improving sewage management indicators and cities, and continuously promoting the healthy development of cities [3, 4]. The sewage treatment industry in some developed countries started early, and the penetration rate and automation rate of sewage treatment plants are at a high level. Some designers monitor the dissolved oxygen concentration of wastewater treatment systems, employ predictive control strategies to maintain the dissolved oxygen concentration at expected values, and evaluate performance indicators such as wastewater quality, ventilation, and pump energy consumption [5, 6]. Several researchers have used the BSM1 activated sludge treatment process to compare optimal control versus prediction. The results show that by predicting and optimizing ventilation time, ventilation consumption can be reduced while ensuring waste compliance [7, 8]. Other researchers have analyzed the denitrification process in detail and applied the BSM model to evaluate the optimal running cost function [9, 10]. Predictive testing based on mathematical models is often difficult in practical applications due to the low accuracy of the models. Artificial neural network widely connects a large number of neurons, has excellent nonlinear method and self-learning ability, and is researching wastewater treatment control and adaptive control based on neural network. Some researchers have proposed design techniques to dynamically optimize the structure of neural networks. The technique uses a competition mechanism to add or remove hidden layer neurons during information processing by determining the effect of hidden layer neurons on the output during the learning process. Compared with the fixed-structure neural network controller for dynamic optimization and adaptation of the neural network structure, the application of dissolved oxygen control has advantages in excess, adaptation time and adaptability [11, 12]. In summary, we can see that intelligent wastewater treatment control algorithms, especially neural networks, are developing dynamically. However, the above optimization control shows that many advanced algorithms have been theoretically verified, but have not been widely used in practical engineering, and are only in the theoretical stage. In this paper, on the basis of consulting a large number of relevant references, combined with the time-varying characteristics and action mechanism of the process neural network and the influencing factors in sewage treatment control, the

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sewage treatment process was designed and the process neural network was optimized. Finally, based on the BSM1 model, the Dissolved oxygen concentration was controlled to verify the effectiveness of this method.

2 Intelligent Control Optimization of Sewage Treatment Process Based on Process Neural Network 2.1 Time-Varying Characteristics and Action Mechanism of Process Neural Network Feedforward process neural network has both its own characteristics and the characteristics of traditional feedforward neural network. Compared with the general neuron grid layer of the conventional neural network, the process neuron grid layer retains the characteristics of the spatial aggregation (multiple input) function of the general neuron grid layer, which is a cumulative function, namely time, “⊗” Extract the result of accumulated time. Hence, the hidden layers of process neurons have the ability to extract both spatial and temporal sides. The temporal aggregation function “⊗” and the excitation function are used to derive the cumulative effect of time, and the spatial aggregation function “⊗” and the excitation function are used to realize the spatial features. Therefore, the lattice layer of process neurons not only has a traditional lattice function that extracts neurons with well-defined fixed characteristics, but also has a lattice layer of process neurons that changes over time. The whole unique process is characterized by the extraction of functional features. Second, its input depends on the characteristics of the time-related function (i.e., the process), and the geometric point input function of the traditional neural network that exists in theory is difficult to practically implement, it can only be executed, and you cannot do it. This will be a mock. Therefore, in the format of input samples, a single sample of a process neural network contains more information, but also contains more useful information, but inevitably increases noise. For discrete data, the requirements for data customization and preprocessing are getting higher and higher, and the data processing process and mechanism are also more complicated. In addition, in terms of the number of sample selections, the process neural network decomposes some limitations of the traditional neural network and the overall information of the sample, but at the same time the total noise amount must be limited by the process. Generate and consider the performance impact of the model. Therefore, under the reasonable noise control and effective preprocessing mechanism, the procedural neural network samples are more representative than the traditional neural network, and thus more representative in the neural network sample selection characteristics. Generalization ability becomes the training process of neural network. Neural network is superior to traditional neural network, and it has been proved theoretically.

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2.2 Influencing Factors in Sewage Treatment Control (1) Dissolved oxygen concentration (DO) In the traditional activated sludge printing and dyeing wastewater treatment process, the most important factor affecting the wastewater quality, system operating costs and pollutant management results is the oxygen content of the solvent in the wastewater. For the biological reaction process in the aerobic zone, filling the aeration tank with appropriate oxygen is the most effective method to limit the dissolved oxygen content in the traditional activated sludge process. However, too much or too little oxygen in the aeration tank will affect the living environment of bacteria. This stress directly affects the rate of dissolution of organic matter in biological reactions. Excessive dissolved oxygen content will promote the active metabolism of bacteria, damage the aggregation function of bacteria, loosen the structure of the traditional activated sludge method in the pool, and form bacteria in the sewage. Without sufficient degradation, bacteria cannot effectively participate in the entire biological reaction, which greatly reduces the decomposition efficiency. (2) Nitrate nitrogen concentration (NO) Nitrate nitrogen content (NO) is another major factor affecting the control efficiency of wastewater processes such as activated sludge and printing and dyeing. Because the nitrate content in industrial wastewater mainly participates in the reactions of nitrification and denitrification in the biochemical reaction tank, it mainly affects the denitrification effect of industrial wastewater, and the nitrate content also affects the operating cost and energy consumption of the system. If the content of sulfur in the anoxic zone is too low, it will affect the denitrification reaction in industrial wastewater, and cannot be completely dissolved into the natural organic matter content in the industrial sludge, thus greatly reducing the effect of denitrification and phosphorus removal. Therefore, reasonable regulation of nitrate nitrogen content is the key to improve the denitrification effect in the process of industrial wastewater treatment. (3) Mixed Suspended Solid Concentration (MLSS) Another major variable that affects the control efficiency of activated sludge treatment is the free-flowing solids concentration in the mix. The mixed freeflowing solids concentration is the total bacterial content in the sewage. Under normal conditions, the solid content concentration of the mixed free flow in the biochemical tank is determined according to the sewage return rate. Due to the stable growth, a large number of bacteria that cannot be decomposed and aggregated are generated. If the mixed suspended solid content is too low, the bacterial activity will be weakened, the production efficiency will be reduced, and the sewage treatment efficiency will be damaged.

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3 Experiment 3.1 Process Neural Network Sewage Treatment Control Process (1) Determine the cycle. Let t = 0 be the start time and T be the optimization period; (2) Generate an initial antibody population. In the randomly generated initial antibody group, each antibody is allocated to the antibody group according to the order of center value, amplitude and weight value of the fuzzy neural network; (3) Evaluation of antibody affinity. For each day of the continuous BSM1 model, first calculate the target function values corresponding to different antibodies, then compare their sizes, and record the antibody with the lowest J value in the target population, and then multiply the J value to record as high relative affinity Antibody. Mark the antibody as the best antibody (4) Antibody update and variation. First, the antibodies are updated and replicated according to the functional principle of the immune system, as well as the regulation and promotion of the function of antibody production, as well as the detection, cloning and mutation of antibodies. When a large number of optimal antibodies are memorized, other antibodies are classified from large to small according to the expected screening probability, and then the number of clones of all candidate antibodies is determined according to all selectable cloning factors, and finally the number of antibodies is determined; (5) Termination conditions. After determining whether the specified number of times has been exceeded, if not, repeat steps 2–4. If it exceeds, the optimization process can be ended, and the dissolved oxygen content value corresponding to the optimal antibody value can be obtained.

3.2 Improved Process Neural Networks Assuming that the range of the original output of the sample is [Dmin , Dmax ], the commonly used data normalization method is shown in formula (1): dk = (Dk − Dmin )/(Dmax − Dmin )

(1)

In fact, the network training process can be transformed into the optimization problem solving process shown in Eq. (2): min J (w) = w

k 

(dk − yk )2

(2)

k=1

In view of the wide range of offline analysis values in the actual control process, simply using the absolute error to characterize the optimization objective will

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inevitably lead to the phenomenon of “non-uniform fitting”. That is to say, if the sample output value is large, this will make the neural network more adaptive to the sample points with small value, thereby achieving overall optimization. In practical applications, the quality of the prediction results is often measured by the relative error of the estimated value, so the network training problem here is the optimization problem shown in Eq. (3). min J · (w) = w

 k   Dk − Yk 2 Dk k=1

(3)

Among them, Yk is the denormalized value of the output of the network.

4 Discussion In order to test the feasibility and effectiveness of the proposed scheme, the dissolved oxygen content was measured using the BSM1 model. BSM1 provides 2 weeks of drainage inflow data under three conditions of sunny, rainy and heavy rain, and the sampling duration is about fifteen minutes. The evaluation results are mainly evaluated against the following three indices: ISE, IAE and Max err. Figure 1 shows the comparison of performance parameters under various methods. It can be seen from the comparison in Fig. 1 that the performance index of dissolved oxygen based on the process neural network control given in this paper is IAE

ISE

Max err

0.35 0.3

VALUE

0.25 0.2 0.15 0.1 0.05 0 PNN

PID METHOD

Fig. 1 The performance comparison of the three methods

MPC

Intelligent Control Optimization of Sewage Treatment Process Based … Table 1 Variable set value performance comparison

IAE

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ISE

Max err

PNN

1.017

0.00923

1.765

PID

3.110

0.0915

3.107

MPC

2.725

0.072

2.846

3.5 3

Value

2.5 2 1.5 1 0.5 0 PNN

PID

MPC

Method IAE

ISE

Max err

Fig. 2 Performance comparison results of different set points

much higher than that of PID control and MPC control, and the reliability and accuracy are also high. In order to better test the stability and adaptability of the indicators given in this paper, this paper carried out the stability comparison test after the change of the specified value of dissolved oxygen content. The performance comparisons are shown in Table 1 and Fig. 2. As shown in Table 1 and Fig. 2, even if the expected value changes at a specific time, the method proposed in this paper can monitor the control of the specified value through the DO concentration and has better performance than the PID control or MPC method.

5 Conclusions The research of process intelligence is an emerging technology developed in recent years. It combines the discipline knowledge of computer, network and artificial intelligence with human practice, and uses its powerful nonlinear mapping ability to model and analyze the complex environment. In this paper, experiments are designed to verify the role of process neural structure in intelligent control of sewage treatment.

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References 1. Kasim N, Nugraha GS (2021) Pengenalan Pola Tulisan Tangan Aksara Arab Menggunakan Metode Convolution Neural Network. Jurnal Teknologi Informasi Komputer dan Aplikasinya (JTIKA) 3(1):85–95 2. Saraiva M, Ribeiro T, Afonso J et al (2021) DOP80 automatic detection of ulcers and erosions in PillCam Crohn’s capsule using a convolutional neural network. J Crohns Colitis 15(Supplement_1):S111–S112 3. Downes LM, Steiner TJ, How JP (2021) Neural network approach to crater detection for lunar terrain relative navigation. J Aerosp Inf Syst 18(2):1–13 4. Yadav S, Suhag RS, Sriram KV (2021) Stock price forecasting and news sentiment analysis model using artificial neural network. Int J Bus Intell Data Mining 19(1):113 5. Srivastava G, Rajpal A, Kazmi AA (2021) A comparative analysis of simultaneous nutrient removal in two full-scale advanced SBR-based sewage treatment plants. Int J Sci Res (IJSR) 10(2):1407–1414 6. Lemar G, Shahar S, Osman A (2021) Influence of sewage treatment plant effluent on the presence of culturable pathogenic bacteria in the water body. Biosci Biotechnol Res Asia 18(1):173–184 7. Singh SK, Kapoor V, Siriya K et al (2021) Performance evaluation of extended aerationbased sewage treatment plants at NCT of Delhi, India. Shanghai Ligong Daxue Xuebao/J Univ Shanghai Sci Technol 23(5):515–525 8. Ruhela M, Wani AA, Ahamad F (2020) Efficiency of sequential batch reactor (SBR) based sewage treatment plant and its discharge impact on Dal Lake, Jammu & Kashmir, India. Arch Agric Environ Sci 5(4):517–524 9. Boujoudar Y, Azeroual M, Elmoussaoui H et al (2021) Intelligent control of battery energy storage for microgrid energy management using ANN. Int J Electr Comput Eng 11(4):2760 10. Nguyen V, Jiao R, Feng D et al (2021) Study on running deviation mechanism and intelligent control of belt conveyor. Vibroeng PROC 36(1990):115–120 11. Mohan G, Bhende CN, Srivastava AK (2021) Intelligent control of battery storage for resiliency enhancement of distribution system. IEEE Syst J 99:1–11 12. Solovyev DA, Kamyshova GN, Kolganov DA et al (2021) Model of an intelligent control system for an irrigation complex. Agrarian Sci J 2:103–108

Research on Dynamic Cost Management System of Power Transmission and Transformation Project Based on 3D Technology Linlin Zhang, Shanwu Huo, Jing Wang, and Qing Ren

Abstract At present, intelligent technology has made a breakthrough. Combined with the actual situation of the construction site of power transmission and transformation projects, applying it to the construction technology management can significantly improve the management efficiency. This paper introduces the framework, construction mode and technical requirements of the power grid project cost dynamic management information system, and takes the 220 kV substation project as an example to introduce the application of the power grid project cost dynamic management system in the power grid project construction management. Keywords Power grid engineering · Cost management · Dynamic management · Information system

1 Introduction In recent years, with the rapid development of China’s economy, urban infrastructure engineering has also been rapidly improved. As the cornerstone of urban development, power engineering is related to the speed and scale of urban development. For power transmission and transformation projects, high cost will hinder economic development, while low cost will lead to incomplete consideration in the construction process of power transmission and transformation projects, unable to ensure the quality of the project, and affect the construction of power transmission and transformation projects [1].

L. Zhang (B) · S. Huo · Q. Ren State Grid Hangzhou Power Supply Company, Hangzhou, Zhejiang, China e-mail: [email protected] J. Wang State Grid Zhejiang Electric Power Corporation, Hangzhou, Zhejiang, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_27

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2 Dynamic Management System Framework of Power Grid Project Cost 2.1 Frame Design The power grid project cost dynamic management system includes four key modules: dynamic information collection, project cost target determination, cost deviation early warning and cost deviation control. Through mutual cooperation, the cost influencing factors can be monitored. In case of cost deviation, take corresponding measures in time to ensure that the project cost is controlled within the target range [2, 3]. The design idea of cost dynamic management technology system is shown in Fig. 1.

start

Dynamic collection of cost information

Determination of project cost objectives

y Abnor mal?

Cost comparison

n y Normal execution of works

Good target control? n Cost deviation warning Deviation control of project

Cost target adjustment

y

Impact control

Fig. 1 Design idea of power grid project cost dynamic management system

Normal execution of works

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2.2 Key Modules 2.2.1

Dynamic Information Collection

The information dynamic collection module further processes and refines the collected data by using technologies such as HTML information metadata extraction, and preprocesses the data by using methods such as isotropy, normalization, standardization and singularity elimination to form structured data that can be directly applied to cost analysis [4, 5]. Based on the pest model, the set of uncertain factors affecting the project cost is determined from the four dimensions of external policy, economy, society and technology. Pearson correlation test is used to cluster the analysis conclusions of technical factors, test the rationality of identification of technical factors, judge the impact stage and quantify the impact degree. The cost monitoring indicators of power transmission and transformation projects are determined according to the screening principles of quantifiable, regular, high sensitivity and long time span [6]. The monitoring indicators of each stage are refined according to the characteristics of investment decision-making stage, design stage, bidding stage, construction stage and final settlement stage, and the whole process data collection is carried out [7].

2.2.2

Cost Target Determination

Firstly, the fuzzy identification algorithm is used to screen the sample projects and construct the historical time series of project cost. The historical price is decomposed into trend price and random price by using ensemble empirical mode decomposition (EEMD); Then, the time series of each component that constitutes the trend price are predicted respectively. The project cost at the subsequent time points is predicted based on the complete set of time series samples, and the discrete analysis is carried out in combination with the random price components in the price decomposition to determine the impact of uncertain factors on the cost of various power transmission and transformation projects.

2.2.3

Cost Deviation Warning

Based on the project management regulations and the fluctuation range of random components of historical data decomposed by EEMD, the target threshold of cost control is determined, and a hierarchical threshold system is established to accurately capture changes and realize the hierarchical early warning of cost. The expert consultation method and the probability distribution method of historical data are used to determine the early warning threshold. According to the different warning conditions, the early warning areas are divided into four early warning areas: yellow, green, light blue and blue light areas. The red light area and the blue light area are

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alarm areas, indicating that the actual value is too high and too low than the target value. It is recommended to take corrective measures [8].

2.2.4

Cost Deviation Control

According to the cause and nature of the deviation [9], it can be divided into controllable deviation and uncontrollable deviation: (1) Controllable deviation: it is defined as controllable deviation if it obviously exceeds the plan and affects other subprojects, and can be controlled by taking measures, such as the cost overrun of a certain item occupies the cost of other subprojects and the progress delay on key lines; (2) Uncontrollable deviation: the deviation that cannot be avoided and controlled, generally caused by objective reasons, is defined as uncontrollable deviation. For controllable deviation, first identify and measure the deviation, then take targeted measures to reduce or avoid such problems again, and verify the correction effect. If it fails to pass the verification, take other measures to continue correction. For uncontrollable deviation, directly adjust the cost control target [10]. By establishing a timely and efficient cost information feedback system, the power grid project cost dynamic management system enables project managers at different levels to browse the real-time cost information within their authority at each node of the project progress. Managers can make corresponding decisions based on allround information to control cost deviation and realize dynamic management of cost control (Fig. 2).

3 Software Implementation of Power Grid Project Cost Dynamic Management System The overall cost dynamic management system software of power transmission and transformation project is designed in the form of hierarchical and component structure,. The logic layers interact closely to ensure the functional integrity and stability of the system. See Fig. 3 for software framework design.

3.1 Presentation Layer The presentation layer is the interface between the whole system and users, which is mainly realized by web design framework technology. Realize the functions of early warning information release and cost deviation display. The first is to display the cost warning information, and different indicator lights show the difference of

Research on Dynamic Cost Management System of Power … Fig. 2 Cost deviation control process

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Deviation analysis

Controllabl e deviation

Adjust control objectives

Deviation identification and measurement Take corrective measures

Correction effect evaluation

Pass verification

Normal implementation of the project

Presentation layer (MX framework) Request data (xml&json) Data control layer (data encapsulation, parsing and encryption) Domain object call Business logic layer Business data entity Data entity layer Data access API Persistence layer (hibernate) SQL call Data storage tier (0race) Fig. 3 Framework design of power grid project cost dynamic management system

alarm degree. Second, preview and display the engineering deviation analysis data from multiple dimensions such as project type and voltage level, so as to provide basis for managers to make management decisions.

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3.2 Data Control Layer The data control layer is mainly used for the encapsulation and analysis of cost data. Extensible markup language is used to encode information in a meaningful structure, which can be understood by computers and people to some extent. It can be used to mark data and define data types. It is a source language that allows users to define their own markup language. It has good scalability, openness, cross platform Fig. 2 cost deviation control process and interoperability, and can provide real-time/historical data storage and access services for real-time business applications.

3.3 Business Logic Layer The business logic layer provides business logic processing functions through domain object responsibilities. The business domain is divided into three parts according to business functions: information collection, dynamic management and application services. The information acquisition part includes: data acquisition node control, data acquisition cycle control; Dynamic management technology includes: monitoring, early warning and feedback control module; The application service module includes: early warning information release platform and deviation analysis display platform.

3.4 Data Persistence Layer The data persistence layer separates the business logic from the specific database to realize the convenient switching between different databases. The application hierarchy is clearly divided to reduce the coupling between various application objects. Business logic no longer depends on specific data access and transaction policies, and there is no hard coded resource search. Realize the storage, centralization, integration, sharing and analysis of real-time and historical data, and provide a standard and unified access mode.

4 Application of Dynamic Management System for Power Grid Project Cost Taking a 220 kV substation project cost dynamic management as an example, the implementation process of the cost dynamic management system is introduced. See Table 1 for the main technical parameters of the demonstration project.

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Table 1 Main technical parameters of the example project Voltage class

Substation type

Number of units in current period

Single capacity

High voltage side circuit

220 kV

Outdoor station

2 sets

180 MVA

2 loops

4.1 Identification of Cost Influencing Factors Input the engineering technical parameters of the sample, screen the influencing factors, select CPI, PPI, silicon steel price and aluminum material from the influencing factor library as the main influencing factors of the project, and automatically calculate the sensitivity coefficient and principal component regression coefficient.

4.2 Cost Target Determination Determine cost control objectives. EEMD model is used to decompose historical cost data of similar projects. The system can analyze the trend component and random component of cost data. Eliminate random components in historical time series cost data and retain trend component data.

4.3 Analysis of Influencing Factors According to the data of the corresponding influencing factors analyzed in the project of this example, the EEMD decomposition model is used to obtain the random component and trend component data, and then the proportional measurement (random component/trend component) is carried out to obtain the time series amplitude of each influencing factor. Finally, 80% interval data of volatility are intercepted to obtain the fluctuation range of influencing factors. Taking copper price as an example, EEMD is used to decompose copper price data to obtain trend component and random component of historical copper price. According to the influencing factors and the trend components of random variables, the fluctuation range of random variables is calculated. Form a sort table accounting for 80%, and take the minimum and maximum values to form a fluctuation range.

4.4 Cost Deviation Warning Forecast and control the cost trend of similar projects of the target project, and analyze the cost trend. Then, according to the actual cost of the example project,

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combined with the relevant provisions of the enterprise on project management and the fluctuation range of the system measurement, the cost deviation threshold system is analyzed, and the project cost is classified for early warning.

5 Conclusion The system can timely monitor the changing factors that will occur in the project, timely predict the impact on the project cost, analyze and judge whether the project cost exceeds the limit, and timely take effective measures to prevent it. The system can quickly and accurately guide the project cost control strategy, improve the initiative and emergency capacity of cost control, provide a reliable basis for the pre judgment and pre control of project cost, reduce unnecessary waste in the construction process, improve the management level of construction enterprises, and optimize the rational allocation of investment resources. Acknowledgements Science and Technology Project Of State Grid Zhejiang Electric Power Corporation (Project Number: 5211HZ19014T).

References 1. Hu J, Zhao S, Nie Q (2021) Research on modeling of power grid information system based on knowledge graph. In: 2021 IEEE international conference on power electronics, computer applications (ICPECA). IEEE 2. Wang S, Zhang L H, Zhang J et al (2021) Research on intelligent identification method for access equipment of grid information system. J Phys Conf Ser 1792(1):012015 (6pp) 3. Yue J, Li C (2020) Study on cascading failures of power grid information physics fusion system based on pattern search. IOP Conf Ser Mater Sci Eng 740(1):012138 (7pp) 4. Kim J, Kim N, Han J et al (2020) Power management system in the microgrid with the proper electric vehicle data preprocessing. In: 2020 International conference on information and communication technology convergence (ICTC), 2020 5. Zhang Q, Li B, Yuan Q et al (2020) Database dynamic update management system for power system. J Phys Conf Ser 1550:052001 6. Roy AK, Biswal GR, Basak P (2020) An integrated rule-based power management and dynamic feed-forward low voltage ride through scheme for a grid-connected hybrid energy system. J Renew Sustain Energy 12(5):056303 7. Mittelstaedt JC (2022) The grid management system in contemporary China: grass-roots governance in social surveillance and service provision. China Inf 36(1):3–22 8. Conte F, D’Agostino F, Grillo S et al (2022) An efficiency-based power management strategy for an isolated microgrid project, 2022 9. Tong J (2022) Smart grid information management system relying on MAS technology and complex scientific management thinking. Discr Dyn Nat Soc 2022 10. Jiao J, Tang Y, Du Y et al (2020) Risk evolution and simulation of power grid project cost management: a system dynamics and life cycle theory approach. In: 2020 Asia energy and electrical engineering symposium (AEEES), 2020

An Algorithm for Constructing Fractal Graph of Frieze Group Based on NIFS Jiahui Zhang, Fengying Wang, Liming Du, and Haiyan Wang

Abstract To construct nonlinear IFS with an equivalence mapping M-set of the frieze groups, we investigate the construction of the M-set and the NIFSs of the mapping of which are the most symmetric in this kind of mapping. According to the symmetry characteristic of pma2 mapping, its coefficients are expressed as parameter vectors in 7-dimensional parameter space. Fixing the values of the five parameters, set up the cross-section composed of the parameters in the seven-parameter space. The step acceleration algorithms is used to get the local critical points, which makes the determinant of the Jacobin matrix of the mapping with parameter in the plane equal zero. The plane is divided into four regions by the orbital characteristics of the critical points, and the general M-set is constructed in the C plane. The filled-in Julia sets are constructed by choosing the parameters from the different parameter regions of the M-set. For the attracting parameter regions, the methods of constructions of NIFSs and their fractals are present according to the multi-orbit characteristics of the mapping. The results show that the visualization of dynamical systems of non-analytic symmetric complex mappings with multi-parameter can be validly researched by the methods provided in this paper and the great number of the chaotic attractors, the filled-in Julia sets and the fractals of NIFSs can be easel constructed from this kind of the mapping family. Keywords Fractal · Frieze group · M-set · NIFS · Step acceleration algorithm

J. Zhang School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China F. Wang (B) · L. Du · H. Wang School of Information Engineering, Suqian University, Suqian, Jiangsu, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_28

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1 Introduction The fractal theory is a prevalent and active new theory and discipline today. The concept of fractal was first proposed by American mathematician Mandelbrot [1], who used complex analytic mapping f (z) = z 2 + c to generate an algorithm full of Julia sets on the dynamic plane and constructed the famous Mandelbrot set (M-set) on the parameter plane. As a source for generating many fractal graphs and chaotic attractors full of Julia sets with rich forms [2]. Many scholars explain the mutual compatibility between chaotic behaviour and symmetry by constructing a mapping equivalent to the expected symmetrical form. In the early research, they determined a part of the equivalent mapping family of the plane discrete symmetry group [3– 8]. With continuous research development, these results have been extended to all discrete symmetry groups on the plane. pma2 mapping is a mapping model in the Frieze plane discrete symmetry group. The iterative function system composed of linear compression affine transformation IFS is an important method to construct fractals in dynamic system graphics. IFS (iterated function systems) is also one of the most vibrant and broad application fields in fractal image processing. The relevant research results, such as the IFS’s definition, mathematical properties, and construction methods, have been continuously updated or expanded [9, 10]. In previous studies on IFS, some scholars have studied analytic and non-analytic complex mappings. For example, Van Loocke et al. discussed the use of complex mapping to construct the fractal attractor of IFS, and then the used radial transformation to form the IFS iterative function system and generate the fractal pattern [11]. Chen Ning et al. took the high-order complex mapping as the research object and discussed the effective method of selecting parameters on the M-set to construct the IFS, which is widely used in Michael Field and Martin Golubisky gave the method of constructing symmetric fractal, and on the basis of it, the method of searching chaotic fractal parameters was extended [12–14]. In the study of plane discrete symmetric group mapping, some scholars also proposed constructing this kind of generalized M-set with various symmetric characteristic mappings and gave relevant definitions. However, what effect does the change in the parameter space of the generalized M-set of such symmetric maps have on the construction of IFS? What kind of rules exists in the constructed fractals? Aiming at these problems, this paper will study the mapping model in the Frieze symmetry group as the pma2 mapping.

2 Analysis on the Properties of Equivalent Mapping of pma2 In the past, the research on the M-set of high-order complex exponential mapping mainly revolves around analytic mapping. The symmetric pma2 mapping studied in this paper is a non -analytical mapping with symmetry. Value points or extremum

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point sets, non-analytical mapping requires solving local extreme points and constructing generalized M-set by examining the dynamics of their orbits, which is also a crucial step in the experimental process. This paper will use non-analytical mapping to solve local extremum points. By examining the dynamic characteristics of the local extremum points of the mapping under given parameters, the parameter space is effectively divided, and a generalized M-set on the parameter space is constructed. Construct corresponding fractals filled with Julia sets and nonlinearities pma2.

2.1 Analysis of Symmetry Properties The Frieze group equivalence mapping contains seven kinds of mapping with different symmetry groups (p111, p112, pm11, p1m1, p1a1, pmm2, pma2). The symmetric group form of pma2 mapping is Gpma2 = {I, T2π , Tπ ° F0 , F90° , R180° }, in which T is translational symmetry, F is reflection symmetry, and R is rotational symmetry. In order to facilitate parameter selection and computer iteration, the parameters of the iterative mapping of the model are expressed in vector form a = (ai , i = 1, . . . n)T = (a1 , a2 . . . an )T , and the pma2 mapping can be expressed as: ) ( ) ( x + a1 y sin(x) + a2 sin(2x) + a3 y 2 sin(2x) x (1) f = y a4 y + a5 cos(x) + a6 y 2 cos(x) + a7 y cos(2x) According to the symmetric property of pma2, in order to reduce the computational complexity, the basic initial iterative region given by the experiment is x ∈ (0, 2π ).

2.2 Non-analytical Analysis It can be known from the Cauchy-Riemann equation that determines whether a complex mapping is analytic: for a mapping f (z) = u(x, y) + iv(x, y), if ∂∂ux = ∂v and ∂u = − ∂∂vx , then the mapping is analytic. Otherwise, the mapping is not ∂y ∂y analytic. Through verification, it can be seen that the pma2 equivalent mapping is non-analytical. Therefore, it is necessary to solve the point where the determinant of the Jacobin matrix of 0 the iterative mapping is taken as the local extreme point. The mapping family studied in this paper meets pma2 mapping the requirement that the Jacobin matrix must be continuous for iterative mapping. The “step acceleration method” of unconstrained optimization is used to solve the point where the absolute value of the determinant of the Jacobin matrix of the mapping is the smallest as the local minimum point of the mapping, that is, the following formula is satisfied:

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| | | | | D f (z) | = ∂ f 1 ∂ f 2 − ∂ f 1 ∂ f 2 | =0 ∂x ∂y ∂ y ∂ x |z=(x,y)

(2)

Specifically, in the process of experiment development, the iteration termination limit is defined as 10−6 .

3 Constructing Generalized M-sets and Full Julia Sets on the Parameter Section of pma2 Equivalent Mappings The parameters of the equivalent mapping pma2 constitute a 7-dimensional parameter space. In this paper, by fixing 5 parameters (a0 , a2 , a3 , a4 , a6 ), another 2 parameters (a1 , a5 ) form a parameter space section C, that is, the x and y coordinate axes of the 2-dimensional space. By investigating the influence of parameter changes on the dynamic characteristics of iterative mapping, the parameter space is divided and the generalized M-set on the parameter section is constructed. It is found through experiments that the number N of local extremum points obtained by searching in the basic domain is different under different parameters, and the iteration orbit of local extremum points has 3 cases: escape, chaos and attraction period. Specifically, the algorithm steps for constructing generalized M-set are as follows: Given parameter {a0 = 0.551, a2 = −0.579, a3 = −0.087, a4 = −0.993, a6 = −0.772, a6 = −0.772}, select parameter combination (a1 , a5 ) to form parameter space section C. Given the drawing window range { a1 ∈ [ − 5, 5],a5 ∈ [ − 3, 3.75]} , the maximum escape radius 10−6 , the maximum number of iterations 3000. Set the step size to 0.1 and determine the error of periodic orbit ρmin . (2) Set the computer screen resolution to 1024×768 and divide the dynamic plane iteration area into grids. (3) Create an empty linked list point for storing local extreme “points_ list”. (4) Select the initial point on the grid for iteration. (5) Select a point in the drawing window to start the outer loop. (6) Carry out optimization steps and set the end limit of “step size acceleration method” σ , The local extremum points are searched and stored in the linked list. (7) Each local extreme point in the chain of extreme points is traversed as the initial point of iteration. (8) Set the “period” and “chaos” flag bits. (9) After removing transient, Calculate the L value twice and compare it. If |f k(aij) | < 10−6 and |L f - L r | < 0.001, stop iteration and record the L value at this time until all points in the list are traversed. Turn to (10), otherwise turn to (7). (10) Color all points in the grid according to the following color division strategy, turn to (11), otherwise turn (5). (11) End. (1)

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C1 C3

C2

Fig. 1 Generalized M-set of pma2 mapping parameter plane

The generalized M-set with fixed parameters constructed by the above method is shown in Fig. 1. The white area is the escape area, the blue region is an attractive periodic region, the red region is chaotic region, the red region is chaotic region, the green region is a mixed parameter region. Parameters c1 ,c2 and c3 in the periodic region (blue part) of the generalized M-set shown in Fig. 1 can construct a full Julia set on the dynamic plane. Figure 2a–c show three full Julia sets under the iterative mapping composed of different parameters, combine with above fixed parameters. During the experiment, it is found that the periodic orbits of the pma2 full Julia set constructed by the equivalent mapping also have the same symmetry properties as the mapping. Figure 2a contains periodic orbits, and Fig. 2b contains 4-4 periodic orbits, and there are multiple attracting periodic orbits that are symmetrically distributed along the origin and do not pass through the origin, Fig. 2c contains 4-2 periodic orbits, two of which cross and pass through the origin.

4 Constructing the Nonlinear IFS Select two groups of parameters in the same periodic parameter region of the generalized M-set in Fig. 2a to construct a nonlinear IFS consisting of two iterative maps, as shown in Eq. 3: / X : f ci

( ) )| ( x x + a1 y sin(x) + a2 sin(2x) + a3 y 2 sin(2x) || = a4i y + a5i cos (x) + a6 y2 cos (x) + a7 y cos (2x) | y

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Fig. 2 Full Julia sets for different parameter 2 X = ∩i=1 xi

⟩

(3)

where the fixed parameters {a0 = 0.551, a2 = −0.579, a3 = −0.087, a4 = −0.993, a6 = −0.772}, Ci = (a5i , a4i ) i = 1, 2; X i is the full Julia sets on the dynamical plane of f ci ; X is the public domain of attraction of two iterative mappings. By selecting two groups of parameters in the same periodic parameter region of the generalized M-set shown in Fig. 3a, and construct the iterative mapping of function (1) respectively by combining the fixed five parameters, a nonlinear IFS iterative function system can be constructed(NIFS). As shown in Fig. 3, the parameters in the yellow area have 2_n periodic orbits (n = 1, 2, ...), and the period values n at different positions are different accordingly. Selecting parameters in this area can form a mapping function (1) with a 2-period orbit. The brown area represents the parameter area with the iterative mapping of the four attraction period orbit; the golden area represents the parameter area with the iterative mapping of the 5 attraction period orbit. The grey area represents the parameter area with 6 attraction period orbits … When the number of orbits is an even number, Fig. 3 Orbital M-set

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Fig. 4 Enlarged view of the boxed area

there may be orbits with the same period, or there may be different periods that are symmetrically distributed along the origin, e.g. (6 : 4_1, 2_2). When the number of orbitals is odd, it may contain orbitals with the same period, or it may contain orbits that are attracted independently by a single line, such as (5 : 4_1, 1_4),the orbits that are symmetrically distributed with the same period. Observing the brown boxed area in Fig. 3, it is found that in this area, the iterative maps constructed by different parameters all have four attractive periodic orbits and show a period-doubling distribution. Figure 3b is a partially enlarged view, from which it can be clearly seen that the orbital distributions of 4_6, 4_12 and 4_24 in the brown parameter area. Figure 4 is an enlarged view of the box area in Fig. 3. It can be seen that this parameter area is covered by the same color, which means that there is four tracks in this area. However, the different period values of the parameters in this area change with it, and there is an apparent period-doubling distribution law. The yellow area in Fig. 3 is 2_2 pparameter area, select 2 parameters (c1 , c5 ) = (0.689, −0.861) and (c1 , c5 ) = (0.758, −0.844) in this area, use these 2 parameters to construct a set of NIFS together with the iterative mapping described in function (1). The attraction fixed point of one of the parameters iterative mappings is selected as the initial iteration point. Then NIFS one of the iterative mappings is selected for iteration. The iterative result is used as the new initial point of the next iteration and repeated. That is, randomly select one iterative map under one parameter, and continuously take the point calculated each time as the initial point before the next iteration to obtain the complete fractal on the plane. The grey part in Fig. 5 is the public domain of attraction of the Julia set with two parameters. Due to the closeness of the parameters, the two periodic orbits of the two maps almost coincide, and the fractal is generated in the common domain of attraction of the two iterative mappings, which is symmetrically distributed (Fig. 6). In order to observe a clearer fractal, one of the fractals in Fig. 6 is partially enlarged, as shown in Fig. 7. In the attraction parameter area of the generalized M-set, in addition to the abovementioned area with multiple attractions, periodic orbits are distributed left and right

260 Fig. 5 Public domain of attraction of the Julia set

Fig. 6 2_2 complete fractal

Fig. 7 Fractal partial enlargement attraction

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(a) Public domain of attraction

(b) NIFS Fractal

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(c) Partial-enlarged Fractal

Fig. 8 Public domain of attraction, NIFS Fractal and partial-enlarged Fractal generated by two parameters (4_2):(c1 ,c5 ) = (−0.9089, 0.6758), (c1 ,c5 ) = (−0.8754, 0.6750)

(a) Public domain of attraction

(b) NIFS Fractal

(c) Partial-enlarged Fractal

Fig. 9 Public domain of attraction, NIFS Fractal and partial-enlarged Fractal generated by two parameters (4_8):(c1 ,c5 ) = (−0.9861, 0.8712), (c1 ,c5 ) = (−0.9382, 0.8372)

symmetrically along the origin. There is also a type of parameter area with multiple attraction periodic orbits distributed left and right symmetrically along the origin and passing through the origin. In such a parameter area, the parameters are selected to construct the fractal. It is found that the fractal is generated near the period point of each orbit and is rationally symmetric along the origin (Fig. 8a). We select two parameters: (4_2):(c1 ,c5 ) = (−0.9089, 0.6758), (c1 ,c5 ) = (−0.8754, 0.6750); (4_8):(c1 ,c5 ) = (−0.9861, 0.8712), (c1 ,c5 ) = (−0.9382, 0.8372) combing with fixed parameters to construct IFS and generate the fractal as shown in Figs. 8b and 9b respectively. Figures 8a and 9a are generated by two iterative mappings with respective public domains of attraction under the group parameters, respectively. Figures 8b and 9b are NIFS fractals generated by the mapping with the rotational symmetry along the origin on their respective public domains of attraction. Fractal distribution in Figs. 8c, and 9c are the fractals clearly visible after partial magnification.

5 Conclusion The Frieze group equivalence map is a class of non-analytical maps with symmetric properties, and the previous methods for solving analytic maps are not fully applicable to this study. This paper uses pma2 mapping model in the equivalent mapping of the Frieze group as an example to carry out related research. In the process of

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experiments, the “step acceleration method” is used in the unconstrained optimization method for solving non-analytical mapping to solve local extremum points. Using the Lyapunov exponent as the criterion, the dynamic characteristics of each extreme point under each group of parameters are determined, and the parameter space is divided. By fixing pma2 mapping N −2 parameters, another two parameters form a parameter space section, and the “step acceleration method” is used to solve the local extremum points of pma2 mapping. According to the dynamic characteristics of the local extremum points under different parameters, a generalized M-set is constructed. On the generalized M-set, the periodic parameter region is further refined according to the complexity of the orbital properties, a Julia set and a public attraction domain with different orbital periods are constructed, select the parameters from the set to form multiple groups of mapped NIFS families, and the fractals of pma2 mapping are successfully constructed, enriching the wonderful fractal world. Acknowledgements This work was supported by Suqian University Scientific Research Fund for Talent Introduction (Research on Analysis, Testing and Anomaly Diagnosis Methods for Complex Systems).

References 1. Barnsley MF (1993) Fractals everywhere. Academic Press Professional, Boston 2. Fletcher A (2019) Attractor sets and Julia sets in low dimensions. Conformal Geometry Dyn Am Mathe Soc 23:117–129 3. Dumont JP, Heiss FJ, Jones KC, Reiter CA, Vislocky LM (1999) Chaotic attractors and evolving planar symmetry. Comput Graph 23(4):613–619 4. Orellana R, Zabrocki M (2021) Symmetric group characters as symmetric functions. Adv Math 390:107943 5. Roelfs M, De Keninck S (2021) Graded symmetry groups: plane and simple. arXiv preprint arXiv:2107.03771 6. Lu T, Li C, Jafari S et al (2019) Controlling coexisting attractors of conditional symmetry. Int J Bifurcat Chaos 29(14):1950207 7. Li C, Sun J, Lu T et al (2020) Symmetry evolution in chaotic system. Symmetry 12(4):574 8. Sun Y, Chen N (2021) Computer graphic analysis of construction of planar dynamic systems with truncation function. J Phys Conf Ser (IOP Publishing) 2037(1):012059 9. Stenflo O (2012) Iterated function systems with a given continuous stationary distribution. Fractals 20(3&4):197–202 10. Abdulaziz A, Said J (2021) On the contraction ratio of iterated function systems whose attractors are Sierpinski n-gons. Chaos Solitons Fractals 150:111140 11. Van Loocke P (2009) Non-linear iterated function systems and the creation of fractals pattenrs over regular polygons. Comput Graph 33:698–704 12. Chen N, Sun J, Sun Y et al (2009) Visualizing the complex dynamics of families of polynomials with symmetric critical points. Chaos Solitons Fractals 42(3):1611–1622 13. Field M, Golubitsky M (1992) Symmetry in chaos. Oxford University Press, New York 14. Sprott JC (1993) Automatic generation of strange attractors. Comput Graph 17(4):325–332

Application of Artificial Intelligence Technology in Distributed Privacy-Preserving Clustering Mining Algorithm Jin Zhang

Abstract With the rapid development of information technology, large-scale data sharing is a new challenge to traditional data mining methods. How to integrate into the distributed environment and obtain accurate mining results on the basis of ensuring the confidentiality of data holders has become a new research field in the field of data mining. This paper aims to study the application of artificial intelligence technology in distributed privacy-preserving clustering mining algorithms. This paper firstly introduces the basic concepts and steps of realizing the foundation of data mining and its realization process. The advantages and disadvantages of different algorithms are compared and analyzed. Next, the concept of confidentiality is introduced, the most common method of privacy protection in data mining is revision, focusing on blockchain technology, encryption technology and peer-to-peer computer security. Finally, further investigate the stored data. Based on the K-mean clustering algorithm, combined with the distribution area, this paper adopts the overall method of homomorphic encryption to minimize the clustering on distributed sites, and regard the security of the results as an intermediate link in the communication process. As long as the cluster process runs in ciphertext mode, public encryption allows the intermediate results of the cryptographic computation process to be protected, and the algorithm can record normal cluster results on a privately secure basis. Experiments show that after the improvement of distributed privacy-preserving clustering mining, the execution time of the mining algorithm is smaller than that of the traditional mining algorithm, and the average clustering accuracy is about 85%, which is high. Keywords Artificial intelligence · Distributed data · Privacy protection · Cluster mining

J. Zhang (B) Shandong Huayu University of Technology, Dezhou, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_29

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1 Introduction In recent years, with the rapid development of technologies such as cloud computing and mobile Internet, various mobile terminals and wireless sensors generate a large amount of data every moment. User services interact with data, and the era of big data has arrived. At the same time, the research and application of big data has become a hot topic, and topics and business related to big data have become the objects of discussion among enterprises and individuals. The broad market prospects of big data attract more and more human and financial resources to invest in it. We believe that in the near future, big data will bring disruptive changes to our industrial production and daily life [1, 2]. In the research on the application of artificial intelligence technology in distributed privacy-preserving clustering mining algorithms, many scholars have studied it and achieved good results. For example: Ren’s secret grouping mining method based on the configuration of the first element is A recommendation. The first component analysis method is an interdisciplinary statistical analysis method that regenerates the original size and some related variables into a new set of independent segmentation variables by means of prediction [3]. Wardoyo proposes a data conversion method for cluster mining based on random extraction. Random mapping is linear variation [4]. This paper firstly introduces the basic concepts and steps of realizing the foundation of data mining and its realization process. The advantages and disadvantages of different algorithms are compared and analyzed. Next, the concept of confidentiality is introduced, the most common method of privacy protection in data mining is revision, focusing on blockchain technology, encryption technology and peer-topeer computer security. Finally, further investigate the stored data. Based on the Kmean clustering algorithm, combined with the distribution area, this paper adopts the overall method of homomorphic encryption to minimize the clustering on distributed sites, and regard the security of the results as an intermediate link in the communication process. As long as the cluster process runs in ciphertext mode, public encryption allows the intermediate results of the cryptographic computation process to be protected, and the algorithm can record normal cluster results on a privately secure basis.

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2 Application of Artificial Intelligence Technology in Distributed Privacy-Preserving Clustering Mining Algorithms 2.1 Improvement of Distributed Clustering Algorithm Protecting Privacy Clustering mining has been widely used in real life, and has been widely used in many application fields such as product research, graphic design, data analysis and process definition. From small to large, people learn how to use the subconscious process of gradual evolution to differentiate things. Stores can define customer group characteristics based on clusters, and determine shopping processes and customer preferences. In recent years, some grouping algorithms, such as K-D means, have been proposed for shared data sources, but these algorithms do not have private security features. Another website that leads to information leakage. In the actual application of grouping, it is always necessary to protect the information of this site from being misused by other sites while exchanging data, that is, privacy security is required. Currently, private security-based clustering algorithms are a rapidly explored problem in cluster mining. Therefore, privacy-based data mining is one of the key elements of data mining research. At present, various solutions have been proposed in cluster data mining to protect private data in data mining and prevent the misuse of the collected data. The literature proposes a method for grouping analysis of this vertical distribution, especially to ensure that each site does not reflect the transaction value of its field when obtaining aggregated results [5]. The literature also proposes a proprietary method for grouping analysis [6]. In this way, each site splits a portion of the original or corrupted data, creates a related world in each site area, and then spreads the world to a highresolution hub. Excellent group distribution through logical sampling. The literature proposes a complex encryption algorithm that limits private dances. It mainly uses the technology of adding Laplacian noise in the process of data acquisition to avoid privacy leakage, and combines the methods of original data analysis and noise interference to achieve privacy security. The literature proposes a K-mean grouping algorithm based on the cluster tree structure algorithm [7]. This partition represents a DCPPD encryption algorithm for external data storage according to the advanced distributed grouping algorithm. The actual data from this website is used for export to other websites reach safety. Scientific data and simulations show that the algorithm can effectively solve the leakage problem in the process of cluster analysis of shared regions, and identify the data security in different locations of lateral distribution.

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2.2 Application of Artificial Intelligence Technology in Data Mining Algorithm Data mining is an independent system in artificial intelligence. It’s about the world of AI, it’s cohesive and connected, it operates on its own rather than its own. The design system can effectively augment artificial intelligence to improve learning and analysis capabilities on the one hand, and can also play a role in the development of analysis, statistics, OLSP and decision support programs on the other hand. In the field of data mining applications, editors can outsource WEB, and can also improve the extraction of a series of text matrix templates, databases, techniques and databases for different fields and different processes. Based on the isolation, identification, and integration rules of the data itself, aggregation algorithms are continuously added and deepened. Therefore, the independent data mining of artificial intelligence is more convenient for data use and analysis in scientific research groups or fields [8, 9].

2.3 Algorithm Selection K-Means algorithm is a distance-based cluster mining algorithm. It uses distance as the evaluation criterion of similarity, and calculates the similarity according to the distance and mean of objects in each cluster. The smaller the distance, the greater the similarity. The ultimate goal of the K-means algorithm is to obtain the clustering result with the highest similarity within the cluster and the lowest similarity between the clusters. Commonly used distance measurement methods include: Euclidean distance, Manhattan distance and Minkowski distance. All three are used to measure inter-individual differences. The Euclidean distance measurement will be affected by the different unit scales of the index, so it is generally necessary to standardize first, and the larger the distance, the greater the difference between individuals. The other two are similar to the Euclidean distance, the larger the distance, the greater the difference between individuals. The standard Euclidean distance calculation formula is as follows [10, 11]: (

D xi , x j

)

┌ |∑ | r ( ( ))2 aq (xi ) − aq x j =√

(1)

q=1

Among them, r represents the number of all attributes of an instance x, a i (x) represents the ith attribute value of the instance x, and D(x i, x j) represents the distance between the instances x i and x j. Manhattan distance and Minkowski distance are defined as follows: Manhattan Distance: d(X, Y ) = |x1 − y1 | + |x2 − y2 | + · · · + |xn − yn |

(2)

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Minkowski distance: d(X, Y ) =

√ p

|x1 − y1 | p + |x2 − y2 | p + · · · + |xn − yn | p

(3)

In this paper, we use Euclidean distance as the criterion for determining the similarity of clusters.

2.4 Privacy-Preserving Distributed K-Means Clustering Mining Algorithm The distributed environment has the characteristics of multi-party participation and multi-party communication. Cooperative computation and sharing of intermediate results are weak links in privacy protection. Based on homomorphic encryption technology and RSA public key encryption technology, this chapter proposes this distributed privacy-preserving data mining algorithm. The basic idea is [12]: (1) The local site S i (=1,…,n, n ≥ 3) calculates the cluster data of the local site, and then sends the calculation result to the central site after homomorphic encryption; (2) The central site calculates the results of the local sites, and then sends them to the local sites for decryption to obtain the calculation results and send them back. (3) The central station judges whether to stop the iteration according to the algorithm convergence conditions. If the iteration is stopped, output the clustering result and complete the clustering process; otherwise, continue to iterate until the convergence condition is met.

3 Application of Artificial Intelligence Technology in Distributed Privacy-Preserving Clustering Mining Algorithm Research Design Experiment 3.1 Improvement of Distributed Clustering Algorithm Protecting Privacy In order to test the performance of PPDK-means and traditional K-means algorithm, the following experiments are designed in this paper. The experimental data comes from the UCI machine learning database in the United States. Includes Adult dataset, Breast Cancer dataset, and Plants dataset.

268 Table 1 Comparison of execution efficiency of two clustering data mining algorithms

J. Zhang Adult

Breast cancer

Plants

K-means

14,276

347

7672

PPDK-means

16,127

392

7913

3.2 Experimental Process This paper mainly conducts experimental analysis on the execution efficiency of the distributed privacy-preserving clustering mining algorithm based on artificial intelligence technology and the clustering accuracy of the algorithm, and mainly conducts comparative experiments. To explore the performance of this algorithm.

4 Research and Experimental Analysis on the Application of Artificial Intelligence Technology in Distributed Privacy-Preserving Clustering Mining Algorithms 4.1 Execution Efficiency Since the efficiency of the algorithm is mainly measured by the time complexity and computational complexity, this paper compares the execution efficiency of the distributed K-means algorithm and the privacy-preserving PPDK-means algorithm by comparing the running time, the experimental results are shown in Table 1. From Fig. 1, the execution time of the privacy-preserving K-means algorithm is slightly longer than that of the standard k-means algorithm. The main reason is that the process of key transmission and encryption and decryption is added at the central site and the local site. However, since the increased operations are dominated by linear operations, the time consumption does not increase much. Among them, the Adult data set has a large number of objects and attributes, so the time consumption increases the most, and the Plants data set is second, but has more attributes, so the execution time of the two algorithms is still significantly different. Due to the smallest size of the dataset, the execution time is quite different from the other two datasets, and the difference in execution time after privacy-preserving-related processing is small.

4.2 Clustering Accuracy The three datasets, the experiments in this paper use 1/2 of the data as training samples and 1/2 of the data as test samples, the experimental results are shown in Table 2.

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18000 16000 14000

tlme

12000 10000 8000 6000 4000 2000 0 Adult

Breast Cancer

Plants

project K-means

PPDK-means

Fig. 1 Analysis of the execution efficiency of two different algorithms on different datasets

Table 2 Comparison of clustering accuracy of two clustering data mining algorithms

Adult

Breast cancer

Plants

K-means

81.2

84.6

90.3

PPDK-means

81.2

84.7

90.5

The experiment is shown in Fig. 2. In the context of horizontally split data, the accuracy of the privacy-preserving K-means clustering algorithm proposed in this paper is between 81 and 91%. For the Adult dataset with the most data samples, the accuracy rate of the K-means clustering algorithm that protects privacy is 81.2%, and the clustering accuracy rate of the Plants dataset with fewer samples but more dimensions is 84.6% and the Breast Cancer dataset with the smallest dimension has a clustering accuracy of 90.3%. It can be seen that the smaller the dataset is, the higher the clustering accuracy of the privacy-preserving K-means algorithm is. Since the encryption and decryption processes all use linear operations, compared with the standard distributed K-means clustering algorithm, the privacy-preserving Kmeans algorithm has basically the same clustering accuracy. Therefore, the privacypreserving K-means clustering algorithm proposed in this paper is effective.

5 Conclusions The development and progress of information technology has made more and more data accumulated in the production and life of various industries. In order to facilitate the classification management and secondary utilization of data, data mining

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project

Plants

Breast Cancer

Adult

76

78

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percent PPDK-means

K-means

Fig. 2 Clustering accuracy analysis of two different algorithms on different datasets

technology came into being and gradually matured. However, as distributed becomes the main data storage mode, traditional data mining technology needs to be migrated to a distributed environment, and security issues also arise. At the same time, in the process of data mining, many data holders are unwilling to be disclosed. In order to solve these problems, mining algorithms for protecting private data are gradually being proposed. Privacy protection of cluster mining algorithm based on public external data, this paper is based on K-mean clustering algorithm, combined with shared area, using artificial intelligence technology to use set-based cryptographic algorithm. Mining, and consider safety issues during the restatement of interim results. As long as the cluster process is in ciphertext mode, public encryption ensures the security of the intermediate effects of computer passwords, and the algorithm can record normal cluster results on the basis of private security. Scientific analysis and experiments have proved this.

References 1. Chen Y, Chen Y (2021) Research on the application and influence of artificial intelligence technology. J Phys Conf Ser 1952(4):042007 2. Wang L, Xu X, Zhao X et al (2021) A randomized block policy gradient algorithm with differential privacy in content centric networks. Int J Distrib Sens Netw 17(12):1385–1394 3. Ren Q (2021) Application analysis of artificial intelligence technology in computer information security. J Phys Conf Ser 1744(4):042221 (7pp) 4. Wardoyo GK, Pratama HB, Sutopo et al (2021) Application of artificial intelligence in forecasting geothermal production. IOP Conf Ser Earth Environ Sci 732(1):012022 (10pp)

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5. Ma J (2021) Intelligent decision system of higher educational resource data under artificial intelligence technology. Int J Emerg Technol Learn (iJET) 16(5):130 6. Zeba G, Dabic M, Iak M et al (2021) Technology mining: artificial intelligence in manufacturing. Technol Forecast Soc Chang 2021(171):1–18 7. Mrzygłód B, Gumienny G, Wilk-Kołodziejczyk D et al (2019) Application of selected artificial intelligence methods in a system predicting the microstructure of compacted graphite iron. J Mater Eng Perform 28(7):3894–3904 8. Yang T, Zhang L, Kim T et al (2021) A large-scale comparison of artificial intelligence and data mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region. J Hydrol 602(6):126723 9. Nguyen L, Vo B, Le NT et al (2020) Fast and scalable algorithms for mining subgraphs in a single large graph. Eng Appl Artif Intell 90(Apr):103539.1–103539.12 10. Asim M, Ahmad S, Shuaib M et al (2020) Adaptation of artificial intelligence in big data mining and its impact: a study. Solid State Technol 63(5):2322 11. Figueiredo E, Macedo M, Siqueira HV et al (2019) Swarm intelligence for clustering—a systematic review with new perspectives on data mining. Eng Appl Artif Intell 82(Jun):313–329 12. Mirza AH (2019) Application of Naive Bayes classifier algorithm in determining new student admission promotion strategies. J Inf Syst Inform 1(1):14–28

Simulation of a Sports Body Index Monitoring System Based on Internet of Things Technology Haoran Gong and Yunhu Si

Abstract The Internet of Things technology is developing rapidly and has become a new norm for modern people to realize the information management of daily life. The Internet data fusion used in wireless medical and health services is also increasing day by day. The main purpose of this paper is to use the Internet of Things technology to carry out research on the simulation of sports body index monitoring system. Through the design and research of the whole system, this paper determines the functions to be possessed by the system and the whole scheme of the monitoring system according to the goals to be realized. The overall performance of the health auxiliary monitoring system was tested. Experiments show that the blood oxygen saturation measured by the system is compared with the blood oxygen saturation measured by medical instruments, and it can be seen that the maximum error of the blood oxygen saturation detection data of this system does not exceed 1%, which meets the requirements of the system. Keywords IoT technology · Sports fitness · Body index monitoring · System simulation

1 Introduction With the further development of science and technology and the prosperity of people’s life, people pay more attention to the health of the body, and have higher requirements for the health monitoring system. The future development will tend to be more miniaturized, intelligent, easy to operate, and low-cost. With the further development of the

H. Gong · Y. Si (B) College of Physical Education and Health, Linyi University, Linyi 276000, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_30

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smart wearable device market, smart wearable devices and a variety of advanced technologies have further cooperation, wearable devices will have lower energy consumption, realize more functions, in terms of human–computer interaction. There will also be further research and development [1, 2]. In related research, Elkhail et al. proposed a system and method for sensor-based monitoring, including computer systems, smart nodes, and RFID reader devices [3]. The system collects data to measure the health of multiple people during an event. Mehmood et al. mentioned that huge advances in embedded systems, miniaturization, and wireless technology have made Wireless Body Area Networks (WBANs) overwhelming applications in e-healthcare, entertainment, sports/gaming training, etc. [4]. Due to the importance of services associated with WBAN applications, they must be highly reliable and fault-tolerant, especially in healthcare monitoring applications that require continuous monitoring of vital patient information for diagnosis. People are more concerned about the health of their bodies, and they are eager to have more convenient and efficient health monitoring equipment to monitor their health anytime, anywhere [5, 6]. This paper uses the Internet of Things technology to carry out research on the simulation of the monitoring system of sports body indicators. Through the overall design and research of the auxiliary health monitoring system, the functions to be possessed by the system and the whole scheme of the auxiliary health monitoring system are determined according to the goals to be achieved. According to the requirements of the system, it is determined to measure the physiological parameters such as body temperature and heart rate. After that, the corresponding sensor and microcontroller are selected to convert the physiological data collected by the sensor. Developed and designed the host computer program of the auxiliary health monitoring system, and tested the overall performance of the auxiliary health monitoring system.

2 Design Research 2.1 The Overall Process of the System The content of this research and design is a health auxiliary monitoring system, which is mainly used for real-time monitoring of family and individual health information in daily life, and real-time feedback [7, 8]. The detailed steps (collection, conversion, storage, judgment, transfer, and display) are shown in Fig. 1. The human body information measured by this system mainly includes body temperature, heart rate, blood oxygen saturation and sweat ion concentration. When measuring information such as body temperature, heart rate, blood oxygen saturation, and sweat ion concentration, select the temperature, heart rate, blood oxygen sensor, and sweat ion concentration measurement modules. Human body information is detected and collected by sensors such as body temperature, heart rate, and blood

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Fig. 1 Structure flow of the health auxiliary detection system

oxygen, and AD conversion is performed by Arduino hardware. The collected data can be displayed by LabVIEW. Enter your personal information (including landline, notes, etc.) on the host computer software LabVIEW, and the information is stored in the Access database synchronously, and then the data is judged in Java to determine the current user’s status. Whether the physiological information is in a normal state. Then, the result determined by Java is stored in the Access database, and the state of the human body can be fed back in real time [9, 10].

2.2 Overall Design of System Hardware The health monitoring system mainly includes two parts: software and hardware, so as to realize functions such as collection, display and storage of human physiological signals. The overall structure of the health monitoring system hardware in this paper is mainly composed of physical health information acquisition module, wireless transmission module, power supply module, main control center and other modules. The main functions of the hardware of the system in this paper are the collection of physiological parameter information, the data conversion of information, the wireless transmission of information, and the power supply of the power module to the entire system [11, 12]. The functional requirements of the entire hardware system are mainly divided into the following aspects: (1) The hardware system is simple in structure, small in size, light and easy to carry; (2) The overall acquisition and transmission process of the hardware system has high accuracy, sensitivity and stability; (3) The power supply module of the hardware system has stable power supply and strong battery life.

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2.3 Cardiovascular Dynamics Parameters (1) Reflected wave enhancement index and ΔTDVP The reflected wave enhancement index (AIr ) is defined as the ratio of radial artery reflected wave to systolic blood pressure, and is expressed as: AIr =

pi − pd × 100% ps − pd

(1)

where ps is the systolic blood pressure; pd is the diastolic blood pressure; pi is the reflected wave point. The strength of the elastic function of the arterial system can be evaluated by calculating the size of AIr . (2) Pulse Wave Velocity In the arterial system, it is calculated according to the distance traveled by the pulse wave during the delay time. The calculation formula is: PW V =

L PT T

(2)

where L is the pulse wave travel distance, and PTT is the pulse wave travel time.

3 Experimental Research 3.1 Design and Use of Database The data sources of this system database are mainly divided into four types: physiological parameter data collected by sensors, personal information data, time information and human health status after multi-condition judgment. (1) The physiological parameter data collected by the sensor include: body temperature, heart rate and blood oxygen saturation. (2) Personal information data represents the identification of personally identifiable information, including: name, gender, age, mobile phone, fixed line, remarks and other information, which are generally entered into the system by the collectors. (3) Time information includes: date, time and other information, which represent the date and time of the current acquisition, and the real-time time information is provided by the host computer software. (4) Health status information includes: normal, low temperature, high temperature, low blood oxygen, low heart rate, high heart rate and other health information. These information data can be stored in the database.

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When the data is stored in the database, it must go through a certain process. After the monitoring system is running, the data collected by the system, the results of judgment and other information are continuously stored in the database.

3.2 System Test (1) During the testing process of the entire system, problems should be discovered and solved as soon as possible to avoid subsequent more complicated problems. (2) Test the entire system to determine whether each part of the entire system can operate either individually or as a whole. (3) Ensure that the entire system can operate normally and can operate stably and lastingly. (4) Ensure that the collected information data is not missing, and that the information has not changed during transmission and storage. (5) Test whether the system can meet the user’s requirements, and find out the places that are not in line with the user’s requirements and then modify and improve it. (6) Whether the accuracy and stability of the sensor can meet the requirements.

4 Experimental Analysis 4.1 ECG Heart Rate Test Under the same conditions, the heart rate of a classmate in a quiet state was detected in real time, and 10 sets of data were selected. The measured heart rate values are shown in Table 1: P1 represents the heart rate value measured by the system sensor, and P0 represents the standard heart rate monitoring. The human heart rate value measured by the meter. It can be seen from Fig. 2 that the heart rate value measured by the sensor of the system and the measurement value of the medical heart rate measurement instrument have an error within 2% in a quiet state, which can meet the requirements of use.

4.2 Pulse Oximetry Test Under the same conditions, the blood oxygen saturation values of 2 students were detected. The detected values are shown in Table 2: S0 represents the human blood oxygen saturation measured by this system, and S1 represents the medical blood oxygen saturation measured by the instrument obtained blood oxygen saturation.

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Table 1 Human heart rate variation values

Serial number

Heart rate P0 beats/min

Heart rate P1 beats/min

Error

1

72

71

1%

2

73

71

2%

3

71

72

1%

4

72

72

0

5

73

73

0

6

72

73

1%

7

73

72

1%

8

74

73

1%

9

72

71

1%

10

73

71

2%

74.5

3%

74 73.5

2%

73 Time

72.5

2%

72 71.5

1%

71 70.5

1%

70 69.5

1

2

3

4

5 6 Number

Heart rate P0 beats/min

7

8

9

0%

10

Heart rate P1 beats/min

error

Fig. 2 Analysis of changes in human heart rate Table 2 Human blood oxygen saturation detection value

No. 3

1

2

Oxygen saturation S0

96%

95%

Oxygen saturation S1

98%

96%

Error

1%

1%

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Table 3 Conductivity detailed data sheet Serial number

1

2

3

4

5

6

7

8

9

Conductivity

123

123

123

126

123

123

120

120

120

It can be seen from Table 2 that the blood oxygen saturation measured by the system is compared with the blood oxygen saturation measured by the medical instrument.

4.3 Functional Test of Sweat Ion Concentration There are two methods of sweat ion monitoring, conductivity test method and color identification method. There are two display methods for the conductivity test method, one is to display through the computer; the other is to display the digital tube. A classmate was tested under the same conditions, and a set of data was selected from the measured data. The specific data are shown in Table 3. As can be seen from Fig. 3, the conductivity of the human skin surface tends to be stable in a quiet state, and when there is a certain mood change, the conductivity will change. The current state of the human body can be known through the change of the value. 127 126 125 Conductivity

124 123 122 121 120 119 118 117

Conductivity 1

2

3

4

Fig. 3 Conductivity detailed data analysis diagram

5

6

7

8

9

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5 Conclusions With the further improvement of people’s material life and spiritual level in modern society, more and more people are in a sub-healthy or even unhealthy state. Therefore, medical and health monitoring service information has received more and more attention in various aspects such as real-time, flexibility, and intelligence. In response to the above problems, a human health monitoring system based on multi-source information fusion came into being. This is very practical for some clinicians and patients. Acknowledgements Fund Project: (1) Innovation and Entrepreneurship Training Program of 2022 for College Students: Analysis on the Treatment of College Students with Exercise-induced Cardiac Arrest and the Improvement of First Aid Support Strategy from the Perspective of Sports Medicine Integration; (2) Fund number: Key laboratory of Emergency and trauma (Hainan medical university), ministry of education (Grant.KLET-202006).

References 1. Pustokhina IV, Pustokhin DA, Gupta D et al (2020) An effective training scheme for deep neural network in edge computing enabled internet of medical things (IoMT) systems. IEEE Access (99):1–1 2. Razzaq MA (2020) Smart campus system using internet of things: simulation and assessment of vertical scalability. Indian J Sci Technol 13(28):2902–2910 3. Elkhail AA, Baroudi U (2021) Internet of things for healthcare monitoring applications based on RFID clustering scheme. Wireless Netw 27(1):747–763 4. Mehmood G, Khan MZ, Abbas S et al (2020) An energy-efficient and cooperative fault-tolerant communication approach for wireless body area network. IEEE Access 99:1–1 5. Junior A, Munoz R, Quezada A et al (2021) Internet of water things: a remote raw water monitoring and control system. IEEE Access 9(99):35790–35800 6. Upendra K, Tiwari, Mittal S et al (2020) Simulation of parking system using internet of things. Xi’an Jianzhu Keji Daxue Xuebao/J Xi’an Univ Archit Technol 12(4):3024–3029 7. Balogun ST, Okon KO, Akanmu AO et al (2021) Safety monitoring of herbal medicines in Nigeria: worrying state of pharmacovigilance system based on WHO core pharmacovigilance indicators. J Herbmed Pharmacol 10(2):202–208 8. Hanak T, Hrstka L, Tuscher M et al (2020) Estimation of sport facilities by means of technicaleconomic indicator. Open Eng 10(1):477–483 9. Prikhodko V, Tomenko O, Matrosov S et al (2021) Strategic issues of public governance in sports development in Ukraine. Sport Sci Human Health 5(1):73–83 10. Podrihalo OO, Podrigalo LV, Kiprych SV et al (2021) The comparative analysis of morphological and functional indicators of arm wrestling and street workout athletes. Pedagogy Phys Culture Sports 25(3):188–193 11. Galluzzi R, Feraco S, Zenerino EC et al (2020) Fatigue monitoring of climbing ropes: Proc Inst Mech Eng Part P: J Sports Eng Technol 234(4):328–336 12. Buchheit M, Simpson BM, Lacome M (2020) Monitoring cardiorespiratory fitness in professional soccer players: is it worth the prick? Int J Sports Physiol Perform 15(10):1–5

Processing of Natural Language Information Hiding Algorithm Based on Machine Learning Zhenpeng Yang

Abstract With the rapid improvement of the Internet, the use of the Internet and daily business activities have become part of people’s lives. An open network environment means that our communications and information may be stolen by criminals, and people’s common demand for information security is growing. Creating a more secure network environment can protect our communication privacy. The purpose of this paper is to use the natural language information hiding algorithm based on Markov chain based on machine learning. Through this algorithm, the text information is compared and analyzed, and higher-level language models will obtain better detection results. By calculating the language model of encrypted text, and using the language model of encrypted text to calculate the degree of confusion between encrypted text and ordinary text, the results show that the larger the number, the higher the accuracy of the calculation result. Keywords Machine learning · Natural language information · Information hiding · N-gram model

1 Introduction This is an era of explosion of science and technology and information, people can more easily obtain all kinds of information, but at the same time, information security is also faced with huge challenges. Through information hiding technology, confidential information can be hidden in ordinary carriers to achieve anonymous transmission of information [1]. Information is the most common transmission carrier in people’s daily life, but due to its low redundancy, its improvement is relatively slow compared with carriers such as images and videos. The traditional text information hiding is Z. Yang (B) Department of Literature and Law, Nanjing University of Finance and Economics Hongshan College, Zhenjiang 212000, Jiangsu, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_31

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realized by modifying the carrier, which has problems such as poor robustness and difficulty in carrier search. Based on artificial intelligence algorithms and machine learning technology, and then learning and exploring in a large amount of data, obtaining new information beyond the data, and integrating this information into various shapes and relationships, we get the law is not was discovered. Mokhnacheva proposes a terminological approach to understand the dynamics of scientific subject improvement based on an analysis of the joint use of key terms. Presents the results of studying the dynamics of key terms in the fields of immunology and microbiology, which underlie the improvement of the SciVal topic-by-category distribution algorithm: topics that are actively improving; topics that are in a steady state; lose relevance or water down topics. Dynamic research results for frequently used keywords in related topics are based on information from relevant publications, i.e. are presented through the most frequently used article citations. It was found that the usage dynamics of certain key terms decreased in the base topic and shifted to related topics [2]. Vangara’s research in natural language processing has reached the point of implementing different machine learning algorithms for better results in text classification. Many previous research work in this area is presented. It discusses the different techniques used for text classification so far and summarizes the advantages and disadvantages of the different techniques. It has been observed that all algorithms work well, but some techniques outperform others [3]. Data hidden in valuable information is extracted from it, and most algorithms can be improved by careful selection of features that play a key role in algorithmic learning. This paper studies the natural language information hiding algorithm, clarifies the classification of information hiding, the emphasis of information hiding algorithm, the concept of machine learning, and uses the natural language information hiding algorithm based on Markov chain to compare the amount of text information and its efficiency. The results show that the ciphertext recognition algorithm based on the Markov chain algorithm can generate high accuracy of ciphertext recognition, and can even achieve accurate classification.

2 Research on Algorithm Processing of Natural Language Information Hiding Based on Machine Learning 2.1 Research on Natural Language Information Hiding Algorithms Domestic and foreign universities and research institutes have carried out some research on information hiding technology. In addition, some large foreign companies such as IBM have also actively participated in the research of information withholding technology. Information hiding, also known as steganography, originated from the ancient Greek “steganography”. Tibetan head poetry, as a relatively simple

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information security technique, still entertains by improving its linguistic charm [4]. Information hiding is used from ancient times. Although today’s ancient information hiding technology belongs to “little tricks”, in today’s highly improved modern Internet technology, information hiding has evolved into a new topic [5]. At present, it is generally believed that hiding information is a sign of secret communication, and the method of image dimensionality reduction is used to hide information in the image. Information hiding has entered people’s field of vision, received more and more attention, and gradually became an important research topic in the field of information security.

2.2 Classification of Information Hiding There are many different types of information hiding in video, audio, image and text information [6]. Among them, text information is the basic way to obtain information, whether it is daily chat, reading or file transfer, text information is widely existing information in the world. The most commonly used scene-based applications can be divided into two categories, digital watermarking techniques and steganography [7]. The basic idea of digital watermarking technology is to embed secure watermark information in host data (including image, text, audio, video, software, etc.). The watermark information can identify the ownership of the work, the unique serial number of the work, the date of production of the work, etc. It identifies the main data of the work, and the embedded watermark information cannot cause major changes to the host data. This watermark can be partially or fully extracted in the future, thus protecting the legitimate rights and interests of the data owner. Digital watermarking can be divided into robust digital watermarking and fragile digital watermarking [8]. Robust digital watermarking is mainly used for the identification of copyright information. The robust digital watermarking technology is used to embed the copyright information into the host data to protect the copyright of the data owner. At the same time, the watermark used for copyright protection must be robust and secure, and be able to resist some malicious attacks. Fragile digital watermarks are mainly used to protect the security of digital products. The fragile digital watermark information is invisible when embedded in the content data. When the content changes, the watermark information will change accordingly. By judging whether the digital watermark has changed, it is judged if the original data has been tampered with computer technology and Internet technology., then the rapid improvement of digital media has become the main carrier of today’s information, digital watermarking technology plays an important role in protecting human copyright, broadcasting management, transaction tracking and so on. At the same time, digital watermarking is also one of the hot issues in the field of information hiding. Stegography is often used for covert communication. While digital watermarking technology is for different purposes of protecting the same host data, the key point of steganography is information hiding, and the method of hiding visible plaintext information by embedding plaintext information is described in more detail. The

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related algorithms usually assume that the third party does not know the existence of the secret channel, so as to achieve the purpose of secret communication between the two parties. The study of steganalysis meets people’s needs in confidential communication.

2.3 Emphasis of Information Hiding Algorithms Information hiding algorithms based on different backgrounds have different requirements for these features. For covert communication, concealment is often the focus of attention [9]. At the same time, stealth is also an important feature, because covert communication often requires high-speed transmission, and real-time transmission in war conditions, and high-capacity can transmit more secret information. Correspondingly, the requirements for robustness and security can be reduced, especially robustness. Since covert communication is generally applied to non-interference channels, the robustness requirements are the lowest [10]. Robustness is the most important part of digital watermarking, and only robust watermarking can effectively protect the rights of copyright owners. In order to ensure the robustness, the algorithm often needs to make full use of the redundant space of the carrier and make further modifications to the carrier, which easily leads to the decline of the concealment of the watermark. In fragile watermarks, tampering sensitivity is the most important feature, which requires the lower the robustness of the watermark, the better. At this point, the requirements for concealment capability and security can be reduced. Different application fields such as covert communication, digital watermarking and fragile watermarking have different requirements for the characteristics of information hiding algorithms. Compare the performance requirements of covert communication, digital watermarking, and fragile watermarking algorithms, as shown in Table 1. Table 1 Compare the performance requirements of covert communication, digital watermark and fragile watermark algorithm Hidden communication

Digital watermarking

Frailty watermark

Robustness

Low

High

Very low

Crypticity

High

Middle

Middle

Embedding

High

Low

Low

Invisibility

High

High

High

Safety

Middle

High

Low

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2.4 Machine Learning Transfer learning is a machine learning method that achieves better classification results in the target domain by utilizing knowledge from other domains. Learning transfer can be divided into different modes according to the content to be transferred. Transfer learning algorithms are classified into four categories according to the content to be transferred, namely relational knowledge-based transfer, parameterbased transfer, feature representation-based transfer, and instance-based transfer [11]. Relation-based knowledge transfer assumes that distributed datasets are not independent, and that one dataset affects the other. Their previous interrelationships due to different domains, the source domain cannot be directly used for the target domain, but some of these samples may be reusable. By increasing the proportion of these samples, sample-based learning transfer can be achieved. The advantage of transfer learning is that when faced with a new task, the existing knowledge can be reused, and the knowledge can be “inferred by analogy” without having to start learning again. Furthermore, the cost of restarting learning can be enormous or impossible.

3 Investigation and Research on Algorithm Processing of Natural Language Information Hiding Based on Machine Learning 3.1 Natural Language Information Hiding Algorithm Based on Markov Chain The natural language information hiding algorithm based on Markov chain is an information hiding algorithm that generates ciphertext based on the statistical characteristics of natural language. The traditional natural language information hiding algorithm first needs a text carrier, and the carrier hides information through modification (synonymous replacement, shape modification, etc.). The disadvantage of this algorithm is that in the case of known carrier text, it is easy to find the ciphertext containing hidden information, and then extract the hidden information from the ciphertext. An information hiding algorithm based on the statistical characteristics of natural language, generates ciphertext according to the statistical characteristics of natural language, does not require a text carrier, and can discover hidden information through text comparison. Encrypted carrier text.

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3.2 N-Gram Model In normal text prediction, it is impossible to use all the text history to predict a certain word. Instead, it is assumed that the current word is only related to the previous word, that is, the order Markov model, also called the N-gram model. The larger the N-gram model, the more accurate the prediction will be. P(w1 . . . wn ) =

C(w1 . . . wn ) N

P(wn |w1 . . . wn−1 ) =

C(w1 . . . wn ) C(w1 . . . wn−1 )

(1) (2)

P(w1 . . . wn ) = C(w1N...wn ) indicates the frequency of (w1 … wn ) appearing, P(wn |w1 . . . wn−1 ) indicates the probability of appearing after (w1 … wn−1 ), and indicates the number of all N-tuples.

4 Analysis and Research of Natural Language Information Hiding Algorithm Processing Based on Machine Learning 4.1 Experimental Results Using the above algorithm, we get a copy of the normal text with the total size in bytes, and use a Markov chain-based algorithm to generate a total of text copies. By detecting the text size, the detection accuracy and size accuracy respectively reach the sum of the detection results, and the text size accuracy can reach the above. However, when the text is smaller than bytes, the classification accuracy drops rapidly due to the statistical characteristics itself. Only when the text reaches a certain length, the statistical data can accurately reflect the characteristics of the text. It can be seen from the table and table that the ciphertext recognition accuracy generated by the ciphertext recognition algorithm based on the Markov chain algorithm is high, and even accurate classification can be achieved. The experimental results for text sizes of 2 and 5 K are shown in Figs. 1 and 2. Figures 1 and 2 show the size and values for different types of text parts, respectively. The text generation algorithm based on Markov chain considers the correlation between before and after words, so the value is relatively small. It is a relatively successful information hiding algorithm with a minimum value. The results show that the smaller the text, the more obvious the fluctuation value and the lower the detection accuracy. This paper proposes a text detection algorithm based on hidden Markov chains. The experimental results show that the algorithm can detect the success rate of the

Processing of Natural Language Information Hiding Algorithm Based …

Number of text segments

287

Miscalculation of paragraph number

500

Value

300

300 200

31

22 Normal text

Markov chain

8

0 NICETEXT

TEXTO

Fig. 1 List of experimental results with a text size of 5 K

Number of text segments

Miscalculation of paragraph number

500

300

Value

300

200

25 Normal text

45

Markov chain

0

10

NICETEXT

TEXTO

Algorithm Fig. 2 List of experimental results with a text size of 2 K

above text, and the detected text size and text success rate reach 98.65% respectively. Due to the limitation of computer space and performance, we adopt an N-gram model in our experiments. As computer performance and storage space increase, we can consider using a higher-level language model, which we believe will achieve better detection results. Also, if we know a lot of encrypted text, we can calculate the

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language model of the encrypted text, and use the language model of encrypted text to calculate the degree of confusion between the encrypted text and the normal text, then the classification accuracy will be higher. Experimental results show that the above classification accuracy can be achieved even when a single sentence is constructed using a markup language model.

5 Conclusions Information hiding is an important direction in the field of information security. It can hide secret information in images, videos and text files. Among various forms of carriers, natural language information is the main way for people to communicate information. Traditional information hiding methods hide secret information in the form of text carriers, which cannot resist re-entry attacks. To overcome this deficiency, most researchers have proposed a natural language information hiding technique that can semantically hide secret information in text. Through the analysis of natural language information hiding technology, it is found that there are some problems in the information hiding ability of existing algorithms. The natural language information hiding algorithm based on machine learning is a general algorithm that can be applied in various fields.

References 1. Richards TB, Croner CM, Rushton G et al (2020) Geographic information systems. J Am Board Family Med 23(4):109–120 2. Mokhnacheva YV, Tsvetkova VA (2021) Development of research topics based on the terminological approach (for example, immunology and microbiology according to Scopus–SciVal Data). Sci Tech Inf Process 48(2):139–145 3. Vangara V, Vangara SP (2020) A survey on natural language processing in context with machine learning. Int J Anal Exp Modal Anal 12(1):1390–1395 4. Sillito AM, Jones HE (2018) Corticothalamic interactions in the transfer of visual information. Phil Trans Royal Soc London. Ser B, Biol Sci 357(1428):1739–1752 5. Fraser SfH (2018) An information system and medical record to support HIV treatment in rural Haiti. BMJ (Clin Res Ed) 329(7475):1142–1146 6. Van BFHD, Gultyaev AP, Pleij C (2018) PseudoBase: structural information on RNA pseudoknots. Nucleic Acids Res 29(1):194–195 7. Rowley J (2018) Strategic information systems planning. Inf Serv Use 15(1):55–64 8. Aea A, Ml A, Rc A (2019) A self controlled simulated annealing algorithm using hidden Markov model state classification. Proc Comput Sci 148(3):512–521 9. Kim EC, Oh KJ (2019) Asset allocation strategy using hidden Markov model and genetic algorithm. J Korean Data Inf Sci Soc 30(1):33–44

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10. Fapohunda KO, Numan PE, Zubair S et al (2018) Bat-inspired hidden node detection algorithm and collision minimization (BIHD-CM) scheme for IEEE802.11AH. Int J Comput Netw Technol 06(2):112–118 11. Eniola V, Suriwong T, Sirisamphanwong C et al (2019) Hour-ahead forecasting of photovoltaic power output based on hidden Markov model and genetic algorithm. Int J Renew Energy Res 9(2):933–943

Color Model-Based Control Algorithm for 3D Printing of FDM Eco-friendly Art Products Diwen Hu and Hao Sun

Abstract With the continuous development of the society, the industrial level is also rising rapidly, and the environmental problems are becoming more and more prominent. Industrial production produces a large amount of waste and toxic and harmful gases that pose a serious threat to human health, and the wastewater, waste gas and solid waste generated during industrial production seriously affect the ecological environment. Therefore, environmentally friendly FDM (green) 3D printing technology is of great importance, which can effectively solve these pollution problems and reduce the waste of resources, improve product quality and efficiency. This paper designs a FDM-type 3D printing temperature control method based on global search PID algorithm, which determines the corresponding temperature range by selecting different colors and comparing it with the environmental control mode to obtain the best solution. The method uses non-toxic and environmentally friendly PLA material and simulates its characteristics to obtain the environmental temperature change pattern, thus providing a reference for environmental friendly FDM product printing control. In addition, FPCA was used for the simulation control test. The results show that this method has better robustness and control accuracy than the traditional PID controller, and it takes into account the aesthetics and environmental protection of FDM-type 3D printing. Keywords 3D Printing technology · FDM environmental art · Global search PID algorithm

1 Introduction With the development and maturity of digital technology, 3D printing technology began to flourish and continue to be promoted. In today’s digital era, people are more D. Hu (B) · H. Sun Hubei University of Technology, Wuhan 430068, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_32

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concerned about environmental protection and energy saving. In recent years, with the continuous improvement of social and economic level, increasingly serious environmental pollution and ecological balance problems, countries all over the world are actively exploring the road of energy saving and green development [1]. Environmental protection products are also getting more and more attention, and the current market is commonly based on color models for 3D printing control algorithms to achieve the work [2]. The system is to study the color as the object to build a simulation environment between the color change of objects and light intensity and other parameters of the relationship, and through the mathematical method to establish its combination with the graphic image degree function to calculate the corresponding data after the results, so as to provide users with more accurate and reasonable reference advice and program design basis [3]. The application of 3D printing technology is in line with the purpose of improving energy saving and reducing consumption with innovation and technology, and it provides an excellent technical platform for changing people’s artistic concept and artistic creativity realization. Color model-based environmental protection products occupy an important position in 3D printing technology, and its main function is based on green color. It achieves the goal of energy saving and emission reduction by analyzing factors such as material characteristics, structural features and color relationships, so as to achieve innovation in product modeling and structural design. A new 3D screen printing process flow system was designed and produced using the traditional CFC (Color DM) control method. The device is able to make reasonable choices between different color patterns according to different raw materials and patterns, and realize 3D printing technology for environmentally friendly products by optimizing the synthesis process parameters [4]. At this stage, FDM-based 3D printing technology cannot meet the appearance requirements of art-related products in terms of accuracy and surface quality. The color and contour values of FDM-based environmental products cannot be calculated directly using the model. Therefore, this paper designs a PID control algorithm based on global search to realize the nozzle temperature control of FDM-type 3D printing, so as to realize the color control of eco-friendly products. The method uses the PID algorithm to make a reasonable prediction of the nozzle temperature, and the parameter setting method is derived through the MATLAB software simulation experiments [5]. The data results obtained from the simulation experiments on the color model are more consistent with the theoretical values, which can prove that the FDM-based environmentally friendly products have good control effects in color design.

2 Color Printing Control Based on Color Model Color model-based 3D printing control method for green products is a new digital image processing technology, which mainly uses the combination of color model and graphics to achieve the effective extraction of information contained in the image of

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the object. It can realize the conversion between digital text and graphic information, use the computer to simulate the texture characteristics of the object surface, use color pixels to represent the brightest and gray value change law in the original data, use the color model to describe the texture characteristics of the object surface, and then use mathematical methods to realize the analysis of information such as pixel point value and gray value change law in the original image, so as to derive the color of the texture model of the object surface information [6]. The 3D printing control algorithm is accomplished by combining the color pixel points with the gray value change pattern and using mathematical methods to predict the data contained in the entire image. The color model used in this paper includes RGB color model and HSI color model. Color modeling refers to the process based on quantitative description of color, which quantifies and analyzes objective things to obtain information such as color and quantity of objects in the external environment, and converts them into color. Color modeling and shape-based products are the two most important applications in the market today. It is a model based on the principles of color science, which is mainly based on the principles of color science, and is designed through the existence of a certain degree of connection and regularity between colors. The use of color modeling technology in industrial production to analyze and modify the environment can make products with better performance and use effect, so as to obtain higher economic benefits. RGB color model can convert the color information in the environment into color, so as to obtain patterns with certain rules and characteristics. The RGB color model is shown in Fig. 1. Fig. 1 RGB color model

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HSI color modeling is a 3D printing control technology that uses the warm and cold changes in color to solve the environmental impact problem, and it can use different colors to control the material in the 3D printing process, so that the product has a good color matching effect. Through the difference between objects with different hues and brightness, the color required for the target object can be accurately determined, and then the final target object can be matched according to its hue to obtain the final target object. The HSI color model includes three main parts in the 3D printing process: color pre-processing, material property determination, and image analysis. As one of the most important expressive factors for visual senses, color has a direct influence on the surface characteristics of objects; while graphics can express various shape information and texture features in different forms, which is also a typical complex three-dimensional digital data acquisition technology [7]. The system uses computers to simulate the human visual perception environment and perform real-time control and adjustment, so as to realize the human–computer interaction function and other related fields that need to be used in a variety of application scenarios. HSI color model is shown in Fig. 2. The process of converting RGB color model to HSI chromaticity can visually display the object color, and visually can judge the environment to people based on the color. The process of converting RGB color model to HSI chromaticity is to compare between the color image and the object itself properties, so as to get different colors corresponding to the colored pixel points, and then realize the visual perception of the target object. RGB color model the conversion to the HSI color model is shown in Eq. (1). Fig. 2 HSI color model

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Fig. 3 3D printing technology

⎧ √ (R−G)+(R−B) ⎪ ⎨ H = arccos 2 (R−G)2 +(R−G)(R−B) , H ∈ [0, 2π ] 3 S = 1 − (R+G+B) [min(R, G, B)], S ∈ [0, 1] ⎪ ⎩ R+G+B I = , I ∈ [0, 255] 3

(1)

3 Process and Material Selection of FDM Type 3D Printing 3.1 3D Printing Method The color of the object is formed in space by physical process, so we can use computer technology to establish and render the 3D solid model. 3D printing technology takes the computer 3D design model as the blueprint, which uses the rapid generation of 3D software, and realizes the interconversion between 3D solid model and entity under computer technology. In this paper, we take environmental protection art products as the research object, based on color modeling and motion simulation method for quantitative analysis of color [8]. Firstly, a simple conceptual model is established and variables, parameters and objective functions are determined, then the relative position relationship between each color block and the time interval generated by their mutual movement are defined according to the color and object shape, the system simulation experiment is realized by MATLAB programming to get the optimal control scheme, and finally the image results are generated by Matlab software. The method of existing 3D printing technology is shown in Fig. 3.

3.2 Process and Characteristics Analysis Environmentally friendly color products 3D printing system is based on green industry, it is a new type of intelligent product manufactured by using metal materials

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processing. It can not only realize the effect of energy saving and resource saving, but also it has high technical content, it is an industry with high market competitiveness and occupies a large share in the market. Advanced design methods and modern digital control theory are used in the process to design 3D printing control technology, and advanced digital models are used in the product modeling and 3D modeling with computer software. The comparative analysis of the processes used in different 3D printing technologies is shown in Table 1. The FDM process involves 2 types of printing materials, and their characteristics are analyzed as shown in Table 2. PLA plastic is widely used in industrial production because of its biodegradable material and environmental advantages. The environmentally friendly FDM plastic has the characteristics of renewable, high corrosion resistance and fatigue resistance, and its use value has exceeded that of traditional metal materials, and it is widely used in the environmental protection field. The development of eco-friendly printing technology is an indispensable part of industrial production in the world today, and better gloss can bring better visual appearance to the printed products. Therefore, this paper proposes a 3D printing scheme to select PLA plastic-based FDM as a specific realization process, and conducts a study related to the color selection, material selection and printing process for the color model. By analyzing data such as chromaticity values and shape and size parameters of FDM products, the main factors affecting their color matching effects are derived. Table 1 Comparative analysis of 3D printing technology processes Process method

Main material

Advantages

Disadvantages

FDM

ABS

High utilization rate Non-toxic material Cheap price

Rough surface Support structure required

PLA

Strong sense of three-dimensionality Not easily deformed

No flexibility

SLS

Plastic powder

High precision

Burning odor Rough surface

SLA

Photosensitive resin material

Smooth surface

Toxic material Expensive

Table 2 Analysis of 3D printing materials Material name

Advantages

Disadvantages

ABS plastic

Excellent mechanical properties and very good impact strength

Easy to print large area products with warping

Easy to remove support

Need to heat up to 80–120 °C

Biodegradable material for reel wire, environmentally friendly

Relatively low impact resistance

PLA plastic

High gloss of the printed product and Low strength in vertical direction no odor during printing

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As mentioned above, various 3D printing technologies based on FDM technology have environmental advantages such as non-toxic, odorless and clean process, and less consumables, which are widely used in various fields. However, many factors need to be taken into account in the production of environmentally friendly products, so its requirements for the processing process are high. The technical requirements of FDM are high, and its 3D printing process for environmentally friendly products needs to take into account the color model and processing process, as well as the inability to print complex structures. Therefore, the global optimized PID control method can be used to solve the problem of low accuracy of FDM molding process, so as to achieve better molding results [9]. The PID control method used is a typical color model with high accuracy and robustness, which can effectively solve the problems existing in traditional control methods. The PID control method is shown in Eq. (2). de(t) + KI u = K p e(t) + K D dt

T e(t)dt

(2)

0

The representation of the error signal e in the above equation is shown in Eq. (3), when the object is moved to the target area, the position of this error signal e in space. As time passes, the FDM color changes will appear more and more complex. e(t) = yd (t) − y(t) = Rd (t) − (x1 (t) − x2 (t))

(3)

The purpose of using PID controller is to control the heating temperature in the 3D printing post-processing unit more effectively, so as to obtain more environmentally friendly 3D printing. Color recognition technology is a new quantitative color analysis method that can be used in different fields such as industry, agriculture, etc. It can improve the appearance of printed models using a minimization cost function as shown in Eq. (4) to achieve the measurement of objects with different colors, and use the same material for better visualization during the printing process. 1 J= T

T  0

x¨

2

x¨max

dt

(4)

The global optimization algorithm performs the search operation in the desirable range space, and it can realize the accurate control of the product color. The global optimization algorithm is used to perform fast and efficient cropping in complex environment, which can obtain higher accuracy measurement data, where the population search method is shown in Eq. (5). P = 2G − R n+1

(5)

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4 3D Proposed Control Algorithm Specific Application Test Results In the traditional industrial product production process, environmental factors have a great impact on its process and quality. Environmentally friendly FDM (green), on the other hand, is aimed at achieving sustainable development, and it uses environmentally friendly technical means to achieve the environmental targets of industrial products. In the traditional FDM process, it is mainly through manual control and mechanical processing and other ways to influence the environmental factors. With social progress and development, people’s living standards and the gradual deterioration of ecological balance, a fast control algorithm based on color model is proposed for this problem, which can not only achieve the environmental indicators of industrial products, but also provide technical support for environmental protection by quantifying the environmental factors, and has high research value. In order to verify the performance of the scheme proposed in this paper, a specific 3D printing implementation was carried out. The color model method was used to analyze the color and shape, and different colors such as gray and black were used as auxiliary variables to control the impact on the material during 3D printing. The algorithm design ideas were revised by calculating the error generated between the time required and the actual effect under the two schemes, and a specific printing control algorithm analysis was conducted [10]. The control algorithm is implemented in the post-processing of the product after the manufacturing process, and the relevant parameters of the proposed optimized PID controller are shown in Table 3. The results of the control curve of the heating temperature in the 3D printing postprocessing device are shown in Fig. 4. It can be seen that the proposed optimized PID controller achieves more stable and accurate control, but also generates higher accuracy and error, so it is necessary to add color variables to the color model. By testing and analyzing the control program of the color model, it was found that the 3D printing system can be automatically controlled in different environments, so that the changes in the environment can be responded to in a timely manner. It can be controlled in real time for the purpose of improving productivity of environmental products, saving energy and reducing costs. However, since a large number of rejects may be generated during the process from production to sales, which affects the quality of products, it is crucial to realize green production by accurately predicting the rejects and finding problems and solving them quickly and timely in the control process. We need to recycle the scrap reasonably and use it as a physical object, Table 3 Relevant parameters

Parameters

Numerical value

Actuator parameters α

4.61 × 1013

Actuator parameter β

1

Actuator parameter γ

1.455 × 109

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Fig. 4 Control curve of heating temperature

which will help to improve the production efficiency of the product and can better achieve the purpose of environmental protection [11].

5 Conclusion In this paper, we analyzed the color model of environmental protection art products as the research object, and conducted simulation experiments under the analysis of the relationship between FDM-type environmental protection garments and color matching, the environmental and psychological impact of color and visual psychology and other factors. Firstly, PLA-FDM process technology with non-toxic materials was chosen, and the coordination between material color and material was studied by using the color relationship of color model. The control algorithm for 3D printing of FDM-like environmentally friendly garments was built on the PS-ST platform, and the pixel point values of each color obtained under different process parameters were simulated by MATLAB software. In this paper, a PID control method based on global random search strategy is designed to solve the surface roughness problem of FDM molding process, and the correctness and efficient performance of the control algorithm are verified through experiments. The specific simulation experimental results on FPGA show that the proposed control method has good stability and control accuracy, and it effectively improves the accuracy and surface quality of FDM-type 3D art product printing.

References 1. Avci E, Grammatikopoulou M, Yang GZ (2017) Laser-printing and 3D optical-control of untethered microrobots. Adv Opt Mater 03:111–114

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2. Schwartz JJ, Boydston AJ (2019) Multimaterial actinic spatial control 3D and 4D printing. Nat Commun 8:88–92 3. Cooreman B (2017) Global environmental protection through trade. In: Product or process— outlining the scope of trade law, vol 05, pp 19–52 4. Santos NP, Lobo V, Bernardino A (2020) Directional statistics for 3D model-based UAV tracking. IEEE Access 7:44–47 5. Liu XD, Wang MY, Sa JM (2020) Supervised-learning-based algorithm for color image compression. ETRI J 42(2):258–271 6. Peng P, Wang L (2019) 3DMRT: a computer package for 3D model-based seismic wave propagation. Seismol Res Lett 90(5):2039–2045 ´ aczka W, Pijanowski M (2018) Polymer composite 7. Bryll K, Piesowicz E, Szyma´nski P, Sl˛ manufacturing by FDM 3D printing technology. MATEC Web Conf 2018(2):88–91 8. Skawinski I, Goetzendorf-Grabowski T (2019) FDM 3D printing method utility assessment in small RC aircraft design. Aircraft Eng Aerosp Technol 865–872 9. Kontovourkis O, Tryfonos G (2020) Robotic 3D clay printing of prefabricated non-conventional wall components based on a parametric-integrated design. Autom Constr 110–114 10. Faure M, Li S (2020) Risk shifting in the context of 3D printing: an insurability perspective. Geneva Pap Risk Insur Issues Pract 4:45–48 11. Rajabi M, McConnell M, Cabral J et al (2021) Chitosan hydrogels in 3D printing for biomedical applications. Carbohyd Polym 260–263

Research on Ecological Environment Early Warning System of Power Transmission and Transformation Project Based on Multi-objective Programming Algorithm Ming Lv and Junda Tong Abstract With the rapid development of society and the operation of the power system, a large number of problems have emerged, which will have a negative impact on the lives of residents. Among them, transmission and substation stations as an important part have a large proportion of the load and ultra-high voltage transmission of large-capacity equipment with safety risks. This paper focuses on the research of transmission and substation project ecological environment early warning system based on multi-objective planning algorithm. This system adopts multi-objective planning algorithm to propose a set of intelligent models applicable to this field, which has the advantages of comprehensive multi-variable, real-time and strong target. This design can provide users with more accurate information data. It adopts adaptive weighted fusion algorithm to fuse multi-sensor information and transmit the fusion results to the cell phone terminal APP in the application service layer, so as to realize real-time monitoring of the ecological environment of transmission and substation projects. It provides real-time information support for decision makers by visualizing and querying ecological environment-related early warning information. Keywords Multi-objective planning algorithm · Transmission and substation engineering · Ecological and environmental early warning system

1 Introduction At present, the construction demand of China’s power transmission stations is increasing, and most of the hubs are built in mountainous areas, so more and more attention is paid to the stability of multi-geological ecological environment construction, but due to the diversity and complexity of the terrain and landscape, M. Lv · J. Tong (B) State Grid Liaoning Electric Power Company Limited Economic Research Institute, Shenyang 110065, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_33

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various factors may occur during the construction of power transmission projects, such as geological conditions, construction techniques and climate [1, 2]. Therefore, the ecological environment warning work of transmission and substation project is very important, in which the ecological environment warning system is another very important link, the system can bring more reliable, safe and more comfortable high for transmission and substation project, which also has a certain protective effect on the natural environment [3].

2 Early Warning System for Ecological Environment of Power Transmission and Transformation Projects 2.1 Overall Structure of Early Warning System With the continuous development of power transmission projects, the scale and quantity of power systems are gradually increasing, but due to its long construction period and large investment amount, it also makes the operation of power grids cause certain impact on ecological environment. In order to ensure that the power supply system can meet the maximum economic benefits as well as social interests, an ecological early warning system based on multi-objective planning theory needs to be established, which can achieve scientific and reasonable management of the power system by incorporating the power system operation into the whole power grid [4]. The overall structure of the ecological early warning system for power transmission and transformation projects proposed in this paper is shown in Fig. 1.

Fig. 1 Overall structure of ecological environment warning system for power transmission and transformation projects

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2.2 Mobile Alert APP Module Design In the whole system, the early warning APP is a very important function, which can provide real-time, comprehensive and timely and accurate information for users. The cell phone terminal APP software of this early warning system includes registration module, data transmission module, data storage module, data query module, early warning module and data visualization module. The registration module mainly includes user registration and user login, which is mainly used for registration, viewing history, browsing weather forecast and statistical analysis of data. Administrator login system can realize real-time monitoring of transmission and substation project operation status information, and it will display relevant results in the background according to the early warning situation [5, 6]. Data transmission module can realize multi-source data network transmission, it can transmit different data in the network in a unified way, so as to realize real-time monitoring of power grid operation status, voltage and current information, which provides the necessary basis for optimization and dispatching work, it also improves the stability and reliability of power grid operation. The data storage module can realize the storage of user registration information and real-time processing of data in transmission and substation projects, thus realizing dynamic update of power system operation status information, which not only improves the efficiency of power grid operation, but also reduces the cost of power supply enterprises. Data query module is the core module of the ecological environment early warning system of intelligent transmission and substation projects. The data query module can collect, analyze and process various data in real time and transmit them to the dispatching center [7]. The early warning module is mainly for early warning of the ecological environment, it will collect and analyze the data of the ecological environment, and then formulate corresponding countermeasures based on the relevant information, so as to optimize the ecological environment, which can improve the stability and sustainability of the ecosystem. The data visualization module is capable of monitoring multi-source data trends and cross data, and it uses Android Canvas, Paints and Gesture to create data graphs and recognize finger touch screen data [8]. The data visualization function structure is shown in Fig. 2.

3 Wireless Sensor Network Node Design 3.1 Network Node Hardware Structure When studying the structure of the network, the communication volume and information transmission rate between the nodes in the power supply system are taken into

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Fig. 2 Data visualization function structure

account, so a data communication infrastructure with high efficiency and practical value is chosen to realize the topology of the power network. The openness of the substation is very strong and covers a wide range, so in order to ensure the safe and stable operation of the power grid and realize the ecological environment warning function of the intelligent power transmission project, it is necessary to establish nodes that can monitor and accurately locate the environmental changes in each area in real time. The nodes are mainly configured by different types of loads and power supply demands in substations, and the status feedback from these nodes is used to realize real-time monitoring of the operation of each power facility and operating parameters of each substation. The more important hardware structure of the network node is the energy supply module, which supplies the raw power energy for each part of the network node and provides the source of energy for the whole network, and it transmits the energy consumed between each node to other power-using units through the power system. In practical application, the advantages of rapid networking and fast transmission of star wireless network distribution mode can be used to provide reliable and stable energy supply for network nodes, which improves the ability of the whole power grid system to resist power shocks and disturbances, and also reduces the ability to pollute the environment [9].

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3.2 Comprehensive Sensor Sensing It includes various types and fields of data, such as environmental parameters, weather conditions and humidity. These signals are collected and processed inside the sensor and converted into the required information output, which is then analyzed by various sensors to obtain conclusions about the overall grid operation and changes in conditions. In addition, environmental factors can be predicted or forecasted or future trends can be determined based on the monitoring results, thus reducing the impact of environmental factors on the ecosystem and achieving a stable ecological development.

4 Early Warning Model Based on Multi-objective Planning Algorithm Due to the complexity of the power system, multiple objectives need to be taken into account when conducting eco-environmental early warning, and different levels may have different impacts on it. It is very difficult to build a model that can achieve the best condition after the operation of the power grid and can meet the needs of users, which requires a model that is applicable to different levels. By calculating these indicators scientifically and rationally, and then selecting the appropriate algorithm to implement the system based on the data obtained. However, multi-objective planning algorithm can solve the above problems and improve the prediction accuracy and precision is an important breakthrough. Multi-objective programming, often referred to as MOP (multi-objective programming), is an important branch of mathematical planning. It is based on mathematical planning and probability theory, and uses computer technology to model the functional structure of the system, the algorithmic process and the program design. Any multi-objective planning problem requires a reasonable optimization of the system and a corresponding mathematical model based on it, which are composed of two basic parts: one is more than two objective functions, and the other is a number of constraints, whose mathematical model of multi-objective planning is shown in Eq. (1). ⎛ ⎜ ⎜ max(min) Z = F(X ) = ⎜ ⎝

⎞ f 1 (X ) f 2 (X ) ⎟ ⎟ .. ⎟ . ⎠

(1)

f k (X )

The early warning model based on multi-objective planning algorithm is an early warning for the ecological environment of current transmission and substation projects. In this model, the power grid system and user requirements are combined and

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analyzed to build a multi-objective planning algorithm-based early warning system for the ecological environment of transmission and substation projects. The model can monitor different areas in real time under the power grid operation state and make corresponding regulation and control plan according to the data analysis results [10].

5 Adaptive Weighted Multi-sensor Data Fusion Method 5.1 Structure and Analysis of Adaptive Weighted Fusion Algorithm The adaptive weighted fusion algorithm refers to the selection of a suitable mathematical model according to different transmission and substation engineering system operation states, and then decompose it into various sub-modules. The main consideration of this method in the solution process is to optimize the objective function so that it has better global adaptability, thus reducing the large deviations in the system operation process, which improves the performance of grid fault warning. This processing can effectively improve the prediction accuracy. However, since there may be a certain degree of information asymmetry in practical situations, this can lead to the inability to accurately estimate the weights and affiliations among the sub-modules, which affects the effectiveness of the application of the method. The adaptive weighted fusion algorithm can adaptively find the corresponding weights of the results measured by each sensor, and finally achieve the optimization of the fusion results. The data fusion results are expressed in Eq. (2). Xˆ =

n 

Wi X i

(2)

i=1

5.2 Estimation of Variance of Multi-sensor Measurements When planning the operation of long-distance power transmission and transformation projects, surveyors often encounter a large number of different types and complex indicators. These data may come from many aspects, and there are many factors that affect them. The sensor has less interference from various external environment and its own conditions, high reliability and large amount of information obtained. Therefore, a more accurate estimated value can be obtained by processing the collected signal to a certain extent, and corresponding processing is performed to obtain a more accurate result. A similar recursive approach is used to estimate the variance of the individual sensor measurements, obtain different levels of prediction, and process

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them accordingly to achieve real-time monitoring of environmental parameters and meteorological conditions in grid operation. The specific method is to measure the data of each sensor in different time periods, set the ith sensor measurement k times, the ith sensor mean value is calculated as shown in Eq. (3). 1 X ik , i = 1, 2, . . . , n X i (k) = k q=1 k

(3)

The adaptive weighted fusion algorithm is used to obtain the corresponding weights of the adaptive sensors, get the corresponding weights, and solve them by the decision tree algorithm, and finally derive the model of the whole transmission project ecological environment early warning system. The multi-sensor measurement variance is estimated using a similar recursive approach to determine the weights of each sensor, and the corresponding weights are obtained by calculating them with the decision tree, which is optimized and improved by the multi-objective planning algorithm.

6 Experimental Analysis The eco-environmental early warning system designed in this paper is mainly programmed in Java language to evaluate the eco-environment by using fuzzy comprehensive judgment technique, and the simulation results are used to verify the practicality of the system for obtaining eco-environmental early warning categories. Set the training set data as Table 1. The data collected from 200 sensors set up around the project were fused and analyzed to determine the early warning index of the system, and the index was evaluated in the whole transmission and substation project, which can provide reference for its future research. Through analysis, it can be seen that most of the data fusion error ranges of the system in this paper are kept below 0.03, and tend to be stable, and the highest error Table 1 Training set data Weather phenomena

Temperatures

Wind power

Humidity

Water level

Early warning category

Clear

Very hot

Breeze

Very low

Very low

Ultra high temperature

Cloudy

Hot

Warm wind

Normal

Low

High temperature

Rain showers

Warm

Gentle wind

Lower

Moderate

Strong wind

Light rain

Cold

Strong wind

Higher

high

Rainfall

Thundershowers

Very cold

Very strong wind

Very high

Very high

Strong rainfall

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does not exceed 0.07. The reason is that the adaptive weighted multi-sensor data fusion method in this system can realize the minimization of the fusion accuracy of sensor acquisition data, thus ensuring the accuracy of the warning information.

7 Conclusion As an important part of the power system, transmission and substation engineering plays a vital role in the whole process of power grid construction. Due to the influence of China’s vast territory and large population, this has led to differences between different regions and provinces. These differences are unavoidable: on the one hand, climate change is detrimental to local economic development, and on the other hand, as the standard of living improves and people become more aware of environmental protection, this makes transmission and substation projects face huge challenges in the actual operation process. Therefore, in order to ensure the health, safety and stability of the power grid, it is necessary to strengthen the monitoring and early warning of the ecological environment during the operation of transmission and substation projects, and the relevant personnel should respond to climate change and disasters in a timely and effective manner to promote the healthy and stable development of the power grid.

References 1. Benmammar B, Benmouna Y, Krief F (2019) A pareto optimal multi-objective optimisation for parallel dynamic programming algorithm applied in cognitive radio ad hoc networks. Int J Comput Appl Technol 2:59 2. Guerrero-Enamorado A, Morell C, Sebastián V (2018) A gene expression programming algorithm for discovering classification rules in the multi-objective space. Int J Comput Intell Syst 1:11 3. Liu S, Liu X, Wang Z et al (2022) Design and application of smart vision sensor using embedded technology in cost management of power transmission and transformation project in Ningxia companies. Wirel Commun Mob Comput 12–15 4. Jin Y, Yu W, Zhao J et al (2021) Research on providing data and space calculation support for site selection of power transmission and transformation projects. J Phys Conf Ser 3:22–25 5. Zhao X, Guo L, Li Y et al (2021) Study on risk management and control of power transmission and transformation project in power grid. In: E3S web of conferences, 253 6. Mo H, Zhao Y, Wen W et al (2020) Ecological regionalization for the ecological protection in power transmission and transformation projects. J Environ Account Manage 2:8 7. Mo H, Liang D, Wen W et al (2019) Interference pathway of power transmission and transformation project on ecosystem. IOP Conf Ser Earth Environ Sci 6:227 8. Abido MA, Elazouni A (2021) Modified multi-objective evolutionary programming algorithm for solving project scheduling problems. Exp Syst Appl 183 9. Pettersson W, Ozlen M (2020) Multiobjective integer programming: synergistic parallel approaches. Informs J Comput 461–472 10. Naghavi M, Foroughi et al (2019) Inverse optimization for multi-objective linear programming. Optim Lett 11–14

Research on Computer Information Security Technology Based on DES Data Encryption Algorithm Zhibo Fu, Guocong Feng, and Jian Wang

Abstract With the continuous development of computer technology, people pay more and more attention to data information security. Traditional cryptographic algorithms can no longer meet the needs of computer communication. Based on DES encryption algorithm as the core idea to design a highly confined conditional memory (MAC), which is used to prevent unauthorized disclosure of important files and sensitive content, it uses ciphertext computing method for secret communication and combines it with ordinary channel to achieve protective measures for data information security. In this paper, a computer information security technology based on ciphertext computing is designed using DES encryption algorithm, which mainly uses triple DES (3DES) encryption algorithm to encrypt the original plaintext data to protect important files and sensitive information so that they can be used for storage and transmission after encryption, which can ensure the integrity of data. Keywords Multi-objective planning algorithm · Transmission and substation engineering · Eco-environmental early warning system

1 Introduction In today’s era, with the continuous development of computer technology, people pay more and more attention to information security. Therefore, people have started to research on information encryption algorithms, and have achieved certain results [1]. The traditional cryptographic system can no longer meet the requirements of confidentiality, integrity and security that we need nowadays, which requires us to use a new encryption algorithm to keep the information confidential under the cryptographic system [2]. However, traditional cryptographic keys, passwords and signatures take time to be recognized by people, which affects data and network Z. Fu (B) · G. Feng · J. Wang Digital Grid Research Institute, China Southern Power Grid, Guangzhou, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_34

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security. In the traditional cryptosystem, we can encrypt the information between the user and the communication server by protecting it from being compromised [3].

2 Computer Communication Data Encryption Based on DES Data Encryption Algorithm 2.1 Security Analysis of Computer Data Communication Based on DES Algorithm DES data encryption algorithm can guarantee the security of communication between computers. It is a general algorithm based on DES data encryption, which encrypts the plaintext data generated by computer communication with a secret key to get the communication ciphertext, and then decrypts the encrypted information with DES algorithm, so as to convert the data into plaintext, and finally uses Lcit function to realize confidential communication. The method aims to protect the information security of both sides of computer communication [4]. A complete communication security protection system is established by analyzing and studying the important technologies involved in the communication process such as sensitive nodes, network layer and transmission links, so that information data can be safeguarded from leakage during the transmission process. In addition, a complete security mechanism architecture model is established, and a key management module is used on this basis. All the keys generated by the communication content and related content must be decrypted and encrypted before they are sent out. The algorithm can effectively solve the problem of information security vulnerability under the traditional cryptographic system, and it has the advantages of high security and modularity of key management, which can effectively guarantee the information security of both communication parties in the process of solving data encryption [5].

2.2 3DES-Based Encryption of Computer Data in Plaintext The confidentiality mechanism based on 3DES encrypted information is currently the most commonly used ciphertext encryption method in the computer field. Its main principle is to set a protective layer in the data file, which is used to effectively control and process the sensitive content involved, so as to protect computer information from illegal tampering. This algorithm can well solve the problems of key leakage and private key misuse in traditional cryptographic systems. Also, it can avoid unnecessary losses caused by different users using the same secret key, which improves the efficiency of the encryption algorithm [6]. In order to enhance the security of computer data communication, the key length of DES encryption algorithm is extended to reduce the encryption complexity of

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Fig. 1 Principle of 3DES algorithm encryption

communication, so the encryption of computer based on DES data information is a hot topic in the current research field. 3DES data encryption algorithm sets three keys to encrypt computer communication data, defining the keys as ka, kb, and kc respectively, thus extending the key to The 3DES data encryption algorithm sets three keys to encrypt computer communication data, defining the keys as ka, kb, and kc, thus extending the keys to 168 bits in length, which implements the process of encrypting computers. Figure 1 shows the principle of 3DES algorithm based on three keys encryption [7]. The data plaintext and ciphertext in the communication process are defined as W and M respectively, and ka, kb and kc are used to encrypt the plaintext. Information security problems in the communication process will cause data to be threatened to varying degrees in terms of transmission, storage and use. Based on this, a cryptography method is proposed, which is based on the DES algorithm, and uses ciphertext to encrypt the two sides of the communication. The algorithm establishes a correspondence between the plaintext (or other means of generating ciphertext) and the key, thus achieving confidentiality protection measures. It has certain security and reliability, and it can also avoid the loss of data during transmission and storage [8]. 3DES algorithm encryption process is described as shown in Eq. (1), and decryption process is described as shown in Eq. (2). W = Ekc[Dkb[Eka[M]]]

(1)

M = Dkc[Ekb[Dka[W ]]]

(2)

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3 3RSA Data Encryption Algorithm for Computer Communication Data Encryption 3.1 3RSA Data Encryption Algorithm With the development of computer technology, there are more and more encrypted data. In order to protect information, people have adopted various methods to keep some important contents confidential, such as: cryptography, firewall, encryption software (e.g. HadMachine), etc. However, in most cases, ciphertext is used to represent plaintext. For example: using digital signatures and other methods to pass useful confidential information contained in a file in different forms between users, receivers and senders, and using digital certificates or keys to achieve authentication, which can protect important confidential information, but also prevent unauthorized persons from using digital data for illegal operations and avoid leaks. The use of electronic mail to complete the signal transmission, etc. are the problems that need to be paid attention to in the development of encrypted data security technology, which has caused a great impact on computer information systems for us to study it. 3DES encryption algorithm can avoid the leakage of data information to a certain extent, which can effectively prevent unauthorized persons from using cipher text to steal important confidential information [9]. However, in order to tamp down the performance of 3DES encryption algorithm to safeguard data communication, RSA encryption algorithm can be used to change the 3DES algorithm and improve its confidentiality, but the performance of this encryption algorithm to protect data information is not ideal, and there are problems such as insecure key management and low decryption efficiency in the process of use. RSA encryption algorithm belongs to asymmetric encryption algorithm, its encryption and decryption process is by encoding the plaintext or key of the message, and then storing the data in the computer system. When a file needs to be transmitted, it is first passed on the original communication path by the data processing center. RSA algorithm is widely used in various secrecy work because of its high compression performance and low complexity and speed, but the application of RSA algorithm in data encryption makes it has security risks such as key loss, password cracking and virus invasion. In the process of RSA decryption, the key function is verified, data integrity is guaranteed, and the security of computer users is greatly improved. Therefore, the security key protection of RSA encrypted data is of great importance. Generating key, plaintext encryption, and ciphertext decryption are the main steps of RSA algorithm, and the key needs to be generated before encryption. The methods of RSA algorithm encryption and decryption are shown in Eqs. (3) and (4): W = E(M) = M e mod i

(3)

M = D(M) = W v mod i

(4)

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3.2 Combination of 3DES Algorithm and RSA Algorithm for Computer Communication Encryption and Decryption Figures 2 and 3 are the process of encrypting and decrypting data communication by combining the RSA algorithm with the 3DES algorithm. In the process of combining these two algorithms, we find that the confidentiality and security of DES-based data encryption technology co-exist and are independent of each other. In order to ensure that secret information is not stolen by illegal elements and to protect users’ privacy from being violated, it is necessary to have an effective and secure communication method to transmit various confidential documents and important contents. In addition, it is also necessary to adopt cryptography or other corresponding methods for key distribution and ciphertext extraction to prevent eavesdropping. In some cases, the information can also be decrypted by encryption algorithms. Due to the confidentiality and security of DES-based data, it is necessary to use a secure, reliable and efficient and fast communication method to transmit secret documents.

Fig. 2 The encryption process of data communication based on RSA algorithm and 3DES algorithm

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Fig. 3 Decryption process of data communication based on RSA algorithm and 3DES algorithm

The process of computer communication data encryption is analyzed as follows: first, the data encryption operation is based on arbitrary numbers and arithmetic functions to obtain 3DES keys, that is, the use of digital signature technology to achieve the encryption of data. Then, the plain text of the computer data to be transmitted is encrypted, and the encryption key is used to convert the plain text of the data into cipher text, so as to realize the security protection of the information content and transmission method stored in the computer. Finally, the DES algorithm is used to securely encrypt the data information flow, and the RSA protocol is used to implement security protection measures such as embedded applications in HadoopLogistics and steganographic operations based on LIBORAC/EYxi functions, so as to ensure the access control of computer storage systems and important confidential documents, important files and network communication contents. The process of decrypting computer communication data is analyzed as follows: the receiving end gets the encrypted data and reads the public key in the server, and recovers the public key in it, and after decrypting the data, it can get its private information, and if the receiving end cannot get the encryption result, it returns to the original file to be read. In this case computer network security technology can effectively prevent and control all possible malicious attacks on the network [10].

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4 Experimental Analysis A data transmission encryption and decryption simulation testbed is built in a LAN communication environment to effectively decrypt data streams by DES encryption algorithm and compare with historical results to analyze the information leakage between network nodes under different algorithms. Five groups of data of the same size are selected for communication, and 50 artificial brute force attacks are set to break the data transmission security key and encrypt the data stream in different traffic segments. The data encryption simulation experiments are conducted simultaneously using 3DES encryption algorithm, DES encryption algorithm and article algorithm, and the results of security performance and encryption/decryption efficiency of each algorithm are analyzed as follows.

4.1 Analysis of the Security Performance of Algorithms The security of the algorithm refers to the operation of the computer in the process of encrypting user information and data, which ensures that the whole system will not be used and stolen by unscrupulous elements. Therefore, there must be a certain degree of reliability for a computer information system with high security performance. The average degree of breakage per transmission after data communication after encryption processing by two algorithms is shown in Table 1. 3DES encryption algorithm is designed to solve the problem of insufficient security of the original DES encryption algorithm. It introduces an encryption algorithm to decrypt the original data, thus improving the security of DES information. Table 1 Degree of breakage of data encrypted communication (%)

Algorithm name

3DES encryption algorithm

DES encryption algorithm

The first set of data

0.00

0.10

The second set of data 0.00

0.32

The third set of data

0.11

0.12

The fourth group of data

0.10

0.24

The fifth group of data

0.00

1.20

316 Table 2 Breakage level of data encrypted communication (ms)

Z. Fu et al. Algorithm name

3DES encryption algorithm

DES encryption algorithm

The first set of data

3.59

1.54

The second set of data 4.28

1.68

The third set of data

4.31

1.71

The fourth group of data

4.09

1.56

The fifth group of data

4.11

1.84

4.2 Analysis of Encryption and Decryption Efficiency of Algorithms Conventional encryption algorithms need to convert plaintext data into characters when decrypting ciphertext, and then use the corresponding method to achieve it. However, the key is set and set by the user before it can be obtained and stored on the internal disk or CD-ROM without public disclosure, so it is difficult to be saved, which makes the confidential document cannot be used and spread to other places. Therefore, in order to protect the secret information from being damaged, it is necessary to use encryption algorithms to decrypt the cipher text, which not only can effectively avoid unnecessary data loss, but also can improve the confidentiality to a certain extent. The response time overhead of each algorithm application in this experiment is shown in Table 2, and the efficiency of algorithm encryption is analyzed. As can be seen from Table 2, DES encryption algorithm takes the shortest time, 3DES encryption algorithm takes the longest time, and its complexity is the highest, and the data ciphertext when encrypted is more fragile than the traditional method. Therefore, it is important to study the computer information security technology based on DES algorithm.

5 Conclusion With the development of the times, people pay more and more attention to the issue of information security, computer technology is also progressing, we need confidential documents, data and personal privacy in our lives are stored with passwords. The traditional method is to use password or password to protect confidential documents inside the computer by setting password or encrypting cipher text into plain text, which will cause information leakage, and this also provides convenience for hackers to commit illegal acts. Although this way can ensure the confidentiality is not leaked, but it is not easy for people to access the content they want to know, and if a large amount of data is used may cause unnecessary losses, and security is not guaranteed.

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Nowadays, information security technology keeps developing and people pay more and more attention to information security, so we should use password technology in computer to combine confidentiality and security to protect data from being leaked.

References 1. Fadhil SA, Kadhim LE, Abdurazaq SG (2021) Protection measurements of computer network information security for big data. J Discr Mathe Sci Cryptogr 7:24 2. Vengala DVK, Kavitha D, Siva Kumar AP (2020) Secure data transmission on a distributed cloud server with the help of HMCA and data encryption using optimized CP-ABE-ECC. Cluster Comput 88–92 3. Du H (2020) Intelligent parallel data encryption algorithm for network communication of CT mechanical scanning information publishing termin. Comput Inf Mech Syst 2:3–5 4. Churi PP (2019) Performance analysis of data encryption algorithm. Int J Recent Technol Eng (IJRTE) 3:8–12 5. Shang Y (2021) Application of mathematical signature technology in computer information security design. J Sociol Ethnol 7:31–35 6. Qian R (2021) Application analysis of artificial intelligence technology in computer information security. J Phys: Conf Ser 4:1744 7. Vijay Kumar Tiwari (2019) About Oracle TDE (transparent data encryption). J Trend Sci Res Dev 1:24–27 8. Rodrigues B, Cardoso A, Bernardino J (2019) Secure remote data collection system using data encryption. IFAC PapersOnLine 52–54 9. Fadhil SA, Kadhim LE, Abdurazaq SG (2021) Protection measurements of computer network information security for big data. J Discr Mathe Sci Cryptogr 24–28 10. Liu B (2019) Discussion on application of data encryption technology in computer network communication security. In: Proceedings of 2019 international seminar on automation intelligence computing and networking (ISAICN 2019). Francis Academic Press, pp 253–256

Stereoscopic Visual Effect Simulation of Film and Television Works Based on 3D Technology Hongxing Qian

Abstract The development of 3D technology has greatly broadened the scope of film and television creation. It not only has a strong visual impact in form, but also has a great tension in content, which has greatly enriched the plot of the film and greatly enhanced the artistic charm of the film. Today, we can use 3D technology to depict real life scenes, and thus bring a strong three-dimensional effect. Therefore, this paper studies the simulation of 3D technology to realize the stereoscopic visual effect (VE) of film and television works (FATW). The principle of stereoscopic vision and the influence of 3D technology shooting skills on SE are discussed. The 3D technology is used to simulate the stereoscopic VE of FATW, so as to strengthen the 3D spatial hierarchy and highlight the sense of spatial existence; It effectively avoids the problems of different lens focus and parallax in the shooting process. It ensures the stability and excellence of the 3D effect, so that the audience can always enjoy the film in a comfortable three-dimensional experience. Keywords 3D technology · Film and television works · Stereoscopic visual effects · Simulation research

1 Introduction The arrival of the digital era has put forward higher technical requirements for video signal transmission. 3D video materials not only have the definition requirements of ordinary HD digital signals, but also have double the data volume of traditional HD video in format. Therefore, during data transmission, the throughput of 3D digital signal is difficult to be equal to that of conventional signal transmission under the condition of the same bandwidth. If special compression coding processing is not H. Qian (B) School of Media and Communication, Changchun Humanities and Sciences College, Changchun, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_35

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carried out, the delay distortion and other problems will inevitably occur in the process of signal transmission and reception. The processing of 3D digital signal transmission mode will directly turn off the stereoscopic effect (SE) of the final picture. The development of 3D animation technology has greatly broadened the scope of film and television creation, applied 3D animation technology to our 3D TV program production, gave full play to the creator’s imagination, enriched the content and form of TV programs, and made efforts to improve the three-dimensional effect of 3D TV by integrating 3D film and television [1]. 3D technology is a powerful means of film and television creation, which represents the aesthetic interest of the current information technology era. While pursuing the three-dimensional effect, we should also pay attention to the connotation of FATW, make reasonable arrangements between them, and strive to present the best three-dimensional artistic effect. 3D FATW are the product of the development of the times and the proof of technological innovation and civilization progress. The development of 3D technology conforms to the development of the times, promotes technological progress in related fields, and forms a relatively stable 3D industry chain. 3D technology is a highly technical means of film and television creation, which represents the aesthetic interest of the current information technology era. It is very easy for the audience to focus on the external form of 3D technology when watching such FATW. This paper studies and analyzes the simulation of 3D technology to realize the stereoscopic VE of FATW, and strives to present the best stereoscopic artistic effect [2].

2 Stereoscopic VE of FATW Based on 3D Technology 2.1 Stereo Vision Principle When people observe things with the naked eye, they perceive the three-dimensional scene according to the monocular and binocular depth perception clues listed in Fig. 1. Monocular depth perception cues come from the visual experience and visual memory accumulated by people through long-term observation and learning of the surrounding environment in the past. Therefore, monocular depth perception cues are also called Billie depth perception cues. According to these visual experience and visual memory, even when watching 2D video, people can accurately observe the occlusion between objects in the picture, the linear perspective of lines, and the near large and far small objects, atmospheric scattering Texture gradient, motion parallax, light intensity change, object shadow and other clues determine the relative distance between the foreground object and the background object in the picture, resulting in depth perception [3, 4]. The images on the retinas of the left and right eyes are synthesized by the brain to form a 3D image, which can establish a three-dimensional scene with the relationship between the upper, lower, left, right, front and back, and form a three-dimensional

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Fig. 1 Depth perception cues

vision. Optic axis convergence is also called binocular convergence. When looking at distant objects, the lines of sight of the left and right eyes are approximately parallel; when looking at a near object, the lines of sight of the left and right eyes converge towards the center to aim at the object. The kinesthetic sensation produced by eye muscles when controlling the changes in the angle of convergence between the two eyes’ lines of sight will provide clues to the brain about the distance of objects.

2.2 3D Technology Shooting Skills Affect SE 2.2.1

Skillfully Use the Composition to Effectively Improve the Three-Dimensional Effect

Usually, the meaning of composition is to coordinate the three-dimensional spatial relationship between height, depth and width on the plane, so as to highlight the theme, sublimate the artistic conception and enhance the artistic effect. In the process of 3D shooting, the meaning of composition is not only to pay attention to the hierarchical relationship of subject connotation. We should also pay attention to the hierarchical relationship of the scene in the 3D stereoscopic space (the hierarchical nature of the stereoscopic picture). Otherwise, the rich layers in the stereoscopic picture will disperse the theme expression of the picture, so that the guest will dominate.

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Select the composition that pays attention to appearance and vertical lines: the contrast formed by the light and dark areas of the side lights in the picture is called appearance. The function of appearance in composition is that the main body of its performance is three-dimensional space. For the same reason, another composition factor related to three-dimensional performance is line. Making good use of appearance and lines is an important condition for 3D camera composition. Paying attention to the composition of appearance and lines can effectively improve the stereoscopic sense of the picture [5, 6]. Practice shows that paying attention to appearance can better express the stereoscopic sense of the subject, and the structure of the picture through vertical lines can balance the picture relationship, and create a sense of stability on the basis of ensuring the stereoscopic sense of hierarchy. At the same time, while strengthening the threedimensional effect, we should also take into account the information expression of the main body of the picture. We should not weaken the connotation of the lens because of the strengthening of the form. In stereoscopic shooting, vertical lines can not only show the spatial hierarchy, but also form a sense of stability. They can give full play to the SE and the expression of picture information, which is a necessary factor for composition [7].

2.3 Simulation of 3D Technology to Realize Stereoscopic VE of FATW 2.3.1

Shooting Angle Enhances SE

Due to the particularity of the picture effect, the choice of 3D technology is different from the tradition. In 3D shooting, the angle has a prominent impact on the threedimensional effect. There is a problem that the superior is better and the mediocre is more mediocre. As the three-dimensional space is easier to visually display the content of the picture, the spatial and hierarchical nature will be emphasized. In a vast space, the improper angle will make the image too monotonous, empty or even isolated. Therefore, the angle must be carefully selected during 3D shooting to enrich the picture content and improve the three-dimensional effect while representing the main body of the lens [8]. Choosing a shooting angle with line perspective effect and understanding the perspective law is the key to the display space of the picture. First of all, from the viewpoint, the object seen is near, far and small. This perspective helps us to emphasize the picture, highlight the subject and weaken the object. Enhance the drama of 3D stereoscopic images. Secondly, the lines are regularly arranged and combined, or parallel and opposite lines. From the viewpoint, it converges to the center point. The farther we go, the closer we get to each other, and finally converge at one point. The perspective selection of this vertical line can not only show the

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existing three-dimensional sense of space, but also improve the depth of space. The picture effect has more space tension on the original basis [9].

2.3.2

SE Presentation of Long Focal Length Lens Under 3D Technology

In 3D FATW, long focal length lens is often used to shoot characters in small scenes, such as close-up lens and close-up lens. The purpose is to highlight the subject, weaken the background, so as to enhance the drama of the story, and pursue the inherent artistic effect of the work. Therefore, telephoto lens is an indispensable part of 3D FATW. In view of the deficiencies of telephoto lens in three-dimensional performance, we need to pay more attention to the construction of its three-dimensional effect, and improve the three-dimensional effect as much as possible while expressing the meaning [10]. The space in 3D pictures is much richer than that in plane pictures. In 3D film and television art creation, the use of telephoto lens often has an original idea, which can condense the author’s “idea” into the lens and present it on the picture. The advantage of telephoto lens over wide-angle lens is that it can create the effect of virtual reality in the picture, so as to achieve the effect of clear primary and secondary points and prominent points. With the change of scene, the scenes integrated into the human eye will become more and more abundant, and the hierarchy and spatiality will gradually increase. In the process of focus change, the scenery in front of people will not disappear in the mind, and new scenery will follow, giving the vision a sense of rhythm and rhythm. The contrast of the front and rear sense of space will bring obvious three-dimensional changes, and the three-dimensional effect will become stronger from the visual experience. To put it simply, it means that the desire to develop is the first to suppress, and the contrast between the front and back highlights the 3D sense. In a word, the unique picture effect of long focal length lens is of great significance in 3D stereoscopic shooting. It is necessary to enhance the three-dimensional dynamic sense and strengthen the three-dimensional effect as much as possible while presenting the artistic effect.

2.4 Application of 3D Technology in Film and Television Shooting When 3D technology is applied to film and television shooting, combined with the imaging characteristics of the lens itself and the space shaping of the stereo camera, the SE of the picture will be stronger. In general, the characteristics of 3D wideangle lens in picture composition and modeling functions are mainly reflected in the following points: (1) Maximize the visual capacity of the picture

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Whether shooting outdoor natural scenery or indoor closed scenes, use 3D wide-angle lens to shoot grand scenes and show. The scale, momentum and quantity are unmatched by other lens performance means. Due to the limitations of the legal range of 3D effects, standard and telephoto lenses in a small space are extremely easy to exceed the legal range of 3D guidance range, which will cause problems such as dazzling and distortion of 3D images. The use of 3D wide-angle lens can extend the guiding range, broaden the legal range, and expand the space for stereo shooting. The large scene pictures taken by 3D wide-angle lens can bring the audience a sense of examining, grasping and controlling [11]. (2) Strengthen 3D spatial hierarchy and highlight the sense of spatial existence The wide-angle lens can strengthen the size contrast of the space scenery, the level contrast of the tone, and the warm and cold contrast of the tone. This contrast is determined by the characteristics of wide-angle lens. This kind of lens is more detailed and exaggerated than human eye observation, and has a strong artistic effect. It makes the contrast of the depth, size and temperature of the subject in the picture frame more concrete and perceptible. Make the visual intuition and distinguishability enhanced. The enhancement of 3D spatial hierarchy is mainly reflected in the sense of spatial distance between scenes and the expansion of the hierarchy by the depth of 3D space possessed by the 3D image itself [12].

3 Application of 3D Technology in Film and Television Shooting In order to remove the influence of the camera zoom operation on the depth map during 2D shooting, firstly, the optical flow motion vector directions of the pixels at the four corners of the video image are detected, and the optical flow motion vector directions at the four corners are used to determine whether the camera has been reduced or enlarged. If it is detected that the direction of the optical flow motion vector on the four corners points to the outside of the image, it is considered that the camera has been enlarged. On the contrary, if the direction of the optical flow motion vector on the four corner pixels points to the inside of the image, it is considered that the camera has been reduced. The optical flow of each pixel (I, J) of the dense optical flow field includes the motion vectors SX (I, J) and sy (I, J) in the horizontal X and vertical x directions, and the sum of the squares of the motion vectors SX (I, J) and sy (I, J) in the pixel (I, J) x and X directions, and then the root can be obtained to obtain the size S (I, J) of the motion vector of the point, that is, the size of the motion vector of the pixel is (1):

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S(i, j ) =

/

325

sx2 (i, j ) + s y2 (i, j)

(1)

The depth information Z (I, J) is represented by an 8 bit grayscale image. The size of the obtained motion vector is mapped to the parallax information of the pixel through formula (2), and the relative depth information can be obtained by inverting the parallax information. Smax and smin are the largest motion vector and the smallest motion vector in the current video image. z(i, j ) = 255 ·

(S(i, j ) − Smin ) (Smax − Smin )

(2)

Calculate the parallax of each pixel of the video post image through formula (1) and formula (2), and then calculate the reciprocal to obtain the relative depth map corresponding to the video post image.

4 Test and Analysis of Stereoscopic VE of FATW Based on 3D Technology In order to verify the impact of the application of 3D technology in FATW on the stereoscopic VE, this paper investigates the feedback and evaluation of the stereoscopic VE of 3D FATW by the audience of different ages by means of online questionnaire. The test results are shown in Table 1 and Fig. 2. According to the data analysis in the above chart, the application of 3D technology in FATW has brought excellent visual experience to the audience. 3D technology is a highly technical means of film and television creation, which represents the aesthetic interest of the current information technology era. The whole process digitalization of 3D film production enables all kinds of data values to be accurately unified in the creation process. Whether it is the convergence point or focal plane parameters, or the perceived depth and parallax, they can be uniformly quantified, so as to effectively avoid the problems of different lens focus and parallax in the shooting process. The unification of 3D parameters and the setting of high standard parameters ensure the stability and excellence of 3D effects, so that the audience can always enjoy the film in a comfortable three-dimensional experience. Table 1 Evaluation of stereoscopic VEs of 3D FATW by audiences of different ages SE

Sound in its place

Rhythm

Full image

Increased legibility

Teenagers

83%

94%

88%

79%

Middle age

88%

89%

85%

81%

Old age

79%

68%

89%

75%

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100% 90%

Evaluation feedback

80% 70% 60% 50% 40% 30% 20% 10% 0%

Sound in its place

rhythm

Full image

Increased legibility

Stereoscopic visual effect of 3D film and television works teenagers

middle age

old age

Fig. 2 Audience feedback on stereoscopic VEs of 3D FATW

5 Conclusions The arrival of the digital age has put forward higher technical requirements for FATW. This paper studies and analyzes the simulation of 3D technology to realize the stereoscopic VE of FATW, applies 3D technology to the shooting of FATW, and has achieved good results. However, there are also some deficiencies. This paper lacks the analysis of 3D digital signal when analyzing 3D shooting technology. The processing of 3D digital signal transmission mode by transmission will directly affect the SE of the final picture. The simulation of stereoscopic VE of FATW by 3D technology needs further research.

References 1. Suominen J, Harviainen JT (2019) Comics as an introduction to media technology: the finnish case—television and Donald Duck in the 1950s and the early 1960s. Historical J Film Radio Television 39(2):1–19 2. Eckhard T (2019) Using 3D stereo vision to inspect bonding wires. Vis Syst Des 24(1):14–16 3. Lee YJ, Park MW (2019) 3D tracking of multiple onsite workers based on stereo vision. Autom Constr 98(Feb):146–159 4. Brickler D, Teather RJ, Duchowski AT et al (2020) A Fitts’ law evaluation of visuo-haptic fidelity and sensory mismatch on user performance in a near-field disc transfer task in virtual reality. ACM Trans Appl Percept 17(4):1–20 5. Bhagya HK, Keshaveni N (2021) Contrast enhancement technique using discrete wavelet transform with just noticeable difference model for 3D stereoscopic degraded video. Int J Innov Technol Explor Eng 10(3):7–13

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6. Kokaram A, Singh D, Robinson S et al (2020) Motion-based frame interpolation for film and television effects. IET Comput Vision 14(6):323–338 7. Tarmawan I, Amalina RN (2019) Cinematic point of view of “pride and prejudice” film on 1995 television serial film and 2005 movie theater. Visualita 7(2):23–29 8. Kim J, Hong S, Hwang S (2019) Automatic waterline detection and 3D reconstruction in model ship tests using stereo vision. Electron Lett 55(9):527–529 9. Tracy T, Flynn R (2020) Irish film and television—2019. Estudios Irlandeses 15(15):296–329 10. Berridge S (2019) Book review: women do genre in film and television. Crit Stud Television Int J Television Stud 14(3):405–407 11. Newsinger J, Eikhof DR (2020) Explicit and implicit diversity policy in the UK film and television industries. J Br Cinema Television 17(1):47–69 12. Islam A, Asikuzzaman M, Khyam MO et al (2020) Stereo vision-based 3D positioning and tracking. IEEE Access 99:1–1

Research on Intelligent Detection Technology of Protection Based on AC Electromagnetic Field Liang Zhang, Bing Wei, Jingyuan Chi, Han Wang, Lijun Fu, and Jian Zhang

Abstract Electromagnetic radiation will cause great pollution to the environment, which is very harmful, and will have a negative impact on people’s health and quality of life. Electromagnetic wave pollution and electronic fog pollution are other names of electromagnetic radiation pollution. They are mainly a kind of pollution phenomenon that the source emits energy into space in the form of electromagnetic waves. It can also be expressed as the phenomenon that the variable electromagnetic field propagates to the distant space in the form of waves and will not return. In order to effectively reduce the harm of electromagnetic radiation, it is necessary to strictly detect the intensity of electromagnetic radiation, detect the electromagnetic radiation pollution in the environment, and take reasonable and effective protective measures, so as to minimize the harm of electromagnetic radiation to the environment. Based on this, the paper mainly discusses the harm of electromagnetic radiation to the environment, and deeply analyzes the key points of electromagnetic radiation environmental detection. Finally, it gives some practical and effective protective measures against electromagnetic radiation environmental pollution. Keywords Protective measures · Environmental detection · Electromagnetic radiation

1 Introduction Because the pollution caused by electromagnetic radiation to the environment has the characteristics of concealment, potential and extensive, it will bring great harm to the working and living environment of human beings. It will not only endanger

L. Zhang (B) · B. Wei · J. Chi · H. Wang · L. Fu · J. Zhang China State Grid Heilongjiang Electric Power Company Limited Electric Power Research Institute, Heilongjiang 150030, Harbin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_36

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people’s health, but also interfere with some sophisticated electronic communication equipment, but also have great potential safety hazards, which is easy to cause some flammable and explosive substances to explode and burn. Therefore, in order to fully reduce the harm of electromagnetic radiation to the environment, we must attach great importance to the environmental detection of electromagnetic radiation [1]. Electromagnetic radiation detection personnel need to make adequate work preparation, fully manage and classify the environmental pollution caused by electromagnetic radiation, and take reasonable and effective protective measures, so as to effectively solve the harm of electromagnetic radiation to the environment [2]. Overview of hazards of electromagnetic radiation The environmental pollution caused by electromagnetic radiation is a kind of energy pollution. The reason for electromagnetic radiation is that the transmission towers of communication base stations, substations, high-voltage lines and other facilities will emit electromagnetic waves with different frequencies and wavelengths in the process of actual work, which will have an impact on the environment. According to the source, electromagnetic radiation can be divided into two categories: natural electromagnetic wave and artificial electromagnetic wave [3]. At this stage, the electromagnetic radiation generated in the environment is mostly artificial electromagnetic wave. Compared with artificial electromagnetic radiation, the natural electromagnetic radiation in the environment is basically negligible. Artificial electromagnetic radiation mainly comes from the equipment used by electromagnetic energy in medical, scientific, industrial and other fields, as well as electrical and electronic equipment, broadcasting, electric traction systems, television, communication systems, radar transmission facilities, etc. There are many kinds of hazards caused by electromagnetic radiation. In serious cases, it will affect people’s living standards and health, and cause certain potential safety hazards [4]. Therefore, the harm of electromagnetic radiation can be classified into the following aspects: first, it brings damage to people’s physical and mental health. In the process of emission, electromagnetic waves passing through the human body will have a negative impact on the human hematopoietic system, nervous system, immune system, endocrine system and other aspects. Electromagnetic waves of different frequencies have different effects on human health [5]. Second, the main harm caused by electromagnetic interference. In the process of substation operation, strong electromagnetic wave will be generated. These electromagnetic radiation will often bring adverse effects on some precision electronic equipment and communication equipment, thus bringing economic losses. Third, it is mainly the potential safety hazards caused by electromagnetic waves [6]. In some cases, electromagnetic radiation will lead to spontaneous combustion or self explosion of flammable and explosive materials, which brings great potential safety hazards to people’s production and life and endangers people’s life and property safety. The harm caused by electromagnetic waves and their causes are shown in Table 1.

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Table 1 The hazards of electromagnetic radiation Harm

Damage to people’s physical and mental health

Cause During emission, electromagnetic waves passing through the human body can negatively affect the human hematopoietic system

Major hazards caused by electromagnetic interference

Safety risks caused by electromagnetic waves

In the process of substation operation, strong electromagnetic waves will be generated, bringing economic losses

Electromagnetic radiation will lead to spontaneous combustion or self-explosion of flammable and explosive materials, bringing huge safety risks to people’s production and life, endangering people’s life and property safety

2 Key Points of Electromagnetic Radiation Environment Detection 2.1 Key Points of General Electromagnetic Radiation Environment Detection Electromagnetic radiation is common in environmental space, and often diffuses in various ways. When detecting this general electromagnetic radiation environment, points are often arranged along the main lobe within a radius of 50 m of the communication base station; The power transmission and transformation lines are evenly distributed within 50 m of the line cross section with a spacing of 5 m; The substation shall be arranged at 5 m outside the fence on each side [7]. The problem that needs to be paid attention to is that in the process of detecting the distribution of points, it is necessary to fully consider the shielding effect of buildings and trees and adjust the detection points. Combined with the actual situation monitored, according to the relevant provisions of electromagnetic radiation protection, the environmental characteristics of electromagnetic radiation, the distribution law of electromagnetic radiation and the environmental quality of electromagnetic radiation in the polluted area are comprehensively analyzed, so as to comprehensively summarize the electromagnetic radiation pollution in this area [8].

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2.2 Key Points of Specific Electromagnetic Radiation Environment Detection 2.2.1

Detection of Electromagnetic Radiation Environment of Mobile Communication Base Station

In the process of wireless communication, the mobile communication base station mainly receives the electromagnetic wave of specific frequency transmitted by the RF transmitting equipment. Therefore, these electromagnetic radiation may have adverse effects on the surrounding environment. Therefore, in the process of detecting the electromagnetic radiation environment of mobile communication base stations, we need to determine the content of detection technology, detection equipment, detection time, detection points, etc. in advance [9]. In the process of actual operation, it needs to be carried out in strict accordance with the corresponding standards. According to the requirements of electromagnetic environment control limits (gb8702-2014), the electric field strength shall not be higher than 12 v/m, and the power density shall not be higher than 40 µw/cm.

2.2.2

Electromagnetic Radiation Environment Detection of High Voltage Power System

In the process of power transmission for the city, the substation mainly transforms high-voltage power transmission into low-voltage power transmission. Due to the electromagnetic effect, a series of electromagnetic radiation will be produced in the substation. These electromagnetic radiation will also bring adverse effects on the environment. Therefore, we need to carefully detect the electromagnetic radiation pollution caused by the urban central substation. The harm of this electromagnetic radiation to the environment is mainly caused by the substation and the high-voltage transmission line itself. Having the characteristics of corona, magnetic field and electric field will not only cause great interference to the radio, but also seriously pollute the ecological environment and affect people’s production and life. In the process of detecting the electromagnetic radiation environment, the electromagnetic radiation detection work shall be scientifically and reasonably arranged in combination with the difference of voltage level and relevant standards and regulations.

2.2.3

Electromagnetic Radiation Environment Detection of Radio and Television System

Radio and television systems are very similar to mobile communication base stations, but also have specific electromagnetic radiation pollution in the surrounding environment. Therefore, in the process of actual detection, it is necessary to pay close attention to the surrounding environment of the radio and television system. In the

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process of inspection work arrangement, it is necessary to carry out inspection from the aspects of inspection technology, inspection equipment, inspection time, inspection potential, etc. The actual inspection work needs to fully consider the surrounding environment to improve work efficiency. Especially in the selection of monitoring points, it is necessary to be flexible and pay attention to the surrounding sensitive points. For example, the surrounding hospitals and kindergartens must be closely monitored.

3 Protective Measures for Environmental Pollution Caused by Electromagnetic Radiation 3.1 Strengthen the Management of Electromagnetic Radiation Environment If we want to improve the living standard and health of urban residents, we must reduce the harm of electromagnetic radiation to the environment. Scientific and reasonable management measures shall be adopted to effectively reduce the electromagnetic radiation pollution generated by the substation. In the process of implementing relevant laws and regulations, it is necessary to guide citizens to actively participate in supervision. In the overall planning of urban construction, electromagnetic radiation management should be included to prevent electromagnetic radiation pollution in the urban environment due to the irrationality of substation planning. Strict control is required for areas prone to electromagnetic radiation pollution in cities. Electromagnetic pollution in urban central areas is often serious, so it is necessary to strengthen its monitoring. The buildings around the substation need height restriction. Reasonable measures shall be taken to control the influence radius and suspension height of the antenna of the indoor micro cellular base station. Details are shown in Fig. 1 [10].

3.2 Make Full Use of Electromagnetic Radiation Control Technology In the process of protecting the environment from electromagnetic radiation pollution, we should pay attention to the application of electromagnetic radiation control technology. First, filtering technology. This technology can effectively reduce electromagnetic interference. Line filtering can intercept useless signals and ensure the passage of useful signals. Second, electromagnetic shielding technology. In the process of shielding high-frequency electromagnetic fields, electromagnetic

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strengthen the management of electromagnetic radiation environment

make full use of electromagnetic radiation control technology

strengthen the protection of electromagnetic radiation environment according to law

Protective measures for environment al pollution caused by electromagn etic radiation

strengthen the publicity of electromagnetic radiation protection knowledge

Fig. 1 Protective measures for environmental pollution caused by electromagnetic radiation

shielding technology is commonly used. And in the process of anti-interference radiation, shielding technology is the most efficient. Third, high frequency grounding technology. This technology is specifically to quickly introduce the RF current generated by induction in the shielding parts into the ground, so that the shielding parts themselves will not become the secondary radiation source of RF, thus greatly improving the efficiency of shielding radiation. Fourth, greening. Trees and vegetation can effectively absorb electromagnetic radiation. Planting trees, flowers and grasses in a large area in the electromagnetic radiation area will gradually attenuate the electromagnetic wave in the transmission process, thus reducing the absorption of electromagnetic radiation by the human body. Fifth, increase the application of electromagnetic radiation protection materials. In terms of clothing, packaging, transportation, construction, etc., the use of materials that can enhance electromagnetic radiation, including metal and other materials, should be eliminated. Therefore, it is necessary to use electromagnetic radiation protection materials to absorb or rebound electromagnetic radiation, so that electromagnetic radiation can be effectively weakened. As shown in Fig. 2.

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Filtering technology

Electromagnetic shielding technology

High frequency grounding technology

Pay attention to the application of electromagnetic radiation control technology

afforest

Increase the application of electromagnetic radiation protection materials Fig. 2 Pay attention to the application of electromagnetic radiation control technology

3.3 Strengthen the Protection of Electromagnetic Radiation Environment According to Law The laws and regulations on electromagnetic radiation pollution protection designated by the government need to be strictly implemented. Electromagnetic radiation sources shall be controlled according to law. Environmental protection approval procedures for construction projects shall be strictly implemented. For those electromagnetic radiation construction projects that may cause damage and pollution to the ecological environment, supervision needs to be strengthened.

3.4 Strengthen the Publicity of Electromagnetic Radiation Protection Knowledge Electromagnetic radiation is invisible in our daily life, so it is difficult to be felt by people, and people pay less attention to it. Although most people know that electromagnetic radiation exists, they know little about the protection against electromagnetic radiation. Therefore, relevant departments need to strengthen the publicity of electromagnetic radiation protection knowledge, so that people have a certain degree

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of protection awareness in daily life, so as to take practical and effective protection measures.

4 Conclusions In a word, with the continuous development and progress of electromagnetic technology, it has brought great convenience to people’s production and life, but it will also bring electromagnetic radiation pollution to the environment, seriously affecting the sustainable development of the ecological environment. Therefore, we should effectively monitor the electromagnetic radiation in the environment and apply practical and effective electromagnetic radiation protection measures, so as to provide a good environment for people’s production and life. Acknowledgements Project supported by State Grid Corporation of China: Research on the technology of electric field control at residential platform near transmission lines (contract number: 8100-202055155A-0-0-00).

References 1. Gong Y, Chi Q, Liu Q et al (2022) Simulation of electromagnetic radiation environmental impact of 5G application base station. J Phys Conf Ser 2242(1):012018 2. Maheshwari RK, Poonia R, Rathore MS et al (2020) Clinical manifestations and protective measures of environmental noise: an overview. Int J Biol Innov 01 3. Meng F, Chen R, Xu Y et al (2021) Centrifuge modeling of effectiveness of protective measures on existing tunnel subjected to nearby excavation. Tunn Undergr Space Technol 112(2):103880 4. Ghislain C (2021) A method and system for detection of electromagnetic radiation. EP3874245A1 5. Sharma S, Laurila T, Rossi J et al (2021) Electromagnetic radiation detection with broad spectral sensitivity and large dynamic range using cantilever-based photoacoustic effect 6. Ke Y, Zhang W, Suo C et al (2022) Research on low-voltage AC series arc-fault detection method based on electromagnetic radiation characteristics. Energies 15 7. Gong Y, Guo X, Liu Q et al (2022) Monitoring and analysis of the current environmental situation of electromagnetic radiation from 5G application base stations. J Phys Conf Ser 2242(1):012026 8. Li Z, Xue F, Liu Y et al (2021) Evaluation of the effects of protective measures implemented in high-rockfill slope engineering based on the discrete element model. IOP Conf Ser Earth Environ Sci 643(1):012154 (12pp) 9. Wang Q, Li C, Xie B et al (2020) A study on the characteristics of electromagnetic radiation during deformation and failure of different materials under uniaxial compression. J Environ Eng Geophys 25(1):139–152 10. Sébastien B, Jean-Jacques Y (2021) Process for manufacturing a device for detecting electromagnetic radiation, comprising a suspended detection element. EP3864386A1

Performance Simulation and Application Research of Typical Directional Valve Based on Neural Network Yuxi Zheng, Yaqin Tang, Zhuotao Zou, and Feng Cao

Abstract The two position four way direction valve is a commonly used hydraulic component, which is widely used in some hydraulic oil circuits. In this paper, a typical cartridge valve consisting of cartridge two position four way directional valve with control components is studied. Based on the hydraulic component design library in the AMESim hydraulic design software, a simulation model of the directional valve is carried out to study the dynamic performance of the above hydraulic system, and the feasibility of this model is also verified. Besides, this paper analyzes the performance of the two position four way directional valve with different damping and spring stiffness coefficients, and discusses its application status, difference and connection based on this typical model. At the end of this basis, according to the simulation results, a BP artificial neural network (ANN) model is proposed to simulate and analyze the dynamic performance of this two position four way directional valve. Keywords Two-position four-way directional valve · Simulation analysis · Neural network

1 Introduction Two-position two-way directional valve is a widely used standard hydraulic component. Due to its simple structure, low cost, large flow capacity and multiple combinations, it is often transformed into two-position multi-way directional valve, multiple directional valve, etc. [1]. Since the directional valve can realize the change of the Y. Zheng · Z. Zou School of Mechanical Engineering, Hefei University of Technology, Hefei, Anhui, China Y. Tang School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, Anhui, China F. Cao (B) School of Electronic and Information Engineering, West Anhui University, Hefei, Anhui, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_37

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liquid flow and the direction of the air flow, and then control the pneumatic and movement direction of the components, it is widely used in almost every hydraulic system and most pneumatic systems. When the fluid flows through the valve port, the change of flow direction and flow rate will cause the change of liquid momentum, which is one of the key factors affecting the performance of the directional valve. In order to detect the state of flow and improve it, many scholars have studied it [2–4]. In this paper, a simulation model of the hydraulic system is established, and the performance of the cartridge two-position four-way directional valve is studied by using the hydraulic cylinder as the actuator and artificial neural network technology.

2 Modeling Analysis of Typical Hydraulic System 2.1 Simulation Platform When establishing the simulation model of the two-position four-way directional valve, we found that ANSYS Workbench and FLUENT fluid simulation software were mostly used to study the mechanical properties of the valve body [5, 6]. For analyzing the working state and dynamic performance of the valve body, the AMESim was mostly used [7, 8].

2.2 Hydraulic Simulation Model In the experimental design, we constructed a theoretical model, as shown in Fig. 1. In Fig. 1, the two-position four-way directional valve consists of four cone valve units and one electromagnetic pilot valve. After four cartridge two-way valves are connected in parallel, the control port is connected to the electromagnetic pilot valve. According to the theoretical model and the working principle of the two-position four-way directional valve, a simulation model is established using hydraulic design library in AMESim software [9–11]. As shown in Fig. 2. The initial parameter settings of the simulation model are shown in Table 1. Our experiment compares the characteristic curves of the cartridge directional valve and the conventional directional valve, and it comes to the conclusion that the dynamics response speed of the cartridge directional valve are faster than another in reversing control process. In the results, the stroke curve of the hydraulic cylinder is shown in Fig. 3. In Fig. 3, 0 ~ 4 s is the incoming stroke, 4 ~ 6 s is the reversing process, and the peak value is the hydraulic cylinder stroke.

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Fig. 1 Schematic diagram of the structure of cartridge two-position four-way directional valve

Fig. 2 A typical simulation model of a two-position four-way directional valve circuit

3 Research on Dynamic Working Performance During the reversing process, the opening and closing of the cartridge valve port will cause the flow to fluctuate. When liquid changes from the static state to the flow state, steady-state hydrodynamic forces are generated which in turn affect the motion

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Table 1 The initial parameters

Name

Numerical value

Load

Mass/Kg

1

pilot valve

Pressure source

0~4s

Cartridge valve

Spool diameter/mm

15

Control port diameter/mm

10

Damping coefficient/(N·s·m-1 )

50

4~8s

Spring rate/(N·mm-1 ) 100 Hydraulic cylinder

Spring Preload/N

5

Hydraulic cylinder bore/mm

25

Rod diameter/mm

12

Stroke/mm

300

Fig. 3 Hydraulic cylinder stroke curve

performance of the actuator. Based on this, we studied the dynamic performance of the system by changing the spring stiffness and damping coefficient. Figure 4 shows the change of the valve port when the cartridge valve is opened and the spring stiffness values are 100 N/mm, 400 N/mm and 700 N/mm respectively. It is not difficult to see that as the spring stiffness decreases, the opening degree of the cartridge valve continues to increase. However, at all times, the change trend is same, and there is a relatively obvious linear proportional relationship. Therefore, with the reduction of the spring stiffness, the movement speed of the valve core will be accelerated, and it can be stabilized faster.

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Fig. 4 The opening of the cartridge valve varies with spring stiffness

Figure 5 shows the change of the valve port when the cartridge valve is opened and the damping coefficients are 50 N s/m, 550 N s/m, 1050 N s/m and 1550 N s/m respectively. It is not difficult to see that with the increase of the damping coefficient, the opening degree of the cartridge valve continues to decrease, and there is an obvious linear proportional relationship. Therefore, with the increase of spring stiffness, the flow rate of the valve decreases, which can effectively reduce the steadystate hydraulic force. Meanwhile, the displacement stabilization time of the valve core will be prolonged. But as the base of the valve opening degree is small, the absolute value of the displacement deviation is also small, and the long stabilization time also eases the degree of fluctuation.

Fig. 5 The opening of a cartridge valve varies with the damping coefficient

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4 Design of Artificial Neural Network Model Through the above research on the dynamic performance of the typical two-position four-way directional valve, it is not difficult to find that the system has a relatively obvious linear regression relationship under different parameter settings. Combined with the widely used artificial neural network (Artificial Neural Network, ANN) BP (Back-Propagation) back-propagation algorithm, the neural network prediction model is constructed. Taking the variation of the spring stiffness of the system parameters as the research object, the AMESim simulation results are exported as a data set. Among them, the dynamic change data of the system are used as the training set when the spring stiffness values are 100 N/mm and 400 N/mm, and the spring stiffness 700 N/mm is used as the test set. The input of the BP backpropagation model is the spring stiffness value and the dynamic response time, and the output is the valve port change when the cartridge valve is opened at the corresponding time. The activation function is a hyperbolic tangent function, and two hidden layers are used. The neural network is shown in Fig. 6. Since the actual results have a good linear relationship, we use a linear regression model to predict the dynamic change results. The sum of squared error in the test set are 0.038 and the relative error are 0.794. This is because the K value is considered to be a constant by the neural network, so it is filtered. At the same time, the number of training sets is small, which is also the reason for the large error. In addition, the number of iterations is small, and the hidden layer of the BP backpropagation model is relatively small. Few factors may also affect the error. Therefore, by adding dynamic performance analysis to the system and training it continuously, it is possible to get a predictable model for

Fig. 6 Neural network diagram

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Fig. 7 Neural network prediction-measured map

changes within a certain range, and then to better predict and explain. The neural network prediction-measured map is shown in Fig. 7.

5 Conclusion Through the simulation analysis of the two-position four-way directional valve, it is not difficult to find that within a certain range, reducing the spring stiffness of the cartridge valve can shorten the reversing time. Within a certain range, increasing the damping coefficient can reduce the steady flow force to make the flow fluctuations tend to be smooth. Besides, reducing the damping coefficient can make the reversing process faster. It is technically feasible to use a neural network to construct a dynamic working condition prediction curve in any state within the range of spring deformation, but in the follow-up research, a motion equation containing K should be derived to replace the linear regression equation used, so as to predict the effect of the spring rate on the system precisely.

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References 1. Petrov O, Kozlov L, Lozinskiy D, Piontkevych O (2020) Improvement of the hydraulic units design based on CFD modeling. Advances in design, simulation and manufacturing II, pp 653–660 2. Jaliel AK, Badr MF (2020) Application of directional control solenoid valves in pneumatic position system. IOP Conf Ser Mater Sci Eng 870:012044 3. Filo G, Lisowski E, Rajda J (2020) Pressure loss reduction in an innovative directional poppet control valve. Energies 13(12) 4. Salloom MY, Samad Z (2012) Magneto-rheological directional control valve. Int J Adv Manuf Technol 58(1–4):279–292 5. Lisowski E, Czyzycki W, Rajda J (2014) Multifunctional four-port directional control valve constructed from logic valves. Energy Convers Manage 87:905–913 6. Khadraoui S, Hachemi M, Allal A et al (2021) Numerical and experimental investigation of hydraulic fracture using the synthesized PMMA. Polym Bull 78(7):3803–3820 7. Jusoh MA, Ibrahim MZ, Daud MZ et al (2020) Parameters estimation of hydraulic power take-off system for wave energy. In: International conference on sustainable energy and green technology 2019, p 463 8. Dumitrescu L, Safta C-A, Cristescu C et al (2021) Mathematical model of the lifting working mode of a mobile platform. In: 10th International conference on energy and environment (CIEM), p 5 9. Hwang H, Nam K (2020) Design of a robust rack position controller based on 1-dimensional amesim model of a steer-by-wire system. Int J Automot Technol 21(2):419–425 10. Syrkin VV, Galuza YF, Kvasov IN, Fedorova MA (2021) Study of the effect of feedback on dynamic stability of hydraulic. J Phys Conf Ser 5 11. Polovnikov EV, Konev VV, Merdanov SM (2021) Modeling the warm-up process for hydraulic actuator components. IOP Conf Ser Mater Sci Eng 1103:012024

Intelligent Multimedia News Communication Platform Based on Machine Learning and Data Fusion Technology Ying Qian

Abstract The emergence of news communication platform is the product of media convergence, and platformization has become one of the mainstream trends of media convergence. In recent years, big data has had a great impact on academia. In the field of news communication, there are also many articles discussing the transformation of news communication from the perspective of big data. The arrival of big data era has a comprehensive impact on the media. It not only subverts the information production and communication of traditional media, but also has a great impact on online media. Therefore, this paper studies and analyzes the intelligent multimedia news communication platform (MNCP). This paper analyzes the operation mechanism and development trend of the intelligent MNCP, as well as the problems faced by the multimedia news platform in the intelligent era, and then discusses the intelligent MNCP based on Machine Learning (ML) and data fusion technology (DFT). By using DFT to test the contact ratio of different forms of audiences to different media and the timeliness and accuracy of the news communication platform to obtain the latest news materials. It verifies the effectiveness of applying ML and DFT to intelligent MNCP, which is of great significance for news communication. Keywords Machine learning · Data fusion technology · Intelligent multimedia · News communication platform

1 Introduction The intelligent MNCP comes from time to time, and the influence of key factors on its development process cannot be ignored. The technical factors as the foundation of the platform, the economic factors as the driving force of the platform and the political factors as the guiding force play an important role in it. Technical factors are the basis Y. Qian (B) School of Media, Guangxi Vocational Normal University, Nanning 530000, Guangxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_38

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of intelligent MNCP. With the innovation and breakthrough of new computer technologies such as big data, artificial intelligence, Internet of things, cloud computing and blockchain technology, the era of deep integration has really come. The time difference between the invention and promotion of technology and its application has been greatly shortened. The inherent platform gene of the news communication platform reduces the rejection of new technology applications and focuses on its advantages while keeping up with the cutting-edge technologies. “Algorithmic news” based on big data and algorithmic technology leads a new way of content production. Its data processing ability and news communication efficiency once made such news communication platforms widely sought after and discussed. Many scholars at home and abroad have studied the intelligent MNCP based on ML and DFT. Sodhro a h develops a QoS aware, green, sustainable, reliable and available (qgsra) algorithm to support multimedia transmission in V2V on the future Internet of things driven edge computing network; During multimedia transmission on the edge computing platform based on the Internet of things, a new QoS optimization strategy is implemented in V2V; Propose QoS indicators, such as green degree (i.e. energy efficiency), sustainability, reliability and availability, to obtain better QoS in the V2V multimedia transmission process of the next generation network [1]. Morocho cayamcela me has established the basic concepts of supervised, unsupervised and reinforcement learning from the perspective of ML, investigated the work done so far in adopting ML in mobile and wireless communication environment, organized literature according to learning types, and discussed the intelligent MNCP of ML and DFT [2]. The development of new technology has led to great changes in the communication pattern and communication concept. In order to get a seat in the fierce media market competition, various media intersect, penetrate and integrate with each other to form a pattern of competition and cooperation. At the same time, at the technical level, the continuously developing and mature digital technology can integrate text, pictures, audio, video Animation, these independent communication technologies, have been rapidly transformed into computer-readable digital forms, thus forming a unified means of multimedia communication and promoting the development of media. ML and data fusion technologies comprehensively help the transformation and upgrading of news communication platforms in content production, information communication, brand planning and management. Intelligent technologies prompt the development direction of news communication platforms and innovate news production and communication methods, which have become the driving force for the development of news communication platforms [3, 4].

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2 Intelligent MNCP Based on ML and DFT 2.1 Operation Mechanism and Development Trend of Intelligent MNCP In the process of connecting customers and users, the news aggregation platform dominated by technical elements agglomerates users with interest, and its algorithm recommendation mechanism builds a bridge for the connection. In the relationship network, although advertisers have not made direct contact with users on the surface, the purpose of advertisers is one of the driving forces for the operation of the entire platform ecosystem. On the basis of giving certain economic benefits, advertisers complete the arrival of advertising through the choice of users and the promotion of the platform. The algorithmic advantage of the news aggregation platform dominated by technical elements is that while aggregating content, it is equally important to serve users on the basis of understanding users and mining interest points, so as to transform user resources into consumer resources required by advertisers. The main operator of the news distribution platform dominated by traffic factors is the Internet giant. The core technology applied by the Internet giants represented by bat (Baidu, Alibaba, Tencent) is often based on the auxiliary technology of the original platform. The diversification of its business and strong operation foundation facilitate the technology introduction and innovation of the news distribution platform. Baijiahao’s “creative brain” integrates Baidu’s powerful artificial intelligence technology, Including intelligent error correction, video understanding and sharing ar materials. The diversity of the platform’s user population will inevitably lead to diversified social relations and promote the energy interaction of the platform ecology. The news distribution platform dominated by traffic elements has the advantage of multiple channels and intelligent distribution, and its rich platform ecology helps it become another major force of news communication [5]. In the process of combining technology, customers, users and other elements, the operation mode construction, relationship structure adjustment and platform operation objectives of the news production platform led by specialized media are always guided by the traditional media. The news aggregation platform led by technical elements focuses on user elements, focuses on user innovation of algorithm technology and optimization of platform services, and realizes the effective connection between users and customers. The news distribution platform dominated by traffic elements relies on the strong basic traffic of Internet giants to connect content producers with distribution channels. Obviously, there are great differences in the basic elements of the three types of news communication platforms, but the emphasis on the key elements of users and customers is the same. The news production platform dominated by specialized media relies on the intelligent production system to share in an open attitude, connecting the media and the production platform, connecting different media, connecting the media and users, and sharing technical products and services with the media.

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2.2 Problems Faced by Multimedia News Platform in Intelligent Era The development trend of news communication platform one of the characteristics of platformization is socialization. Today, social networking is no longer a function required by users to maintain contact, but exists as a lower infrastructure for product expansion. As an instrumental client, news and information media are difficult to form strong contact with users, and some technology-based news communication platforms lack long-term loyal user groups. The introduction of social networking will undoubtedly become a shot in the arm for headlines. Therefore, the content push of thousands of people and thousands of faces can not avoid copying or imitation in technical means. The news communication platform regards the user information and labels as the original data of accurate push with the help of technology, but the user information is relatively independent and cannot build a social network, which is the current problem [6, 7]. The emergence and agglomeration of a large number of non professional content producers. While the open content production platform provides access for diversified content producers, the emergence and agglomeration of a large number of non professional content producers have also brought new severe problems to the news communication platform. Under the guidance of the news communication platform, the Internet platform has become the main flow direction of traditional media and professional news, which may make the media become the “migrant workers” of the Internet platform, thus accelerating the marginalization of traditional media. In response to the unbalanced development trend between Internet platforms and traditional media platforms, traditional media are not waiting to die. Traditional media platforms are competing for the right to speak, distribution and independence from Internet platforms. In the huge flow of content creation, distribution and platform management based on artificial intelligence, the income of content creators is closely related to the number of clicks, readings and other exposure data, and false news is rampant. Therefore, it is particularly important to integrate ML and DFT into the intelligent MNCP [8, 9].

2.3 Intelligent MNCP Based on ML and DFT The decentralization of media and the diversification of media choices provide the premise for the user centered theory. The algorithm mechanism pushes the user centered theory to the forefront. While customizing the news information, it restricts the users to the news information environment that users choose and strengthen, excessively caters to the needs of users, and even leads to the practice of the unique user centered theory. Obviously, the user needs on which the user only centrism is based are relatively narrow and one-sided information. The user centrism calls for a

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more intelligent information screening and recommendation mechanism in order to truly and comprehensively insight and distinguish the real user needs [10, 11]. When random optimization algorithm is used in computer parallel mode, data generation methods usually include “online data generation” and “offline data generation”. Online data generation means that the news data accessed by the audience is generated immediately according to the real distribution of the data; The off-line data generation is that Mr. Shi becomes an off-line data set that conforms to the distribution of real news, and the working nodes repeatedly sample the training data from the off-line data set through uniform distribution. Data parallelism: when the training data is too large to be shared in the public memory, the news data set needs to be divided and then allocated to each work node. The work node uses local data to train the model, which is called “data parallelism”. When the training data dimension is high, the data can be divided by news type, which is called “data dimension division”. Compared with data sample division, data dimension Division has a higher degree of coupling to model properties and optimization algorithms [12].

3 ML and DFT 3.1 Basic Process of Distributed ML Generally, ML algorithms are given data D and model Ψ. Then, the objective function is iterated until convergence, which can be expressed as Eq. (1). Where the superscript r refers to the number of iterations, and the objective function Δ Ψ Acts on data D and model state u, while function f acts on Δ Ψ The result of the calculation then produces an update of the model status. U (r ) = F(U (r −1) , Δψ(U (r −1) , D))

(1)

There are mainly two parallel modes of distributed ML: one is that in the face of massive training data, the training data needs to be segmented. The methods are based on random sampling (with the method of putting back) and based on scrambling segmentation, and then distributed to multiple computing nodes. Each work node has a copy of the model. The local data DQ is used to update the copy of the model. Then each work node communicates to announce each other’s latest models or updates to the models. Finally, the training results of each node are integrated through the aggregation logic to obtain the global model. The iteration formula in the data parallel mode can be rewritten into formula (2). U (r ) = F(U (r −1) ,

q ∑ 1

Δψ(U (r −1) , D))

(2)

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In distributed ML in model parallel mode, the sub models of each working node are closely coupled. Each node needs to obtain the intermediate calculation results related to it. These intermediate calculation results come from other sub models, which may be activation values or error values. Otherwise, training cannot continue. Therefore, the distributed ML framework in model parallel mode has high requirements for communication. It is still the main conflict that the model training time is too long because of the massive training data. Data parallelism is the most common parallel mode of distributed ML.

3.2 Multimedia DFT Multimedia DFT includes feature image matching. Feature based image matching first extracts the feature set of the image to be matched, then finds the similar matching point pairs in the feature set through similarity measurement to form the corresponding matching point pair set, and finally selects an appropriate global transformation model to estimate the transformation parameters in the global transformation model through this set of matching point pairs. The key of feature-based image matching is how to extract and select robust features, and how to overcome the problem of mismatching. (1) Direct average fusion method The direct average fusion method is to average the pixel values corresponding to the overlapping areas of two images. As shown in formula (3), (a, b) and (a, b) respectively represent the pixel values of the two images to be stitched at point (x, y), and H (a, b) represents the pixel values of the fused image at point (x, y). ⎧ ⎨ H1 (a, b), (a, b) ∈ H1 H (a, b) = 21 (H1 (a, b) + H2 (a, b)), (a, b) ∈ H1 ∩ H2 ⎩ H1 (a, b), (a, b) ∈ H2

(3)

This image fusion method is simple and fast, but the effect is not satisfactory. The color transition of the fused part is very uneven, and the human eye can clearly observe the seam. (2) Weighted average fusion method The weighted average fusion method is similar to the direct average fusion method, but the seam of the fused image is smoother. This method is to add the pixel values of the overlapping area of the two images according to a certain weight, as shown in Eq. (4), K1 and K2 respectively represent the weight of the corresponding pixel points in the overlapping area of the two images, and k1 + k2 = 1 (0 < k1 < 1, 0 < k2 < 1).

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⎧ ⎨ H1 (a, b), (a, b) ∈ H1 H (a, b) = k1 H1 (a, b) + k2 H2 (a, b), (a, b) ∈ H1 ∩ H2 ⎩ H1 (a, b), (a, b) ∈ H2

(4)

4 Experimental Test Analysis In order to verify the effectiveness of applying the DFT and ML algorithm proposed in this paper to the intelligent multimedia news platform, in the era of traditional media, the means of news reporting is relatively simple, which is far from meeting the audience’s focus needs. The development of digital technology and network technology has brought a diversified broadcast platform, and the integration between different media can make the audience more convenient to obtain information, At the same time, they are more and more inclined to receive instant information including text, pictures, audio, video, animation, forums and other forms. By using DFT, this paper tests the contact ratio of different forms of audiences to different media, as shown in Table 1 and Fig. 1. It can be seen from the figure that under the DFT, different groups and different types of audiences have different preferences for contacting the media. In the process of production and development, the multimedia news platform should firmly grasp this characteristic change of the audience, clarify the psychology of the target audience, formulate the development strategy of diversification, and spread creative news products. Next, through the statistics of the past two months, the timeliness and accuracy of the intelligent MNCP to obtain the latest news materials under the combination of DFT and ML algorithm are tested. The test results are shown in Fig. 2. It can be seen that the combination of DFT and ML algorithm applied to the intelligent multimedia news platform is of great help to obtain the latest news materials in Table 1 Comparison of media exposure ratio of different audiences Media

Modern open type

Adaptive following type

Advertising alienation

Moderate random type

Fusion adaptation

Career striving type

Television

98.7

98.9

99.0

98.7

98.9

99.0

Radio broadcast

41.3

33.5

22.8

28.8

45.6

38.5

Network

67.1

39.4

31.0

31.3

51.4

58.4

Newspaper

71.1

64.1

42.6

49.6

88.8

77.9

Magazine

52.9

35.6

24.7

28.9

51.8

50.6

Film

24.4

11.2

8.3

8.6

16.6

21.7

2.9

1.5

1.1

1.5

1.8

2.9

53.1

43.0

38.9

36.0

54.1

58.5

Mobile TV Car TV

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120

Contact ratio(%)

100 80 60 40 20 0

television

radio broadcast

network

newspaper

magazine

film

mobile TV

Car TV

Contact ratio of different forms of audiences to different media Modern open type

Adaptive following type

Advertising alienation

Moderate random type

Fusion adaptation

Career striving type

Fig. 1 Contact ratio of different forms of audiences to different media

current politics, military, urban, humanities and entertainment, and can better ensure the authenticity of the news; It verifies the effectiveness of applying ML and DFT to intelligent MNCP, which is of great significance for news communication.

5 Conclusions Since the development of MNCP, the platform trend has become its remarkable feature. This paper applies ML and DFT to intelligent MNCP and achieves good results. However, there are also shortcomings. The news communication platform should guarantee and supervise the authenticity and legitimacy of its content in the process of disseminating information. Whether intelligent algorithms cater to the audience or the selective contact psychology of the audience, it is easy to create information cocoons. Regardless of the right and wrong of “algorithm kidnapping content”, it plays down the chief editor’s work. The intelligent MNCP based on ML and DFT needs further study in the future.

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entertainment

News material

humanity

urban

military

Current politics 0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Timeliness and accuracy Accuracy

timeliness

Fig. 2 Timeliness and accuracy of news materials obtained by news communication platform

References 1. Sodhro AH, Obaidat MS, Abbasi QH et al (2019) Quality of service optimization in an IoTdriven intelligent transportation system. IEEE Wirel Commun 26(6):10–17 2. Morocho-Cayamcela ME, Lee H, Lim W (2019) ML for 5G/B5G mobile and wireless communications: potential, limitations, and future directions. IEEE Access 7(99):137184–137206 3. Panova E, Shumakova E (2019) Precedence in the era of multimedia communication. Przeglad Wschodnioeuropejski 10(1):357–369 4. Harris AD (2019) New chairman sets sights on collaboration and communication. Air Conditioning Heating Refrig News 266(9):8–9 5. Bangotra DK, Singh Y, Selwal AK (2020) An intelligent opportunistic routing protocol for big data in WSNs. Int J Multimedia Data Eng Manag 11(1):15–29 6. Dardari D, Decarli N (2021) Holographic communication using intelligent surfaces. IEEE Commun Mag 59(6):35–41 7. Ali ES, Hasan MK, Hassan R et al (2021) ML technologies for secure vehicular communication in internet of vehicles: recent advances and applications. Secur Commun Netw 2021(1):1–23 8. Rodrigues IR, Barbosa G, Filho AO et al (2022) Modeling and assessing an intelligent system for safety in human-robot collaboration using deep and ML techniques. Multimedia Tools Appl 81(2):2213–2239 9. Usman M, Jan MA, Jolfaei A (2020) SPEED: a deep learning assisted privacy-preserved framework for intelligent transportation systems. IEEE Trans Intell Transp Syst (99):1–9 10. Molinara M, Ferdinandi M, Cerro G et al (2020) An end to end indoor air monitoring system based on ML and SENSIPLUS platform. IEEE Access (99):1–1

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11. Kahraman E, Gokasan TA, Ozad BE (2020) Usage of social networks by digital natives as a new communication platform for interpersonal communication: a study on university students in Cyprus. Interact Stud 21(3):440–460 12. Challita U, Ferdowsi A, Chen M et al (2019) ML for wireless connectivity and security of cellular-connected UAVs. IEEE Wirel Commun 26(1):28–35

Digital Economic Dispatch System Based on Improved Genetic Algorithm Yuan Li and Xin Yu

Abstract China has entered the stage of rapid economic development, and its comprehensive national strength has advanced by leaps and bounds. Digital technology not only promotes the digital transformation of traditional industries, but also drives the development of emerging industries. The development of digital economy has become the only way for economic development. Genetic algorithm is a highly abstract algorithm, which can be applied in many disciplines and has become a hot topic in interdisciplinary research. The purpose of this paper is to realize and optimize the digital economic dispatching system based on the improved genetic algorithm. In the experiment, through the comparison between the improved genetic algorithm and the standard algorithm, the improved genetic algorithm is used to find the optimal digital economic dispatching system, so as to make up for the shortcomings of the classical genetic algorithm. Keywords Genetic algorithm · Improved genetic algorithm · Digital economy · Digital communication scheduling system

1 Introduction With the rise of digital economy, the application of digital technology makes it possible for traditional industries to develop “low consumption and high income”, and also opens up a situation for the industry [1]. The traditional industries of developed countries are giving way to the emerging digital industries. Developing countries should also absorb the wave of digital economy, guide the economic transformation and upgrading of all walks of life, and independently win the dominant power of improvement. The rapid improvement of digital economy has improved the industrial transformation and upgrading, and brought changes to the production mode and Y. Li (B) · X. Yu Shandong Institute of Commerce and Technology, Jinan, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_39

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personal consumption mode of enterprises. In addition, the development of traditional industries has also been digitalized. The rapid development of digital economy has driven the industrial transformation and upgrading, and brought changes to the commercial production mode and personal consumption mode. The development of traditional industries has also been digitalized. It can be seen that the development of digital industry has an increasing impact on China’s economy. Digital industry is a high-tech industry with high added value and low pollution. Its development has multiple effects, such as improving economic benefits of enterprises and reducing pollution. Negrie’s research shows that the emergence of industrial technology has changed many industries, among which intelligent scheduling is one of the most important industries. He has contributed to scheduling research by suggesting the integration to forecasting into activity scheduling and the integration of a field-synchronized equipmentin to the framework. The meta reasoning of optimal scheduling is realized by genetic algorithm related to DT simulation, and provides multi generation scheduling alternatives evaluated by the same simulator. To define how to implement different framework modules to the actual use cases created in the laboratory pipeline environment, it also provides a practical proof [2]. Sahraeish believes that cloud programming is considered the most difficult management problems, especially when it comes to the best way to manage. The above problems can be solved by genetic algorithm based on management. The main goal is to manage functions with higher utilization. Therefore, a solution is adopted and a new job creation model is proposed. Manufacturing time, cost and usage are analyzed according to two other existing scheduling models. The results showed significant improvements in cost, manufacturing time and availability [3]. Ibrahim M f believes that the vehicle steering problem has many applications in practical systems. The optimal trajectory of the vehicle will affect the increased economic benefits. The goals of this issue include reducing mileage. The three penalties under consideration are the ability penalty, the ability to work on time and the ability to work overtime. Freight and logistics companies are introduced into the research. The proposed method obtained by genetic algorithm is better than the existing methods and previous algorithms. In addition, the influence of iteration times on driving distance, penalty times and fitness value is also analyzed [4]. Therefore, a full understanding of the current situation of the development of digital economy and a clear understanding of the specific impact of digital economy on economic development will help to introduce new momentum into China’s economic development, find support, promote high-quality economic growth and achieve sustainable economic growth. The paper is given that the genetic algorithm describe the digital economy and its scheduling system, and to clarify the concept and algorithm flow of genetic algorithm. In the experiment, through the comparison and calculation of the optimal solution of algorithm, the improved genetic algorithm is used to find the optimal digital economic dispatching system, which promote the productivity of the algorithm and realizes and optimizes the economic dispatching system.

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2 Research on Digital Economic Dispatch System Based on Improved Genetic Algorithm 2.1 Definition of Digital Economy Digital economy is based on digital knowledge and information technology. Under its guidance, it will change the manufacturing and marketing process of multidisciplinary construction work, thus changing manufacturing, enterprise management services and vitality services: a new form of digital integration [5]. Its definition is based on access to goods and services and digital companies. According to the IMF, digital programming represents the penetration and expansion of digital technology in many industries and systems. Internationally, digital economy generally refers to digital resources, and its scope is larger than that of the Internet.

2.2 Digital Economic Dispatching System The static economic task of the digital economic dispatching system is to make reasonable load distribution for each dispatching cycle within the dispatching cycle. The dynamic economic dispatching includes checking the relationship between each planning period within the planning period. Its advantage is that it is closer to the actual operation of the system and the dispatching result is more accurate [6]. The so-called dynamic economic dispatching research needs to take into account the changes of physical quantities in successive periods before and after, not only the continuous change of system load with time, but also the change rate of corresponding unit output. Its purpose is still to make the system achieve economic optimization in the whole dispatching cycle. The dynamic economic optimal dispatching strategy is proposed to track the demand of digital economy in real time. However, to achieve complete real-time tracking, the current dynamic optimization algorithm is difficult to achieve dynamic optimization calculation in a small scheduling period due to its large amount of calculation [7]. Therefore, the research on optimization algorithm also needs to be more in-depth. The purpose is to find a dynamic optimal scheduling algorithm with less computation and good global optimization performance.

2.3 Improved Genetic Algorithm (1) Genetic algorithm As an artificial intelligence algorithm, genetic algorithm is widely used in production field. The application of genetic algorithm in practical production has achieved good economic benefits and the development of organisms [8]. The

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main reason is the crossover and variation between chromosomes, which realizes gene recombination. On the basis of biological genetic mechanism, there are different schemes to deal with different codes according to different problems, and a variety of coding methods have been developed to imitate biological genetic characteristics. Therefore, genetic algorithm does not solve the problem itself, but encodes the solution. Therefore, how to convert the solution or solution of the problem into coding is the key of genetic algorithm [9]. Genetic algorithm performs genetic operation on individuals in the coding domain, and calculates and selects individuals in the solution domain. The process from the solution space to the encrypted space is called encoding, and the process from the encrypted space to the solution space is called decoding. The problem is not actionable or illegal, which means that the solution of chromosome decoding does not meet the constraints of the problem. Illegal means that the chromosome does not conform to the decoding rules and cannot be decoded. (2) Specific process of genetic algorithm The research object is all individuals in a population. The method guided by randomization technology is used to efficiently search a coded parameter space. In this process, its genetic operations include selection, crossover and mutation. Its core contents include parameter coding, initial population setting, fitness function design, genetic operation design, and control parameter setting [10]. The specific process is shown in Fig. 1. (3) Improved genetic algorithm During the solution process, the intercept probability and mutation probability of the algorithm will change with the evolution [11]. The crossover Fig. 1 The idea of genetic algorithm

encoding initialize the population

to assess the individual fitness in the population

evolution

select

cross connection

heteromorphosis

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value and mutation probability of the improved genetic algorithm are not fixed values, but the probability values change adaptively with the population fitting. The closer the fitness value of the population in the algorithm is to the larger value, the smaller the probability of individual transformation or mutation, or even zero (the individual fitness value is the maximum value). When the population is close to a constant, the mutation probability method is very suitable. In the initial stage of population evolution, the individual is in an unstable state, and the better individual is likely to be in a constant state, so this method is not suitable. If different individuals cannot be identified as global optima at the early stage, the possibility of falling into local optima during development will increase.

3 Investigation and Research on Digital Economic Dispatch System Based on Improved Genetic Algorithm 3.1 Improved Genetic Algorithm Fitness function, also known as fitness function, is used in the genetic algorithm of individual fitness to measure the quality of individuals or chromosomes in a population. Fitness function is an evaluation function that is transformed from objective function to evaluate the individual quality in the community. It can not only provide driving force for the algorithm, but also provide basis and criteria for genetic selection. Generally speaking, the value of fitness function is always positive, and the fitness of an individual is directly proportional to the size of his constitution. However, the value of the objective function cannot always be positive, and the selection pressure must change proportionally under different circumstances. Therefore, in order to meet the requirements of fitting function, it is necessary to convert the objective function into a certain form. The algorithm formula is as follows: 

g(x) −g(x)

(1)

cmax − g(x), g(x) ≺ cmax 0, other s

(2)

cmax + g(x), g(x)  cmax 0, other s

(3)

f (x) =  f (x) =  f (x) =

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3.2 Parameter Setting The setting of parameters will have a significant impact on the nature or function of genetic algorithm, especially the convergence of the algorithm. The number of individuals in the population has a significant change on the speed and calculation of genetic algorithm. If the number of individuals is too small, the diversity of individuals in the population will become smaller, it is difficult to reach high-quality research points, and may converge to the local optimal solution. However, if the number of individuals is too large, the amount of calculation is too large, which will reduce the efficiency of the account. Therefore, the number of individuals in the population should be sufficient. Similarly, the values of crossover probability and mutation probability should not be too high or too small, and the range should be adjusted according to specific problems. Although configuration is not an important step of genetic algorithm, it is the factor to determine the optimal value, so the genetic algorithm setting must be accurate and need to be modified many times.

4 Analysis and Research of Digital Economic Dispatch System Based on Improved Genetic Algorithm 4.1 Example Verification As to the basic genetic algorithm, in order to verify the algorithm, the standard genetic algorithm is easy to fall into local optimization, while the improved genetic algorithm has the function of global search and short time. In the standard genetic algorithm, the fitness function only obtains the local optimal solution 79.753, while the improved genetic algorithm can obtain the global optimal solution 79.7565, as shown in Table 1 and Fig. 2. The results show that the improved genetic algorithm is better than the standard genetic algorithm, and the time is reduced, and the computational efficiency is improved. Based on the content and principle of genetic algorithm, an improved genetic algorithm is proposed, and the improved genetic algorithm is used to find the optimal digital economic scheduling system to make up for the shortcomings of Table 1 Comparison of the optimal solutions, means, and run times

Algorithm

Optimum solution

Average value

Performance period

Improve the genetic algorithm

79.7534

79.4576

28.6745

Standard genetic algorithm

79.7565

79.3614

5.3213

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90 79.7534 80

79.7565

79.4576

79.3614

70

Value

60 50 40 28.6745

30 20 10

5.3213

0 Improve the genetic algorithm

Standard genetic algorithm

algorithm optimum solution

average value

performance period

Fig. 2 Improved genetic algorithm and standard algorithm legend

classical genetic algorithm. By constructing genetic mechanism, the algorithm is not easy to reach the partial optimal level; the use of fitness function not only improves the level and ability of the improved function, but also improves the stagnation of the original algorithm in the later stage. It can be seen from the experiment that the algorithm greatly improves the computational efficiency and convergence accuracy.

5 Conclusions Digital economy leads to the gradual decline of traditional economy with the rise of material consumption, and the positive development of emerging industries supported by digital technology is more conducive to the realization of green economy in the future. The digital dispatching system can integrate different components into a whole. More funds and resources should be invested to build the communication infrastructure, so that the digital dispatching system can realize the system data with reliability and stability. Based on the improved genetic algorithm, the digital economic scheduling system is optimized, which not only improves the search ability, but also improves the efficiency, so as to promote the level of solving economic scheduling problems.

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Acknowledgements This work was supported by Shandong Institute of Commerce and Technology Funds (A110, C110).

References 1. Pedram H, Sharifabadi HH, Raji M et al (2020) A genetic algorithm-based tasks scheduling in multicore processors considering energy consumption. Int J Embedded Syst 13(3):264–265 2. Negri E, Ardakani HD, Cattaneo L et al (2019) A Digital Twin-based scheduling framework including equipment health index and genetic algorithms. IFAC-PapersOnLine 52(10):43–48 3. Sahraei SH, Kashani M, Rezazadeh J et al (2019) Efficient job scheduling in cloud computing based on genetic algorithm. Int J Commun Netw Distrib Syst 22(4):447–467 4. Ibrahim MF, Putri MM, Farista D et al (2021) An Improved genetic algorithm for vehicle routing problem pick-up and delivery with time windows. Jurnal Teknik Industri 22(1):1–17 5. Mirzaie K (2020) An improved memetic genetic algorithm based on a complex network as a solution to the traveling salesman problem. Turk J Electr Eng Comput Sci 28(5):2910–2925 6. Lou Q, Chen J, Niu H et al (2021) An integrated energy system scheduling optimization method considering economy, stability and environmental protection. J Phys Conf Ser 1914(1):012–017 7. Pyne S (2019) Scheduling of dual supercapacitor for longer battery lifetime in safety-critical embedded systems with power gating. Comput Digital Tech IET 13(6):429–442 8. Mahmudy W, Sarwani M, Rahmi A et al (2021) Optimization of multi-stage distribution process using improved genetic algorithm. Int J Intell Eng Syst 14(2):211–219 9. Patil PB, Shastry M, Ashokumar D (2020) A novel approach for prediction of cardio vascular disease: an improved genetic algorithm approach using classifiers. Test Eng Manag 2020(83):5246–5251 10. Kumari R, Gupta N, Kumar N (2020) Image segmentation using improved genetic algorithm. Int J Eng Adv Technol 9(1):1784–1792 11. Paulis D, Cecchetti, Olivieri et al (2019) Efficient iterative process based on an improved genetic algorithm for decoupling capacitor placement at board level. Electronics 8(11):1219–1220

Research on Target Detection and Anti-Attack Method Based on Neural Network Yi Wu, Jinchi Zhang, Hao Luo, Zhihao Zhong, and Xiaosheng Shi

Abstract The well trained neural networks have performed much better than human beings in many daily task, like object detection and image classification. The security has also been an important issue while neural network makes our daily life much more convinient. Adversarial attack can attack on the input with small perturbation to let network make wrong prediction. Based on the information that attacker can get during the attack, we can divide the attack methods into white box attack and black box attack. Currently, all the black box attack methods are designed for image classification, and there is no black box attack for object detection. In this paper, we successfully apply the Boundary Attack which was originally designed for image classification on object detection task. Keywords Neural network · Black box attack · Image classification

1 Introduction Target detection is one of the common tasks of neural network, and neural network should be able to detect many objects in the field of vision. The detection task includes positioning task and classification task, which is more complicated than the simple image classification task. For example, intelligent driving is a target detection scene, and the vehicle-mounted neural network should detect the objects on the road surface Y. Wu · X. Shi (B) Guangdong Intelligent and Connected Vehicles Innovation Center Co., Ltd., Guangzhou 511434, Guangdong, China e-mail: [email protected] Y. Wu · J. Zhang · H. Luo · X. Shi GAC R&D Center, Shenzhen 511434, Guangdong, China Z. Zhong Open Security Research, Shenzhen 518000, Guangdong, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_40

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in real time, and decide the next operation according to the detection results (classification + positioning). Compared with simple image classification, because more scenes in life are target detection scenes, it is more practical to study the confrontation attacks on target detection scenes than the attacks on image classification.

2 Background Compared with the image classification task, the target detection task needs more complex networks (such as SSD and RCNN). Attacking this kind of network is a more complicated task, and a slight disturbance will have a huge impact on the output. Therefore, the design of disturbance should be more elaborate, so as to make the detector go wrong with a small enough disturbance. Compared with the whitebox attack, the black-box attack can use less information, and more often, it directly uses the classification result or the confidence of classification to attack, but cannot know the information in the calculation process and the information related to the model, so the attack is more difficult. Up to now, because of the complexity of the target detection task and the high difficulty of black box attack, all the counter attack methods for target detection are white box attacks. However, the white-box attack requires too much authority of the attacker, so it is necessary to obtain more model parameters. In the actual application scenario, the model parameter information is unlikely to be output outside the model. Therefore, black box attack is a more practical attack method [1, 2]. If the target detector can be attacked by black box attack, it may bring great threat in real life. In this paper, the black box attack algorithm HopSkipJump Attack [3], which was originally applied to image classification, was modified and adapted to the target scene, and the attack was successful.

2.1 Object Detection In computer vision, target detection, segmentation and image classification are common tasks [4, 5]. Object detection will detect every object in the picture, and the neural network will give the coordinates and categories of the objects. We can draw positioning boxes according to the given coordinates, and visually mark the object categories on each positioning box. The traditional target detection algorithm extracts features manually, the accuracy of this manual feature extraction method is not high enough, and the amount of calculation is too large. Later, a target detection algorithm based on neural network was developed. Neural network can automatically extract features, and its accuracy and calculation speed are greatly improved compared with manual extraction. The target detection algorithm based on neural network can be roughly divided into two categories. One is the two-stage detection algorithm represented by RCNN, which has higher accuracy but slower speed. The other kind is the one-stage detection

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algorithm represented by SSD, which will have faster detection speed but slightly lower accuracy. In some scenes with high real-time requirements, such as automatic driving scene [6, 7], SSD model [8–11], which has faster detection speed, will be preferred.

2.2 Counter Attack In 2014, Szegedy and other scholars found that the trained high-precision model can be misjudged only by adding slight disturbance to the input, and the added disturbance is small enough that human beings can hardly detect it [12]. For example, after adding a disturbance to a picture, the human eye cannot detect the difference between the pictures before and after the disturbance. Anti-sample attack against AI system means that the attacker can cheat the target system by constructing specific input samples without changing the target machine learning system. For example, an attacker can modify the non-critical features of a malicious sample so that it is judged as a benign sample by an anti-virus system, thus bypassing detection. Samples specially constructed by attackers to carry out escape attacks are usually called “confrontation samples”. Adversarial Example is an interesting phenomenon in machine learning models. Attackers can make machine learning models accept and make wrong classification decisions by adding subtle changes to the source data that are difficult for human beings to recognize through their senses. The biggest difference between an attack and other attacks against AI systems is the difference of the target. Conventional attacks on AI system, the object of attack is the model itself of AI system, which makes the model itself shift, and then can’t give a correct judgment. Even if the input sample is benign, it will lead to wrong judgment. To some extent, this attacked model is a bad one that can’t be used normally. The object of attack against attack is the input, and only the input is disturbed, without any attack on the model, so only those deliberately disturbed inputs can make the model produce wrong output. The AI model in this case is a good model that can work normally. Because confrontation samples are indistinguishable from human beings, this concealment may bring great harm. For example, if the traffic signs on the road have been maliciously disturbed, intelligent driving cars may misjudge the contents of the signs, which will lead to wrong operation and very dangerous consequences. For example, if a road sign that is forbidden to pass is identified as a speed limit sign, it will drive on a road that should not have passed. Confrontation can be divided into white-box attack and black-box attack according to the attacker’s mastery of model information. White box attack is characterized by knowing the algorithm used by the model, the parameters used by the algorithm, and other characteristic information of the model. The attacker can interact with the machine learning system in the process of generating antagonistic attack data, and can know the gradient information in the calculation process of neural

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network. Some white-box algorithms will use the gradient information to guide the direction of resisting disturbance, which means that the neural network is completely transparent to the attacker when running. Compared with white-box attack, it can get much less information. Therefore, black-box attack is also a more difficult attack way than white-box attack [13, 14]. At the same time, in real application scenarios, the deployment and operation of neural network systems have good protection measures, so white-box attacks are more often only applicable to academic research. However, the black-box attack can be realized by acquiring less model information, which is more in line with real application scenarios. In terms of attack targets, confrontation attacks can be divided into targeted attacks and untargeted attacks. In the multi-classification problem, the targeted attack will specify which kind of input the neural network wants to classify after the attack before the attack, and it will move in the direction of improving the confidence of the targeted class during the attack. However, the untargeted attack is successful as long as the classification results of the input images before and after the attack are inconsistent, and it is not mandatory to classify them into any kind. Therefore, the targeted attack will be more difficult to achieve than the untargeted attack.

2.3 HopSkipJump Attack HopSkipJump Attack (HSJA) is a decision-based attack in the framework of optimization, which is used to generate specific and non-specific examples of confrontation. These samples are optimized for the minimum distance of “L2 distance” or “∞ distance”. This algorithm is iterative in nature, and each iteration involves three steps: 1. Estimation of gradient direction; 2. Step search by geometric series and boundary search by dichotomy; 3. Theoretical analysis of optimization framework and gradient direction estimation. This not only provides a reference for selecting super parameters, but also demonstrates the necessary steps in the proposed algorithm. This attack method only depends on the decision of the model, and does not need more information. It realizes a novel and unbiased gradient direction estimation at the decision boundary, and puts forward a method to control the error deviating from the boundary.

3 Method Hopskipjump attack is a black box attack algorithm applied to image classification. The basic network of image classification is CNN, so Hoppjump attack is an effective attack method on CNN. As mentioned earlier, target detection can be divided into two tasks, one is location and the other is classification. The basic network of classification task is also CNN network, so we make use of the similarity between target detection

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and image classification task on CNN network, and modify and adapt HopSkipJump Attack, so that it can be applied to target detection task. X represents the sample, and the process of finding the classification boundary of the sample is as follows: x := x + ξ v(x, δ) v(x, δ) = ∇ S(x, δ)/∇ S(x, δ)2 The attack flow of the algorithm is shown in Fig. 1. The original sample should be a picture containing multiple targets. After the detector detects multiple targets in the picture, we artificially select one of the targets as our attack target, obtain the coordinates of the target, and set it as the target area. In the subsequent attack process, we only need to care about the classification results in the target area. After the target area is selected, HSJA is applied to the target area. The algorithm first defines a classifier C (model): C(x) := arg max Fc (x)

Fig. 1 HopSkipJump Attack algorithm attack flow chart

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Given an input x, define the following function:       Fc x ' − Fc x ' . When there is no target: Sx ∗ x' := max   ' When you have a goal: Sx ∗ x ' := Fc x ' − max  Fc x . ' For perturbed images, if and only when Sx ∗ x > 0, this is a successful attack, which guides the direction of perturbation and searches for the best sample according to this direction. Monte Carlo method is used to estimate the gradient, and the step length is estimated according to the calculated gradient, and the length that should be pushed in the direction of the gradient is obtained. Knowing the step length and direction of each step, the sample point can gradually move along the boundary and gradually reduce the distance from the original sample. In this way, we can approximately transform a target detection problem into an image classification problem, and the classification result of the target area is the input required by our HopSkipJump Attack algorithm. The detector will output the confidence level of each category in the target area, and the algorithm will determine the attack direction according to the confidence level, so that the samples will move continuously along the classification boundary, and ensure that the L2 distance between the confrontation sample and the real sample will be continuously reduced without changing the classification result, that is, the difference between them will be smaller and smaller in visual effect. When L2 decreases to a certain value, the human eye can no longer recognize the gap between them. At this time, the classification result of the target area is our pre-specified category (error category), and it is very similar to the original image. We can call this kind of picture a confrontation sample, that is, the attack is successful.

4 Experimental Data In the experiment, the data set we used is VOC2007 data set, a classic target detection data set. Since HopSkipJump Attack can change the classification result of the target area at the beginning of the attack, we have raised the requirement for the success of the attack and added the L2 limit of 500. When attacking, we designate a target in the picture as the attack object, and apply the method mentioned in Chap. 3 to it. We attacked 122 images, and on the premise of limiting the maximum L2 value, we judged whether the recognition results before and after the attack were consistent. Among them, 77 images (63%) are successful and 45 images (37%) are unsuccessful. Taking Fig. 2 as an example, the detector can detect five cars in the figure, and gives the classification category and confidence. Figure 3 is the result after the first round of attacks. It can be seen that target0 can’t be detected, but it is quite different from the original image. At this time, L2 is 6809, which doesn’t meet the requirement that the confrontation sample can’t be detected by human eyes. Therefore, the goal of the next round of attacks is to make the target area closer and closer to the original image. The final attack result is shown in Fig. 4. There is almost no difference between the target0 area and Fig. 2, but the detector cannot identify the cars in the target0

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Fig. 2 Initial detection results

Fig. 3 Results after the first round of attacks

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Fig. 4 Final attack result

area. Therefore, after several rounds of attack iterations, the algorithm successfully reduced L2 to 499. The difference between the disturbed image and the original image is so subtle that the human eye can’t separate it, and the detector can’t detect the objects in our designated target area, thus successfully realizing the confrontation attack on the designated target and generating confrontation samples.

5 Conclusions The neural network has surpassed human performance in many fields, such as image and natural language processing, but the existence of anti-attack brings great challenges to the security of neural network. In the field of image, most of the anti-attack methods are aimed at the task of image classification. After our experimental verification, the anti-attack algorithm for image classification can be applied to the task of target detection after modification and adaptation. And when L2 is set to 500, the attack success rate can reach 63%. If the limit of L2 is increased, more samples can be successfully attacked, that is, the success rate will continue to rise, so this attack method can be considered as an effective attack method.

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References 1. Li Y, Li L, Wang L, Zhang T, Gong B (2019) Nattack: learning the distributions of adversarial examples for an improved black-box attack on deep neural networks. In: Proceedings of the 36th international conference on machine learning, PMLR vol 97, pp 3866–3876 2. Cheng M, Zhang H, Hsieh C, Le T, Chen P, Yi J (2019) Query-efficient hard-label blackbox attack: an optimization-based approach. In: 7th International conference on learning representations, ICLR 2019 3. Chen J, Jordan MI, Wainwright MJ (2020) HopSkipJumpAttack: a query-efficient decisionbased attack. In: 2020 IEEE symposium on security and privacy (SP). IEEE, pp 277–1294 4. Dhillon A, Verma GK (2019) Convolutional neural network: a review of models, methodologies and applications to object detection. Progress Artif Intell 2020(9):85–112 5. Cai Z, Fan Q, Feris RS, Vasconcelos N (2016) A unified multi-scale deep convolutional neural network for fast object detection. In: Computer vision—ECCV 2016, vol 9908. Springer International Publishing, Cham, pp 354–370 6. Heisley D, Levy S (1991) Autodriving: a photoelicitation technique. J Consum Res 18:257–272 7. Wang K, Li F, Chen C, Hassan MM, Long J, Kumar N (2022) Interpreting adversarial examples and robustness for deep learning-based auto-driving systems. IEEE Trans Intell Transp Syst 23:9755–9764 8. Liu W, Anguelov D, Erhan D et al (2016) SSD: single shot MultiBox detector. In: Computer vision—ECCV 2016, vol 9905. Springer International Publishing, Cham, pp 21–37 9. Cheng B, Wei Y, Shi H, Feris R, Xiong J, Huang T (2018) Revisiting RCNN: on awakening the classification power of faster RCNN. In: Computer vision—ECCV 2018, vol 11219. Springer International Publishing, Cham, pp 473–490 10. Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1440–1448 11. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137–1149 12. Szegedy C, Zaremba W, Sutskever I et al (2014) Intriguing properties of neural networks. In: 2nd international conference on learning representations, ICLR 2014—Conference Track Proceedings 13. Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 International conference on engineering and technology (ICET). IEEE, pp 1–6 14. O’Shea K, Nash R (2015) An introduction to convolutional neural networks. arXiv.org. Cornell University Library, Ithaca. arXiv.org

Autonomous Driving System of Intelligent Connected Vehicle Based on ASAM Standard Huiyu Xie, Pengchao Zhao, Zhibin Du, Bolin Zhou, and Jun Jiang

Abstract ICV is the final stage of the development of autonomous driving. It is the unification of intelligence and networking, and the concentrated embodiment of the new four modernizations of automobiles. The purpose of this paper is to study the simulation of the autonomous driving system of intelligent networked vehicles based on the ASAM standard. The calibration system adopts the CCP protocol as the communication protocol. The design of the communication card mainly includes the design of hardware circuit and software program. Apply the developed calibration software to the unmanned driving system, design the underlying controller, and automatically control the operating mechanism. In the simulation environment, the steering control and vehicle speed control are monitored in real time to realize the optimization of the control parameters. Experiments have verified the effectiveness of the developed system in real vehicle applications. The ICV automatic driving system works stably, can better complete the monitoring and calibration tasks in the real vehicle debugging process, and achieves the expected design goals. Keywords ASAM standard · Intelligent connected vehicle · Autonomous driving · System simulation

1 Introduction With the advent of the era of intelligent connected vehicle, the level of autonomous driving of cars has been continuously improved, and the popularity of connected autonomous vehicles has also increased [1]. Numerous technology companies and start-up companies began to flood into it, promoting the development of a series of related technologies in the fields of transportation, communication, and computers H. Xie (B) · P. Zhao · Z. Du · B. Zhou · J. Jiang China Automotive Technology and Research Center Co., Ltd.-China Automotive Data (Tianjin) Co., Ltd, Tianjin 300393, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_41

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[2, 3]. Looking at the research and development of connected autonomous vehicles at home and abroad, emerging auto companies led by Tesla and Internet companies represented by Google have leading technical levels in this field. Connected autonomous driving and its related industries will usher in huge development opportunities [4]. Further, as an important part of the intelligent transportation system, the development and application of the intelligent control technology of autonomous driving can bring great help to the management and control of the intelligent transportation system [5]. The testing of smart cars has become a common technical difficulty and hotspot in the academic and industrial circles of smart cars and autonomous driving at home and abroad [6]. Ahmed H U provides a comprehensive review of research on car following models for popular microsimulation tools for vehicles with human and robotic drivers. Specifically, based on the dynamic behavior of the vehicle and the driving environment, the following logics such as the Wiedemann model and adaptive cruise control technology are reviewed. In addition, some newer “V-ready (autonomous vehicle-ready) tools” in the micro-simulation platform are discussed [7]. Fakhir I proposed an evolutionary computing technique that can solve key problems in the field. The CLARION cognitive model will be used for this purpose. To drive in mixed traffic environments, intelligent vehicles must make real-time strategic-level decisions. The CLARION model has many inference modules and obtains combined outputs from these modules. The performance of each module directly depends on the internal and external parameters of those modules. Most cognitive architectures have the ability to learn from the environment and act according to a given input. CLARION is one of the best cognitive models for such learning-based problems [8]. Under this development background, the research on ICV is in line with the development trend of the times and the development direction of the industry [9]. Based on the in-depth analysis of the ASAM standard and CCP communication protocol involved in the calibration system, this paper completes the design of the overall scheme of the calibration system, including the software and hardware scheme of the communication card and the software scheme of the calibration system. On the basis of mastering the CCP protocol, the design of the calibration software is completed. The design mainly includes program development of system management module, session establishment module, measurement module, calibration module and FLASH programming module. The simulation of the automatic driving of the car is carried out, the control of the vehicle by the control system is realized, and the safety and traffic efficiency of highway driving are improved.

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2 Research on the Simulation of Intelligent Connected Vehicle Automatic Driving System Based on ASAM Standard 2.1 ASAM MCD Standard Model ASAM is the acronym for the Association for Automation Engineering and Measurement Systems, whose members are international automakers, component manufacturers and automation service providers. The group first integrates technology solutions developed by member companies. These frameworks include programs, data templates, file formats, and APIs for test automation control modules [10, 11]. Adherence to these principles ensures interaction between tools developed by different vendors and that data exchange does not require interpretation [12].

2.2 Design Scheme of Calibration System The hardware modules of the entire calibration system are as follows: (1) The PC is equipped with software updates, which are the basis of the entire calibration system. The user should be provided with a simple and friendly interface, which is convenient for the user to manage the entire computing process through one computer [13, 14]. The computer first completes the realtime data communication, including the recording and real-time display of the parameters that need to be monitored in the ECU, receiving the control settings modified by the ECU regenerator, updating the ECU system online, storing and copying calibration process [15, 16]. (2) The CAN-CAN communication card is a low-cost, easy-to-develop, highreliability, and high-performance bus. It is a serial data communication system independently developed by Bosch data exchange [17]. The computer-aided interface provides intuitive and concise operation for the calibrator. The calibrator does not need in-depth understanding of the ASAM standard, and can easily complete the monitoring by controlling the operating system and measurement of breed parameters. Update software mainly includes system control, session installation, unit measurement, unit measurement and FLASH programming [18].

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2.3 Analysis of the Application Requirements of the Calibration System in the Unmanned Low-Level Controller Unmanned driving technology has developed rapidly in recent years and has become a hot field of current automotive technology research. The technical content involved in unmanned driving technology is various and complex, and the design method of system layering is particularly important. In the development of unmanned technology, the upper-level unmanned control system and the lower-level chassis execution system are generally designed separately, and the layered design makes system development and maintenance more convenient. Unmanned driving needs to realize the automatic control of the operating mechanism, mainly including the automatic control of the accelerator, braking, steering and gear position. The original car is an ordinary heavy-duty truck with automatic transmission, and its operating mechanism needs to be modified in mechanical and electrical structure to meet the needs of automatic driving.

3 Investigation and Research on the Simulation of Autonomous Driving System of Intelligent Connected Vehicle Based on ASAM Standard 3.1 Simulation In this project design, the relevant code block corresponds to mdlInitializeSizes to initialize variables. mdlStart performs socket initialization operations. mdlOutputs continuously receives data, calculates coordinates, and controls the trajectory of the simulated car. Among them: recvfrom is responsible for receiving vehicle latitude and longitude, heading angle, speed and other information from the data acquisition visualization system. solution_of_geodetic_problem implements the geodetic theme solution algorithm, which converts latitude and longitude coordinates into scene coordinates that can be recognized by simulation software. writeTraffData writes the scene coordinates into the simulation car module to control the running track of the simulation car.

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3.2 PID Control The cruise control controller is mainly suitable for the situation that the driver releases the accelerator pedal and accelerator pedal, so that the speed of the vehicle is automatically maintained at the set cruise speed, thereby reducing the driver’s operation intensity. PID control algorithm. The mathematical expression of PID control is: ⎡ 1 u(t)K p ⎣e(t) + Ti

t

⎤ de(t) ⎦ e(t)dt + Td dt

0

(1)

e(t) = r (t) − c(t) Among them, pK is the proportional control coefficient, iT is the time constant, and dT is the differential time constant. Since the vast majority of control systems in engineering are implemented by computers, it is necessary to distinguish PID control. The discretization process refers to using a series of KT sampling time points to represent the continuous time t, replacing the integral control line with addition, and replacing the differential control line with incremental form. The discretization process is expressed in mathematical formulas as follows: ⎧ t = KT ⎪ ⎪ ⎪ ⎪ ⎪ t  ⎪ k ⎪  ⎨ e(t) ≈ T e( j ) ⎪ j=0 ⎪ 0 ⎪ ⎪ ⎪ ⎪ e(K T ) − e[(K − 1)T ] e(k) − e(k − 1) d[e(t)] ⎪ ⎩ ≈ = dt T T

(2)

Among them, T is the sampling period; K is the sampling sequence number.

4 Analysis and Research of Automatic Driving System Simulation of Intelligent Connected Vehicle Based on ASAM Standard 4.1 Overall Program Architecture The overall program is extended on the calibration software framework on the ECU side. The program is divided into three layers: the underlying driver layer, the service layer and the application layer. Because it involves digital signal processing, analog signal data acquisition and analog voltage output, etc., as shown in Fig. 1, the bottom

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CAN driver

EEPROM driver

AD drive

Bottom driver layer

PWM drive

watchdog driver

IO driver

RTI driver Fig. 1 The underlying driver layer of the underlying controller

drive part adds IO bottom drive, AD bottom drive, and PWM bottom drive modules. CAN communication needs to communicate with the whole vehicle network and the upper-level driverless system, using CAN1 and CAN2 two CAN channels to achieve, corresponding to the need to write the CAN bottom driver. The final application layer adds data acquisition tasks, driving mode discrimination tasks, gear control tasks, speed control tasks, remote control tasks, mode indicator control tasks, and CAN network overtime supervision tasks. These tasks are all periodic tasks, and the time divided by the RTI reference timer is used as the final task control time.

4.2 Simulation Results This time, the PID control of the vehicle speed is used to verify the calibration function. The upper-level unmanned driving system sends the step speed test command, the bottom-level controller system receives the instruction, calculates the accelerator output and braking amount, and the wire-controlled accelerator cooperates with the active braking to realize the adjustment of the accelerator speed. Use the calibration download function to modify the PID parameters of the wire-controlled throttle and brake online to realize the optimal control of the vehicle speed. At the same time, the

Autonomous Driving System of Intelligent Connected Vehicle Based … Table 1 Data monitoring results

Time (s)

Target speed

379 Current speed

10

4.8

5.1

20

7.7

7.2 13.8

30

14.8

40

6.5

6.8

50

3.5

3.1

Fig. 2 Longitudinal speed control effect

data monitoring function of the calibration software is enabled to monitor the target speed and current speed status in real time, as shown in Table 1. Because the control object is the engine, the overall control will have a certain lag in response. On the other hand, if the gear adopts an automatic transmission, the clutch will be disengaged and the power will be interrupted when shifting, resulting in the vehicle speed not being able to follow the original speed rise. It can be seen that the speed can be tracked and controlled according to the vehicle speed command, and the maximum error in the control process is 1 km/h, as shown in Fig. 2, which can meet the operation control requirements of the real vehicle.

5 Conclusions Self-driving technology separates the “human” from the driving activity, thereby avoiding traffic accidents caused by driver error or performance error. Therefore,

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how to plan a successful transportation in a complex driving environment, and how to manage vehicles to track the planned route smoothly and accurately, are of crucial scientific research and practical value. This paper discusses the automatic driving control system of the Intelligent Connected Vehicle, and analyzes the statistical results of the basic vehicle automatic driving control system by writing the intelligent networked vehicle management system and simulation scenarios. It is easy and convenient to achieve the purpose of system identification and error tracking and positioning.

References 1. Omar HM, M Uk Ras S (2022) Integrating anti-swing controller with px4 autopilot for quadrotor with suspended load. J Mech Sci Technol 36(3):1511–1519 2. Kriebitz A, Max R, Lütge C (2022) The german act on autonomous driving: why ethics still matters. Philos Technol 35(2):1–13 3. Damien AR (2018) Car minus driver: autonomous vehicles driving regulation, liability, and policy. Comput Internet Lawyer 35(5):1–18 4. Devi TK, Srivatsava A, Mudgal KK et al (2020) Behaviour cloning for autonomous driving. Webology 17(2):694–705 5. Demmel S, Gruyer D, Burkhardt JM et al (2019) Global risk assessment in an autonomous driving context: impact on both the car and the driver. IFAC-PapersOnLine 51(34):390–395 6. Lopez J, Sanchez-Vilarino P, Sanz R, et al (2021) Efficient local navigation approach for autonomous driving vehicles. IEEE Access 99:1–1 7. Ahmed HU, Huang Y, Lu P (2021) A review of car-following models and modeling tools for human and autonomous-ready driving behaviors in micro-simulation. Smart Cities 4(1):314– 335 8. Fakhir I, Kazmi AR, Qasim A et al (2020) A Learning-based autonomous system for driving a car using CLARION. Int J Comput Sci Inf Secur 14(12):730–740 9. Kolarova V (2020) Exploring the elements and determinants of the value of time for manual driving and autonomous driving using a qualitative approach. Transp Res Rec 2674(12):542– 552 10. Kim M, Lee T, Kang Y (2020) Experimental verification of the power slide driving technique for control strategy of autonomous race cars. Int J Precis Eng Manuf 21(3):377–386 11. Baumann MF, Brändle C, Coenen C et al (2019) Taking responsibility: a responsible research and innovation (RRI) perspective on insurance issues of semi-autonomous driving. Transp Res Part A Policy Pract 124(C):557–572 12. Claussmann L, Revilloud M, Gruyer D et al (2019) A review of motion planning for highway autonomous driving. IEEE Trans Intell Transp Syst 99:1–23 13. Wienrich C, Schindler K (2019) Challenges and requirements of immersive media in autonomous car: exploring the feasibility of virtual entertainment applications. i-com 18(2):105–125 14. Engin SN, Etlik UB, Korkmaz B et al (2021) A fuzzy logic-based autonomous car control system for the JavaScript Racer game. Trans Inst Meas Control 43(5):1028–1038 15. Mijic D, Vranjes M, Grbic R et al (2021) Autonomous driving solution based on traffic sign detection. IEEE Consum Electron Mag 99:1–1

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16. Alberti E, Tav Era A, Masone C et al (2020) IDDA: a large-scale multi-domain dataset for autonomous driving. IEEE Robot Autom Lett 99:1–1 17. Ahn H, Berntorp K, Inani P et al (2020) Reachability-based decision-making for autonomous driving: theory and experiments. IEEE Trans Control Syst Technol 99:1–15 18. Kuhn CB, Hofbauer M, Petrovic G et al (2020) Introspective failure prediction for autonomous driving using late fusion of state and camera information. IEEE Trans Intell Transp Syst 99:1–15

Smart Clothing Try-on System Based on Data Analysis Algorithm Peipei Zhao, Ning Yang, and Dan Yu

Abstract The development of modern digital clothing technology enables clothing enterprises to collect and analyze the data and needs of different customers at a lower cost, and customize them through a flexible and flexible production system. Mass customization will make it possible to bring personalized goods and services to each customer. This paper aims to study the smart clothing fitting system based on data analysis algorithm. This paper briefly introduces the application of data analysis technology in smart clothing try-on, and discusses the domestic and foreign fields of 3D body scanning, 3D body measurement technology based on 3D body scanning data, and clothing retrieval technology. Focus on technical issues in 3D scanning, and use intelligent design concepts and algorithms to achieve 3D scanning. Applying data analysis technology to the smart clothing try-on system can remove redundant invalid data and preprocess the user data stored on the platform, which greatly improves the inefficiency of the try-on system. Experiments have shown that the virtual clothing generation speed of the algorithm constructed in this paper is about 2 s faster than that of the traditional virtual try-on system, which greatly improves the efficiency of try-on. Moreover, using the wisdom of the try-on system, consumers of various clothing are satisfied. Compared with before use, the degree has increased by about 30%. Keywords Data analysis · Smart clothing fitting · Automatic anthropometric measurement · Computer simulation

1 Introduction In the B2C business environment, all kinds of customers (here refers to customers in the broadest sense, including clothing retailers, retailers, and customers) can buy P. Zhao (B) · N. Yang · D. Yu Art Department, Changchun Humanities and Sciences College, Changchun, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_42

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their own satisfactory products in the virtual clothing store without leaving home, which not only saves the company’s sales Cost and store cost, but also save the customer’s purchase time and purchase price. Modern online shopping platforms all sell clothes online, but the clothes they provide are all 2D photos, and customers can only get information on the color, style and other information of the clothes through the photos. Satisfaction with clothes depends to a large extent on individual efforts, especially the three elements of clothing, figure and clothing should be considered closely. Support the design of clothing inspection service software. However, there are still many gaps in our country’s development in this regard. The survey shows that nearly 50% of people are unwilling to buy clothes online, and many consumers who have tried online clothing business are dissatisfied, such as the size is not suitable, and the figure is not ideal. After trying it, the colors don’t match, etc. Of the respondents, 88% expressed concern about not being able to try before purchasing [1, 2]. In the research of smart clothing fitting system based on data analysis algorithm, many scholars have studied it and achieved good results, for example: Wang W’s contour measurement method with white light layer is used to capture 3D human body data. It uses a white light source to create a single wave for the object. When the irregular shape of the object produces the expected edges, the generated process will represent the shape of the object’s surface, seen by 6 cameras, and then merge the very deep image into the depth image when filling [3]. Jafari M Jafari M develops 3D image size extraction 3DM software, which automatically identifies important areas of the human body, interacts with human body rotation and length measurement, provides cross-sectional and side views, and performs dimension design extraction. Develop a macro program or short program to automate the extraction process [4]. This paper briefly introduces the application of data analysis technology in smart clothing try-on, and discusses the domestic and foreign fields of 3D body scanning, 3D body measurement technology based on 3D body scanning data, and clothing retrieval technology. Focus on technical issues in 3D scanning, and use intelligent design concepts and algorithms to achieve 3D scanning. Applying data analysis technology to the smart clothing try-on system can remove redundant invalid data and preprocess the user data stored on the platform, which greatly improves the inefficiency of the try-on system.

2 Research on Smart Clothing Fitting System Based on Data Analysis Algorithm 2.1 Virtual Try-on System Virtual try-on methods are an emerging field with broad development expectations. The system adopts advanced advanced fabric physical property simulation technology, reads the fabric’s physical properties through a fabric scanner, and reproduces the fabric’s thickness, hardness, weight, color pattern, gloss and other textures

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with today’s images. The virtual 3D rendering of clothing models is combined with an automated computing system, without the need to sew materials manually, but through data input, to quickly and efficiently complete automatic modeling of clothing of different sizes and styles. The virtual try-on system can capture the virtual simulated image or real image scanned by the customer himself, select the model according to the image, verify the virtual try-on result, and really eliminate the problem of the appearance of the supplier and the appearance of the buyer. Virtual 3D virtual try-on system is designed to meet the trend of the personal production era, allowing users to see 3D clothes assembled to their own size on a computer screen, as if they were trying on themselves. Therefore, the virtual try-on system of 3D clothing should first reflect the real appearance of the user, and then make 3D clothing on this basis, and use the 3D clothing simulation technology to reveal many general effects behind the clothing [5, 6]. The more refined and controllable the body parts are, the more realistic the clothes you try on will be. However, too many control features will increase the difficulty of mathematical design of the clothing model, which is not necessary. The data obtained from anthropometric measurements are field data with only scalar properties. These dimensions only have a sense of length and size, with no concrete information. How to convert these measurements into 3D clothing data. This is the first question to design when developing a 3D garment model. The size of each control part of the garment is determined by the user according to the size chart of different types of body items. In this paper, the 2D line model and the 3D human body model are combined, and the 3D coordinate modeling model is used to construct the characteristics of the clothing. The advantage is that templates can be simplified and made easier to learn. The human body is a complex body, the geometric features are difficult to understand, and the human body has different shapes. Because this article is mainly based on the role of the customer after the wardrobe is completed, it does not emphasize the modeling of a lot of human body information. The 3D information required by the model is flexible, so the number of typed levels and typed designs to analyze decreases, and performance degrades. This geometric model not only ensures that the main part of the garment is the correct size, but also that some other parts are not significantly different in size in general. The 3D clothing model generated by geometric information is to create space on the clothing surface through the geometric shapes of the human body, including height, shoulder width, bust circumference, waist circumference, hip circumference and other geometric shapes, and then create a control space that requires Bezier surfaces and avoids the use of other methods. Prepare for the choppy effects that can occur when combining different branch components in surface models and surface build speeds [7, 8].

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2.2 Application of Data Analysis in Smart Clothing Fitting System Data analysis includes data preprocessing, data storage and management, data analysis and mining, and data visualization. Data analysis algorithms and data analysis tools are inseparable from the analysis and processing of large volumes and various types of data. According to different forms of data generated in different application scenarios and different processing forms, the data analysis techniques and presentation methods to be applied are also different. When processing data, you must first understand the relevant types before you can choose relevant technical solutions to solve them. Because the street light data itself has a wide variety and a large number of repetitions, two questions are raised according to the characteristics of the data, one is the prediction of the power consumption of the street light equipment in the time series, and the other is how to use the algorithm to provide decisionmaking for the power consumption settings of the equipment and other issues., the convolutional neural network time series CNN is selected for these two problems. LSTM prediction model and particle swarm PSO algorithm to solve. Through data analysis, a large amount of redundant data can be removed for the smart clothing try-on system in this paper, and various data of consumers can be preprocessed and collected in advance, so that the data processing of each try-on can be greatly reduced speed, greatly improving the efficiency and performance of the smart clothing try-on platform [9, 10].

2.3 Structured Light Ranging Algorithm The smart clothing fitting system in this paper mainly uses the structured light ranging algorithm to measure the human body, and the 3D body scanner uses linear structured light to measure the field coordinates of the human body. The laser irradiates point A on the surface of the object, the angle between the optical axis of the camera and the laser line is α, and the camera shoots point A at P. Obviously, CP is directly related to the information depth of OA. The position of point A and the position of P are shown in the figure. The value of OA can be calculated after scaling the parametric device in advance. Might as well set OA = r, CP = r' , OD = d, CD = d' , AD O = PDC = θ, then the calculation method of r can be mathematically deduced as follows [11, 12]. In ΔAO D, from the law of sine, we get: d r = sin θ sin(π − θ − α) Thus there are:

(1)

Smart Clothing Try-on System Based on Data Analysis Algorithm

r=

d sin θ d dsinθ = = sin(θ + α) sin θ cos α + cos θ sin α cos α + ctgθ sin α

387

(2)

In ΔDCP, there are: ctgθ =

d' r'

(3)

Therefore, the calculation formula is as follows: r=

r'

r 'd cos α + d ' sin α

(4)

3 Research and Design Experiment of Smart Clothing Fitting System Based on Data Analysis Algorithm 3.1 Program Description The whole system is developed with VC++, and the start interface of the system is developed in the MFC environment, providing the interface for users to input body shape information and select clothing version, and store the user information in the database. When the user tries on the clothing, read from the database. Take body shape information and pass relevant parameters to the clothing fitting model. The running interface of the software is developed under Win32 Console Application under VC++. The clothing model drawing part of the system is in Cloth.cpp. The twodimensional array defined at the beginning of the program is the three-dimensional space coordinates of the clothing, and these coordinate values are the coordinates of each feature control point of the clothing model.

3.2 Experimental Design The main purpose of the experiment in this paper is to test the function of the smart clothing fitting system based on the data analysis algorithm constructed in this paper. The first is to test the virtual clothing generation speed of the fitting system, and compare the speed with the traditional virtual fitting system. Secondly, this paper explores the role of smart try-on in Internet clothing sales by comparing the consumer shopping satisfaction before and after using the smart try-on system for four types of clothing.

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Table 1 Comparison between the generation time of smart clothing try-on clothing in this paper and the generation time of traditional virtual try-on clothing 1

2

3

4

5

Traditional virtual try-on

2.6

2.9

3.2

2.7

2.9

This article is smart try on

0.4

0.6

0.4

0.5

0.4

Fig. 1 Clothing generation speed for two try-on systems

4 Experiment Analysis of Smart Clothing Try-on System Based on Data Analysis Algorithm 4.1 Simulated Clothing Generation This paper tests the performance of the smart clothing try-on system in this paper, mainly tests the virtual clothing generation speed of the system, and compares the virtual clothing generation speed of 5 pieces of clothes by comparing it with the virtual clothing generation speed of the traditional virtual try-on system., record the time of virtual generation, the data is shown in Table 1. As can be seen from Fig. 1, the virtual clothing generation speed of the smart clothing try-on system in this paper is greatly improved compared to the generation speed of the traditional virtual try-on system, with an average increase of more than 2 s, which greatly improves the user experience of the try-on system.

4.2 Comparison of Consumer Satisfaction This paper investigates the role of the smart clothing try-on system constructed in this paper, and records the customer satisfaction before and after the use of four types of clothing, including tops, pants, shoes and hats. The data are shown in Table 2.

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Table 2 Comparison of consumer satisfaction before and after the use of the smart try-on system in this article Jacket

Pants

shoe

hat

Before use

71

65

69

72

After use

95

97

93

96

120

per cent

100 80 60 40 20 0 Jacket

Pants

shoe

hat

clothes before use

After use

Fig. 2 Comparison of consumer satisfaction before and after using the try-on system for various garments

It can be clearly seen from Fig. 2 that after the smart clothing try-on system constructed in this paper is used, the consumer satisfaction of all kinds of clothing has been greatly improved, from about 70% before use to about 95% after use, which perfectly demonstrates the role of the smart clothing system in this paper.

5 Conclusions With the development of modern digital clothing technology, the customization of many garments is possible, which will bring burden and personalized functions to each customer. The key supporting technologies for building such e-commerce machines include 3D body scanners and anthropometric automated measurement technologies as well as advanced garment and digital simulation technologies. Taking into account the current situation of the clothing industry and the development culture of the global clothing industry, combined with data analysis algorithms, researches on 3D full body scanner, automatic acquisition of human body size, construction of 3D human surface model, and virtual clothing try-on technology were carried out.

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A spring model is defined and a very robust analysis is done in detail and a solution to the flawed model is given based on the Euler method. The square grid algorithm based on the standard grid method is used to determine the specificity of the twodimensional cloth, and the grid generated by the grid can fully meet the specific requirements of converting two-dimensional cloth into three-dimensional clothing. Acknowledgements 1) An Study on “Brand Integration” Teaching Mode of Garment Specialty under “the Belt and Road”. 2021 Jilin Association for Higher Education (JGJX2021B29). 2) Research on the Training Mode of Applied Talents in Colleges and Universities of Jilin Province under the Concept of “Integration of Five Education”. 2022 Jilin Institute of Higher Education (JGJX2022D646).

References 1. Wang J, Li D, Wang Z et al (2021) Research on enterprise employee information system based on big data analysis. J Phys Conf Ser 1748(3):032025 (6pp) 2. Shuqing, Lin, Allam et al (2019) Research on real-time database recovery method of smart grid system based on IEC61970 standard. IOP Conf Ser Earth Environ Sci 242(2):22046–22046 3. Wang W (2020) Research on smart antenna system based on beamforming algorithm. IOP Conf Ser Mater Sci Eng 782(4):042049 (6pp) 4. Jafari M, Hasanlou M, Arefi H (2019) SRTM DEM enhancement using a single set of PolSAR data based on the polarimetry-clinometry model. Int J Remote Sens 40(23):8979–9002 5. Chou CL, Chen CY, Hsieh CW et al (2021) Template-free try-on image synthesis via semanticguided optimization. IEEE Trans Neural Netw Learn Syst 99:1–14 6. Lage A, Ancutiene K (2019) Virtual try-on technologies in the clothing industry: basic block pattern modification. Int J Cloth Sci Technol 31(6):729–740 7. Ma J (2021) University ideological and political education management based on K-means mean value algorithm. J Phys Conf Ser 1852(4):042023 8. Mana SC, Sasipraba T (2021) Research on cosine similarity and pearson correlation based recommendation models. J Phys Conf Ser 1770(1):012014 (6pp) 9. Herrera-Franco G, Montalván-Burbano N, Carrión-Mero P et al (2021) Worldwide research on geoparks through bibliometric analysis. Sustainability 13(3):1175 10. Hao X, Zhang H, Wang Y et al (2019) A framework for high-precision DEM reconstruction based on the radargrammetry technique. Remote Sens Lett 10(11):1123–1131 11. Yesypenko OA, Trybrat OO, Vashchenko OO et al (2022) Influence of the configuration of an asymmetric carbon center on the parameters of the nuclear magnetic resonance spectra of inherently chiral N-(1-Phenylethyl)Amides of Calix Arene Acetic Acids: determination of the absolute stereochemical configuration of the macrocycle. Theoret Exp Chem 58(1):54–60 12. Herber R, Lenk J, Pillunat LE et al (2022) Comparison of corneal tomography using a novel swept-source optical coherence tomographer and rotating Scheimpflug system in normal and keratoconus eyes: repeatability and agreement analysis. Eye Vis 9(1):1–12

Design of English Automatic Translation System Based on Machine Intelligent Improved GLR Algorithm Can Wang

Abstract The traditional English translation system is mainly based on words and sentences to explain the translation, but the accuracy of the traditional system is relatively low, it cannot express the content of the translation well, and the machine translation system is deficient in English words and sentence patterns to some extent. Therefore, this paper adopts an intelligent recognition model of English translation based on improved GLR algorithm to design an English translation system, which is based on artificial neural network to simulate, analyze and process the machine translation process, etc. The main purpose of creating an intelligent recognition model for English translation is to analyze the sentence types, structural features and semantic characteristics of the translation process through the neural network algorithm, which can systematically collect and process English signals. Keywords Machine intelligence algorithm · Improvement of GLR algorithm · English automatic translation system

1 Introduction Nowadays, the world has entered the information age. People put forward higher and more standard requirements for translation work. The traditional English translation methods can no longer meet the translation requirements, which requires us to improve and innovate on the basis of the traditional methods [1]. Machine intelligence is a new translation method based on artificial intelligence and human brain neural network technology, which uses the computing technology and information processing ability of computer to analyze, organize and summarize complex text sets and apply them comprehensively, so as to realize the translation of texts [2]. This C. Wang (B) College of Tourism, Changchun University, Changchun 130000, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_43

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paper mainly introduces the GLR series expression method based on machine intelligence algorithm, and through this system, the linguistic forms described in traditional general-purpose literature are used as carriers, which are processed by machine intelligence algorithm to transform the text into linguistic forms with specific characteristics, and through this method, an improved translation system based on machine intelligence is realized [3]. In recent years, global trade has gradually increased, and international communication has become more and more frequent. As an indispensable language in international communication, English plays an important role in people’s daily life and work. In response to the market demand in China, major universities have opened English majors, but there are still shortcomings in the curriculum of English in major universities, which leads to the low interest of students and their weak translation ability, which requires the use of machine intelligence to make up for these defects [4]. Therefore, it is necessary to design a translation system based on machine intelligence to improve the GLR algorithm that is suitable for Chinese conditions and can realize the combination of text and computer automation [5].

2 English Intelligent Recognition Algorithm 2.1 Create a Phrase Corpus When creating a phrase corpus, the first thing we need to do is to count the number of words contained in the words to all relevant words and then analyze these data. Machine translation is a computer-based process to realize automatic literacy functions and memory tasks, and it does not completely rely on the human brain’s perceptual information processing system to complete its work activities. Therefore, the establishment of machine translation system needs to consider the following aspects: firstly, the computer should be studied as a whole; secondly, it is necessary to analyze the information expressed according to the words themselves; lastly, the Fourier transform technique is used in data processing to realize automatic and intelligent control of the processes such as feature extraction and classification [6]. In addition, the intelligent degree of machine translation process needs to be considered in the design, so as to realize the automatic classification of information expressed by different features. Figure 1 shows the information flow of phrase corpus. The translation model has more than 700,000 words and contains 2000 phrases, and it can accommodate more than 3000 words for translation, including 100 species. The model extracts potential patterns and trends by processing a large amount of data. The English-Chinese automatic translation system based on machine neural network is a computer-based translation system with intelligent features, which can realize information sharing and learning memory mapping. At the same time, it can realize the memory storage of translation results. The model uses the processing of a large amount of data to uncover potential patterns and trends, and uses them as the basis

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Fig. 1 Phrase corpus information flow

for intelligent translation. It also uses neural network technology combined with machine learning methods to analyze the text content and translation information and transform them into intelligent translation models [7]. The system can effectively help translators read and understand the English content quickly and accurately, and also realize automatic correlation between texts.

2.2 Phrase Corpus Lexical Recognition Phrase lexical recognition can help translators understand the meaning of words and automatically extract the information to be expressed in the computer, so as to realize the classification and recognition of target words. It uses the content of the annotated phrase corpus, automatically extracts the information to be expressed in the computer and translates it, and realizes the effective translation of the content of the word phrase corpus and the translated text, which enables the reader to get the desired meaning of the original text quickly. This effectively system improves the ability of machine translation process to handle phrase corpora. The GLR algorithm is widely used in lexical recognition, which is used to determine the contextual relationship of a phrase and to identify the intended meaning by identifying it. In the process of machine translation, it is able to handle complex and difficult words and phrases with unclear semantic relations well. In addition, GLR does not detect grammatical ambiguities, which leads to the failure to recognize the intended meaning. Therefore, semantic analysis is needed in the translation process, and it should be de-emphasized

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and calibrated again, so that the translated words can be lexically accurate and thus achieve a better expression of the author’s intention. Generally, the accuracy and precision of GLR algorithm in word recognition results cannot be guaranteed because of its high chance. In the translation process, since there are differences between different words, this phenomenon can be solved by machine intelligence algorithms. The phrase pre/post-text likelihood of the improved GLR algorithm is calculated using quadratic clusters, as shown in Eq. (1). G E = (VN , VT , S, α)

(1)

If P denotes any action in α and exists in V, then by derivation Eq. (2) can be obtained. P → {θ, c, x, δ}

(2)

The improved GLR algorithm prescribes recognition of word translations as a machine intelligence translation system based on the least mean squared deviation algorithm (FIR). The key lies in how to design a program that can automate the recognition and decoding process, automatically generate translations and effectively improve efficiency and accuracy, and have a high degree of accuracy in recognition [8].

3 English Translation Intelligent Recognition Model 3.1 Model Design Process The overall model design is planned for the functions to be realized by the English translation intelligent recognition model, and the English translation system is designed by the improved GLR algorithm based on machine intelligence, so as to maximize the functions of the model in English information retrieval. This model can achieve automatic acquisition and processing of various information in the translation process, and it can combine text and speech content to achieve the functions required by the machine-intelligent improved GLR algorithm based on machine intelligence. The model design flow is shown in Fig. 2.

3.2 English Signal Processing English is a tool for human communication, however, the inherent characteristics of the language itself as well as the different levels of people’s perception of the world

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Fig. 2 Model design flow

and the differences in cultural backgrounds lead to certain differences in its expressions and contents. All these factors will affect the deviation in the translation process and the accuracy of the translation results. At the same time, because the English speech signal has strong time-varying and low signal-to-noise ratio characteristics, which makes it possible to cause misunderstanding loss in practical application, this reduces the practical value of English and its usage effect. In order to improve the accuracy, it is necessary to process the collected speech signals, including framing, weighting, endpoint monitoring and windowing [9]. A new translation method can be implemented using digital filters for speech signal weighting. The principle is a machine learning algorithm based on artificial neural network. In this model, firstly, the speech signal is input to the BPSK data set for pre-processing, and then the BPSKN pair K-2, C(DF) and N are used as the intermediate weight coefficients and weight values respectively; Then, the neural network algorithm is used to process the output signal, and the result is displayed in digital form. This method can effectively reduce the defects existing in the manual translation process. The realization of the translation method can not only reduce the data processing speed in the artificial neural network, but also improve the accuracy

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of the automatic speech translation system’s understanding of English information to a certain extent, so as to achieve the purpose of improving the translation quality.

4 Extraction of Feature Parameters Extracting feature parameters is a very important link in translation tasks, and some details are often ignored in the traditional research process. However, in order to achieve the quality and accuracy requirements of the translation, it is necessary to identify the text to a certain extent. Therefore, how to quickly and effectively obtain the required feature information and correctly match it to a specific target language is particularly critical. In the process of feature parameter extraction, Fourier transform is mainly used to calculate the continuous spectrum of the signal, and the feature vector of the image is converted into a Fourier transform matrix, and then the neural network model is used to process the signal. However, the Fourier transform matrix is computationally intensive, so it is necessary to make a certain degree of subtraction of various noise parameters involved in the signal processing when performing feature extraction. The main function of the fast Fourier transform FFT algorithm is to classify different feature points in the image to achieve the extraction of text information.

5 Design of English Automatic Translation System 5.1 Operating Environment Arrangement The experimental system designed this time is based on the layout of the embedded environment. The model needs to complete a large number of text translation tasks in the scene. In order to reduce time consumption and improve efficiency, it requires a reasonable arrangement of data distribution. The main tool of the system is FAST, in data processing, it automatically recognizes the translated text and converts the text recognized into executable instructions [10].

5.2 System Architecture Design The design of English automatic translation system mainly contains two parts, algorithm and software, which is mainly designed for different translation products based on machine intelligence perspective, using computer vision, image processing and other technologies to automatically analyze and judge the translation text. The system adopts both master–slave algorithm and regression mainstream programming

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Fig. 3 Overall structure of English automatic translation system

methods in the process of implementation. Based on the embedded ARM environment, the automatic translation system software is designed and developed, and the software can automatically complete the recognition of different translated texts. In the process of translation, it can be automatically corrected by machine intelligence algorithm, and it can realize fast accuracy rate with high accuracy good translation. The overall structure of the English automatic translation system is shown in Fig. 3.

6 Experimental Analysis 6.1 Validity Verification Through the design of this algorithm, we can know that machine intelligence is automatically analyzed and processed based on artificial neural network technology. The system not only needs to consider the functional structure of the human brain and the knowledge system, but also needs to consider factors such as language environment, time dimension, and semantic characteristics. What has the greatest impact on the translation is the difference in detail information extraction and text comprehension. In view of the cultural background of different countries, the words used by translators in the translation process may have a certain degree of deviation or error rate. Therefore, after the improvement of machine intelligence, suitable machine intelligence improvement methods are adopted for different translated texts, and the translations are automatically analyzed and processed under the premise of ensuring accuracy. Related experiments were carried out in this paper, as shown in Table 1.

398 Table 1 Accuracy of English translation before and after proofreading

C. Wang Experiment serial number

Translation accuracy Before proofreading (%)

After proofreading (%)

1

57.2

99.4

2

72.6

98.8

3

68.5

97.4

4

72.4

98.9

5

75.2

98.4

6.2 Comparison of Recognition Node Distribution The recognition nodes in this system mainly focus on the translation of text, which means “the” for English words. Since the degree of machine intelligence is not high and there are large differences between different languages, the recognition results may have certain deviations. Therefore, in order to achieve the desired effect and ensure the requirements of high accuracy and efficiency, a lot of experimental research and comparative analysis are needed to determine the optimal value of each parameter and the response time (maximum absorption capacity) and operation amount under the best combination of algorithms, so as to obtain a machine intelligent translation model with good robust performance that meets the desired purpose. Figures 4 and 5 are the distribution of the system identification node control points. The node control points in Fig. 4 have a compact distribution, and it makes the English translation process contextual. The incoherence issue was resolved. In Fig. 5, the nodes of the recognition system based on syntax and phrases are loosely distributed. Fig. 4 Distribution of nodal control points of the identification system in the text

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Fig. 5 Distribution of computer intelligent proofreading system section control points

7 Conclusion The research focus of this paper is the combination of machine translation and artificial intelligence applications, and the traditional language processing process is improved on the basis of the automatic text translation system algorithm, it also implements key technologies such as English word, sentence, and sentence pattern recognition, thereby realizing the transformation of traditional translation methods. In the machine translation system, based on image processing and recognition technology, the neural network model is used to convert word and sentence information into image data for recognition. The method obtains English and Chinese digitization through a computer vision algorithm and outputs it to the translation, then converts the word into a digitized English sentence through a translation algorithm and outputs it to the translation, and combines the artificial intelligence application platform (AI) to convert the text into a specific language for expression. As the translation description object, the realization of the automatic decoding process is completed. With the development of computer and multimedia technology, the field of machine translation is becoming wider and wider. Therefore, this paper focuses on how to use artificial intelligence tools to complete the automatic translation of English, and proposes an improved GLR algorithm based on machine intelligence.

References 1. Ban H, Ning J (2021) Design of english automatic translation system based on machine intelligent translation and secure internet of Things. Mob Inf Syst 55–58y 2. Johnstone A, Scott E, Economopoulos G (2006) Evaluating GLR parsing algorithms. Sci Comput Program 3:51

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3. Zhu R (2020) Research on the optimization of english automatic translation by weakening grammar rules. J Phys Conf Ser 2:1533 4. Negri M, Ataman et al (2017) Automatic translation memory cleaning. Mach Trans 24–26 5. Zhang Y (2017) Research on English machine translation system based on the internet. Int J Speech Technol 1017–1022 6. Garcia-Mendez S, Fernandez-Gavilanes et al (2019) A system for automatic english text expansion. IEEE Access 2019(1):123320–123333 7. Singh S, Kumar et al (2018) Attention based English to Punjabi neural machine translation. J Intell Fuzzy Syst 2018(5):1551–1559 8. Sak A (2019) Machine translation system modeling based on sentences comparison. J Phys Conf Ser 02:111–117 9. Jesus Martins DB, de Medeiros Caseli (2015) Automatic machine translation error identification. Mach Trans 19–24 10. Géczi Z, Iványi P (2018) Automatic translation of assembly shellcodes to printable byte codes. Pollack Periodica 16–20

Financial Risk Monitoring Analysis Based on Integrated SVM Data Stream Classification Algorithm Chunyun Yao

Abstract In recent years, with the continuous development of China’s national economy, financial risks have become more and more complex, and present characteristics such as multi-level and complexity. Financial risk monitoring is an important means to prevent and resolve financial systemic crises, which has also become the main method to prevent, control and manage possible problems and potential dangers in the operation of the financial industry. In the traditional statistical classification methods, the data volume is large and unstable, which cannot accurately reflect the specific manifestations of financial risks. Therefore, when classifying data in an integrated SVM system, a combination of statistical vectors and support vector machines can be used to quantify and analyze a large and complex sample set. In this paper, we combine the method of financial risk classification and SVM support vector machine to establish a monitoring and analysis model based on integrated SVM data flow, and study the data integration optimization of financial risk monitoring system. Keywords SVM support vector machine · SVM data stream classification algorithm · Financial risk monitoring and analysis

1 Introduction With the continuous development of financial risk classification technology, there are more and more ways of financial risk classification [1]. Based on data mining and SVM support vector machine combined with information processing technology has become a current research hotspot. In the current Internet era, the financial industry is facing more complexity and diversity, which make the financial risk monitoring and analysis based on integrated SVM data classification system have more obvious advantages and better application prospects. For financial risk classification, the C. Yao (B) School of Business, Xi’an Siyuan University, Xi’an 710038, Shaanxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_44

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analysis and monitoring technology based on integrated SVM data flow is the future development direction for financial institutions [2]. The purpose of financial risk monitoring and control is to reflect the changing conditions of the financial market in a timely and accurate manner, which provides financial institutions with effective and reasonable risk management services, and proposes corresponding measures to prevent and resolve potential losses based on the monitoring results [3]. Therefore, it is also important for financial institutions to continuously improve the monitoring of their business processes and determine preventive measures by grasping and identifying in real time the factors affecting risk changes and development trends. The data flow classification algorithm can quickly and accurately predict what kind of risk problems are brought about by various types of transaction behavior in a certain period of time in the future, so that when abnormalities are found in the financial operation process, corresponding countermeasures can be taken in time to avoid further expansion of harm. Therefore, in the process of financial operation, risk monitoring and control is crucial and complex [4, 5].

2 Integrated SVM Data Stream Classification Algorithm 2.1 Support Vector Machine Support vector machine (SVM) is a classification method proposed by Kohious, a famous American scholar in the field of machine learning, in 1977. It uses a training sample point as a primitive to identify an unknown data set, and then determines its class according to a specific classification rule, and then performs statistical analysis and decision making. The main principle is to use a linear statistical regression model to fit the parameters to be measured to obtain the optimal individual and feature distribution functions, and then determine the best decision table (PWM) value based on the least squares estimation method, and use it to predict the possible future trend changes, so as to achieve the monitoring and prediction of financial risks [6]. The support vector machine (SVM) is shown in Fig. 1. Fig. 1 Support vector machine (SVM)

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Fig. 2 The idea of classification of support vector machines

The real valuable application of the SVM method is for solving nonlinear problems, which focuses on analyzing the data stream through a nonlinear mapping and a statistically based vector space, and then mapping the original information to a sample set and using the support vector machine method to predict the possible occurrence of financial risks in a certain time in the future. The classification idea of support vector machine is shown in Fig. 2. The kernel function is a statistical method used for machine learning to classify data with random samples, which treats data objects with some differences in spatial domains and attributes as random variables, and predicts possible events by analyzing this function. The kernel function is given the Mercer kernel K(x, y), which is represented as shown in Eq. (1). ∫b K (x, y)ψ(x)d x = λψ(y)

(1)

a

In the mapping and identification of financial risk features, the original data need to be pre-processed and, on this basis, transformed into a density distribution using classification methods in vector space. Since the process of financial information collection is affected by weather and geographic environment, it is necessary to ensure that the sample size is relatively large and has a certain regularity. At the same time, the acquired information should also be compressed and analyzed (including clustering) to obtain more effective feature mapping and identification results. After data pre-processing, in financial risk feature mapping and identification, the original data need to be pre-processed and, based on this, transformed into a density distribution using classification methods in vector space [7]. Suppose the set of functions tensor by the Mercer kernel is the feature space, denoted as F. The original sample space is denoted as X. If we make the following nonlinear mapping of the sample space X to the feature space F as shown in Eq. (2): √ √ √ φ(x) = ( λ1 ψ1 (x), λ2 ψ2 (x), . . . λk ψk (x), . . .)

(2)

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The Mercer kernel can be obtained from the above equation as shown in Eq. (3): K (x, y) =

∑

λi ψi (x)ψi (y) = (φ(x) · φ(y))

(3)

i

It can be seen from this: when we mirror the sample space into the feature space by nonlinear mapping, we can find the corresponding feature space in a certain domain, so that we can judge the financial risk by the distribution of the feature space. In practical applications, we usually process some data with certain regularity, randomness and obvious statistical properties and typical co-temporal characteristics.

2.2 Integrated SVM Data Stream Classification Algorithm The integrated SVM data stream classification algorithm is developed on the basis of support vector machine, which is an integrated classifier composed of multiple SVM-based classifiers, and it can perform multi-dimensional classification description, and it can monitor and identify financial risks in real time. Among them, the data stream detection framework is based on CDSMM algorithm and SVM support vector machine based on Kalman filtering algorithm, and the monitoring of financial risks is achieved through the extraction of data stream features. The base classifier weights are derived by calculating the classification accuracy of the current data blocks: the method of combining the current data blocks can effectively improve the classification algorithm in financial risk monitoring and it also provide support services for different categories subsequently [8]. The algorithm can effectively enhance the impact of concept drift instances on the training of new classifiers and reduce the accuracy of financial risk classification, while the integrated SVM data stream based classification tested under different types can effectively improve the accuracy of new category samples on prediction results. The symbols in the DSES-VM algorithm are defined as shown in Table 1. The flow of the integrated SVM data stream classification algorithm is as follows: Step 1: Construction of integrated classifier EC. the integrated classifier EC is obtained by collecting data on financial risks and then classifying them using the integrated SVM algorithm in financial risk detection, the classification results are compared with the real sample values by collecting previous data and using the integrated SVM classifier C-tree model. Step 2: Noise filtering. In the financial risk monitoring system, one important part is noise, which has a direct impact on the whole data processing process. The noise filtering method is mainly used to filter the signal through filtering, reconstruction and statistics, and then the noise is removed and the features of the SVM classifier are used to analyze what potential risks exist in the sample data generated under different parameters. Step 3: Weight update. Based on the eigenvalues at a certain moment, the probabilities of all attribute sample points in a certain neighborhood at the current state

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Table 1 Definition of symbols in DSES-VM algorithm Symbols

Symbolic description

EC

Integrated classifier

S

Current data block

d

The amount of data in the training set corresponding to the base classifier

num

The number of base classifiers in the current EC

K

Capacity of the integrated classifier

C_i

The ith base classifier in EC

E_i

The ith data in S

ErrInst

Example dataset of misclassification

Weight_X

Array of weights of integrated classifiers updated from the dataset

C_new

Constructed new classifier

Right_new

Correct rate of C_new

C_new’

Constructed replacement classifier

are obtained, and the density and weights of the data points contained in the vector and in each dimension at that moment, as well as their correlation coefficients, are calculated. The weight update method calculates the relationship matrix between the weights and the contained data volume at each moment, and finds the vector at that moment. The integrated SVM algorithm is used to monitor the financial risk based on its features of real-time, fast and classification capability. Step 4: Concept drift detection. The classification error rate of the data block S on EC is calculated and used to detect the concept drift value. The integrated SVM data classification method can be used to quantify financial risks, which not only provides effective monitoring and control measures for financial institutions, but also reduces their supervision efforts and costs to a certain extent. Step 5: Classifier tuning and updating. Once drift is detected and the number of base classifiers in the integrated classifier reaches K, an error classification buffer is used for classification; if the detected samples contain a large amount of information in the base set, the error aggregation technique is used to classify them. Taking advantage of the integrated SVM algorithm for financial risk monitoring can effectively reduce the identification of anomalous data and improve the prediction accuracy.

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3 Financial Risk Monitoring System Based on Integrated SVM Data Stream Classification Algorithm 3.1 Financial Risk Monitoring System Functional Design The system functional modules are roughly divided into:data collection platform, research task platform, data aggregation platform, institutional warning function, data comprehensive query function, system basic information configuration module, data analysis application platform several functional modules. Data collection platform: In the process of financial risk monitoring and classification, the data collection platform is the core of the whole financial risk monitoring. Through the comprehensive collection of a large amount of real and accurate information can effectively help people discover and identify potential or known abnormal factors in a timely manner, and this can also provide financial institutions with more information for decision making. The financial data collection platform is a useroriented, computer-based approach to the entire business process, which utilizes an integrated SVM approach to achieve the mining and processing of large amounts of real information. Research task platform: Financial risk monitoring task platform is a system integrating SVM data collection, processing and display. In response to the current situation of financial risk management, financial regulators require financial institutions to strengthen the analysis and application of big data, which provides effective information support for financial institutions. Through the establishment of a comprehensive evaluation index system based on data mining, quantitative models and other tools on the Internet to achieve financial risk monitoring, and according to the index system to put forward the corresponding regulatory measures, so as to reduce and prevent the probability of risk occurrence of financial institutions, which can improve the level of security management of Internet enterprises [9]. Data Aggregation Platform: Financial risk monitoring and management is a comprehensive monitoring of the entire financial industry. In the process of data collection, processing and analysis, it is necessary to grasp and accurately identify potential risk sources in real time. By establishing an effective financial database, it can summarize and organize various business flow information, and effectively analyze and monitor it, so as to realize the function of financial risk monitoring and early warning [10]. Institutional early warning function: While monitoring financial institutions in real time, the financial risk early warning system also needs to make accurate judgments on their potential crisis behaviors and take necessary measures in a timely manner so as to avoid or reduce losses. Comprehensive data query function: In financial risk monitoring, the comprehensive query function refers to the analysis of transaction data of financial institutions and the risk measurement based on the characteristics of financial data to reduce and prevent the probability that risks may occur or have been found to occur, so as to achieve the analysis and prediction, identification as well as evaluation of the

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relationship between the internal transaction behavior of financial institutions and the external environment. The statistical method and neural network technology can effectively realize the effective supervision of risk. System base information configuration module: The basis of financial risk monitoring and control is information configuration. In the whole process, through the processing of a large amount of data, the effective information is transformed into resources that can be used. Data analysis platform: In financial risk monitoring, the data analysis platform plays a crucial role. Financial data analysis refers to the formation and utilization of effective information through statistics on the transaction activities occurring in financial institutions, and then making comprehensive and accurate assessment and judgment on the operation activities and risk management of financial institutions based on such information.

3.2 Application of Integrated SVM Data Stream Classification Algorithm in Financial Risk Monitoring Financial risk monitoring is the real-time monitoring of the operation status of financial institutions, which will determine the potential or occurred problems and deal with them accordingly in a timely and accurate manner according to the internal and external environmental factors of financial institutions. Financial risk monitoring also conducts real-time monitoring of various business activities engaged in by financial institutions and takes timely measures according to the problems found and revealed. It can predict financial risks by means of data analysis, statistical calculation and information extraction, and make accurate judgments on the operating conditions and potential problems of financial institutions based on the corresponding processing of the obtained results. Traditional methods usually use artificial neural network models to identify and evaluate financial risks, but the uncertainty of neural network models is high, and the recognition accuracy and precision are low. The integrated SVM system enables the monitoring and control of financial risks with the help of this technology: Firstly, a structure of indicators that can be used to monitor the internal business activities of financial institutions in real time is established. Secondly, extracting financial risks from sample data and predicting potential problems and solutions by fusing the processed information. Finally, the integrated SVM model is used to monitor the internal business activities of financial institutions in real time, and the results are fed back to the decision-making level to provide a basis for the internal control of financial institutions.

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4 Conclusion Financial risk monitoring refers to the real-time monitoring of the operating status of financial institutions. It can timely detect potential crises and losses that may affect the overall development of the financial industry, and it can reduce or even eliminate hidden dangers to a certain extent. With the continuous advancement of China’s economy and society and the improvement of the level of science and technology, the rapid progress of Internet technology, communication networks, and computer software and hardware systems has provided us with convenient conditions, and at the same time, it has also brought new challenges to the banking industry. Financial risk monitoring is the real-time monitoring of the flow of funds involved in the operation of financial institutions, and it can detect potential crises and losses in time to reduce or avoid risk losses. Through the research on financial risk monitoring, this paper proposes a data flow classification algorithm based on integrated SVM, and uses this technology to realize real-time monitoring and prediction of possible crises and losses during the operation of financial institutions. Acknowledgements This work was supported by: Big Data Research and Innovate Team in Management (Projects of Xi’an Siyuan University).

References 1. Kavassalis P, Stieber H, Breymann W et al (2018) An innovative RegTech approach to financial risk monitoring and supervisory reporting. J Risk Financ 1:19–23 2. Rampini AA, Viswanathan S et al (2020) Risk management in financial institutions. J Financ 591–637 3. Candian G, Dmitriev et al (2020) Risk aversion, uninsurable idiosyncratic risk, and the financial accelerator. Rev Econ Dyn 299–322 4. Dominika B, Sujata M, Abirami K et al (2022) Identifying priorities for research on financial risk protection to achieve universal health coverage: a scoping overview of reviews. BMJ Open 3:12–16 5. Adelusi EA, Anifowose OL (2022) Assessment of financial risk and its impact on an informal finance institutions profitability. Can Soc Sci 1:18–20 6. Gilchrist S, Wei B, Yue VZ et al (2022) Sovereign risk and financial risk. J Int Econ 77–80 7. Ye R, Xie Y, An N et al (2022) Influence analysis of digital financial risk in China’s economically developed regions under COVID-19: based on the skew-normal panel data model. Front Publ Health 10–12 8. Osipov VS, Krupnov YA, Semenova GN et al (2022) Ecologically responsible entrepreneurship and its contribution to the green economy’s sustainable development: financial risk management prospects. Risks 2:10–14 9. Rabbani Abed G, Heo W, Lee JM (2022) A latent profile analysis of college students’ financial knowledge: the role of financial education, financial well-being, and financial risk tolerance. J Educ Bus 97 10. Ghadge A, Jena SK, Kamble S et al (2021) Impact of financial risk on supply chains: a manufacturer-supplier relational perspective. Int J Prod Res 59

A Virtual Culture Brand Image Design System Based on Corba/Java Technology Hongyan Yuan, Rongzeng Hou, and Weicheng Li

Abstract With the rapid development of virtual brand visual image design, the aesthetic and artistic demands of its design are also constantly improving. The purpose of this paper is to design a virtual culture brand image system based on CORBA/JAVA technology. This paper first expounds the background of the research and summarizes the related technologies involved. Then it analyzes the system architecture of the virtual culture brand image design system, and elaborates the functional requirements of the middleware. Through the filtering function of spatial index, a large number of spatial objects irrelevant to specific spatial operations are excluded, thereby improving the efficiency of spatial operations. Taking the product image design case of an electrical enterprise with a single brand strategy, it can be found that the average scores of the sense of stability and the sense of atmosphere of the transformer products are 3.97 and 3.95, respectively, which are greater than the average scores of the sense of intelligence and technology (3.25 and 3.32). Therefore, in terms of the expression of product concept, the expression of “stable and important” is obviously better than that of “intelligent technology”. Keywords Corba/Java technology · Virtual culture · Brand image · Design system

1 Introduction From the point of view of products, in today’s market with a wide range of items, the differences between similar products in functions, materials, technologies, etc. [1]. When consumers’ purchasing desire is aroused by visual stimulation, a clear product image will help the product to be strongly and continuously stimulated at the visual level, thereby promoting the consumption of the product [2]. H. Yuan (B) · R. Hou · W. Li Shandong Institute of Commerce and Technology, Jinan 250103, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_45

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There are few studies at home and abroad on the virtual culture brand image design system based on CORBA/JAVA technology. Corbala-Robles L studied the heat recovery economics of the local wastewater treatment plant (WWTP) for the treatment of concentrated effluent, since the higher the concentration, the more heat is generated in the treatment pond [3]. Forster M tested a Bayesian decision process model using cost and outcome data from real ProFHER tests [4]. Gilal F G combines intrinsic motivation between product design and designer accessories. To this end, people from three countries were surveyed and found that cognitive decision-making generally applies to people in Pakistan, South Korea and China [5]. From the enterprise’s point of view, in terms of brand strategy, product image and publicity can maximize the brand image and gain a foothold in the homogeneous commodity market. This paper studies the virtual culture brand image design system based on CORBA/JAVA technology. In product sales, a successful product and brand image can attract potential consumers and consolidate existing consumers. From the actual production level, a perfect product image system can reduce the production cost of the enterprise and reduce the industrial risk.

2 Research on the Design of Virtual Cultural Brand Image Based on CORBA/JAVA Technology 2.1 Factors Affecting Brand Image Design (1) The way of expression of product image A unique image built by defining specific specifications for the appearance. The cultural characteristics of a branded product described in this way are: popular characteristics—often with specific design norms to control design quality [6, 7]. (2) Visual elements of product image design There are many visual elements to be considered when designing. Products with different characteristics and functions should choose appropriate elements from different fields for product image designs [8, 9]. (3) Emphasis of product image design for various types of products Product image design in all aspects related to branding and products. If every product image designer needs to plan the above points [10, 11].

2.2 Establishment of Brand Image Human sensibility is rational sensibility, and pure perceptual knowledge only exists as the germ of consciousness in the prehistoric period when human consciousness

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was formed and in the psychological activities of animals and infants. From the established form of human knowledge development, there is no perceptual knowledge without logical agency [12].

2.3 CORBA/JAVA Technology CORBA (Architecture Application Request) is the application software architecture and object specification of OMG (Object Management Group), based on a set of standard languages, interfaces and frameworks.

2.4 The Main Functions of the Virtual Culture Brand Image Design System The main function of the virtual culture brand image design system is to combine brand image design with multimedia technology to achieve an interactive brand image design, making the image more vivid and highly visible and realistic. The image can be individually modified and designed, and can also be replicated in other products or services. Specifically, its main functions include: the transmission of information, the combination of brand image design and multimedia technology and the organic combination of virtual image and multimedia technology. Among them, the virtual culture system mainly includes: virtual character image display, virtual commodity information display, virtual product effect display and virtual product application five functional modules.

2.5 Overall System Structure Design The overall structure of the system is: main page, icon wall, image display and operation panel. The main page is the overall display interface of the system, and the icon’s are the image display of the characters in the picture. The main interface mainly includes Swiss software, Java software, CAD software and website interface, video playback software, etc. The icon’s are the website interface. The main page contains five modules: the icon wall, the video playback software, the interface design and production module, the web browsing module and the web page creation module. The video playback software is used to play video data streams used to play image data streams. The web browsing module is used for web browsing data; functional modules include: characters, animations, videos, pictures, etc.; the web creation module is used for creating graphics; the web browsing module is used for creating images.

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3 Design and Research of Virtual Culture Brand Image Design System Based on CORBA/JAVA Technology 3.1 System Development Environment The development of this system is based on the following software and hardware environments: Computer: Intel(R) Celeron(R) CPU430 @1.80G. Operating System: Microsoft Windows Version 2020 Service Pack 2. Version version: jdk1.6.0_32.

3.2 System Architecture The server starts, starts the service process, including the user management process, the system service process and so on. The server monitors the running status of the equipment. If there is abnormal information such as alarms, the server will store the alarm information in the database.

3.3 Spatial Index Each vertex of the input triangular mesh model is defined as V(v0, v1,…,vn), and the piecewise linear scalar field of the mesh model is: f (v) =



f i ϕi (v)

(1)

Among them, fi is the function value at the vertex vi of the mesh model; ∅i (v) is the piecewise linear basis function. ∅i (v) takes 1 at the mesh vertex vi and 0 at other vertices, and the gradient corresponding to the scalar field f is: ∇ f (v) =



f i · ∇ϕi (v)

(2)

3.4 Initial Feature Set Spatial Feature Design The spatial features are intended to reflect the spatial structure of the code as well as the information level characteristics. Among the spatial features, the number of

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program branches, code block depth and McCabe complexity reflect the structural characteristics of the code, the Halstead metric reflects the capacity complexity of the code, and the function cohesion and code entropy reflect the level of cohesion and information of the code. The spatial characteristics reflect code features that are deeper than text-level features and are an important complement and component of the feature set. (1) Function cohesion The lack of cohesion in functions has a negative impact on source code quality and is associated with the number of defects. And it is easy to have functionally complex functions in beginner’s code. This system takes function cohesion into account, based on Python code, and is judged by the degree of overlap in vocabulary between different blocks of statements in a function. The algorithm for calculating function cohesion is shown in Algorithm 1. The formulae for the degree of lexical overlap and function cohesiveness are as follows. |B1[T oken] ∩ B2[T oken]| |B1[T oken] ∪ B2[T oken]| functional cohesion = max(degree of lexical overlap[Bi,Bj]) degree of lexical overlap[B1,B2] =

(1)

(2) Code entropy Code entropy (information entropy): Entropy is often regarded as the complexity, degree of disorder or amount of information in a signal or data set. The entropy of a code is calculated using the following formula, where xi is a Token in the code fragment and count (xi) is the number of occurrences of xi. count (xi ) p(xi ) =  n j=1 count (x j ) H (X ) = −

n 

p(xi ) log2 p(xi )

(2)

(3)

i=1

(3) Halstead complexity Halstead complexity uses two metrics, Program Vocabulary and Program Volumn, with the formulas Program Vocabulary : n = n1 + n2 Program Volume : V = (N1 + N2)*log2 (n)

(4)

(4) McCabe complexity McCabe complexity is calculated using the following formula. V (G) = e − n + 2

(5)

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CORBA middleware

System function module

XML serialization and deserialization

Alarm module Client Topology Module Fig. 1 System function module

4 Facility and Development of Virtual Culture Brand Image Design System Based on CORBA/JAVA Technology 4.1 System Function Module The functions of the system designed in this paper are shown in Fig. 1: CORBA middleware: According to the interface requirements of the alarm module, the interface file of the alarm module is initially designed and completed. The implementation process of the CORBA middleware mainly includes compiling files and generating client stub files and server IDL skeletons.

4.2 Video Frame Rate The video frame rate is the number of frames per second output, often expressed in terms of FPS, and the choice of video frame rate is very important. This is because the limit of human eye recognition is around 24 fps, once it is lower than 24 fps, the image will feel stuck, but it is not the higher the better, because once the frame rate is higher than 30 fps, the file size of the video will be too large, which will lead to a waste of storage space. In this design, the frame rate of the stored video is specified using the VideoWriter class constructor; the system’s video sampling frame rate is determined by the internal timer1, so when setting the frame rate of this video, it needs to be adjusted in conjunction with changes to the above two parameters. The general steps to test the optimisation are. (1) Modify the video save frame rate FPS in the VideoWriter in the project to vary from 15 to 30.

A Virtual Culture Brand Image Design System Based on Corba/Java … Table 1 Frame rate test results

415

Actual output Project setting Actual output Project setting frame rate frame rate frame rate frame rate 10

10

20.5

21

11

11

22

22

11.2

12

22.8

23

13.5

13

23.8

24

14

14

25

25

15

15

24.8

26

16

16

24.5

27

17

17

24.3

28

18.5

18

24.1

29

(2) Set the value of timer1 to 1*0.8/FPS in ms and multiply it by 0.8 to reduce the time interval between two frames, thus increasing the frame rate. (3) Recompile the project file to generate the program file at the current frame rate setting and transfer it to the system board for operation. (4) Run the program, display the saved video at the frame rate using PotPlayer, record the actual frame rate with the current parameters and compare and analyse the results. Repeating the above steps, the relationship between the set frame rate and the actual output frame rate is shown in Table 1. The analysis in Fig. 2 shows that in this design, the actual output frame rate of the video increases as the set frame rate increases when the set frame rate is below 25FPS. When the frame rate is set above 25FPS, due to the embedded system and the complexity of the algorithm, once the frame rate is set above 25FPS, the system may not be able to process in time and the frame rate will be capped and the actual output frame rate will remain the same or even decrease slightly. In summary, the frame rate should be set to 25 when using VideoWriter, and the internal timer1 should be set to between 32 and 40 ms.

4.3 Evaluation Experiments The evaluation objects are 3 transformer products and 3 reactor products designed, a total of 6 product designers. The mean value is obtained after statistical calculation, as shown in Table 2. From Table 2, it can be found that the average evaluation value is between 2.7 and 4.5, of which the average value between 3.0 and 4.0 accounts for 63%, indicating that the expression of the 6 product solutions to the product concept of “stable and vigorous, intelligent technology” is in the general to comparative conforming stage.

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35 30

Rate

25 20 15 10 5 0 1

2

3

4

5

6

7

8

Actual output frame rate

Project setting frame rate

Actual output frame rate

Project setting frame rate

9

Fig. 2 Frame rate test line graph

Table 2 User comprehensive evaluation Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

Sense of atmosphere

4.1

3.9

4.0

4.0

4.0

3.7

Sense of stability

4.3

4.1

4.3

4.2

3.8

3.1

Sense of technology

3.2

3.5

3.6

3.3

3.2

3.1

Sense of intelligence

3.5

3.8

3.7

2.7

3.5

2.3

It can be seen from Fig. 3 that the score line of “sense of stability and atmosphere” is basically above the discounted score of “sense of intelligence and sense of technology”, the score of the former is basically higher than that of the latter, and the line of the former is generally higher The latter slowed down. It can be seen from the above that in terms of the expression of product concept, the expression of “stable and important” is obviously better than the expression of “intelligent technology”.

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4.5

assessment score

4

3.5

3

2.5

2 plan 1

plan 2

plan 3

plan 4

program category

plan 5

plan 6

sense of atmosphere sense of stability sense of technology sense of intelligence Fig. 3 Evaluate mean line chart

5 Conclusions At this stage, virtual cultural brand image design technology has been widely used in the fields of cultural arts, cultural creativity and animation production. Through the study of the virtual cultural brand image design system, the Corba/Java platform has been used to develop modules such as multi-person graphical interaction interface, video playback, animation and virtual image, and to interface them with the Corba software, providing an interface for the virtual cultural brand image design system. The application of virtual cultural brand image design technology to cultural and creative product design can improve the efficiency and effectiveness of product design, thus realising the design and development goals of cultural and creative products. Based on the status of product image in the market, this paper analyzes and compares the differences at home and abroad. Starting from the meaning and concept of brand identification and product image, this paper realizes that creating a brand identification system is the long-term mission of the company’s own long-term development, and it is also the embodiment of corporate responsibility.

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References 1. Choi HY, Yoon HH (2019) An analysis of the influence on the festival brand design: focused on the gimje horizon festival. J Tour Stud 31(3):71–93 2. Ren L, Xu L (2021) Brand image design of gumuge mangtuan paper. Art Des Rev 09(1):80–89 3. Corbala-Robles L, Ronsse F, Pieters JG et al (2018) Heat recovery during treatment of highly concentrated wastewater: economic evaluation and influencing factors. Water Sci Technol 78(11):2270–2278 4. Forster M, Brealey S, Chick S et al (2021) Cost-effective clinical trial design: application of a Bayesian sequential model to the ProFHER pragmatic trial. Clin Trials 18(6):647–656 5. Gilal FG, Zhang J, Gilal RG et al (2020) Integrating intrinsic motivation into the relationship between product design and brand attachment: a cross-cultural investigation based on selfdetermination theory. Eur J Int Manag 14(1):1–27 6. Grgan Y (2018) LLSTRATF MARKA TASARIMINDA GNCEL UYGULAMALAR/contemporary approaches in illustrative brand design. Int J Interdisc Intercult Art 4(4):129–141 7. Khoo C, Reay S, Potter E et al (2020) Engaging young people in the co-design of a brand and online platform for a public health organization. J Des Bus Soc 6(2):165–187 8. Kurniullah AZ, Aprilia N (2019) Design for anti-brand counterfeit and brand protection through a study of semiotics and consumer vals (values and lifestyle). Int J Dev Res 9(8):28929–28937 9. Ipaki B, Movahedi Y, Amirkhizi PJ (2018) A research on the use of metaphor design in promoting brand identity. J Graphic Eng Des 9(2):17–20 10. Amron A (2018) The influence of brand image, design, feature, and price on purchasing decision of apple iOS smartphone in Surakarta, Indonesia. Int J Soc Sci Hum Invention 5(12):5187–5191 11. Appiah E, Danquah JA (2020) Designing a culturally relevant television brand identity using culture-orientated design model. Int J Art Cult Des Technol 9(1):30–46 12. Kim SY (2020) Disastrously creative: K-pop, virtual nation, and the rebirth of culture technology. TDR Drama Rev 64(1):22–35

“Digital New Infrastructure” Contributes to the Construction of New Power Systems Guang Chen, Wanting Yin, Di Wang, Yihan Zheng, and Xiaonan Gao

Abstract This paper first introduces the basic concepts and constituent elements of “digital new infrastructure”. Secondly, from “promoting the large-scale development and utilization of new energy, Help the new power system to achieve power change, Accelerate energy production to clean, low-carbon”, “improve the efficiency and efficiency of new power systems, To promote business collaboration and efficient collaboration among multiple entities, Promote energy use to become more efficient and intensive”, “to ease information asymmetry, Improve market transparency, Enhance mutual trust among all parties, Reduce various costs”, “reshape the value creation system of new power systems, Support energy enterprises to actively innovate business models”, “improve the risk identification and safety protection capability of the energy system, Effectively defuse various security risks, To ensure the safety and reliable operation of the new power system” and other five aspects of the in-depth analysis of the “digital new infrastructure” supporting “the main mechanism of the new power system construction”. Finally, targeted improvement suggestions are put forward for the actual situation of energy enterprises to promote the “digital new infrastructure”. Keywords Digital new infrastructure · New power system · Cost

1 The Connotation of the “Digital New Infrastructure” “Digital new infrastructure” is an infrastructure based on the new generation of information and communication network, driven by digital technology and Internet concept, which can support enterprises to promote digital transformation and realize G. Chen (B) · W. Yin · D. Wang · X. Gao State Grid Energy Research Institute Co. Ltd., Beijing 102209, China e-mail: [email protected] Y. Zheng Leysin American School in Swizerland, Leysin, Switzerland © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_46

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China's State Grid released the "digital new infrastructure" ten key construction tasks

Power grid digital platform

smart energy comprehensive services

energy big data center

Energy Internet 5G application

The Application of electric power big data

The Application of electric power artificial intelligence

power Internet of Things

Energy blockchain applications

energy industry cloud network

Power Beidou application

Fig. 1 The state grid corporation of China released the top ten key construction tasks of “digital new infrastructure” in 2020

ecological integration and innovation. “Digital new digital infrastructure” represented by 5G, big data center, cloud computing, Internet of Things, artificial intelligence, blockchain and industrial Internet is the key area of China’s infrastructure construction. At the moment, building the new power system in the energy industry, the “digital new infrastructure” can accelerate the construction process of the new power system from five aspects and help achieve the “dual-carbon” goal [1]. Since 2020, power grid enterprises attaches great importance to play to the important role of “digital infrastructure” and value, actively deploy and promote grid digital platform, energy data center, power data, power Internet, energy industry cloud, intelligent energy services, power artificial intelligence, energy block chain key tasks, strongly support the energy power industry to promote the digital transformation process and actively build a modern energy system (See Fig. 1 for details).

2 The Main Mechanism of “Digital New Infrastructure” to the Construction of New Power System The “Digital new infrastructure” can support the construction of the new power system in five aspects (see Fig. 2 for details).

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Fig. 2 The main mechanism of the “digital new infrastructure” that promotes the construction of a new power system

2.1 The “Digital New Infrastructure” Can Promote the Large-Scale Development and Utilization of New Energy Sources [2], Help the New Power Systems to Achieve Power Reform, and Speed up Clean and Low-Carbon Energy Production New energy has the characteristics of randomness, volatility and uncertainty. Its large-scale and high-proportion of grid connection will bring many risks and challenges to the power balance and safe and stable operation of the new power system. With a large number of new energy such as wind and solar energy access, power grid facing volatility, randomness, security problems of continuous impact, an urgent need to accelerate the grid digital transformation, relying on digital technology for power grid, promote power grid to more intelligent, more ubiquitous, more friendly energy Internet, actively build a new power system. Big data, the Internet of things, artificial intelligence, such as “the digital new infrastructure” can rely on digital means aggregate all kinds of adjustable load, energy storage resources, realize flexible access,

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accurate control, enhance the friendliness of new energy grid, stability, enhance the given ability of new energy, to help new power system power conversion, upgrade. The Internet of Things can comprehensively monitor the electrical, physical, environment, space, behavior and other variables and parameters of massive new energy equipment [3]. Relying on image recognition, machine learning, deep learning and other technologies, AI can play an important role in the site selection of photovoltaic power stations and wind farms, the design of wind turbine blades and photovoltaic panels, the prediction of renewable energy generation, and the assessment of new energy consumption capacity. Industrial Internet platforms such as “New Energy Cloud” can provide important support for the development and utilization of new energy sources such as wind power, photovoltaic power and power generation in scientific planning, efficient construction and safe operation.

2.2 “Digital New Infrastructure” Can Improve the Efficiency and Efficiency of the New Power System, Promote Business Collaboration and Efficient Collaboration among Multiple Entities, And Promote the Efficient and Intensive Energy Utilization [4] “Digital new infrastructure” can break the barriers between the traditional energy industry, realize the oil, coal, natural gas, electricity, heat and other different types of energy connectivity, depth fusion and efficient utilization, as well as the source network load storage of each link of considerable, measurable, controllable, improve the comprehensive utilization efficiency of the new power system and the overall efficiency. 5G and artificial intelligence combined with robots and drones can accurately identify and intelligently analyze the defects of power transmission and distribution equipment and lines, greatly improving the efficiency and level of inspection operations. The Internet of Things can realize the interconnection between information collection systems, adjust and optimize the backbone transmission network of oil, gas and power grid, and improve the energy transmission efficiency. Block chain with the help of encryption algorithm [5], consensus mechanism, smart contract, timestamp technology, can solve the participants in the process of new power system construction, data and information diversification, build a decentralized trust system, promote energy business collaboration between upstream and downstream enterprises and efficient collaboration, build mutually beneficial and win–win energy ecosystem.

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2.3 “Digital New Infrastructure” Can Ease Information Asymmetry, Enhance Market Transparency, Enhance Mutual Trust Among All Parties, and Reduce the Costs of Investment, Operation, Maintenance, and Transactions “Digital new infrastructure” such as energy big data centers and artificial intelligence can improve the precision and effectiveness of power grid investment, catalyze new business forms and new models in many energy fields such as virtual power plants, and reduce the overall scale of power grid investment. For example, the duration of 5% peak load in many places in China is often very short. If the peak load demand is met by the way of building new generating sets and supporting power grids [6], it will generate great infrastructure construction costs and produce redundant power supply capacity. Virtual power plants can mobilize the distributed energy users such as energy storage system, controllable load and new energy vehicles to actively participate in the demand response, so as to realize the optimal allocation and full utilization of social resources. Artificial intelligence can optimize the operation mode and dispatching mode of the power grid, and improve the transmission capacity of the lines. Blockchain can improve the transparency of electricity market transactions and significantly reduce the cost of electricity purchase and sale between different market entities. Blockchain provides a platform for equal cooperation for all entities in the energy field, which can reduce the information asymmetry in each link of the value chain, greatly reduce the risk and cost of credit cooperation among participants, and realize the efficient collaboration and collaboration among multiple centers on the basis of equality. Compared with previous production organizations, production collaboration under blockchain technology is larger and cheaper than before.

2.4 “Digital New Infrastructure” Can Reshape the Value Creation System of the New Power System, Support Energy Enterprises to Actively Innovate Business Models, and Promote the Continuous Emergence of New Subjects, New Business Forms and New Models in the Energy Field The “new digital infrastructure” such as 5G, big data, cloud computing, mobile Internet and blockchain are changing the energy production, operation, transmission, trading and other links, and reshaping the value creation system of the energy system. Big data, the Internet of things, block chain “digital new infrastructure” changed the change of the identity and status between energy producers and consumers, make “energy consumer” from virtual to reality, energy production and marketing process from “one-way” to “closed loop”, support energy enterprises to accelerate

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the process of data assets, actively release data elements value. For example, energy producers can not only sell energy products or services to energy consumers in one way, but also buy back excess energy products or data use rights from energy consumers [7]. At the same time, energy manufacturers can also use the Internet of Things, sensors and other digital devices to actively obtain user feedback data, and modify the type, quality and method of energy products based on the feedback data, so as to facilitate secondary sales or multiple sales. In this process, energy consumers also shift from the pure demand side to the “dual roles” of both demand side and supply side. Therefore, with the help of the extensive application of the Internet of Things and other technologies, the energy production and marketing process has shifted from “one-way” to “closed-loop”. In addition, 5G, big data, the Internet of things, artificial intelligence “digital infrastructure” can support energy enterprises in energy efficiency analysis, energy efficiency management, intelligent operations, demand response and other comprehensive energy services, and create energy storage, micro market, and virtual power plants, micro grid, comprehensive energy services, energy electricity, wisdom car networking, power big data credit, “photovoltaic + 5G communication base station”, “micro grid + charging pile” and a large number of new forms, new mode of energy.

2.5 “Digital New Infrastructure” Can Improve the Risk Identification and Safety Protection Capabilities of Energy Systems, Defuse Various Security Risks, and Ensure the Safe and Reliable Operation of New Power Systems [8] “Security” is not a new problem for power grid companies. Moreover, “security” has increasingly become a key factor and an important variable that power grid enterprises must fully consider in the development process. But the connotation of “security” has expanded greatly in the past. In the past, when power grid enterprises say security, it may refer more to power supply security, power grid security and personal safety. Now, network security, data security, information security, infrastructure security and other non-traditional security increasingly occupy an important position (See Fig. 3 for the various safety problems faced by power grid enterprises). In particular, the conflict between Russia and Ukraine and the power outages caused by cyber attacks have occurred in recent years, which have raised the security and protection of energy and power infrastructure to an unprecedented important position. In this case, the power grid enterprises must coordinate the safety and development, actively improve the safety protection capacity, effectively respond to and resolve the multiple security challenges, and ensure the safe and stable operation of the power grid and the safety and reliability of the energy infrastructure. Based on the power big data, the energy regulatory agencies can carry out the credit evaluation and analysis of the power market participants, comprehensively

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Fig. 3 All kinds of security problems faced by power grid enterprises

evaluate the basic attributes, market behaviors and other data and information of the market participants participating in the power market transaction, prevent the credit risks of the power market participants, and provide support for the supply chain finance and other applications [9]. Artificial intelligence can detect and accurately identify and automatically deal with dangerous behaviors such as network attacks, and eliminate potential security risks in time. Blockchain can store the equipment information, defense information and other data in the new power system to ensure that safety events can be monitored and traced, and significantly improve the safety management level of the new power system [10].

3 Related Suggestions Since 2020, the energy and power enterprises have continued to increase their investment in the “digital new infrastructure”, and the “digital new infrastructure” projects in various key areas have been accelerated, laying a foundation for the construction of the new power system and the high-quality energy development. In the future, it is suggested to strengthen the systematic planning of the relevant systems and mechanisms of the “new digital infrastructure”. We will accelerate the establishment and improvement of policies, regulations and standards on the development and utilization of data resources, the sharing and reuse of digital infrastructure, and the comprehensive promotion of business models and income distribution.

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It is suggested to increase the research and development layout, integration innovation and application of advanced digital technology in the energy field. Follow up the latest progress of basic and forward-looking digital technology, combine the pain points and difficulties of energy enterprises in production, operation and service aspects of energy enterprises, and carry out the integration, innovation and application of digital technology and energy technology. It is suggested to promote the construction of “digital new infrastructure” security protection system with overall thinking. According to the requirements of network security law, data security law, password security law, key information infrastructure and other laws and regulations, formulate and improve the security assessment, risk assessment and confidentiality review system for “new digital infrastructure”; take data security as the key direction of “new digital infrastructure”, and strengthen the whole life cycle of data security management and data security governance capacity building. Acknowledgements This work was supported by the science and technology project of State Grid Corporation of China, called “Research on Two—way Data Value Mining Technology and Typical Application of “Electric Power—Economy” (Project Code: 1400-202157207A-0-0-00)”.

References 1. Sin Security guard (2021) Seize the new digital infrastructure opportunities to promote the digital transformation of the power grid. Electric Power Equip Manag 02:17–19 2. Chonghui M (2020) Thoughts on the implementation of the new digital infrastructure strategy. Inf Commun Technol Policy (11):51–56 3. Na Z (2021) Emergency identification of supply chain security risk aggregation for new infrastructure projects. Int J Front Sociol 3.0(7.0) 4. Levitt J (2022) Invest in maintenance, not just new infrastructure. Fleet Maintenance 26(2) 5. Wang KB, Li JY, Zhang YJ, Chen X, Wang B (2013) The construction and management of technology innovation base on college students. Appl Mech Mater 464(464–464) 6. Tavakkoli S, Macknick J, Heath GA, Jordaan SM (2021) Spatiotemporal energy infrastructure datasets for the United States: a review. Renew Sustain Energy Rev 152 7. Yu H (2021) Analysis of the impact of the new infrastructure on economic growth——based on empirical test charging pile of new energy vehicles. IOP Conf Ser Earth Environ Sci 769(4) 8. Zhu L, Ma Z, Huang H, Yan M (2021) Research on cybersecurity risk prevention and control of new infrastructure. J Phys Conf Ser 1856(1) 9. Zheng W, Barker A (2021) Green infrastructure and urbanisation in suburban Beijing: An improved neighbourhood assessment framework. Habitat Int 117:102423 10. Wang C, Guan S (2020) Research on evaluation of new infrastructure development degree. In: Proceedings of 2020 international conference on the frontiers of innovative economics and management (FIEM 2020), pp 148–153. https://doi.org/10.26914/c.cnkihy.2020.016372

3D Map Modeling Technology Based on Unmanned Vehicle Environment Perception Information Wenjun Xue, Ke Xiao, Jielun Zhao, Leiyu Wang, and Yifan Guo

Abstract Artificial intelligence and Internet technology are developing, and driverless driving is also rapidly setting off a boom, driverless cars have begun to surround everyone. In the research of unmanned vehicle system, simultaneous localization and mapping (SLAM) technology is one of the main research methods to solve the positioning of unmanned vehicles. The main purpose of this paper is to conduct research on 3D map modeling based on the related technologies of unmanned vehicle environment perception information. In this paper, the environmental information perceived by the unmanned vehicle is mainly based on three-dimensional modeling on the map, and the display of the underground space is emphasized. The experimental results show that the average CPU occupancy rate for running the complete SLAM algorithm is 31.5%, while the average CPU occupancy rate for only positioning without mapping in known maps is 20.6%, so the map preservation and reuse algorithm proposed in this paper is very large. To a certain extent, the consumption of real-time visual positioning algorithm on running memory is reduced, so that the system can be applied to more scenes. Keywords Unmanned vehicles · Environmental perception information · 3D maps · Modeling technology

1 Introduction Unmanned driving technology is an important branch of artificial intelligence. Unmanned driving technology brings a lot of convenience to people, but also deeply affects everyone’s life. The realization of driverless technology is inseparable from high-precision maps. Through high-precision maps, the vehicle can be positioned in W. Xue · K. Xiao (B) · J. Zhao · L. Wang · Y. Guo Northern Automatic Control Technology Insitute, Taiyuan 030006, Shanxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_47

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real time, which can help the vehicle perceive the surrounding environment and its own position [1, 2]. In a related study, Iglesias proposed dual-grid flight to obtain a three-dimensional (3D) representation of the road environment, ensuring unbiased line-of-sight output [3]. Then, the dense cloud point is derived by moving the structure in the multi-view stereo process. In addition, the influence of 3D modeling parameters on the results was also evaluated. Milea proposed a new MDE method capable of processing aerial images [4]. In the past few years, the technological society has developed rapidly, and driverless cars have frequently appeared around, and have become a hot issue in today’s social research. This paper mainly uses the relevant technology of unmanned vehicle environment perception information to study 3D map modeling. This paper first analyzes the perception and positioning technology of the unmanned vehicle system, which lays the foundation for subsequent research on planning, decision-making, and navigation.

2 Design Research 2.1 3D Coordinate Kinematics Model of Unmanned Vehicle Introduction to the coordinate system of the unmanned vehicle: During the movement of the unmanned vehicle, it is necessary to use the coordinate system as a reference to describe the motion state and position information of the vehicle body. The Cartesian coordinate system is simple and intuitive, and is suitable for describing the motion state of unmanned vehicles. This paper is based on the Cartesian coordinate system [5, 6], and the posture of the robot in the global coordinate system OwXwYwZw is set as, (x, y, z) is the position coordinate of the unmanned vehicle in the Cartesian coordinate system, (ϕ, θ, ψ) is the angle between the vehicle body pose and the X-axis, Y-axis, and Z-axis of the global coordinate system. In the three-dimensional coordinate system, it is used to represent the running speed of the unmanned vehicle. In order to simplify the calculation, the slope of the ground in the experimental scene is ignored, and the position and attitude of the unmanned vehicle at time t (referred to as the pose) are simplified as.

3D Map Modeling Technology Based on Unmanned Vehicle … Fig. 1 Creating a process using 3DMAX

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3DMAX

Stereo modeling

3DS+BIM 3D geographic information software

2.2 Overview of 3D Modeling Methods for Underground Space 2.2.1

General Approach to Creating 3D Modeling of Underground Space

With the support of 3D modeling integrated management system in 3D geographic information technology, there are mainly the following methods for 3D modeling of underground space. (1) Complete modeling using third-party modeling software According to the design plan of the underground space, the underground building space and various components are modeled with the support of 3DMAX [7, 8]. This process is shown in Fig. 1. (2) Complete modeling using 3D geographic information software itself The simulation effect of the model constructed by this method is difficult to guarantee, especially for objects with irregular shapes. This method is used for modeling because it is a model from the software itself, so in the post-analysis process, the work efficiency is guaranteed. And because the model belongs to the internal object of the program, it can also be directly analyzed in 3D [9, 10]. (3) Automatic batch modeling method using Revit software According to the above analysis, combined with the characteristics of the two modeling methods, a third modeling method is proposed, which can not only ensure the fineness of the model, but also not affect the operation of the system [11, 12].

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2.3 Selection of Feature Points The specific gray-scale centroid algorithm flow is as follows: (1) Take the circular area B around the corner point P. (2) Find the grayscale weighting of the x-axis: m 10 =



x · I (x, y)

(1)

x,y∈B

(3) Find the grayscale weighting of the y-axis: m 01 =



x · I (x, y)

(2)

x,y∈B

(4) The rotation angle of the corner point can be expressed as: θ = arctan(m 01 , m 10 )

(3)

where x, y represent the pixel coordinates, I(x, y) represents the grayscale of the pixel point, and θ represents the direction of the corner point.The corner direction is obtained by the improved FAST corner detection, and then the corner coordinates are projected to the ox' y' coordinate system: 

x ' = x cos θ − y sin θ y ' = x sin θ + y cos θ

(4)

The same projection transformation is performed on the surrounding 256 pixel pairs, and then the grayscale size of the pixel pair is compared to determine the value of the pixel pair to 0 or 1.

3 Experimental Study 3.1 Underground Spatial Data Modeling Based on the Perception Environment of Unmanned Vehicles (1) Draw the elevation and grid Under normal circumstances, the “Elevation” command in Revit can only be displayed in stereo or section views, so to achieve effective reading of data information, it is necessary to build an elevation view first. Click the “Elevation” menu of the browser, customize the view name, the system will automatically jump to the elevation view, and then draw the elevation as needed. It is worth

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3D underground space modeling in cities

Scene browsing

Function module editing and analysis

Building Module Query

Building module measurement and marking

Fig. 2 Module functional structure diagram

noting that the grid in Revit can be automatically synchronized to other threedimensional or section graphics with only one drawing. (2) Functional realization of the underground space module based on the perception environment of unmanned vehicles The three-dimensional module construction is carried out for the underground space. The construction content and specific links include: 1. First, ensure the accurate geographic information function, that is, the underground space management is connected with the relevant geographic information system. 2. Ensure that the module has authenticity and visibility. 3. With the support of the system, the 3D space analysis performance is strengthened to ensure that the 3D space can provide the query services and related business analysis functions required by users. The function diagram of the 3D underground space building module is shown in Fig. 2.

3.2 Map Upload and Positioning In the uploading stage of the map, the system will sequentially read the map information according to the storage order of the map, generate a visual dictionary online based on the description words of the read feature points, and store the co-view key frame Id and the corresponding key frame according to each key frame. Weights, generate co-views online. After the map is uploaded, it can be relocated and tracked in known maps. The following introduces the relocation and positioning method in the known map. The relocation algorithm in this paper is: (1) Read the current frame, extract feature points, and calculate the visual word bag vector. (2) Traverse all words vi of the visual word bag vector of the current frame. (3) Find all keyframes ki with word vi in the map and set them as candidate frames. (4) Through feature point matching, calculate the map points mi of the current frame and these key frames.

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(5) Obtain the current frame pose through the PnP algorithm, and use the RANSAC algorithm to make the obtained current frame pose satisfy the largest number of map points mi that have corresponding pixels in the current frame. (6) Using the uploaded map, find the common view key frame, and project the corresponding map point to the current frame.. (7) Projection obtains the reprojection error, constructs the BA optimization problem model, and optimizes the current frame pose. (8) The relocation is successful.

4 Experiment Analysis 4.1 Comparison of CPU Usage Table 1 shows the occupancy rate of the computer CPU (Central Processing Unit) when running the complete SLAM algorithm in real time and the CPU occupancy rate when running the map saving and reusing algorithm when locating the module in the known map. As can be seen from Fig. 3, the average CPU occupancy rate of running the complete wheel speed pulse-visual-inertial tightly coupled SLAM algorithm is 31.5%, while the average CPU occupancy rate of only positioning without mapping in known maps is 20.6% Therefore, the map preservation and reuse algorithm proposed in this paper greatly reduces the consumption of real-time visual positioning algorithm on running memory, so that the system can be applied to more scenes. Table 1 Comparison of CPU usage

Algorithm

SLAM (%)

Position (%)

CPU1

36.5

14.6

CPU2

23.3

21.4

CPU3

26.0

25.0

CPU4

9.2

21.2

CPU5

19.2

24.0

CPU6

22.2

14.9

CPU7

73.2

18.6

CPU8

42.3

25.5

Average value

31.5

20.6

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80.00% SLAM

70.00%

position

Proportion

60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00%

CPU1 CPU2 CPU3 CPU4 CPU5 CPU6 CPU7 CPU8 average value

Algorithm

Fig. 3 Comparative analysis of CPU usage

4.2 Comparison of Map Preservation and Reuse Algorithms The map and preservation and reuse functions developed in this paper will be similar to VINS-Mono from the map size, save time, load time, etc. This section conducts experiments on the two algorithms in multiple scenarios, and the experimental data of the two algorithms are shown in Table 2. As can be seen from Fig. 4, this paper has obvious advantages in terms of map size, map saving time and map loading time compared with the map saving and reuse function of VINS-Mono. Table 2 Comparison of map preservation and reuse algorithms Algorithm

VINS-Mono

The algorithm in this paper

Experimental scenario packet size

Laboratory hall 2G

Underground garage 16.9G

Mechanical building hall 3.77G

Map size

376 MB

414.3 MB

361.2 MB

Save time

2.16 s

11.12 s

8.56 s

Load time

1.62 s

10.21 s

6.81 s

Map size

11.5 MB

81.1 MB

33.2 MB

Save time

0.72 s

7.12 s

0.29 s

Load time

0.13 s

1.02 s

0.19 s

map size(MB) save time

load time

16.9G

map size(MB)

2G

Fig. 4 Comparison and analysis of map preservation and reuse algorithms

0

50

100

150

200

250

300

350

400

450

load time

save time

3.77G

0

2

4

6

8

10

12

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5 Conclusions Positioning technology is an important prerequisite for realizing autonomous navigation and decision planning of unmanned vehicles. Various application and functional requirements have promoted the rapid development of unmanned technology in the field of mobile unmanned vehicles. Unmanned vehicles with terrain adaptability and high intelligence have important research value.

References 1. Gbopa AO, Ayodele EG, Okolie CJ et al (2021) Unmanned aerial vehicles for three-dimensional mapping and change detection analysis. Geomatics Environ Eng 15(1):41–61 2. Salari A, Erricolo D (2021) Unmanned aerial vehicles for high-frequency measurements: an accurate, fast, and cost-effective technology. IEEE Antennas Propag Mag 99:2–12 3. Iglesias L, Santos-Berbel CD, Pascual V et al (2019) Using small unmanned aerial vehicle in 3D modeling of highways with tree-covered roadsides to estimate sight distance. Remote Sens 2625(11):1–13 4. Miclea VC, Nedevschi S (2021) Monocular depth estimation with improved long-range accuracy for UAV environment perception. IEEE Trans Geosci Remote Sens 99:1–15 5. Ramyar R (2020) Learning from tradition: the role of environment perception layers in space making—the case of the Persian Garden. J Urban Manag 9(2):238–249 6. Isaac MSA, Ragab AR, Garcés EC et al (2022) Mathematical modeling and designing a heavy hybrid-electric quadcopter, controlled by flaps. Unmanned Syst 10(03):241–253 7. Cho E, Kim H, Park S (2020) Driving environment perception and grid map generation system using deep learning based mono vision. Trans Korean Inst Electr Eng 69(2):356–361 8. Yoon JI, Kim JH (2020) The influence of the perception of restorative environment on place attachment for visitors to Han River Park: grounded on attention restoration theory. Korean J Leisure Recreation Park 44(3):1–13 9. Baldo AH, Elnimeiri M, Haroun HM et al (2020) Sudanese paediatric residents perception towards training environment in Sudan medical specialisation board, 2020. Sudanese J Paediatr 20(2):126–135 10. Choi J, Park I (2020) The influence of entrepreneur’s perception of entrepreneurial environment on business operation plan. Korean Policy Sci Rev 24(3):27–47 11. Sawyer AO (2022) Imagining daylight: Evaluating participants’ perception of daylight in work environments. Indoor Built Environ 31(1):96–108 12. Gareis M, Parr A, Trabert J et al (2021) Stocktaking robots, automatic inventory, and 3D product maps: the smart warehouse enabled by UHF-RFID synthetic aperture localization techniques. IEEE Microwave Mag 22(3):57–68

Traffic Image Classification Algorithm Based on Deep-Learning Yi Ren and Lanjun Cong

Abstract With driving record image data set, it’s hard to choose the most suitable classification algorithm model for predicting traffic message channel. Because the data set has features of time series, common deep learning model can not extract the features. The models combine EfficientNet and Bi-GRU algorithms. And the experimental prediction precision, recall and F1-score are up to 66.67%, 71.62% and 68.47% respectively. Finally, the feasibility of the proposed recommendation algorithm selection is demonstrated. Keywords Image classification algorithm · Traffic message channel · Time series · Deep learning

1 Introduction For the sustainable development of cities, building smart cities has become the development trend of the world. Smart transportation belongs to smart city and is the core of smart city. The rich road information of intelligent transportation has promoted the development of driverless. Intelligent perception of road environment is an important part of driverless technology. According to the perceived road environment information, automatic route planning has become the main purpose of driverless. Real time traffic is really important in path planning. The real-time and accuracy of road information is the key factor to determine the path planning. As a key technology in intelligent transportation systems (ITS), traffic flow forecasting has received more attention than before because of deployment of ITS [1]. The traffic management platform combine with AI technology for real-time traffic data mining and flexible traffic

Y. Ren · L. Cong (B) School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_48

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flow control [2]. It is also necessary to consider the spatial interaction information between other regions [3]. However, it continues to be a challenge until today [4]. In this paper, the fusion of convolution model EfficientNet and recurrent neural network models to extract data features of driving record image for forecasting traffic message.

2 Relevant Theory In recent years, with the development of image convolution network, image classification algorithm models have developed rapidly, and the emergence of convolution models has attracted more attention to deep learning. EfficientNet systematically studies the balance between network depth, width and resolution. The development of deep learning in travel time prediction and traffic is very rapid. Deep learning neural network is a popular technique in forecasting traffic flow. A new model based on Deep Belief Networks [5] was proposed to predict traffic flow. With the increase of real-time traffic flow data, online learning for traffic prediction is developing rapidly [6]. Guo [7] et al. predict flow with 3D convolutional neural networks. TGCLSTM [8] is a convolutional recurrent neural network to forecast traffic.

2.1 Convolution Module Classic convolutional models include Vgg, Resnet, EfficientNet etc. Vgg uses small convolution kernels, compared with using a large convolution kernel, the amount of convolution calculation is reduced, the speed and efficiency of training are improved, and models with different depths of 11, 13, 16, and 19 convolution layers are tried, which increases the depth of the neural network, so that more activation functions, richer features, and stronger discrimination capabilities can be incorporated into the model, which improves the performance of the network and reduces the error rate. As the number of convolutional layers in the neural network increases, the learning ability of the neural network will deteriorate. The ResNet neural network introduces a residual module through a short-circuit connection to improve the performance of the model. The input feature map is processed by the convolution layer and the activation function and then fused with the input feature map, so that the neural network refers to local features and global features, which avoids the model from learning only the local feature map, and will not cause the model to have abnormal gradient values. In order to obtain better accuracy of convolutional neural network, it is usually to expand the convolutional neural network as much as possible under the inherent hardware resources, from the three dimensions of network depth, width and image

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resolution. Google brain team systematically analyzed the impact of the expansion of various dimensions on the accuracy of the model, balanced the network depth, width and image resolution, and achieved higher accuracy with fewer parameters and fewer floating-point operations. Eight models from EfficientNet-B0 to B7 are selected for comparison. The neural network model EfficientNet is selected. The goal is to maximize the accuracy of the model through different combinations of network depth, network width and image resolution within the limited range of hardware resources. It is an optimization problem. Use composite scaling. The composite coefficient will unify the depth, width and image resolution of the scalable network.

2.2 LSTM Module For time series data, only using feature extraction model to process data can not get good results. We also need to introduce a time series model to take the extracted features as input, and finally get the classification [9]. Since it is necessary to judge the road condition information according to the driving record video sequence images, the images of each sequence have reference significance. It is necessary to refer to sequence images over a period of time. In order to remember the characteristics of time series images, LSTM [10] model is selected. As show in Fig. 1. It can remember the characteristics of the previous moment, fuse the current characteristics, and grasp the theme of the time series. The principle is that the forgetting gate can ignore some unimportant historical information. The forgetting gate is based on the result of the calculation of the previous neuron hidden layer h t−1 and current the input data then generates a value between 0 and 1 through the sigmoid function to forget unimportant information.     f t = W f · h t−1 , xt + b f

Fig. 1 LSTM neural unit

(1)

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The input gate affects the data information input at the current time. h t−1 and xt are passed into the activation function to affect the result of updating gate. New candidate memory units are obtained by tanh activation function Ct' .     i t = σ Wi · h t−1 , xt + b f

(2)

    Ct' = tanh Wc · h t−1 , xt + b f

(3)

Forgetting gate multiply by one state plus input gate multiply candidate state. Ct = f t × Ct−1 + i t × Ct'

(4)

The state value is obtained from −1 ~ 1 through the tanh function. The value is calculated with the output gate to determine the output state information.     ot = σ Wo h t−1 , xt + bo

(5)

h t = ot × tanh(Ct )

(6)

Because the unidirectional model can only remember unidirectional information, for the subject of driving record image classification, the current output should be related to the past state and the future state. The two-way model is more suitable for such tasks. The Bi-LSTM and Bi-GRU models are more suitable for the experimental scenario of this paper.

2.3 GRU Module Another time series model is GRU. It uses update gate and reset gate. As show in Fig. 2. The update gate Z will compute the current hidden state h with previous time ˜ Reset gate r determine how much previous information state and new input state h. should be ignored. If the previous information is not relevant or irrelevant to the current input, it will be reset. As show in formulae (7, 8). Reset gate r and update gate Z are calculated through the last hidden state h t−1 and the input xt . The new hidden state is calculated through reset gate r and the input xt . As show in formula (9). The update value is calculated through forgetting gate and current information. As show in formula (10).    r j = σ Wr · h t−1 , xt

(7)

   Z t = σ Wz · h t−1 , xt

(8)

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Fig. 2 GRU model

   h˜ t = tanh W · rt ⊗ h t−1 , xt

(9)

h t = Z t ⊗ h˜ t + (1 − Z )h t−1

(10)

3 Model in This Paper After the input image is processed by the convolution model, the full connection layer is spliced, and then the Softmax function is used to calculate the final classification results. The accuracy of EfficientNet, VGG and ResNet in the training set and test set is compared, as show in Figs. 3, 4. Fig. 3 Accuracy comparison of convolution models on training set

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Fig. 4 Accuracy comparison of convolution models on validation set

The comparison results of the accuracy rate of the model using the training data set and validation data set show that EfficientNet is better than other models in terms of convergence speed and accuracy rate, indicating that the model is easier to train and its model ability is also stronger than VGG and ResNet. Using EfficientNet feature extraction model as input to the model of time series. Correlate the characteristics of time series pictures according to their memory function. Finally, the output value of the time series model is obtained and transmitted to the final output neural network layer to obtain the three classifications result value, so as to obtain the road condition information of this time series. As shown in Fig. 5. The network structure is composed of 16 mbconv+2 conv+1 global average pooling+1 FC classification layer, which shows the input and output of each layer of EfficientNet-B0 structure diagram. Where mbconv is mobile inverted bottleneck convolution (mbconv). It is composed of depth separable convolution and senet. This experiment takes EfficientNet-B0 to B7 as the basic model of image feature processing, and then fuses the time series model. The ELSTM model architecture is constructed by combining EfficientNet and LSTM/GRU/RNN. As shown in Fig. 6.

4 Experiment and Analysis In this paper, training model framework is tensorflow with NAVIDIA V100 32G GPU in Linux ubuntu.

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Fig. 5 EfficientNet-B0 structure

Fig. 6 EfficientNet+ LSTM/GRU/RNN model

4.1 Data Set The driver recorder images dataset is Tianchi Datasets public dataset. The size of train time series 1500, which contains 7000 images, and the test dataset contains images of 600 time series set, which contains 2800 images. The image sizes in database are all 1264 × 720.

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4.2 Algorithm Evaluation The road conditions include unobstructed, slow and congested. The score considers the accuracy of road classification of each image sequence, and uses weighted F1score as the evaluation index of the algorithm. Weighted F1-Score: Scor e =

n 

wi × F1i

(11)

i=1

(i = 0 unobstructed, i = 1 slow, i = 2 congested);

4.3 Experimental Results The six models proposed in this paper are compared. The basic models of time series are RNN, LSTM and GRU. Each model selects B0 ~ B7 models with different extended dimensions for EfficientNet. The accuracy, recall and F1-score were calculated. As shown in Fig. 7, F1-score curve for comparison. Experiments show that EBGRU (EfficientNet+ bidirectional GRU), EfficientNetB4 feature extraction model plus bidirectional GRU model can obtain the best accuracy, recall and F1-score. It can be seen that the LSTM model memory ability is very strong in the gated structure, but its structure is complex. GRU not only has strong memory ability, but also has a simpler gating structure than LSTM. Moreover, the bidirectional GRU model learns the information in two directions on the time axis, and gets better results after fusion. Fig. 7 Comparison of F1-score of EfficientNet Time series six types of time series models

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5 Conclusions Six deep learning models are proposed based on the road condition image classification algorithm of deep learning. (1) (2) (3) (4) (5) (6) (7)

EfficientNet combined with RNN model (ERNN) EfficientNet combined with Bi-RNN model (EBRNN) EfficientNet combined with LSTM model (ELSTM) EfficientNet combined with Bi-LSTM model (EBLSTM) EfficientNet combined with GRU model (EGRU) EfficientNet combined with Bi-GRU model (EBGRU) EfficientNet model (EM).

The experimental results show that the design scheme is feasible, and the effect of model (6) is the best among the 7 models. Among them, the bidirectional GRU selected by the time series model has a better effect on the classification of driving record images. The research of this paper lays a foundation for future in-depth learning on the classification of driving record images. Later research work can consider the choice of model algorithm to further improve the classification effect.

References 1. Haydari A, Yılmaz Y (2022) Deep reinforcement learning for intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 23(1):11–32 2. Lana I, Ser JD, Velez M et al (2018) Road traffic forecasting: recent advances and new challenges. IEEE Intell Transp Syst Mag 10(2):93–109 3. Nallaperuma D et al (2019) Online incremental machine learning platform for big data-driven smart traffic management. IEEE Trans Intell Transp Syst 20(12):4679–4690 4. Song C, Lin Y, Guo S et al (2020) Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. AAAI Conf Artif Intell 34(1):914–921 5. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554 6. Van Lint J (2008) Online learning solutions for freeway travel time prediction. IEEE Trans Intell Transp Syst 9(1):38–47 7. Guo S, Lin Y, Li S et al (2019) Deep spatial–temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans Intell Transp Syst 20(10):3913–3926 8. Cui ZY, Henrickson K, Ke RM et al (2020) Traffic graph convolutional recurrent neural network: a deep learning framework for NetworkScale traffic learning and forecasting. IEEE Trans Intell Transp Syst 21(11):4883–4894 9. Davis GA, Nihan NL (1991) Nonparametric regression and short-term freeway traffic forecasting. J Transp Eng 117(2):178–188 10. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

The Application of 3D Cable Laying in Substation Based on Genetic Algorithm Wenzhe Xu, Wenhua Tao, Shudong Lv, and Jiabing Shi

Abstract Cable laying design is one of the most complex and tedious tasks in the design of substation power plants. Manual design will consume a lot of manpower. At the same time, the traditional two-dimensional design cannot truly represent the design scene, and it is prone to design errors for collision checking. Compared with traditional design methods, substation power plant design based on 3D design software is more intuitive and accurate, and has been widely used in the industry. However, no matter what the design method is, the cable laying path algorithm has always been dominated by traditional algorithms, which are often limited by complex designs and large data requirements. This paper mainly studies the optimal routing path problem of cable laying in 3D design scenarios. In this paper, AutoCAD software is used to establish a three-dimensional cable design system for substations. Using this system to design cable laying paths can reduce labor design costs. The cable path optimization module of the system uses genetic algorithm (GA) to optimize the laying path. This paper compares the laying optimization results of the basic GA and the improved GA. Compared with the basic GA, the improved GA improves the laying capacity by increasing the algorithm operation time. Keywords Genetic algorithm · Cable laying · 3D design · Laying path

1 Introduction With the continuous expansion of the scale of current power grids, in the process of power grid construction in various cities, in order to improve the overall planning of the city and consider the reliability factors of cable laying, the process of urban power grid planning and construction has gradually eliminated overhead lines. The laying method with this limitation is more obvious. The proportion of power cables used in W. Xu · W. Tao (B) · S. Lv · J. Shi Zhejiang Electric Transmission & Transformation Co. Ltd, Hangzhou 310016, Zhejiang, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_49

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the laying form of power channels during the laying process has been continuously increased, which has also made a qualitative leap in the reliability and security of the power network. At present, many scholars have discussed the application of 3D cable laying in substations based on genetic algorithm. For example, a scholar proposed to use the improved Djkstra and genetic algorithm to solve the optimal laying route by combining the objectives of the shortest cable laying path, the least number of turns and the least number of staggered layers [1]. A scholar described the design and implementation of the 3D cable design system based on the A-Start algorithm, and developed software based on the AutoCAD platform [2]. A scholar combined the genetic operator of the improved genetic algorithm with the cable laying problem in the actual project, and verified the optimized new algorithm through simulation experiments. A simple software for ship cable layout was developed [3]. A scholar applied the ant colony algorithm to the process of laying 3D pipeline and cable. In the process of 3D pipeline routing, the heuristic ant colony algorithm was used to realize the path planning of the aero-engine cable line. The superior characteristics achieve the effect of fast search speed and high global convergence rate [4]. Although the genetic algorithm is widely used in cable laying, the genetic algorithm also has shortcomings, and the cable laying problem can be solved by improving the algorithm. This paper first introduces the concept and operation process of genetic algorithm, and then proposes the difficulty of substation cable laying path design. This paper establishes a three-dimensional cable laying path design system, and uses genetic algorithm to optimize the cable laying path. In the substation cable laying based on genetic algorithm. In the algorithm simulation experiment, the optimal path of the cable laying path is obtained through algorithm iteration.

2 Genetic Algorithms and Cable Laying 2.1 Genetic Algorithm (GA) The algorithm idea of GA is to simulate the process of chromosome evolution, and iteratively perform replication, crossover and mutation operations on the chromosome represented by the problem solution code, so that the final result can be adapted to all groups to obtain the optimal solution [5]. The basic operation process of the genetic algorithm is as follows: Initialization: The initialization of the parameters of the encoding algorithm. The current number of iterations t is initialized to 0, the number of iterations is initialized to T, and an individual is randomly generated as the initial value for the initialized population P(0). Assess individual fitness: Calculate the fitness of each individual in the population P(t) using the roulette strategy.

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Selection operation: Individuals with high fitness are selected and inherited or inherited through crossover operation, and those with low fitness are eliminated. Crossover operation: The population P performs crossover operation, and it will generate new individuals according to a certain crossover probability and crossover method. Termination condition judgment: if the iteration terminates, the algorithm terminates, that is, the current iteration number t = T. The group with the highest output fitness is the optimal solution. The mapping relationship in the genetic algorithm can be expressed as:

 f (x) =

f (x) = f a (x)

(1)

L − f a (x), f a (x) < L 0, else

(2)

Among them, f a represents the objective function, and f(x) is the mapping relationship, and L is a constant.

2.2 The Main Difficulties in Cable Laying Path Design 1. The path is complicated The high safety requirements and degree of automation of modern substations determine the complexity of its main tray network. Reasonable routing resource allocation, efficient routing design, and convenient routing modification must rely on efficient design software [6]. 2. There are strict distance isolation and physical isolation requirements With a series of isolation guidelines developed to ensure safety, it is unthinkable to rely on human control in the design of each cable. 3. It is difficult to count the amount of cable laying There are hundreds of cables of various types including power, control, measurement and communication, and the laying quantity must be counted separately for item procurement management. If the traditional design method is used for manual counting, it is time-consuming, labor-intensive and inaccurate [7]. In order to solve the above problems, at the same time ensure the design quality, meet the requirements of engineering design progress and the needs of engineering construction management, it is very necessary to use a set of professional cable laying software.

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3 3D Design Modeling of Substation Cable Laying Under the development environment of Visual C++ 6.0, this paper adopts the method of ObjectARX object-oriented programming, and uses the powerful graphics function of AutoCAD software to establish a three-dimensional cable laying and cable tray model, as shown in Fig. 1. And research the optimization algorithm of cable laying, realize the graphic output of the design result, make the cable laying computer-aided design take a solid step in the direction of three-dimensional design. There are many engineering methods for cable laying design work, which can be divided into various forms such as one-time construction, multiple constructions, and original engineering transformation. However, no matter what kind of construction method is used, for the cable laying design work, it is necessary to carry out standard identification of cable laying design such as collision detection for cable laying. Therefore, in the cable laying design work, if there is a strong correlation with the previous construction design or the original project, the previous design drawings are required for the three-dimensional modeling of the cable laying design work [8, 9]. The database prepares the necessary data for design calculations, which are sourced from bridge, substation and cable system blueprints and cable inventories. At the same time, the database also saves the calculation results of some functional modules. The communication between the database and the program is realized through the 0bjectARX and the database interface. The substation and bridge modeling modules are the basis of other functional modules. Through the interface between Visual C++ and AutoCAD, this module goes deep into the AutoCAD graphics database, and uses the classes and functions provided by the 0bjectARX development tool to build plant and bridge models. The data required for modeling is obtained through the interface between ObjectARX and the database. The ID of the established bridge entity is stored in the AutoCAD graphics database, and the data is prepared for the cable route optimization module and the design result output module [10]. In the process of computer-aided design of cable laying, the first step is to calculate the direction of the cable. In this step, the cable routing optimization module is used, which calculates the direction of the cable and the actual length of the cable, and prepares the data for the characteristic section statistics module. The calculation results of this module are stored in the database in the form of data tables. The function of the statistical module of cable tray characteristic section is to count the cables passing through the characteristic section of the cable tray. After calculating the cable direction, the bridge characteristic section statistics module calculates and counts the cable distribution in each characteristic section according to the data provided by the cable path optimization module. The cable distribution is then stored in a database to provide data for the cable sorting module, the cable crossfinding module, and the output module. The function of the cable sorting module is to select the cable placement position according to the cable path and the statistics of the characteristic cross-section data of the cable tray through which the cable passes [11]. This module designs the human–machine interface, allowing technicians to

Fig. 1 Cable laying system functional modules

Substation, bridge modeling module

Interface between Visual C++ and AutoCAD

Design result output module

Crossover Cable Lookup Module

Cable Sorting Module

Cable Tray Feature Section Statistics Module

Cable route optimization module 0bjextARX and database interface

Cabling Database

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participate in the design. Technicians use this module for cable sequencing with manual intervention. The data output by the cable sequencing module is divided into two parts. The first part is the position of the cable in each characteristic section. This data is provided for technicians to check the position of the cable and the statistics of the completion data in the later stage of construction. The second part is provided for the cable cross-finding module, and the cable cross-finding module uses this part of the data to calculate the position of the crossing cable and the statistics of the crossing situation. The function of the cross-cable search module is to calculate and statistically analyze the cross-section of cables in the characteristic section of each cable tray. This module will analyze each characteristic section of the cable tray and the distribution of cables in the relevant section, and then determine whether the cables passing through the section are crossed. Finally, make a statistical analysis of the cable crossover and provide it to the technicians. If the crossover is serious, the technicians can make appropriate adjustments [12]. The design result output module can graphically output the data information of the cable tray, substation and cable direction, and vividly express the spatial position and cable direction of the cable tray, so that the cable laying designer can easily check the substation, cable and cable tray.

4 Application of Genetic Algorithm in Cable Laying Path Optimization 4.1 Optimization of Cable Radiation Path Based on Genetic Algorithm This paper takes the cable laying of the substation as an example. Suppose a 220 kV substation is built in a province. The substation has three power distribution devices of 220 kV, 110 kV and 10 kV, which are respectively connected to the equipment in the main control room. In this paper, the ant colony optimization algorithm is used to simulate the optimization of the cable laying path from the main control building of the substation to the power distribution equipment on its 220 kV side. In the process of cable laying, it is considered that most of the cables are laid during the secondary laying under the condition that the cables have been laid relatively well, and the process of multi-point laying is also based on the single-point cable laying research. Therefore, in this paper, the most basic laying method in the cable laying process is single-point laying, that is, the cable line laying method with single starting point and single end point. After abstracting the cable laying network, this paper takes the 40-point cable laying network as a single-point calculation example of single-point cable laying to study the laying method of cable current carrying capacity.

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Fig. 2 Path laying distance

Because the genetic algorithm simulates chromosome evolution and continues to iterate by encoding chromosomes, it has its own unique advantages and disadvantages in the running process of the algorithm. In the optimization process of the cable laying path this time, the optimal laying method of the direct cable path is used for laying, and the genetic algorithm is used for research and verification in the process of path optimization. In this experimental simulation process, the genetic algorithm parameters are set for the 40-point cable laying scene, and the final laying result is shown in Fig. 2. Set the parameters of the genetic algorithm to optimize the algorithm. After certain parameter settings, it can be concluded that the optimal solution of the cable laying path 264 can be obtained in the fifth algorithm iteration after the optimization of the genetic algorithm. After the 25th algorithm iteration, the The average set distance tends to be stable. In the operation process of the algorithm, 35 iterations are selected as the maximum number of iterations of the ant colony algorithm. At this time, the shortest distance is at the global optimal value and the difference between the average optimal path and the shortest path is 28. At this time, the operation time is 1.63 s.

4.2 Improvement of Cable Laying Path Based on Improved Genetic Algorithm When the basic genetic algorithm is used for the laying of power cables in the process of cable laying, it is impossible to take into account the influence of the reference factor of the current carrying capacity during the cable laying process on the path of the cable laying, and the improved genetic algorithm is used to carry out Further

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Table 1 Comparison of laying results

Basic GA

Improved GA

Path length

197

234

Ampacity (A)

352

468

Operation time (s)

1.63

4.75

The optimal number of path iterations

5

12

optimization can increase the ampacity. The laying comparison results for the two laying scenarios are shown in Table 1. By comparing the basic genetic laying algorithm and the improved genetic algorithm, it can be concluded that the running time of the improved GA is longer than that of the basic GA. However, considering the chromosome coding problem when improving the GA, it is actually equivalent to the starting point in the process of optimizing the laying. Symmetrical path selection is performed on chromosomes equal to the end point, so the computational scale of the improved GA is equivalent to twice the number of chromosomes for pathfinding optimization, and the crossover probability and mutation probability need to be temporarily adjusted in the selection process of each path., which will also increase the burden of arithmetic operations. Compared with the basic GA, by comparing the calculation results, it can be concluded that compared with the basic laying, the ampacity value obtained by the laying in the results of the ampacity optimization laying is increased by 32.95% compared with the basic laying, and the ampacity path length ratio is relatively 18.78% increase in foundation laying. The improved GA essentially sacrifices the computation time and increases the scale of the computation to improve the degree of optimization of the algorithm.

5 Conclusion This paper develops an easy-to-use, efficient and optimized 3D cable design system based on AutoCAD platform. When designing the cable path of the substation, the designers only rely on their own experience to design. In many cases, the cable path cannot be optimally optimized, resulting in a waste of engineering materials, and because the cable is expensive, it seriously affects the overall project. In this paper, an optimized and efficient genetic algorithm path selection is realized, which reduces the use of cable radiation materials. The software realizes the function of automatic path selection according to the cable channel and the cable information of the starting end designed by the designer. The algorithm needs to meet the requirements of laying large-scale cables at the same time, and fully consider the professional constraints of cable engineering, and can calculate the optimized laying path in a short time to achieve the purpose of saving materials and improving economic benefits.

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References 1. Tsatsulnikov AF, Lundin WV, Sakharov AV et al (2018) Formation of three-dimensional islands in the active region of InGaN based light emitting diodes using a growth interruption approach. Sci Adv Mater 7(8):1629–1635 2. Mustafa A, Heppenstall A, Omrani H et al (2018) Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm. Comput Environ Urban Syst 67(Jan.):147–156 3. Giassi M, Goteman M (2018) Layout design of wave energy parks by a genetic algorithm. Ocean Eng 154(Apr.15):252–261 4. Tatematsu A, Terakuchi S, Yanagi T et al (2020) Lightning current simulation of 66-kV substation with power cables using the three-dimensional FDTD method. IEEE Trans Electromagn Compat PP(99):1–11 5. Dawid H, Kopel M (2019) On economic applications of the genetic algorithm: a model of the cobweb type*. J Evol Econ 8(3):297–315 6. Benacerraf BR (2019) Three-dimensional volume imaging in gynecology. Obstet Gynecol Clin North Am 46(4):755–781 7. Petrovic C, Liu Y (2017) Three-dimensional magnetic critical behavior in CrI_3. Phys Rev 97(1):014420.1–014420.6 8. Yamoto T, Nishibayashi H, Ogura M et al (2020) Three-dimensional morphology of the superior cerebellar artery running in trigeminal neuralgia. J Clin Neurosci 82(Pt A):9–12 9. Devarajan H, Punekar GS, Srikanth BN (2018) E-field computation in 765kV substation using CSM with reference to occupational exposure. IET Gener Transm Distrib 12(7):1680–1685 10. Baghmisheh AG, Estekanchi HE (2019) Effects of rigid bus conductors on seismic fragility of electrical substation equipment. Soil Dyn Earthq Eng 125(Oct.):105733.1–105733.16 11. Perovi BD, Tasi DS, Klimenta DO et al (2018) Optimising the thermal environment and the ampacity of underground power cables using the gravitational search algorithm. IET Gener Transm Distrib 12(2):423–430 12. Nastati I (2018) Wind farm cable laying overcomes obstacles. Dredg Port Constr 52(597):11–11

Research on the Digital Measurement Method of Ship Flow Based on Computer Vision Jianchao Xia

Abstract In today’s time, the economy is developing faster and faster, and ships have become one of the essential means of transportation for people to travel. The ship traffic flow is the number of ships going upstream and downstream through a certain water area, and it is the traffic flow of ships in a specific water area, so it is very important to measure the ship traffic flow. The traditional way of measuring ship traffic flow is mainly by measuring the flow of ships in each direction, and then using the manual measurement method for data statistics and analysis according to the needs of users. This traditional method not only consumes a lot of human and material resources, but also has low efficiency and poor accuracy and objectivity of data statistics. In order to solve the defects of the traditional measurement method, this paper proposes a new concept of digital measurement method of ship flow based on computer vision to realize dynamic statistics of data volume, which mainly adopts the real-time target detection technology of YOLO v3 and Sort target tracking algorithm to realize the measurement of ship flow, and the method uses the way of marker line, so as to realize fast and accurate target detection of inland waterway ships and upstream and downstream flow of waterway. The method uses marker lines, so as to achieve fast and accurate target detection of inland vessels. Keywords Computer vision · Ship traffic · Digital metering methods

1 Introduction With the rapid development of computer technology, the demand for data management and related equipment in the shipping industry is getting higher and higher. The traditional rough measurement method can no longer meet the requirements J. Xia (B) Department of Quality, Safety and Environmental Protection, Wuhan Second Ship Design and Research Institute, Wuhan 430205, Hubei, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_50

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of modern shipping industry for real-time and dynamic data [1]. Therefore, it is of great practical significance to use advanced scientific theoretical methods to realize the digital measurement method of ship flow, which is important to improve the economic efficiency and international competitiveness of shipping enterprises [2]. In order to realize the detection of ship flow, a lot of research has been conducted at home and abroad on the automatic statistics of ship traffic and the measurement of ship flow, and five kinds of measurement methods have been researched to identify ship targets and detect ship flow, namely, laser measurement method, infrared imaging method, radar imaging method, AIS monitoring method and video monitoring method [3]. Among them, the laser measurement method uses the principle of laser distance measurement to monitor the ship in real time and dynamically, so that the coming and going direction of the ship can be tracked dynamically, and then the error is fed back to the user based on the results obtained from data calculation [4]. However, this method is easy to miss when multiple vessels are interlaced, and it is easily affected by environmental factors during the measurement process. Infrared imaging method is to identify by detecting the infrared difference between the ship target itself and the background, but infrared imaging has certain limitations, and it is influenced by environmental factors, so in the ship flow measurement needs to consider a variety of circumstances. Radar imaging method is to locate the ship through the data of radar sensor, so as to realize the measurement of ship flow [5]. The AIS monitoring method is to install cameras on the ship, process the collected signals, and feed the data to the computer. But the AIS equipment needs to keep the camera running normally and keep the video data clear, so as to ensure that the final ship traffic value is accurate, stable and reliable [6].The video monitoring method can be dynamic, and it can monitor the ship traffic in real time. Video monitoring technology is also considered as the prototype of computer vision technology, and this paper focuses on the measurement of ship traffic by computer vision technology [7].

2 Computer Vision Technology Computer vision technology is a method of digital control based on images, which mainly includes the use of machine language to complete the analysis of object data, so as to get object images, the principle of computer vision technology is shown in Fig. 1. In the research process, we can use this relevant information to get some useful information, such as the size, shape and position of the object, so as to get the data we need. The relevant people can process this information to get the relevant data. When analyzing ship traffic, we can quickly and accurately obtain the information and calculate the corresponding quantity by computer. With the continuous development of science and technology, computer vision technology has become the most active and widely used technology in the current research field. It also has the advantage of fast processing speed, so computer vision is gradually being widely used in various industries.

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Fig. 1 Principles of computer vision technology

Computer vision technology is a kind of computer vision, which includes image processing technology, image recognition technology, and image processing technology. Image processing technology: In the actual measurement process, we need to digitize the acquired original image, and computer vision is mainly used to obtain the spatial data information from the camera. Therefore, the grayscale histogram method can be used to achieve the image pre-processing, grayscale histogram as shown in Fig. 2. This method can eliminate the grayscale distribution in the image to a certain extent, which makes the original graphics without distortion, thus improving the measurement accuracy and reducing the error [8]. Meanwhile, the gray histogram and tilt direction of the image can be used to control the ship flow. Image recognition technology: Computer vision has a wide range of applications, mainly in the industrial, medical and transportation fields. In the actual production process, the use of machine vision can identify and process a large amount of information, so as to achieve the automatic monitoring of the production process and achieve real-time control. Since the effect of image recognition is relatively good and can obtain high economic value, so it is now widely used in various industries, such as: intelligent systems and other related aspects. Image processing technology: Computer vision is a computer image processing technology to complete the analysis and study of raw data, so as to achieve the purpose of information exchange, storage and identification. In practical application, the use of computer image processing technology can effectively enhance the required intuition, and also improve work efficiency. In addition, it can help managers to understand the user’s demand for dynamic operation of the ship in time and provide

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Fig. 2 Grayscale histogram

more accurate and reliable services, and this can also effectively avoid the errors brought about by manual operation errors [5].

3 The Principle of Ship Traffic Measurement Vessel traffic measurement refers to the processing of data with the help of computer vision technology to obtain the corresponding results. The most important and effective way is to use computer to realize the measurement of data information, and the data can be analyzed by computer to get the ship traffic information. The measurement of ship traffic flow mainly includes target detection algorithm and target tracking algorithm. The target detection algorithm is mainly based on YOLO v3 for real-time target detection, which is mainly monitored dynamically by installing cameras, and then the data processing is carried out according to the image information obtained from the cameras, so as to get the actual movement information of the ship, and the data is aggregated, and the calculated flow parameters to determine the target positioning. The target tracking algorithm is based on Sort for real-time target detection, which calculates the actual motion of the ship and uses it to estimate the flow data. The method is based on the Sort platform, where a 3D model is created and then computer vision technology is used for dynamic measurement and static monitoring, and then the target location is determined based on the image information obtained from the camera and the IMOv3 software that provides the acquisition signal to obtain the simulated image sequence feature parameter values. These data are compared with the original data by calculating a ship traffic data sheet to obtain the final results.

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3.1 Target Detection Algorithm In the actual measurement, due to the limitations of the conditions, the acquired data often have a certain degree of deviation, the use of computer vision technology to analyze and identify these errors is a proven method. YOLO is mainly based on a single convolutional neural network target detection model, through which the ship’s traffic is pre-processed, so as to achieve data analysis based on computer vision technology. The convolutional neural network is a feed-forward neural network with deep structure, which is trained to process the error signal by the forward network and deep neural network. It has good global adaptability in pre-processing the data, and it can quickly analyze and describe the original non-decisional feature points effectively. Compared with traditional detection methods, convolutional neural network has the following advantages: it can identify non-decision feature points, and it can realize the proposed detection of any object or event, and obtain accurate results. At the same time, it has a high learning speed, which can well overcome the influence of subjective factors on the detection accuracy.

3.2 Target Tracking Algorithm After collecting the data, it is necessary to establish a mapping relationship between the information and calculate the corresponding values between them. However, it is very difficult and time-consuming to obtain the required parameters because of the practical problems in obtaining the original source images that cannot be matched with the target features. The use of computer vision technology can be a good solution to the above situation: first of all, the data object and its location, size and other information are obtained through real-time computer positioning and measurement, then vector calculation is performed to obtain the exact value required, and finally the data is analyzed according to the calculation results [9]. The ship traffic counting function relies on Sort multi-objective tracking algorithm to implement, which uses the ship on the front and back two images to detect whether a specific point intersects with the marker line to achieve an accurate counting function, and the function is based on the digital measurement of traffic in a computer vision way, which can well solve the error problem arising from the environmental factors such as traffic and water that exist in the ship when performing the counting. Sortdependent multi-target tracking algorithm in the multi-target tracking problem can be simply understood as an algorithm to find the matching optimal solution of several targets in the front and back frames. Define W(i, j) to represent the matching weights between the current ith trajectory and the jth detection frame, and W(i, j) is specifically defined as shown in Eq. (1).  w(i, j ) =

dm(i, j), (uncon f ir me d shi ps) k( j )dm(i, j ) + (1 − k( j )dc(i, j )), (uncon f ir me d shi ps)

(1)

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where dm(i, j) is the similarity between the predicted and detected results of the Kalman filter for the ship’s trajectory, which is a common method to describe the position of the target object in the image. The algorithm can calculate the data between each part of the ship based on the parameters generated by the ship in the actual navigation process, and pass this information to the computer through the vector machine. dm(i, j) is specifically defined as shown in Eq. (2). dm = (x j − yi )T Si−1 (x j − yi )

(2)

dc(i,j) is the cosine distance between the eigenvectors of two objects, which is a mathematical expression to represent the eigenvectors of an object. In practice, there are often some errors and chance factors that lead to errors, which can be avoided by using computer vision technology to analyze and study them. dc(i,j) is defined as shown in Eq. (3).   dc = min 1 − R Tj Rk(i )

(3)

4 The Design of Digital Measurement System of Ship Flow In the vision system of computer, the hardware equipment is its foundation, therefore, the hardware layout design is especially important for the whole system. For the digital measurement of ship traffic, data acquisition, processing and analysis need to be done with the help of a special software platform, so the requirements for data processing and analysis become more stringent for the software system. With the increasing time and the increasing number of demands, computers are becoming more and more complex in terms of resources, and the hardware layout needs to be planned and adjusted in order to better realize the above functions. 1. Device for image acquisition, control and transmission. It consists of all-weather camera, directional control head and data transmission control module, which is presented on the screen of the computer in the form of graphics on the ship’s mainframe. By using the special digital camera, the waters such as sea section, buoy board and water ship shore slope are monitored in real time. When these data are abnormal, the difference between the network connection points can be automatically adjusted to ensure that the information transfer process can be timely and accurate acquisition of the required signal. The parameters are collected by sensors and then converted into standard voltage or current output form for transmission to the central controller (PLC) on the control platform and digital metering, which can simultaneously realize the processing and analysis of data and calculate the flow rate, thus improving the efficiency of information transmission.

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2. Acquisition of image data. The all-weather acquisition and transmission unit is installed under the bridge to realize real-time monitoring of the bridge. The use of computer vision technology allows for effective acquisition of large amounts of data and the ability to represent images in digital form, thereby improving measurement accuracy. Computer graphics methods are applied to analyze and calculate massive information in ship flow management measurement, and at the same time, massive data can be represented in digital form, thus realizing real-time monitoring of ship flow. 3. Image data processing. The computer vision-based image flow meter can analyze the information of ship’s inlet and outlet, and it can accurately reflect the hydrological situation and each index in the seawater in the measured water depth range. Firstly, multiple continuous static frames are acquired by the camera and sent to the back-end of the server after pre-processing such as eliminating background information and noise reduction for data analysis, and then the linear relationship between the flow value and the historical voyage difference is obtained by preprocessing. Finally, the pre-processed image data is used to realize the precise analysis and judgment of ship flow and heading by YOLO algorithm, and realize the control of ship dynamic flow.

5 Model Training and System Testing Before system testing, data information such as the parameters of the model and the error rate of the model need to be pre-processed. The calculated values are analyzed to determine whether the results are correct. In order to ensure accurate experimental results, we have to start using the computer vision software after establishing the ship flow control measurement points, so we have to use the machine learning tool KAPP to train the target positions and derive the corresponding quantitative relationship values first, and then use MATLAB to write the program to conduct experiments on the measurement tasks with the parameters already set. The design of KAPP mainly includes: KDD and calculation formula, data collection methods and program writing and other parts, where the most critical is the analysis of computer vision software MATLAB related algorithm parameters, combined with the actual measured values to determine the required value size and the number of relationships, and then use VisualTack web programming to implement the digital flow measurement system model database. The rapid development of waterway information construction, the use of computer technology for chart identification is an important tool in the construction of waterways. The easiest way is to count the flow of ships on the waterway, and use computer vision technology to measure the flow of the waterway, which can reflect the current state of ships in different stages of navigation in a period of time intuitively, and at the same time this can control the flow of ships effectively.

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6 Conclusion In summary, the main methods for ship traffic measurement are historical data methods and image analysis based techniques, of which dynamic volumetric upvalue reflectance photometry is the most widely used in measuring the surface of a ship. Firstly, the RGB values of the target are reflectively photometrically measured by YOLO v3 real-time target detection algorithm to obtain its 3D image, and then the graph of ICLOv3 real-time is used to determine where the ship is located over a period of time. At the same time, the Sort target tracking algorithm is used to perform reflectance photometry on the actual condition of the ship, obtain its 3D image, and compare this digital measurement with the data provided in the IMUORB system for analysis. In addition, the actual measurement status of the ship is determined using marker lines and the position is compared and analyzed with the data provided in the IMUORB system, resulting in indices of dynamics, accuracy and controllability. In practice, it can accurately measure the navigating vessels and the upstream and downstream flows, and control their sailing time in different waters through the changes of vessel flows, thus achieving real-time monitoring and maintenance of vessels, and this can also effectively prevent the problem of traffic congestion at sea.

References 1. Vahid R, Alexandre Hideki O, Zhiyong H et al (2022) Biomedical applications of computer vision using artificial intelligence. Comput Intell Neurosci:22–23 2. Cuong Pham V, Giap Nguyen H, Khang Doan T et al (2020) A computer vision based robotic harvesting system for lettuce. IJMERR (11):9–12 3. Scharstein D, Dai A, Kondermann D et al (2021) Guest editorial: special issue on performance evaluation in computer vision. Int J Compu Vis:4–8 4. Zhi Z, Shuncheng C (2021) A collaborative target tracking algorithm for multiple UAVs with inferior tracking capabilities. Front Inf Technol Electron Eng 10:22–24 5. Liu M, Ren Z, Wang C et al (2019) Kernelized correlation filter target tracking algorithm based on saliency feature selection. In: Abstracts of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE 2019) Part II:36–38 6. Jingxian L, Yang L, Le Q (2021) Modelling liquefied natural gas ship traffic in port based on cellular automaton and multi-agent system. J Navig 3:74–77 7. McCarthy Arlie H, Peck Lloyd S, Aldridge DC (2022) Ship traffic connects Antarctica’s fragile coasts to worldwide ecosystems. Proc Natl Acad Sci USA 3:119–123 8. Zhi Z, Shuncheng C (2021) A collaborative target tracking algorithm for multiple UAVs with inferior tracking capabilities. Front Inf Technol Electron Eng (10):22–24 9. Boškovi´c Ð, Orlandi´c M, Arne Johansen T (2020) A reconfigurable multi-mode implementation of hyperspectral target detection algorithms. Microprocess Microsyst:78–80

Development of Virtual Imaging Technology for Engineering Surveying and Mapping Based on Genetic Algorithm Donghuan Qiu

Abstract The rapid development of big data technology and the rapid progress of virtual imaging technology for engineering surveying and mapping have contributed new tools and new methods to surveying and mapping work. The acquisition of engineering surveying and mapping data is developing in the direction of digitization, real-time and multi-dimensional. The purpose of this article is to study the virtual imaging technology of engineering surveying and mapping based on genetic algorithm, which is of great significance to the design, construction and operation of engineering projects. This paper designs a virtual imaging system for engineering surveying and mapping based on genetic algorithm, designs a set of online surveying and mapping management process through B/S architecture, divides the responsibilities of office administrators and field surveyors, and transfers the surveying and mapping work to the browser side. It also provides a visual construction interface to conduct surveying, mapping and stakeout operations in a more intuitive and convenient way. The system has a built-in algorithm conversion module, which uses the calculated spatial coordinates of the rover as the source data, and automatically converts it into geodetic coordinates, plane coordinates, and construction coordinates, avoiding manual intervention, improving the convenience of surveying and mapping and ensuring data reliability. Taking the hydropower engineering surveying and mapping work in the third district of A High School as the test application scenario, it can be found by comparing the test results that the measurement errors in the horizontal and vertical directions are within 0.5 cm and 1 cm in the vertical direction, respectively. Keywords Genetic algorithm · Engineering surveying and mapping · Virtual imaging technology · System design

D. Qiu (B) Ningxia Institute of Science and Technology, Shizuishan, Ningxia, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_51

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1 Introduction With the continuous development of information technology, great progress has been made in the scientific research. Applications of world-class technology include topographic and mapping research, scientific and topographic technology research and development technology development, and the development of new applications. The application of remote sensing technology, digital map management and retrieval have achieved satisfactory results [1]. The rapid development of genetic algorithms and related technologies, and running through many fields of life, surveying and mapping technology has broken through the end of market simulation, considering the description of three-dimensional areas, and enabling topography, research and mapping become possible [2]. Domestic and foreign scholars have conducted in-depth research on genetic algorithms and virtual imaging technology for engineering surveying and mapping. The Davis A M study improves comparable image quality by using virtual isocenters and variable magnification during refinement to avoid imaging conflicts. The accessories most sensitive to attack are the skeleton and the kV detector. With powerful amplifiers, the kV detector is able to increase the magnitude of the vacuum angle around the area of greatest impact, while collecting enough data to maintain the viewing area. Both techniques are used independently and in conjunction with the quality of the resulting image, according to standard cycle characterization tests [3]. David H uses Genetic Algorithms (GA) to model the learning behavior of a set of exercise and rational factors interacting in a systematic environment. GA behavior is analyzed in two parts of the spider web model, one where the company makes only quantitative choices and the other where the company first decides to exit or stay in the market and then how much to publish. Simulations of different coding systems are given, employing the mathematical technique of genetic algorithms of statedependent fitness functions to explain surprising differences, rather than the results of different configurations, in particular differences between codes, and the cohesion of related genetic algorithms characteristic [4]. Joseph, Y, J Study side effects calculation (CPT) and weight measurement and static material modeling calculations using genetic algorithms; the process produces better statistics than standard utility-based logit models or using advanced techniques. CPT-based logit models have high probability and statistical significance. However, performance indicators suggest that in a rapidly changing field, drivers may be discouraged by the impact of their decisions [5]. It can be seen that there are many research achievements in both genetic algorithm and engineering surveying and mapping technology, but very few combine the two, which is also the innovation of this paper. In this paper, genetic algorithm is applied to engineering surveying and mapping virtual imaging technology. Using virtual reality modeling technology, we can easily establish a measurement space for measurement. On the one hand, the accuracy is improved, and the other is On the one hand cost savings. Virtual measurement can avoid inaccuracy and inaccuracy in actual measurement, completely avoid noise interference, and make the image itself as close as possible to the actual position of

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the subject, thus creating a perfect image and high reliability. For optical measurement verification, and to acquire measurement data under the following complete conditions. Create virtual reality platforms to facilitate improvements such as event sampling, imaging and editing simulated images.

2 Research on Virtual Imaging Technology of Engineering Surveying and Mapping Based on Genetic Algorithm 2.1 Genetic Algorithm The genetic algorithm is supported by the natural evolution of minerals and endows offspring with nearly identical body symbols. The best children have the best chance of survival and improve the health of the baby over time. Children have combined attitudes from both parents. If the design of the genetic algorithm is accurate, the population will be grouped to the optimal solution. Reproduction involves two genetic manipulations, mating and mutation, where the chromosomes (solutions) of two parents are randomly combined to form a child and a selected species is randomly mutated into another gene [6, 7].

2.2 Engineering Surveying and Mapping Technology 1. CORS technology The two major RTK network technologies of CORS system are: Level 3 technology and Trimble virtual port technology (MAC technology). In the virtual base station technology, the fixed traffic station sends the original data to the control center through the data communication line, instead of directly sending the maintenance information to the user’s mobile phone; and selects the exercise of the best reference point for calculation. Orbital errors, ionospheric and modulus errors, and differential correction data; the control center usually corrects the errors and sends the highest frequency signal received to the mobile station for RTK network positioning services. VRS technology solves the limitation of traditional RTK performance distance and ensures accurate measurement. Users only need a GPS receiver to complete millimeter and centimeter positioning tasks [8, 9]. 2. 3D laser scanning technology The 3D laser monitoring system determines the three-dimensional polar coordinates of the application center, and calculates the three-dimensional coordinates of the measurement space by measuring the horizontal and vertical angles of the laser and the distance from the center to the application center. Each point can be represented in polar or Cartesian coordinates with volume information [10, 11].

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3. UAV tilt photogrammetry technology Combined with the GPS/IMU system installed on the UAV platform, it is processed through related software, including POS data and image control point data, as well as cloud point reception, digital spelling, and 3D photogrammetry. The UAV photography system has the characteristics of short preparation time, flexible operation control, no need for special clearance and landing area, small workload, high resolution, and little influence by weather conditions such as clouds [12, 13].

2.3 The Principle of Virtual Scene Imaging If we know the characteristics, orientation and size of space objects, as well as the internal and external space of the camera, we can establish a mathematical model of image transformation according to the laws of images, and use mathematical simulation methods to develop simulated images [14, 15]. To create a mock map, we have to set up an event coordinate system by following steps to write out the geometric descriptions that should be measured and put them in the appropriate positions in the fields Set the camera position, angle and other external bases and Focus Length, Image Model build On the basis of the interior of the art gallery, strategic changes are made according to the orientation, size and other details of the object [16, 17].

3 Development and Research of Virtual Imaging Technology for Engineering Surveying and Mapping Based on Genetic Algorithm 3.1 Development Environment This system design uses ASP.NET as the basic interactive technology platform, and the terminal service is developed and deployed based on this platform. ASP.NET technology is based on .NET, and all .NET compatible languages can be used to write applications, such as Microsoft Visual Basic.NET and Microsoft Visual C. In addition, ASP.NET can use Microsoft’s Microsoft.NET Framework technology, which simplifies the development work of developers.

3.2 Production of Point Marker Simulation Images The ideal point has no size and is a concept of position. For the purpose of precise positioning, this paper mainly considers the simulation of circle and square point

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marks. { G(x, y) =

√ B, / (x − x0 )2 + (y − y0 )2 > R √ F, (x − x0 )2 + (y − y0 )2 > R

(1)

Among them, (x0 ,y0 ) is the center coordinate, R is the radius, B is the background gray value, and F is the gray value of the point mark. If the Gaussian function is selected to describe, the circle mark can be expressed as G(x, y) = Ce−[(x−x0 )

2

+(y−y0 )2 ]/R 2

+B

(2)

where is the magnitude of the C Gaussian point.

4 Analysis and Research on Virtual Imaging Technology of Engineering Surveying and Mapping Based on Genetic Algorithm 4.1 Design of Virtual Imaging Technology for Surveying and Mapping The genetic algorithm-based virtual imaging system for engineering surveying currently covers four types of mechanical measurement tasks: known alignment points, arbitrary point marking, debris measurement, and slope placement. The system collects the measurement values within the specified countdown (10 s by default), and filters and processes the average value. After the end, the measurement results are displayed, including: measurement field, plane and structural coordinates, field point parameter deviation, PDOP, HDOP, VDOP, instrument height, measurement time, measurement difference and data separation diagram within the measurement time, as shown in Fig. 1. Known Alignment Points: Identifying marks is the most basic search function in the machine field, and can be widely used in bridge construction, walls, stage systems, and other scenarios. The project first determines the coordinates of the airport site, and supports the introduction of two modes of aircraft coordinates and construction coordinates. The inspector then places his position on the rover and finds the field in the center of the radar map from the architect’s point of view. Arbitrary point marking: Random field marking means that the coordinates of two field points form a constraint line. As the rover moves, space to the finish line is displayed. After online browsing, the measurement can be started and the current coordinates of the measurement field can be obtained. Scenarios for such tasks include inability to directly access field measurement sites, such as those removed

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Base station known coordinates

control point data

Base station measurement datacoordinates

Rover measurement datacoordinat

Server settlement service space coordinates Projection transformation

normal high Geodetic coordinates

plane coordinates flat curve Construction coordinates Fig. 1 Coordinate transformation structure diagram

from the ground. The visual interface design of any point junction is basically the same as that of the known point junction. Fragment measurement: Fragment measurement works very flexibly. You can directly create task containers, select any measurement field, and record measurement data. Multiple output programs can stay in the same task. Application scenarios include: perimeter design, area measurement, etc. Finally, data such as Data images and area View Images can be exported. Slope placement: In traditional road construction, the task of measuring slope is often very complex and time-consuming. The terrain mapping system greatly simplifies this process through digital graphics, and the inspector can control the distance to the slope section in real time by moving the embedded computer.

Development of Virtual Imaging Technology for Engineering Surveying … Table 1 Test result table

471

Serial number

X/m

Y/m

H/m

1

4,597,075.1286

505,331.2877

658.9516

2

4,598,075.1802

503,897.2819

658.9967

3

4,598,075.1306

504,389.2703

658.9504

4

4,597,075.1657

504,388.2765

658.8859

5

4,597,075.5598

504,388.5688

658.5668

6

4,597,075.4656

504,388.5462

658.5462

7

4,597,075.5676

504,388.3598

658.5679

8

4,597,075.5689

504,389.5546

658.2614

4.2 Test Results The construction site test can be started to verify the completeness of the system functions and the accuracy of the measurement results. Because the control point data is known through static observation and has very high accuracy, it can be used as a target point and compared with the system measurement results to judge the measurement error. The test site of the system is place A, the plane coordinates of the selected control point are (4,497,075.1754, 505,221.2806, 657.6543), and the known point task is established according to the parameters, and the measurement time is set to 8 s, that is, 8 sets of measurement data are recorded and averaged within 8 s After filtering, the measurement results table shown in Table 1 can be obtained, which is expressed in spatial coordinate format, where X, Y, and H represent the length, width, and height of the plane coordinates, respectively. Compare the task result data in Table 1 with the original coordinates of the control points, and perform the difference operation on X, Y, and H, and obtain the difference map as shown in Fig. 2. By comparing the test results, it can be found that the system can guarantee the measurement error within 1 cm in the horizontal direction and 2 cm in the vertical direction, which meets the needs of engineering measurement. The average measurement accuracy of the system in the horizontal direction is higher than that in the elevation direction.

5 Conclusions This paper systematically studies the virtual imaging technology, discusses how to design the engineering surveying and mapping virtual imaging system based on genetic algorithm, the system development environment, the production of pointmarking simulation images, and the final test of the system effect. By analyzing the traditional surveying, a new surveying and mapping operation method is proposed,

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deviation value

1 0.8 0.6 0.4 0.2 0 1

2

3

4

5

6

7

8

testing frequency X/m

Y/m

H/m

Fig. 2 Test difference graph

aiming to meet the requirements of the specification, achieve the goal of one survey and multi-purpose, and reduce the workload of the field, thereby improving work efficiency. The reality of the currently generated virtual scene is not strong enough, and it is obviously different from the real scene. The next step will focus on the settings of lighting, materials, textures, and the use of software to generate complex structures to be tested, so as to better simulate the actual situation.

References 1. Denault D, Khan et al (2018) 3D Virtual imaging made accessible and inexpensive. J Investig Med 66(1):269–269 2. Mozaffari S (2018) Parallel image encryption with bitplane decomposition and genetic algorithm. Multimed Tools Appl 77(10):1–21 3. Davis AM, Pearson EA, Pan X et al (2018) Collision-avoiding imaging trajectories for linac mounted cone-beam CT. J Xray Sci Technol 27(2):1–16 4. Dawid H, Kopel M (2019) On economic applications of the genetic algorithm: a model of the cobweb type*. J Evol Econ 8(3):297–315 5. Joseph YJ, et al (2018) Genetic algorithm to estimate cumulative prospect theory parameters for selection of high-occupancy-vehicle lane. Transp Res Rec 2157(1):71–77 6. Hussain A (2020) Interactive 360-degree virtual reality into eLearning content design. Int J Innov Technol Explor Eng 10(2):1–4 7. Johnson A, Pandey A (2019) Three-dimensional scanning—a futuristic technology in forensic anthropology. J Indian Acad Forensic Med 41(2):128–131 8. Shah P, Chong BS (2018) 3D imaging, 3D printing and 3D virtual planning in endodontics. Clin Oral Invest 22(2):641–654

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9. Fautier T (2020) Cloud technology drives superior video encoding. SMPTE Motion Imaging J 129(10):10–13 10. Uspeneva MG, Astapov AM (2020) Application of modern technologies of engineering and geodesic works for surveying of main gas pipelines. Interexpo GEO-Siberia 1(1):50–63 11. Saragih TH, Mahmudy WF, Anggodo YP (2018) Optimization of Dempster-Shafer’s believe value using genetic algorithm for identification of plant diseases jatropha curcas. Indones J Electr Eng Comput Sci 12(1):61–68 12. El-Zammar D, Eng J, Schulz T (2022) Implementation of an emergency department virtual follow-up care process in a community-based hospital: a quality improvement initiative. BMJ Open Quality 11(2):E1171–E1172 13. Sathaporn, Opasanon, Elise et al (2018) Noisy genetic algorithm for stochastic, time-varying minimum time network flow problem: transportation research record 2196(1):75–82 14. Pan JS, Kong L, Sung TW et al (2018) A clustering scheme for wireless sensor networks based on genetic algorithm and dominating set. J Internet Technol 19(4):1111–1118 15. Esfahlan ZJ, Hamdipour A, Zavaraqi R (2020) Investigation of the compliance of Persian abstracts and graduate theses and Dissertations of engineering fields of Tabriz University with international standards ISO 214. J Eng Educ 22(85):85–105 16. Moza M, Kumar S (2018) Routing in networks using genetic algorithm. Int J Commun Netw Distrib Syst: IJCNDS 20(3):291–311 17. Sivasankaran P, Radjaram B (2021) Quality benchmarking in business performance using surveying technique—review. Int J Res—GRANTHAALAYAH 9(8):175–184

Path Planning Algorithm of Intelligent Unmanned Inspection Vehicle Ning Wang, Junren Shao, Zhenlin Huang, Tao Yang, Xing Wen, Shaosen Li, Liuqi Zhao, Jinwei Zhu, and Yuheng Zhang

Abstract The rapid development of modern power grids has put forward new requirements and challenges for power inspection operations. Traditional manual inspection methods have been not easy to satisfy the needs of power grid inspections. In response to the problem, a large number of electric power enterprises and scientific research institutes have carried out extensive research and technical research, and have achieved a series of advanced results in the field of electric power inspection technology and application. The purpose of this article is to analyze the path planning algorithm for intelligent unmanned inspection vehicles. The operation process and body design of the inspection vehicle are analyzed, and the application of GA algorithm in path generation and planning is introduced. A piecewise Bezier curve generation path is proposed. A special simulation experiment is designed for the BOBCIGA algorithm conceived in this paper, and the influence of the parameter values in different fitness functions on the experimental results is deeply analyzed. According to the results, it can be intuitively seen that the BOBCIGA algorithm mentioned in this article close to the optimum and meets the needs of the actual project. Meanwhile, compared with the simulation results of the A* algorithm, the BOBCIGA algorithm can obtain a good solution in the complex, changeable and random obstacle environment. Keywords Unmanned inspection vehicle · Intelligent inspection · Path planning · Planning algorithm

N. Wang (B) · Z. Huang · X. Wen · L. Zhao · J. Zhu · Y. Zhang Operation and Maintenance Center of Information and Communication, CSG EHV Power Transmission Company, Guangzhou, Guangdong 510700, China e-mail: [email protected] J. Shao · T. Yang · S. Li China Southern Power Grid Co., Ltd. Kunming Bureau of EHV Transmission Company, Kunming, Yunnan 650214, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_52

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1 Introduction The traditional manual inspection has been unable to keep up with the pace of the development of modern substations. From this long-term trend, unmanned inspection vehicles instead of manual inspections are an important idea for future development [1, 2]. Because unmanned inspection vehicles are easier to find problems than inspection personnel. Although unmanned inspection vehicles for substations are only engaged in the simplest inspection work, this is a trend and an unstoppable trend. In short, putting the unmanned inspection vehicle into operation can not only directly reduce the equipment loss caused by the negligence and missed inspection of the operation and maintenance personnel, but also improve the quality of the power grid operation, which has the effect of killing two birds with one stone [3]. Intelligent unmanned inspection is the only way for the development of power inspection [4]. Saravanakumar proposed simple coordination strategies such as modified RRT (MRRT) and collision avoidance using accessible partitions to avoid collisions on the road. It is displayed in the Python window using the Python software. The proposed algorithm can create routes and guide vehicles around obstacles in a short time to avoid collisions. Schafle TR has developed a safety scheduling system based on the characteristics of mobile robots [5]. The concept of the algorithm is compared with the proposed algorithm design method, and the performance of the algorithm is evaluated by Monte Carlo simulation. Compared with possible related routing algorithms, the proposed algorithm significantly improves the security without significantly increasing the travel time and computation time [6]. Learning how to design intelligent vehicle detection algorithms to improve detection performance has important practical implications. This paper takes the inspection path planning problem of intelligent unmanned inspection vehicles as the research object, takes the shortest inspection time as the objective function, establishes an integer programming model, and designs a genetic algorithm to solve it according to the characteristics of the model. The performance of the algorithm is evaluated through simulation experiments, including algorithm comparison experiments to prove the correctness; and algorithm parameter sensitivity experiments to analyze and obtain the optimal parameter settings for the genetic algorithm to solve the problem in this paper [7].

2 Research on Path Planning Algorithm of Intelligent Unmanned Inspection Vehicle 2.1 The Operation Process of the Inspection Vehicle The operation process is as follows:

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1. The intelligent inspection cloud platform customizes inspection tasks and pushes the tasks to the edge computing server in the inspection vehicle [8]. 2. The edge computing server starts to execute the task according to the task start condition, moves the vehicle to the first inspection point, and controls the vehicle to open the sunroof and lift the lifting platform; 3. After the vehicle arrives at the designated inspection point, the inspection vehicle control module notifies the edge computing server that the inspection vehicle is in place and has stopped. 4. The edge computing device controls the robotic arm to move to the specified position, controls the camera pan/tilt, and takes pictures according to the robotic arm control information of the inspection point. 5. After the GPU server obtains the photo, it calculates whether the shooting target is located in the available position of the screen. If there is a problem that the angle of the photo cannot be obtained, it will automatically generate the control information of the robotic arm and feed it back to the edge computing device to control the robotic arm to continue taking pictures until the shooting. To the qualified inspection photos [9]. 6. After the inspection of the current inspection point is completed, repeat operations 2, 3, 4, 5, and 6. 7. After the inspection of the last inspection point is completed, the edge computing device controls the robotic arm and the lifting platform to return to the initial position, closes the sunroof, and sends a “return to warehouse” command to the inspection vehicle control module to complete the operation [10].

2.2 Body Design The whole vehicle can be divided into two parts: the vehicle body and the equipment warehouse. The vehicle body and equipment box are listed in the equipment list of “Business Load Equipment Power Consumption”. The ordinary PTZ camera is located on the top of the vehicle, and the lifting rod can be used to achieve Vertical 1000 mm lift. The top of the equipment box adopts a car-level customized sunroof as a whole, which can be opened and closed through program control [11, 12]. The inspection vehicle adopts the trackless navigation method, and the whole vehicle adopts the combined navigation technology of laser radar, IMU and vision fusion, and the repeated positioning error of the inspection is less than ± 10 mm. It supports the intelligent obstacle avoidance function, which can identify objects such as isolation belts, warning signs, obstacles, etc. that affect the normal driving of the vehicle, perform intelligent obstacle avoidance, and reach the designated location. The ability to automatically walk and stop according to pre-set tasks or routes. Equipped with audio capture and playback equipment, which can be accessed through secure wireless communication; The unmanned vehicle needs to integrate a 6-axis robotic arm to realize the integrated control of the vehicle and the robotic arm. According to the inspection

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rules, send control commands to the robotic arm, and inspect the equipment of the converter station. The load is 5 kg, the arm span is 940 mm, and the weight is 25 kg. Programmable control. The robotic arm supports collision detection. Based on the underlying program, within the collision force range of 0-250N, for different process requirements, including handling load and speed, the strength can be adjusted by 10%–100%. When the robot encounters obstacles, Carry out emergency stop protection to avoid unnecessary damage to personnel and equipment. To achieve safe human–machine cooperation, the collision is stopped enough, and the work can be resumed quickly without complex reset.

2.3 Path Planning Algorithm A variety of heuristic algorithms summed up by natural laws have been proposed and widely used in various combinatorial optimization problems. The heuristic algorithm can solve the approximate optimal solution of large-scale optimization problems in a reasonable time, and obtain a higher solution speed by sacrificing the quality of the solution, which has high practical value in the field of engineering applications. 1. A* algorithm The A* algorithm is based on the Djikstra algorithm, and the criterion of finding the distance from the current point to the target point is added to its heuristic function; the Djikstra algorithm was originally used to solve the TSP problem. It takes the starting point as the center, continuously calculates the distance to the closest point, and recursively calculates and selects the next path point based on the principle of the minimum distance, so as to find the final target point through node traversal. This algorithm has a high success rate in searching for the shortest path and has good robustness. The disadvantage is that there are too many nodes to be traversed. The A* algorithm can obtain the optimal path by searching fewer nodes than the Dijkstra algorithm. Generally speaking, the Dijkstra algorithm is used to search for the shortest path, while the A* algorithm is used to search for the optimal path. 2. Genetic algorithm The genetic algorithm realizes the search process of the optimal solution of the function by simulating the process of natural evolution. Through multiple iterations, the approximate optimal solution of the large-scale combinatorial optimization problem can be obtained in a short time. It is the most basic and widely used. One of the heuristic algorithms. Genetic algorithm is a search algorithm for solving optimization. It is developed due to the phenomenon of biological evolution, so it is named genetic algorithm. The algorithm starts from a population with a set of possible solutions, firstly encodes the population, calculates the fitness value of each individual, selects, crosses and mutates according to the

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principle, and eliminates it. This process is repeated over and over to create the best individual that satisfies the constraints.

3 Design and Investigation of Path Planning Algorithm for Intelligent Unmanned Inspection Vehicle 3.1 Improved Genetic Algorithm Path Planning The starting point and destination information of the vehicle, including the current explicit obstacle area, can be obtained in advance. Therefore, the problem at hand is: in the case of as little collision as possible, in the complex, changeable, random obstacles, passability areas Constructs one or more paths within the range that satisfy the condition. Its mathematical model is as follows: F = min{S(Ai )}

(1)

Ai = (ai1 , ai2 , . . . a13 ) = ((xi1 , yi1 ), (xi1 , yi2 ), (xin , yin ))

(2)

F-objective function; Ai-the moving distance of the i-th vehicle; S-the moving distance of the vehicle; Based on this, the BOBCIGA algorithm proposed in this paper is as follows: (1) Randomly generate M random solutions to form the first group Pi (2) Initialize BOBCIGA parameter threshold parameter ⟁ (3) Chromosomal coding genes (4) While t = 1 < Maxiter where Maxiter is the maximum iteration do (5) Fitness evaluation for all populations pt (6) Use the cross-compilation process to generate a new population Ct and add it to pt (7) Replace each solution x ∈ Ct with the desired mutation rate and add to pt (8) Calculate the variance Q of all solutions (9) When (Q > η) indeed (10) Use the summation method to generate the total Ct and add to pt (11) Replace each solution x ∈ Ct with the desired mutation rate and add to pt (12) Calculate the difference Q of all solutions (13) Last (14) Evaluate all submitted results (using a two-way evaluation system) (15) Select the best orientation of the M chromosome for reproduction (introduction) (16) If stopping criteria are met, stop the study and return to the population (17) else (18) t = t + 1 (19) Decrease as follows: Decrease as follows: p = p'

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(20) End if (21) End while In this paper, the reciprocal of the objective function value is used as the fitness function of the genetic algorithm. The better the path planning scheme, the less time it takes to inspect, the smaller the objective function value, and the larger the fitness function. The roulette method is used to select the population, that is, the higher the fitness value of the chromosome, the more likely it is to inherit high-quality genes. Among them, the specific process of roulette selection is as follows: 1 Calculate the fitness value fi of each chromosome in the population (chromosome number ie ∈ {i ∈ 1,2,…,G), G is the population size. 2. Calculate the ratio of the fitness value of each chromosome in the population to the sum of the fitness values of the population, as the probability of each chromosome being inherited to the parent population: ri , ri = f i /

G 

fi

(3)

i=1

3. Calculate the cumulative probability of each chromosome: ai , ai =

i 

rj

(4)

j=1

4. Randomly generate a uniformly distributed number q between [0, 1], if q is less than a1, select chromosome 1 to enter the parent population, otherwise select chromosome k, where ak-1 < q ≤ ak. We want to achieve such a goal: other parameters are within a reasonable range to ensure that the generated path segment is as short as possible. This paper proposes the composition of segmented Bezier curves. The connection of segmented curves can form the entire Bezier curve. The control points of the Sel curve form a Bezier polygon, and the Bezier curve is completely contained within the polygon’s convex hull. The endpoints of the curve coincide with the first and last control points of the Bezier curve. The tangent vectors at the endpoints of the Bezier curve are denoted as P1-P0 and Pn-Pn-1 pointing along the first and last span of the polygon.

3.2 Simulation Experiment Parameter Settings The experimental program was implemented using C++ language programming in the Microsoft Visual Studio Ultimate 2013 environment, and the calculation was run on a computer configured with Windows 1064-bit operating system, 16 GB memory and 3.60 GHz processor. All experimental results are the average of the results of

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10 runs of the same experiment. The configuration of relevant algorithm parameters during the experiment is described as follows. The performance parameters of the vehicle are necessary parameters in the algorithm optimization process. In order to make the experimental effect as close to the reality as possible, we set the average speed of the vehicle as 10 m/s. The BOBCIGA generation algorithm is called for learning, and 3 test scenarios with different degrees of complexity are designed: including scene size, number of obstacles, obstacle area, starting point position, end position and population size; running time (seconds), path length, adaptation. The evaluation indicators of the three dimensions are used as experimental data.

4 Analysis and Research on Path Planning Algorithm of Intelligent Unmanned Inspection Vehicle 4.1 Sensitivity Analysis of Algorithm Parameters The parameters such as continuous access task probability and cross probability are the same. In a specific problem, it should be analyzed according to the characteristics of the problem, and it must be set to an appropriate value to play a positive role in solving the algorithm. In order to obtain appropriate parameter values, in the scenario where there are 20 task nodes and 10 parking nodes distributed in the patrol inspection of unmanned vehicles, the interval between the test values of the continuous access task probability is set to 0.2, and 0.1 is used as the test value. The starting point of, 0.9 is the end point of the test value, that is, the probability of the continuous access task for the experiment is 0.1, 0.3, 0.5, 0.5, 0.7, 0.9. Under each probability value, run the algorithm in this paper 10 times, and take out the corresponding objective function The optimal value, the average value and the worst value are used to determine the optimal value of the probability of continuous access to the task. The experimental results are shown in Table 1. It can be seen from the above experimental results that the different values of the probability of continuous access tasks have an impact on the solution effect of the algorithm to a certain extent. When the value of the continuous access task Table 1 Objective function values under different sequential access task probabilities Successive access task probability

Best

Average

worst

0.1

38.5

40.1

41.8

0.3

36.7

38.2

40.9

0.5

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35.1

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38.9

39.9

0.9

39.3

41.2

41.5

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objective function values

45

Average

worst

40 35 30 25 20 15 10 5 0 0.1

0.3

0.5

0.7

0.9

successive access task probability Fig. 1 Change in objective function value

probability changes incrementally, the change of the objective function value also shows a certain trend, as shown in Fig. 1.

4.2 Comparison with the Simulation Results of the A* Algorithm Although the A* algorithm can reduce the possibility of obstacle collision in the global path planning, the path generation length is long and time-consuming. The specific experimental data is shown in Fig. 2.

SCENES

C B A 0

5

fitness value

10 15 20 REDUCTION RATE path length

25

30

running time/sec

Fig. 2 Data reduction rate of comprehensive simulation experiment compared with A*

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Through specific experimental calculations, this paper proposes the BOBCIGA algorithm. Compared with the A* algorithm, the effective solution space has been significantly improved: in the search path time, at least 13% of the time can be saved; the path length can be reduced by at least 20%. The fitness value can be reduced by at least 13%. A better path is generated. Through the above comparison, it shows that the BOBCIGA algorithm proposed in this paper can get better results.

5 Conclusions The intelligent unmanned inspection vehicle is a new type of electric inspection method that has been vigorously promoted in recent years, and has been widely used in electric inspection operations in many places in China. This paper mainly studies the path planning problem in the inspection operation mode of the intelligent unmanned inspection vehicle, establishes the relevant mathematical model, and designs the key solution algorithm according to the characteristics of the problem, and obtains research results with certain practical value. On the basis of the research of this paper, the main problems worthy of in-depth study and to be solved are as follows: In this paper, in the inspection path planning problem of unmanned vehicles, only the workload and work efficiency of one vehicle are considered to be limited. After expanding to a certain range, more inspection forces need to be added to complete it efficiently. For example, a combination of multiple drones and vehicles can be dispatched, so that the combination can be coordinated to complete the inspection, and the overall path optimization can be studied. The synergy between multiple isomorphic and heterogeneous individuals can make the problem extremely complicated, but it is also a very valuable research direction.

References 1. Lee J, Yoon S, Kim B et al (2021) A new image-quality evaluating and enhancing methodology for bridge inspection using an unmanned aerial vehicle. Smart Struct Syst 27(2):209–226 2. Fuentes-Hernández C, Elvira-Hernández EA, Huerta-Chávez OM et al (2021) Aerodynamic analysis of unmanned aerial vehicle with hawk shape for monitoring oil leakage. Revista UIS Ingenierías 20(3):135–146 3. Tan CH, Ng M, Shaiful D et al (2018) A smart unmanned aerial vehicle (UAV) based imaging system for inspection of deep hazardous tunnels. Water Pract Technol 13(4):991–1000 4. Blaney S, Gupta R (2018) Unmanned aerial vehicle-based sounding of subsurface concrete defects. J Acoust Soc Am 144(3):1190–1197 5. Saravanakumar A, Kaviyarasu A, Jasmine RA (2021) Sampling based path planning algorithm for UAV collision avoidance. Sadhana 46(112):1–8 6. Schafle TR, Uchiyama N (2021) Probabilistic robust path planning for nonholonomic arbitraryshaped mobile robots using a hybrid A* algorithm. IEEE Access PP(99):1–1 7. Zbyryt A, Dylewski L, Morelli F et al (2020) Behavioural responses of adult and young white storks ciconia ciconia in nests to an unmanned aerial vehicle. Acta Ornithologica 52(2):243–251

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8. Kubat M, Smyczy´nski P, Granosik G (2018) Unmanned air vehicle selection criteria for inspection and transport tasks. Pomiary Automatyka Robotyka 22(3):23–32 9. Ribeiro RG, Júnior JRC, Cota LP et al (2020) Unmanned aerial vehicle location routing problem with charging stations for belt conveyor inspection system in the mining industry. IEEE Trans Intell Transp Syst 21(10):4186–4195 10. Sliusar NN, Batrakova GM (2018) Environmental monitoring of the waste disposal sites with the use of unmanned aerial vehicle. Ecol Ind Russ 22(8):44–49 11. Márquez FPG, Sánchez PJB, Ramírez IS (2022) Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring. Struct Health Monit 21(2):485– 500 12. Nagarajan PR, Mammen V, Mekala V et al (2020) A fast and energy efficient path planning algorithm for offline navigation using Svm classifier. Int J Sci Technol Res 9(1):2082–2086

Multiple-Processes Integratived Management System of Construction Project Based on Ant Colony Algorithm Lei Lei

Abstract In today’s increasingly fierce social competition, how to achieve the multiobjective optimization of a project with “short construction period, low cost and high quality” is an urgent problem that enterprises must solve. This article focuses on the research on the multiple-processes management of construction projects based on ant colony algorithm. On the basis of the literature, the relevant theories of Multithread management of engineering projects and ant colony algorithm are understood, and then the multi-objective construction project based on ant colony algorithm is studied. The integrated management system is designed, and then the algorithms cited in the design system are tested. The test results show that the improved algorithm in this paper has excellent solution performance and can adapt to complex engineering management systems. Keywords Ant colony algorithm · Engineering project · Integrated management · Multi-Objective optimization

1 Introduction In the process of project development, the contractor pays attention to the construction period, the developer pays attention to the cost, and the owner pays attention to the quality [1, 2]. From a historical perspective, the environmental protection goals of construction projects are particularly important to society, and the project is also responsible for the safety of itself and the people around it [3, 4]. The profit requirements are so inconsistent, how to integrate the interests of all parties is also a problem that must be solved when the many goals of an engineering project are optimized [5, 6]. With economic growth, engineering projects require more and more control L. Lei (B) School of Management, Wanjiang University of Technology, Maanshan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_53

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targets, and the relationship between them is becoming more and more complex. Traditional management and optimization techniques are difficult to solve [7, 8]. In view of the multi-objective optimization research of project engineering, some researchers have proposed multi-objective optimization models for cost and quality time periods to facilitate the use of project managers. The model uses work structure decomposition to subdivide the work during the construction period into a series of activities, including materials, management, equipment and construction work, and introduces a construction quality-cost-duration optimization model based on the nonlinear integration of construction activities, aiming at the construction project make a reasonable resource plan for construction period, quality, cost, etc., and use genetic algorithm in the model to obtain an optimized model. The results show that the model helps project managers to better optimize the balance between construction time, quality and cost. This has significant advantages over traditional models [9]. Some researchers have introduced the ant colony algorithm into the predetermined research field of cost engineering projects, such as applying the weight adjustment method to single target construction and cost targets, and using the ant colony algorithm to find the Pareto solution. Through examples, this method has been proven to have multiple advantages in overall optimization and search efficiency [10]. In summary, there are many researches on engineering project optimization, but the research on the introduction of deep learning into project optimization still needs to be studied in depth. This chapter first studies how to apply ant colony algorithm to multi-thread integrated management of engineering projects. Based on the literature, This paper analyzes the advanced nature and related theoretical problems of using ant colony algorithm in multi-thread integrated management of engineering projects. The calculated engineering multi-objective integrated management has carried out the research on the scheme design, and the experiment of the control system of the engineering design has been carried out. Finally, the corresponding research results are obtained by using the experimental results [11].

2 Research on Multiple-Processes Integrative Management System of Construction Project 2.1 Advantages of Multiple-Processes Integrative Management of Construction Project 1. Integrated integration. In terms of resources, integrated management integrates various resources inside and outside the business into the management field. In other words, it is considered to be an important means of managing human resources, materials, materials and information within the enterprise [12]. The

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field of resource selection and integration includes all types of resources. Through careful selection, evaluation and integration, various resources are effectively integrated to achieve optimization goals. In terms of management elements, with the advent of the information age, the integrated management has become a very important management element, prompting it to occupy a large percentage of integrated management activities. Integrated management not only plans to integrate the management technology itself in terms of management technology tools and methods, but also includes the integration and integration of management technology, manufacturing technology and information technology. 2. Complexity. The main activities are: (1) The elements of integrated management not only include all the basic elements inherent in the business, but also all the resources available for external selection and completion of tasks in the business. Therefore, in this relationship, the change of a certain factor causes the influence of other factors, and it will be affected and changed by various other factors. (2) The integrated management system has a diversified and comprehensive functional structure, and each level becomes the basic project of the upper level. (3) The integrated management system learns, reconstructs and continuously improves its hierarchical structure and functional composition during its generation and development. (4) Comprehensive management is an inevitable product of the natural environment, and requires people to develop continuously with the changes of the natural environment. (5) Comprehensive management emphasizes the initiative of the environmental complex. Human knowledge and initiative can foresee environmental development trends in comprehensive management and take measures in accordance with specific management goals.

2.2 Ant Colony Algorithm It takes a long time for the algorithm to use the pheromone communication between ants to find an optimization method, and it is not easy to maintain the diversity of the team. The algorithm defines an external BP set, which includes all non-dominant solutions currently monitored in the entire ant colony. Looking for the most sparsely distributed non-dominated solutions in all BPs, and the current main direction of ant optimization is their location. Assuming that there are p non-dominated solutions x in the current BP set, use the following equation to calculate the distance between each solution and another solution. ┌ | k |∑ di j = √ (1) ( f t (xi ) − f t (xi ))2 t=1

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In the formula, K is the number of objective functions, i = 1,2…p, j = 1,2…p, calculate the shared function value according to the following formula: { S(di j ) =

1−

di j σsgar e

0

(2)

In the formula, σsgar e means the niche radius.

3 Design of Multiple-Processes Integrative Management System for Construction Project Based on Ant Colony Algorithm 3.1 Architecture of Multiple-Processes Integrative Management System for Construction Projects Construction involves managing many technical projects. Each project includes contracts, schedule, quality, investment, risks, plans, documents, etc. The involved design, supervision, construction, operation and other departments and units communicate and coordinate. Such management tasks are arduous and require effective management of large amounts of information. The engineering management subsystem mainly relies on the engineering project database and uses information as a means to systematically manage the project quality, investment, schedule, and contracts of the engineering department. Project management supports management decisions while constructing management decisions. An integrated project management platform integrating government construction authorities, construction units, construction units, and supervision units. Figure 1 shows an example of a project management system.

3.2 Project Library Management Project management involves multiple projects, and each project involves management tasks such as workflow, schedule, and investment. Many participating departments and units have difficulty communicating and coordinating, and need effective management of large amounts of information. Good project library management can help research and establish projects, and promote project investment. The project construction process completely adopts the main methods of project management, schedule management and investment, strictly manages the schedule of the project, and contributes to the smooth implementation of the project.

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Multi-objective integrated management system for engineering projects

Project library management

Project decision management

Project contract management

Project schedule management

Project operation stage

Fig. 1 Example of project management system

The system collects and maintains basic information of various projects and important information of project engineering research through comprehensive management of the preliminary process, construction period, submission completion, work process, project progress and investment of each project, monitors and coordinates subsequent projects, and improves investment development department’s overall coordination and monitoring of project operations and projects to improve management.

3.3 Project Decision Management In the decision-making stage of a construction project, scientifically demonstrate and compare multiple projects, investment projects, investment time, and implementation, mainly through project establishment and preliminary feasibility studies. The most important part of the decision-making stage is to define project safety, investment, construction period, quality goals and other goals. The decision-making stage of a project is to manage the scale and investment of the project, and is an important part of determining the success or failure of the project. If the definition of a project is not clear or there is ambiguity in the decision-making stage of the project, it will affect the subsequent design process of the project, leading to design defects and errors, leading to an increase in the chain effect of construction problems. Even after the project is underway, some deficiencies in the decision can be found through the feedback of the later stage of the project, but the current

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progress of the project has already caused a greater impact on the project due to the increase in construction progress and project progress. Therefore, considering the significant impact on project construction in the early stage of project construction, the management of the design and decision-making process should be managed in an integrated manner. The work of the project decision-making stage is mainly determined by the decision-making level, but because the decision-making level lacks project design experience, the legal person responsible for the initial design of the project should also participate in the decision-making process. Project design for proper placement of the project. Another option is to hire professionals to provide advice and assistance related to the owner. The sooner the design unit gets involved in the decision-making process, the better, because the project needs maintenance.

3.4 Project Contract Management When choosing the form of contract, the project has the characteristics of integration of design and construction, namely contract DB (design-construction) and EPC (design-supply-construction). Generally, the project distribution method is adopted, which can be adopted in the form of organization. This is also the project management model currently being actively promoted in our country. At the same time, when determining the target of the contracting work, it can be applied flexibly according to the actual situation of the project. For example, the DB project management model can be extended on the basis of the preliminary design. In other words, general engineering contracting includes construction design and construction operations. In some cases, preliminary design can also be included in the scope of general contracting.

3.5 Project Progress Management Project progress management refers to the formulation of blueprints for the work content, procedures, duration, the relationship between the various stages of the project and the implementation of the plan, and the preliminary design until the completion of the project. The ultimate goal of schedule management is to ensure that all project schedule goals are achieved. This part contains functional modules such as annual plan management, project plan preparation management, project period management, period plan review management, project log management, period plan monitoring management, and progress analysis management.

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3.6 Project Operation Stage The operation stage mainly refers to the whole process of smart technology work from the beginning to the disassembly. Project maintenance and protection, expansion and conversion requirements to normal operation procedures are key issues in the integrated management of intelligent system engineering projects. As far as the system engineering project is concerned, the scale of the project is so large, and the implementation phase of the construction project is also very complicated. The post evaluation of the technical project is carried out at any stage of the implementation of the technical project or during the period after it is put into production. The meta-evaluation of a construction project refers to the degree to which the expected goals are achieved when defining the strategy when defining the project. It is mainly composed of project objective evaluation, construction process evaluation, profit evaluation, sustainability evaluation, etc. The comprehensive results of the post-project evaluation can be used to store solutions for building intelligent systems. This is an extension of an engineering project and can accumulate rich experience in related project processing.

3.7 Application of Ant Colony Algorithm in Project Integrated Management 1. Delete the problem area The process of finding food for wild ants is ongoing, but the subject of this article is all process nodes during the event. When ants walk on the network graph, they can only follow the network path of the process that meets the requirements. In this article, abstract artificial ants search for cost-quality construction sites in the 3D period. The search path is a network diagram composed of n processes in the project. The coordinates of all process modes in this 3D space form an array nXm. In other words, all the ants are looking for this array. Naturally settled ants move on continuous levels, but the points left by the ants are clear. This corresponds to any node process in this article. Therefore, finding food from ants is essentially the same as finding a path in multi-function mode. 2. Search taboo table structure The taboo table related to the algorithm is divided into the node taboo table and the alitransit taboo table. In the TSP problem, there are no obstacles to movement between cities. That is, for the current city node, the next node to be selected is a city other than the intersecting city. For the project activity itself, the execution of some activities restricts the execution of other activities, and there are direct restrictions between processes. Ant can only search for the nodes allowed in the taboo table.

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3. Delete the search path In the process of looking for food, the ants will detect the accumulation of pheromone around and choose which direction to go. The process of finding paths and creating solutions in the network diagram is similar to the process of ants looking for food. Therefore, every time the ant goes through a path, it stores the intersecting nodes in an accessible set. Then continue to select the next node in the same way, which can obtain the pattern execution order from the beginning of the process to the end of the process, that is, the understanding of the construction process can be completed.

4 System Testing In this paper, the improved algorithm will be solved by using the basic ant colony algorithm introduced above, run 4 times, the population size is 35, the pheromone priority factor is 3, the heuristic factor is 4, and the number of iterations per run is 40. The best solution for each run is the same. The results are shown in Table 1: It can be seen from Fig. 2 that the improved algorithm has excellent solution performance. The basic ant colony algorithm application solution is the second solution in the Pareto solution suite. However, as the weight changes, the solution solved by the basic ant colony algorithm may change again. The use of this method is very subjective, and it is very tedious and burdensome to convert it into a target utility function before solving the multi-purpose model. The results prove that the model and improved algorithm proposed in this paper are obviously superior in solving such multi-objective optimization problems. The solution is completely selected by the decision maker according to the needs, and may be more suitable for actual engineering problems. Table 1 Improved algorithm test results

Pattern sequence

Time optimization (%)

Cost optimization (%)

Quality optimization (%)

1

18.7

11.2

9.2

2

22.7

10.6

9.3

3

29.4

4.8

2.8 10.2

4

22.7

7.8

5

26.7

5.3

2.1

6

13.4

13.1

14.8

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35.00% 29.40%

30.00%

26.70%

Degree

25.00%

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22.70%

20.00% 18.70% 14.80% 13.40% 13.10%

15.00% 11.20% 9.20%

10.00%

10.60% 9.30%

10.20% 7.80% 5.30%

4.80% 2.80%

5.00%

2.10%

0.00% 1

2

3

4

5

6

Type Time optimization

Cost optimization

Quality optimization

Fig. 2 Improved algorithm test results

5 Conclusions This paper studies the multiple-processes integrative management of construction projects based on ant colony algorithm. After understanding related theories, designs the multiple-processes integrative management system of construction projects based on ant colony algorithm, and then tests and tests the improved algorithm of this paper. The results show that the model and improved algorithm proposed in this paper have obvious excellent characteristics when solving such multi-objective optimization problems. The solution can be fully selected by decision makers according to needs, and can be more suitable for actual engineering problems.

References 1. Jonkers RK, Eftekhari Shahroudi K (2021) A design change, knowledge, and project management flight simulator for product and project success. IEEE Syst J 15(1):1130–1139. https:// doi.org/10.1109/JSYST.2020.3006747 2. Dynamic routing optimization algorithm for software defined networking. Comput Mat Continua (2022) 70(1):1349–1362. https://doi.org/10.32604/cmc.2022.017787 3. Zhang Y, Wei HH, Zhao D et al (2020) Understanding innovation diffusion and adoption strategies in megaproject networks through a fuzzy system dynamic model. Front Eng Manag (1):1–16 4. Shdid CA, Andary E, Chowdhury AG et al (2019) Project performance rating model for water and wastewater treatment plant public projects. J Manag Eng 35(2):65–73

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5. Sharafati A, Naderpour H, Salih SQ et al (2021) Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms. Front Struct Civ Eng 15(1):61–79 6. Iriondo I, Montero JA, Sevillano X et al (2019) Developing a videogame for learning signal processing and project management using project-oriented learning in ICT engineering degrees. Comput Hum Behav 99(Oct.):381–395 7. Vanhoucke M, Coelho J (2018) A tool to test and validate algorithms for the resourceconstrained project scheduling problem. Comput Ind Eng 118(Apr.):251–265 8. Boje C, Bolshakova V, Guerriero A et al (2022) Semantics for linking data from 4D BIM to digital collaborative support. Front Eng Manag 9(1):104–116 9. Ahuja R, Sawhney A, Arif M (2018) Developing organizational capabilities to deliver lean and green project outcomes using BIM. Eng Constr Archit Manag 25(10):1255–1276 10. Ibrahim CKIC, Costello SB, Wilkinson S (2018) Making sense of team integration practice through the “lived experience” of alliance project teams. Eng Constr Archit Manag 25(5):598– 622 11. Hatami E, Arasteh B (2020) An efficient and stable method to cluster software modules using ant colony optimization algorithm. J Supercomput 76(9):6786–6808. https://doi.org/10.1007/ s11227-019-03112-0 12. Sennan S, Ramasubbareddy S, Balasubramaniyam S et al (2021) MADCR: mobility aware dynamic clustering-based routing protocol in internet of vehicles. China Commun 18(7):69–85

Moving Image Processing Technology and Method Based on Neural Network Algorithm Xinyu Liu and Yingwei Zhu

Abstract With the convenience brought by the rapid development of information, teaching reform has also set off a heat wave. The emergence of Internet+ , multimedia technology, virtual reality technology and artificial intelligence has made education continue to develop towards informatization, and mobile learning has become a way for students to learn. This paper provides a reference for the reform of exercise classes in colleges and universities, enriches the teaching methods, improves the learning quality of students, and makes the action image processing technology better applied to physical education classrooms. In this paper, according to the characteristics of the human motion system, the training actions of sports are collected. After the 3D action data is properly calibrated and normalized, the motion vector is used as the training input method. This method conforms to the laws of the human motion system. In terms of temporal feature extraction, this paper proposes a multi-level long short-term memory neural network structure for action recognition. The action image processing technology improves the accuracy of movements more significantly, and proves that it has a certain promotion effect on the self-editing ability and innovation ability of students’. The final results of the research show that the accuracy of action recognition has been improved to a certain extent after image data processing. The accuracy of action recognition in images has increased from 67.13% to 91.23%, and the accuracy rate has remained above 90% after action image processing which proves the effectiveness of the neural network algorithm processing method. Keywords Virtual reality · Artificial intelligence · Image processing technology · Temporal features

X. Liu · Y. Zhu (B) Physical Education College of Bohai University, Jinzhou, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_54

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1 Introduction Physical education has undergone the transformation from cramming to multimediaassisted teaching, which has changed the traditional teaching mode and provided convenient conditions for the reform of modern physical education. The network resources are rich and the amount of information is large, and the use of modern media and technology can screen the best information [1]. Action image processing technology can enrich and develop the teaching methods and means of physical education, and is the key to realize the modernization of education. Therefore, the research on the image processing technology of sports under the neural network algorithm has certain practical significance. In recent years, many researchers have carried out research on the method of sports action image processing technology based on neural network algorithm, and achieved good results. For example, Shahroudy A believes that multimedia technology assisted teaching content is vivid and vivid, and is equipped with exquisite pictures. The impact of color can stimulate students’ interest in learning, turn passive into active, and make students love learning and want to learn [2]. Liu C believes that multimedia technology resources are very large, and the information is rich, the teaching knowledge is timely, and the video can be updated in time, so that students can enrich their horizons and stimulate students’ active cognition [3]. At present, scholars at home and abroad have carried out a lot of research on the safety detection and evaluation methods of pressure vessels. These previous theoretical and experimental results provide the theoretical basis for the research of this paper. This paper is based on the theoretical basis of the neural network algorithm, combined with the application analysis of the sports action image processing technology, and through a series of experiments to verify that the neural network algorithm has a certain feasibility in the sports action image processing technology method, it can be seen from the experimental data, the improved 3D neural algorithm can directly extract the temporal features of the image, and the recognition effect is naturally enhanced compared to the dual-stream neural network. The optical flow image sequence is added, which not only increases the amount of training data, but also enables image recognition to have the characteristics of iterative optimization, which enhances the overall performance of image recognition.

2 Related Theoretical Overview and Research 2.1 Definition of Basic Concepts of Sports (Take Aerobics as an Example) 1. Basic features of aerobics Aerobics is an aerobic exercise that uses the basic skills of dance in the accompaniment of music and is used for strengthening the body, beautifying the body

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and entertaining. Aerobics is divided into three types of fitness, athletics, and performance. The characteristics of competitive aerobics: the main purpose of competitive aerobics is to obtain excellent results in competitions, and the movements are arranged under specific rules and regulations. The movements are difficult and require high technical skills and artistic performance. Generally, it is more suitable for young people with strong cardiopulmonary function and good physical fitness [4, 5]. The characteristics of fitness aerobics: fitness aerobics is a fitnessoriented exercise that integrates fitness, entertainment and disease prevention. Also known as mass aerobics, the exercise intensity and movement difficulty are relatively low, and the popularity is strong, which can be used by the whole society. The characteristics of performance aerobics: performance aerobics is an ornamental aerobics with the theme of spreading, introducing and driving the development of aerobics, and it is a unique form of aerobics in my country. Based on the above, fitness aerobics and performance aerobics are the most suitable for college students. They have low exercise intensity and low requirements for sports skills, which is conducive to cultivating students’ interest, improving students’ physical quality, and establishing a lifelong awareness of physical exercise, and suitable for the age and psychological characteristics of college students. 2. Analysis of action content of aerobics courses In terms of categories, aerobics is divided into competitive aerobics, fitness aerobics and performance aerobics. Fitness aerobics is most suitable for physical education in colleges and universities, and is most in line with the requirements of the “Standard” and the physical and psychological characteristics of college students. From the purpose of teaching, fitness aerobics generally includes freehand exercises, light equipment exercises and water exercises, but suitable for college teaching. It is freehand gymnastics, freehand aerobics also includes mass aerobics, Latin aerobics, hip-hop, fighting, etc. [6]. Because light equipment exercises and water exercises have strict requirements on the teaching environment, students have high basic level requirements, and the school’s teaching facilities are almost unsatisfactory, such projects are basically carried out in health clubs. Theoretical teaching is an indispensable part of aerobics teaching in colleges and universities. Simple technical movements can not meet the needs of college students for aerobics learning. Only by systematically learning more professional knowledge of aerobics courses, students can fully understand aerobics, deepen their understanding of aerobics, and stimulate students’ enthusiasm. Interest, master scientific fitness methods [7, 8]. Physical aerobics, yoga, and cheerleading with strong performance are in line with the characteristics of college girls who love beauty, and have a good training value for girls to shape their bodies, thereby stimulating their interest in learning. Kickboxing and hip-hop can fully demonstrate the strength of high school boys, conform to the psychological and physical characteristics of college boys, and promote their interest in learning aerobics.

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2.2 Image Action Processing Technology Based on Neural Network This paper studies the human action recognition algorithm based on 3D convolutional neural network, 2D convolutional neural network with long short-term memory network, and 2D convolutional neural network with ResNet residual network structure [9]. Although the two-dimensional convolutional neural network can effectively extract image features, it does not have the ability to extract features in the time dimension. The addition of long short-term memory network and the use of threedimensional convolutional neural network can well obtain information in the time dimension. 1. Three-dimensional convolutional neural network The three-dimensional convolutional neural network is mainly used in video classification. It is improved from the two-dimensional convolutional neural network, so that the timing information in the video can be well used. Threedimensional convolution is a three-dimensional convolution kernel sliding, and the time dimension is regarded as the third dimension. 3D convolution stacks multiple consecutive frames into a cube, and then uses the 3D convolution kernel to slide in this cube. Each feature map in the convolutional layer is connected to multiple adjacent consecutive frames in the previous layer, thereby capturing continuous motion information, thus solving the problem that 2D convolutional neural networks cannot extract temporal features. The 3D convolution kernel also has the property of weight sharing, and the lines of the same color in the figure represent weight sharing. 2. Network structure First, a recognition experiment is performed on the UCF101 dataset in the simplest network structure with a 3D convolutional neural network. A total of two convolution modules are set up, and each module contains a 3D convolution kernel, a BN layer, a ReLU activation function, and a Dropout layer. Finally, a max-pooling method and three fully connected layers are used for the final classification of actions [10, 11]. A total of four convolution modules are set up, the first is a convolution kernel, which is mapped using the ReLU activation function. Then, the extracted features are sent to the second convolution kernel, which is mapped by the Softmax activation function, and then sent to the third convolution kernel to extract features after the maximum pooling layer and the Dropout layer. This layer uses the ReLU activation function. Mapping, followed by connecting the fourth convolution kernel, this module has the same structure as the second convolution module, uses the Softmax activation function to map, then connects the maximum pooling layer, the Dropout layer, and then connects the Flatten layer and the first full layer. The connection layer, then the Dropout layer, and finally the second fully connected layer for action classification and recognition [12].

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3 Experiment and Research 3.1 Experimental Method RNN is a connection model that captures the dynamics of sequences through recurrent structures in network nodes. Unlike standard feedforward neural networks, recurrent neural networks can preserve the state of information from context windows of arbitrary length. Using the recurrent structure, RNN can model the context information of time series, and the calculation process is as follows;   h t = H Wxh x t + Whh h t−1 + bh

(1)

  y t = o Who h t + bo

(2)

In the above formula, where Wxh, Whh, Who represent the connection weight from the input layer x to the hidden layer h, the connection weight from the hidden layer h to itself, and the connection weight from the hidden layer h to the output layer y, respectively. bh and bo are the two bias vectors, and H and O are the activation functions in the hidden and output layers.

3.2 Experimental Requirements This experiment is mainly aimed at the research on the fusion development of neural network algorithm and sports action image processing technology. The experiment uses 3D neural network algorithm to process the image data of sports. The recognition accuracy rates obtained before and after image data processing are used, and the advantages and disadvantages of different algorithms are verified by analyzing the recognition rates under three algorithms: 3D neural network algorithm, improved 3D neural algorithm and dual-stream neural network algorithm.

4 Analysis and Discussion 4.1 Action Recognition Accuracy Analysis Before and After Sports Image Data Processing The experiment uses 3D neural network algorithm to process the image data of sports, and analyzes the accuracy of action recognition before and after the image data processing. The experimental data is as follows.

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It can be seen from Fig. 1 and Table 1 that the accuracy of action recognition has been improved to a certain extent after image data processing. The accuracy of action recognition in image 1 has increased from 67.13% to 91.23%, and the accuracy of action recognition in image 2 has increased from 69.28%. % increased to 92.16%, the action recognition accuracy rate in image 3 increased from 70.12% to 90.88%, the action recognition accuracy rate in image 4 increased from 74.13% to 93.78%, and the action recognition accuracy rate in image 5 increased from 72.33% to 94.25%, and the accuracy rate remains above 90% after the action image processing, which proves the effectiveness of the neural network algorithm processing method.

100 90

91.23

Experimental data

80 70

67.13

92.16

70.12

69.28

94.25

93.78

90.88

74.13

72.33

60 50 40 30 20 10 0

Experimental variables Fig. 1 Analysis of motion recognition accuracy before and after sports image data processing

Table 1 Analysis of motion recognition accuracy before and after sports image data processing

Action image

Before image data processing(%)

After image data processing(%)

Image one

67.13

91.23

Image two

69.28

92.16

Image three

70.12

90.88

Image four

74.13

93.78

Image five

72.33

94.25

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4.2 Comparative Analysis of Experimental Recognition Rates of Different Algorithms The experiment proves the effectiveness of the neural network algorithm processing method through the analysis of the accuracy of action recognition before and after the sports image data processing. The experiment continues to compare and analyze the experimental recognition rates of different algorithms. The experimental data is shown in the following figure. As shown in Fig. 2, by comparing the recognition rates of the 3D neural network algorithm, the improved 3D neural algorithm and the dual-stream neural network algorithm, the improved 3D neural algorithm has a higher recognition rate for image action models than other algorithms. The recognition rates of these algorithms are 61.33%, 87.44% and 77.52% respectively. The improved 3D neural algorithm can directly extract the temporal features of the image, and the recognition effect is naturally enhanced compared to the dual-stream neural network. The optical flow image sequence is added, which not only increases the amount of training data, but also enables image recognition to have the characteristics of iterative optimization, which enhances the overall performance of image recognition.

Fig. 2 Comparison and analysis of the experimental recognition rates of different algorithms

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5 Conclusions This paper is based on the theoretical basis of neural network algorithm, combined with the application analysis of sports action image processing technology, and through a series of experiments to verify the feasibility of neural network algorithm in sports action image processing technology method. The analysis of the accuracy of action recognition before and after data processing and the experimental data of the comparative analysis of the experimental recognition rate of the same algorithm shows that the accuracy of aerobics action recognition has been improved to a certain extent after the image data processing, and the improved 3D neural algorithm compared with other algorithms. The recognition rate of the action model is higher. Under the rapid development of the Internet, more and more attention has been paid to the field of artificial intelligence. With the maturity of neural network algorithms, various identification and classification problems have better solutions. Action image processing technology has always been a hot research topic in the field of computer vision. Its main research content is to recognize and classify human actions in videos, and it has broad application prospects in many aspects of daily life.

References 1. Johansson G (2018) Visual motion perception. Sci Am 232(6):76–88 2. Shahroudy A, Liu J, Ng T et al (2019) NTU RGB+D: a large scale dataset for 3D human activity analysis. Comput Vis Pattern Recognit 1(5):1010–1019 3. Liu C, Hu Y, Li Y et al (2017) PKU-MMD: a large scale benchmark for continuous multi-modal human action understanding. arXiv: Comput Vis Pattern Recognit 5(6):51–55 4. Han F, Reily B, Hoff W et al (2020) Space-time representation of people based on 3D skeletal data: a review. Comput Vis Image Underst 1(5):85–105 5. Vemulapalli R, Arrate F, Chellappa R et al (2021) Human action recognition by representing 3D skeletons as points in a lie group. Computer Vis Pattern Recognit 4(5):588–595 6. Cho K, Chen X (2019) Classifying and visualizing motion capture sequences using deep neural networks. Int Conf Comput Vis Theory Appl 5(6):122–130 7. None (2019) Structured time series analysis for human action segmentation and recognition. IEEE Trans Pattern Anal Mach Intell 36(7):1414–1427 8. Moeslund TB, Granum E (2019) A survey of computer vision-based human motion capture. Comput Vis Image Underst 81(3):231–268 9. Baccouche M, Mamalet F, Wolf C et al (2020) Sequential deep learning for human action recognition. Hum Behav Understand 12(4):29–39 10. Grushin A, Monner D, Reggia JA et al (2021) Robust human action recognition via long short-term memory. Int Jt Conf Neural Netw 5(5):1–8 11. Lefebvre G, Berlemont S, Mamalet F et al (2019) BLSTM-RNN based 3D gesture classification. Int Conf Artificial Neural Netw 5(6):381–388 12. Ellis C, Zain Masood S (2019) Exploring the trade-off between accuracy and observational latency in action recognition. Int J Comput Vis 101(3):420–436

Building Fall Safety Early Warning System Based on Directed Weighted Network Xinyu Zhang, Xiaoxuan Wang, and Jinmei Lin

Abstract Directed Weighted Network (DWN) are ubiquitous in nature. It is of great practical significance to evaluate and analyze the importance of their nodes. With the continuous maturity of the network research field, hypernetworks have gradually entered the research field of scholars. At this stage, China’s construction safety management has gradually changed from post emergency treatment to prewarning and prevention. In order to improve the level of construction safety early warning, this paper studies and analyzes the building fall safety early warning system (SEWS) based on DWN. This paper briefly analyzes the establishment principle of the early warning index system of building falling safety accidents, and constructs the early warning system of building falling safety based on the DWN; Finally, through the experimental test and analysis, the test results obtained from the early warning model of building falling accidents are compared with the sample data. The results show that the test results of the early warning model of falling accidents are highly consistent with the conclusions of the expert group, that is, based on the DWN, the knowledge, experience and thinking mode of safety experts to judge the safety situation are well obtained and learned, The effectiveness and feasibility of building fall SEWS based on DWN are verified. Keywords Directed weighted network · Building · Falling from height · Safety early warning system

1 Introduction The construction industry is one of the national pillar industries. Economic development, safety needs and safety investment funds of construction enterprises restrict and X. Zhang · X. Wang (B) · J. Lin Urban Construction College, Guangzhou Huali College, Guangzhou 511325, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_55

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promote each other. The study of building fall SEWS is not only the need of the development of construction enterprises, but also the need of national economic and social development. Based on the DWN, this paper establishes an early warning decisionmaking system architecture, which can collect real-time information dynamically to control and adjust the construction safety risk events. By predicting the quantitative analysis of the results of the current technical scheme or construction scheme, it can intuitively reflect the value and practicality of the scheme. Domestic and foreign scholars have conducted a long and many studies on the safety risk accidents of building falling from height (FFH). Most of them can not directly and effectively guide the safety management of building FFH, and most of the research fields are the mechanism analysis of the causes of accidents and the preventive measures of accidents; There is relatively little research on safety risk accident early warning. It was first proposed by American experts in the 1960s on the basis of combining the two theories of crisis and risk management. In the early stage, it was more applied to national safety early warning, and gradually developed into enterprise management in the long development [1]. Although relevant experts and scholars in China have done some research on the early warning of construction safety risks, such as neural network safety status evaluation method, vector machine safety accident causes and early warning method, grey theory safety risk accident prediction method, etc., they are too complex and difficult to be applied to the safety management of accident scene [2]. Therefore, this paper proposes to study the building fall SEWS based on the DWN. In order to improve the early warning level of building safety, this paper comprehensively uses the DWN theory to identify the causes of building falling accidents, identify the influencing factors of building safety from multiple angles, establish the safety evaluation index system of construction site, remove the attributes of redundancy or interference, and finally design a building falling SEWS with the help of advanced computer technology, Provide new ideas and theoretical support for the prevention research and practice of construction safety accidents, achieve prevention first, prevention and control combination, dynamic tracking, improve the level of safety management, and try to avoid the loss of property and personnel caused by safety accidents [3, 4].

2 Research on Building Fall SEWS Based on DWN Construction engineering is a complex human–computer interaction process. Through the analysis, it can be concluded that people, objects, environment and management are the main factors leading to construction safety accidents. However, as a highly professional technical work, the impact of technology on safety is very important. If we can’t identify the influencing factors of safety risk in the early stage of safety risk early warning, the basis of early warning decision is inaccurate and invalid, so the identification of safety risk factors is more important. Safety risk identification

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refers to the process of using certain methods and means to identify various risks that affect the objectives of construction projects, and summarize, judge and identify various risk factors [5]. The risk identification of construction safety risks refers to the early warning of falling safety at the height of the building by collecting the summarized data and documents of various influencing factors that can cause the construction safety risk objectives to fail to proceed smoothly or can cause safety risk events in the construction stage.

2.1 Establishment Principles of Early Warning Index System for Building Falling Safety Accidents The key to the early warning of construction safety accidents is the establishment of early warning index system. Whether the selection of indicators is reasonable and comprehensive will not only affect the early warning results of construction projects, but also affect the safety management measures taken according to the early warning results. Therefore, the establishment of early warning index system should follow certain principles. Scientific principle: the scientific principle not only ensures the objectivity and effectiveness of the sample data obtained, but also makes the early warning results obtained based on the sample data closer to the actual situation of the construction project [6]. The selection of all early warning indicators in this paper is extracted from the law of a large number of construction safety accidents and the theory of accident causes, and obtained through questionnaire survey and data analysis. It is the main influencing factor objectively existing in the construction project, which meets the scientific requirements. Systematic principle: systematic means that the establishment of early warning index system should be from the perspective of system, which can not only reflect all aspects related to safety in construction projects, but also reflect the functional relationship between indicators. It can comprehensively and systematically reflect the safety status of construction projects and meet systematic requirements. Principle of practicality: practicality means that the selection of early warning indicators not only exists in theory, but also can guide the safety management of construction projects in practical application. If the selected indicators are only theoretical and lack of practicality, then the existence of indicators will have no practical benefits. The selection of early warning index system in this paper can not only early warning the safety status of construction projects, but also guide the safety management of projects according to the early warning results, which meets the practical requirements [7, 8]. Feasibility principle: feasibility means that the availability of data should be considered in the selection of early warning indicators, so as to ensure that the indicator data can be obtained by collecting relevant information or sorting, and it

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is easy to quantify. Comparability means that the selection of early warning indicators should not only reflect the actual situation, but also compare the advantages and disadvantages of indicators at the same level. Generally, the value of indicators should be relative.

2.2 Construction of Building Fall SEWS Based on DWN Design principle: the safety risk early warning system of building FFH is to evaluate the safety status of the construction site, and the situation of the construction site is changing in real time. To ensure the accuracy and effectiveness of the early warning system, it must be required that the human, material and machine information obtained is dynamic, which changes with the change of the construction site environment, and can make the dynamic information reflected in the information module [9]. In order to improve the quality of construction safety early warning, it is necessary to ensure the accuracy of the data, and the accurate data is often the on-site data at the first time, so the SEWS is constructed by using the DWN, and the data standard, format and data storage and exchange must be standardized, so as to ensure the requirements of accurate, clear and consistent information [10]. Early warning information module framework: as the basis of building fall safety risk early warning, real-time information collection on the construction site through DWN technology and BIM case base as the information source of building construction safety risk early warning [11]. According to the basic steps of information management structure system design and the operation principle of DWN technology, the architecture of early warning information module can be expressed as shown in Fig. 1. The main advantage of this module is that the data collection mainly comes from the field collection of DWN technology, project information and case sensors

Fig. 1 Early warning information module architecture

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and readers, as well as network communication tools that can be transmitted to the network, so as to achieve the purpose of rapid and efficient. The data processing layer is mainly to embed information standardization tools in the building fall safety system. It is to format and standardize the real-time collected field data, and is designed to realize information interaction and collaboration with BIM. In the model layer, the standard data transferred into the BIM information model is threedimensional and 4D, which can establish the dynamic information model of building fall safety from the perspective of building safety [12].

3 DWN In the study of complex networks, the relevant topological quantities in the network are usually calculated to analyze the topology of the whole network. The main network statistical parameters considered are: degree and degree distribution of nodes, average path length between nodes, edge weight and its distribution, and node unit weight. In general, the distance between two nodes in a network is usually expressed by the shortest path length from one node to another. The path length between nodes is the total number of edges in the path they form a connection. In the DWN, the edge weight (hereinafter referred to as edge weight) reflects the close relationship between nodes, and is also an important topological attribute in the network. Then, similar to the degree and degree distribution of nodes, there is also the distribution of edge weights. Since there is a direction for the edge connection, the edge connection of the node also includes the edge out weight and the edge in weight. The distance of all nodes under each path length will form a distance matrix L. therefore, by calculating the distance between nodes in all path lengths, a total of N distance matrices can be obtained: 

L (1) , L (2) , . . . , L (N )



(1)

However, for node i to node j, only one effective distance SIJ is required. Therefore, according to the idea of the shortest path in the network diagram, the minimum distance between nodes in all path lengths is taken as the final effective distance. The specific calculation method is as follows:   (2) (3) li j = min li(1) j , li j , . . . , li j

(2)

According to the method of minimum distance, we can get the effective distance between all nodes, and finally form the effective distance matrix of the nodes of a network:   L = li j N ×N

(3)

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The effective distance of nodes in the network represents the degree of interaction between nodes. The smaller the distance, the greater the interaction between nodes. The average effective distance from a node to other nodes in the network can reflect the importance of the node in the whole network, which is conducive to improving the prediction accuracy of the traditional accident early warning model of the building fall SEWS. Complex networks generally contain complex relationships between things. When studying complex networks, we usually need to use models to abstract the whole complex network system. Because some basic characteristics of complex networks conform to the basic framework of graph theory, graph theory has been favored by researchers of complex networks. Real complex networks usually contain a large number of independent individuals, and there are complex relationships between individuals in different real networks.

4 Application Analysis of Security Early Warning System Based on DWN If the value range of an index in the training sample is relatively large, on the one hand, it may dominate the training process of the DWN, weakening the role of other early warning indicators; On the other hand, it is easy to cause the training time of the DWN to be too long and the convergence speed to be slow. Therefore, in order to avoid such a problem, the training samples of the DWN are normalized, that is, the sample data of the building fall safety accident early warning model are mapped to the (a, b) interval, where 0 < a < b < 1. There are many ways to map data to (0,1) intervals. In order to make the output results of the improved early warning model simple and clear, the scoring of early warning indicators in this paper adopts the percentage system, so the method of y = x / 100 is used to normalize the data. After the 113th training, the error meets the design accuracy requirements, and the early warning improvement model converges. Compare the test results obtained from the improved early warning model of building falling safety accidents with the sample data, as shown in Table 1 and Fig. 2. It can be seen from the chart data that the results obtained from the SEWS of the building FFH DWN are in good agreement with the target results, and the maximum error between the two is 2.27%, which shows that the test results of the FFH safety accident early warning model are highly consistent with the conclusions of the expert Table 1 Comparison table of test results Sample Target results Test result Error

1

2 0.85

0.78

3

4 0.91

0.8448

0.7828

0.8923

−0.61%

0.36%

−1.95%

0.85 0.8602 1.2%

5

6 0.74

0.76

0.7372

0.7781

−0.38%

2.27%

509

1

3.00%

0.9

2.50%

0.8

2.00%

0.7

1.50% 1.00%

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test result

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-1.50%

0.1

-2.00%

0

-2.50% 1

2

3

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sample Target results

test result

error

Fig. 2 Comparison and error of test results

group, that is, based on the DWN, the knowledge of safety experts to judge the safety situation is well acquired and learned Experience and thinking mode verify the effectiveness and feasibility of building fall SEWS based on DWN.

5 Conclusions Building fall safety early warning plays an important role in improving the level of building safety management. In this paper, a building fall SEWS model based on DWN is established. According to the reduced index system, the collected sample data are trained and learned, and the appropriate function is selected to complete the establishment of the early warning model. In practice, the early warning model is used to analyze the project safety, and good feedback is obtained according to the actual project status and early warning results. Although this paper has achieved the expected goal, there are still some imperfections, mainly in the following aspects: because the collection of construction site data is relatively difficult, the types of projects are relatively limited, and the scope of use of the model is limited. In future research, detailed research can be carried out for different types of building FFH and project types to expand the scope of use of the model; In terms of the selection of conditional variables, due to the limited research objects, there is a lack of some influencing factors, such as the level of construction technology. The future building fall SEWS based on DWN needs further research.

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Acknowledgements 2021 Guangdong University scientific research platform and scientific research project—young innovative talents project “cause analysis and Preventive Countermeasures Research of building falling accidents” (2021KQNCX153); 2016 Guangdong Key Discipline Cultivation Project—Civil Engineering, issue No. [Yue Jiao Yan Han 2017] No. 1.

References 1. Dewi M, Heni et al (2019) Reliability analysis of safety system on fire hazard factory building (Study Case at PT. Semen Baturaja). J Phys Conf Ser 1198(8):82008–82008 2. Spenger D, Geiselhart K (2022) Kleinrumige Verteilung von Gesundheitsbedingungen in Stdten. Standort 46(2):76–83 3. Tommaso C, Teresa et al (2019) Rockfall forecasting and risk management along a major transportation corridor in the Alps through ground-based radar interferometry. Landslides 16(8):1425–1435 4. Malone D (2019) Earthquake response system takes the guesswork out of seismic safety. Build Des Constr 60(8):58–58 5. Tourtelot J, Ghattassi I, Roy RL et al (2021) Yield stress measurement for earth-based building materials: the weighted plunger test. Mater Struct 54(1):1–13 6. Abe S, Suzuki N (2021) Scale-invariant statistics of period in directed earthquake network. Eur Phys J B 44(1):115–117 7. Sheen A (2019) Salt dome safety warning. Highways 88(4):62–62 8. Tongal H, Sivakumar B (2021) Transfer entropy coupled directed–weighted complex network analysis of rainfall dynamics. Stoch Env Res Risk Assess 36(3):851–867 9. Bolla M et al (2019) Estimating parameters of a directed weighted graph model with betadistributed edge-weights. J Math Sci 237(5):611–620 10. Lee JH, Ostwald MJ, Dawes MJ (2022) Examining visitor-inhabitant relations in palladian villas. Nexus Netw J 24(2):315–332 11. Borrett SR, Scharler UM (2019) Walk partitions of flow in ecological network analysis: review and synthesis of methods and indicators. Ecol Indic 106(Nov.):105451.1–105451.16 12. Wiratsudakul A, Wongnak P, Thanapongtharm W (2022) Emerging infectious diseases may spread across pig trade networks in Thailand once introduced: a network analysis approach. Trop Anim Health Prod 54(4):1–11

Maritime Mobile Communication System Based on Wireless Mobile Ad Hoc Network Jia Liu

Abstract With the development of maritime mobile communication, mobile AdHoc network will play an increasingly important role as a candidate key technology for realizing personal area network. The maritime mobile communication network based on AdHoc network will be able to significantly improve the shortcomings of the existing network. The purpose of this paper is to study the realization of maritime mobile communication system based on wireless mobile ad hoc network. The design concept of the system software is introduced, the main flow chart is given, and the software realization method of the specific function modules is introduced. The performance of the Ad Hoc maritime mobile communication network system designed in this paper and the traditional system using the AODV routing protocol are compared and tested. Under the same conditions, the AODV routing protocol has higher end-to-end throughput and lower packet loss rate. In this regard, the AODV routing protocol has a relative advantage in adapting to the dynamic topology changes of maritime wireless networks. Keywords Wireless mobility · Ad Hoc network · Maritime mobile communication · Communication system

1 Introduction With the advent of the wireless ad hoc network development stage, people expect a new, safe, efficient and convenient form of maritime communication [1, 2]. Ad Hoc Network is an acronym for Wireless Mobile Ad Hoc Network. This is a mobile network not supported by wired infrastructure. The transmission distance of wireless devices is limited and compensated by various forward transmission techniques, thus J. Liu (B) Tianjin Aerial Survey Technology Center, Beihai Navigation Support Center, Ministry of Transport, Tianjin 300222, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_56

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expanding the network and providing users with a wide range of services, transmissions and services [3, 4]. At present, Ad Hoc network is mainly used for military, business, political communication (such as wireless local area network and wireless local area network) and post-disaster emergency rescue [5]. The characteristics of Ad Hoc network are related to the complex and changeable maritime communication and the changing topology structure. It has broad application prospects to apply the concept of Ad Hoc network to mobile maritime communication [6]. At present, maritime communications mainly rely on International Mobile Satellite (INMARSAT), Single Sideband (SSB) and Very High Frequency (VHF) communications. Mazzali N presents new application scenarios and advanced techniques, including reference designs that implement superframes, predistortion, robust synchronization chains, and plug-and-play channel interleavers. It has been demonstrated through software simulation and hardware testing that DVB-S2X can be a universal technology enabler for land mobile, aviation and maritime satellite scenarios in addition to more traditional VSAT scenarios, even under very challenging conditions (e.g. very low signal-to-noise ratio) [7]. Vatambeti R proposes a novel Gray Wolf Trust Accumulation (GWTA) model in a wireless mesh network architecture to identify attacks through the best features of the GWTA model. Furthermore, the predicted attacked node is replaced to the last location on the network medium to prevent packet loss. Furthermore, the effectiveness of the proposed model is demonstrated by obtaining less packet loss and high throughput [8]. Therefore, the research and development of advanced communication equipment is of great significance for strengthening ship traffic management, ensuring ship safety, and improving the ability to deal with marine accidents [9]. This paper is divided into five parts. The first part mainly introduces the research background of this paper, the significance of the wireless mobile ad hoc network applied to the maritime mobile communication system and the work done in this paper; the second part introduces the key technology of the wireless mobile ad hoc network, The wireless mobile ad hoc network is analyzed in detail, and the modular programming of the system is studied. The third and fourth parts introduce the wireless mobility model using the AODV routing protocol, simulate various network scales and compare and analyze the main performance indicators of the routing protocol. Section 5 concludes the paper.

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2 Realization of Maritime Mobile Communication System Based on Wireless Mobile Ad Hoc Network 2.1 Key Technologies of Ad Hoc Network 1. Channel access technology Due to the propagation properties of electricity, the network (channel) of wireless communication is a channel. Wireless communication occurs when neighboring nodes receive radio signals sent by the same node. In the field of communication systems, resource allocation of wireless multi-channel networks is a problem solved by MAC address control [10, 11]. Due to the unique characteristics of mobile networks, intermediate network technologies used in mobile communication systems and traditional network access technologies based on distributed networks (such as IEEE802.3) cannot be directly applied to the portability of mobile phones. Ad-hoc networks should plan appropriate network access policies [12]. 2. Discovery and maintenance of multi-hop routes Wireless networks are heterogeneous networks where each node is a receiver and router [13]. When the sending node (source) and the data receiving node (node) cannot communicate directly, other intermediate nodes are needed to help them complete the communication through storage and transmission, and the same is true for multi-channel networks. From source to destination, a communication “path” is directed by many other adjacent processes. How to find the right path is the main problem; when the network topology changes, routing is the main problem. Process accounting and process maintenance together constitute the management strategy [14]. The main goal of private network routing protocol design is to require the calculated route to have good characteristics such as fewer hops, stable and reliable network, and low delay. It requires a protocol process to quickly adapt to topology changes, and the maintenance cost of the system is low [15]. 3. Other key technologies Other technologies in wireless networks include: network and information security in wireless networks, reliable QoS technology, energy control and management, network connectivity, etc. [16].

2.2 Selection of Maritime Mobile AdHoc Network Routing Protocol The AODV routing protocol is a routing protocol that can be provided to mobile nodes in an AdHoc network. It can provide fast adaptation for AdHoc network in various situations, such as: power connection status, low processing speed and memory capacity overload, low network utilization and traffic analysis, etc. AODV

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uses destination sequence numbers to ensure that routing loops do not occur (especially if routing control messages are sent externally), to ensure that other long-range routing problems such as “count to infinity” problems do not occur [17, 18]. The AODV algorithm framework enables mobile network operators to easily, independently and span multiple hops to set up and manage AdHoc networks. AODV allows a mobile node to quickly find a route to a destination, but when the destination is inactive (no communication established), the node does not need to continue routing to the destination. AODV allows mobile networks to respond to failures in a timely manner and adapt to changes in network topology. The AODV protocol can avoid routing signals and can provide faster connections by avoiding the “compute to infinity” problem of the Bellman-Ford algorithm. When the link goes down, the mobile site will be notified by a message to stop using the lost link for output.

2.3 The Main Program Flow Design of the System The main program flow chart of the system is shown in Fig. 1: When the system board is powered on or reset, the main control chip C8051F020 first configures the resources of the chip itself, and then initializes the corresponding peripheral devices. After the initialization is completed, the system executes the key scan task. When it is judged that a key is pressed, the voice compression and coding Fig. 1 System software flow chart

start

Start the IDE

Create a task

Receive compressed data, process compressed data

send data

received data

Data processing

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task is performed until the data is transmitted. If the button is not pressed, the system has been waiting to receive data, and after receiving the data, the voice compression and decoding task is performed.

2.4 System Modular Program Implementation 1. Key scan task module This is the first task performed by the main program task module to decide when to start voice transmission. When it is found that a certain pin changes from low level to high level, it can be judged that the button connected to the pin is pressed, and voice transmission starts; when the pin level is always low, it is in a state of waiting for data to be received. 2. Voice task module This part is divided into two parts: sender and receiver: For the sender, C8051F020 receives data and clock signals through pins P2.0 and P2.2 respectively, and then extracts the valid voice data, adds the system frame header 0 × 13EC and the address routing information of voice transmission to form a system frame. The serial port is connected to the digital radio station to transmit. In this way, C8051F020 receives compressed voice data from AMBE2000 every 20 ms, and then packs and sends it out according to the system frame. For the receiving end, when data arrives, C8051F020 first receives 1 word (16bits) of data through the serial port and judges whether it is 0 × 13EC (ie frame header). If the data is the frame header, C8051F020 continues to receive the following valid compressed voice data. Then it judges whether the routing information conforms to the selected route, and then extracts the effective compressed voice data from the receiving array. Finally, it reconstructs the voice frame according to the data input frame format of the AMBE-2000 chip, and writes it into the AMBE-2000 for decompression.

3 Simulation Experiment of Maritime Mobile Communication System Based on Wireless Mobile Ad Hoc Network 3.1 Simulation Environment According to the scale of Ad Hoc network, the network can be divided into small-scale network, medium-scale network, large-scale network and ultra-large-scale network. Considering the practical application of maritime mobile communication, the number of ship nodes is set to 5 ~ 40 ships in medium scale. The system simulation in this

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paper is under Linux system, version RedHat9.0, simulation software NS-2Ver2.30. This paper compares and simulates the existing maritime VHF communication station and the maritime mobile communication network based on Ad Hoc from the aspect of packet arrival rate. Among them, in the simulation with the average motion speed of the nodes as the parameter, we select 40 nodes in combination with the actual ship motion scene, and set the maximum speed to 18 m/s, about 40 knots.

3.2 Package Delivery Rate In the process of network simulation, the ratio of the number of packets successfully received by all destination nodes to the number of packets sent by the source node is called the end-to-end packet transfer rate of the network. The calculation steps are shown in formula (1), and the packet delivery rate corresponds to the packet loss rate, see formula (2). PDR =

Nreceived Nlost + Nreceived

(1)

PLR =

Nlost Nlost + Nreceived

(2)

The larger the packet delivery rate PDR, the smaller the number of lost data packets Na, and the better the channel quality and network performance. The corresponding packet loss rate PLR is also smaller.

4 Analysis of Maritime Mobile Communication System Based on Wireless Mobile Ad Hoc Network 4.1 Performance Analysis of Simulation Results Table 1 shows the comparison of the packet arrival rate (PDR) between the Ad Hoc maritime mobile communication network system using the AODV routing protocol and the traditional communication equipment system when the network scale is 10 nodes to 40 nodes. As shown in Fig. 2, when the number of network nodes is small (below 25 nodes), the packet arrival rates of both systems are very high, about 95% or more. However, the PDR of the maritime mobile communication system based on the Ad Hoc network is relatively low. This is because the Ad Hoc network has a process of route discovery and maintenance, and the network state is relatively complex, while the existing VHF marine radio communication belongs to point-to-point direct communication, there

Maritime Mobile Communication System Based on Wireless Mobile … Table 1 System comparison

517

Node

Legacy system

This article system

5

98.5

99.4

10

96.4

97.2

15

95.4

95.5

20

95.1

95.1

25

90.8

92.3

30

88.3

90.1

35

87.7

89.8

40

80.3

88.7

Packet arrival rate

120 100 80 60 40 20 0 5

10

15

20

25

30

35

40

number of nodes legacy system

This article system

Fig. 2 The relationship between the packet arrival rate and the number of nodes

is no need for complex operations such as forwarding and routing establishment. With the increase of the number of nodes, the PDR of both has a rapid decline, and the decline speed of the existing VHF system is faster than that of the Ad Hoc network. This is because with the increase of the number of nodes, the probability of random communication between nodes increases, and the channel contention phenomenon in the existing system emerges, while the maritime mobile communication network based on Ad Hoc can open up multiple links in the network.

5 Conclusions An Ad Hoc network is a special wireless network that is different from existing networks. The characteristics of distributed access and multi-hop routing make the network have broad application prospects in military and civilian fields. The design

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of the system is from the initial theoretical study to the subsequent concrete reinforcement. Through repeated experiments and result analysis, the feasibility of the Ad Hoc maritime mobile communication network system is proved. The system can greatly improve the packet arrival rate, which is very important to reduce the slow speed of the maritime mobile communication system. However, there is still a lot to improve and improve in the design. The single-threaded processing mode of microcontrollers limits the ability to process multiple signals in parallel. If possible, use a processor with strong parallel processing capabilities.

References 1. Magnussen LI, Carlstrm E, Srensen JL et al (2018) Learning and usefulness stemming from collaboration in a maritime crisis management exercise in Northern Norway. Disaster Prev Manag 27(1):129–140 2. Mobo FD, FRIEDR (2018) Effectiveness of social media in monitoring cadets performance on shipboard training in selected maritime schools using system quality metrics. Orient J Comput Sci Technol 11(2):103–106 3. Bostrm M (2021) Other-initiated repair as an indicator of critical communication in ship-to-ship interaction. J Pragmat 174(2):78–92 4. Bonnar T (2019) The first steps in mitigating contemporary risks to our strategic sea lines of communication. Martime Report Eng News 81(8):26–26 5. Kim Y, Song Y, Lim SH (2019) Hierarchical maritime radio networks for internet of maritime things. IEEE Access PP(99):1–1 6. Broadbent M (2019) Maritime missions. Air Int 96(5):28–28 7. Mazzali N, Boumard S, Kinnunen J et al (2018) Enhancing mobile services with DVB-S2X superframing. Int J Satell Commun Network 36(6):503–527 8. Vatambeti R (2020) A Novel wolf based trust accumulation approach for preventing the malicious activities in mobile Ad Hoc network. Wireless Pers Commun 113(4):2141–2166 9. Sarabia-Jacome D, Palau CE, Esteve M et al (2019) Seaport data space for improving logistic maritime operations. IEEE Access PP(99):1–1 10. Sushma EAT (2021) A review of the cluster based mobile Adhoc network intrusion detection system. Turkish J Comput Math Edu (TURCOMAT) 12(2):2070–2076 11. Willners JS, Toohey L, Petillot YR (2019) Sampling-based path planning for cooperative autonomous maritime vehicles to reduce uncertainty in range-only localisation. IEEE Robot Autom Lett PP(99):1–1 12. Abdallah RM, Dessouki A, Aly MH (2018) The resonant tunneling diode characterization for high frequency communication systems. Microelectron J 75(MAY):1–14 13. Arienzo L (2019) Green RF/FSO communications in cognitive relay-based space information networks for maritime surveillance. IEEE Trans Cognit Commun Netw PP(99):1–1 14. Thomas A (2019) Social networks found: within Chinese space events. Space Chronicle 72(1):3–8 15. Elhoseny M, Shankar K (2020) Reliable Data transmission model for mobile Ad Hoc network using signcryption technique. IEEE Trans Reliab 69(3):1077–1086 16. Bindhu M, Jackson B, Asha S (2020) Secured routing using neighbour coverage with minimum overhead in Ad Hoc networks. J Comput Theor Nanosci 17(4):1867–1870 17. Borkar GM, Mahajan AR (2020) A review on propagation of secure data, prevention of attacks and routing in mobile ad-hoc networks. Int J Commun Netw Distrib Syst 24(1):23–57 18. Sadeghi M, Behnia F, Amiri R (2021) Maritime target localization from bistatic range measurements in space-based passive radar. IEEE Trans Instrum Meas PP(99):1–1

Cryptographic Reverse Firewalls in Cyber-Physical Systems: Preliminaries and Outreach Wanda Guo

Abstract Edward Snowden’s revelation about global surveillance programs makes cryptographic researchers to pay more attention to security under compromised machines. Based on the idea of protocol divertibility and the algorithm-substitution attacks, a group of researchers worked out the cryptographic reverse firewalls which have the characteristics of functionality-maintaining, security-preserving, exfiltration-resistant, and relatively ‘transparent’. They then further implemented a CCA-secure message transmission protocol using the firewall, while another group of researchers complemented their work by applying the reverse firewalls to MPC protocols to achieve security against active adversaries. Keywords Reverse firewall · Divertibility · Multiparty computation

1 Introduction In 2013, Edward Snowden disclosed global surveillance programs that were managed by the National State Agency and the Five Eye intelligence alliance. This incident makes some cryptographic researchers to focus on protecting secret messages from being leaked under surveillance and compromised machines. Based on several formerly proposed security properties of symmetric encryption, Ilya Mironov et al. proposes the cryptographic reverse firewall [1] (CRF) which owns a set of security properties that can effectively protect the communication parties from surveillance. The proposed cryptographic reverse firewall implements three important security features, which are maintaining the underlying protocol’s functionality, security perseverance and exfiltration-attack resistance.

W. Guo (B) School of Cybersecurity and Privacy, Georgia Institute of Technology, Atlanta, GA, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_57

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Maintaining Functionality. The idea of maintaining the functionality of the underlying protocol comes from the algorithm-substitution attacks (ASA) which is introduced in [2]. ASA intends to substitute the original and secure encryption scheme to another encryption scheme implemented by the attacker to obtain sensitive information transmitted between two parties. Meanwhile, the attacker should ensure that no anomaly should be noticed by the user about the attack. Also, the security the paper proposes is only useful to this kind of functionality-maintaining attackers. Preserving Security. For most security notions, this property is very similar to exfiltration resistance. But there are some exceptions, where these two properties are different. In the context of CRFs, they are not the same if the attacker is computationally unbounded, or he has access to oracles. Another exception happens when the security requirements are simulation-based [1]. Exfiltration Resistance. The implementation of preserving security in the design of CRF comes from [3] which introduces protocol divertibility. A protocol is divertible if any two parties communicating using this protocol can’t distinguish between communicating directly with each other and communicating via an intermediary. Papers based on Cryptographic Reverse Firewalls. As indicated in the project proposal, although I found three papers that cites the paper introducing CRFs and seems valuable, I only include two of them which shows a direct connection with the paper. The paper [4] designs a message-transmission protocol based on the CRF which is interactive and concurrent IND-CCA secure. The authors applied the reversefirewall framework to different kinds of protocols to find the best answer. Finally, they managed to prove the CCA-security in key agreement protocols and rerandomizable encryption schemes. Another paper that cites the one about CRF is [5]. This paper limits the scope to computationally secure MPC protocols [6], and explores the security when adding reverse firewalls to their original cryptographic models. The logical order of these five papers is shown in Fig. 1. This paper is divided into five parts. Part 1 briefly introduces the papers covered later. Part 2 introduces two papers that some ideas about reverse firewall come from. Part 3 introduces the definition of CRFs and its application. Part 4 includes another two papers that utilize the definition of the reverse firewall to go further. Part 5 concludes the paper.

Fig. 1 Logical order of the papers about CRF

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2 Previous Work The idea of designing CRFs is originated from a cluster of previous work in similar areas [7, 8]. This part briefly introduces previous work as a basis of designing CRFs.

2.1 Algorithm-substitution Attacks Algorithm-substitution attacks (ASA) were discussed several times in several papers, but none of them formally models it and practically launches it [2]. The paper about ASAs analyzes algorithm-substitution attacks both offensively and defensively. This paper limits the scope to ASA under symmetric key settings and then introduces two attacks and one defending method. In an algorithm-substitution attack, the attacker substitutes the encryption scheme with the one he designs, and keep the decryption scheme unchanged. The only thing that explicitly changes in the encryption scheme is that the attacker’s scheme takes a key called ‘big-brother key’ as an additional input. This process is called subverting encryption in the paper. The paper introduces two kind of attacks that share the idea of ASA. One is called IV-replacement attacks. These attacks targets stateful scheme that puts a random nonce into the ciphertext. Another kind of attack are called biased-ciphertext attack. The difference between the IV-replacement attack and biased-ciphertext attack is that the attack recalculates the random value in the original encryption scheme. This paper gives inspiration to the functionality-maintaining characteristic of CRFs. The paper about CRFs extends the security notion mentioned in this paper from several aspects. First, it claims that the firewall can achieve security in multi-party communications, where only two-party communications are discussed in this paper. Second, CRFs can preserve security not only against the functionality-maintaining attackers, but also those who breaks the security of the underlying protocols.

2.2 Divertible Protocols Divertibility of protocols is where the idea of exfiltration resistance in CRFs comes from. The paper about protocol divertibility [3] also includes the notion of atomic proxy cryptography, but it’s unrelated to the idea of CRF paper. So, this part only summarizes the part that introduces the divertibility. The paper proposes a set of definitions that are stronger than the original definition of divertibility. The new definition is called ‘perfect divertibility’. Assume that A and B are the two parties that uses the same protocol to communicate with each other, and X and Y are the sets of private inputs and common inputs. Then W is the set of relations of Y to X. To be perfectly divertible, the protocol is required to be extensible,

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which means that the views of B on W and on A and the views of A on W and on B are equal, and prefectly indistinguishable. What the CRF paper goes further than this paper is that it takes asynchronous multi-party communication [9] into consideration, and roots the idea of warden into the reverse firewalls. Also, CRFs achieve better security properties while being unable to see the message they forward. CRFs are better for practical use as mentioned in the paper, because they can satisfy different security needs from different parties.

3 CRFs: Definition and Evaluation 3.1 Cryptographic Protocols In the paper, multiple-party interactions can be defined by a cryptographic protocol P, where all the parties involved are stateful. The protocol works as follows: At first, there is a procedure that can do the initial set-up process for all the parties and is run, which takes a security parameter as an input. What it does is to generate and to output one state for each involved communication party, which we call their respective input; public parameters and a schedule of messages [1]. As the schedule of communication indicates, the involved parties start to transmit data from/to each other. Each party has an associated next message algorithm and a message receipt algorithm implemented. This former is run when it must output a message while the latter is utilized to update the communication parties’ states. After the protocol is finished, the output is generated for each communication party and the final result is returned. The identifiers of a protocol are the parties that use the protocol and the setup procedure. The identifiers of these parties are receipt, next and output algorithms. A complete record of messages, or so-called transcript will be sent when a protocol is run. There are also other definitions concerning input, security and functionality requirements.

3.2 CRFs: Definition and Security Properties After defining the crytographic protocols, this section defines the cryptographic reverse firewalls and its security properties. To provide a basis for Sect. 4, I unified the definitions from [1, 4] and [5]. Definition of CRF. There is no explicit definition of CRF, because it is always combined with different parties for use. For a certain party which are identified by three algorithms defined in Sect. 3.1, the composed party with a firewall can still be defined by these three algorithms. What is different is that the message input of the receipt algorithm is now the output of the firewall instead of the original message,

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and the output of the next algorithm is the result which the firewall processes the output of the original next algorithm and outputs. The definition of CRFs is shown in (Eq. 1). W ◦ P := (r ecei veW ◦P (σ, m) = r eceive P (σ, W (m)), nextW ◦P (σ ) = W (next P (σ )), out putW ◦P (σ ) = out put P (σ ))

(1)

Definition of CRF Security properties. Generally, CRFs must hold the characteristics described below. Functionality-maintaining RFs. A CRF is functionality-maintaining if for any number of CRFs added to a functional protocol, the functionality of the protocol is always the same as its original functionality. In this part, the authors emphasize that only the protocols that function properly are discussed. Security-preserving RFs. A CRF is security-preserving if any number of compromised parties exist in the communication scenario, and the protocol together with the firewalls deployed at these parties’ side still maintains the security when no party is compromised. If the adversaries are functionality-maintaining, this property is called weak security-preserving. Otherwise, it’s called strong security-preserving. Exfiltration-resistant RFs. A CRF is exfiltration-resistant if the advantage of an adversary who tries to gain message from the communication protocol with firewalls deployed is always negligible. If the adversaries are functionality-maintaining, this property is called weak exfiltration-resistant specifically. Otherwise, it’s called strong exfiltration-resistant. Valid Transcript. A valid transcript is defined as all the messages that are identified by all parties when the protocol runs properly. No protocol running failure generates a valid transcript. Detectable Failure. In the protocol, a possible event of failure of a party will be identified if there are unambiguous transcripts existing in the communication. There is a polynomial-time algorithm that can decide whether a transcript is valid and the firewall outputs invalid symbol if the transcript it runs on is invalid.

3.3 CRFs: Evaluation The paper evaluates the possibility of implementing CRFs by constructing a scheme that can assess a set of private functions. The design of the scheme utilizes the definition of garbled circuit and the idea of a oblivious data transmission protocol. What they do is to use the Oblivious transfer protocol as the input phase described in Sect. 3.2.1 and to use the garbled circuit scheme as the output phase. Then, they prove the security of the whole composed protocol and CRFs. After that, the paper shows two examples of adapting arbitrary existing protocols with CRFs. The examples are the adapted version of the aforementioned composed protocol, and a general design of firewall in two-party communication scenarios.

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4 CRF: Further Research This section introduces two research papers that uses the CRFs to either implement secure multi-party computation or a secure message transmission protocol.

4.1 Message Transmission with Reverse Firewalls This paper shares two authors with the paper proposing CRFs. It utilizes the reverse firewall to design a concurrent CCA-secure message transmission protocol, which can preserve security if machines are compromised. In the definition part, apart from referring to the definition of CRFs, the paper also defines the message transmission protocol and the key agreement protocol, along with their security (CPA-security, CCA-security, security against passive adversaries and security against active adversaries). Then, the paper discusses a simple scenario, where a public-key encryption is performed and two firewalls are deployed respectively to the sender and the receiver. It is proved that if the scheme is rerandomizable, then the firewall preserves security and resists exfiltration; if the scheme is key malleable, the firewall maintains the functionality of the underlying protocols. In the next two parts, the paper introduces key agreement protocol [10] to the original encryption scheme (which implements a authenticated key agreement protocol), and designs corresponding reverse firewalls for sender and receiver. What it concludes is that they can get a CCA-secure message transmission protocol by adding reverse firewalls for either sender or receiver.

4.2 Using Reverse Firewalls to Construct Actively Secure MPCs This paper designs a multi-party computation (MPC) protocol that achieves active better security than the protocols introduced in [1], which can preserve security if not all the parties in the protocol are compromised by attackers that breaks the functionality of the protocol. The protocols it introduces are summarized in Fig. 2. Apart from mentioning the definition of CRFs, the paper also defines admissible transmission, controlled-malleable NIZK proof system [11] and malicious adversaries. Then it focuses on the security properties that the computationally secure MPC protocols with CRFs deployed achieves. The paper tries to design secure MPC protocols under different scenarios. The first protocol that is proposed is a coin-tossing protocol that utilizes the cryptographic reverse firewalls. Its general designing idea is to construct a commit and proof framework. In the protocol, one party first initializes a string and proves that it has zero

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Fig. 2 Structure of Secure and CRF-compatible MPC Protocols

knowledge in this string. Then each of all the communication parties excluding this party send strings to the first party. In the end, the first party tries to prove its zero knowledge to all the strings mentioned above. Another proposed protocol is an input-commitment protocol. How the CRF is utilized is that it commits rerandomizes the commitment, rerandomizes the proof, and finally send the commitment-proof pair broadly. The final protocol is an authenticated computation protocol, which uses the CRFs to rerandomize the proof to prevent the commitment-proof pair from leaking secret information. The final result of these implementation is that utilizing the multi-party coin-tossing protocol, authenticated computation protocol and input commitment protocol, the compiled MPC protocol is actively secure.

5 Comparison and Discussion of Cited Papers The paper proposing CRF is the center topic we discuss in the paper. It is based on the two papers about ASAs and divertibility, and two other papers cite this paper for further research. This section discusses the strengths and weaknesses of these papers and summarizes them. The paper about ASAs is the first paper to discuss ASAs formally, and it proposes the design of the attacks and the method about how to defend it. These points are its highlights. What is insufficient is that the paper only discusses ASAs under

526 Table 1 Summary of the discussed papers

W. Guo Paper

Strength

Weakness

EUROCRYPT’98

Proposed ASA

Not Discuss non-FP Attacker

CRYPTO’14

Defined Perfect Divertibility

No Criteria for Divertibility

EUROCRYPT’15

Proposed Secure CRF

Only Included 2-party Case

CRYPTO’16

Proposed Enhanced CRF

No Performance Justification

ASIACRYPT’21

Proposed a New MPC Design

Lack Concrete Instantiation

symmetric key settings. Also, it doesn’t discuss the condition when the attacker isn’t functionality-preserving. The paper about protocol divertibility did some extended research about the definition of divertibility. It proposes a stronger security notion called perfect divertibility, which is its highlight. Its shortcoming is that it doesn’t set a criterion for the computationally divertible protocols discussed in the paper. The paper about CRFs proposes a brand-new definition which can help preserve security and maintain functionality of underlying protocols even if some machines of the users are compromised by attackers that may not be functionality-maintaining. What it lacks is that it only presents the model under two-party settings, rather than the models and protocols it claims to present which should be under multi-party settings. The paper about message transmission protocol applies the reverse firewall to the existing message transmission protocols and key agreement protocols. Its shortcoming is that there is no justification about the performance of the new protocol, which may make it not useful in practice. The paper about actively secure MPCs utilizes the reverse firewalls to construct a secure MPC protocol against active adversaries. The protocol set includes the augmented coin-tossing protocol, the input-commitment protocol and the computation protocol with authentication. The question the paper leaves open is how to develop robust instance of firewalls for threshold cryptography schemes and its performance analysis. Another future research direction is to improve the performance of the designed protocol in terms of its efficiency and round-optimality. The summary for the comparison and discussion below is shown in Table 1.

6 Conclusions After Snowden’s revelation about NSA’s mass surveillance programs, researchers start to research on preserving security under the condition that the host machine

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of one or several parties in a communication model is/are compromised. Based on the definitions of divertibility and ASAs, researchers work out the CRFs which are functionality-maintaining, security-preserving and exfiltration-resistant. But it only implements a prototype which needs to be adapted and applied further. Therefore, a group of authors refined the model and designed a CCA-secure authenticated key agreement protocol. Another group of researchers successfully applied this new kind of cryptographic reverse firewalls to design new MPC protocols which achieve active security and weakly exfiltration-resistant.

References 1. Mironov I, Stephens-Davidowitz N (2015) Cryptographic reverse firewalls. In: Annual international conference on the theory and applications of cryptographic techniques, pp 657–686 2. Bellare M, Paterson K, Rogaway P (2014) Security of symmetric encryption against mass surveillance. In: Annual cryptology conference, pp 1–19 3. Blaze M, Bleumer G, Strauss M (1998) Divertible protocols and atomic proxy cryptography. In: International conference on the theory and applications of cryptographic techniques, pp 127–144 4. Dodis Y, Mironov I, Stephens-Davidowitz N (2016) Message transmission with reverse firewalls: secure communication on corrupted machines. In: Annual international cryptology conference, pp 341–372 5. Chakraborty S, Dziembowski S, Nielsen JB (2020) Reverse firewalls for actively secure MPCs. In: Annual international cryptology conference pp 732–762 6. Ishai Y, Khurana D, Sahai A, Srinivasan A (2021) On the round complexity of black-box secure MPC. In: Annual international cryptology conference, pp 214–243 7. Burmester M, Desmedt Y, Itoh T, Sakurai K, Shizuya H (1999) Divertible and subliminal-free zero-knowledge proofs for languages. J Crypto, pp 197–223 8. Lenstra A, Hughes J, Augier M, Bos J, Kleinjung T, Wachter C (2012) Public keys. In: Advances in cryptology—CRYPTO, vol. 7417, pp 626–642 9. Knott B, Venkataraman S, Hannun A, Sengupta S, Ibrahim M, van der Maaten L (2021) Crypten: secure multi-party computation meets machine learning. Adv Neural Inf Process Syst 34:4961–4973 10. Soni M, Singh DK (2021) LAKA: lightweight authentication and key agreement protocol for internet of things based wireless body area network. In: Wireless personal communications, pp 1–18 11. Mao X, You L, Cao C, Hu G, Hu L (2021) Linkable ring signature scheme using biometric cryptosystem and Nizk and its application. In: Security and communication networks, pp 1–14

Information Collection System of Learning City Based on Big Data Technology Shifa Lu

Abstract City is a highly concentrated regional type of human society and an important part of society. Under the influence of lifelong education, lifelong learning and learning society, the concept of Learning City(LC) also came into being and continues to develop, indicating that the development of the city has ushered in a new stage and a new height. Based on BDT, this paper designs the LC information acquisition system, briefly analyzes the characteristics of BDT and data acquisition and processing technology, takes urban traffic information as an example, analyzes the LC traffic information acquisition system, and verifies the effectiveness of the LC information acquisition system based on BDT proposed in this paper. Keywords Big data technology · Learning city · Information collection · System design

1 Introduction The emergence of BDT has brought mankind into a new era of information technology. The importance of BDT is obvious, because BD information covers all aspects of human life in the Internet era. People’s daily life, work and learning behaviors will leave data traces. This kind of data with extremely wide coverage and easy access to technical means shows a new perspective for the research of various business organizations and scientific research institutions. The continuous deepening and improvement of BD analysis technology and industrial chain provides new research ideas and means for the collection, processing and application of traffic information in learning cities. Therefore, this paper studies the traffic information collection(TIC) system of learning cities through BDT. S. Lu (B) Tianjin Open University, No.1 Yingshui Road, Nankai District, Tianjin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_58

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As BD plays an increasingly important role in the transportation of learning cities, the role of traffic flow prediction in traffic management becomes more prominent. Relevant scholars have carried out extensive and in-depth research on applying BD to predict learning cities in combination with various algorithms. Kalman filter theory and neural network methods have been applied to the prediction and verification of traffic flow; Compared with the research and development of using BD in foreign learning cities, China’s BDT started late, and the research and application achievements in intelligent transportation since the 1990s are relatively not high-end [1]. According to the needs of BD analysis, this paper establishes a TIC and analysis system based on BD to realize the effective collection of basic road information, time information and vehicle identity information. Establish the system system of traffic information calculation and storage of the distributed file storage system based on BDT, and establish the system storage platform for the subsequent system design; Combined with the actual case, the system is tested and verified. Thus, the TIC, storage and system implementation based on BDT are preliminarily realized, which provides a new technical means for urban traffic to obtain structured, real-time and accurate traffic information, and also lays a foundation for subsequent traffic information technology integration, data mining and practical application [2, 3].

2 Design of Information Collection System for LC Based on BDT 2.1 LC Learning cities emphasize the concept of lifelong learning and create the corresponding atmosphere, create learning organizations and build lifelong education systems, integrate the construction of resource platforms, and finally promote the common development of people and cities, which have become the key points of learning cities. This paper believes that a LC refers to a harmonious city that integrates all kinds of learning resource platforms in an open society and cultivates a lifelong learning cultural atmosphere in order to protect and meet citizens’ lifelong learning rights and needs, promote the coordinated and sustainable development of the city, take learning, innovation and development as the driving force, and based on the establishment of lifelong education system and the construction of various learning organizations. This is a regional learning society based on cities, a product of the era of knowledge economy, a modern city integrating learning, work and life, and a new model of urban management and development [4].

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2.2 Technical Characteristics of BD BD technology usually manifested in that the scale of data volume is far beyond the processing capacity of traditional computers, and a computing system suitable for this data level needs to be used. At the same time, the update speed of network BD is far faster than that of ordinary data, because the number of network users is large, and a huge amount of data is generated all the time.

2.2.1

Data Acquisition and Processing Technology

Mining, collecting and formatting useful data from an open network environment is the basis of the research on the spatial characteristics analysis of learning cities and the analysis and prediction of residents’ spatio-temporal behavior characteristics. Data collection refers to the process of mining the data needed for analysis and research to collect urban information from the Internet, classifying and storing the collected data according to standards and processes, and generating database index files [5, 6]. There are also great differences in the data and organization methods of various Internet platforms, which makes the network environment an infinitely growing heterogeneous data integration environment. Images, audio data, video data and other data in different storage formats are mutually organized in the Internet. The Internet itself has become a huge database, and it is not relational, but semi-structured or unstructured [7].

2.2.2

Data Analysis Tools

The application of BD in the field of information collection in learning cities should first start with the visual analysis of the obtained data. Due to the limitation of the knowledge structure of information collection personnel in learning cities, everyone is at a loss in front of a large amount of data. One of the reasons for this is that the amount of data that can be collected and used in urban design is very large, and traditional data analysis methods and existing data analysis tools will have performance bottlenecks when facing such a large amount of data [8]. In order to meet the demand of geometric growth of data volume in cities, new tools should be able to quickly acquire, select, analyze and display the information needed for urban design in real time [9].

2.3 Learning Urban TIC System Based on BD At present, among various TIC methods, manual collection has low cost performance. For Fred detection, a magnetic strip for identification needs to be added to each

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Fig. 1 Frame diagram of TIC system

vehicle. Wave frequency detection and magnetic frequency detection are disturbed by the surrounding electric and magnetic fields. At the same time, because BD involves some private data, such as the personal information of car owners, it is not suitable for public disclosure. Therefore, in the information collection, this study mainly uses video detection and recognition technology to collect road information, time information and license plate information.

2.3.1

Framework of TIC System in Learning Cities

This paper studies TIC based on the perspective of traffic managers, using video recognition. It mainly collects traffic road information, traffic time information and vehicle identity information. The system framework of TIC is shown in Fig. 1.

2.3.2

Key Technologies of Traffic Information Acquisition System

Video capture technology is widely used in the field of traffic engineering because of its unique advantages. The key technology of information acquisition in this paper is video acquisition. Image processing is at the core of video detection technology. The amount of image processing data is huge, and other information needs to be recorded for statistical calculation and analysis. Therefore, the development of image recognition in video detection also determines the development of video detection technology. Image detection technology has experienced knowledge related types, motion related types, stereo vision types, pixel intensity types and other types [10, 11]. After years of development, video detection technology has gradually matured. At the same

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time, it has been widely paid attention to because of its many advantages. The main advantages are as follows: The installation is simple, economical and practical. When video detection technology is applied to the road, it does not need to bury the coil below the road like coil detection, so there is no need to damage the road during installation and maintenance. The camera is also very convenient to move and adjust, and the cost of maintenance and upgrading is greatly reduced. The original equipment can be recycled for many times. Intuitive and reliable, easy to manage. The equipment is visible, intuitive and reliable, which is convenient for managers to make pre-warning. The detection range is relatively wide, and at the same time, it can also obtain more and richer information. It can take into account the on-site video and has a high value density. The video camera can record traffic video, which is of great value to the restoration and reproduction of the road scene, and is of great significance to traffic accidents and traffic safety. Little impact on the environment. The erection of cameras will not affect the surrounding environment, and there is no interaction between them, so the pollution to the environment, landscape and vision will be minimized. The video detection system consists of video acquisition, unit responsible for transmission, video detection host (host can be installed on site, or information can be transmitted to the server remotely), industrial computer and detection related software system. The installation height of the camera is generally 720 m. Due to the huge amount of information, the detection host is placed on the remote server, and the data detected by the system can be transmitted to the remote data center through the communication interface. BD video detection technology: use the camera installed on the road to collect the vehicle information of the intersection. During the installation process, the position should be corrected and adjusted to determine the road information of the intersection, including the road information such as the width, height and distance of the intersection. A virtual “vehicle detector” is set on the scene image to simulate the induction coil, that is, a line or block diagram area is set on the road of image acquisition. During image acquisition, it is necessary to compare the gray value of the background of each frame collected. The image information collected should be processed by removing interference factors, and the time information should be recorded in combination with timers, clocks and other auxiliary tools, so as to obtain the information of traffic parameters such as vehicle flow, vehicle speed, road occupancy [12].

2.4 Road Basic Data Acquisition The research goal of this paper is to develop TIC and processing software, and realize the traffic evaluation of intersections. The main collected information is divided into road information, time information and vehicle identity information. It is difficult to collect road information and time information. The collection of vehicle identity

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information is the focus of this chapter, and support vector machine is mainly used for license plate recognition. The acquisition of basic road data mainly comes from road design data. The data of road planning and design at each intersection are entered into the server as basic standby data. In this study, separate cameras are set for each lane, so each camera only tracks, captures and records the traffic conditions in that lane.

2.4.1

Identification and Collection of Vehicle Identity Information

The collection of vehicle identity information mainly comes from the collection of vehicle license plate character information, because in our country, the license plate information is equivalent to the ID card issued by the traffic management department for each vehicle, and each license plate can indicate the identity of the vehicle and the owner’s information. The core technology of license plate detection is the image recognition technology of license plate recognition. Image recognition algorithm based on support vector machine algorithm is used for license plate recognition. The requirement of the algorithm is to cut each license plate and divide it into many NxN size blocks, and then distinguish the word blocks into license plate and non license plate areas, and then classify the feature vectors. The license plate is pretreated with exposure and white balance. The purpose of pretreatment is to keep the picture in the best state, remove noise, enhance contrast, and zoom the picture appropriately, etc.

2.4.2

Traffic Information Integration and Processing

The information collected by the camera exists in the form of analog signals, which are converted into digital signals and transmitted to the data center for filtering and storage. After data collection, data processing should be carried out. The current methods of data processing mainly include data extraction, data fusion, data mining and data aggregation, road condition data matching and conversion, and relevant and valuable data mining should be carried out according to the actual situation. Select the service mechanism and method of information publishing and sharing according to the actual situation. The collected traffic information generally has the characteristics of multiple sources, multiple dimensions, different tenses and large data scale. Therefore, the integrated processing method of heterogeneous distributed database is mainly adopted in the storage, and the data format, relevant specifications and network interface all use standard format.

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3 Urban Information Collection Model Based on BDT The data preprocessing operation is realized by obtaining the correlation matrix between the historical speed and flow. First, we divide the two kinds of data into several sample points based on the collection time of the two kinds of data. Secondly, we discretize the sample point values we collected, namely the speed value and the flow value. The method of discretization is to divide these sample points into NV and NH intervals, namely G1, G2, gnv and H1, H2, ⟁, HNS. We can get NV in the following form × NH association rules: G i ⇒ H j , i = 1, . . . N , j = 1, . . . Nh

(1)

The incidence matrix (including support matrix and confidence matrix) in association rules can be obtained by the following formula: ni j sup por t (G i ⇒ H j ) = f (G i ∪ N j ) = ∑ Nv ∑ Nh i=1

j=1

ni j

= sup por ti j

(2)

sup por ti j con f idence(G i ⇒ H j ) = f (G i ∪ N j ) = ∑ Nv = con f idencei j (3) j=1 sup por ti j The frequency of traffic speed and traffic flow in the interval GI and HJ is NIJ. Transforming the flow value into the speed range is based on the association rules between the two, and can achieve a certain degree of support and credibility at the same time.

4 Case Analysis of TIC in Learning Cities In order to verify the effectiveness of the TIC system of LC based on BDT proposed in this paper, a LC is selected, and the traffic evaluation of the intersection is mainly based on the comparison of the traffic volume of the intersection and the traffic capacity of the intersection. Through the comparison, the congestion evaluation of the intersection at this time is obtained. This study mainly studies the traffic evaluation of urban intersections, mainly involving cars and buses. The basic data mainly includes the distribution map of the pavement entrance of the intersection, the lane width of the entrance in all directions, and the signal cycle distribution. The signal light timing table of the intersection. Due to the uneven traffic flow in the north–south and east–west directions, different timing is designed. The straight travel time in the north–south direction is 33S, and the left turn time is 24 s; The straight travel time in the east–west direction is 24 s, the travel time of left turning vehicles is 19S, and the right turning vehicles are not controlled by the traffic lights.

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Table 1 and Fig. 2 show the vehicle information statistics of the intersection from 17:00 to 18:00 on Saturday afternoon, which are derived from the field vehicle information statistics and the data provided by the local traffic management department. According to the actual situation of the location and the data in the chart, the number of direct vehicles at the south entrance is the most. Because of weekends, the number of vehicles from south to north is the most. There are few right turning vehicles at the south entrance and left turning vehicles at the north entrance. Based on the above chart and the calculation method of the stop line method, the traffic flow at the intersection is analyzed and calculated to obtain the traffic capacity of the left turning, straight going and right turning entrance roads, and then calculate the saturation of the entrance roads to calculate the saturation degree of the intersection. It verifies the effectiveness of the learning urban TIC system based on BDT proposed in this paper. Table 1 Intersection data sheet East entrance

West entrance

South entrance

North entrance

578

805

603

452

Proportion of left turning vehicles

0.27

0.16

0.29

0.08

Proportion of straight driving

0.49

0.41

0.59

0.76

Right turn ratio

0.17

0.46

0.08

0.15

Traffic volume (veh / h)

Traffic volume (veh / h)

900

0.8

800

0.7

700

0.6

600

0.5

500

0.4

400

0.3

300 200

0.2

100

0.1

0

0 East Entrance

West entrance

South entrance

North entrance

Entrance passage Traffic volume (veh / h)

Proportion of left turning vehicles

Proportion of straight driving

Right turn ratio

Fig. 2 Traffic volume and transfer ratio of different entrance roads

Transfer ratio

Entrance passage

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5 Conclusions Based on BDT, this paper designs a LC TIC system, and verifies the effectiveness of this system design through an example analysis. However, due to the limited technical conditions, there are still deficiencies in this paper. Traffic information has the characteristics of multi-dimensional, complex and dynamic, so there are high requirements for the accuracy and efficiency of information collection. BD analysis technology depends on high-quality data and efficient data management, so data quality and data management have also become the focus of BD research.

References 1. Sun M, Zhang J (2020) Research on the application of block chain BD platform in the construction of new smart city for low carbon emission and green environment. Comput Commun 149(Jan.):332–342 2. Senju T, Takeuchi N, Kozono N et al (2022) Biomechanical comparison of a horizontal mattress, cross suture and vertical mattress for repair of a tendon weave in a porcine model. J Hand Surg (Asian-Pacific Volume) 27(03):439–446 3. Mark SM et al (2019) Data science%information technology%learning health%machine learning%translational research. EGEMS (Washington, DC) 7(1):1–1 4. Putra IAI (2019) Evaluating information technology investment with new information economic (NIE): a study case greenspan packaging system co. ltd. J Comput Theor Nanosci 16(12):5151–5161 5. Habib MN, Jamal W, Khalil U et al (2021) Transforming universities in interactive digital platform: case of city university of science and information technology. Educ Inf Technol 26(1):517–541 6. Bharati S, Podder P, Thanh DNH et al (2022) Dementia classification using MR imaging and clinical data with voting based machine learning models. Multimed Tools Appl 81(18):25971– 25992 7. Vasilev IA, Petrovskiy MI, Mashechkin IV et al (2022) Predicting covid-19-induced lung damage based on machine learning methods. Program Comput Softw 48(4):243–255 8. Ghallab H, Fahmy H, Nasr M (2020) Detection outliers on internet of things using BDT. Egypt Inform J 21(3):131–138 9. lvaro Edgar González-Aragón P, Socorro Aída B-Y, Irigoyen-Camacho ME et al (2019) Relationship between erosive tooth wear and beverage consumption among a group of schoolchildren in Mexico city. Clin Oral Investig 23(4):1–9 10. Masoud AA, Aal A (2019) Three-dimensional geotechnical modeling of the soils in Riyadh city, KSA. Bull Eng Geol Env 78(1):1–17 11. Yousuf T, Khan I, Yousuf T et al (2020) Socio-economic profile of silk weavers: a micro- level study of Srinagar city. Eur Acad Res 1(3):319–331 12. Ducruet C, Carvalho L, Roussin S (2019) the flight of icarus? incheons transformation from port gateway to global city the flight of icarus? incheons transformation from port gateway to global city. Ann Reg Sci 49(3):619–642

A DOA Estimation Algorithm Based on Unitary Transform Wenchao He, Liyan Li, and Ting Xu

Abstract When DOA angle is in the middle of grids, the performance of DOA off-grid algorithm decreases significantly. As a result, the unitary transform method is used to convert the received array signal into real domain, and estimate DOA by Off-Grid Sparse Bayesian Learning. We improve the present off-grid Bayesian algorithm, and proposed a new method using Off-Grid Sparse Bayesian Learning called RV-Root-SBI. The simulation results show that the estimation performance proposed is better than that of the original off-grid Bayesian algorithm. When DOA angle falls at the midpoint of grid, the performance of OGSBI algorithm and RootSBI algorithm decreases significantly, but the proposed algorithm can still estimate DOA angle effectively. Keywords Unitary transform · Sparse Bayesian learning · DOA estimation

1 Introduction Array Signal DOA estimation is a hot research area in signal processing. The DOA estimation of far-field narrowband signals is studied in this paper. The sensor array is a uniform linear array (ULA). For the DOA estimation of ULA phased array radar, the Esprit Algorithm and MUSIC algorithm are widely used because of their high spatial resolution and simplicity. The Music Algorithm has been proved to have similar performance to the maximum likelihood for incoherent sources when a large amount of sampling data is required [1]. But the estimation performance of these two subspace-based Algorithms decreased in the case of small snapshots and low signal-to-noise ratio. In recent years, the rise of signal sparse reconstruction (SSR) and compression sensing (CS) provides new ideas for DOA estimation. Using W. He (B) · L. Li · T. Xu College of Science and Engineering, Changchun Humanities and Sciences College, Changchun, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_59

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sparse reconstruction theory, the problem of DOA estimation is transformed into the problem of reconstructing sparse signals from multiple measurement vectors. The typical algorithms include l1 -SVD [2], CS-MUSIC [3], SA-MUSIC [4], OMP [5] and SBL [6–8]. All of these algorithms construct a complete dictionary. If the estimated DOA angle falls on the dictionary, the estimation accuracy will be better. In practice, however, it is often difficult to meet this requirement. When the dictionary grid is too dense, it will increase the amount of computation. In order to solve this problem, a sparse Bayes Algorithm (OGSBI, Root-SBI) is proposed in [9, 10]. Since the computational load of sparse Bayes Algorithm is still very large, literature [9] reduces the computational load by SVD method. Literature [11] improves the estimation accuracy of DOA by combining unitary transform with MP Algorithm. Literature [12] combines the ideas of Literature [9] and [11], and combines unitary transform with OGSBI algorithm to improve the DOA estimation accuracy of the Algorithm. In OGSBI Algorithm, the idea of DOA off-lattice estimation is to make first-order linear approximation between the grid and the actual DOA angle, and to approach the actual DOA angle gradually by iterative method. Therefore, this algorithm is a biased estimate in theory. The Root-SBI Algorithm proposed in [10] can estimate the actual DOA of the target by solving the roots of polynomials, and its estimation performance is better than that of OGSBI Algorithm. In this paper, the unitary transform is applied to the Root-SBI Algorithm, and the unitary transform RV-Root-SBI Algorithm is proposed. The theory and simulation results show that the DOA estimation performance of this algorithm is better than that of the original algorithm.

2 The Related Mathematical Basis 2.1 Unitary Transformation Theory According to Literature [11], there is the following Theorem: Theorem 1: If a matrix is a central hermitian Matrix, then U PH AU Q is a real matrix. In theorem 1, matrix U P and U Q are unitary matrix. And when P is even, [ ] 1 I iI UP = √ 2 J −i J

(1)

Matrix I is identity matrix with dimension of P/2. And when P is odd, ⎡ ⎤ I 0 iI 1 ⎣ √ UP = √ 0 2 0 ⎦ 2 J 0 −i J

(2)

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Matrix I is identity matrix with dimension of (P − 1)/2. Matrix U Q has the same form as U P . | | Theorem 2: For any matrix Y , the constructed matrix Y J P Y ∗ JQ is centrohermitian. In theorem 2, Y is a matrix with dimension of P × Q, and J P is an exchange matrix with dimension of P × P. matrix JQ has the same definition as J P . According to theorem 2, formula 10 |can be constructed as a centro-hermitian | matrix, and then the augmented matrix Y J P Y ∗ JQ can be converted to a real matrix by theorem 1. | | Y = Y J P Y ∗ JQ

(3)



Y = U PH Y U Q

(4)

2.2 Array Signal Model For far-field narrowband signals, the received signal of ULA Array can be expressed as: Y (t) = A(θ )X (t) + N (t)

t = 1, 2, . . . T

(5)

where Y is the signal receiving array, M × T dimensional Matrix, M is the number of elements, T is the number of snapshots. X is an incoherent cell, a matrix of K × T dimensions, and K is the target number. N(t) is a Additive white Gaussian noise. A(θ ) is expressed as: T  2πd A(θ ) = [a(θ1 ), a(θ2 ), . . . a(θk )], a(θk ) = 1, vθk , . . . , vθ(M−1) , vθk = e j λ cos(θk ) k where the array element spacing is d = λ/2 and θk ∈ [0◦ , 180◦ ].

3 The Proposed Method For a uniform linear array model, the receiving signal model is shown in formula (5), and According to formula (4), the formula (5) can be converted into real numbers. And formula (4) can be seen as a Sparse Bayesian Model with Sparse dictionary expressed as U PH A(θ ) to replace the steering vector matrix. And then DOA can be estimated by sparse Bayesian algorithm. Sparse Bayes is calculated as follows:

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The signal model is rewritten as equation

Y = U PH Y U Q

(6)

Initialization the parameters of the algorithm with α0 = 0, δ (0) = 0, ρ = 0.01, a = 1, b = 0.001, l = 0. β = 0, ϕ(β) = U PH ϕ(β). We calculate ∑ K and μ K by

−1 H −1 ∑ = βA A + △ , θ θ



μt = β∑ A H yt , θ

△ = diag(δ)

(7)

t = 1, 2, . . . T



(8)

And update δ and β by

δines β new =

=

−T +

/

T 2 + 4ρ

T t=1

[ t ]

ii

(9)

ρ

T ∗ M + (a − 1) 2 T    b + t=1  yt − A μt  + T ∗ tr (A ∑ A H ) θ θ 2 θ



(10)



 where ρ = 0.01 ⊃ and  μt (μt ) H + . The above parameters are updated by iterative method until the satisfactory precision is obtained. Finally, by searching for the angle at which the peak of the energy spectrum corresponds, an estimate of the DOA can be obtained. The energy spectrum is calculated as follows:    1  n 2 (11) P n = E X  2 T





n

where X represents the estimated value of x corresponding to the nth grid, the nth row of the signal recovered through sparsity. The above algorithm is on-grid SBL which the estimation performance decreased. An off-lattice Algorithm is often used to solve this problem. The Steering Vector Matrix in the signal model is converted to





a(θk ) ≈ a(θ n k ) + b(θ n k )(θk − θ n k )





(12)

where b(θ n k ) = a ' (θ n k ) and θ n k is the grid angle closest to the signal source location θk . In [10], the method of finding roots through polynomials is an unbiased estimation without noise. The Algorithm is as follows:

A DOA Estimation Algorithm Based on Unitary Transform

(U PH ai' ) H [U PH ai

543

T T     2 (μti  + γ ii ) + T γ ji (U PH a j ) − μ∗ti · y t−i ] = 0 (13)

t=1













j/=i



t=1



φ (i )





ϕ (i )

 ai is the ith column of A ; μti is the ith cell of μt ; γi, j is the cell in with ith θ  ' H row and jth column. yt−i  yt − j/=i μti a j and ai  dai /dv . (·) represents θt conjugate transpose. The formula (13) can be transformed into the following form:



⎡ ⎢ ]⎢ ⎢ v , 1, v −1 . . . , v −(M−2) ⎢ ⎢ θt θt θt ⎢ ⎣

[







M(M−1) (i) φ 2 ϕ2(i) 2ϕ3(i )

.. . (M − 1)φ2(i)

⎤ ⎥ ⎥ ⎥ ⎥=0 ⎥ ⎥ ⎦

(14)

Equation (13) is a problem of finding the root of a polynomial, which has (M-1) roots in the complex plane. By definition, the actual root should be in the unit circle. But because of the noise, the root of the polynomial is probably not on the unit circle, so we choose the root nearest to the unit circle as our estimate. The actual DOA angle can be updated according to formula (14)

new

θ i∗

= arccos



λ · angle(z i ∗ ) 2π d

(15)

4 Simulation 4.1 Simulation 1: DOA Estimated by Once A 7-element uniform linear array with incident angles of 40.3 degrees and 80.3 degrees and grid spacing of 4 degrees has a DOA estimation at 0 DB as shown in Fig. 1.

4.2 Simulation 2: RMSE Experiment 2 was to compare the performance of DOA estimation algorithms under different SNR. The incident angles of the uniform linear array with flow pattern of 7

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elements were randomly selected in the range of [60,70] and [100,110] by uniform distribution, the spacing between grids was 2 degrees, the signal-to-noise ratio was −5 db ~ 10 DB, the number of snapshots was 30, and 200 Monte Carlo experiments were carried out. The root mean square error is defined as: ┌ | N k  | 1  (θ i j − θi )2 RE MS = √ N k i=1 j=1

(16)

Figure 2 compares the performance of OGSBI, Root-OGSBI and RV-RootOGSBI. According to Fig. 2, the performance of the proposed algorithm is obviously better than the other two algorithms (Fig. 3).

4.3 Simulation 3: Probability of Resolution Versus SNR In Experiment 3, the success rates of OGSBI, Root-OGSBI and RV-Root-OGSBI were tested. The experimental conditions are as follows: the number of array elements is 7, the number of snapshots is 30, the incidence angle is randomly selected by uniform distribution in the range of [60,70], [100,110], and the search grid is 2 degrees. Conduct 200 Monte Carlo Experiments. It is defined here that the conditions for DOA estimation to be successful are:

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5 Conclusion Compared with the traditional algorithms, the lattice sparse Bayes Algorithm has obvious advantages in the case of small snapshots and low signal-to-noise ratio. In this paper, the Matrix unitary transformation technique is applied to the discrete sparse Bayes Algorithm, the Root-SBI Algorithm is improved, and the RV-Root-SBI

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Algorithm is proposed. The simulation results show that the proposed Algorithm has higher success rate and smaller root mean square error than the original algorithm.

References 1. Stoica P, Nehorai A (1989) MUSIC, maximum likelihood, and Cramer-Rao bound. IEEE Trans Acoust, Speech Signal Process 37(5):720–741 2. Xu X, Shen M, W X et al (2022) Direction of arrival estimation based on modified fast off-grid L1 -SVD. Electro Lett 58(1):32–34 3. Kim J, Li O, Ye J (2012) Compressive MUSIC: Revisiting the link between compressive sensing and array signal processing. IEEE Trans Inf Theory 58(1):278–301 4. Boot T, Nibbering D (2020) Subspace methods(Book chapter). Adv Stud Theoret Appl Econometrics 52:267–291 5. Bai GT, Tao R, Zhao J et al (2017) Parameter-searched OMP method for eliminating basis mismatch in space-time spectrum estimation. IEEE Trans Signal Process 138:11–15 6. O’Shaughnessy, RM (2020) Sparse Bayesian learning with dynamic filtering for inference of time-varying sparse signals. IEEE Trans Signal Process 68:388–403 7. Mecklenbrä GP, Christoph F, Santosh N et al (2020) DOA estimation in heteroscedastic noise with sparse Bayesian learning. Appl Comput Electromagn Soc J 35(11):1439–1440 8. Huang KD, Yang ZY (2021) Robust Sparse Bayesian learning for sparse signal recovery under unknown noise distributions. Circ Syst Signal Process 40(3):1365–1382 9. Yang Z, Xie L, Zhang C (2013) Off-grid direction of arrival estimation using sparse Bayesian inference. IEEE Trans Signal Process 61(1):38–43 10. Dai J, Xu W, Chang C (2017) Root sparse Bayesian learning for off-grid DOA estimation. IEEE Trans Signal Process Lett 24(1):46–50 11. Yilmazer N, Koh J, Sarkar TK (2006) Utilization of a unitary transform for efficient computation in the matrix pencil method to find the direction of arrival. IEEE Trans Antennas Propag 54(1) 12. Yang GAO, Jun-li CHEN, Guang-li YANG (2017) Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning. J Commun 38(6):177–182

3D Animation Character Simulation Technology Based on Swarm Intelligence Algorithm Chulei Zhang

Abstract The illusory world built by people through 3D animation simulation technology allows people to experience the incomparably beautiful audio-visual experience that the real world brings us, and the new 3D art has also found a stage for its own expansion. There is no doubt that 3D animation based on computer technology has gradually surpassed 2D animation and become the general trend of future animation development. This paper takes the design of 3D animation characters as an example, and uses 3D MAX software to build a 3D animation character simulation system based on particle swarm algorithm. Animated character models. Keywords Particle swarm algorithm · 3D animation characters · Simulation technology · 2D animation

1 Introduction In the field of 3D animation character simulation, character behavior modeling is a very important research topic. Whether the character behavior in the character simulation result is natural and intelligent depends largely on whether the character behavior model is suitable. Character behavior model is the core component to control the intelligent behavior of virtual human, so the modeling of 3D characters must pay attention to whether the behavior of characters can bring people a sense of reality. At present, the research on 3D animation character simulation technology has achieved good research results. For example, a scholar uses the IBR method to collect the pose images of each character model from various viewpoints around it for each frame, and then play them during real-time simulation. The IBR method can reduce the overhead of drawing a 3D human geometric model by using a 2D C. Zhang (B) Changchun Humanities and Sciences College, Changchun 130117, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_60

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impostor, but the user’s viewpoint during real-time simulation and the camera’s viewpoint cannot be exactly the same when capturing photos, so this method inevitably produces abnormal shaking of characters [1]. The skeletal system based on Maya’s adaptive mesh proposed by relevant personnel has good bendable characteristics, can automatically change the shape of the mesh according to the current animation character pose, and can keep the total area of the mesh coverage area unchanged. The meshes between each joint point are smoothly joined, which is very suitable for the deformation of two-dimensional character images. In terms of realism of deformation results, the continuity of curve derivatives can be controlled to ensure the smoothness of deformation and bending regions; in terms of real-time deformation calculation, real-time deformation calculation can be ensured through localization of influencing factors and GPGPU graphics hardware acceleration [2, 3]. Character shaping in 3D animation simulation technology has reached a certain height, and the development of animation is more prosperous. Not only that, the unique charm of animation characters can also promote the development of related industries. This paper first expounds the concept of two swarm intelligence optimization algorithms, namely ant colony algorithm and particle swarm algorithm, and then introduces the 3D animation character simulation technology, and then designs a 3D animation simulation system to optimize the characteristics of animation characters through particle swarm algorithm to build a mannequin. Finally, this paper analyzes the realization of human–computer interaction of the simulation system, and uses the system to create the proportion of animated characters by referring to the proportion of real characters.

2 Related Algorithms and Technologies 2.1 Swarm Intelligence Optimization Algorithm 1. Ant colony algorithm Ant colony algorithm is a biometric random search algorithm. Ants move differently, making it more difficult to find food individually, but when the entire colony works together, the results and efficiency of foraging are different. As the number of steps on the road increases, the more pheromone remains, other ants can find the direction of the ant colony based on the pheromone. Ant colony algorithm usually chooses a path without any prior knowledge, and the path search gradually moves towards the optimal path [4]. 2. Particle swarm algorithm The particle swarm algorithm regards each bird in the group as a particle, and uses mathematical techniques to calculate the position of each bird in the search space according to the particle’s position at that time. The search space of the flock corresponds to the movement interval of the particles, the food at the search position is related to the best result of the objective function to be captured, and

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the distance between the bird and the food is used. As a judgment on the pros and cons of each particle solution. Therefore, the more closely a region is related to the optimal outcome of the objective function, the better the outcome for that region, which is often referred to as fitness. The current historical state of each particle in the population is considered to be the best state of each individual, and all particles in the population currently find the historical optimal position as the global optimal position. During the whole path planning process, due to the constant competition and cooperation of each particle, the group is constantly approaching the optimal solution [5]. Using particle swarm optimization to solve optimization problems is like capturing the position information of a bird flying in the sky and implementing a mathematical model function called particles. The following is the particle update speed and position formula: l l l l l Mi,l+1 j = Mi, j + e1 d1 ( pi. j − Ni, j ) + e2 d2 (g j − Ni, j )

(1)

l l Ni,l+1 j = Ni, j + Mi, j

(2)

Among them, e1 is the individual learning factor, e2 is the social learning factor, d1 and d2 are random numbers uniformly distributed in [0,1], k represents the number of iterations, p and g are the optimal position solutions, and M represents particle update Velocity, N stands for update position.

2.2 3D Animation Character Simulation Technology 1. Image-based modeling and rendering Perceiving facial expressions is one of human instincts, so traditional 3D image technology will make facial expressions realistic a very difficult task. An obvious way to do this is to use image-based tools. It bypasses most of the difficulties through human intervention and restrictions. The general approach is to 3D warp the head model with existing 2D image textures [6]. One of the most successful illusions produced by post-processing video or motion picture film is 2D warping. Exaggerated distortion caused by deformation between two objects will be produced in one image. 3D warping techniques can be used in computer graphics. 3D deformation techniques rely on general polygon mesh head models, involving special head photos and special expressions of the head. Although it is deformed to adapt to different expressions, because the captured expressions are texture-mapped to the same type of mesh, the deformation of facial expressions is simple, and it is no longer necessary to specify the corresponding target. This approach effectively uses 2D warping techniques in 3D space, transforming a 2D plane onto a 3D mesh [7, 8]. 2. MPEG-4 standard

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The facial model of MPEG-4 is a collection of a series of facial parameters FP and facial animation parameters FAP, each FAP corresponds to a special facial action, which is used to deform the facial model in a neutral state. The facial model deformation corresponding to some FAP values at a certain moment can generate an animation sequence, and a complete expression animation is a series of animation sequences [9]. For a specific FAP, the given FAP value indicates the magnitude of the corresponding motion. For an MPEG-4 terminal that uses its facial model to interpret the FAP, it must predefine animation rules to generate facial motions for each FAP. The terminal either uses its own animation rules, or downloads a face model and the corresponding animation definition table FAT.

3 3D Animation Character Simulation Design System Based on Particle Swarm Algorithm The system was developed on the 3D production software 3D MAX. The system can be divided into three modules by function, as shown in Fig. 1. Among them, the expression animation module is realized on the basis of the real touching facial expression model. When constructing a 3D animated character image in this system, the particle swarm algorithm is used to optimize the character feature points. During modeling, the morphological points and facial feature points of the model are outlined according to the animated character image. The particles in the particle swarm are equivalent to these By connecting the feature points, the basic shape of the animated character can be designed, making the three-dimensional character image more threedimensional and full [10]. 1. Character Action Module Human body structure kinematics scientifically explains how various parts of the human body change during exercise through in-depth analysis of the human body structure. In 3D animation, although the characters are virtual, their behaviors and movements have human characteristics. And with the continuous

3D animation simulation system

Character Action Module Fig. 1 System architecture

Expression animation module

Human-computer interaction module

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improvement of animation production level, the modeling of characters in 3D animation is developing more and more towards the direction of realism, which requires the movement of characters to be more realistic and more in line with the laws of motion of real people. Therefore, in the animation production process, the key frame of the character should also follow the principle of human structure kinematics in form [11]. The principle of balance: The balance of a character refers to that the character’s body is in a stable and balanced state during the movement process. Whether a character in motion can be in a state of balance is the most basic requirement for judging the rationality of a character’s action in animation. Only in a state of balance can the characters be able to complete some vivid and rich body movements according to their own subjective wishes [12]. Three-axis principle: It refers to the understanding of the laws of human body dynamic modeling from the three parts of the human head, chest, and hip, and the amplitudes of the three axes: oblique, positive, and lateral. The various poses and expressions of the human body are formed by the movement and turning of these parts around these three axes. Because of these three axes, like the three axes of the three-dimensional space coordinate, they are perpendicular to each other. Therefore, the magnitude of the movement and turning of a certain link or limb of the human body around a certain axis must be reflected in the inclination of the other two axes perpendicular to it. Therefore, as long as the axis movements of the three main links of the human body, the head, the chest, and the crotch in the dynamics are analyzed, no matter how the dynamics of the human body changes, it can be grasped [13]. 2. Expression animation module In the OpenGL development environment, the double buffer mechanism is adopted, and the expression animation system is realized based on the timing mechanism of MFC. The double buffering mechanism ensures smooth rendering speed, and the frame rendering rate is 25 frames per second, which can ensure smooth operation of the picture. The animation displays common facial expressions, and the user can select the facial expressions to be displayed through the menu. 3. Human–computer interaction module This module of the system provides the functions of rotating, zooming, and panning the model, so as to facilitate the user to operate and demonstrate the model, and observe the generated model from different angles. The human– computer interaction module provides manual annotation of the feature points of the input front and side photos.

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4 System Implementation and Application 4.1 System Implementation 1. Realization of human–computer interaction The human–computer interaction module requires the input image to be in 24-bit BMP format with unlimited length and width. The frontal face photo in the image must be frontal and located in the middle of the image. The height of the image is the height of the face in the photo. The photo and profile photo should be taken at the same focal length. The system provides a good human–machine interface. On the one hand, the model can be controlled through the mouse, and the mode can be opened through a series of shortcut keys. For example, Ctrl + 1 selects the model entity mode, Ctrl + 2 selects the model linear mode, Ctrl + 3 selects the model point mode, the shortcut key F displays the front model, and the shortcut key P displays the side model, thus providing a good human–machine interface [14]. 2. Expression of emotions of animated characters There are many ways to express emotions. In live-action film and television works, the most common way for actors to express an emotion, such as happiness or sadness, is through changes in facial expressions. In animation works, animated characters often convey different emotions through facial expressions, but animated characters are different from real people after all. A real person can perform with rich facial changes without any scruples, while an animated character is virtualized by a computer. In addition, in a large panorama or distant view, due to the relatively small picture, it is difficult for the audience to even see the faces of the animated characters clearly. Then at this time, the expression will lose the function of conveying emotion. At this point, we need to use the body movements of the animated characters.

4.2 System Application As shown in Table 1 and Fig. 2 are the parameters of the human body model. This parameter is the basic shape of the characters measured on real people such as adult men, adult women, and children. The design of 3D animation characters should be designed with reference to the proportions of real characters, so as not to Causes the proportion of animated characters to be distorted, and the structure is not coordinated. Therefore, according to this parameter, the prototype of the three-dimensional animation character can be designed in the simulation system.

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hip circumference chest circumference Arm length height

waistline item circumference crotch height

group

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adult female

adult male

parameter Fig. 2 Parametric information

5 Conclusion To achieve realistic effects of three-dimensional animated characters, it is necessary to model the shape and image of the animated characters through high-tech technology. In this paper, a 3D animation simulation system is designed, the facial expressions and gestures of 3D animation characters are designed by using each module of the system, and the proportion design of 3D animation characters can be completed by imitating the morphological parameters of real characters, breaking through the technical barriers of 2D animation and making the proportions of the animated characters are more coordinated and more in line with the three-dimensional concept.

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References 1. MortazaviAli (2020) Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm. Adv Eng Softw 142(Apr.):102790.1–102790.13 2. Shimura T, Nagasawa T, Shikazono N et al (2021) Electrochemical reaction mechanism of LSM-YSZ composite cathode based on 3D simulation of oxygen diffusion and oxygen labeling experiment. ECS Trans 103(1):1339–1349 3. Eom H, Han D, Shin JS et al (2019) Model predictive control with a visuomotor system for physics-based character animation. ACM Trans Graph 39(1):1–11 4. Pandit D, Zhang L, Chattopadhyay S et al (2018) A scattering and repulsive swarm intelligence algorithm for solving global optimization problems. Knowl Based Syst 156(Sep.15):12–42 5. Park S, Ryu H, Lee S et al (2019) Learning predict-and-simulate policies from unorganized human motion data. ACM Trans Graph 38(6):1–11 6. Lee M, Hyde D, Bao M et al (2018) A skinned tetrahedral mesh for hair animation and hair-water interaction. IEEE Trans Visual Comput Graph PP(99):1–1 7. Santesteban I, Otaduy MA, Dan C (2019) Learning ||| ased animation of clothing for virtual Trymn. Comput Graph Forum 38(2):355–366 8. Choi M, Wi JA, Kim TH et al (2021) Learning representation of secondary effects for fire-flake animation. IEEE Access PP(99):1–1 9. Tarokh M, Ho HD (2019) Kinematics-based simulation and animation of articulated rovers traversing uneven terrains. Robotica 37(6):1057–1072 10. Peng XB, Abbeel P, Levine S et al (2018) DeepMimic: example-guided deep reinforcement learning of physics-based character skills. ACM Trans Graph 37(4CD):143.1–143.14 11. Won J, Lee J (2019) Learning body shape variation in physics-based characters. ACM Trans Graph (TOG) 38(6):1–12 12. Wang Y, Wang D, Pang W, Miao C, Tan AH, Zhou Y (2020) A systematic density-based clustering method using anchor points. Neurocomputing 400:352–370 13. Wang Y, Wang D, Zhang X, Pang W, Miao C, Tan AH, Zhou Y* (2020) McDPC: multi-center density peak clustering. Neural Comput Appl:1–14 14. Parmar M, Wang D, Zhang X, Tan AH, Miao C, Jiang J, Zhou, Y*. (2019) REDPC: a residual error-based density peak clustering algorithm. Neurocomputing 348:82–96

The Construction of Power Grid Operation Monitoring Platform Driven by High-Tech Information Technology Chengfang Gao, Jinman Luo, Haobo Liang, and Xiaoji Guo

Abstract The power grid operation monitoring system relies on a variety of hightech information technologies, aiming at the management of power companies, to monitor the company’s operating business data and information. Based on the construction of the power grid operation monitoring platform, this paper focuses on the analysis of operation and maintenance material requirements, and uses the IOWHA algorithm to predict the power grid operation and maintenance material requirements. Small, indicating that the use of the power grid operation monitoring system can effectively predict future business needs by analyzing historical business data. In addition, this paper uses Web to realize the system user interface, and submits the monitoring results to the system client through Apache and CGI interface, so that the system can complete the business process under the high-tech information technology. Keywords Power grid operation monitoring · High-tech information technology · IOWHA algorithm · Operation and maintenance materials

1 Introduction Through the power grid operation monitoring platform, various business tasks can be effectively detected, such as troubleshooting power grid operation failures, performing power grid maintenance work, and managing enterprise data resources. Therefore, this paper hopes to rely on the support of the existing business system and use high-tech information technology to establish a multi-functional power grid operation monitoring platform to provide support for the comprehensive monitoring detailed data and operation data analysis of the operation monitoring business. C. Gao (B) · J. Luo · H. Liang · X. Guo Dongguan Power Supply Bureau, Guangdong Power Grid Co., Ltd, Dongguan 523000, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_61

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Nowadays, the research on the construction of power grid operation monitoring platform driven by high-tech information technology has achieved fruitful results. For example, in the process of modern enterprise operation, power grid companies build and deploy centralized management and control analysis departments. Through measures such as monitoring the overall operation data of power companies and analyzing business operations, they can provide corresponding company operation efficiency analysis reports for the company’s senior management to assist company strategy. Decision direction and put forward opinions and suggestions [1, 2]. There are many management lines in the power enterprise, and the business of each branch cannot be effectively and smoothly connected. In order to improve the management level of the entire group and optimize the existing management resources, the company re-analyzed the management system and resources of each business, and proposed to build an enterprise comprehensive monitoring center. Comprehensive business analysis management system and business analysis talent system, comprehensively develop the company’s operational capabilities, and assist the improvement of power grid enterprise management [3, 4]. Although the power grid operation monitoring platform has realized real-time monitoring in business data analysis, talent management, etc., the detection function of the current monitoring platform is not perfect. This paper first analyzes the necessity of building a power grid operation monitoring platform, then proposes the non-functional requirements for designing the operation monitoring system, and then uses various high-tech information technologies to design the system structure, for example, the system page integration design adopts B/S structure and realizes it through Portlet. Finally, the operation monitoring system of this paper is applied to the material monitoring of operation and maintenance of power enterprises. Based on the IOWHA algorithm, the material consumption of the previous year is used to predict the consumption of the next year, so as to realize the rational allocation of resources.

2 Necessity and Demand Analysis of Power Grid Operation Monitoring Platform Construction 2.1 Necessity of Construction of Power Grid Operation Monitoring Platform 1. The need for lean management Through the construction of a unified information-based operation system, based on the integration of various operating data of each power station and each system, using the efficient interaction between the handheld terminal and the production management platform, connecting with management systems such as PMS/OMS, and combining with the experts accumulated in the system The

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standardization of work cards, job tickets, etc. and the optimization of business processes through improved production plan management have become the practical needs of lean management [5]. 2. The need for scientific decision-making In the past, the production management of electric power majors and the decision-making of real-time fault handling depended more on personal experience and knowledge, and did not systematically organize, summarize and refine the knowledge and experience of various experts, historical data of equipment operation, and various equipment. This kind of failure plan has been sublimated into an organic and overall scientific decision-making basis; in addition, there is no standardized, informatized, networked mechanism and platform to assist scientific decision-making; It is impossible to form a joint force when it is really needed to provide strong support for the production management and real-time fault handling of the electric power profession [6]. When a unified data integration and sharing platform is established, various equipment operation and trend analysis reports can be generated according to management needs through statistics, analysis and mining, and combined with production carrying capacity management and control, so as to assist all management levels of the company to effectively Formulate work plans, guide production and equipment procurement, evaluate equipment operation status, etc. [7]. 3. The need for state-based maintenance The construction of the power grid operation monitoring platform can effectively integrate GIS ultra-high frequency partial discharge monitoring, switchgear monitoring, DC equipment status monitoring, protection device status information analysis, auxiliary equipment status monitoring and other information that can effectively reflect equipment status. The real-time operation monitoring data of equipment and the company’s existing PMS/OMS system can better realize the efficient development of equipment condition evaluation and condition maintenance work [8]. 4. The need for data sharing Implementing “lean management” requires collecting a large amount of basic data on production management and equipment operation, and it is necessary to standardize and organically manage various production, operation, and management information of power companies. Upward, it can provide the data center with professional data of substation in a timely and standard manner according to the requirements of the superior, and can realize the association with the power grid control center, PMS, OMS and other systems, data can be shared, business processes can be connected, and system functions can be linked; On the level, it can share data in both directions with power transmission, cable, and local companies; on the down, it can collect and centralize the data and information of each department, each plant, and each system of each substation company, and through data transmission and Shared means can better support safety task management, on-site work management and control, and real-time fault handling [9, 10].

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2.2 Non-Functional Requirements of Operation Monitoring System The design of non-functional requirements is mainly aimed at the performance requirements of the system. In accordance with the guidelines stipulated by the state, some requirements that meet the Fujian power grid are formulated. By combining the ODS National Grid-level data center with the typically designed BW system, the three-tier architecture required for data mining is completed. Therefore, the non-functional requirements of the system need to include the following requirements: Versatility: As a part of the basic system of the enterprise, data mining should have a more standard positioning in the system. From a business perspective, the technical application of data mining itself is not directly related to it, so it must be used for all sub-systems. The system produces general principles. After using the new system with the business expansion, the data mining of the new system should also be able to meet the requirements of the current system, and meet the same technology type for calculation and display, so that the investment cost caused by secondary development can be reduced in deployment [11]. Adaptability: It is another important non-functional requirement of the design system to keep design margins as much as possible. The system needs to reserve room for future expansion capabilities. After all, computer technology is always developing gradually, and it is indispensable to maintain the adaptability of the system at a high level. Efficiency: The data mining effect for each link requires a large amount of data processing. As the business volume of the system increases, the system usage capacity caused by the increase of its users will inevitably be affected to some extent. At this time, some optimizations need to be carried out to maintain the high efficiency of the system.

2.3 IOWHA Algorithm IOWHA is developed based on the ordered weighted average (OWA) algorithm. OWA algorithm is a method of integrating discrete data, which can effectively gather discrete data information [12]. IOWHA Definition: Let < v1, a1 >, < v2, a2 > . . . < vm, am > be m two-bit arrays, let: gk (⟨v1, a1⟩, ⟨v2, a2⟩, . . . ⟨vm, am⟩) = ∑m

1

ki i=1 av−index(i )

(1)

Then the function gk is called the m-dimensional induced order weighted harmonic averaging algorithm formed by v1, v2, …, vm, which is represented by IOWHA. In the formula, vi is called the induced value of ai, and v-index(i) is The subscripts of the

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T i large numbers in v1, v2, …, vm are arranged in order, k = (k1, ∑mk2, . . . km) is the weight system of IOWHA, and the establishment condition is i=k ki = k, ki ≥ 0, i = 1,2, …,m. Theil inequality coefficient definition, let:

/

∑N t=k

τ=

( x1t −

∑N t=k

1

Ʌ

xt

)2 (2)

( x1t )2

Ʌ

xt is the combined predicted value, xt is the actual observed value, t = 1, 2, …, N. τ is the Theil inequality coefficient of the reciprocal sequence of xt and the reciprocal sequence of xt . Ʌ

3 Design of Power Grid Operation Monitoring System 3.1 System Interface Integration Design The business management system needs to open the access address of the corresponding function and open the system interface to realize the interface integration, which can minimize the impact on the meta-system. Usually, there are URL method and Portlet method to realize the interface integration. Through the open interface, the business system uses Portlet to extract the background data of the original business management system to realize interface integration. The company’s established business systems are mostly B/S structures. The operation monitoring system is integrated with the auxiliary analysis and decisionmaking system, and each business system is integrated by URL. Users do not need to log in repeatedly when switching to different system work pages. The operation monitoring system and the portal directory realize the single signon of users through the synchronization of user authentication information. At the same time, the portal directory integrates the operation monitoring (control) workbench display page through the interface integration method to realize the display of monitoring and analysis related information in the portal.

3.2 System Data Integration Design The current status of data management is that the data of each professional business management information system is managed in a decentralized manner, while the monitoring and analysis of the transportation supervision system needs to build a unified data management center. The most effective way to solve the current decentralized data management is by means of data integration. Data with inconsistent

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sources, inconsistent data standards, and inconsistent data formats are logically integrated for data monitoring and analysis in the operation monitoring system.

3.3 Monitoring Module Design Over time, the network structure and business of the Internet of Things for power grid equipment will change. In order to adapt to the changes in network structure, network equipment and business, the monitoring module of the on-site monitoring platform is implemented through plug-ins and supports customizable functions. The actual situation of the Internet of Things network of grid equipment to increase and decrease services. When a new device needs to expand a new monitoring service, it is very convenient to add a new monitoring plug-in; when a certain detection service is not needed or the detection service is redundant, it can also be quickly deleted from the management platform.

4 Realization and Application of Power Grid Operation Monitoring System Under High-Tech Information Technology 4.1 Implementation of Web-Based Operation Monitoring System For the display of monitoring and control results, the UI (user interface) of the entire management platform is implemented using Web. After the core main program executes the monitoring, the control and monitoring results are divided into two parts for processing. For the monitoring part, a caching mechanism is used to save the monitoring results to a cache belt, and to control the execution of commands, the execution results need to be submitted to the user in time, no longer cached, and directly submitted to the client through the Apache and CGI interfaces. Because the monitoring has a certain periodicity, the client refreshes the monitoring status also has a certain periodicity. During the refresh cycle, the client extracts the corresponding monitoring results from the cache of the core main program through Apache, and implements it on the client. The image display of the monitoring results, the specific process is shown in Fig. 1.

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cache

user (client)

HTTP

561

Monitoring results

Apache (server)

core main program

control commands Fig. 1 System implementation process

4.2 Application of Power Grid Operation Monitoring System 1. Trend analysis of operation and maintenance materials usage The power grid operation monitoring system designed in this paper can be used to monitor the operation and maintenance materials of power companies. Figure 2 shows the use of power cables and overhead insulated wires obtained by a power company using the operation monitoring system in a certain year. In that year, the overhead insulated wires shared 1366.05 km and the power cables shared 344.69 km.

Fig. 2 The trend of power grid operation and maintenance materials usage

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Table 1 Forecast forecast actual usage for next year Overhead Insulated Conductor power cable

Number of forecasts

The actual amount

Error

2968.28

3049.75

2.67%

774.33

739.61

4.69%

2. Operation and maintenance material consumption forecast Use the system to monitor the use of materials for operation and maintenance in a certain year, and then use the IOWHA algorithm to predict the use of materials in the next year through the use of the year. The results are shown in Table 1. As shown in Table 1, by predicting the consumption of the next year based on the material consumption of the previous year, the error between the predicted consumption and the actual consumption of the two materials is within 5%, indicating that the monitoring system in this paper has a high prediction accuracy in predicting the material consumption of operation and maintenance.

5 Conclusion This paper preliminarily explores the construction theory and method of the power grid operation monitoring platform, studies the IOWHA algorithm based on Theil inequality coefficient, and designs the power grid operation monitoring system by using data mining technology and high-tech information technology such as Web. The material requirements for operation and maintenance are predicted, which effectively helps users deal with actual business problems, and accumulates practical experience for further improving and improving the operation monitoring level of power grid enterprises in the future.

References 1. Kazmina IV (2020) The impact of digital information technology on improving the competitiveness of high-tech enterprises. In: Proceedings of the Voronezh state university of engineering technologies 82(2):174–180 2. Kazmina IV (2020) The main trends in the development of information and telecommunication technologies when creating high-tech products. In: Proceedings of the Voronezh state university of engineering technologies 81(4):291–297 3. Kumar SD, Vinodh SV, Praveen J et al (2018) Renewable grid monitoring and control by Internet of Things(IoT). Int J Pure Appl Math 119(15):1749–1755 4. Mohammed NS, Selman NH (2020) Home energy management and monitoring using ubidots platform. Al-Furat J Innovat Electr Comput Eng 1(3):14–21

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5. Chukkaluru SL, Kumar A, Affijulla S (2022) Tensor-Based dynamic phasor estimator suitable for wide area smart grid monitoring applications. J Control Autom Electr Syst 33(3):955–964 6. Sufyan M, Zuhaib M, Rihan M (2020) An investigation on the application and challenges for wide area monitoring and control in smart grid. Bull Electr Eng Inform 10(2):580–587 7. Silva-Ortega JI, Valencia-Ochoa G, Escorcia YC (2018) Monitoring electromagnetic fields and safe operation levels in electrical power transmission lines. Chem Eng Trans 67(67):715–720 8. Cai L, Thornhill NF, Kuenzel S et al (2018) A test model of a power grid with battery energy storage and wide-area monitoring. IEEE Trans Power Syst PP(1):1–1 9. Kaushal J, Basak P (2018) A novel approach for determination of power quality monitoring index of an AC microgrid using fuzzy inference system. Iranian J Sci Technol Trans Electr Eng 42(4):1–22 10. Meena V, Pant V (2020) Dc micro grid monitoring and protection. Int J Sci Res 5(5):1644–1649 11. Kesavan T, Lakshmi K, Gnanamalar SR et al (2018) Local search optimization algorithm based monitoring and controlling of virtual power plant for distribution network. Int J Pure Appl Math 119(12):1851–1863 12. Kumar C, Kundu RP, Chitturi S et al (2018) Synchrophasor: key insight into the power system dynamic monitoring. Water Energy Int 61(3):30–37

Design of Tag Recommendation Algorithm in Travel APP Intelligent Guidance System Fei Meng

Abstract With the rapid improvement of Internet technology, it has also brought about changes in people’s travel patterns. Internet technology has brought great convenience to the transmission of travel information, which brings convenience to people’s travel. The more intelligent software is travel APP. At present, there are many APPs for travel services in the market, and the competition is extremely fierce. The research purpose of this paper is the research and design of the tag recommendation algorithm in the travel APP intelligent guidance system. In the experiment, the questionnaire survey method and the label recommendation method are used to construct a reasonable and systematic evaluation of the satisfaction index system of M company’s travel APP service. The experimental results show that the overall satisfaction score of M company is relatively stable. Keywords Travel APP · Intelligent guidance system · Recommendation system · Tag recommendation algorithm

1 Introduction In recent years, the improvement of the tertiary industry has also brought many opportunities in the service industry. At the same time, tourism has improved rapidly. On the one hand, people have more and more disposable income, which provides the economic and social foundation for the improvement of tourism, and people can travel anytime, anywhere. People’s demand for travel APP is more and more diversified, and at the same time provide users with good use [1]. At present, the service quality of each APP is different, which affects the experience of customers. Users are not satisfied with the service of a certain travel APP, which may directly affect the benefits of the travel company. Afzaal M focus that F. Meng (B) Shandong Institute of Commerce and Technology, Jinan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_62

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travel reviews is a source of information for travelers about tourist attractions. Unfortunately, some reviews are irrelevant and become noisy data. Feature-based impulse classification methods have shown great promise in noise suppression. However, there is not much research on manual aspect recognition such as irregular, rare and basic recognition, leading to misclassification. This paper proposes a feature-based sensory classification technique that can not only identify points very well, but also perform segmentation tasks with high accuracy. The process has been implemented as a mobile application to help travelers find the best restaurant or hotel in a city, and the performance has been evaluated by performing tests on real data sheets with good results [2]. The interaction between cultural heritage, tourism and education in Rui N should be studied in the area made from the macro or micro view of the urban fabric. The site of Shiyitang in Lugang has been investigated. An augmented reality travel system is improved on a smartphone-based platform for a new application scenario using 3D scenes transformed from space clouds to portable interactive units. ARTS includes a real-time environment visualization module, a scene rotation module, and a real-time guided imagery module. The system facilitates place generation, prediction and superimposition, annotation, and visual customization by using software tools improved with ARKit [3]. Based on this, it is particularly necessary to comprehensively improve the travel APP service to improve user satisfaction. This paper studies the definition and improvement of mobile applications, divides the categories of travel APPs, studies and analyzes the theory and working principles of recommendation systems and tag recommendation algorithms. In the experiment, the questionnaire survey method and the label recommendation method are used to construct a reasonable and systematic evaluation of the satisfaction index system of M company’s travel APP service. The experimental results show that the overall satisfaction score of M company is relatively stable, and there is no extreme situation. All satisfaction scores are between 3.4211 and 3.6414, which means that the overall satisfaction of M company’s travel APP fluctuates less.

2 Research on Tag Recommendation Algorithm and Travel APP Intelligent Guidance System 2.1 Mobile Application Definition An APP refers to an application that appears on a mobile phone or other mobile device. Mobile APPs can be divided into paid APPs and free APPs according to the standard of whether to pay or not [4]. The domestic mobile application market mainly includes Apple’s APP Store and the Android download market of the Android system. There are also a variety of different applications in different application malls, which are rich in variety and cover all aspects of life. However, the improvement of tourism APP still needs continuous enrichment. The tourism application improvement of mobile APP is different from other APPs. It not only needs positioning services,

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but also has certain requirements for catering services. It needs a comprehensive database system and positioning system. When people choose related mobile travel APPs, they will choose different APPs according to their own needs and preferences. Only by constantly optimizing themselves and focusing on consumers, modern APPs can improve mobile travel APPs that are popular in the market to the greatest extent.

2.2 Mobile Application Improvement The improvement of mobile applications is a continuous process of twists and turns. From the original OTA (Online Travel Agency), through the overall planning of browsing information, commodity platforms, tourism government, transportation modes, and accommodation service systems, it finally integrates A small mobile APP has achieved a huge breakthrough and innovation in the tourism services operated by traditional brick-and-mortar stores [5]. Traditional travel websites that provide travel services and mobile Internet mobile APPs together form an OTA system. The former is based on hotels and airlines. Through the Internet, a series of comprehensive websites, vertical website recommendation websites and review websites have been established to carry out the tourism system. Comprehensive inclusion. With the improvement of the times, the mobile APP has replaced the original website model to a certain extent. Behind this trend is the increase of agency networks, consumers’ comprehensive pursuit of tourism needs and the popularity of mobile phones and mobile APPs. Modern has gradually established a travel service system with smartphones as mobile terminals. On the basis of inheriting the traditional PC-side website, it has greatly improved and improved the service capabilities, technology level, functional services and personalized customization of online travel. Innovation has completed the structural adjustment and industrial upgrading for the improvement of mobile phone applications [6].

2.3 Division of Travel APPs In the initial design of the existing travel APP, the user groups were divided from the strategic level, and different function choices were made according to the commonality of the user groups to meet different user experience needs. It can be divided into four categories: sharing category (SNS social service), information category (UGC user-generated content + SNS social service), reservation category (OTA online travel agency + O20 e-commerce model + group purchase), transportation category (LBS radio information network to obtain the location information of mobile terminal users) [7]. The functional division of the travel APP is shown in Fig. 1.

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Users

APP location

Sharing class

Information class

Functions

Reserooking class

Traffic class

Fig. 1 Functional division of the tourism APP

2.4 Recommendation System and Tag Recommendation Algorithm 1. Recommendation system A recommender system is an information filtering system that predicts a user’s rating or preference for an item based on the user’s hobbies and purchasing behavior [8]. Since each person has different interests, the recommended results also vary from person to person, and the recommended results received by different user groups are often different. The general recommendation process of the recommendation system is shown: obtaining user preferences; establishing user interest model: matching the feature attributes of the item itself and the user’s preference for the item, constructing the user interest model; using algorithms to recommend [9]. At present, there are mainly several recommendation algorithms: collaborative filtering recommendation algorithm, content-based recommendation algorithm, knowledge-based recommendation algorithm and hybrid recommendation algorithm. 2. Theory and working principle of tag recommendation algorithm The theoretical recommendation system of the recommendation algorithm refers to a tool that mines the different points of interest of each user from the historical records of a large number of users, and recommends specific products to each user, so as to achieve personalized and customized services. The recommendation system is proposed to address the problem of information overload, and is improved based on information retrieval technology and information filtering technology, but it is still fundamentally different from the above two technologies [10]. The above technologies require the user to actively provide corresponding key information, while the system just filters the information and then feeds back the appropriate information to the user. For the recommender

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system, it does not require the user to actively provide keyword information, but the system learns the user’s preference based on the user’s past historical behavior, and makes recommendations accordingly. In real life, the needs of users are sometimes ambiguous, and even users cannot really recognize their needs. At this time, it is very difficult to ask users to actively provide keyword information to the system, which is the pain point that the recommendation algorithm can solve. The recommendation system is still essentially simulating the recommendation behavior of human society, and the recommendation algorithm will have different forms depending on the specific business scenarios, but in general, its general model includes the following three modules: user module, recommendation object module and recommendation algorithm module [11].

3 Application of Tag Recommendation Algorithm in Travel APP Intelligent Guidance System 3.1 Data Collection The sample size was 400, and some of the questionnaires were not fully answered, so the questionnaire was judged to be invalid, with only 20 copies and 380 valid questionnaires, with an effective rate of 98.0%. Due to the lack of necessary literature references, it is necessary to construct a reasonable satisfaction index system for evaluating M Company’s travel APP services, and analyze the company’s current basic status through the evaluation data of users’ satisfaction with the company’s travel APP services.

3.2 Research Content Research methods Generally speaking, before a comprehensive survey is conducted, a small-scale preliminary survey of the designed questionnaire is required to verify whether the content of the questionnaire is reasonable. Preliminary findings of a small-scale survey show that there are no significant data differences between online and offline samples collected due to the collection channel. Therefore, based on the convenience of research and the operability of sample collection, on the premise of ensuring the availability and quality of samples, this paper sets up a combination of online and offline research methods to fill in the questionnaire.

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3.3 The Nearest Neighbor Algorithm Based on Label Vector This method finds the nearest neighbor set by calculating the cosine similarity based on the label vector. First, we assume that there are N labels, then each user u is represented by an N-dimensional vector: u = (q1 , q2 , . . . , q N ), where the vector elements are not represented as follows: qi =

Nqi Nq1 + Nq2 , . . . , +Nq N

(1)

Among them, Nqi represents the number of times that the user u uses the label ti , so it can be considered that qi represents the possibility that the user u uses the label ti . The cosine similarity of user u and user v is defined as follows: ∑N

u×v /∑ N 2 2 i=1 (u qi ) × i=1 (vqi )

sim(u, v) = /∑ N

i=1

(2)

4 Satisfaction with Travel APP Intelligent Guidance System For the satisfaction of the intelligent guidance system, the satisfaction score of each index is calculated first, and then the statistical results of the questionnaire are comprehensively evaluated. As stated in the research ideas, 5 points are assigned to very satisfied, 1 point to very dissatisfied, and so on, and the total score of each indicator is calculated respectively. The results of each index satisfaction score are shown in Table 1 and Fig. 2. The results show that the true and reliable score satisfaction value of the product is the highest 3.6414, while the lowest score satisfaction standard is that the function is easy to master, and its value is 3.4211. This shows that users are currently not very satisfied with the easy-to-master functions of M Company’s travel APP. Therefore, Table 1 Evaluation factors of satisfaction scores of each index Factor of evaluation

Dissatisfied

General

Satisfied

Score

Functional selection is perfect

0.0415

0.5215

0.6201

3.5412

The product is true and reliable

0.1515

0.5141

0.6414

3.6414

The APP is running stable

0.0521

0.5634

0.6394

3.5172

Features are easy to master

0.0654

0.6411

0.6584

3.4211

The operation instructions are clear

0.0458

0.5841

0.6698

3.5741

Personalized service

0.0452

0.5485

0.5474

3.5685

User preference perception

0.04123

0.6841

0.6864

3.5941

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4 3.5

Value

3 2.5 2 1.5 1 0.5 0

Content Dissatisfied

General

Satisfied

Score

Fig. 2 Comparison diagram of tourism APP guidance system satisfaction

M company should focus on starting from the standard of low satisfaction score and make comprehensive improvements. In addition, the overall satisfaction score of Company M is relatively stable, and there is no extreme situation. All satisfaction scores range from 3.4211 to 3.6414, which means that the overall satisfaction of Company M’s travel APP fluctuates less.

5 Conclusions Travel APPs serve users, and users are also very concerned about their satisfaction. In some aspects, travel APP satisfaction directly affects user experience, and ultimately affects the economic benefits of travel APP companies. With the rapid improvement of the Internet, various information resources are showing a blowout trend, and it is difficult to provide users with high-quality information services only by relying on search engines. In order to alleviate the problem of information overload, major companies have built recommendation systems. On some websites, users can add social tags to their favorite items at will. These tags can not only reflect the user’s preferences and attitudes, but also reflect the intrinsic properties of the items. Therefore, tag-aware recommender systems take the social tags generated by these collaborative

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actions as a kind of content information. In order to solve the problems of sparsity, multi-word synonymy and polysemy caused by tags, some deep learning-based tagaware recommendation models have been proposed, which greatly improves the recommendation performance.

References 1. Castaeda JA, Martínez-Heredia MJ, Rodríguez-Molina MN (2019) Explaining tourist behavioral loyalty toward mobile apps. J Hospitality Tourism Technol 10(3):415–430 2. Afzaal M, Usman M, Fong A (2019) Tourism mobile app with aspect-based sentiment classification framework for tourist reviews. IEEE Trans Consum Electron 65(2):233–242 3. Rui N, Jacob J, Coelho A et al (2018) Leveraging pervasive games for tourism: an augmented reality perspective. Int J Creative Interfaces Comput Graph 9(1):1–14 4. Blapp M, Mitas O (2018) Creative tourism in Balinese rural communities. Curr Issues Tourism 21(7–12):1285–1311 5. Kim S, Park DE, Fu Y et al (2021) The cognitive development of food taste perception in a food tourism destination: a gastrophysics approach. Appetite 165(5):105310–105310 6. Dickinson JE, Filimonau V, Cherrett T et al (2018) Lift-share using mobile apps in tourism: the role of trust, sense of community and existing lift-share practices. Transp Res Part D: Transp Environ 61(2):397–405 7. Shyam S, Arun J, Gopinath KP et al (2021) Biomass as source for hydrochar and biochar production to recover phosphates from wastewater: a review on challenges, commercialization, and future perspectives. Chemosphere 286(9):131490–131490 8. Ahb A, Albah B, Abfm A et al (2019) iMars: intelligent municipality augmented reality service for efficient information dissemination based on deep learning algorithm in smart city of Jeddah. Procedia Comput Sci 163(2):93–108 9. Roberto P, Emanuele F, Primo Z et al (2019) Design, large-scale usage testing, and important metrics for augmented reality gaming applications. ACM Trans Multimed Comput Commun Appl 15(2):41–41 10. Moon J, Heo J, Lee WS (2018) Effect of service on the emotion and satisfaction in coffee business. J Tourism Leisure Res 30(3):177–190 11. Kirillova K (2018) Phenomenology for hospitality: theoretical premises and practical applications. Int J Contemp Manage 30(11):3326–3345

Practice of Plant Factory Visualization System Based on Internet of Things Technology Shaowei Sun and Dan Li

Abstract This paper designs a visual control system for plant factory (automatic greenhouse) based on agricultural Internet of Things technology. The system uses Zigbee and NB-iot to build a network to collect environmental sensor data. The sensor data is processed by the upper computer, which makes the parameters of environmental factors visible. This system design uses PLC master controller, which can well adjust and control the environmental factors in the greenhouse, to create a good growth environment for crops. The system is simple, reliable and practical. It can meet the needs of the current agricultural industry and has a good development prospect. Keywords Plant factory · Internet of Things · Visualization system

1 Introduction In the production process of plant factory, there are insufficient labor force, unable to accurately collect the environmental parameters of crop growth, and unable to accurately control remote equipment. To solve these problems, the research and practice of visual monitoring system of plant factory is carried out based on the key technology of agricultural Internet of Things [1]. A good environment is a necessary condition for the normal growth of crops. Greenhouse temperature, humidity and other environmental factors have an important impact on crop growth. Therefore, it is important to monitor and analyze the changes of greenhouse environmental information in time for guiding agricultural operation, ensuring the healthy growth of crops and improving the quality of agricultural products. However, the traditional greenhouse information monitoring relies S. Sun · D. Li (B) Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_63

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on artificial or with the help of simple instruments to complete, it is low efficiency, human interference factors, monitoring scope is limited, and it is difficult to achieve accurate water, medicine, fertilizer and environmental control. Therefore, to carry out the design of this topic, real-time monitoring of crop growth data, can achieve remote production management, automation, production management and circulation process is more rapid and efficient, improve the production efficiency per unit time. Through the IOT of agriculture in the process of light intensity, air temperature, soil humidity and the concentration of our fleet, and other data are collected and stored in a database, through the database and real-time camera information interaction, give full play to the characteristics of the wireless communication is flexible and convenient, without wiring, effectively avoid the existed defects and insufficiency in the traditional cable. The intelligent control of the visual system improves the decision reliability, fault tolerance and stability of the greenhouse environmental control system. The agricultural production and management are accurate and controllable, the dosage of fertilizers and pesticides is accurate and scientific and controllable, and the residual rate can be effectively controlled, thus ensuring the quality and safety of agricultural products and increasing the economic added value of agricultural products. The effective combination of Internet of Things technology and agricultural production development can improve agricultural production efficiency, at the same time, it can also realize the precision of production management, improve the output ratio of unit area, space or unit factor input, that is, improve the input–output efficiency.

2 General Design of Plant Factory Visualization System A visual monitoring system of plant factory based on IOT is designed, which can manage intelligent greenhouse remotely by computer. The plant factory visual monitoring system has environmental information sensing system, wireless network communication system and environmental parameter control system. The overall design of the intelligent greenhouse monitoring system is shown in Figs. 1, 2 and 3. The environmental information sensing system adopts Wireless Sensor Networks (WSN). As network architecture, WSN adopts wireless sensor network design based on NB-iot and Zigbee [2–5]. The ZigBee terminal node connects to the temperature and humidity sensor, PH sensor, light intensity sensor, and CO2 concentration sensor to realize environmental parameter information sensing function. The data of wireless network communication system is transmitted wirelessly to the PC platform for data processing. The wireless network communication system uses NB-iot and the existing cellular network to form the central structure of the star topology with Zigbee, and carries out multi-port information output in the information module. After processing the data of various environmental parameters collected by the upper computer platform of the system, the data is sent to the PLC master controller through a certain communication format in the form of network. PLC automatically controls the corresponding control equipment according to the environment threshold set by

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Humiture sensor Photosensitive sensor

Zigbee terminal node 1

CO 2 sensor Other sensors Fig. 1 Block diagram of data acquisition system

Zigbee terminal node 1 Zigbee terminal node 2

ZigBee coordinator

NB-iot

Zigbee terminal node n Fig. 2 Block diagram of wireless network system

Monit or Cont rol roll ing curtain PLC main control ler

Cont rol water curt ain Cont rol t he sun l amp Cont rol C O2 concentrati on

Fig. 3 Block diagram of the drive control system

the system, and starts the heating and cooling, humidification and dehumidification, ventilation and other control equipment.

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3 Design of Environmental Information Sensing System In order to achieve the information acquisition of the local greenhouse, Zigbee sensor node is used to get the environmental parameter and crop growth information in the greenhouse. WSN consists of multiple Zigbee nodes, and each Zigbee terminal node is connected to a variety of environmental parameter sensor modules [6–8].

3.1 Selection of Temperature and Humidity Sensor A good environment is necessary for crops to grow properly. Greenhouse temperature, humidity and other environmental factors have an important impact on crop growth. Timely monitoring and analysis of greenhouse environmental information changes are important means for guiding agricultural operation, ensuring the healthy growth of crops and improving the quality of agricultural products. Environmental sensor is the first element to realize measurement and control, and the key of control depends on the accuracy and reliable conversion of sensor, that is, accurate capture of original signal. Because of its advantages of high reliability, high integration, high resolution, high precision and strong anti-interference ability, digital temperature and humidity sensor is often selected as the temperature sensor of various temperature and humidity monitoring systems. Digital sensor has good performance in measuring accuracy, linearity and consistency. At the same time, the digital sensor is easy to reuse and replace, without repeated calibration. Therefore, the design of environmental information sensing system uses digital temperature and humidity sensor SHT21. Sensor SHT21 can monitor the temperature and humidity of the environment, can obtain humidity accuracy ± 2% (relative humidity 20 ~ 80%), temperature accuracy ± 0.3 °C (ambient temperature 25 ~ 42 °C). SHT21 internal circuit contains A/D circuit for digital signal conversion. In addition, the SHT21 resolution can be changed from 8/12 bit to 12/14 bit by command. It can detect the state of electricity and output checksum at the same time, which helps to improve the reliability of communication.

3.2 Selection of Light Sensor Photosensitive sensors are mainly responsible for collecting light intensity in the environment. There are many kinds of photosensitive sensors, mainly IIC and RS45 bus communication. This design uses BH1750FVI photosensitive sensor, which adopts IIC bus communication mode. It has a measurement range of 1–65535 lx. Its measurement accuracy is 0.5 lx, and the cost is low.

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4 Design of Wireless Network Communication System 4.1 Network Design Based on NB-Iot and Zigbee Technology Data such as light intensity, air temperature, soil moisture and CO2 concentration in plant factories are collected through the Agricultural Internet of Things [9–13]. The system gives full play to the flexible and convenient characteristics of wireless communication without wiring, effectively avoids the defects and deficiencies existing in the traditional wired way, and builds the star topology network structure based on NB-iot and Zigbee [14]. ZigBee technology is a wireless communication technology with low complexity, low power consumption, low data rate and low cost. ZigBee chip CC2530 conforms to IEEE802.15.4 0–9 protocol. Connect the ZigBee Coordinator to the NB-iot module BC95 through a serial port to connect the sensor network to the Internet [15]. The ZigBee Coordinator is the control center of the ZigBee network. It collects data from each node and sends the data to the BC95 module through a serial port. The BC95 module transmits data to the cloud platform or remote control center over the NB-iot network. Instructions from the remote control center travel through the NB-iot network to the ZigBee coordinator, who forwards the instructions to sensor nodes. Since the system uses Zigbee coordinator node as gateway node, the system software should include the function of coordinator as well as the function of data forwarding between two heterogeneous networks. The CC2530 connects to the BC95 through a serial port. The CC2530 processes the ZigBee protocol, and the BC95 processes the NB-iot protocol. The system controls the BC95 module by sending AT instructions through the serial port, so as to realize the transmission and reception of NB-iot network data.

4.2 The Module Design of Network Communication Software Firstly, the ZigBee network data receiving module processes the feedback information from ZigBee nodes and sends the data to the BC95 module. Secondly, the serial port sensor data receiving module processes the information returned by BC95 module. If data needs to be delivered, it is forwarded to the corresponding ZigBee node. Thirdly, the timer module sends a heartbeat frame to periodically feedback the status data. Fourthly, the environment anomaly alarm module deals with the feedback state. If the data exceeds the threshold, the system sends an SMS alarm through GPRS [16].

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5 Design of Environmental Parameter Control System 5.1 The Hardware System Design of Main Controller PLC The climate change in the north of China is relatively big, such as the temperature difference between day and night, the sunshine time is short, which is not conducive to the growth of crops, however, building greenhouse to improve the growth rate of crops is an effective way. At the same time, in order to increase the economic added value of agricultural products, it is necessary to improve the decision-making credibility, fault tolerance and stability of greenhouse environmental regulation system through various regulatory controls. Agricultural production and management are accurate and controllable, the amount of fertilizer and pesticide is accurate, scientific and controllable, and the pesticide residue rate can be effectively controlled, so that the quality and safety of agricultural products can be guaranteed. There are many growth factors in plant factories and the environmental factors in greenhouse have strong coupling with each other. We use PLC as the main controller, design environmental parameter control system. The system adopts server-client terminal control mode, which changes from the traditional single-factor control mode to the multi-factor control mode, which can effectively improve the environmental control effect of greenhouse.

5.2 The Application Software Design of Main Controller PLC There are many environmental control factors in plant factories, and the coupling of environmental factors may make the control system out of order, so sequential control is adopted [17]. The parameters of each control part were initialized, and the temperature, humidity, CO2 concentration and light intensity were gradually controlled after parameter setting. PLC battery self-test and the initialization of control parameters of each part, when the PLC battery can work normally, and the initialization of control parameters of each part is completed, then adjust each control part in turn. The initialization operation of temperature system includes giving the set temperature value, setting each parameter of PID operation, and selecting the upper and lower temperature range suitable for plant growth. After the completion of initialization, the system carries out D/A conversion, and then manual/automatic control mode switching, alarm and alarm processing Settings. Humidity control also has two modes: automatic and manual. The manual control mode is similar to the temperature manual control, and the automatic control mode is realized by comparing instructions. After A/D conversion, the sampling value of the humidity sensor is compared with the digital quantity corresponding to the default value. After comparison, the water curtain is controlled by the output module to achieve the purpose of humidity control.

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6 Transformation of Scientific Research Achievements The teaching thinking of reverse design of scientific research results into teaching cases is the key technology of curriculum construction in application-oriented universities. In order to meet the needs of different types of talents in economic and social development, the classification and development of higher education has been widely recognized, and ordinary undergraduate colleges have changed from “one column” to “two columns”. Research-oriented universities based on the cultivation of research-oriented talents and local undergraduate institutions based on the cultivation of application-oriented talents. Adopting the idea of reverse design of application-oriented talent training mode reform, the paper explores the solution of application-oriented undergraduate curriculum reform and breaks through the traditional research-oriented talent training mode. The industry needs to define the objectives and graduation requirements of talent cultivation, then determine the knowledge structure and ability structure that students should have, then reverse which courses, which practice links, and finally develop teaching plans. The first stage: the research results described in this paper are decomposed and designed into a case library of practical teaching, which is applied in the practice of students after class to further expand the form of educational innovation and entrepreneurship. The first is to set up a course integrating theory and practice to increase the practical proportion of scientific research results in the course. Secondly, design activities are carried out around the theory class. Focusing on ability and quality training such as product test design competition and innovation and entrepreneurship competition, innovation work is carried out through the assessment of participants in college students’ scientific research projects. Finally, relying on the practice base of scientific research institutions and school alliances, integrating internal and external resources of the school, it is mainly to complete the practice of students ’independent innovation and entrepreneurship. The second stage: Based on the reverse design of scientific research results into teaching case resources, Jilin wisdom Agricultural Engineering Research Center will be built into a large-scale experimental training and practice center integrating production, teaching and scientific research, and become a platform for practical teaching and collaborative innovation. It has contributed to the completion of the project of “Jilin Province Agricultural Applied Talents Training Research Base” and to the realization of stage goals. In order to build a platform for collaborative education, innovation and entrepreneurship, collaborative innovation, social service and practical teaching, we should actively build a practical collaborative innovation platform for agricultural engineering research center. The curriculum construction of application-oriented undergraduate colleges is the inevitable requirement and important content of the first-class undergraduate education in colleges and universities. The teaching thinking of reverse design of scientific research results into teaching cases is a results-oriented education idea, which has important guiding significance for the curriculum construction of applicationoriented universities. In the aspect of curriculum construction, it is necessary to define

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the curriculum objective, optimize the teaching content, optimize the teaching team, construct excellent curriculum resources, innovate the teaching mode and reform the curriculum evaluation. Finally, improve the students’ practical ability and innovative ability, in order to achieve the goal of reverse design talent training.

7 Conclusion This paper uses PLC programmable controller technology for hardware design, according to the temperature environment characteristics control room temperature. Detect and collect temperature, humidity, carbon dioxide concentration, lighting and other parameters in the greenhouse. Of course, there are still challenges due to the constraints of the situation [18]. Due to limited time and knowledge, hardware and software are not suitable for complex environments, mainly automatic temperature control, and other environmental factors that may affect temperature are not perfect. The intelligent greenhouse monitoring system includes upper computer, NB-iot, Zigbee coordinator gateway, acquisition node and PLC main controller of lower computer. The wireless information delivery network system based on NB-iot and Zigbee solves the problem of less network coverage area, and makes the cellular network more flexible and the cost is greatly reduced. PLC programmable controller (simple programming, strong anti-interference, low development cost) uses the sequential control method to process the environmental factor data information. However, it is still a big problem to measure and calculate the effective values of environmental factors. The effective values of environmental factors are not stable and accurate due to the seasonal change, and the effective values of environmental factors in winter and summer fluctuate at the edge of the appropriate temperature range. The next research direction of this paper is to realize the automatic measurement of the appropriate environmental parameter interval. Acknowledgements This work was supported by National College Students Innovation and Entrepreneurship Training Program “Development of visual agricultural eye system”, Project No. GJ202211439002. This work was supported by Jilin Provincial Development and Reform Commission Project “Research on Internet of Things Information Fusion Basic Platform and Its Key Technologies for Plant Factories”, Project No.2022C047-9.

References 1. Khapugin AA (2019) Benefits from visualization of environmental factor gradients: a case study in a protected area in central Russia. Rev Chapingo Serie Cienc Forestales Ambiente 25(3):383–397 2. Kho EP, Chua SND, Lim SF, Lau LC, Gani MTN (2022) Development of young sago palm environmental monitoring system with wireless sensor networks. Comput Electron Agric 193

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3. Dash RK, Cengiz K, Alshehri YA, Alnazzawi N (2022) A new and reliable intelligent model for deployment of sensor nodes for IoT applications. Comput Electr Eng 101 4. Chehri A, Chaibi H, Saadane R, Hakem N, Wahbi M (2020) A framework of optimizing the deployment of IoT for precision agriculture industry. Procedia Comput Sci 176:2414–2422 5. Maraveas C, Piromalis D, Arvanitis KG, Bartzanas T, Loukatos D (2022) Applications of IoT for optimized greenhouse environment and resources management. Comput Electron Agric 198 6. Zhang L, Yang X, Li T, Gan R, Wang Z, Peng J, Hu J, Guo J, Zhang Y, Li Q, Yang Q (2022) Plant factory technology lights up urban horticulture in the post-coronavirus world. Hortic Res 7. Zhang P, Li D (2022) EPSA-YOLO-V5s: a novel method for detecting the survival rate of rapeseed in a plant factory based on multiple guarantee mechanisms. Comput Electron Agric 193 8. Rihan HZ, Aljafer N, Jbara M, McCallum L, Lengger S, Fuller MP (2022) The impact of LED lighting spectra in a plant factory on the growth, physiological traits and essential oil content of lemon balm (Melissa officinalis). Plants 11(3) 9. Liu Y, Mousavi S, Pang Z, Ni Z, Karlsson M, Gong S (2021) Plant factory: a new playground of industrial communication and computing. Sensors 22(1) 10. Lee JH, Shibata S, Goto E (2021) Time-course of changes in photosynthesis and secondary metabolites in canola (Brassica napus) under different UV-B irradiation levels in a plant factory with artificial light. Front Plant Sci 12 11. Li Y, Wu L, Jiang H, He R, Song S, Su W, Liu H (2021) Supplementary far-red and blue lights influence the biomass and phytochemical profiles of two lettuce cultivars in plant factory. Molecules 26(23) 12. Ares G, Ha B, Jaeger SR (2021) Consumer attitudes to vertical farming (indoor plant factory with artificial lighting) in China, Singapore, UK, and USA: a multi-method study. Food Res Int 150(PB) 13. Chen D, Mei Y, Liu Q, Wu Y, Yang Z (2021) Carbon dioxide enrichment promoted the growth, yield, and light-use efficiency of lettuce in a plant factory with artificial lighting. Agron J 113(6) 14. Senouci MR, Mellouk A (2019) A robust uncertainty-aware cluster-based deployment approach for WSNs: coverage, connectivity, and lifespan. J Network Comput Appl 146(C):102414– 102414 15. Azimbek K, Israr U, DoHyeun K (2021) Optimization-assisted water supplement mechanism with energy efficiency in IoT based greenhouse. J Intell Fuzzy Syst 40(5):10163–10182 16. Gauhar Fatima S, Kausar Fatima S, Mehrajuddin M (2019) Designing The IOT Basestation for greenhouse monitoring. Int J Adv Res Eng Technol (IJARET) 10(2) 17. Pucheta JA, Schugurensky C, Fullana R, Patiño H, Kuchen B (2005) Optimal greenhouse control of tomato-seedling crops. Comput Electron Agric 50(1):70–82 18. Weaver GM, van Iersel MW, Velni JM (2019) A photochemistry-based method for optimising greenhouse supplemental light intensity. Biosyst Eng 182:123–137

Blockchain Technology Drives the Transformation and Upgrading of Audit Mode Shiya Zhou and Wenbin Liu

Abstract In recent years, blockchain, as a distributed shared database generated based on cryptography technology, is silently changing our world. Due to the valuable characteristics of blockchain, such as openness, transparency, non-counterfeiting, traceability and decentralization, the information obtained based on blockchain data analysis is of great value. Under the influence of blockchain, the audit field is also undergoing changes. Centering on the research topic that blockchain technology drives the transformation and upgrading of audit mode, this paper discusses how blockchain technology drives the transformation and upgrading of audit mode from the three aspects of technological innovation, audit process and risk control. Keywords Block chain · Technological innovation · Audit process · Risk control

1 Introduction In recent years, the application of big data analysis in financial crisis warning, financial fraud detection, audit evidence analysis and audit risk assessment has greatly promoted the development of data-driven audit technology, while the emergence of blockchain technology has led the transformation and upgrading of modern audit mode [1]. Block chain, as a kind of distributed Shared database, based on cryptography techniques are stored in the data or information that is transparent, not fake, traceability, decentralization, and so on characteristics, these characteristics make when block chain technology used in the audit, can break through the inherent S. Zhou Wuhan Technology and Business University, Wuhan, Hubei, China W. Liu (B) Wuchang Institute of Technology, Wuhan, Hubei, China e-mail: [email protected] Department of Public Basic Courses, Wuhan Technology and Business University, Wuhan, Hubei, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_64

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defect of traditional auditing business model store complete sharing books, Ensure data integrity and security, improve information transparency from the source of accounting information, change the internal audit environment [2]. The concept of “blockchain” was first put forward in 2008, and has attracted widespread attention since 2017. This topic focuses on blockchain audit, and the research of domestic and foreign scholars in this field took shape in early 2016. Specifically, the current research mainly focuses on three aspects: the impact of blockchain on audit, the application cases of blockchain audit, and the problems existing in the application of blockchain technology in audit. First, theoretical research on the impact of blockchain on audit: When it comes to the impact of blockchain technology on audit, the vast majority of scholars believe that blockchain technology is likely to bring earthshaking changes to the audit industry. The principle of blockchain mainly includes four aspects: distributed ledger, public knowledge mechanism, smart contract and cryptography. The characteristics of decentralization, imtamability and autonomy brought by these four aspects to blockchain can help reduce audit costs and audit risks and greatly improve the efficiency of traditional audit. Second, the study of blockchain audit application cases: in the auditing industry, the Big Four accounting firms took the lead in the layout of blockchain. For example, Deloitte developed a set of Rubix platform and built Deloitte’s Perma Rec, a global distributed accounting book, which achieved the purpose of successfully connecting with SAP and other financial systems [3]. On August 10, 2018, China’s first blockchain e-invoice was launched in Shenzhen, which enabled auditors to break through the limitations of the accounting vouchers of the audited units in traditional auditing, and the inspection procedures could be “checked to the end”. In addition, blockchain technology is applied to bank audit, insurance audit, and affordable housing allocation audit. Third, research on the problems of blockchain technology in audit application: first, blockchain technology itself still has some defects, mainly writing efficiency is relatively low and node and capacity problems; Secondly, as a decentralized distributed ledger, the introduction of blockchain will inevitably lead to the full disclosure of the transaction ledger of the audited entity, so the business secrets of the audited entity may be leaked, and confidentiality is threatened [4]. Finally, it raises a question about the feasibility of complete self-governance in today’s social conditions. It is believed that due to the immutable nature of blockchain, once dirty data occupies the vast majority of the whole chain, the credibility of the whole blockchain will be greatly compromised. Under the influence of blockchain, the audit field is also undergoing changes. Centering on the research topic that blockchain technology drives the transformation and upgrading of audit mode, this paper discusses how blockchain technology drives the transformation and upgrading of audit mode from the three aspects of technological innovation, audit process and risk control.

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2 Blockchain Technology Drives the Transformation and Upgrading of Audit Mode The transformation and upgrading of audit mode driven by blockchain technology can be realized from three aspects: technical innovation, audit process and risk control.

2.1 Technological Innovation Manual phase can be seen from Table 1, 1.0, 2.0, information technology of the traditional audit model phase, in this stage, because of the audit procedures need to rely on artificial to complete, often using field visits, hire a third party assessment methods such as offline, requires a lot of time, money, human resources, such as the phase information can achieve real-time communication feedback, There is information lag phenomenon, and the lack of effective analysis and monitoring data technical tools at this stage, can not judge the authenticity and reliability of audit evidence [5]. In the 3.0 data analysis stage, when big data analysis technology is applied to audit work, relevant audit evidence can be extracted in real time to improve audit efficiency and quality. However, data may be tampered with or fabricated during the whole process from data generation to final audit, and the authenticity of audit evidence cannot be fully guaranteed. With the rapid development of Internet technology, the technology to make the audit into 4.0 semi-automatic and automatic block chain stage, distributed books as a reliable medium can store all electronic information, through the hash algorithm into a fixed number of random Numbers, permanent storage in the block structure, the data are distributed storage, update and backup feature in time, Ensures that data will not be lost or corrupted. Block chain should be to ensure “chain” of data are to achieve consensus information, the information open and transparent, can be traced and tamper-resistant, parties can undertake cross validation, any one party accounting entity can unilaterally intentionally hide trading, can reduce the audit risk from the source of audit evidence, ensure the authenticity of audit evidence, realize the whole process of audit [6]. Through distributed ledger (open, flat, equal system structure), real-time update and self-management of audit data can be realized through the whole network broadcast, which can effectively improve the independence and transparency of audit data. Consensus mechanism—(Pool verification pool, share authorization certificate mechanism, equity certificate mechanism, proof of work mechanism) spontaneously check, verify and confirm the audit data of each transaction, which can effectively improve the authenticity and security of audit data; Timestamp—The hash value randomly generated from the checked and verified audit data will form a timestamp recording the detailed transaction time that will never be deleted, which can effectively improve the continuity and traceability of audit data. Digital signature (publickey publickey, privatekey privatekey) identifies digital information on audit

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Table 1 Comparative analysis of different stages of audit development Audit development stage

1.0 manual phase

2.0 information technology phase

3.0 data analysis stage

4.0 semi-automatic and automatic stages

Information interaction mode

Centralized

Centralized

Decentralized

Distributed

Sources of the audit

The manual book

Double entry book

Digital ledger

Distributed ledger

The audit tool

The calculator

Excel and other computer software

Big data analysis technology

Blockchain technology

Audit quality

Low

Low

Middle

High

Authenticity level Low

Low

Middle

High

Audit time point

After the event

During and after the event

Before, during, after

After the event

data, which can effectively improve the integrity of audit data. Homomorphic encryption—the premise of “available, invisible”, “privacy protection, data security” realizes the analogy analysis and result calculation of audit data, which can effectively improve the privacy of audit data.

2.2 Audit Process As one of the five emerging technologies of network security, the development of audit blockchain cannot be separated from them. But so far, about the document of block chain technique is applied to the audit seldom contact with big data, intelligent, mobile Internet, cloud computing, Internet of things, only put research vision block chain and auditing itself, and mostly concentrated in the chain’s influence on the audit, the block chain audit application cases, block chain technology applied in the audit of the problems exist in three aspects [7]. Among them, in terms of audit application cases of blockchain, the main literature studies the positive effect or possible positive effect after application, while the research literature on the impact of the current audit process is very little. By studying the impact of blockchain technology on the audit process, this paper discusses the transition period from the current “riskoriented audit stage” to the unknown new audit stage, and takes into account the five technologies of “great wisdom and cloud things”. How to appropriately introduce blockchain technology into audit practice in the next few years. To solve the problems that lead to low audit efficiency and high audit cost in current audit practice, for example, information asymmetry leads to a large number of audit work demands of “checking accounts and data”, and audit sampling leads to limited scope of audit evidence collection. At the same time, after the introduction of blockchain

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technology, accounting firms and auditors, as the main body of social audit, should change the existing audit thinking, adjust their business and improve their professional competence; What role should the institute of Certified Public accountants, government departments and other relevant parties play in this process? The essence of blockchain audit is to collect blockchain data, carry out data analysis, and conduct extended forensics according to the analysis report of audit data. First, information sharing should be realized [8]. Auditors can call various electronic information within the scope of authorization according to their work requirements to form authentic and reliable audit evidence, improve audit efficiency and reduce audit costs. Second, strong deterrence of fraud. In the blockchain audit, the object and source of transactions can be easily traced, forming a strong deterrent to fraud [5]. At the same time, the work of auditors is also subject to relevant supervision, effectively preventing collusion. Three is to realize the continuous track to follow, block structure there is a will never be deleted records the detailed transaction time timestamp, guarantee the continuity of data and traceability, the auditor questions suspects, the audit object implementation were stored in the rectification of block chain, attach a timestamp, can keep the follow up and never recorded in real time [9]. The fourth is to build an audit early warning mechanism. The smart contract mechanism is used to set various detailed rules. The blockchain audit system will automatically process abnormal records, remove abnormal data in a timely and effective manner, and build a real-time early warning system to improve audit efficiency [10]. Block chain audit mode, the audit will focus on the recording, tracking, validation, into a complex indepth analysis, to overcome the inherent defects of the existing audit mode, reduce the audit cost, improve the efficiency of the audit, the audit mode to a new stage of development, See Table 2. Audit early warning mechanism under blockchain technology. The transformation of audit methods should not only make reasonable use of artificial intelligence technology, but also not be “dominated” by artificial intelligence technology. It should follow the professional ethics requirements of “fairness, inclusiveness, transparency, responsibility, reliability and security, privacy and confidentiality”, and explore a new path of human–machine collaboration and mutuallybeneficial development. By studying the impact of blockchain technology on the audit process, this paper discusses the transition period from the current “risk-oriented audit Table 2 Audit early warning mechanism under blockchain technology New trading data

Formation block

Transaction data does not pass validity checks

Elimination abnormal data

Transaction data passes validity checks

The smart contract Formation new mechanism passed blocks the test The smart contract Elimination mechanism failed abnormal data the inspection

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stage” to the unknown new audit stage, and takes into account the five technologies of “great wisdom and cloud things”. How to appropriately introduce blockchain technology into audit practice in the next few years. To solve the problems that lead to low audit efficiency and high audit cost in current audit practice, for example, information asymmetry leads to a large number of audit work demands of “checking accounts and data”, and audit sampling leads to limited scope of audit evidence collection. At the same time, after the introduction of blockchain technology, accounting firms and auditors, as the main body of social audit, should change the existing audit thinking, adjust their business and improve their professional competence.

2.3 Risk Control Under the background of “great wisdom shift cloud things”, blockchain technology itself is already a relatively mature technology, and its characteristics of decentralization and imtamability can effectively suppress various risks caused by information asymmetry in the market. Auditing, which primarily performs a third-party verification function, may be revolutionized by this technology in the future. However, the replacement of any industry is not a matter of overnight, can not be overnight. Therefore, in the short term, how to better integrate blockchain into the existing audit mode, gradually adjust the existing audit process and method, and how to adjust the behavior of the audit subject and all relevant parties are undoubtedly the real top priority for the audit practice. The current audit mode is risk-oriented audit mode, as shown in Table 3. Risk-oriented audit mode. It can be seen from the audit risk model that there are three variables in audit risk, which should be controlled if audit risk is to be reduced [11]. One is to reduce the inherent risk, inherent risk are inherent in accounting statements of material misstatement or omission, to reduce the inherent risk must improve the quality of financial statements, with the progress of science and technology, statements fraud means more hidden, audit difficulty increasing, and the pattern of distributed storage block chain to ensure the interlocking data in financial statements, To ensure the authenticity and integrity of accounting information, control the inherent risk [12]. Economic responsibility audit is a balance sheet, income statement, statement of changes in owners’ equity, and cash flow statement and other financial statement analysis as the foundation, focus on the assets, liabilities, profits and losses of quality condition, cash flow, etc., the methods of trend analysis, structure analysis enterprise debt paying ability, operation ability, profitability and development ability of the structure change; The management responsibility orientation emphasizes the influence of off-balance sheet financing, related party transactions, commitments, contingencies and post-term matters on the overall business strategy, investment decision and risk management of the enterprise on the basis of the analysis of foreign affairs. Based on the analysis of information in notes to financial statements, social responsibility orientation focuses on environmental standards, environmental risks, environmental countermeasures, environmental protection, community contributions, employee welfare, public welfare donations and other

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Table 3 Risk-oriented audit mode Audit risk

Inherent risk

Material misstatement or omission in the financial statements

The distributed storage mode of blockchain ensures the interlocking of financial statement data

Control risk

Material misstatement or omission at the identification level

The openness and transparency of blockchain can help supervise enterprises to be honest and trustworthy and form a good internal control environment

Check risk

Slow information Blockchain is decentralized and collection and distortion of immutable. Accounting audit evidence information and the whole process of its formation are completely stored in the blockchain, so that auditors can obtain accurate and reliable audit evidence in real time

behaviors that bear social responsibility [13]. Second, it can reduce control risks. The openness and transparency of blockchain can help supervise enterprises to be honest and trustworthy, and form a good internal control environment. Blockchain uses a variety of scientific information technologies to realize automatic inspection and eliminate abnormal data through intelligent contract mechanism, and effectively improve the accuracy of internal control implementation [10]. Three is can reduce the inspection risk, the traditional audit mode in the face of the screening data needs to be various, looking for audit evidence often takes longer, easy loss of timeliness, increase the risk of detection, and chain block has the characteristics of decentralization and tamper-resistant, intact all the whole process of accounting information and its formation in block chain, Auditors can obtain accurate and reliable audit evidence in real time, which can solve problems such as slow information collection and audit evidence distortion, and realize intelligent audit [14, 15]. In the research related to blockchain audit, the vast majority of scholars only discuss from the perspective of blockchain technology, but ignore the common impact of big data, intelligence, mobile Internet, cloud computing, Internet of things, etc. For example, blockchain can only ensure that the whole process of financial data in the chain is true and not tampered with, but before the chain, if it can be combined with the Internet of things technology, then it can be directly traced back to the corresponding data in the audit through the Internet of Things, which has a great impact on the overall process such as inventory monitoring.

3 Conclusions This paper analyzes the transformation and upgrading of auditing mode driven by blockchain technology in detail from three aspects: technological innovation,

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auditing process and risk control. First, the evolution of audit development stage is analyzed, and blockchain audit has unique advantages compared with traditional audit and big data audit [11]. Secondly, the analysis of blockchain audit overcomes the inherent defects of the existing audit mode, reduces the audit cost, improves the audit efficiency, and promotes the audit mode to a new stage of development; Third, study how to reduce audit risk, realize audit intelligence, and complete audit transformation and upgrading. This paper proposes to improve the existing audit mode from three aspects of technological innovation, audit process and risk control, and provides theoretical reference for further perfecting the audit mode. Promote in-service audit through blockchain technology, so as to supervise audit risks of audited units in real time and correct audit problems of audited units in a timely manner. Under the background of “great wisdom shift cloud things”, blockchain technology itself is already a relatively mature technology, and its characteristics of decentralization and imtamability can effectively suppress various risks caused by information asymmetry in the market. Auditing, which primarily performs a thirdparty verification function, may be revolutionized by this technology in the future. However, the replacement of any industry is not a matter of overnight, can not be overnight. Therefore, in the short term, how to better integrate blockchain into the existing audit mode, gradually adjust the existing audit process and method, and how to adjust the behavior of the audit subject and all relevant parties are undoubtedly the real top priority for the audit practice. On this basis, the technology of block chain and “wisdom, moving cloud” five technologies are more profound study, through the theoretical research and normative analysis, to explore under the background of “the big wisdom moving cloud”, in the next few years should be how to block the chain technology to absorb the audit practice in work, solve the current audit practice leads to low efficiency, high audit cost audit problems; And how firms, auditors, CPA associations and government departments should prepare after the introduction of blockchain technology. Acknowledgements This work was supported by Research on the influence of block chain technology on the whole audit process under the background of big intelligence moving cloud (Fund project number: A2021011).

References 1. Tiwari K, Khan MS (2020) Sustainability accounting and reporting in the industry 4.0. J Cleaner Prod 2020(1):1–14 2. Gladston A, Mohan AP, Mohamed Asfak R (2020) Merkle tree and blockchain-based cloud data auditing. Int J Cloud Appl Comput 2020(3):54–66 3. Fuller SH, Markelevich A (2020) Should accountants care about blockchain? J Corp Acc Fin 2:34–46 4. Sinha S (2020) Blockchain-opportunities and challenges for accounting professionals. J Corp Acct Fin 2:65–67 5. Moffitt KC, Rozario AM, Vasarhelyi MA (2018) Robotic process automation for auditing. J Emerg Technol Account 15(1):1–10

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6. Tušek B, Ježovita A, Halar P (2021) The importance and differences of analytical procedures’ application for auditing blockchain technology between external and internal auditors in Croatia. Econ Res—Ekonomska Istraživanja 34(1):1385–1408 7. Yu Z, Yan Y, Yang C, Dong A (2019) Design of online audit mode based on blockchain technology. J Phys: Conf Ser 1176(4):042072 (7pp) 8. Ahmad A, Saad M, Mohaisen A (2019) Secure and transparent audit logs with blockaudit. J Network Comput Appl 145:102406 9. Mahbod R, Hinton D (2019) Blockchain: the future of the auditing and assurance profession. Armed Forces Comptroller 64(1):23–27 10. Rozario AM, Thomas C (2019) Reengineering the audit with blockchain and smart contracts. J Emerg Technol Acc 16(1):21–35 11. Dyball MC, Seethamraju R (2021) The impact of client use of blockchain technology on audit risk and audit approach—an exploratory study. Int J Auditing 25(2):602–615 12. Sheldon MD (2020) Auditing the blockchain oracle problem. J Inf Syst 13. Robert N, Deniz A (2019) Auditing cloud-based blockchain accounting systems. J Inf Syst 34(2):5–21 14. Jans M, Hosseinpour M (2019) How active learning and process mining can act as continuous auditing catalyst. Int J Account Inf Syst 32:44–58 15. Kshetri N, Loukoianova E (2019) Blockchain adoption in supply chain networks in Asia. IT Prof 21(1):11–15

Research on Blockchain Technology Applications for Digital Currency Xiaoqi Yu and Minghong Sun

Abstract Blockchain, a focus of enterprises and governments for the latest decade, has been widely used on a range of economic and social area, especially for digital currency. The special attributes and technique advantages of blockchain contribute to the current digitalization. Therefore, this paper researches on the application of blockchain on digital currency, which is enhanced by two technical supports, four mechanism designs and application layers. Technical support can help to strengthen the credibility and security of digital currency in blockchain system. The four mechanism designs also support the integration between blockchain and digital currency, which makes the application on currency more secured, unchangeable and more convenient. As well, in the paper, benefits and drawbacks are also studied like high criminal rate with lack of regulation and wider economic gap between developing and developed societies, which need to be further considered in the road of currency digitalization. Keywords Blockchain technology · Digital currency · Benefits and drawbacks analysis

1 Introduction Blockchain is profoundly meaningful for the construction of programmable economic system and social system, and thus it has been up surging and widely studied by the industries and academies. With the development of modern currency, digitalization is an inevitable trend. The digital currency integrated with blockchain technology is a popular topic and has been further researched in current years. With X. Yu Dalian Royal Highschool, Dalian, Liaoning, China M. Sun (B) Dalian Neusoft University of Information, Dalian, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_65

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quantitative and quality analysis method and case study method, this article investigates blockchain technology and its application to digital currencies such as Bitcoin, and discusses the opportunities and challenges they may meet, which hopes to provide useful guidance and reference for further related researches. The paper is divided into five parts: The first part is the introduction. The second part illustrates the overview of blockchain. Thirdly, applications of blockchain on digital currency and the methods are studied. Then, discussions on advantages and potential risks on the application are shown. The last part is conclusion.

2 The Overview of Blockchain 2.1 Definition and Attributes of Blockchain According to IBM, the definition of Blockchain is a shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a business network [1]. Blockchain is categorized into three types: Public Block Chains, Consortium Block Chains and Private Block Chains. (1) Public Block Chains. It is the earliest and widest-used block chain. Any individual or group in the world can send a transaction under the system, and the block chain can ensure the transaction. Anyone can participate in its consensus process. (2) Consortium Block Chains. The bookkeepers are multi pre-nodes selected by a group, and all those nodes decide the production of each block. Other nodes can participate in the transaction, but do not involve in the bookkeeping process. (3) Private Block Chains: It uses the ledger technology of the block chain for bookkeeping, and has exclusive writing rights to the block chain. Based on its technological categories, Yao and Ge summaries blockchain with following four attributes: (1) Decentralized. The nodes in self-contained block chain itself realise information self-verification, transmission and management by distributing accounting and storing them. (2) Openness. The block chain’s data is open to everyone, except for the private information of the parties to the transaction, which is encrypted. (3) Independence. All the nodes can validate and exchange the data automatically without any intervention. (4) Security. People can’t manipulate and modify network data. (5) Anonymity. Verifying the personal identity information of each block node is not necessarily, and the information transmission can be carried out anonymously [2].

2.2 Current Development of Blockchain As a typical decentralized technology, blockchain has experienced an immense evolution since 2016. For its development in economy, blockchain treats all the individuals as nodes, promoting the improvement of the existing financial system rules to build

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Fig. 1 Global blockchain enterprise scale from 2018 to 2023

a shared and win–win financial development ecosystem. The development progress of Blockchain can be divided into three stages from 1.0 era to 3.0 era according to The Bretton Woods 2015. As Fig. 1 shows, a steady growth in the global blockchain market size can be witnessed in 2020, reaching $4.31 billion. Due to the regulation of Europe and America, the growth of the market decreased by 53.97% compared with 2019, and is expected to reach $14.533 billion in 2023 [3]. In terms of blockchain patent applications, according to the data released by IPR daily and incoPat innovation index research center, among the top 100 enterprises, China occupies 55% of them, followed by 26% in United States. Japan and South Korea accounts for 5% respectively, and 4% in Germany. Finland, the United Kingdom, Canada, Ireland and Antigua and Barbuda with 1% each, as shown in Fig. 2 [4]. For blockchain enterprises, as Fig. 3 shows, according to the data of the China Academy of Information and Communication Technology, the United States has the largest number of blockchain enterprises as high as 27%, followed by 24% in China. Figure 4 demonstrates the sector distribution on usage of blockchain around the world, and it is noticeable that the digital currency occupies the rate as high as 35%, which is more than one third of the whole. Thus, the application of blockchain on digital currency is the trend that needs to be focused on [5].

3 The Digital Currency Integrated with Blockchain Technology 3.1 Definition of Digital Currency Digital currency is an unregulated, digitalised currency. It is usually distributed and managed by developers, accepted and used by members of a specific virtual community. The European Banking Authority defines the digital currency as a digital representation of value that is not allocated by a central bank or authority and is not linked to fiat money, but is accepted by the public so that it can be used as a means of payment and can also be transferred, stored or traded electronically. Like bank notes,

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Fig. 2 Distribution of blockchain industry-related patents in the first half of 2021

Fig. 3 Enterprises in blockchain area

digital currencies are essentially credit coins, but they can reduce operating costs and be used more efficiently in a wider range of areas. From the perspective of some existing digital currencies, decentralised mechanisms are running behind them, and the trust system is mainly established through the distributed accounting method. However, these digital currencies still have the same fundamental flaws as private currencies in history: unstable value, weak credibility, limited acceptable range, and easy to produce large negative externalities.

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Fig. 4 Industry classification of global blockchain enterprises

3.2 Current Development of Digital Currency With the rapid evolution of the digital economy and the increasing demand for digital financial services, the digital transformation has led global monetary and financial system to a new era. Many central banks of economies have put efforts into the research and development of digital currencies. According to a report published by Finbold on January 1, 2021, the worldwide number of cryptocurrency categories was 8153, and as of December 31, 2021, the number was 16,223. An increase of about 98.98% can be witnessed compared to January. Finbold data shows that 8070 new tokens were created in the crypto industry in 2021, with an average of about 21 new cryptocurrencies being launched on the market every day [6]. In total, about 5000 of these new currencies were added to the crypto market in January–October 2021, compared with more than 3000 of them entering the market in November and December, according to separate data.

3.3 The Digital Currency Integrated with Blockchain Technology Blockchain is a kind of chain-type data structure that combines data blocks in a sequential manner according to the chronological order. Digital currency is the most typical and widespread application of blockchain since its creation. The data information combined, for example, in the Bitcoin system, is the transfer information, including the payer, the payee and the number of bitcoins. There are two of characteristics of blockchain: decentralised and hard to falsify the figures contained, which

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Fig. 5 Digital currency technical framework with blockchain

further secure digital currency [7]. So when blockchain is applied to digital currencies, the role of third-party intermediaries can be eliminated, enabling peer-to-peer links between customers and sellers, and speeding up transactions at a cost savings. For applications of blockchain on digital currency, two main technical supports are used, with four mechanism designs, as shown in Fig. 5. 1. Technical support Technological innovation is the source driving force for the development of digital currency, and technical support is the most basic technique to help the integration of blockchain and digital currency. Technological support is divided into foundation support and privacy protection. The foundation support constructs a new trust system and enhances the credibility of value delivery in the digital currency system through the decentralised and autonomous consensus mechanism. As a distributed accounting database, blockchain ensures the security of value transmission in the digital currency system through innovations in searching mechanism, traceability mechanism, verification mechanism and encryption mechanism. Blockchain technology is based on a peer-to-peer distributed network, which makes data tamper-proof, traceable and enhances the reliability of value delivery in the digital currency system. In the blockchain-based digital currency system, privacy protection technology is an important foundation for security applications. On the one hand, the anonymity of digital currency requires that users’ legitimate information, including identity, location and balance. These will not be exposed when the distributed node consensus is reached and transaction records are made public on the whole network. On the

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other hand, the anonymity of digital currency cannot be separated from the needs of safe operation and legal supervision, so as to avoid it becoming a criminal tool for criminals. 2. Mechanism design Mechanism design is an important part of digital currency system architecture design to secure and stabilize digital currency system. It includes consensus mechanism, incentive mechanism, currency mechanism and issuance mechanism. The first one is consensus mechanism, which is a system that verifies and ensures the transaction with voting by specific nodes in a short time period. It is the basis to ensure the consistency and security of digital currency system operation. The main goal of the consensus mechanism is to achieve high efficiency and secure consensus of each node in decentralised power network. Incentive mechanism is the mechanism with interests to attract users [8]. It is considered as the core of the operation of the digital currency system. With distributing economic benefits under the framework of rules, and the transaction verification is carried out at the same time of mining. The existing researches mainly focus on mining incentive mechanism and transaction pricing mechanism. The third one is currency mechanism. It is used to assess the price of digital currency and enhance the stability of the currency. This mechanism is mainly applied to digital currencies issued by private enterprises. These currencies usually are also lack of national credibility endorsement. Under reasonable evaluation as well as specific conditions, the value of digital currencies can maintain certain stability. The final mechanism is issuance mechanism, showing how the digital currencies are distributed. It is influenced by the issue mode, the total amount of issue, the output speed, the currency of issue and so on. According to different issuing methods, it can be divided into mining mechanism and non-mining mechanism. For example, Bitcoin, one currency under the mining mechanism, is distributed by competition of calculation between ‘The miners’. The one who use shortest time can gain one page of account to get Bitcoin. In the other hand, non-mining mechanism can issue currency in the network and accounting directly. 3. Application layer With support from two technical support and four mechanism designs, application layer is finally achieved. It is consisted by consuming side, technique side and controlling side. Digital currencies are usually issued by controlling layer (such as central bank) through the nodes in blockchain. Then it can be passed on consumers. Meanwhile, controlling layer will monitor the technique layer to ensure the safety of distributing digital currencies. This distributed system framework can deal with massive concurrent transactions effectively, achieving high scalability by adopting the technology of data reading separation, data multiple copies, sub-database sub-table and so on. With multi-node, system security is guaranteed, and efficiency of transaction processing is improved.

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4 The Benefits and Risks of Digital Currency Integrated with Blockchain Technology 4.1 Benefits of Digital Currency Integrated with Blockchain Technology With support from blockchain, digital currency can be more secured, tamper-proofed and more convenience. Digital currency is essentially a string of characters with key information. The information will not be changed or erased with the transaction, and blockchain can help to achieve transparency of digital currency by tracing to the source. Some of the stored information can also be viewed at any time, making the transaction clearer. Relevant regulators can monitor and track the flow of digital currency when necessary, reducing the probability of money laundering. So the currency is more secured. Also, the currency is presented as information, so there is no need to worry about counterfeiting. Also, the information of digital currency with blockchain is difficult to be changed, the advantage of digital currency in blockchain system is tamper-proofed, which further supports security of digital currency. As long as one node in the entire system is working, the whole block chain is secured. These servers are called nodes in the system. Nodes are the computers in the blockchain network, including mobile phones, mining machines and servers. But now the node is the mine or mining pool which help allocating digital currencies. The nodes provide storage space and computing power for this system. If the information stored in the block chain needs to be changed, consent of more than half of the nodes and modify the information in all the nodes have to be gained, and these nodes are usually held by different subjects. So it is extremely difficult to tamper with the information in the block chain. Besides, digital money is convenience to use in blockchain system. For enterprises, when market needs the information feedback, blockchain can improve efficiency and accuracy of it since it is easier to trace to root of figures and statistics. Nationally, the issuance and exchange of digital currency becomes efficient because processes like printing, publishing and distribution are eliminated. For the development of globalisation, the transaction of digital money can occur across national borders. Unlike normal currencies, which need to go through foreign exchange institutions and record transaction information, digital currencies use blockchain technology to transfer money online, and the transaction will not be recorded under the same blockchain system.

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4.2 Potential Risks of Digital Currency Integrated with Blockchain Technology Although there are advantages of digital currency with blockchain technology, it also has drawback side. Firstly, for the society, the anonymity of blockchain, which do not force users to disclose personal information, makes it possible for criminals to take advantage [9]. The trading process of digital currency is peer to peer without limitations of space and time. This convenient way to trade has also attracted the attention of speculators [10]. Due to the anonymity of blockchain, regulators cannot check the personal information from users. Criminals can carry out money laundering, tax evasion, gambling and other illegal activities through this trading platform, and escape from the legal net and the monitoring of the regulatory authorities, which greatly increases the difficulty of law enforcement officials to deal with criminal activities. Due to the difficulty of legal supervision, digital currency platform has become a hiding place for many criminals. So there is potential risk for increasing online criminal rate. Secondly, the operation of the digital currency consumes a huge amount of electricity and resources. Due to the board range of blockchain system, countries with abundant of technology resources will link together for the further development of digital currency to get wealthier. But considering about development level of countries all over the world, developed countries can use efficient power equipment, greatly shorten the process of trading, but many developing countries economic strength is relatively weak. In the development and application of digital currency in developed countries, they control the majority of wealth. The unbalanced evolution of economic power makes developed countries control the discourse power in digital currency, and the value of digital currency will change accordingly, causing economic losses to many developing countries and causing heavy losses to investors. The blockchain can broad the range of usage of digital money, but wider the gap between developed and developing country.

5 Conclusion The paper has overviews of blockchain and digital currency and demonstrates the application of blockchain on digital currency. Attributes of blockchain can enhance the strength of digital currency, facilitating the transfer efficiency and accuracy. Technical support and mechanism design that support application on digital money, making the currency tamper-proofed and secured. Although the current blockchain technology still has problems of limited regulation and insufficient equality, digital currencies in the market are developing for different dimensions under the support of the technology, and they keep brining benefits to the current society.

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Acknowledgements This work was supported by ‘Research on innovative applications of blockchain technology of the high-quality development of Liaoning digital credit investigation system’, the program of Basic Research Project of Education Department of Liaoning Province, 2022.

References 1. Bhuvana R, Aithal PS (2020) Blockchain based service: a case study on IBM blockchain services and hyperledger fabric. Int J Case Stud Bus IT Educ (IJCSBE) 4(1):94–102 2. Yao ZG, Ge JG (2017) An overview of blockchain. E-Sci Technol Appl 8(2):3–17 3. China Institute of Information and Communication (2021) Blockchain 2021 white paper [R/OL], pp 6–8 4. Data Science (2021) Online rating system development using blockchain-based distributed ledger technology [R/OL] 5. Tapscott D, Tapscott A (2016) Blockchain revolution, pp 36–43. China CITIC press 6. Shaker M, Shams Aliee F, Fotohi R (2021) Online rating system development using blockchainbased distributed ledger technology. Wireless Netw 27:1715–1737 7. Ozili PK (2022) Central bank digital currency research around the world: a review of literature. J Money Laundering Control 1:1–20 8. Bentov I, Lee C, Mizrahi A et al (2017) Proof of activity: extending bitcoin’s proof of work via proof of stake [EB/OL] 9. Schwartz D, Youngs N, Britto A (2017) The ripple protocol consensus algorithm [EB/OL] 10. Chen X, Liu F (2020) Research on the elements of legal digital currency architecture based on blockchain technology. Comput Sci Appl 10(11):1984–1992

Construction of Fuzzy Control Evaluation System for Regional Economy Based on VIKOR Algorithm Yan Zuo

Abstract With the continuous improvement of economic globalization and the level of science and technology, the acceleration of regional integration and the increasingly close pace of regional urbanization, the fuzzy control model is of great significance to the development of regional economy and makes the development of regional economy more reasonable. It calculates a clear and definite function expression by mathematical method, which has the advantages of small calculation, easy realization and easy understanding. In this paper, a comprehensive evaluation system of regional economy based on fuzzy mathematical model and object-oriented is established under the framework of VIKOR. Based on VIKOR algorithm, the fuzzy control evaluation system of regional economy is designed in this paper. Firstly, the concept and characteristics of regional economy are introduced, then the application characteristics of VIKOR algorithm are studied, and then the performance of the system is evaluated by software simulation. The experimental results show that the fuzzy control evaluation index has good performance in the system response time and step response time, which indicates that the fuzzy control evaluation system optimized by the algorithm can meet the actual needs of users. Keywords VIKOR algorithm · Regional economy · Fuzzy control · Evaluation system

1 Introduction With the rapid development of computer technology, computer applications have been widely used in economy, life and other fields. Regional economy refers to the relationship between the quantity of products produced by a country or region in a Y. Zuo (B) College of Accounting, Zhanjiang Science and Technology College, Zhanjiang 524094, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_66

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specific period of time and the output [1, 2]. It not only includes the sum of market transactions of a unified scale, in which the total amount and quality of all goods and services in the region remain basically stable and the ability to meet people’s needs is equal (the relative distance is long), but also covers that when other relevant factors have an impact on it, the regional economic system can maintain normal operation under this condition [3, 4]. With the rapid development of economy, there are complex and diverse contradictions among countries and regions in the region. Therefore, it is of great significance to study the fuzzy control model of the system. At present, many scholars at home and abroad solve these problems through mature and practical methods in different fields. The famous American experts proposed that the most important and widely used fuzzy set theory is the Velt algorithm. It can make judgment, reasoning and comparison according to the actual situation of the evaluated object, and can use the results of its quantization processing as input and output information for the control strategy design [5, 6]. Many domestic experts have adopted some basic concepts when constructing fuzzy mathematical models, such as “finding first”, “knowing later”, etc. The fuzzy algorithm can combine the qualitative description with the quantitative calculation. At the same time, it also uses the fuzzy set theory, membership degree transformation and other technical means to conduct quantitative analysis and Research on the relationship between the degree of regional economic development and the constraints [7, 8]. The above research has laid the research foundation for this paper. Fuzzy control is a method based on mathematics. It can describe the controlled object by computer, and then connect it with the experience and judgment results given by people. In this paper, two strategies are proposed when establishing the model, dynamic programming method and deterministic network. The system uses Vista (time window), AGM and other theories to build the model and get the optimal solution, and then applies it to practice to verify whether the optimization problem is effective. In order to predict the future development trend and Prospect of the region more accurately, this paper uses cam neural network algorithm to design and realize the fuzzy control of the region.

2 Discussion on Fuzzy Control Evaluation System for Regional Economy Based on VIKOR Algorithm 2.1 Regional Economy Regional economy refers to a kind of economy formed by a country in a certain period of time, taking a certain region as the center, through the optimal allocation and combination of resources, labor and other production factors, which has higher social benefits and economic value, and can drive the development of other industries and produce greater pulling effect. It not only includes the utilization efficiency of natural

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resources, the optimization and upgrading of industrial structure and the length of industrial chain, but also involves the environmental conditions of the whole region [9, 10]. Regional economics mainly studies how to maximize the function of limited natural resources. For each country, its own natural resources and human resources are limited and non-renewable. Therefore, in order to improve the level of economic development in this region and take corresponding measures to achieve sustainable development, we need to make rational and effective use of these resources and maximize their allocation. Regional economics believes that the result of human’s deepening understanding of nature is that there is no connection between people and the objective world itself. Therefore, we can’t judge only by subjective experience, but should establish a systematic theoretical framework based on the interaction between things, and make it more meaningful and practical through certain technical means [11, 12].

2.2 Fuzzy Control Evaluation Fuzzy control is a multivariable and nonlinear mathematical model. In practical application, it can be refined and evaluated. The system can decompose complex problems into multiple single objective functions or sub objective functions. Therefore, it is one of the feasible, effective and necessary means to realize the optimal value estimation and tracking control process by fuzzy operation processing of these specific objects. However, traditional methods generally require a lot of expert experience to get the results, and at the same time, they often cause shortcomings such as large amount of calculation. Therefore, using fuzzy mathematics theory to establish a model can overcome the above defects, and can obtain more accurate evaluation effect and accuracy. Fuzzy control mainly uses mathematical methods to quantify the parameters of the system, so that it has a better effect in practical application. By establishing the model, the influencing factors in the region are combined with other relevant factors to form a nonlinear multivariable control system with certain constraints and boundary conditions, which can reflect the development of regional economy. Its basic principle is that, first of all, based on the controlled process itself, it has the characteristics of being clear and relatively independent, and can be explained and defined with general knowledge. Then choose a suitable way according to the actual situation to transform these features into simple, practical and easy to understand. Fuzzy control is a very effective mathematical method to deal with the complex relationship between uncertain system variables and achieve the goal by accurately describing the object.

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2.3 VIKOR Algorithm The fuzzy control model based on VIKOR method is to determine an optimal parameter and extend it to various fields by mathematical modeling of input variables under the framework of Rost content. It is based on the combination of mathematical principles and computer technology to establish an intelligent system based on fuzzy model. The algorithm realizes automatic control by quantifying the input variables to obtain the input data set, and then calculating the corresponding control parameters on the output. This method can simplify complex problems, simplify their internal structure, and is easy to understand and apply to multi-objective optimization. It is especially suitable for nonlinear time-varying (uncertain) dynamic programming and multi-objective coordinated tracking in uncertain environments. The shape and distribution of triangle U(x) membership function are determined by three parameters, as shown in Eqs. (1) and (2), s and f determine the function range, D determines the vertex position, that is, the peak value, and the reference function type in the membership function editor is trimf. u(x) =

x−s ,s < x < d x−d

(1)

u(x) =

x −s ,d < x < f x −d

(2)

The format of Z-type membership function is shown in formula (3), where x is the independent variable and s and D are parameters to determine the shape and distribution of the curve y = zmf(x, [sd])

(3)

In the application of fuzzy control, first of all, we need to initialize the system to determine the functional relationship between various regions. Then the input vector is established according to the known dimension and output value. By comparing the actual value with the target value, the corresponding parameters are obtained. Finally, according to the control strategy set in the parameter calculation model, the performance of the controlled object is optimized or the requirements it should meet when running under uncertain conditions are changed, so as to optimize the overall system.

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3 Experimental Process of Fuzzy Control Evaluation System for Regional Economy Based on VIKOR Algorithm 3.1 Design Principles of Fuzzy Control Evaluation System The evaluation model of fuzzy control is a multivariable nonlinear system, its parameters are difficult to accurately describe, and it is difficult to determine in practical applications. Therefore, a comprehensive index system is established for quantitative analysis. 1. The principle of combining systematicness and hierarchy. This method takes into account other relevant theoretical knowledge such as fuzzy mathematics and neural network, and also takes into account the characteristics that these factors have a great impact on the level of regional economic development. 2. The design of fuzzy control model should consider its impact on system performance, not only from the economic benefits. Therefore, when establishing the system, we must fully understand the various factors that may occur in the study area in the future and their change rules. 3. The system has good dynamic characteristics. In practical application, the parameters obtained will also change with the influence degree of equivalent factors in time and space. Therefore, the appropriate method should be selected according to the specific situation.

3.2 Regional Economic Fuzzy Control Evaluation System Framework The establishment of fuzzy control model is a mathematical method based on fuzzy set theory. This method describes the complex system, and then uses language symbols to express its information in this process. This method can easily express the uncertain factors and accurate data and quantify them to achieve the optimal solution or approximate predictive output. At the same time, it can also be used to solve the control strategy design problems with strong robustness, which can not be described by linear relations, and there are many fuzzy variables. Fuzzy control is a kind of incomplete system described by calculating deterministic information, in order to achieve the optimal goal of the system. It can be seen from Fig. 1 that the following framework structure is mainly used in this paper: (1) visa gives the matching rules and control criteria between the fuzzy membership function of regional economy and the corresponding parameter values. (2) According to different types of cities, PID controller design based on vise model is constructed, and optimization research is carried out from three aspects: impact minimization algorithm, robust control method and accurate fuzzy controller.

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K

Fuzzy controll er

KU

DE

Fig. 1 Fuzzy control evaluation framework

3.3 Index Test of Regional Economic Fuzzy Control Evaluation System In the process of system testing, we need to evaluate various indicators, including regional economic development level, industrial structure, etc. In order to ensure the integrity and comprehensiveness of the system. This topic uses expert scoring method to determine the weight. The system is described by a certain mathematical model, and then the relationship between various variables is determined according to the established index system. Therefore, in order to accurately reflect the dynamic changes of regional economy in the process of actual operation, this theory needs to be used. Data calibration is required after the completion of software and hardware design. The optimal function value is determined by calculating the corresponding relationship between various parameters, and then a reasonable, accurate, stable and better state space distribution structure is established according to the actual situation. Finally, it is applied to the model construction after the regional economy, so as to ensure that the model can meet the accuracy requirements of the research content.

4 Experimental Analysis of Fuzzy Control Evaluation System for Regional Economy Based on VIKOR Algorithm 4.1 Evaluation Index Test of Regional Economic Fuzzy Control Evaluation System Table 1 is the experimental data of the performance test of the fuzzy control evaluation system. In this simulation process, there is a certain deviation between the output of the model and the actual value, and all parameters in the region are not real physical quantities. The control model is fuzzed. However, because the system is completed under the software development platform, the virtual prototype has the characteristics

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Index

Step response

Weight

System response time

Linkage index

0.45

0.14

2

Achievement indicators

0.32

0.32

2

Potential indicators

0.45

0.24

1

2.5

0.5 0.45

2

0.4 0.35

1

Data

0.2

Time

1.5

0.3 0.25

0.15 0.1

0.5

0.05 0

0 Linkage index

Achievement indicators

Potential indicators

Index Step response

Weight

System response time

Fig. 2 Index of the fuzzy control evaluation system

of real-time, scalability and superior performance, and the fuzzy adaptive method based on VIKOR technology has high requirements for it, so this paper selects a relatively simple, convenient, practical and practical genetic algorithm to simulate the design. As can be seen from Fig. 2, this paper has carried out relevant tests in three aspects of linkage index, achievement index and potential index in the regional economy. The fuzzy control evaluation index has good performance in the system response time and step response time, which shows that the fuzzy control evaluation system under the optimization of the algorithm can meet the actual needs of users.

5 Conclusion Fuzzy control is a very effective stochastic system, which can be modeled mathematically and has been widely used in many fields. This paper mainly introduces the research and design of regional economic fuzzy prediction model based on VIKOR

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method, and takes regional economy as the research object. Firstly, this paper introduces the evaluation model and relevant theoretical basis under the framework of VIKOR, then describes the construction process of fuzzy adaptive evaluation system based on VIKOR model with a specific case, and finally uses the algorithm to verify and analyze the corresponding parameter values designed by the system. Acknowledgements This work was supported by Project: Research on the Output Efficiency of Agricultural Economy in Leizhou Peninsula (No. CJKY201913).

References 1. Abdulkareem KH, Arbaiy N, Zaidan AA, Zaidan BB, Albahri OS, Alsalem MA, Salih MM (2020) A novel multi-perspective benchmarking framework for selecting image dehazing intelligent algorithms based on BWM and group VIKOR techniques. Int J Inf Technol Decis Mak 19(3):909–957 2. Khanali H, Vaziri B (2020) An improved approach to fuzzy clustering based on FCM algorithm and extended VIKOR method. Neural Comput Appl 32(2):473–484 3. Ampririt P, Qafzezi E, Bylykbashi K, Ikeda M, Matsuo K, Barolli L (2022) IFACS-Q3S—a new admission control system for 5G wireless networks based on fuzzy logic and its performance evaluation. Int J Distributed Syst Technol 13(1):1–25 4. Abdygalievich AS, Barlybayev A, Amanzholovich KB (2019) Quality evaluation fuzzy method of automated control systems on the LMS example. IEEE Access 7:138000–138010 5. Boutouba M, El Ougli A, Tidhaf B (2019) Performance evaluation of conventional and fuzzy control systems for speed control of a DC motor using positive output Luo converter. Int J Intell Eng Inform 7(1):4–18 6. Sokolov G, Stepchenkov YA, Rogdestvenski Y, Diachenko YG (2022) Approximate evaluation of the efficiency of synchronous and self-timed methodologies in problems of designing failuretolerant computing and control systems. Autom Remote Control 83(2):264–272 7. Abbasi MA, Baac HW (2022) Wave-mode configurable ultrasonic non-destructive evaluation system using optoacoustic prism. IEEE Access 10:54720–54729 8. Amarasinghe PAGM, Abeygunawardane SK, Singh C (2022) Adequacy evaluation of composite power systems using an evolutionary swarm algorithm. IEEE Access 10:19732– 19741 9. Arens S, Schlüters S, Hanke B, von Maydell K, Agert C (2022) Monte-Carlo evaluation of residential energy system morphologies applying device agnostic energy management. IEEE Access 10:7460–7475 10. Hernández-Solana A, Garcia Ducar P, Valdovinos A, García JE, de Mingo J, Carro PL (2022) Experimental evaluation of transmitted signal distortion caused by power allocation in inter-cell interference coordination techniques for LTE/LTE-A and 5G systems. IEEE Access 10:47854– 47868 11. Karimoddini A, Khan MA, Gebreyohannes S, Heiges M, Trewhitt E, Homaifar A (2022) Automatic test and evaluation of autonomous systems. IEEE Access 10:72227–72238 12. Riquelme-Dominguez JM, Martinez S (2022) Systematic evaluation of photovoltaic MPPT algorithms using state-space models under different dynamic test procedures. IEEE Access 10:45772–45783

Predictive Analysis of Key Performance Indicators of Distributed Database Based on Machine Learning Algorithm Tingting Wu

Abstract With the advent of the Internet era, data is growing at an alarming rate, and the current global data traffic is huge. The era of big data presents three major characteristics, namely, large amount of data, various types, and real-time nature, which pose challenges to the existing database systems. For big data, distributed databases play a huge role. The purpose of this paper is to predict and analyze the key performance indicators of distributed databases based on machine learning algorithms. In the experiment, the PM2.5 data for the whole year of 2020 was divided into 2 different quarters to analyze and predict the air pollution indicators of M city based on a hybrid model based on distributed database and machine learning algorithm. Using the Boosting Tree algorithm, the indicator forecast analysis is performed on the PM2.5 forecast in the first quarter and the PM2.5 forecast in the second quarter. Keywords Machine learning algorithms · Distributed databases · Key performance indicators · Indicator predictive analysis

1 Introduction Computer technology and the Internet industry are improving rapidly, and the Internet has penetrated into various fields such as our life, learning, social interaction, entertainment, etc. Especially in the era of mobile Internet, everyone’s mobile terminal is sending out a large number of network requests based on different needs every day, resulting in a large number of network requests. Content, Internet content and service providers also generate a large amount of data every day, and large and small enterprises and institutions also generate a large amount of useful or useless data every day. With the rapid improvement of the Internet, we have entered the era of big data [1]. T. Wu (B) Shandong Vocational College of Science and Technology, Weifang, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_67

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Massive data contains a lot of knowledge value, and machine learning is a key technology that can extract useful information from massive data. E Sevinç believes that high performance, small PC hardware and high-speed LAN/WAN technologies make distributed data systems an attractive research area, where query optimization and distributed data design are two important issues. Since dynamic programming is not possible for query optimization in distributed databases, a new query optimization method based on genetic algorithm is proposed, and its performance is compared with stochastic and optimal algorithms. Experiment on synthetic databases with fixed relationships but no horizontal or vertical sharding. The network link is assumed to be Gigabit Ethernet. A comparison with the optimization results shows that the new formulation of the genetic algorithm produces only optimization results, and we achieve an improvement compared to previous algorithm-based algorithms [2]. Yulianto AA believes that while a data warehouse is designed to support decisionmaking activities, the most time-consuming part is the extraction and transformation process. In academic data warehouses, when the data source comes from the shared database of the college, although the database is representative, it is not easy to integrate. This article describes how to implement a variable load strategy in a distributed data warehouse. After streaming the data in the data creation area, we perform drilldown analysis to identify all tables in each data source, including content analysis. Then, the cleaning, validation, and data delivery steps put the different data sources into the data warehouse. Since improving a data warehouse using a bottom-up, multimethod approach, we have discovered three types of operations that extract from data source tables: merge, merge, and merge. Purify and organize step-by-step results by creating consistent metrics across data source analysis, refinement, and processes [3]. A distributed processing tool for big data with strong versatility, ease of use, and excellent performance can analyze data. This paper studies the related theory of distributed database, introduces its data consistency and Raft algorithm, and an introduction to machine learning. In the machine learning classification algorithm, Softmax logistic regression, support vector machine (SVM) and random forest (RF) are introduced respectively. In the experiment, the PM2.5 data for the whole year of 2020 was divided into 2 different quarters to analyze and predict the air pollution indicators of M city based on a hybrid model based on distributed database and machine learning algorithm. Using the Boosting Tree algorithm, the indicator forecast analysis is performed on the PM2.5 forecast in the first quarter and the PM2.5 forecast in the second quarter.

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2 Research on Predictive Analysis of Key Performance Indicators of Distributed Database Based on Machine Learning Algorithm 2.1 Introduction to the Theory of Distributed Database Since this paper needs to implement a distributed database, it is necessary to introduce the concepts and algorithms related to distributed storage [4]. The following is a brief overview of data consistency in distributed storage and an easy-to-understand strong consistency algorithm, the Raft algorithm [5]. 1. Data consistency In order to improve the reliability and availability of the system, the multicopy mechanism is mainly used in distributed storage, that is, for the same piece of data, multiple identical copies need to be stored on multiple nodes. For a distributed storage system with multiple copies, if the node where one of the copies is located goes down, requests can be routed to other copies, ensuring the reliability and availability of the entire storage system. At the same time, for applications that do not require high consistency, multiple copies can also be used to provide read and write services at the same time to improve the overall performance of the system. For a distributed storage system using a multi-copy mechanism, how to maintain the consistency between the copies is a key content that must be considered. Important CAP Theory in the Distributed Domain. The main contents of its theory are as follows: C (Consistency): means consistency. Consistency here refers to strong consistency, which requires multiple copies in the system to be consistent to the outside world at any time. A (Availability): Indicates availability [6]. It means that if some replicas in the system cannot provide services due to downtime or partition, the entire system can still work normally. P (Partition Tolerance): Represents partition tolerance [7]. This means that the entire system can allow network partitioning, that is, allowing isolated sub-networks to exist in the entire system. 2. Raft algorithm Among the strong consensus algorithms, the Paxos algorithm is considered a classic and is also very influential in academic theory. However, because its content is difficult to understand, it is difficult to implement in engineering practice, so an easy-to-understand consensus algorithm Raft [8] appears. The Raft algorithm is theoretically based on a replicated state machine. For each node in the cluster, they are in the same state initially, and execute the same sequence of commands in the same order during the cluster running process, and finally they reach the state will be consistent. The basic principle and main process of the Raft algorithm are briefly introduced below. In the Raft algorithm, nodes have three main role states: Leader, Candidate and Follower [9]. Their main tasks are as follows: (1) Leader: This is the leader in the

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entire Raft system, responsible for processing client requests, and encapsulating the requests into logs and sending them to other nodes in the system. At the same time, the leader will periodically send heartbeat messages to other nodes in the system to maintain the identity status of the leader. When no network partition occurs, there is only one Leader node in the entire Raft system. (2) Candidate: This is the transitional role among the three roles. When the follower node does not receive the heartbeat message for a long time, it will initiate a voting election and convert it into a candidate. Candidates can become Leaders when they receive votes from more than half of the nodes in the system. (3) Follower: This is a common role in the Raft system, used to respond to the Leader’s request. And restart a round of election after waiting for a timeout.

2.2 Introduction to Machine Learning Experts and scholars have always hoped that machines can be more intelligent, and artificial intelligence came into being. In the earliest days, machines acquired intelligence by endowing them with logical reasoning ability and computing power. At that time, AI programs could realize the proof of famous theorems. However, due to the lack of machine knowledge, true intelligence could not be realized [10]. Subsequently, people began to “teach” prior knowledge to machines to obtain intelligent machines, but knowledge is huge, and this idea is prohibitive. Early artificial intelligence made machines more intelligent according to human settings, and the resulting intelligent machines could not achieve true intelligence and could not surpass humans. As a result, humans have come up with the idea of “self-learning” by machines, and machine learning methods are methods by which machines can learn from data. Nowadays, machine learning has been widely used in the fields of image audio and video processing and biological macromolecular structure prediction. The core of machine learning is the design and analysis of algorithms that aim to allow machines to automatically learn information from data [11]. For the research of machine learning, we must first understand its classification: (1) Supervised learning: classify and label data samples, and then use machines to learn the characteristics of data samples, and trained machines are used to deal with unclassified and unlabeled data. Samples, typical algorithms of this type include decision trees, support vector machines, etc. (2) Unsupervised learning: All data samples in the dataset are unlabeled and their categories are unknown. The model needs to learn the internal structure of the data by itself and apply it to new data. This type of algorithm is usually used for clustering, such as the common K-means clustering algorithm. (3) Semi-supervised learning: There is no very clear boundary between supervised learning and unsupervised learning. In fact, there is such a learning mode: using two sample data sets, one with labels and one without labels, the model Learning to generate appropriate classifications on two sample datasets can be referred to as semi-supervised learning. Classical machine learning methods have achieved great

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success in many fields, however, data such as speech has multi-dimensional characteristics, and traditional machine learning methods are difficult to process such high-dimensional data.

2.3 Machine Learning Classification Algorithms 1. Softmax logistic regression Logistic regression analysis is a multivariate analysis method, which is usually used in two-class and can be extended to multi-class research problems. It analyzes the relationship between the observations of the dependent variable and the corresponding influencing factors of some independent variables [12]. The transition from a logistic regression model to a Softmax logistic regression model is equivalent to an extension of binary classification to multiclass problems. In daily research, Softmax logistic regression model is very suitable for solving practical problems of multi-classification. 2. Support Vector Machine (SVM) The SVM classification algorithm originated from the generalized portrait algorithm in pattern recognition. SVM is a binary linear classifier that uses supervised learning on the training data. The maximum margin hyperplane obtained by training and learning the training set samples is the decision. Boundary [13]. SVM is an algorithm originally designed for binary classification problems. It can solve multi-classification problems by constructing multiple decision boundaries in an orderly manner, that is, each decision boundary determines the attribution of one classification to all other classifications, and the class of the sample is based on its impact on all decisions. The category with the highest score in the discrimination result of the boundary is selected. The algorithmic complexity of the support vector machine configured by the linear kernel function is the number of support vectors multiplied by the dimension of the input vector. 3. Random Forest (RF) Random Forest is derived from Random Decision Forest and is registered as a trademark. In the actual machine learning classification problem, the random forest algorithm builds a random forest prediction model by randomly sampling objects and explanatory variables, which is equivalent to generating multiple decision trees, all of which can realize object classification [14]. Random forest is composed of decision tree, which is a basic classifier with the advantages of high readability and fast classification speed. By continuously learning from the training dataset, the test samples are classified, and if M decision trees are used to vote, the time complexity of the algorithm is 1. One of the functions of random forests is to be used as a classifier, and in addition, to identify important explanatory variables. It is also a commonly used function of random forest. This study is used to screen high-information AISNP combinations that can stably distinguish the three major populations in northern East Asia. The “average reduction value” indicates the

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degree to which each predictor reduces the accuracy of the random forest prediction model, and the value represents the contribution of the explanatory variable to the classification accuracy.

3 Investigation and Research on Prediction of Key Performance Indicators of Distributed Database Based on Machine Learning Algorithm 3.1 Research Content The PM2.5 data for the whole year of 2020 is divided into 2 different quarters to analyze and predict the air pollution indicators of M city based on a hybrid model based on distributed database and machine learning algorithm. The PM2.5 data from January 1, 2020 to January 10, 2020 were set as the training samples for the first quarter, and the PM2.5 data from June 1, 2020 to June 10, 2020 were Consider Q1 validation samples; PM2.5 data from July 1, 2020 to July 10, 2020 are set as training samples for Q2, and from December 1, 2020 to December 1, 2020 The daily PM2. data is considered a validation sample for the second quarter.

3.2 Boosting Tree Algorithm The Boosting Tree algorithm uses an additive model and a forward step-by-step algorithm, and the basic models are all decision tree models. The meaning of the forward step-by-step algorithm is to perform model optimization synchronously on the basis of superimposing the new base model, that is, each new superimposed model will fit the residual generated by the previous model fitting. From the interpretation of the algorithm model, M is the number of decision trees; T (x, θm ) is a decision tree; θm is the parameter of the corresponding decision tree. Boosting Tree is an additive model of decision tree: f M (x) =

M 

T (x, θm )

(1)

m=1

The Boosting Tree model adopts a forward step-by-step algorithm, where assuming f 0 (x) = x, the model at the m step is: f M (x) = f M−1 (x) + T (x, θm )

(2)

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4 Predictive Analysis and Research of Key Performance Indicators of Distributed Database Based on Machine Learning Algorithm 4.1 PM2.5 Forecast for the First Quarter Put the segmented data into a distributed database, and compare the four single forecast results together in the PM2.5 forecast for the first quarter to determine whether the proposed hybrid forecast model has better forecast performance. Judging the pros and cons of the model is still based on the accuracy and stability of the model, and the evaluation indicators of the prediction model are still MAE, MSE and MAPE. The specific comparison results are shown in Table 1 and Fig. 1. Table 1 Forecast results for Q1 PM2.5 Standard

WNN

BPNN

Elamn NN

GRNN

MAE

3.8744

3.9857

4.4125

5.8418

MISE

33.6444

33.8484

33.8747

48.6547

MAPE (%)

11.57

11.59

12.69

16.87

Optimal input level

6

9

7

1

Optimal implicit layer

9

15

24

20

60 50

Value

40 30 20 10 0 MAE

MISE

WNN

BPNN

MAPE

Content Elamn NN

Optimal input level

GRNN

Fig. 1 PM2.5 forecast index comparison chart for the first quarter

Optimal implicit layer

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4.2 PM2.5 Forecast for the Second Quarter In this data comparison study, it can be clearly seen that among all the predicted results, the four smallest are MAE, MSE and MAPE values. The forecast results for the second quarter are shown in Table 2 and Fig. 2. To sum up, compared with the forecast PM2.5 results in the first quarter and the forecast PM2.5 results in the second quarter, the air quality indicators in the second quarter have better results, indicating that their forecasting performance is more complete. Table 2 Forecast results for the second quarter Standard

WNN

BPNN

Elamn NN

GRNN

MAE

4.847

4.897

5.024

5.874

MISE

37.874

38.547

38.987

58.968

MAPE (%)

13.04

13.08

13.58

19.87

Optimal input level

7

12

9

3

Optimal implicit layer

10

18

28

24

Optimal implicit layer

Content

Optimal input level

MAPE

MISE

MAE

0

10

20

30

40

50

60

Value GRNN Fig. 2 Data comparison of the second quarter

Elamn NN

BPNN

WNN

70

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5 Conclusions Facing the current massive data information of hundreds of thousands of TB or PB level, traditional computer technology has been unable to meet the needs of data processing in terms of storage capacity and computing power. How to efficiently store and analyze massive data is the current academic what is being studied in the world and industry. Because of this, we need a processing tool for big data with strong versatility and excellent performance. Data analysis and mining in the traditional sense mainly use statistical methods similar to databases, while machine learning algorithms refer to analyzing a large number of sample data. Learning certain patterns or characteristics to generalize, identify, and predict unknown outcomes or unobservable data. Its ideas originally originated from modern statistical learning theory. Therefore, the prediction analysis and research of key performance indicators of distributed databases based on machine learning algorithms are of great significance.

References 1. Teluguntla P, Thenkabail PS, Oliphant A et al (2018) A 30-m Landsat-derived cropland extent product of Australia and china using random forest machine learning algorithm on google earth engine cloud computing platform. ISPRS J Photogramm Remote Sens 144(11):325–340 2. Sevinç E, Co¸sar A (2018) An evolutionary genetic algorithm for optimization of distributed database queries. Comput J 54(5):717–725 3. Yulianto AA (2020) Extract transform load (ETL) process in distributed database academic data warehouse. APTIKOM J Comput Sci Inf Technol 4(2):61–68 4. Gao J, Nuyttens D, Lootens P et al (2018) Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosys Eng 170(1):39–50 5. Comertpay B, Gov E (2022) Identification of molecular signatures and pathways of obese breast cancer gene expression data by a machine learning algorithm. J Transl Genet Genomics 6(1):84–94 6. Revel-Vilk S, Chodick G, Shalev V et al (2021) Study design: development of an advanced machine learning algorithm for the early diagnosis of Gaucher disease using real-world data. Mol Genet Metab 132(2):91–91 7. Das S, Chakrabarti S (2021) Classification and prediction of protein–protein interaction interface using machine learning algorithm. Sci Rep 11(1):1761–1761 8. Ruby J, Kumar PSJ, Pan M et al (2018) Support vector machine based retinal therapeutic for glaucoma using machine learning algorithm. Int J Med Eng Inform 5(3):7–7 9. Jiang X, Sohail M (2022) Evaluation method of human resource management efficiency of chain retail enterprises based on distributed database. Comput Math Methods Med 2022(1):1–8 10. Solihah B, Azhari A, Musdholifah A (2021) The empirical comparison of machine learning algorithm for the class imbalanced problem in conformational epitope prediction. JUITA J Informatika 9(1):131–131 11. Ge YF, Yu WJ, Cao J et al (2020) Distributed memetic algorithm for outsourced database fragmentation. IEEE Trans Cybern 99(99):1–14 12. Plante A, Weinstein H (2021) A machine learning algorithm for the detection of function-related rare events in MD trajectories of biomolecular systems. Biophys J 120(3):301–301

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13. Mohsin SA, Younes A, Darwish SM (2021) Dynamic cost ant colony algorithm to optimize query for distributed database based on quantum-inspired approach. Symmetry 13(1):70–70 14. Mikhailutsa O, Melikhova T, Stoiev V et al (2020) The study of the economic feasibility of using distributed database technology when building an electronic system for voting. WSEAS Trans Bus Econ 17(1):478–486

Design of Regional Economic Information Sharing Based on Blockchain Technology Bin Ji and Zhiheng Zhang

Abstract We have developed a new data sharing platform by using blockchain technology to realize the exchange of economic information on this platform. On the other hand, the architecture of monitoring function is built to realize the functions of location sharing, information updating and storage, and enhance the storage capacity and information security. In terms of software, blockchain technology is used to build data interaction data links between regions. Keywords Network technology · Blockchain · Economic network

1 Introduction The emergence of network information technology promotes the development of regional economy, and also brings a lot of information. In order to facilitate the retrieval of accurate data of regional economy, the idea of sharing data is introduced into the development of regional economy. Some outstanding researchers first put forward the concept of sharing, and advocated the construction of another system to strengthen the dissemination and exchange of information between regions. The blockchain technology has built an obvious architecture with strong non damping technical characteristics. Regional economy refers to the production complex produced by the interaction of internal factors and external conditions of economic development in a certain region. A regional development entity that takes a certain region as the scope and closely B. Ji (B) International Economics and Trade Major, Jilin Agricultural Science and Technology University, Jilin, China e-mail: [email protected] Z. Zhang Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_68

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combines with economic factors and their distribution. Regional economy reflects the objective laws of economic development in different regions and the relationship between connotation and extension. The theory of regional economic development includes: the gradient theory of regional economic development, the radiation theory of regional economic development, the growth pole theory of regional economic development, and the comparative theory of regional economic development. In terms of hardware, ok6410 development board with the highest performance and most suitable for information sharing, RSIC processor with high cost performance and powerful functions, and other auxiliary input/output component frameworks are selected to make the system run smoothly and greatly increase its smoothness [1]. Blockchain is a virtual ledger, in which each bookkeeping detail record is a block. These records are distributed to everyone on the network, and they can read and add, but cannot be modified. Adding, deleting, modifying and checking CRUD cannot delete and modify them. If you want to delete and modify them, you can add a new record to offset the original value, such as the previous “borrowing error” account in financial accounting, A “lending” record with the same amount will be added later for offset; For another example, listed companies often issue quarterly statement revisions. Last month, they made a profit of 5 million yuan (Chinese listed companies sold a suite of flats). The next month, they announced that last month’s profit was revised to 3 million yuan. Such words are like spilled water. They can’t be taken back. They can’t be modified with erasers. In finance, if you want to change database records, you can use SQL update to modify records, and it is a crime to alter financial books. A block in a blockchain is such a record linked to the previous record, just like a kindergarten child holding hands, staring at one another, like a linked list LinkedList, so that no one can tamper with the chain. Of course, this high serialization, like the serialization of transaction ACIDs, causes performance limitations. Therefore, one of the bottlenecks restricting the wide application of the blockchain is its throughput and concurrency performance, These are the sacrificial aspects caused by its high transaction security. Blockchain is also a distributed transaction database to ensure high consistency. It maintains a growing and orderly transaction record list. Its distributed transaction implementation is different from the centralized database distributed transaction implementation. The latter usually uses Paxos or Raft for consensus aggregation, and ultimately changes the status of all server nodes to a consistent state; The distributed transaction mechanism of the blockchain is also different from the traditional 2PC two-part transaction mechanism, which is mainly implemented by locking the resources of transaction participants. The transaction mechanism of the blockchain is very similar to the transaction mechanism of event sourcing. Blockchain not only guarantees the high transaction integrity of transactions, but also combines or couples the security encryption algorithm. This integrated design is very suitable for various money related transfer transactions, smart contracts or cryptocurrencies and other applications.

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2 System Framework We have built a traceable hardware framework of the sharing system to improve the data storage and data security of the regional economic information sharing system. In the traceability link, the development board, as the main system storage body, executes a series of operating procedures, such as data storage, data storage and data sharing, to display the information interface. You need to go through the development committee. In addition, other system hardware needs to complete each task according to the requirements of the development board. The local device and the remote device can establish network connection through the air interface to form a “wireless” network and realize the sharing of multiple devices in the system [2]. Using the system hardware architecture with tracking function and information sharing, that is, by using the USB hub to expand the USB interface of the development board, the development board can support more local and remote devices. It is impossible to associate events with time points in a distributed system, which is an unsolved problem. Until Nakamoto invented the blockchain work to prove this solution, decentralized ledgers can be implemented. The work of the blockchain has proved that it is a SHA-2 hash value that meets certain requirements. This value is very difficult to find. The difficulty is that the hash is smaller than a specific number. The smaller the number, the fewer input values and the higher the difficulty of finding it. It is called “proof of work” because it is known that the value with this hash is very rare, which means that finding a new value requires a lot of trial and error, that is, “work”. This, in turn, means consuming “time”. Bitcoin search difficulty is dynamically adjusted so that a correct hash value can be found every ten minutes on average. That is to say, the blockchain can’t decide who can calculate the hash value as quickly as possible (because the server clock of each participant can’t be as accurate as the watch). Then, if it is delayed for a period of time, such as 10 min, who can calculate who wins first, and then multiply quickly. The longer the chain is, the better it will be. This is an additional judgment condition for the possibility of having two winners within 10 min. We may as well set the velocity as V, then we get the following formula  V1 =

C1 − C2 = |t1 − t2 | C2

 (1)

where C represents the capacity and T represents the time offset. Through these capacities and time offsets, the speed is affected, and the size of the optimal block D is determined to make the system more readable.  D = C2 ×

  di+1 − d1 T − T' × 1− − k2 k1 vt

(2)

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The system hardware framework designed in this paper has the characteristics of high positioning, high sensitivity and low power consumption. Therefore, it can retrieve multiple shared terminals at the same time to update the local economic information data of shared user terminals in real time. Screening and treatment shall be carried out after availability is guaranteed. The time subtraction in the absolute value in formula (1) represents the time delay of the network, and the value range of the best block is determined through this delay.

3 Software Design 3.1 Two Chain Storage Structures Are Used Two chain storage modes are installed in the shared system using blockchain technology. When extracting information from the network, it caches the information into the information extraction pool and waits for a new block to be generated. After generating a new block, the information is packaged from the extraction pool into blocks according to the information extraction amount and query content, and the block information is verified using the integrated network. In the above data storage process, the generation rate and the block capacity of the new block determine the data write rate of the block chain. In the process of calculating the speed, MySQL database is selected as the database, so that the calculation information can be imported through the link and the program can run normally [3]. In this chain storage structure, in order to keep the number of columns of the hash value at any time, the storage structure is placed in the transaction segment, which not only enables the function to operate normally, but also reduces the possibility of malicious changes to the system. The specific storage structure is shown in Fig. 1.

3.2 Block Information Sharing Based on the above research, a block is constructed using the blockchain technology, which is the lowest P2P network of the system. Users in each area under the P2P network; If you acquire and share information, all ends are treated as equal node rights. If the network accesses a new node, it must clarify the node location and status of other nodes in the network to achieve blockchain synchronization. Therefore, you need to write more IP addresses of usage nodes. The DNS node scans the network records that can provide DNS services. The IP addresses of active nodes in the network are used to add information to the system. Entering a new node in the blockchain network can send a random request to the DNS node. This will obtain the location and status information of other active nodes in the network. Because the node is active, some nodes may not be online, but the

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Fig. 1 Storage structure

system cannot return to the network active node IP address list before responding to the request of the new node [4]. However, in this process, the node may return to other locations, which requires the monitor to monitor and describe the monitoring content in detail, so that the modified program can run normally. The decoding process is almost a part of the process. This series of mechanisms can solve many problems that may actually encounter in the process of digital transactions in a distributed network environment of public, anonymous and mutual distrust. This corner may certainly be difficult to understand, but now I need to stop and stop your mind, you will have a few minutes to close your eyes and taste a place where the design is excellent. After obtaining other IPS, try to add a new blockchain node to transmit its own version of data, including but not limited to the system version and synchronized blocks, so as to connect with another node. After receiving the message, the peer node sends its own release message to the new node. With the confirmation of the two terminals, the two nodes successfully complete the link. When a node enters the blockchain for the first time, the node also needs to download on the largest blockchain in the whole network. After the node is completed, this node will randomly select a node in a network to synchronize the data block. At this time, the new node will send a request header information to the synchronization node. It is shown in Fig. 2.

4 Experimental Test Stage In order to improve the program so that it can run normally, so as to ensure that other unknown users can not tamper with the program, a large number of virtual

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Fig. 2 Request internal information

Table 1 Information change Economic information

Experimental group

Control 1

Control 2

Control 3

Social economic

0.82

0.88

0.45

0.80

Market economic

0.91

0.72

0.77

0.53

experiments need to be set up to highly compare the information put into the memory, and interrupt programs are set up. Five computers are used for comparison. When the program is tampered with and the modified results are not output, it means that the program is successful. However, in order to ensure the integrity of the experiment, the results of the program are also compared through economic data. The modified information is shown in Table 1. It can be seen from the table that the social and economic information of control 2 is basically the same as that of the experimental group, except that the social and economic information of control 2 is quite different from that of the experimental group. This proves the unity of the experiment in most cases, but we must prevent some emergencies, such as improving the safety of the system. It can be seen from Table 2 that in the actual application range, most of the head industries use the sharing system described in the article. It can be seen that the system also has absolute advantages and positions in the application range.

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Table 2 Comparison of users of shared system Industry

Experiment group (%)

Control 1 (%)

Control 2 (%)

Control 3 (%)

Financial

96.2

0.7

1.4

0.6

Large enterprises

95.4

1.5

0.7

1.5

Private enterprises

54.3

1.4

1.3

1.5

5 Summary For the economic information sharing system, the use of blockchain technology is undoubtedly one of the most effective ways. Blockchain can summarize and share the vast majority of information so that they can play a role in this field. The dual chain storage function of blockchain technology also plays an important role in this system [5]. Nowadays, the technical problems are constantly solved by emerging systems, but the data processing still does not reach the application level, at least there is a gap from complete sharing [6]. Icloud is often used in the apple system to store information and realize face-to-face sharing among users. However, economic information is much more complicated than transmitting photos, because its ability to process information still needs to be further strengthened [7]. However, the designed information sharing solves the information processing functions encountered in the sharing, such as calculating the statistical information through the database related algorithms, and transmitting the information to the cloud through the blockchain technology for troubleshooting and sharing [8]. The shared economy is taking full advantage of advanced technologies such as cloud computing and big data, and this new economic system is based on Internet technology. There are many idle resources in the society, and it is not possible to play an original role in the single use, but the role which can be demonstrated is very remarkable by the fusion of it. The shared economy is the bond between these idle resources, and the resource can be drawn out sufficiently, and it can be jointly introduced into the economic society, and it can change the conventional business model. The shared economy is a new business model, the product of “Internet + ”, and its share is the core principle. After sharing bicycles, the idea of sharing has gradually permeated into each field. In pursuit of the essence of the shared economy, this is to pass the right to use products and services that utilize Internet technology under certain conditions, so that social resources can maximize the availability, and all resources need not be restricted by time and space It can be used more effectively. Japan is currently lacking in resource constraints, and environmental pollution is in a serious situation, and the economy must introduce a shared economy to achieve conversion. The shared economy is based on big data and cloud computing, with the “Internet +” guidance, growing rapidly in many fields such as finance, medical care and public services, and in 2016, shared economies had a loan scale of more than 10 billion yuan. The development of the economy is very rapid, and it is widely applied in many fields, and

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the degree of approval of the society is high, and the scale of the loan is expanded, and the problem which appears continuously in the development process is not neglected. The newly built economic big data platform integrates the data information of all economic sectors such as industry and commerce, taxation, statistics, commerce, economy and information technology, and development and reform, and realizes the correlation analysis, mutual verification, and integrated application of data. The platform realizes the data sharing and application of all streets and departments at district level, forms the application mode of “one management, multiple use” of data, and makes the information system extend to all streets and departments, so that information sharing is barrier free. In the near future, the blockchain technology will reach new processing modes. The data storage pictures involved in these modes have been explained in the text. Through the intercommunication of multiple blockchains and the sharing of hash values, the information will be perfectly processed and reflected in the database [9]. The processing mode used in the test phase is also being applied by the majority of computer users. Through the comparison of data displayed by multiple computers, the tampering situation of information is known and monitored and interrupted [10–12].

References 1. Kim M, Lee J, Park K et al (2021) Design of secure decentralized car-sharing system using blockchain. IEEE Access PP(99):1 2. Kong Y, Petrov D, Risnen V et al (2021) Path-link graph neural network for IP network performance prediction. 17 3. Noh T, Choi J (2022) Cell-free MIMO systems powered by intelligent reflecting surfaces. 20 4. Rajasoundaran S, Prabu AV, Routray S et al (2022) Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks. Comput Commun 187:71–82 5. Koutlia K, Bojovic B, Ali Z et al (2022) Calibration of the 5G-LENA system level simulator in 3GPP reference scenarios. 89 6. Gomez AS, Zhivkov T, Gao J et al (2021) The need for speed: how 5G communication can support AI in the field. In: UKRAS21 conference “robotics at home”. p 137 7. Jafary P, Supponen A, Repo S (2022) Network architecture for IEC61850-90-5 communication: case study of evaluating R-GOOSE over 5G for communication-based protection. Energies 15 8. Tomyuk ON, Avdeeva OA (2022) Digital transformation of the global media market: in search for new media formats. Econ Consultant 37 9. Nowak TW, Sepczuk M, Kotulski Z et al (2021) Verticals in 5G MEC—use cases and security challenges. IEEE Access PP(99):1 10. Richards DR, Lavorel S (2022) Integrating social media data and machine learning to analyse scenarios of landscape appreciation. Ecosyst Serv 55 11. Rodriguez I, Mogensen RS, Fink A et al (2021) An experimental framework for 5G wireless system integration into industry 4.0 applications. Energies 14 12. Ochoa MH, Siller M, Duran-Limon HA et al (2021) Access network selection based on available bandwidth estimation for heterogeneous networks. Comput Stand Interfaces 78–79

Building Structure Optimization Based on Computer Big Data Chao Li, Qiufan Chen, Huafei Huang, and Qiong Zeng

Abstract Rapid urbanization makes the spatial structure of buildings increasingly complex, and the rapid expansion of construction scale leads to the unreasonable layout of urban functions and road network form, which leads to a series of urban problems. The research and optimization of the spatial structure of buildings are particularly important for the healthy and sustainable development of cities. Multisource big data provides new ideas and methods for the study of architectural spatial structure. This paper mainly carries on the research of building structure optimization based on computer big data. This paper first analyzes the demand of big data in the construction industry. According to the analysis results, it uses big data to optimize the design of building structure and uses PKPM model to analyze the optimization results. Through the analysis results, it can be known that the building structure optimized by big data meets the requirements of the building structure code. Keywords Big data · Building structure · Structure optimization · PKPM model

1 Introduction In recent years, with the rise of big data, industrial data that can be used to study building structures are more abundant. Big data provides an opportunity for building structure. Due to the shortage of traditional data and the limitation of acquisition channels, big data, as an alternative to traditional data, has the characteristics of accurate and effective acquisition method, sufficient data volume, high precision and wide coverage, making big data have an unparalleled role in the analysis of the internal spatial structure of buildings [1]. At present, big data technology has been widely used in urban space research, such as urban planning, establishment of urban functional areas, spatial flow characteristics of residents and network analysis [2]. C. Li · Q. Chen · H. Huang · Q. Zeng (B) Guangzhou Huali College, Guangzhou 511325, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_69

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With the development of network data mining, the collection and analysis of resident behavior data and the maturity of data visualization technology, relevant institutions and urban geographers have increasingly realized the importance of big data [3]. At the same time, big data provides a favorable way for the reform of various disciplines and the research and innovation of urban spatial structure. As the foundation of big data analysis, data generation and data collection are very important links, and data mining is the key. The construction industry is now moving from the traditional computer-aided design (CAD) stage to the application of building information modeling (BIM) in order to achieve the design and management of the whole life cycle of projects that cannot be achieved in the past [4]. As the foundation of the project collaboration platform, BIM model not only supports the communication and collaboration of designers, engineers, party A, builders and other project parties, but also plays an important role in integrating the model and collecting and storing various information in the project life cycle. The application of big data enables the integration of previously scattered and fragmented information in the construction industry, making it possible to effectively analyze and utilize massive data generated by the construction industry [5]. With the application of big data technology, the information of the construction industry will increase exponentially. According to the estimate of Glodon, the average life cycle of a building will generate about 10 T level of data, which means that the construction industry has a huge ocean of data that cannot be applied [6]. The data ocean of the construction industry needs to be exploited, and scholars at home and abroad have conducted research on data mining and analysis of information in the construction industry [7]. The study of urban spatial structure and morphological characteristics by big data is more and more perfect, and the methods of urban form analysis and optimization are more comprehensive. The study of the spatial structure of buildings can improve the operation efficiency of the city, and the construction of the city lies in guiding the rationalization of the structure of the building space.

2 Building Structure Optimization Based on Big Data 2.1 Demand for Big Data in the Construction Industry As a special manufacturing and service industry, the construction industry, like other industries, has data mining needs for the industry’s big data. The structural analysis of buildings is generally based on the finite element analysis based on certain assumptions and combined with the empirical formula obtained from experiments. The results obtained from simple analysis are difficult to be consistent with the actual experimental results. For the experimental data of structural analysis, the data mining technology such as clustering and association analysis can be used to process, to find out valuable information from a large number of experimental data, to establish a more accurate and more in line with the actual situation of the

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hypothesis model, so that the prediction of the analysis object of structural analysis is more consistent with the actual situation [8]. From the perspective of building energy consumption, large public buildings have huge energy consumption, and the effective management of energy consumption in these public buildings is also related to the huge energy saving problem. In the process of the public building equipment operation, real-time energy consumption data have been collected accumulation, as the growth of the time, the energy consumption data to form the large amount of data, and as a result of its multi-dimensional attribute characteristics and the existing data noise, the absence of data, the data processing of the conventional data analysis methods. After some data preprocessing, the data is reduced to and screened, and the data mining technology is used to find out the rules of energy consumption, and the energy consumption analysis model is established, which can be used for energy consumption prediction and energy consumption optimization of public buildings [9]. From the point of construction progress control, construction project of the actual project progress will be influenced by various factors resulting in advance and delay, such as deviation of construction design, construction preparation, construction management, the supply of labor and equipment problems and weather conditions, etc., and some has not been discovered or pay attention to the causes of the construction schedule delays. In construction engineering, construction record for each with problems of informatization construction can be cumulative collection and storage, combined with the problems of the time, place, object and consequences of each respect such as the record of information, can build on the progress delay problem of the data warehouse, used for data mining work schedule problem. Due to the multi-dimension and multi-level nature of construction management data, association algorithm can be used to mine the strong association rules of various factors or multi-factors on construction schedule delay, and the obtained data can be used to assist site managers in making decisions to deal with unnecessary schedule delay [10].

2.2 Building Structure Optimization Process It is a necessary process of structural design to check whether the strength, stiffness, stability and displacement Angle of the whole structure are within the limit and meet the structural safety. According to the standard design requirements, the formula is used to calculate the components and the whole structure to judge whether the structure is safe. 1. Beam calculation The strength of the beam member needs to be calculated to check whether it meets the structural limit, and the structure will not be damaged. According to the structural design principle, the strength of the beam member is obtained by connecting different battery blocks.

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Shear stress ratio: According to the specification and design experience, if the beam section is not weakened, it is not necessary to calculate the shear strength of the beam. The stiffness check of the beam is the deflection check of the beam. The insufficient stiffness of the beam will cause large deformation. In this paper, the deflection of the beam is checked according to the specification, and the section of the member is optimized according to the deflection of the beam. According to the specification requirements, the design value of axial pressure, stability coefficient, design value of maximum bending moment, modulus of wool section, stability coefficient of axial compression member, influence coefficient of section and other parameters are successively calculated, and the overall stability and local stability of the beam are output. 2. Column calculation Different from the strength input values of beams, the important indexes affecting the strength of columns are compressive strength and flexural strength respectively, so they should be calculated and summed separately. According to the specification requirements, the parameters such as the net section area of the checked section, the net section modulus of the checked section to the X-axis and Y-axis and the plasticity development coefficient of the section are successively input. The output value can output the column strength of a single member or the strength of all the column members of the whole building. In order to meet the normal use requirements of the structure, the axial stress members should not be too soft, but should have a certain stiffness, to ensure that the members will not produce excessive deformation. When the condition of not having to calculate the overall stability is not satisfied, the calculation should be carried out according to the formula of the overall stability of the beam specified in the Code for Design of Steel Structures. βmx Mx N + ≤ 1.0 ϕx A f γx W1x (1 − 0.8N /N E' X ) f

(1)

where • N—design value of axial pressure within the calculated component range (N); • N E' X —parameter, N'Ex = π 2EA/(1.1λ2x); • ϕx Stability coefficient of axially compressed member in the plane under the action of ϕx bending moment; • Mx the design value of the maximum bending moment within the range of the component segment calculated by Mx ; • W1x wool section modulus of W1x to the maximum compression fiber in the bending moment action plane; • ϕy Stability coefficient of axially compressed member outside the plane of ϕy bending moment.

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Local stability: The local stability of box section is mainly reflected in the width to thickness ratio of component section. The width to thickness ratio should be controlled according to the formula to meet the requirements of local stability: √ t ≤ 30 235/ f x = 30 b

(2)

3. Intelligent structure optimization design For varying building schemes, parameterization can quickly establish different structural system models according to the corresponding shape coefficients of different building schemes. Gradually optimize the corresponding structural system, refine the size of structural components, materials, loads and other information.

3 Structural Optimization Safety Verification 3.1 PKPM Model Analysis In this paper, PKPM design software will be used to model the optimization results. According to relevant data, at present, PKPM has 90% market share in our design industry. Its operation is built on top of the system of CAD drawing software, has the advantages of simple operation, quick calculation and analysis, enjoys an absolute status and advantage in the architectural design industry in our country. The software can meet the requirements of the construction industry, will be the first time to adjust the software according to the update of norms, the construction industry has brought a positive role can not be ignored. At the same time, PKPM not only improves the working efficiency and drawing quality of designers, but also reduces the drawing work and pressure of designers during design. The calculation part of PKPM is carried out by the finite element calculation module SATWE, and the output results of this paper are also provided by this module. The finite element analysis software is developed for the analysis and design of architectural structures, and its basic theory comes from shell element theory of threedimensional composite structures. The software can not only show the stress state of the structure more in line with the actual situation, but also show the relationship between components more accurately.

3.2 PKPM Modeling The design life of the building is 50 years; Building seismic fortification category: class C; Building structure safety grade: three; Frame seismic grade: three; Foundation design: Class C. Basic wind pressure: 0.72 KN/m2 ; The seismic fortification

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Table 1 Limit value of column axial compression ratio 1 level

2 level

3 level

Frame structure

0.60

0.65

0.75

Frame shear wall structure

0.70

0.80

0.85

Partial frame supported shear wall structure

0.65

0.75

intensity is 6°, the design basic acceleration is 0.15 g, and the design earthquake group is the third group. The engineering load is as follows: floor dead load is 4 KN/m2 , live load is 4 KN/2; Activity room, corridor live load of 4.5 KN/m2 ; The roof dead load is 6 KN/m2 , live load is 3 KN/m2 ; No roof dead load is 5 KN/m2 , live load is 0.5 KN/m2 ; The load on the outer wall line is 9 KN/m2 , and the load on the inner wall line is 5 KN/m2 . With reference to similar engineering structures, relevant PKPM structural design models were established, and loads and constraints were added to the models.

4 Analysis of Experimental Results 4.1 The Axial Compression Ratio Column (wall) axial compression ratio N/(fcA) refers to the ratio of the product of the design value of column (wall) axial pressure and the full section area of the column (wall) and the design value of concrete axial compressive strength. Limiting the axial compression ratio can make the wall column have better ductility and can deal with the effect of earthquake on the building structure. When the axial compression ratio is too large, it means that the ductility of the building structure is poor and cannot meet the seismic requirements. But the axial compression ratio is too small will cause excess structural performance, no good economic performance. The maximum axial compression ratio of the column is 0.65. The seismic grade of the building is a three-level frame structure. According to Table 1 and Fig. 1, the limit value of axial compression ratio is 0.80, which meets the requirements of the code.

4.2 Displacement Than The ratio of the maximum horizontal displacement between the floor and the floor height Δ U/h calculated by the elastic method under the action of wind load or the standard value of frequent earthquakes should conform to the following provisions: the ratio of the maximum displacement between the floor and the floor height Δ U/h

Axial compression ratio of column

Building Structure Optimization Based on Computer Big Data 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

1 level

2 level

635

3 level

Seismic grade Frame structure Frame shear wall structure Partial frame supported shear wall structure

Fig. 1 Calculation results of limit value of column axial compression ratio

Table 2 Limit value of the ratio of maximum displacement between floors to floor height

Structural system

Limit value

Frame

1/600

Frame shear wall

1/850

Barrel in barrel

1/900

Conversion layer

1/900

should not be greater than the limit value in Table 2 for a high-rise building with a height not more than 150 m. By calculating the structure, the maximum displacement ratio of the structure is obtained: the maximum interlayer displacement Angle of the direction is 1/1057 < 1/600, which meets the specification requirements.

5 Conclusions In traditional structural design, the preliminary structural design often has a direct impact on the formal drawing, while the preliminary structural design generally lacks consideration of the project cost. In this paper, the cost of prefabricated components is taken as the objective function. By the size of the prefabricated, the size of the reinforcement, prefabricated self-respect, prefabricated steel content and spacing of reinforced build constraint condition, big data is adopted to establish the mathematical model of optimization design, avoid the designer during the preliminary design of structure, focusing on whether the structure can meet the specification requirements of the indicators, Ignoring the project cost is also the standard to measure the quality of the design results.

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Acknowledgements The Project of Young Innovative Talents in Colleges and Universities in Guangdong Province in 2021, “Study on the transformation of alternative nursing places in large public buildings under the outbreak of epidemic—a case study of Zengcheng District, Guangzhou” (No.2021KQNCX151) The Project of Young Innovative Talents in Colleges and Universities in Guangdong Province in 2019, “Study on the mechanism of introducing Phoenics to form high density urban guidelines under complex terrain”. (2019KQNCX203)

References 1. Carrizosa E, Guerrero V, Hardt D et al (2018) On building online visualization maps for news data streams by means of mathematical optimization. Big Data 6(2):139–158 2. Kabanikhin S, Krivorotko O, Takuadina A et al (2020) Geo-information system of tuberculosis spread based on inversion and prediction. J Inverse Ill-Posed Probl 29(1):65–79 3. Moayedi H, Mosavi A (2021) Suggesting a stochastic fractal search paradigm in combination with artificial neural network for early prediction of cooling load in residential buildings. Energies 14(6):1649 4. Frankiewicz T (2018) Technology focus: production and facilities (December 2018). J Petrol Technol 70(12):54 5. Clark A, Zhuravleva NA, Siekelova A et al (2020) Industrial artificial intelligence, business process optimization, and big data-driven decision-making processes in cyber-physical systembased smart factories. J Self-Gov Manag Econ 8(2):28–34 6. Marino CA, Marufuzzaman M (2020) A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics. Comput Ind Eng 143(May):106392.1–106392.14 7. Mishra B, Das H, Dehuri S et al (2018) [Studies in Big Data] Cloud computing for optimization: foundations, applications, and challenges vol 39. In: Consolidation in cloud environment using optimization techniques. https://doi.org/10.1007/978-3-319-73676-1(Chapter6):123-151 8. Yun Y, Kim SK (2017) Simulation of aseismic structure optimization of buildings based on finite element numerical simulation and improved genetic algorithm. Bol Tec/Tech Bull 55(19):171– 177 9. Loyola M (2018) Big data in building design: a review. Electron J Inf Technol Constr 23:259– 284 10. Nomura M, Matsumoto S, Sakino Y et al (2017) Optimization of placement of response control damper on plane of high-rise building structure using ESO method. J Struct Constr Eng (Trans AIJ) 82(742):1885–1891

Design of Aero-engine Start-Up Fuel Control System Based on Particle Swarm Algorithm Binhui Li, Shiyin Wang, and Chen Chen

Abstract Aero-engine is one of the most complex mechanical equipment that can be built by the industry at present. Its complex structure, nonlinear dynamic characteristics and complex and diverse engineering technologies required for normal operation can be called the Mount Everest in the industry. As the power source of aircraft, aero-engine affects the performance, economy and reliability of aircraft, and has important strategic and economic significance. The purpose of this paper is to design the fuel control system for aero-engine starting based on particle swarm optimization. In the experiment, the algorithm of combustion chamber is used to analyze and study the law of steady state fuel control system of aero-engine. Keywords Particle swarm algorithm · Aero-engine · Engine fuel · Control system

1 Introduction As the heart of the aircraft, aero-engines are a kind of highly complex thermal machinery, known as the “flower of industry”. They directly affect the power performance, safety, reliability and economic benefits of the aircraft [1]. The working environment of aero-engines is very harsh, generally including high temperature and high pressure, high load, high speed and often accompanied by strong vibration, etc. It involves many disciplines and is a comprehensive project, so its design and construction are much more difficult. The measurement of surface temperature of aero-engine hot-end components is very important for aero-engine upgrade research and improvement, condition monitoring and fault diagnosis. Zeidan M particle swarm optimization algorithm is one B. Li (B) · S. Wang COMAC Shanghai Aircraft Design and Research Institute, Shanghai 201210, China e-mail: [email protected] C. Chen Shenzhen Transsion Holdings Co. Ltd., Shanghai, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_70

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of the good ways to solve NP-hard problems. Therefore, it is of great significance to improve the optimal particle swarm optimization. Shifted service scheduling is such a problem. In this paper, the optimal particle swarm optimization IPSO algorithm is used to solve the scheduling problem of permutation flow shop. The improvement is achieved by implementing a separate optimization procedure that replaces the original group with another group that is closer to the optimal solution. The performance of IPSO is evaluated by several evaluation tests and several random problems generated by uniform distribution and compared with PSO. The results show that IPS outperforms PSO [2]. In his research, Kumar S improved a systematic approach using advanced particle swarm algorithms to determine optimal injection molding conditions to minimize warpage and shrinkage of thin-walled contact structures. Polybutylene terephthalate and polyethylene terephthalate were injected into thinwalled composites under different process conditions: melting temperature, dwell pressure, and dwell time. In addition, we use an orthogonal design for simulation analysis to illustrate the interaction of the above parameters. Based on the above criteria, regression models were used to improve predictive mathematical models for shrinkage and warpage. Analysis of variance was performed to determine statistical significance between injection parameters and growth models. The improved model is further optimized using a new and improved particle swarm optimization algorithm to obtain the process parameter values of the model [3]. In order to carry out timely, accurate and efficient fault diagnosis of aero-engines and ensure the flight safety of the aircraft. Through the research background and significance, this paper introduces the fuel system and aero-engine control system, including the characteristics of the control system and the improvement of the control system, and studies the theory of particle swarm optimization, including the parameters of classical particle swarm optimization and particle swarm optimization. In the experiment, the algorithm of combustion chamber is used to analyze and study the law of steady state fuel control system of aero-engine.

2 Research on Fuel Control System for Aero-engine Starting Based on Particle Swarm Optimization 2.1 Research Background and Significance From design to final manufacturing of aero-engines, it is a complex and difficult system engineering, mainly manifested in a wide range of subject knowledge, including aerodynamics, combustion, fluid mechanics, chemistry, structural strength, materials science and control system science, etc., and the improvement cycle is long, the improvement cost is high, and the research risk is high [2]. In the world, only the United States, Britain, France, Russia and other countries have the ability to independently improve and put into use advanced aero-engines. Therefore, being

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able to independently design, improve and manufacture aero-engines has become a symbol of a country’s highest industrial level and a symbol of comprehensive national strength. Nowadays, aero engines have improved from the earliest turbojet engines to turbofan, turbojet, turboprop, turboshaft and other types of engines. Turbofan engines have high thrust, low noise, low fuel consumption, and high flight efficiency. Therefore, it is widely used in civil aviation aircraft. The high bypass ratio turbofan engine can adapt to various complex climatic environments in long-distance flight, and the low flight cost makes it the first choice for large civil aviation aircraft. Foreign turbofan engines started early and have mature technology. They have been widely used in airlines of various countries, while the engines of civil aviation aircraft of major domestic airlines are almost all imported. In recent years, the country has vigorously improved research in the field of aero-engines. The improvement of military and civil engines in my country is in full swing, and good results have been achieved. However, the research on turbofan engines in my country started relatively late, and it is comparable to Western aero-engine powerhouses. There is still a large gap [3]. As a complex dynamic system that integrates multiple disciplines, aeroengines have the characteristics of high technical difficulty, high requirements for work reliability, and high test risks, resulting in high improvement costs and long improvement cycles. Therefore, countries are exploring low-cost, A improvement plan for a short-cycle, high-efficiency aero-engine.

2.2 Fuel System The fuel system consists of three parts: fuel supply; fuel heating; air-assisted direct injection system [4]. The low-pressure air-assisted direct injection system includes air nozzles, fuel nozzles, pressure reducing valves, and air compressors. The fuel is delivered to the injector through the oil pump and the pressure regulating valve, and the injection pressure is controlled by the pressure regulating valve. In this test, the injection pressure is stabilized at 8 bar. Compressed air was supplied by an air compressor, and the pressure was set at 6.5 bar. The fuel heating device is a PTC heating piece attached to the surface of the injector fixture. The maximum heating temperature can reach 120 °C. The temperature can be flexibly controlled by the ECU. When the temperature reaches the set temperature, the ECU will automatically disconnect the power supply thereby stopping the heating. Compared with the incylinder direct injection method of pure fuel injection, the low-pressure air-assisted direct injection method has the following advantages: (1) The spray cone angle is smaller and the penetration distance is longer; (2) After the fuel is injected into the air cavity, it will be compressed. The compressed air is surrounded by air, and the compressed air will be broken when injected into the cylinder, so the fuel will also be broken into smaller sizes, thereby improving the atomization effect [5].

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2.3 Aero-engine Control System (1) Characteristics of the control system Aero-engine is the “heart” of aircraft and other aircraft. It is a complex system with multi-variable coupling, nonlinearity and high real-time performance. The continuous improvement of aero-engine technology and the upgrading of aircraft are inseparable from the advanced control system of aero-engine [6]. The features of advanced control systems for aero-engines are: 1. It can give full play to the performance characteristics of aero-engines and improve the reliability of the engine system; 2. Facilitate system analysis and health monitoring; 3. From the separate control of each system, to the improvement of integrated and integrated control; 4. The engine control is closely integrated with the aircraft control, so as to reduce the difficulty of the pilot’s operation and reduce the load [7]. (2) Improvement status of control system In recent years, research institutions in various countries have vigorously carried out research on aero-engine control systems, moving towards the direction of advanced control systems [7]. The aero-engine control system has experienced the improvement process from single-variable control to multi-variable control with dozens of variables, from hydraulic mechanical control system to full-authority digital electronic control system, which can be roughly divided into the following four stages: 1. Initial stage In the initial stage, the control system of the aero-engine is mainly a single-variable hydraulic-mechanical control system, which generally controls the fuel flow of the main combustion chamber through hydraulic machinery. The frequency domain response curve of the system is used to obtain the desired phase margin and amplitude margin [8]. At the same time, the performance of the engine control system is analyzed by methods such as step response. 2. Growth stage In the growth stage, the control system of aero-engine is still mainly based on the hydraulic-mechanical control system. In the design method of engine control, the two control loops can be decoupled to achieve the effect of controlling two single loops respectively. Related control technologies such as variable geometry control begin to mature at this stage. 3. Electronic stage The modern control theory gradually matures, the rapid improvement of digital electronic technology and computer technology has begun to be applied in industry, and the aero-engine control system has entered the electronic stage

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[9]. At this stage, with the continuous improvement of aero-engine performance and the continuous expansion of functions, the improvement of the single-variable hydraulic-mechanical control system has been unable to meet the increasingly expanding functions of the engine. The aero-engine control system began to try to use multi-variable digital electronics. Control System.

2.4 Particle Swarm Optimization Theory (1) Classical particle swarm algorithm Particle swarm optimization, also known as particle swarm optimization [10]. Particle swarm optimization is a kind of swarm intelligence algorithm. Its main idea is to randomly initialize some particles, which carry information that can interact with each other, and then gradually find the target position of the population through iterative update, and judge the pros and cons of these particles. Through fitness, it is mainly updated gradually through the motion trajectory without more complicated operations, and it follows the example particles in the current population to gradually optimize to find the global optimal solution. Particle swarm optimization is simple to implement, has fast convergence speed, and is used in many optimization fields. The basic idea of particle swarm optimization algorithm is to realize the information exchange between particles and guide the next decision-making of particles through “swarm intelligence” in the academic field. (2) Parameters of particle swarm algorithm There are many parameter elements in particle swarm optimization, among which the main elements are learning factor, inertia weight and speed limit, and the secondary factors include population size, number of iterations, fitness settings, etc. [11]. At the beginning of the particle swarm algorithm, the particle swarm needs to be initialized, and some hyperparameters need to be set before initialization, such as learning factor, population size and inertia weight. The setting of the population size also has a great impact on the performance of the particle swarm optimization. Therefore, a reasonable population size needs to be set. The inertia weight represents the proportion of the displacement that the particle can move each time. When the setting is larger, the particle speed is more affected by individual cognition and social experience, and vice versa. When these parameters are set, the particles need to go through the initialization phase. For the initialization process, the algorithm needs to ensure that the particles are dispersed in the solution space as much as possible. The higher the dispersion of particles, the greater the probability that the algorithm will finally converge to the vicinity of the optimal solution. The next step is to set the speed threshold so that the particles can traverse the solution space as much as possible to avoid falling into a local optimal state. The setting of the fitness calculation method depends on the specific problem, and the appropriate fitness calculation method can effectively reflect the pros and cons of the particle position. The last step is

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to set the number of iterations, which indicates the number of times the particle needs to move in the solution space. Before reaching convergence, the larger the number of times is, the better the effect will be, but it should not be set too large, which will only waste computing resources. At the same time, it should not stop too early, which will cause the algorithm to fail to converge. Therefore, the setting of the number of iterations is often determined by a combination of experience and experiments.

3 Investigation and Research on Fuel Control System for Aero-engine Starting Based on Particle Swarm Optimization 3.1 Research Content In the steady state, the engine performance control laws can be divided into four types: the maximum state (full afterburner) control law, that is, when the throttle is placed at the maximum state position, the change law of the controlled parameters with the flight conditions; afterburner throttle state control The law, given the flight height and Mach number, the change law of the controlled parameters with the flight conditions; the intermediate state control law, when the throttle is placed in the intermediate state position, the change law of the controlled parameters with the flight conditions; the throttle state control law, given the flight altitude and flight Mach number, the controlled parameters vary with the throttle position. Since the DGEN380 does not have an afterburner and its geometry is not adjustable, it is only necessary to study the change law of the engine operating parameters in the throttle state and the intermediate state.

3.2 Combustion Chamber Related Algorithm In order to improve the accuracy of the combustion chamber model, this paper adopts the method of variable specific heat in the thermodynamic calculation of the combustion chamber, and the gas adiabatic index at the combustion chamber components is. The constant pressure heat capacity of the combustion chamber gas is: Cp = R

k k−1

(1)

The total gas flow at the combustor outlet is: W4 = W3 + q m f

(2)

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The total gas temperature at the outlet of the combustion chamber is: T4 = T3 +

qm f Q m C P W4

(3)

4 The Law of Steady State Fuel Control System of Aero-engine Based on Particle Swarm Algorithm The steady-state operating point of the engine model with the same flight environment and different throttle lever positions (PLA) is simulated to study the throttle state control law of the engine under steady-state conditions. In the actual engine control system, the PLA position reflects the speed command signal, which is the input to the engine controller. From the point of view of the aerodynamic thermodynamic model, the position of PLA cannot be used as the direct input of the model, so this paper collects the fuel supply when the position of the throttle lever ranges from 20 to 70% under the design working conditions, and takes 10% as the variation. The change data is used to study the law of the changes of the parameters of the engine with the fuel supply under the steady state. In the designed working state, the flight altitude is 20,000 ft, and the actual fuel flow corresponding to different throttle stick positions measured by experiments is shown in Table 1 and Fig. 1. Keep the flight conditions unchanged, use the previous method to continuously simulate the engine model under different fuel supply, solve its steady-state coworking equations, and obtain the engine state parameters at different throttle lever positions under the design working conditions (sequentially). Oil supply, low pressure speed, high pressure speed, fan boost ratio, compressor boost ratio, high pressure turbine drop pressure ratio, low pressure turbine drop pressure ratio, bypass ratio, total thrust are shown in Table 2 and Fig. 2. The data in the table and the data collected in the actual test are drawn and compared, so as to observe the trend of each parameter changing with the oil supply and test the accuracy of the model. Table 1 Gas bar position corresponds to fuel flow Sample

1

2

3

4

5

6

PLA%

20

30

40

50

60

70

Actual fuel flow rate (kg/s)

0.0254

0.0261

0.0274

0.0279

0.0281

0.0289

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0.0289 0.0281 0.0279 Actual fuel flow rate (kg / s)

0.0274 0.0261

Content

0.0254

70 60 50

PLA%

40 30 20 0

10

20

30

40

50

60

70

80

Value 6

5

4

3

2

1

Fig. 1 Comparison of the actual fuel flow rate at different throttle bar positions

Table 2 Changes of engine parameters with throttle bar position PLA (%)

20

30

40

50

60

70

Actual fuel flow rate (kg/s)

0.0254

0.0261

0.0274

0.0279

0.0281

0.0289

B

9.51

8.89

8.54

8.12

7.89

7.84

FIN

135

360

460

687

854

1054

5 Conclusions As one of the most intelligent industrial crystallizations, aero-engines have the characteristics of complex structure, long manufacturing cycle and high improvement cost. The aero-engine control system is an important part of the engine, and it is also an important guarantee for the safe and stable operation of the engine within a wide flight envelope. As the “jewel in the industrial crown”, the reliability of aero-engines is the key to ensuring the safety of aircraft operation. If the potential faults in its operation cannot be found in time, and the inspection and maintenance are neglected, it will not only increase the degree of damage, affecting its normal operation, and even

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serious accidents may occur, causing huge losses to life and property safety. Therefore, the design of aero-engine starting fuel control system based on particle swarm algorithm is of great significance.

References 1. Sun SH, Yu TT, Nguyen TT et al (2018) Structural shape optimization by IGABEM and particle swarm optimization algorithm. Eng Anal Boundary Elem 88(1):26–40 2. Zeidan M, Al-Abaji MA, Ahmed M (2021) Improved particle swarm algorithm for permutation flow shop scheduling problems. Invest Operacional 42(2):195–203 3. Kumar S, Singh AK, Pathak VK (2020) Modelling and optimization of injection molding process for PBT/PET parts using modified particle swarm algorithm. Indian J Eng Mater Sci 27(3):403–415 4. Ali HK, Taha AM, Hasanien HM (2021) Performance improvement of wind generator using hybrid particle swarm algorithm and grey wolf optimizer. Int J Energy Convers (IRECON) 9(2):63 5. Ellahi M, Abbas G, Satrya GB et al (2021) A modified hybrid particle swarm optimization with bat algorithm parameter inspired acceleration coefficients for solving eco-friendly and economic dispatch problems. IEEE Access 99(99):1

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6. Jalaee SA, Shakibaei A, Horry HR et al (2021) A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran. MethodsX 8(6):101226–101226 7. Aliwi S, Al-Khafaji N, Al-Battat H (2021) A single-branch impedance compression network (ICN) optimized by particle swarm optimization algorithm for RF energy harvesting system. J Phys: Conf Ser 1973(1):012080–012080 8. Jafari M, Salajegheh E, Salajegheh J (2021) Optimal design of truss structures using a hybrid method based on particle swarm optimizer and cultural algorithm. Structures 32(1):391–405 9. Afzal A, Ramis MK (2020) Multi-objective optimization of thermal performance in battery system using genetic and particle swarm algorithm combined with fuzzy logics. J Energy Storage 32(1):101815–101815 10. Smfa B, Sea B (2020) A classifier task based on neural turing machine and particle swarm algorithm. Neurocomputing 396(1):133–152 11. Dorogyy YY, Doroha-Ivaniuk OO, Ferens DA (2019) Resources distribution model of critical IT infrastructure with clear parameters based on the particle swarm algorithm. Èlektronnoe Modelirovanie 41(2):23–38

Safety Verification Technology of Urban Power Grid Under Strong Convective Weather Based on Laser Point Cloud Data Weinan Fan, Hongbin Wang, Junxiang Liu, Zhong Xu, and Yong Wang

Abstract In recent years, with the continuous update of 3D technology, the accuracy and resolution of lidar have been continuously improved, and the cost has been reduced. Obtaining data through lidar has become a hot topic. Accurate and fast perception of the surrounding environment has become more and more important topic. The main purpose of this paper is to study the safety verification technology of urban power grid in strong convective weather based on laser point cloud data technology. This paper mainly uses a variety of power flow analysis methods to propose and check the maintenance plan evaluation indicators, and proposes fault setting standards that meet the regional operating characteristics to sort out the risk level of the entire network equipment, and realize the real-time and offline calculation functions of risk failure and safety verification. Experiments show that for the table point cloud, within the reasonable parameter selection range, 1.8–3.2% of the point cloud is defined as outlier elimination, and for the corridor lidar point cloud, 2.6–6.3% of the outliers in the point cloud are defined as outliers was eliminated. Keywords LiDAR point cloud · Point cloud data · Urban power grid · Security check

1 Introduction In recent years, with the continuous increase of the power grid scale, the continuous investment of intelligent equipment, and the continuous expansion of the network scale, power dispatching has also received more and more attention in the safe operation of the power grid. With the increasing dependence of social development on power and energy, the economic losses caused by power grid failures and power outages are also becoming heavier. Accidents are also common [1, 2]. W. Fan (B) · H. Wang · J. Liu · Z. Xu · Y. Wang Guangdong Guangzhou Power Supply Company, Guangzhou 510000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_71

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In a related study, Ohno et al. developed a method to superimpose point clouds on the actual environment to aid in 3D laser measurements of the environment, allowing operators to use camera images to inspect scanned point clouds or unscanned areas in real-time [3]. Umehara et al. proposed a method to automatically divide point cloud data into feature units and identify features from projected images with added depth information [4]. Experiments verify that the proposed method can accurately identify and extract these features. Based on laser point cloud data technology, this paper studies the safety verification technology of urban power grid in strong convective weather. Based on the real-time power grid data, network parameter data and user load data obtained from PMIS, EMS and DMS as the basis for analysis and research, a real-time and offline analysis system for a full-dimensional regional power grid is constructed to realize collaborative analysis and joint calculation of the main distribution network, and to control the operation of the power grid. Provide technical auxiliary support to realize the function of evaluating and early warning of the available capacity of the distribution network; the function of checking the safe and stable operation of the power grid; the function of loop closure and power flow analysis and its application function.

2 Design Study 2.1 Current Situation and Analysis of Engineering Application In the existing scheduling application system, there are the following problems: 1. There is no complete model for the regional power grid of 35 kV and below, and various calculations cannot be performed. 2. The power flow calculation, safety check and loop closure simulation of the regional power grid still adopts the manual calculation mode, which requires a lot of labor and the accuracy is not high. 3. The regional power grid has many risks and influencing factors, lacks relevant risk analysis software, and the N − 1 scan is not perfect. 4. There is a lack of guidance for grid reconfiguration in maintenance mode and heavy-load mode, and an optimized isolation scheme and power restoration scheme cannot be given. In order to solve the above problems, the local power grid dispatcher obtains a complete model and section by collecting the models and data in DMS and EMS, and obtains a complete model and section with the idea of model splicing based on boundary naming and mapping. The testing process is developed, verified and practical, and the auxiliary decision-making system for regional power grid dispatching and operation has been developed, which has realized the check of the safe and stable

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operation of the power grid, the calculation of the closed loop power flow and the auxiliary decision analysis of heavy load or exceeding the limit, and the available capacity evaluation and early warning., network reconstruction optimization and other functions [5, 6].

2.2 Grid Fault Data Identification Method After the power grid fault signal alarm occurs, in order to complete the identification of the correctness of the information for the first time for the power grid alarm information, and effectively delete the fault information accuracy rate of the upper screen in the daily monitoring of power grid dispatching, you can collect different correlations between the data or you can Realize the verification of power grid monitoring information [7, 8]. When a real fault occurs in the power grid, the protection rules of the faulty equipment in the power grid are triggered, the protection outlet trips, and the related switches around the equipment also take related actions. The equipment action rules after the occurrence of similar faults, and reversely compare the switching action, power flow change, signal display, protection alarm and other information, and judge the reliability and authenticity of the accident information according to the degree of comparison, so as to realize the alarm Correctness of information identification [9, 10]. For example, when a transmission line fault occurs in the power grid, such as a tripping caused by a line grounding short circuit, comprehensive statistics on the signal actions at both ends of the tripped line will usually generate at least twelve equipment displacement and protection action information, such as the first set of protection action signals, the first set of protection action signals, and the Two sets of protection exit trip signal, interval accident total signal, etc. Therefore, when the dispatcher on duty monitors the fault signal of the substation equipment, the fault information discrimination mechanism can be activated. Taking the trigger time of the first accident signal as the cross section, the rules are checked for all the collected signals within a certain time range. If the signal matching degree is high, it can be judged as grid fault information. If the grid fault rule signal matching degree is low, it will be classified as an invalid alarm signal, so as to realize the preliminary research and judgment of the grid fault information. Taking this model as a model, the main fault equipment of the power grid is comprehensively classified and the equipment fault rule base is constructed, and the equipment action mechanism under various fault environments, that is, the action rules, is fully clarified. The identification method can realize the comprehensive identification and screening of power grid fault signals [11, 12].

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2.3 Point Cloud Statistical Filtering Algorithm (1) Algorithm principle The point cloud data set generated by lidar sometimes receives the echo signal incorrectly, which will lead to some wrong values, and due to the influence of the measurement error caused by the scanning result, other processing of the point cloud will inevitably occur. Errors are generated, so remove these errors. Outlier point clouds are usually scattered point clouds outside most point clouds. This noise has a great impact on the imaging, segmentation and subsequent target recognition of point clouds. The spatial distance of such points is greater than that of ordinary point clouds. distance between. (2) Algorithm steps 1. Calculate the total number n of point clouds, find its neighborhood through k neighborhood search for point p, and calculate the average distance between point p and each point in its neighborhood; 2. Establish a set {d1 , d2 , d3 ,…dn }, calculate the mean and standard deviation, such as formulas 1, 2 μ= ┌ | | σ =√

n 1∑ di n i=1

1 ∑ (di − μ)2 n − 1 i=1

(1)

n

(2)

3. The maximum threshold value based on Gaussian distribution is obtained from the above formula dmax = μ + α × σ, where a is the standard deviation coefficient, and the size of a determines the threshold value range. 4. Compare μ and dmax and process to remove outlier noise points.

3 Experimental Research 3.1 Point Cloud (PC) Reduction In order to speed up the processing speed of the laser point cloud algorithm, the necessity of simplifying the laser point cloud is getting higher and higher. For some massive point cloud data, there will be great pressure on data processing. In order to prevent this situation, some methods can be found to compress and simplify the massive PC data. On the premise of ensuring the main characteristics of the point cloud, try to Possibly reduce the amount of data in the point cloud Most point cloud reduction methods reduce the number of vertices in the model, thus speeding up the algorithm.

Safety Verification Technology of Urban Power Grid Under Strong … Fig. 1 Simplified schematic diagram of point cloud

A

B Calculate the center of gravity of all point clouds in

Build Spatial Grid

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C in place of points in the grid

Point cloud downsampling is a way to appropriately change the level of visual detail, thereby improving the overall performance of the application. Specifically as shown in Fig. 1. Its main idea is: the voxel grid method is based on the grid method, which divides the whole PC into several voxel grids of the same size, usually divided into cubes with side length L, and all point clouds are included in the In the grid, calculate the center of gravity of all PCs in the grid, and use the center of gravity to replace the rest of the PCs in the entire grid to compress the number of PCs, if the PC density distribution is uneven, it may affect the PC characteristics. Specific steps are as follows: 1. First, determine the size of the voxel grid according to the number and volume of the initial point cloud. Since it is necessary to ensure that the overall characteristics of the point cloud remain roughly unchanged, the selection of the grid size is very important. The side length L of the voxel grid is set. The definitions are as follows. L= g=

√ s/g

a3

N Lx L y Lz

(3) (4)

Among them, a is the adjustment factor of the grid side length, through which the grid size can be adjusted, g represents the average number of point clouds in each unit volume, and the side length is controlled by the number, and s is the proportional coefficient. N is the number of point clouds in the overall PC model, Lx , Ly , and Lz represent the maximum span of the PC in the x, y, and z axis directions, respectively, and the product is the overall volume of the point cloud. The selection is determined by the size and distance of the object to be measured and the number of point clouds that need to be simplified. 2. The center of gravity reflects the distribution of the point cloud in the grid, so as to ensure the characteristics of the point cloud to a large extent and achieve simplification. The center of gravity is calculated as Eq. 5:

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xcenter =

h ∑ xi h i=1

ycenter =

h ∑ yi h i=1

z center =

h ∑ zi h i=1

(5)

For models with relatively uniform point cloud density, the voxel grid method has a simple principle and an easy-to-implement algorithm, and has a good simplification effect.

3.2 Lidar Function Control Module When the lidar is scanned, the USB interface is used for communication between the upper and lower computers. The Chinese name of USB is universal serial interface. Compared with serial communication, it has the advantages of supporting hot swapping, fast transmission, and can be connected to multiple devices. The mechanism of automatic error checking has the advantage of transmitting data in large batches. The theoretical speed of its interface is 640 MB/s. Since the amount of point cloud data collected by lidar is large and requires continuous transmission, it is used for large-scale point cloud data. Cloud data communication, at the same time through the USB data, the information exchange between the lidar and the upper and lower computers can be carried out. The communication between the upper computer and the lidar is divided into three modules, which are parameter settings, start scanning, and stop scanning. The host computer can use the host computer parameters to adjust the parameters such as the scanning angle of the lidar, and transmit the parameter signal to the lidar control module, and control the lidar scanning through the lidar control system, start scanning, and transmit the data after scanning with the object. To the host computer and display the laser point cloud, if the scanning is aborted, the stop scanning signal will be transmitted to stop the lidar from running.

3.3 Data Acquisition and Processing Module When the lidar is working, the ranging data sent by the lidar is received through the USB communication module to complete the subsequent data analysis, display and processing functions, and the distance data scanned by the lidar is transmitted through the USB communication function. On the PC side, it is converted into a 3D

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point cloud by calculation, and displayed on the PC side, and then enters the filtering and segmentation module, and saves the point cloud data before and after processing. The laser radar host computer sends an instruction to start scanning, the laser radar starts to work, the laser pulse and the MEMS galvanometer are synchronized, and then the scanned distance value is converted into a time difference signal through AD, and the laser measurement distance value is obtained by calculation, and then transmitted through communication. Enter the host computer to convert the point cloud data into laser point cloud coordinates, and save it on the PC side, and then filter and segment the point cloud to realize 3D point cloud display. In the VS2015 environment, QT is used to build the display interface of the host computer. QT has many advantages in GUI interface. QT is based on C++ language and supports multiple operating systems. It only needs to be developed once to transplant different systems, and also provides a signal/response response mechanism, with high security, and it supports the rendering of 3D graphics and OpenGL, which is of great help for subsequent 3D graphics display. The left side of the lidar host computer display interface is the writing module, which can be set to display text information such as point cloud coordinate information and rgb color images, etc., and the right side of the interface is the point cloud display module, which can display point clouds through OpenGL. OpenGL is a 3D graphics display library. Because it can be used in a variety of environment platforms, and has the advantages of easy operation, good integration, many corresponding interfaces, and stable operation effects; it is suitable for 3D display, virtual reality, 3D animation, etc. matching method. Here, by building a subclass of QT relay QOpenGLWidget, using the rewritten virtual function feature, initialize and display the point cloud, and set the camera position, world coordinates, rendering, etc. for the initialization virtual function. Use the right-handed coordinate system and use the draw function for point cloud display.

4 Experimental Analysis 4.1 Point Cloud Downsampling The voxel 3D grid is a cube with a side length equal to 1 cm. The number of points before downsampling is 460,400, after filtering, it is 67,194, which greatly reduces the number of PCs, the outline shape of the PC before and after downsampling is basically It remains unchanged, saving a lot of computing resources for various subsequent operations, as shown in Table 1. In the process of PC simplification, as the number of lidar scan lines increases, the number of PCs will also increase greatly, so the voxel grid coefficients can be adjusted according to actual needs for reasonable simplification.

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Table 1 Filtering time before and after PC reduction Data processing situation

Before PC reduction

After PC reduction 0.2

Grid side length (cm) Table PC filtering time (s) Corridor PC filtering duration (s)

0.5

1

27.32

16.81

7.54

2.98

6.70

3.69

1.63

0.53

Table 2 Number of PCs before and after filtering Data processing situation

Before filtering

The number of neighbor points k

After filtering 30

Scale factor a

30

50

50

1

2

1

2

Number of table PCs

67,194

65,016

65,909

65,063

65,935

Number of corridor PCs

28,160

26,370

27,328

26,744

27,424

4.2 Comparison of the Number of PCs Statistical filtering can better remove outlier noise generated by lidar during scanning, and has different filtering effects under different parameters, as shown below. It can be seen from Table 2, it can be seen that the statistical filtering effectively filters out some outlier noise points. At the same time, it can be seen that the fewer the number of points queried in the neighborhood, the more the number of PCs filtered out., because the more points in the calculated neighborhood, the larger the calculated threshold will be, so that the wider the range of defined outliers is, the less the number of PCs will be filtered out. The smaller the value of the scale coefficient is, the smaller the range of outliers is, and the more points are filtered out. If it is too small, some useful information PCs may be misjudged, and it will be regarded as noise removal. If it is too large, it cannot be Most of the noise is removed, which affects the subsequent PC processing. For the table PC, within the reasonable parameter selection range, 1.8–3.2% of the PC is defined as outlier rejection. For the corridor lidar PC, 2.6–6.3% of the outliers in the PC are rejected.

5 Conclusions With the continuous expansion of the scale of power grid construction, the electrical connection between equipment becomes more and more complex, the coupling relationship between sections is more closely, the characteristics and shape of the power grid have undergone great changes, and the level of stable operation and the state of equipment are mutually restricted. In the period of rapid development of the power grid, the power flow and wiring methods of the power grid change greatly, and the

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safety and stability characteristics change rapidly, which puts forward higher requirements for the ability of power grid security analysis and evaluation. Safe and stable operation check is an important security line of defense to ensure the stable operation of the power grid, and has important social significance.

References 1. Ballal MS, Kulkarni AR (2021) Synergizing PMU data from multiple locations in Indian power grid-case study. IEEE Access 99:1 2. Aissou BE, Aissa AB, Dairi A et al (2021) Building roof superstructures classification from imbalanced and low density airborne LiDAR point cloud. IEEE Sens J 99:1 3. Ohno K, Date H, Kanai S (2021) Study on real-time point cloud superimposition on camera image to assist environmental three-dimensional laser scanning. Int J Autom Technol 15(3):324–333 4. Umehara Y, Tsukada Y, Nakamura K et al (2021) Research on identification of road features from point cloud data using deep learning. Int J Autom Technol 15(3):274–289 5. Abegg M, Boesch R, Schaepman ME et al (2020) Impact of beam diameter and scanning approach on point cloud quality of terrestrial laser scanning in forests. IEEE Trans Geosci Remote Sens 99:1–15 6. Vassilakis E, Konsolaki A (2021) Quantification of cave geomorphological characteristics based on multi source point cloud data interoperability. Z Geomorphol 63(2–3):265–277 7. Kranjec N, Ekada MT, Kobal M (2021) Predicting tree species based on the geometry and intensity of aerial laser scanning point cloud of treetops. Geodetski Vestnik 65(2):234–259 8. Kogut T, Slowik A (2021) Classification of airborne laser bathymetry data using artificial neural networks. IEEE J Sel Top Appl Earth Obs Remote Sens 99:1 9. Altyntsev MA, Saber KH (2020) Impact of mobile laser scanning data preliminary processing results on accuracy of generating digital models of road pavement. Interexpo GEO-Siberia 1(1):74–85 10. Walters R, Hajna NZ (2020) 3D laser scanning of the natural caves: example of kocjanske jame. Geodetski Vestnik 64(1):89–103 11. Bott F, Hoffmeister D (2020) Vergleich von Laserscanningdaten zur Vegetationsdetektion im Gleisumfeld. ZEV-Zeitschrift Eisenbahnwesen und Verkehrstechnik – J Railway Transp 144(11/12):1–10 12. Munir N, Awrangjeb M, Stantic B (2020) Automatic extraction of high-voltage bundle subconductors using airborne LiDAR data. Remote Sens 12(3078):1–27

Blockchain Computing Resource Allocation and Benefit Sharing Based on Artificial Intelligence Technology Jian Liu

Abstract With the rapid development of Internet of Things technology, the emergence of more and more Internet of Things devices has brought more application requirements, which has also promoted the development of cloud computing and edge computing, making servers with sufficient computing power (such as Cloud servers, edge servers, etc.) provide services for computing needs. The purpose of this article is to study blockchain computing resource allocation and revenue sharing based on artificial intelligence technology. The common problems of optimizing revenue sharing and the proportion of computing resources are studied, and the optimal computing power and revenue sharing scheme is proposed to maximize the total net revenue of users and servers and ensure the fairness of the two. Based on the idea of revenue sharing, this paper introduces the revenue sharing ratio into the problem, and establishes a problem model related to the total net revenue of users and servers and the distribution of computing power and the distribution ratio. Aiming at the non-convex optimization feature of the problem, we decompose the problem vertically and propose a hierarchical algorithm to solve it. The simulation results show that the solution results of the algorithm proposed in this paper meet the target requirements and are consistent with the linear search calculation results, and the calculation performance has been significantly improved. Keywords Artificial intelligence · Blockchain computing · Resource allocation · Revenue sharing

1 Introduction With the development of the Internet of Things technology, as well as the increase of various Internet of Things and smart terminal devices, more and more data is J. Liu (B) Shandong Polytechnic Vocational College, Jining 272000, Shandong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_72

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generated at the edge of the network, and these have brought about changes in the computing model [1]. Edge computing is becoming a good supplement to cloud computing, bringing more resources to the edge of the network where data is generated [2]. However, the current management and distribution of edge computing resources still mostly adopt the traditional centralized model, which violates the original intention of network computing, and cannot adapt to the highly dispersed and heterogeneous characteristics of edge computing resources, and maintains the traditional central management model. The inherent problems of individual failure points and trust must be effectively resolved and developed into a decentralized model [3, 4]. Based on the hard-to-crack decentralization feature and data traceability, blockchain provides a way to do this. Users can download the consensus algorithm calculation tasks to the server deployed at the edge of the wireless network for completion, thereby reducing the time required to complete the calculation tasks and effectively improving the overall benefits of users [5, 6]. In order to protect the privacy and security of mobile data in the Internet of Things (IIoT) transaction environment, Ning Z proposed a mobile computer base in response to the limited bandwidth and computing power of small base stations. (MEC) The ability of the blockchain to observe the movement of devices will cause common problems in bandwidth allocation and computing resources to improve the long-term usefulness of all mobile devices. They split the word problem into two parts to reduce the size of the working space. Next, a deep particle swarm optimization (DRPO) algorithm is proposed to solve these two problems. The algorithm uses the best particle swarm algorithm to avoid unnecessary searches for decision-making solutions. The simulation results verify the effectiveness of the method from several aspects [7]. Huang Y proposed a blockchain system based on headset restrictions. The new blockchain system is scalable because it allows the most advanced devices to share storage resources evenly and efficiently. They found the best peer-to-peer meeting to store transaction data and came up with a modern block storage plan to recover lost blocks. They developed powerful data acquisition and storage reduction algorithms to adapt to changes in network topology. The proposed blockchain system can even reach consensus on the most advanced low-power machines through the new printing press [8]. Research on the distribution and revenue sharing of blockchain computing resources based on artificial intelligence technology is of practical significance. The main research content and contributions of this paper are summarized as follows: Aiming at improving the revenue of edge computing service providers, we study the optimization of computing resources based on revenue sharing. After the user wins the consensus competition and obtains the digital currency revenue, the revenue will be distributed a certain percentage of the revenue to the edge server provider, with the goal of maximizing the overall net revenue. For the objective function, we propose a distributed algorithm to effectively solve the calculation power allocation method and sharing ratio that maximizes the overall net profit, and provide chart data to compare with CVX and linear search results to verify the effectiveness of our design algorithm.

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2 Research on Blockchain Computing Resource Allocation and Benefit Sharing Based on Artificial Intelligence Technology 2.1 Edge Computing Technology Based on Blockchain First of all, the existing research on edge computing generally adopts a three-level structure, namely the terminal device layer [9]. Based on this three-level structure, the existing integrated architecture can generally be described as: 1. Local network based on private blockchain Generally in this type of local network, access rights are controllable and identities are clear. Therefore, private blockchains that do not require a strong consensus mechanism are generally deployed to reduce management risks, reduce management costs, and obtain low latency and stable operating performance [10]. Generally, according to different communication systems, node processing capabilities and security requirements, it can be divided into two management modes. One is centralized management, where the edge server initiates transactions and is responsible for adding and deleting devices [11, 12]. Devices can communicate with each other only when they have obtained the permission of the edge server. Another management mode allows devices and edge servers to participate in the blockchain. The device acts as a light node in the blockchain, mainly receiving firmware updates or sending transaction data to other peer devices. In both modes, the edge server mainly controls the local network, and provides outsourced storage and computing services to low-energy devices in a safe manner based on the local blockchain. Some of the work will be included in the higher-level In the blockchain. 2. Server peer-to-peer network based on public blockchain In addition to leading the local network, the edge server also has the function of mutual storage and forwarding of messages for data playback, data sharing, and collaborative computing. From a higher perspective, edge servers adapt to environmental changes by adding or deleting edge nodes, and perform lightweight analysis on themselves and other peer nodes for self-management. Considering the processing power, a light distributed agreement protocol should be deployed on the edge server to ensure low latency and high performance requirements. For clouds with powerful computing and storage capabilities, distributed blockchain clouds can provide a competitive low-cost, secure, and on-demand computing infrastructure. Since the peak P2P server network and distributed cloud range is much wider than the local network, it is suitable to use public blockchains, such as Ethereum that supports smart contracts.

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2.2 Edge Artificial Intelligence Computing This article does not discuss the use of proprietary hardware to run artificial intelligence algorithms on single-ended devices, because this situation is not essentially different from the current artificial intelligence algorithms running in large data centers. The number of devices is by no means limited to terminal devices. On the contrary, the most advanced artificial intelligence will make full use of the available data and resources of terminals, edge servers and cloud data centers. Multiple devices cooperate with computers to generate artificial intelligence. It can be a single artificial intelligence algorithm that completes calculations through multiple devices to protect data privacy or improve efficiency, or it can be a single device or a small group of devices. A device or small set of devices can be removed by an autonomous agent that has consumable knowledge and is guided by selfish goals. In order to achieve the goals of the group, it interacts with other agents, such as exchanging knowledge and data or making joint decisions. Multiple agents connected and organized in a network are also called group intelligence. Artificial intelligence processes and extracts important event information, generates semantic digital analysis, and stores the results in the blockchain. Blockchain provides many common financial services. Including the exchange of data transmitted outside of artificial intelligence analysis and important information obtained from analysis, in order to make full use of valuable information on the Internet.

3 Investigation and Research of Blockchain Computing Resource Allocation and Revenue Sharing Based on Artificial Intelligence Technology 3.1 Numerical Simulation Assuming that the D2DECN system is based on D2D communication under the LTE standard, the transmission power of the IoT device is 0.125 W, and the path loss model is L = 62.3 + 32log10 (d), where d is the distance between the task owner and the computing resource provider. For the setting of calculation parameters, the number of calculation cycles required to process each unit bit of data is 376 times. In order to verify the effectiveness of the resource trading system and task offloading scheme proposed in D2D-ECN, the feasibility and cost of the system are analyzed in this section, and resource pricing based on game theory and the task offloading algorithm based on QPSO are given. The numerical simulation results.

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3.2 Data Preprocessing Benefits of resource providers: If the currently available communication or computing resources are greater than the resources required by the task owner, according to the resource provider’s reputation value in descending order, the right to provide resources is allocated to the corresponding resources in a certain proportion Providers; secondly, after obtaining the optimal resource pricing, in the same way, priority will be given to higher resource prices to resource providers with high reputation. This incentive mechanism can encourage more users to participate in distributed resource transactions, thereby effectively ensuring the reliable operation of the system. The income of the resource provider is defined as shown in Formula 1: U S P ( pi , Ri ) =

N 

( pi RiT − εi RiT )

(1)

i=1

  Among them, εi = εib , εic is a resource overhead proportional coefficient greater than zero. The benefit of task owner: In the resource transaction application of D2D-ECN, the satisfaction of task owner can be expressed as a function of the resource R and the resource price P. The profit function of TOi designed in this paper is shown in formula 2: Ui (Ri , pi )Δ50(1 − exp(−μib Rib ) − exp(−μic Ric )) − pib Rib − pic Ric

(2)

Ui is the weight parameter, which reflects the degree of resource demand of the task owner, and pi is the unit price corresponding to the obtained communication resources and computing resources.

4 Analysis and Research of Blockchain Computing Resource Allocation and Revenue Sharing Based on Artificial Intelligence Technology 4.1 Single-Edge Server Revenue Sharing Model Analysis The system model diagram of this paper is shown in Fig. 1. In the system model diagram, there is an edge server and a set of intelligent terminals that participate in the consensus. I = (1,…,i,…,I}, the scene graph will be introduced in detail below. The system model in this paper only studies the scenario of a single edge server, and considers that the benefits of edge computing service providers are more in line with the overall benefits. The edge server provides certain computing resources xi for the

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Smart terminal 1

Smart terminal i Edge server Base station Smart terminal I

Fig. 1 Single edge server revenue sharing model

smart terminal, and the smart terminal shares a certain percentage of the revenue to the edge server provider after completing the PoW consensus and adding blocks to obtain the revenue. First, all smart terminals send the local {xiloc }i∈I computing power to the edge server 1, and the edge server 1 calculates the provided computing power {xiloc }i∈I . A after receiving the local computing power of all smart terminals, and does not exceed the total computing power Ctot it can provide. After this group of intelligent terminals perform consensus calculations, assuming that user i is the first to get the result, it will send the result to other nodes to verify the correctness of the result. If the result is correct, it can get benefits. Then user i will share part of the revenue received to the edge computing provider, and the user’s own proportion is set to yi , then the edge computing provider’s proportion is (1 − yi ).

4.2 Simulation Analysis Figure 2 shows the impact of differential pricing and consolidated pricing procedures on the source provider’s total revenue. The figure shows that with the increase in the number of employers, the overall income of source suppliers under a single tariff system is: This is because, in terms of the overall income of resource providers, resource allocation has become the largest share of decision variables. This problem is broken down into network programming problems. Participants receive the same materials, so if the job owners are different, the total income will be equal. In contrast, the gross domestic product (GDP) of suppliers under the differentiated fee system tends to increase exponentially with the increase in the number of service providers. If the number of service providers is small and the current supply of resources exceeds demand, the source provider may not be able to exchange all resources with the

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service provider, reducing the current unit cost and reducing the total income. As the number of job holders further expands and due to job holders. The existing potential resources caused by resource competition, the differentiated pricing process will lead to different resource allocation rates based on actual resources. The quotation results are shown in Table 1. For the uniform pricing strategy, if the resource allocation is carried out from the perspective of the task owner’s income, when the unit resource price is lower, the task owner can obtain a higher overall income, and task owners increases and increase,

Fig. 2 Comparison of total revenue of resource providers under different pricing strategies

Table 1 Comparison of total revenue of resource providers under different pricing strategies

Number of task owners

Uniform pricing p = 10

Uniform pricing p = 15

Differentiated pricing

2

80

160

20

4

80

160

40

6

80

160

60

8

80

160

80

10

80

160

100

664

J. Liu

and finally show a steady upward trend. For a differential pricing system, given the total number of sources, when the number of service owners is small, the demand for the source is less than the supply. Therefore, as the number of users increases, general revenue is on the rise. With the further increase in the number of users, the general income of the owners will decline due to resource competition among the owners.

5 Conclusions The current management mode of edge computing resources is relatively rigid, and there are still a large number of devices with certain resources at the edge of the network, and these resources are often idle and not fully utilized. Therefore, it is necessary to explore a more open and flexible method to effectively organize the many resources at the edge of the network and make full use of them to achieve the purpose of complex data processing at the edge of the network. However, in a decentralized and heterogeneous environment, the traditional centralized resource management model is no longer suitable for this type of distributed computing platform due to its inherent disadvantages. Therefore, this article aims to explore a decentralized edge computing resource allocation mechanism to adapt to the decentralized edge network environment. At the same time, a solution based on blockchain is proposed to rebuild a trust order in a decentralized mode to achieve decentralized and credible resource allocation.

References 1. Liu T, Wu J, Chen L et al (2020) Smart contract-based long-term auction for mobile blockchain computation offloading. IEEE Access 8(99):36029–36042 2. Zhao N, Wu H, Chen Y (2019) Coalition game-based computation resource allocation for wireless blockchain networks. IEEE Internet Things J 99:1 3. Silvestre M, Gallo P, Ippolito MG et al (2018) A technical approach to the energy blockchain in microgrids. IEEE Trans Ind Inf 14(11):4792–4803 4. Nandi ML, Nandi S, Moya H et al (2020) Blockchain technology-enabled supply chain systems and supply chain performance: a resource-based view. Supply Chain Manag 25(6):841–862 5. Guo F, Yu FR, Zhang H et al (2020) Adaptive resource allocation in future wireless networks with blockchain and mobile edge computing. IEEE Trans Wireless Commun 19(3):1689–1703 6. Liu Y, Yu R, Li X et al (2019) Decentralized resource allocation for video transcoding and delivery in blockchain-based system with mobile edge computing. IEEE Trans Veh Technol 99:1 7. Ning Z, Sun S, Wang X et al (2021) Intelligent resource allocation in mobile blockchain for privacy and security transactions: a deep reinforcement learning based approach. Sci China Inf Sci 64(6):1–16 8. Huang Y, Zhang J, Duan J et al (2021) Resource allocation and consensus of blockchains in pervasive edge computing environments. IEEE Trans Mobile Comput 99:1

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9. Guo S, Qi Y, Yu P et al (2020) When network operation meets blockchain: an artificialintelligence-driven customization service for trusted virtual resources of IoT. IEEE Network 34(5):46–53 10. Zhang X, Zhu X, Chikuvanyanga M et al (2021) Resource sharing of mobile edge computing networks based on auction game and blockchain. EURASIP J Adv Sig Process 2021(1):1–23 11. Fu S, Fan Q, Tang Y et al (2020) Cooperative computing in integrated blockchain-based Internet of Things. IEEE Internet Things J 7(3):1603–1612 12. Feng J, Yu FR, Pei Q et al (2020) Joint optimization of radio and computational resources allocation in blockchain-enabled mobile edge computing systems. IEEE Trans Wireless Commun 99:1

Integrated Development of Smart City and Smart New Media Yue Luo and Yao Huang

Abstract The media industry should be connected with the future. At this stage, the media industry is in a critical period of reform and transformation. The promotion of national strategies such as “media integration”, “Internet +” and “triple play” has provided comprehensive support and implementation path for the media industry. This paper mainly studies the integrated development of smart city and smart new media. Firstly, he put forward the application principles of the smart city concept, which is divided into three parts: overall planning, urban management and people’s livelihood services. Then it expounds the concept of smart new media, and puts forward the strategy and method of integrated development of smart city and smart new media. Through a questionnaire survey, investigate the convenience of citizens for smart cities and smart new media. From the results of the questionnaire survey, we can know that the integrated development of smart city and smart new media will help promote urban development and improve citizens’ well-being. Keywords Smart city · Smart new media · Integrated development · Questionnaire survey

1 Introduction With the technological innovation in the field of science and technology, people’s lifestyle has changed. The concept of smart city came into being with the development of the information age. As one of the hottest words nowadays, it is considered to be one of the most effective ways to eliminate the development problems in cities, and has attracted wide attention at home and abroad. Tracing back to the smart city and its research, the western developed countries, especially the United States, as the representative, started earlier. As early as the Y. Luo · Y. Huang (B) Institute of Media, Changchun Humanities and Sciences College, Changchun, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_73

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1990s, the international conference with the theme of “smart city, rapid system and global network” held in San Francisco, the United States, made “smart city” the first choice for sustainable development and competitiveness enhancement among cities in the world. However, no systematic consensus was reached at this meeting. The academic circles generally regard the concept of “smart earth” proposed by IBM as its predecessor [1]. Two years later, the concept of smart city evolved from the concept of smart earth, providing a technical guide for urban development and construction. In the white paper “smart city in China” published by IBM, IBM gives a more detailed description of “smart city”. Smart city is able to make full use of information technology and communication means to sense, analyze and integrate various key information of urban operation core system, so as to make intelligent response to various needs including people’s livelihood, environmental protection, public safety, urban services, industrial and commercial activities, Create a beautiful city life for mankind. Among them, “perception”, “connectivity” and “intelligence” are three pronouns that describe the main characteristics of smart cities [2, 3]. Around this, individuals, government agencies and business organizations will all have synergy effects. Domestic scholars described the essential role of emerging information technology in innovation effect. Taking Hangzhou as a typical case, the author illustrates the importance of information technology and network infrastructure construction in the construction of smart city [4]. Smart city is not only a type of city, but also a new way of life. If only the smart city is viewed from the perspective of smart city, the article will be too limited. Therefore, this paper organically integrates the concept of smart city with smart new media, and forms a diversified research system through purposeful investigation and Research on the development status of smart new media at home and abroad, so as to lay a theoretical foundation for the application and development of China’s concept of smart city in smart new media.

2 Smart City and New Media 2.1 Application Principles of Smart City Concept 1. Principle of overall planning The clearest direction for the construction of a smart city is to put aside appearances, provide high-quality services to the public in a down-to-earth manner, and bring convenience to the public. It is widely used in government offices, intelligent teaching, intelligent transportation and other fields. There is no fixed direction, but the general direction of its development is to sort out the problems faced by the city according to its own urban conditions, carry out overall planning, and take the needs of the citizens as the first essence, Set up a unique city

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image through continuous exploration, and give full play to the wisdom under its smart city concept [5]. 2. Urban management principles In the urban management with citizens as the main body, the position of the main body is people, and the city is developed under the management of people. In the process of development, people exist as the leader. With the rapid development of the city, we should fully listen to the opinions and suggestions of the citizens, and always take the vital interests of the citizens as the starting point for comprehensive consideration. As the manager of smart technology, people can provide corresponding solutions according to the specific conditions of citizens, so emphasizing the concept of human led smart city is an effective means of urban development [6]. 3. Principle of people’s livelihood service The application of the smart city concept can be seen everywhere in citizens’ lives, and is closely related to medical treatment, education, travel and other aspects of citizens’ lives. The concept of smart city can be organically combined with citizen service to solve many problems in life. Therefore, adhere to the construction concept of “people-oriented” and solve the problems in urban development through the construction of smart cities. For example, smart medical care, which is highly discussed in the society, has largely solved the problem of difficult medical treatment for citizens, making citizens fully feel the satisfaction brought by smart cities [7].

2.2 Smart New Media New media has a strong information capacity. The information capacity of traditional media, such as magazines and newspapers, is limited by the length of articles. However, new media developed on the Internet are not limited by the length of content. The broad audience can browse the content through hyperlinks, information push and other forms, which virtually expands the number and types of information received by the audience. The forms of new media communication are diverse. Before the birth of new media, the most obvious deficiency of traditional media in the process of information dissemination is that its information presentation form is relatively single. Newspapers and periodicals can only transmit graphic information to the audience, radio can only transmit sound information to the audience, and television can only transmit text, image and sound information to the audience. However, the new media, which integrates various technologies, integrates pictures, words, audio and video, and has new forms of communication such as live broadcast and interaction, which is beyond the reach of traditional media [8]. New media communication is highly interactive. In the process of information transmission, traditional media such as newspapers, periodicals, television, radio and so on, due to the small information capacity and the limitation of linear information

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transmission, the traditional media mainly focus on one-way communication. Before the emergence of new media, due to the limitations of the times and the advantages of information dissemination, the dominant power of information dissemination was firmly in the hands of traditional media, and was in a strong position in the whole process of information dissemination. However, the audience could only be in a passive position to receive information, and had no dominant power to independently select the dissemination information in the face of a large amount of information. However, with the arrival of the new media era, it has greatly changed the dominant and strong position of the traditional media in the process of information dissemination. First of all, the audience can actively choose what information to browse according to their own interests, and can choose to refuse to accept the communication content they do not need and are not interested in, which greatly ensures the autonomy of the audience and the right to choose information. Secondly, the information dissemination characteristics and interactivity of new media help the arrival of the era of “everyone is a media” and promote the emergence of more and more we media people. The audience can choose the appropriate platform to release information, so as to continuously improve the audience’s sense of participation [9]. New media communication is highly personalized. The birth and development of new media rely on the birth and development of network technology. With the gradual popularization of 5 g network, it will greatly improve the online experience of the audience while improving the speed of the audience. Relying on big data technology and algorithm technology, most apps can track users’ use paths in real time, and then conduct personalized analysis on users’ reading habits and content preferences, so as to push differentiated content, provide personalized services according to individual user portraits, and promote the audience to have a better sense of experience in the process of using new media [10].

2.3 Integration of Smart City and Smart New Media In the face of the deep integration needs of media development, and the business needs of city and county-level media integration platforms, county-level financial media centers and regional media joint operations, it should be considered from the top-level design, and will be built from the perspective of solving its own needs, giving consideration to internal applications and providing external services, so as to promote the diversification, diversification and multi platform development of media integration development. Relying on the integrated design idea of “perception, overall planning, coordination and decision-making” in the smart city architecture, media integration needs to promote the transformation of content production and communication pattern from the four aspects of “public opinion hot spot early warning analysis”, “overall command and scheduling”, “shared coordination” and “decision-making impact”. Build an integrated platform for resource integration, unified management,

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multi-channel distribution and standard interface to realize the production mode of information resource interconnection and collaborative sharing. Relying on the support of information network perception and source feedback, a public opinion hot spot early warning analysis system is established to control the whole process of material collection, hot spot tracking, communication effect and public opinion monitoring from the aspects of people’s livelihood, culture, economy and politics. Based on the media information collection and cloud recording system, capabilities such as hot source detection, content transmission effect evaluation and new media operation analysis based on massive data are embedded, which can realize the collection and analysis of information throughout the network. Keywords can be customized for effect and public opinion analysis, help journalists and editors provide rich material resources, and carry out risk assessment and early warning for sensitive materials and sources such as national politics and figures. It can effectively reduce the risk of public opinion. In the “central kitchen” integrated media platform, strengthen the central command and dispatching ability, coordinate a series of process management, such as topic reporting, topic selection, division of labor, collection, import, scheduling, distribution, archiving, transmission, statistics, assessment, and form a cross departmental and cross line topic selection and reporting group in the system around the topic selection goal. In the construction of the service platform, media can simultaneously meet the needs of government service convergence, media integration and information interaction, and make use of the multi terminal media matrix such as traditional media, PC websites, apps and small programs to form a comprehensive service system with diverse entrances, unified content sources, diversified access channels and online and offline integration. At the same time, the personnel information collection system is used to carry out real name authentication and live detection for platform users, so as to realize the effective collection of natural person and license data, as well as all government services and data collection, record all matters in the whole life cycle of individuals, and provide real online service. At the same time, relying on the platform user system, we will build a virtual all-in-one card for citizens to realize the integration of multi entity cards such as resident identity authentication, medical security, public transport, book borrowing, disabled card, elderly care and disability assistance, scenic spot Garden card (Leisure Card), provide entrance checksums for various online service applications, and solve the problem that user identity authentication cannot be verified online in the event of not meeting for approval. At the same time, in the offline scene, Virtual all-in-one card can be promoted as life service applications such as identity certificate, micro payment (bus and subway), ticket verification, building access control, etc., so that the public can enjoy all services, realize the convenience of life services, feel the wisdom of the city, and make the public feel richer, more secure and more sustainable in their sense of gain, happiness and security.

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3 Questionnaire Survey on Integration of Smart City and Smart New Media 3.1 Questionnaire Design In order to make the research more effective, this paper uses the method of questionnaire survey to collect relevant data in the city. The survey objects cover all ages and educational levels. A total of 150 questionnaires were sent out this time. The scope of the survey population is limited to Changchun permanent residents. The city’s permanent residents mainly refer to the people who live, work, study and work in Changchun for a long time. All the questions in this questionnaire are single choice questions, mainly about the communication content, communication form and data presentation of the integration of smart city and smart new media. The purpose of designing the questionnaire into two main parts is to learn about the changes in the attitude of the survey audience, so as to clarify the integration of smart city and smart new media, and conduct more scientific analysis and evaluation.

3.2 Questionnaire Reliability Test Results In this paper, the reliability test of spssu is used to test the validity of the questionnaire survey results. The test formula is as follows: rx x =

2rhh 1 + rhh

r =1−

Sd2 Sx2

(1)

(2)

(1) Where RXX is the reliability value and rHH is the correlation coefficient of the two half test scores; (2) Where, R is the reliability value, SX is the variance of the difference between the two half test scores, and SX is the variance of the total test scores.

Integrated Development of Smart City and Smart New Media Table 1 Public attention to statistical results

673

Focus frequency

Number of persons

Proportion (%)

Often

54

36

Sometimes

33

22

Very seldom

36

24

Never

27

18

60

40% 35%

50

Number

25%

30

20% 15%

20

Percentage

30% 40

10% 10

5%

0

0% Often

Sometimes

Very seldom

Never

Focus frequency Number of persons

Proportion

Fig. 1 Residents pay attention to the statistical results of smart city and smart new media

4 Questionnaire Results 4.1 Attention to Smart Cities and Smart New Media As shown in Table 1 and Fig. 1, 36% of the citizens said they would often pay attention to the development of smart cities and smart new media and browse relevant news; 22% of the residents said they would sometimes pay attention to relevant developments. The above results show that more than 50% of residents pay more attention to smart cities and smart new media.

4.2 Recognition of Smart City and Smart New Media As shown in Fig. 2, 72% of the citizens recognize the integrated development of smart city and smart new media. These citizens believe that the integrated development of smart city and smart new media can greatly improve the convenience of life. There

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Commonly

30% Relatively recognized

Percentage

Acceptability

Not recognized

20% 10%

Quite recognized

0% 0

20

40

60

80

100

Quantity Number of persons

Proportion

Fig. 2 Statistical results of recognition of smart city and smart new media

are also a small number of citizens who worry that the development of smart cities will endanger personal privacy and security.

5 Conclusions With the development of the new generation of information and communication technology and its extensive and in-depth application in various fields of the city, smart city has become a hot topic in recent years, and smart city construction has also become a city development plan pursued by all regions. Based on the concept of smart city, through the analysis and study of excellent design cases, this paper summarizes the laws and methods, refines and integrates the elements of the development concept, smart technology positioning, service attitude and development potential of smart new media integration, successfully applies them to the process of smart city and new media integration, and sorts out a complete design scheme. Due to the limited time and the limited information about the integration of smart new media, there may be some problems in the whole process of writing the paper, such as inadequate summary, insufficient in-depth and comprehensive analysis. In the future, more time and energy need to be invested to further improve it and make it more perfect.

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References 1. Adapa S (2018) Indian smart cities and cleaner production initiatives—integrated framework and recommendations. J Cleaner Prod 172(pt.3):3351–3366 2. Picardal C, Pugliese B, Rhee S et al (2020) Bellevue smart: development and integration of a smart city. J—Am Water Works Assoc 112(2):28–37 3. Lytras MD, Serban AC (2020) E-government insights to smart cities research: European Union (EU) study and the role of regulations. IEEE Access (99):1 4. Finger M, Audouin M (2019) [The Urban Book Series] The governance of smart transportation systems (towards new organizational structures for the development of shared, automated, electric and integrated mobility). In: Regulating transport platforms: the case of carpooling in Europe. pp 13–35. https://doi.org/10.1007/978-3-319-96526-0 5. Batalla JM et al (2017) Efficient media streaming with collaborative terminals for the smart city environment. IEEE Commun Mag 55(1):98–104 6. Fahmfam G, Hamidi H (2019) Factors affecting the development and management of smart city approach using a combination of big data and the internet of things and cloud computing technologies. Iran J Inf Manag 34(2):557–584 7. Smirnov EA, Kashtanov VG, Denk VV et al (2021) Trends in the innovative development of smart cities. Vestnik Universiteta 5:28–36 8. Sushma M (2017) A comparative study of smart city versus smart villages startup, opportunities and challenges. Asian J Manag 8(4):1315–1321 9. Macke J, Casagrande RM, Sarate J et al (2018) Smart city and quality of life: citizens’ perception in a Brazilian case study. J Cleaner Prod 182(MAY 1):717–726 10. Maulana A, Haerah K (2021) Smart city development innovation strategy and challenges for the government of Jember regency. IOP Conf Ser: Earth Environ Sci 717(1):012008 (7pp)

Intelligent Logistics Transport Prediction of Forest Products Based on BP Neural Network Learning Algorithm Qian Chen, Ning Li, and Siyu Deng

Abstract In this paper, BP neural network and Support Vector Machine (SVM) are used to predict the flow of forest products in Heilongjiang Province, China. Taking some relevant parameters such as GDP and forestry fixed assets investment as network input, the complex multivariate data are processed by principal component analysis, and the dimension reduction index reduces the weakly related input variables to improve the prediction efficiency. Then, the BP neural network and SVM prediction models are established, and the error data of the two are compared, Finally, it is concluded that the MAPE value for the support vector machine model is 2.3014%, which indicates that the model can provide a lower prediction error rate than BP, and the BP network model needs further improvement. SVM model accurately predict the logistics freight volume of forest products, and provide reasonable and feasible production and management planning for forest product enterprises. Keywords Forest products logistics · Principal component analysis · Neural network prediction · Support vector machine prediction

1 Introduction Modern logistics has formed a considerable scale of social module relying on the Internet of things and other advanced technologies. As a circulation link of the supply chain, logistics has linked the upstream and downstream industries and helped their development, including forest products logistics developed from forestry. The development of forest products logistics transportation can effectively support the Q. Chen (B) · S. Deng School of Logistics Management and Engineering, Zhuhai College of Science and Technology, Zhuhai, Guangdong, China e-mail: [email protected] N. Li Northeast Forestry University, Heilongjiang, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_74

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production and management of forest products, promote the improvement of forestry operation efficiency, and favors the growth of forestry sustainably. For the moment, the research direction of forest product logistics mainly displace towards logistics distribution costs, system simulation and modeling, and the efficiency of forest product logistics circulation. Elyakime and Cabannettes believe that forest product circulation needs to focus on communication and cooperation for the sake of better enhance the efficiency of forest product circulation [1]. Newton et al. lay his highlights on the circulation efficiency of forest products is influenced by natural and social factors [2]. Rauch built a simulation model of raw material supply for Forest Fuels under random risk conditions such as pests in a risk assessment scenario [3]. Akhtari et al. studied the distribution cost, distribution location, timber storage methods and costs of forestry logistics, and found that supply chain design, transportation and material costs are important elements affecting supply chain costs of forest products industry [4]. Ma analysed the characteristics of forest biomass for power generation and studied the supply chain system management of biomass-derived forest products in order to reduce the cost of the supply chain of forest biomass feedstock in conjunction with the supply chain issues of various biomass-derived forest products [5]. The research on machine learning prediction for wood is also the focus of the related field at present. Li and Wang confirmed the feasibility of the optimization algorithm by improving the sparrow optimization algorithm for predicting the mechanical properties of wood [6]. Fathi et al. obtained more accurate data by studying the variation of wood moisture content and wood mechanical properties through an improved composite neural network algorithm [7]. However, the research related to the prediction of wood production through machine learning is relatively thin. The research in this paper bridges this weak point and helps the supply chain and commercial sector to forecast the production capacity of related wood products for better scheduling.

2 Principal Component Analysis Principal component analysis (PCA) is a way to lessen the dimension of complex multivariate data. This method seeks the basic structure of the observation data by analyzing some dependent relationship among several variables, and uses a few independent variables to represent the original structure of the basic data [8]. In this paper, SPSS software is selected as the integration and analysis tool of affecting factors of forest products flow in Heilongjiang Province to determine the reliability coefficient between various elements and highway transportation mode. Due to too many indicators, in order to better predict and analyze, the method of PCA is used to reduce its dimension, so that multiple variables are replaced by a small number of variables. 2007–2017 The original data of annual highway freight volume of forest products and related influencing factors are as follows Table 1:

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Table 1 Raw data of influencing factor indicators Particular Per year capita GDP

Forestry Forestry Timber Total Highway Highway fixed assets production production population operating freight investment value mileage volume

2007

18,685

48.13

499.24

549.22

3825

140,909

51,996

2008

21,879

49.56

580.32

530.57

3825

150,846

35,424

2009

22,621

83.4066

2010

27,266 105.85

693.37

571.43

3826

151,470

36,486

916.76

336.79

3831

151,945

40,582

2011

33,024 186.783

1093.81

301.89

3834

155,592

44,420

2012

35,938 174.2134

1281.38

219.63

3834

159,063

47,465

2013

37,935 124.6

180.6

212.79

3835

160,206

45,288

2014

39,468

97.4013

195.7

156.77

3833

162,464

47,173

2015

39,699 128.0583

204.2

116.39

3812

163,233

44,200

2016

40,432 138.5455

219.9

91.18

3799.2

164,502

42,897

2017

42,699 152.7454

175.2

70.58

3789

165,989

44,127

2.1 PCA of Highway Freight Volume of Forest Products Table 2 shows the practicability of PCA by KMO and Bartlett sphericity test. When the KMO value is higher than 0.9, the data is very suitable for further PCA. When the KMO value is between 0.8 and 0.9, the data is appropriate. In the range of 0.7 and 0.8 for the KMO, it means that the PCA is appropriate. In the range of 0.6 and 0.7, it means that the PCA can also be performed. When the KMO value is between 0.5 and 0.6, the data cannot be further analyzed5, it is not suitable for PCA. From the above table, KMO measurement value is 0.548 better than 0.5, and Bartlett’s sphericity test value is 91.631, the equivalent value threshold is sig. = 0.000 0.05, indicating that it is suitable for PCA. The eigenvalue of the first principal component (pc) is 3.880, which explains 55.434% of the data [9]. The eigenvalue of the second pc is 1.514, which explains 21.633% of the data. The eigenvalue of the third pc is 1.005, which explains 14.354% of the data. The sum of the eigenvalues of the first three pc accounts for 91.420% of the total eigenvalues. The first three pc extracted by Ming can basically reflect all the information of the original variables. Because most of the variables have good explanatory ability, but there are still a few indexes with poor explanatory ability, the rotating factor load matrix is used Table 2 KMO and Bartlett spherical test

KMO And Bartlett spherical test Kaiser Meyer Olkin metric with sufficient sampling

0.548

Bartlett’s sphericity test

Approximate chi square

91.631

df

21

Sig

0.000

680

Q. Chen et al.

Table 3 Arithmetic results for each factor

Coefficient matrix Element 1

2

Per capita GDP

0.251

− 0.049

Forestry fixed assets investment

0.364

0.341

3 0.056 − 0.064

0.159

0.572

− 0.156

Timber production

− 0.233

0.065

− 0.109

Total population

− 0.014

0.385

0.195

Forestry GDP

Highway operation mileage Highway freight volume

0.267 − 0.072

− 0.073

− 0.180

0.001

0.919

to polarize the factor load coefficient to 0 or 1, so that the larger load is larger, the smaller one is smaller, and the consequence are more explanatory. Finally, the score of pc is shown in Table 3. The score of PCA: F1 = 0.251X1 + 0.364X2 + 0.159X3 − 0.233X4 − 0.014X5 + 0.267X6 − 0.072X7 F2 = −0.049X1 + 0.341X2 + 0.572X3 + 0.065X4 + 0.385X5 − 0.073X6 + 0.001X7 F3 = 0.056X1 − 0.064X2 − 0.156X3 − 0.109X4 + 0.195X5 − 0.180X6 + 0.919X7 The comprehensive pc scores were as follows: F = 0.554F1 + 0.216F2 + 0.144F3

3 Neural Network Analysis 3.1 Determination of the Number of Nodes in Input Layer and Output Layer The BP neural net replicates the human mind neural network for error correction since it is a multi-layer feedforward neural net [10]. The message from the input layer is initially routed by the BP neural network through the hidden units, and

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Input

Hidden

681

Output

Fig. 1 BP network structure

Fig. 2 Method of determining inputs and outputs

eventually propagates forward to the output units. After comparison with the actual data, opposite transmission of the mistake is made to the input layer, and the weight and bias of each layer are adjusted in turn. As shown in Fig. 1 [11]. In the model, the three factors obtained from the above PCA are used as the input layer data, and the number of input nodes is 3. Taking the freight volume of Heilongjiang Province as the prediction object, the number of input nodes is 1. as shown in Fig. 2. Because the input index is three-dimensional and the opposite index is one, the number of input nodes of BP is three and the number of output nodes is one. The network index data of Heilongjiang Province from 2007 to 2017 is divided into training set and verification set. The data from 2007 to 2012 is used as training set, and the data from 2013 to 2017 is used as verification set. The road freight volume of Heilongjiang Province is predicted by BP model, and the predictive impact of PCA-BP model is tested.

3.2 Determination of Hidden Layer Node Number If there are as many input nodes as there are output √ nodes, The ideal range for the hidden layer node count can be obtained from m + n + a, a can be any integer from 1 to 10. In this experiment, the number of input nodes is 3, and the number of

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Fig. 3 Evolution curve of gradient and other parameters with training algebra

output nodes is 1. Therefore, the number of hidden layer nodes ranges from 3 to 12. By comparing the mean square error (MSE) of the predicted value and the actual value of the training set corresponding to different number of hidden layer nodes, it is found that the value of hidden layer nodes with the minimum MSE is the best case, which is 6. The minimum inaccuracy of training set is 10−8 , and the neural network stops training in the 139th generation. At this time, it has reached the best network performance, and the corresponding MSE is 9.7924 × 10−9 ; the correlation coefficient of training set is 1, which indicates that the fitting degree of model training is very high; from the training stage Fig. 3, we can see that the gradient decreases with the increase of training algebra, and there is no over fitting phenomenon in the training process [12].

4 Support Vector Machine Regression Analysis 4.1 Parameter Setting of Support Vector Machine Support vector machine is a machine learning method discovered by Cortes and Vapnik [13]. It can effectively solve the problems of classification and regression. In order to compare and analyze with the prediction model established by BP neural network, the model uses its corresponding sample data, that is, the data from 2007 to 2012 as the training set and the data from 2013 to 2017 as the verification set, and carries out unified normalization. Epsilon SVR model is adopted in the model, and the value of loss function P in E-SVR is set to 0.1. Firstly, cross validation CV method is used to roughly optimize

Intelligent Logistics Transport Prediction of Forest Products Based … Table 4 Prediction data of SVM model

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Test set

Raw data

BP forecast data

SVM forecast data

2013

45,288

46,576

43,908

2014

47,173

48,194

44,871

2015

44,200

40,575

43,232

2016

42,897

35,434

43,205

2017

44,127

35,424

43,830

the training parameters c and G, with the value range between (−8, 8), and then fine selection is made according to EPS = 10(−4) . Finally, all parameter pairs that can make the training set achieve the highest verification accuracy are centered, A pair of C and G with the minimum penalty coefficient C are selected as the optimal regression parameters, and the final optimal parameter values C = 2 and g = 0.5 are obtained [14].

4.2 Prediction Results of SVM Model The data of model pair validation set from 2013 to 2017 are shown in Table 4: The error rate may be used to assess the model’s correctness. The ratio of the square of the variance between the projected value and the actual value to the number of observations is known as the root mean square error, or RMSE. It may be used to gauge a collection of data’ dispersion and more accurately depict the actual context of the anticipated value’s mistake. Table 5 demonstrates that the RMSE value of SVM model is much smaller than that of BP model, which shows that the prediction effect of the model is good. The line graphic also shows how the model’s ability to forecast works. Figure 4 demonstrates that the SVM model may better suit the raw signal, and after a given period of training, achieve the impact of low error with the raw data, but the BP model varies significantly and has a large error with the raw data. Table 5 Comparison of prediction data between BP model and SVM model

Error

BP model error data

SVM model error data

MAE

4419.7985

1050.9126

MSE

29,453,796.1525

1,664,800.3141

5427.1352

1290.2714

RMSE MAPE

10.0655%

2.3014%

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raw data

2014

2015

BP model prediction data

2016

2017

SVM model prediction data

Fig. 4 Comparison of the predicted values of the improved model and the BP model

5 Conclusion According to the comparison of prediction results, the regression model of support vector machine has better prediction performance than BP neural network model. In this study, BP model and SVM model were used to establish the prediction model of forest product flow. The established SVM model can predict the parameters to a high extent. In practice, the prediction model can be optimized according to the requirements of wood product logistics on transportation environment, transportation mode and transportation parameters, so as to transport wood more scientifically and reasonably. From the case analysis of this paper, the establishment of prediction models based on PCA-BP and PCA-SVM is helpful to estimate the logistics demand of forest products under the condition of fully obtaining information. The comparison between BP model and SVM model can also provide a more feasible choice for similar research directions, so as to reduce algorithm trial and error and improve research efficiency. Acknowledgements This work was supported in part by 2021 Guangdong Teaching Quality Project: Construction of Intelligent Logistics Application Teaching and Research Office and by 2021 Zhuhai College of Science and Technology Teaching Quality Engineering Project (ZLGC20210205) Intelligent Logistics Application and by 2021 Zhuhai Institute of Science and Technology Teaching Quality Project (ZLGC20211001)Construction of Intelligent Logistics Application Teaching and Research Office and by 2022 Discipline Co construction Project of Guangdong Social Science Planning(GD22XGL68) Research on Supply Chain Collaborative Innovation of Integrated Circuit Industry in Guangdong Province under the Double Cycle Pattern.

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References 1. Elyakime B, Cabanettes A (2009) How to improve the marketing of timber in France? Forest Policy and Economics 11(3):169–173. https://doi.org/10.1016/j.forpol.2009.01.001 2. Newton P, Watkinson AR, Peres CA (2011) Determinants of yield in a non-timber forest product: Copaifera oleoresin in Amazonian extractive reserves. For Ecol Manage 261(2):255– 264. https://doi.org/10.1016/j.foreco.2010.10.014 3. Rauch P (2010) Stochastic simulation of forest fuel sourcing models under risk. Scand J For Res 25(6):574–584. https://doi.org/10.1080/02827581.2010.512876 4. Akhtari S, Sowlati T, Day K (2014) The effects of variations in supply accessibility and amount on the economics of using regional forest biomass for generating district heat. Energy 67:631– 640. https://doi.org/10.1016/j.energy.2014.01.092 5. Ma N et al (2022) Simulation study on complex systems of forest biomass power generation supply chain in China. Computational Intelligence and Neuroscience 2022:7202352. https:// doi.org/10.1155/2022/7202352 6. Li N, Wang W (2022) Prediction of mechanical properties of thermally modified wood based on TSSA-BP model. Forests 13:160. https://doi.org/10.3390/f13020160 7. Fathi H, Nasir V, Kazemirad S (2020) Prediction of the mechanical properties of wood using guided wave propagation and machine learning. Constr Build Mater 262:120848 8. Song Q et al (2021) New approaches in the classification and prognosis of sign clusters on pulmonary CT images in patients with multidrug-resistant tuberculosis. Frontiers in Microbiology 12:714617. https://doi.org/10.3389/fmicb.2021.714617 9. Sakiyama F, Lehmann F, Garrecht H (2021) A novel runtime algorithm for the real-time analysis and detection of unexpected changes in a real-size SHM network with quasi-distributed FBG sensors. Sensors (Basel, Switzerland) 8:2871. https://doi.org/10.3390/s21082871 10. Yu X-C, Sun D, Li X (2011) Preparation and characterization of urea-formaldehyde resinsodium montmorillonite intercalation-modified poplar. J Wood Sci 57:501–506 11. Saito Y, Hatanaka T, K Uosaki T, Shigeto H (2003) Neural network application to eggplant classification. KES 12. Amabilino S, Bratholm LA, Bennie SJ, Vaucher AC, Reiher M, Glowacki DR (2019) Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality. J Phys Chem A 123(20):4486–4499 13. Cortes C, Vapnik VN (2004) Support-vector networks. Machine Learning 20:273–297 14. Yang H, Cheng W, Han G (2015) Wood modification at high temperature and pressurized steam: a relational model of mechanical properties based on a neural network. BioResources 10:5758–5776

Distributed 3D Interior Design System Based on Intelligent VR Technology Jianfeng Wang, Lulu Liu, and Linzi Li

Abstract Intelligent virtual reality technology (VRT) is a very new art form, which gradually matures with the progress of science. As one of the most popular research topics in the twenty-first century, VRT has been integrated into various industries. This paper studies the distributed 3D interior design system based on intelligent VRT for the application of VRT in ID and the requirements of distributed 3D ID. The research results show that the ID can fully apply VRT to conduct a comprehensive prospective analysis of the early design scheme. It has greatly improved the quality of engineering design, reduced expenditure, improved the efficiency of scheme design and approval, gradually changed the way of modern ID, and formed a virtuous circle of modern interior and exterior design mode. In the ID scheme, various details can be presented to people more realistically, so that the experiencer can fully and carefully understand the intention of the designer. Keywords Intelligent VR technology · Distributed system · 3D interior design · Design research

1 Introduction After entering the twenty-first century, the whole human society is developing at a high speed. Human cognition and perception are changing with the development of science and technology and cultural progress. People try to rely on computer network and VRT to develop a more diversified space environment and enjoy the colorful virtual world visual feast. ID is gradually increasing its connection with VRT. In the future, the promotion of the needs of the public to promote the diversified display J. Wang · L. Liu Art Design College, Hanyang University, Ansan, Gyeonggi-Do, South Korea L. Li (B) Graduate School of Techno Design, Kookmin University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_75

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and interaction of ID has a good prospect for the application of VRT in ID to show the diversification of scheme design. VRT will become a very important technical support means in the future, making ID more convenient. This paper studies the distributed 3D ID system based on intelligent VRT. In many developed countries, VRT has developed to a certain height, and is no longer limited to interior or architectural landscape design. More research institutions and companies have seen the good prospects of VRT, and quickly entered the research of VRT and the development of related products. The 3D design software used in China is mainly the computer software of 3D simulation. In the domestic interior and landscape design industry, 3D software is the most widely used industry. 3D software is mainly embodied in computer-aided interior and landscape design [1]. Such software technology can be used to create and complete the building facade, interior scenes and decorations, and can also express the surrounding environment in a rich way. Compared with traditional hand drawn renderings, the application of this software technology makes the scheme display more comprehensive and clear, and the entire scene space can be observed at will during the scheme stage of the project [2]. At present, VRT has been widely used in many industries, and has been inseparable with people’s lives. How to use a more convenient way to express the language of ID is still an urgent problem in the traditional design methods and planning. Therefore, this paper starts from the characteristics of 3D software VRT, analyzes the differences between its general environment and concept, so as to find that the application of VRT plays a crucial role in the reconstruction of interior space design [3, 4].

2 Analysis of Intelligent VRT and Distributed 3D ID System 2.1 Concept of 3D Software VRT Virtual reality (VR) technology it means that VRT is regarded as a new type of human–computer communication. In such a scenario, the environment observed by the user is a colorful, shaped, three-dimensional space. The sound created by the virtual world can also be heard, and the limbs can also feel the gravity and power from the virtual world. In other words, people can feel the virtual environment brought to us by computers in the form of their perception of the real world [5, 6]. Today’s virtual reality system should include the following three important features: immersion, interaction and imagination. The virtual reality system structure is shown in Fig. 1. Immersion: Immersion is to make users feel the virtual space environment in audiovisual aspect through the sensor or interaction.

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Fig. 1 Characteristics of virtual reality system

Interaction: Interaction refers to the action characteristics of people, which are generated by the interaction between natural actions and virtual space in the virtual world. If the current virtual environment can simulate users’ hands, it will greatly improve the realism [7, 8]. Imagination: Visual perception is the most important information source for users in the process of using virtual display technology. Through the construction and thinking of virtual world, people can also deepen their understanding of the real world. At present, all kinds of complex environments in real life can be presented to people with the help of VRT, so that people can get the possibility to put their imagination into practice [9].

2.2 3D Scene Data Organization A good organization of 3D scene data can not only store and utilize efficiently, but also facilitate the efficient implementation of real-time high realism rendering algorithm through fast traversal method. The organization of scene data not only determines the efficiency of data storage, but also affects memory management, visual clipping calculation, level of detail, calculation and collision detection. The 3D scene is divided into indoor scene and outdoor scene. The indoor scene has its own characteristics, so the applicable technologies are different. The indoor scene is small and the object is close to the observer, so LOD technology is basically useless. There are a large number of large obstructions in indoor scenes, even small ones. The indoor scene management engine must first have the characteristics of easy data management, and should support data reuse to minimize storage space and achieve efficient visual computing. This is particularly important for complex large-scale indoor scenes [10]. Assuming that the light intensity of the incident point p is known, the reflected light intensity of the point p is determined by the material of the point p, and the material can be regarded as the reaction parameter of the object to the incident light. In the generation of realistic graphics based on physics, the reflection characteristic

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of the material is generally described as the bidirectional reflectance distribution function. The BRDP of the point p has two parameters, the direction of the incident light and the reflected light β I and β o: f (βi , βo ) =

d L o (βo ) L i (βi )Cosθi dβi

(1)

The r(p, βi ) It is a ray casting function. Substituting the above equation into the rendering equation, we get:  L(P, β O ) = L o ( p, βo ) +

L o (r ( p, βi ), −βi ) fr (βi , p, βo ) cos θi dβi

(2)

In practice, direct illumination and indirect illumination are solved separately. For each point p in the scene, the radiation intensity at this point is required, and the incident light integral at this point needs to be calculated. Therefore, the ray tracing algorithm will cast several secondary rays at this point to estimate the direct light, indirect light specular reflection light, refraction light, etc.

2.3 ID Requirements Based on VRT 2.3.1

Fast 3D Modeling

Modeling speed is one of the main factors studied in this paper. Therefore, compared with many powerful modeling software in the market, VR ID modeling software provides not only the necessary basic modeling functions, but also the command set for fast input of commonly used indoor component models, which requires careful design. In ID, build a 3D model from the wall. Define the default wall thickness and floor height. Enter two points to determine a straight wall, and three points to determine an arc wall. Continuous input walls can form a room, and adjacent walls are automatically connected at the corner. Rectangular rooms can be input quickly through two diagonal points. For continuous input rooms, overlapping walls need to be detected and combined to remove unnecessary redundant wall data [11]. Doors, windows and door/window sleeves exist in their host walls, so the fastest way to operate is to drag the doors or windows in the model library to the wall. Before placement, auxiliary temporary dimension display should be provided, such as the distance between the edge of the door/window opening and the wall edge, so that users can quickly locate the exact position of the doors and windows.

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Construction Drawing Output

Automatic generation of construction drawings can greatly reduce the repeated work of designers, but because the drawing output standards of each enterprise are not uniform, it is a huge challenge to meet different standards. For rooms composed of walls, doors and windows, each enterprise can formulate its own standard legend, and the software can generate the original plan survey map, reconstruction plan, floor material grid distribution map, etc. that meet the requirements according to different standard sets [12].

2.3.3

Rendering and Animation Output

Rendering is the most effective way for designers to show their design ideas to customers before construction, but the traditional way is to make some models required by modeling software and export them to rendering software for a long time to obtain the required results. If the customer is not satisfied, you need to modify the model, adjust the materials and lights, and render again. On the basis that we can quickly input a complete 3D model, the advantage is that we can output renderings at any angle and at any position.

3 Analysis of ID System Based on Intelligent VRT 3.1 ID Process ID business process In modern interior decoration design, people pay great attention to the selection of large furniture and electrical appliances, and the adoption of soft decoration such as lamps, paintings, cushions, plants and curtains, in addition to hanging ceilings, paving floors, wall treatment and making some hard decoration such as wood veneer modeling. Conceptually, the ID process can be divided into two stages: hard decoration and soft decoration design. The application of CAD software involves two processes: modeling and visualization. Therefore, we summarize the workflow of ID into three steps, as shown in Fig. 2. Hard decoration seems to be the background of our painting or the background of a drama, while soft decoration plays a role in creating home taste and finishing touch.

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Fig. 2 Three steps of ID workflow

3.2 ID Based on Intelligent VRT In the hard decoration stage, the main models are mainly cubic, cylinder and other basic three-dimensional voxels and their combinations. The model is not complex, and the main demand is to be fast and convenient. It needs to provide fast parametric input and modification functions for common components, such as doors, windows and even rooms. The modification function should have the function of saving the modification history of a single object, so that the user can cancel the modification or even go back to the previous step. Soft decoration models are complex and changeable, but there are common model libraries that can be imported and used, which can greatly accelerate the modeling speed. Therefore, the collection, sorting, search and other management functions of soft decoration model libraries should be provided. The 3D models output from modeling should be used as the input of visual design. Model data should be simplified as much as possible without losing visual accuracy to improve rendering speed. In VR design software, different areas may have different ground materials. In order to conform to the working habits of the original designer and provide the function of dividing functional areas, complete ground objects are divided into multiple surfaces with different material settings according to the division of functional areas, so that the system can automatically output the grid layout of ground materials. After the completion of the hard decoration phase, designers can import the furniture, potted plants, lamps, sanitary wares and various decoration models commonly used in the Model Manager, and put these objects into the original scene where only walls and doors and windows appear empty. These models have their own materials and texture settings, which can be used after importing. Of course, designers can modify the appearance of these models as needed to make them more in line with the requirements of the current design scheme. In order to preview the effect of the indoor scene, the designer may not fully follow the workflow shown in Fig. 2. It is entirely possible to set materials, textures and lights at any stage. During the entire design workflow, the adjusted and improved model elements, such as furniture, lamps or materials, can be used as reusable model elements and saved in the model library managed by the Model Element Manager for future needs.

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Fig. 3 Flow chart of visualization system

4 Design and Implementation of Distributed 3D ID System Based on Intelligent VRT 4.1 Design and Implementation of Modeling System Figure 3 is the flow chart of the visualization system. From the figure, we can see that the system must preprocess the scene model data, including BREP model gridding, scene optimization, material and texture assembly, and light and camera model configuration, before the system is handed over to ACDA for ray tracing rendering and physical calculation.

4.2 Channel Model of Indoor Distributed Antenna System The indoor radio propagation environment is more unfriendly than the outdoor radio propagation environment. It is prone to multipath propagation, reflection, diffraction, lack of line of sight conditions, masking fading, proximity to interference sources and other phenomena that cause dramatic fluctuations in the characteristics of the wireless channel, which have a significant impact on the signal reception power of indoor propagation. Generally speaking, the indoor environment has partitions that cause various transmission losses, which can be divided into hard partitions that constitute the building structure, and soft partitions composed of movable people or furniture. Table 1 shows typical partition losses for different types of partitions within a building. When the transmitter and receiver are not in the same building, another important factor for indoor signal propagation is the penetration loss of the building, which is mainly composed of dielectric penetration loss and diffraction loss. The signal strength received inside the building increases with the building height, and the

694 Table 1 Typical zoning losses in buildings

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Partition loss (dB)

Metal

26

Aluminum alloy wall

20.4

Concrete wall

13

Foil insulation in walls

3.9

Double gypsum board wall

3.4

Layout partition

1.4

transmission loss decreases with the increase of frequency when the diffraction loss is ignored.

4.3 Visual Design and 3D Virtual Walkthrough Through virtual roaming, customers can pre check whether each corner of the home environment really meets their own demands. The designers can communicate with customers and modify at the same time. The communication is more smooth, reducing or eliminating design defects to a large extent. Designers can instantly modify the design scheme and quickly preview it according to customers’ opinions. It is unnecessary for customers to wait too long. Its advantage is that customers can participate in the design more, bringing customers a different feeling. The ID system based on VRT can greatly improve the customer’s consumption experience. Unlike the original pile of construction drawings, designers rely more on oral explanations. Customers cannot try or try it out in advance as they buy other goods. Because the home project involves hundreds of materials, construction processes and industry quality standards, customers are vulnerable. Due to lack of professional knowledge, Finally, it is inevitable to cause local design defects. The VRT allows customers to experience in advance, subverting the traditional consumption mode.

5 Conclusions Nowadays, the application of VRT in distributed 3D design plays a crucial role in shaping and reconstruction of environmental space. It puts human experience in the first place, realizes the transformation from space creation to space presentation, and achieves immersive visual effects; There are some deficiencies in the research process of this paper: 3D software VRT is related to art design, so the application of 3D virtual technology should not be separated from the thinking and methods of art design, but should be integrated into the unique creative ideas of designers, so that

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VRT can really help people’s design. The development of VRT is a difficult project. The distributed 3D ID system based on intelligent VRT needs further research.

References 1. Nigam S, Kapoor V (2022) Perception of clients towards the 2D and 3D types of design presentation of the projects in interior design. Int J Creative Interfaces Comput Graph 13(1):1– 15 2. Zhou Y, Ji J, Zhao W, Zhu S, Liu H (2022) Modulated vibration reduction design for integralslot interior permanent magnet synchronous machines. IEEE Trans Ind Electron 69(12):12249– 12260 3. Liu YuHan (2021) Application of ray tracing algorithm based on intersection optimization in 3D interior design. J. Intell. Fuzzy Syst. 40(4):5787–5794 4. Tanneau M, Anjos MF, Lodi A (2021) Design and implementation of a modular interior-point solver for linear optimization. Math Program Comput 13(3):509–551 5. Volpato Filho CJ, Vieira RP (2021) Adaptive full-order observer analysis and design for sensorless interior permanent magnet synchronous motors drives. IEEE Trans Ind Electron 68(8):6527–6536 6. Chatterjee S, Chaudhuri R, Vrontis D (2022) Dark side of instant messaging: an empirical investigation from technology and society perspective. Aslib J Inf Manag 74(5):928–955 7. Sitanggang N, Luthan PLA, Dwiyanto FA (2020) The effect of google sketchup and need for achievement on the students’ learning achievement of building interior design. Int J Emerg Technol Learn 15(15):4–19 8. Liu Y (2020) Practical Analysis on the Integration of Fair-faced Concrete Decorative Elements into Modern Interior Design. Journal of Landscape Research 12(02):9–12 9. Jeamwatthanachai W, Wald M, Wills G (2019) Building rating system: an instrument for building accessibility measurement for better indoor navigation by blind people. Journal of Enabling Technologies 13(3):158–172 10. Moon K, Gao P, In J, Glynn R, Rosado LC, Studio RLON, Hong FT (2020) Demo hour. Interactions 27(2):8–11 11. Yu B, Liu H, Cheng H, Gao P (2022) The impact of cross-border knowledge management on internationalizing Renminbi: lessons from the belt and road initiative. J Knowl Manag 26(1):257–267 12. Reily B, Gao P, Han F, Wang H, Zhang H (2022) Real-time recognition of team behaviors by multisensory graph-embedded robot learning. Int. J. Robotics Res. 41(8):798–811

Application of Decision Tree Mining Algorithm in Data Model Collection of Hydraulic Engineering Equipment Guang Yang and Xin Guo

Abstract After years of continuous reform and development, the economic field has undergone earth-shaking changes. At this time, water conservancy construction project enterprises are also changing their own business strategy, the water conservancy construction project equipment management of the single management into the diversity of engineering projects management, construction projects are relatively close to become relatively scattered. Enterprises improve the algorithm, the decision tree mining algorithm for water conservancy project equipment data collection, data training, establish data model, so as to analyze the characteristics of equipment, good and bad performance, so that the equipment can be better analyzed and used, so as to improve the use time of equipment. This paper studies the application of decision tree mining algorithm in water conservancy construction project equipment management, explains the working mechanism of water conservancy construction project equipment management application. Data analysis shows that the application of decision tree mining algorithm in water conservancy construction project equipment management effectively promotes the improvement of the application effect of water conservancy construction project equipment management. Keywords Decision tree mining algorithm · Water conservancy construction project · Equipment management · Algorithm application

1 Introduction National policy is heavily tilted towards the water sector, which is developing rapidly. In this context, more equipment is selected and used, and the equipment needs to be G. Yang (B) QianNan Polytechnic For Nationalities, Duyun 558022, Guizhou, China e-mail: [email protected] X. Guo Anhui Agricultural University, Hefei 230036, Anhui, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_76

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used more rationally, which can improve the service time of the equipment. Water conservancy and hydropower project is a huge project system, its equipment management is a major focus, its reasonable use will affect the technical performance of machinery, performance engineering quality, affect the market competitiveness of the final product. The industry improved the algorithm, using the decision tree mining algorithm, to establish the model of equipment, equipment data analysis. The application of decision tree mining algorithm in water conservancy construction project equipment management is conducive to the progress of water conservancy construction project equipment management application. As for the research of decision tree mining algorithm, many scholars at home and abroad have studied it. In foreign studies, Ghosh proposed to use logistic regression, decision tree and random forest methods to evaluate the sensitivity of soil erosion in the Mayurakshi River Basin in eastern India, and verified the ROC curve and Kappa statistics. A soil erosion inventory map was made using 150 rill and gully erosion prone sites, of which 70% sample sites were randomly selected for modeling and the remaining 30% sites were used for model validation [1]. Ziweritin developed decision trees and neural network models that are considered one of the fastest and easiest to use techniques with the ability to learn from classified data patterns. These models can be used to detect anomalies in normally measurable results, provided the student is healthy, problem-free and taking the exam. Existing methods lack merit and completeness to effectively detect irregularities between students’ continuous assessment and test scores [2]. Dinesh proposed to realize the fake news detector by comparing the performance of machine learning classifiers, so as to carry out higher classification of political fake news. Materials and methods: Two groups of decision tree algorithm and naive Bayes algorithm are considered. The algorithm has been implemented and tested on a dataset of 44,000 records. Through programming experiments, N = 10 iterations of each algorithm were performed to identify fake news and real news classification at different scales. Results: Through experiments, the average accuracy of decision tree algorithm is 99.6990 [3]. The development momentum of water conservancy projects is very good, and the use of equipment shows an increasing trend, so the reasonable management and use of equipment is a very important issue [4, 5]. In order to improve the use efficiency of equipment, relevant departments have stepped up data modeling for the management of water conservancy construction project equipment, carefully analyzed various influencing factors in the equipment, and analyzed the performance parameters of the equipment in good condition. In this way, enterprises can use these data more reasonably and scientifically. The application of decision tree mining algorithm in equipment management of water conservancy construction projects promotes the innovation of equipment management application of water conservancy construction projects [3, 6].

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2 Design and Exploration of the Application of Decision Tree Mining Algorithm in Water Conservancy Construction Project Equipment Management 2.1 Decision Tree Mining Algorithm The decision tree mining algorithm is a tree structure that takes the value of the attribute as the branch node of the and sample attribute [7, 8]. Among them, data source adopts data mining method to obtain data, which is generated by analyzing and summarizing large-scale sample attributes using the principles of information theory. The root node of the decision tree is the largest attribute information content in all samples. The middle node of the tree is the attribute point that contains the largest amount of information content in the root tree of the sample subset. The classic ID3 decision tree mining algorithm has many features, which include the following: Neat sample space, small amount of tests, high mining rate, small number of decision nodes, noise, and high fitness of discrete data, as shown in Fig. 1. However, the disadvantage of this algorithm is that it must follow the information entropy theory and meet the requirement of attribute gain. The final result value is often multiple values, it is difficult to obtain the optimal. This algorithm can promote the decision to reach the global optimal value. The calculation of information entropy is a very important step, which usually requires decision tree algorithm mining [9, 10]. In this paper, the value of information entropy is calculated first, and then improved, and the process is redesigned by the improved results. In ID3 type decision tree algorithm, multiple attribute values often appear, and the number is usually selected as the weight coefficient, and this parameter is put into the calculation of attribute information entropy. Given that A contains m element values, the probabilities of these values are p1, P2… PM can be expressed. All m element values in A can be expressed by using specific data sets. The algorithm formula is {θ1 , θ2 , ...θm }, these element values map to their information entropy, and these values can be expressed by the algorithm as noise data and discrete being suitable

Fig. 1 Characteristics of ID3 decision tree mining algorithm

Search space complete

Fewer tests

The decision tree having fewer nodes

Classification mining speed being fast

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G(θ1 ), G(θ2 ), ...G(θm ). After the algorithm is upgraded, A can be calculated, and the formula is as follows: G( A) = m

m 

pi ∗ G(θi )

(1)

i=1

The improved decision tree algorithm can be divided into five parts: Step 1: If any value exists Ai , the complete expression can be expressed {θ1 , θ2 , ...θm i } by assuming that this data has an element value m i . The probability that the values of all the elements in these expressions are produced p1 , p2 , ... pm i , and the information entropy expression that each probability maps to is G(θi ) =

n  2ti f i t +fi i=1 i

(2)

Step 2 Calculate the entropy of attribute Ai based on Eq. (1). Step 3 repeat step 1, 2, and obtain Ai+1 Ai+2 , … And then choose the lowest entropy value as the node. Step 4 Repeat Step 1 to step 3 to continue the subsequent nodes [11, 12]. Step 5 If the generated element values are set as subset points when the algorithm is calculated again, then the generation of decision tree is complete. If this step is not reached, proceed with the above steps.

2.2 Existing Problems in Equipment Management of Water Conservancy Construction Projects, as Shown in Fig. 2 (1) Lack of scientific equipment selection

Fig. 2 Problems existing in water conservancy construction project equipment management

Lack of scientific equipment selection

Internal equipment rental system was not established

Equipment use management lags behind

There being shortterm behavior

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At present, water conservancy and hydropower construction projects generally implement the bidding system, in the preparation of bidding documents to provide construction organization design documents, which equipment selection and configuration is mostly based on the completion of the period of the target, and the benefit of the target is considered less, lack of strict accounting; At the same time, some units in order to achieve the purpose of winning the bid, to cater to the preference of the construction unit, artificially increase some equipment; In the actual construction preparation, the project manager department also has the idea that the task is not important and whether the profit and loss is small. In the selection of equipment, there are often greedy and new and foreign psychology, but cannot seriously prepare the construction organization design, often based on experience and feeling, rationality is out of the question. (2) The internal equipment rental system has not been established As one of the production factors, construction equipment occupies a considerable proportion in modern construction, because the project department is a temporary construction organization, and the establishment of a perfect internal equipment rental market is the key to reflect the project responsibility, rights and benefits [13, 14]. But now most water conservancy and hydropower construction enterprises are still mainly based on the establishment and accounting of professional companies. When the project manager department needs, professional companies will organize equipment to enter the market by themselves. After the completion of the project, enterprises will conduct accounting for the company at the end of the year, and it is difficult to establish the market of production factors. (3) The use and management of equipment lags behind Equipment use management lag is a very serious problem [15]. If the lag time error is very large, it will seriously affect the later production benefit of the enterprise, at the same time, it also increases the wear and tear of the equipment, thus increasing a huge expenditure. At this time the most need to solve the problem is to build a complete set of equipment use management system. (4) Short-term behavior Projects often exists short-term behavior, will be in a short period of time several times more than the daily workload, thus greatly increase the rule of the use of the equipment, but also seriously violated the law of equipment maintenance management, imbalance of equipment, equipment maintenance can’t be normal maintenance, the equipment will be greatly to wear, so its use time will be discounted, The equipment will definitely be scrapped ahead of time.

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3 Explore the Application Effect of Decision Tree Mining Algorithm in Water Conservancy Construction Project Equipment Management Assuming X and Y are input and output variables respectively, and Y is a continuous variable, the training data set is given D = {(x1 , y1 ), (x2 , y2 ), ..., (x N , y N )}

(3)

For the partition of input space, subspace and its output value correspond to leaf nodes one by one. Here, by traversing each feature variable and its corresponding feature value in turn, assuming that the current segmentation variable is the JTH variable and the corresponding segmentation eigenvalue is S, two regions can be divided and defined:  α1 ( j, s) = {x|x ( j ) ≤ s} (4) α2 ( j, s) = {x|x ( j) > s} By traversing all the input characteristic variables and their eigenvalues, the current optimal segmentation point can be found, and then the current space can be divided into two sub-regions according to the segmentation point. If the two subregions can no longer be divided, the corresponding optimal output value can be obtained, which is expressed as 

βˆ1 = ave(yi |xi ∈ α1 ( j, s)) βˆ2 = ave(yi |xi ∈ α2 ( j, s))

(5)

By using decision tree data mining algorithm, the items of water conservancy construction project equipment management are abstracted into X and Y in formula (1). The training set is composed, and the data processing process is the same as formula (4) and (5), and the optimal output value of water conservancy construction project equipment management is finally obtained.

3.1 Pursue the Balance Between Equipment Use and Maintenance First, as a project, there is always a long-term plan, and each sub-project also has a detailed schedule. The preparation of the schedule plan should not only consider the influence of hydrology, weather and the total period of time required, but also consider the production capacity of the leading construction machinery and supporting machinery, otherwise the preparation of the plan will become empty talk because the machinery does not have the corresponding production capacity.

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Second, the specific implementation of each plan is inseparable from the organization of construction machinery, how to allocate the limited machinery reasonably to the sub-projects, in order to make full use of the best mechanical benefits, equipment management personnel must consider the comprehensive balance of mechanical selection and earthwork. Third, through reasonable equipment organization, make full use of the difference between the progress of each sub-project, the dispatch of similar equipment between sub-projects, in order to coordinate the maintenance of equipment, which is very important in the need for long-term overtime work in the project.

4 Investigation and Analysis of Application of Decision Tree Mining Algorithm in Water Conservancy Construction Project Equipment Management In order to verify the effectiveness of the proposed algorithm, three test data sets are selected to perform data mining experiments. The Hill Valley dataset is a kind of test data used in the laboratory, which tests the data that the curves formed by connecting multiple data will form a bump. In this process, 100 pieces of data are connected to form a curve and then tested to see if the surface they form is concave or convex. Hill Valley data set stores a large amount of data, up to 1212, among which the data sample reaches 100 attributes. The operating system used this time is Windows. Data set implementation of water conservancy construction project equipment management testing, as shown in Table 1. Table 1 shows that there are three data sets in the table, namely Hill Valley, Balance Scale and ABC. There are two algorithms used, namely ID3 (ID3 algorithm) and Decision tree mining algorithm. The table contains two groups of information, including the number of training nodes and Mining precision during the test. The data are shown in Table 1. It can be seen from the table that the same data set training requires fewer training nodes for the Decision tree mining algorithm. Meanwhile, the training accuracy of Decision tree mining algorithm is higher. It can be seen that the decision data mining algorithm has higher accuracy when processing the equipment management data of water conservancy construction projects. Table 1 Testing situation of water conservancy construction project equipment management Date set

Node number ID3 algorithm

Mining precision Decision tree mining algorithm

ID3 algorithm

Decision tree mining algorithm

Hill valley

320

260

0.83

0.97

Balance scale

271

210

0.90

0.98

ABC

120

90

0.76

0.85

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Fig. 3 Water conservancy construction project equipment management chart

Figure 3 clearly shows that the decision tree mining algorithm is more efficient in the data accuracy of water conservancy construction project equipment management application. The blue line represents the decision tree mining algorithm, and the green line represents the ID3 algorithm. As can be seen from the figure, the blue line is longer. The test results show that the application of decision tree mining algorithm in water conservancy construction project equipment management is more efficient.

5 Conclusions The economy is in continuous development, at this time the construction of water conservancy projects is also in rapid progress. In actual development, enterprises need to pay special attention to the management efficiency of water conservancy project construction equipment. If the equipment cannot be treated, then the equipment will often be damaged in advance, and its service time will be greatly reduced. In this paper, decision tree mining algorithm is adopted to calculate the existing data in water conservancy construction project equipment management. Based on the obtained data, equipment can be better used, so as to optimize the use of equipment and improve the use time of equipment. Decision tree mining algorithm has very efficient data processing and collection ability, and has efficient data decision-making ability, so it can better manage and use water conservancy construction project equipment. The application of decision tree mining algorithm in water conservancy construction project equipment management is conducive to the development and deepening of water conservancy construction project equipment management application.

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References 1. Ghosh A, Maiti R (2021) Soil erosion susceptibility assessment using logistic regression, decision tree and random forest: study on the Mayurakshi river basin of Eastern India. Environmental Earth Sciences 80(8):1–16 2. Ziweritin S, Baridam BB, Okengwu UA (2022) A Comparative analysis of neural network and decision tree model for detecting result anomalies. Open Access Library Journal 9(3):15–15 3. Dinesh T (2021) Higher classification of fake political news using decision tree algorithm over Naive Bayes algorithm. Revista Gestão Inovação e Tecnologias 11(2):1084–1096 4. Ayinla IB, Akinola SO (2021) An improved collaborative pruning using ant colony optimization and pessimistic technique of C5.0 decision tree algorithm. International Journal of Computer Science and Information Security 18(12):111–123 5. Sonza RL, Tumibay GM (2020) Decision tree algorithm in identifying specific interventions for gender and development issues. Journal of Computer and Communications 08(2):17–26 6. Varade RV, Thankanchan B (2021) Academic performance prediction of undergraduate students using decision tree algorithm. SAMRIDDHI A Journal of Physical Sciences Engineering and Technology. 13(SUP 1):97–100 7. Balasubramaniam V (2021) Fault detection and diagnosis in air handling units with a novel integrated decision tree algorithm. Journal of Trends in Computer Science and Smart Technology 3(1):49–58 8. Mijwil MM, Abttan RA (2021) Utilizing the genetic algorithm to pruning the C4.5 decision tree algorithm. Asian Journal of Applied Sciences 9(1):45–52 9. Kumar MRRBS (2021) A C4.5 decision tree algorithm with MRMR features selection based recommendation system for tourists. Psychology (Savannah, Ga.) 58(1):3640–3643 10. Karthigaikumar P (2021) Industrial quality prediction system through data mining algorithm. Journal of Electronics and Informatics 3(2):126–137 11. Agyapong K, Acquah J, Asante M (2020) An optimized page rank algorithm with web mining, web content mining and web structure mining. International Journal of Engineering Technologies and Management Research 4(8):22–27 12. James N, Godfrey BM (2020) District water conservancy contrivance an approach for water resource management in lessening water scarcity and floods in Rwanda-East Africa. IJSRP 10(7):101–108 13. Bhat MA, Abbasi T, Abbasi SA (2020) A household-scale on-site embodiment of the novel bioreactor SHEFROL for treating greywater. Taiwan Water Conservancy 68(2):26–35 14. Nosov VV (2020) Appraising the service life of dangerous engineering equipment by acoustic emission diagnosis. J Mach Manuf Reliab 49(12):1072–1083 15. Schindlerová V, Buko M, Ajdlerová I (2020) Potential of using burning equipment in the engineering company and metallurgy. Manufacturing Technology 20(2):244–249

ArcGIS-Based Landscaping Management System Juechao Tan and Qianying Lu

Abstract Information technology as the representative of “ArcGIS” technology applied to landscape management, can improve the level and quality of landscape management services, to promote the process of urban greening management, to achieve the management information of landscaping. The purpose of this paper is to study the gardening management system based on ArcGIS. By starting with the management aspects of greening-related data, this paper provides an in-depth study and analysis of the needs of the Bureau of Landscape Architecture in greening management, optimizes for business processes, and clarifies the efficient management role played by information systems. In the traditional way of greening management has not been able to adapt to the rapid development of urban planning and construction, it discusses how to improve the efficiency of the greening management department and improve the quality and efficiency of greening management. The experimental results show that the system can realize the statistical analysis management of greening resources. Keywords ArcGIS technology · Landscaping construction · Management system · Cost measurement

1 Introduction Along with the economic and social development, urban garden construction and landscape construction is also the basis and guarantee of urban greening work. J. Tan (B) Software Engineering Institute of Guangzhou, Guangzhou 510990, Guangdong, China e-mail: [email protected] Q. Lu Guangdong Urban and Rural Planning and Design Institute Co. Ltd., Guangzhou 510000, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_77

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With the help of efficient and scientific greening management system can reduce people’s pressure, improve human health and enhance human welfare, the construction of “gardening management system” has become an inevitable trend of modern garden development, and the construction of garden information technology is gradually changing the lifestyle and management of the garden industry. In addition, by reducing the cost of garden construction, the efficiency of garden management will also be significantly improved [1, 2]. For a long time, garden management has been at the original management level. Garden information is managed using traditional paper-based data, which is inaccurate, inefficient in real time, and does not create a system to manage as people change [3, 4]. More efficient access to data, documentation, and statistics is often needed when developing conservation work plans, management practices, or new structural plans. Mismanagement of some data may cause great inconvenience to managers. Landscape construction is in a period of rapid development and various landscape data are being updated rapidly, such as massive information control, scientific analysis and management of information, effective data extraction and its application to green management for sustainable and coordinated development of landscape management and cities are issues that must be addressed in landscape management [5, 6]. In landscaping management, the customization of green plant damage compensation schemes is usually achieved by manual measurement, paper records, and manual calculations, which are time-consuming, deviant, and inefficient. Nakash proposed an automatic GIS customization technique based on green damage compensation schemes. It can improve the calculation efficiency and reduce the compensation errors by using new mapping technology to collect urban landscape data and establish a landscape spatial database. An urban landscape management information system based on ArcSDE architecture technology was developed. Spatial and network data engine. A fast, accurate and automated personalized landscape loss compensation scheme using high-precision portable positioning technology [7]. The management of landscape seedlings is of great significance and determines to a large extent the degree of greening in urban and rural areas. Dash discusses the content and specific requirements of seedling management starting from landscape construction, analyzes the landscape construction methods for different types of seedlings, and proposes feasible recommendations for seedling management and maintenance methods [8]. Ogbuabor integrates AutoCAD survey elements into a library based on geographic data dictionary, element classification and coding standards while maintaining integrity and speed, please use ArcGIS and idata technologies to compare and analyze survey data and stored data ArcGIS stores spatial data based on ArcMAP desktop editing environment. Idata creates geographic information on ArcGIS platform database as a personal geodatabase and converts it from DWG to MDB [9]. With the help of mobile communication network, big data platform and other technical means, it can provide various support for urban landscaping management evenly and timely, so as to achieve scientific development and accurate layout of urban landscaping management [10, 11].

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This paper constructs an ArcGIS-based landscaping management system, combining the current situation, classification, mode and development trend of landscape management, focusing on the application direction of ArcGIS technology in landscaping management, the landscaping management system constructed in this paper can be applied in urban management, and also can provide a scientific management platform for open, efficient and scientific government management.

2 Research on ArcGIS-Based Landscaping Management System 2.1 System Objective To realize the sharing of urban greening and data related to urban greening, which can be permanently stored and queried in real time, and to realize dynamic management. The process of landscaping requires access to reference including park topographic maps, plant list tables, upper quality analysis tables and other types of graphic information, which are stored scattered and are quite laborious to find, and most of the landscaping construction drawings and effect drawings are sun maps, which are very easy to discolor and break over time, making the original wood excellent reference information becomes unpleasant [12]. Therefore, the system can access to various graphic information and flow with the pieces, and the system can pass the corresponding various types of attribute data generated during the case into the attribute library when the project is completed, so as to achieve data sharing and utilization.

2.2 Mobile GIS With the development of mobile technology, GIS information is stored on mobile computers in digital map format and carried around, so that staff can access enterprise-level geographic information anytime and anywhere. Mobile GIS can satisfy functions such as display of information. In ArcGIS on mobile GIS provides ArcPad to achieve simple GIS operations and Mobile ArcGIS Desktop System to achieve advanced GIS operations. currently, with the development of mobile GIS, the gradual development of powerful functions, it is more and more widely used. We are familiar with the “City Manager” which is being used by the city administration, which enables real-time transmission of information between the field environment and the supervision center. In this greening census field data collection process, it is found that the traditional measurement and management methods are no longer effective management methods, so look forward to the late construction of the system to establish a professional, relatively full-featured handheld mobile system for landscaping information.

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2.3 Support Vector Machine Based Target Cost Measurement The cost composition of landscaping projects is complex and involves more uncertainties, which makes it difficult to establish an accurate mathematical model. Support vector supporting small sample learning provides an effective method for target cost measurement of landscaping projects in landscaping management system. The regression algorithm of support vector machine is as follows: For the sample set {(xi , yi )}(i = 1, 2... n), xi is the input vector, yi is the target output, and n is the number of samples, the learning process is simplified SVM according to the structural risk minimization criterion and Lagrange multiplier method as: max

n 

yi (ai −

i=1

ai∗ )

−ε

n 

1  (ai − ai∗ )(a j − a ∗j )k(xi , x j ) − 2 i=1 j=1 n

(ai −

ai∗ )

i=1

n

(1) The system uses the RBF kernel function and the regression function is f (xi ) =

n 

(ai − ai∗ )k(x, xi ) + b

(2)

i=1

The model needs to be trained iteratively until the required accuracy is achieved.

3 Investigation and Study of ArcGIS-Based Landscaping Management System 3.1 ArcEngine Based Framework Design The design of the overall framework of this system is mainly divided into data layer, logic layer and application layer. This architecture is hardware composition is more flexible compared to secondary structure, which makes the program logically simpler and greatly improves maintainability. (1) Data layer In this project, based on 0racle10G database integrated database management greening data, through the database engine ArcSDE, link database and secondary development components, call spatial data and attribute data and return the result information to the user tide through the form in the system interface. (2) Logic layer

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In the logic layer, components are used to encapsulate all functions for database operations. The user operates these functions through the interface designed by the application layer. If the user commands change, causing changes to the interface. For this there is no need to modify the data layer and application layer, only the corresponding code needs to be changed. (3) Application layer The application layer is directly oriented to the user, and the menus need to be designed to be easy to use and conform to the user’s operating habits. Software users send requests to the logical layer to achieve the operation of the data, do not care about the specific implementation process of the system, do not need professional knowledge to understand the composition of the system, directly handle the data they need, before using the mainstream Windows Forms, easy to use for the majority of users. ArcGISEngine supports the creation of new and extended GIS desktop applications, including the main GIS navigation editing, information query, data analysis and rendering functions.

3.2 Development Environment of the System The system was developed on the AE platform, which is an application development platform designed by ESRI. When using AE to issue packages for GIS secondary development, it can be completely detached from the ArcGIS desktop platform, which facilitates the development of GIS. The specific configuration of the whole system is as follows: (1) Hardware environment: CPU > 2. 1Ghz; memory > 4G; video memory > 1G; hard disk > 300G. (2) Software development environment: ArcGIS Engine 10 Runtime,. net Framework3. 5, FME2011 (3) System operating environment Database: oracle 10 g/11 g. Operating system: Windows 7/XP. Development platform: Visual studio 10.0. Database engine: ArcSDE 10.0. GIS server: ArcEngine 10.0.

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Permission management Landscape information management system Greening query

Greening analysis

Greening resource management

Fig. 1 Functional architecture diagram

4 Analysis and Research of Landscape Management System Based on ArcGIS 4.1 System Function Architecture The landscaping information management system includes the system administrator side and operator side, which realize the functions of basic operation, permission management, greening query, greening analysis, greening resource management, etc. respectively. Each functional module corresponds to the corresponding type of business requirements. And there are many data connections between each module. The functional architecture of the system is shown in Fig. 1.

4.2 Green Space Planning Management Process (1) Green space declaration: optimization of green space declaration procedures, without the use of traditional manual statistics, directly use the greening integrated management system to query municipal planning and related documents, complete the preliminary feasibility study, research and preparation work for online declaration, greatly improving the efficiency of green space declaration. (2) Green space approval: new green space project classification function, the project will be divided into administrative licensing services and nonadministrative licensing services, according to different types of green space in accordance with different standards for approval, improve the efficiency of

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green space approval, and the approval results are publicized, increasing the transparency of the services of government units. (3) Green space ledger management: new implementation of the update of green space construction planning information function, the relevant users can timely query the green space construction, but also convenient to maintain the green space ledger, improve the efficiency of the Bureau of green space construction planning. (4) Green space reconstruction and expansion management: online approval of green space reconstruction and expansion, while timely updating of green space ledger information and green space construction planning information.

4.3 Greening Maintenance Management Process Greening maintenance work is one of the core of garden management business, which is mainly responsible for the inspection and supervision of the city’s gardening plants and garden facilities, responsible for supervising various illegal felling and transplanting of trees, destruction of garden facilities, encroachment on green areas and other violations of urban greening management, and responsible for guiding and coordinating the comprehensive urban gardening maintenance work. (1) Pest and disease statistics and prediction: Newly set the greening maintenance cycle, analyze and compare historical data, predict the risk of pests and diseases business function, able to conduct statistical analysis of historical information, providing strong support for garden plants to resist pest and disease attacks, and also able to save a lot of human and material resources. (2) Pesticide tool use registration: the use of computer registration of pesticide tools using the management system, compared with the traditional manual registration method greatly improves the efficiency of work, making management more intelligent. (3) Maintenance work progress management: new video pictures and other recording methods, can upload the work progress in real time, convenient for managers to make decisions. (4) Maintenance record management: New online maintenance work record is added to improve the statistical analysis of maintenance work progress, which can manage maintenance work more efficiently. And administrative approval personnel can also query the progress of maintenance work in a timely manner.

4.4 Greening Resources Statistical Analysis Management Greening resources include green areas, gardens, scenic spots, green belts, etc. within the city, which is a huge and complex data system. The statistical analysis of greening resources includes green space information statistics, greening structure analysis,

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Table 1 Statistical analysis

Type of park green space

Number

Proportion(%)

Community park

43

47.7

Roadside green space

32

35.8

Comprehensive park

8

8.8

Historic garden

5

5.5

Other types of parks

2

2.2

and greening rate calculation analysis. Urban green space information statistics is to organize and classify green space information after collecting and summarizing urban green space information, statistical green space contains plant status information, maintenance situation, etc.. The system provides the statistical analysis processing of its query results again, and users can analyze them intuitively, as shown in Table 1. Figure 2 shows the statistical analysis chart of M city. number

Proportion(%)

60

value

50 40 30 20 10 0

type of park green space Fig. 2 Proportion of various parks and green spaces in M City

ArcGIS-Based Landscaping Management System

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5 Conclusions At present, there is more and more information on landscaping, and the first challenge to be solved in urban landscaping management is to correctly grasp the large amount of information and apply it to the management of our urban landscaping system through meticulous analysis. The development and application of the system in this paper has the process of analysis, design, research, construction, and finally test through practice, and in practice, debugging, modification and improvement. This study is mainly the application of ArcGIS in the management of landscaping, there are still some problems in the system design, in the system research and construction and future applications will continue to summarize the adjustment, the problem will be solved.

References 1. Khan F, Das B, Mishra RK (2022) An automated land surface temperature modelling tool box designed using spatial technique for ArcGIS. Earth Sci Informatics 15(1):725–733 2. Brahma B, Wadhvani R (2022) Visualizing solar irradiance data in ArcGIS and forecasting based on a novel deep neural network mechanism. Multim Tools Appl 81(7):9015–9043 3. Pinos J, Dobesová Z (2019) ATTA converter: software for converting data between ArcGIS and TerrSet. Earth Sci Informatics 12(1):117–127 4. Koo H, Chun Y, Griffith DA (2018) Integrating spatial data analysis functionalities in a GIS environment: spatial analysis using ArcGIS engine and R (SAAR). Trans GIS 22(3):721–736 5. Misuraca G, Pasi G (2019) Landscaping digital social innovation in the EU: structuring the evidence and nurturing the science and policy debate towards a renewed agenda for social change. Gov Inf Q 36(3):592–600 6. Abood A, Feltenberger D (2018) Automated patent landscaping. Artif Intell Law 26(2):103– 125 7. Nakash M, Bouhnik D (2022) A system that will do magic: organizational perspective on the technological layer in knowledge management. Aslib J Inf Manag 74(6):1089–1102 8. Dash R, Ranjan KR, Rossmann A (2022) Dropout management in online learning systems. Behav Inf Technol 41(9):1973–1987 9. Ogbuabor GO, Augusto JC, Moseley R, van Wyk A (2022) Context-aware system for cardiac condition monitoring and management: a survey. Behav Inf Technol 41(4):759–776 10. Kimble M, Allers S, Campbell K, Chen C, Jackson LM, King BL, Silverbrand S, York G, Beard K (2022) medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata. Bioinformatics 38(19):4589–4597 11. Bernd DC, Beuren IM (2022) Do enabling management control systems stimulate innovation? Bus Process Manag J 28(2):461–480 12. Basak S, Dey B, Bhattacharyya B (2022) Demand side management for solving environment constrained economic dispatch of a microgrid system using hybrid MGWOSCACSA algorithm. CAAI Trans. Intell. Technol. 7(2):256–267

Construction of English Grammatical Error Correction Algorithm Model Based on Deep Learning Technology Jiaying Meng and Zhifan Wang

Abstract Syntactic EC (GEC) is one of the most important research tasks in natural language processing (NLP). The purpose of GEC is to detect and correct grammatical mistakes in the text. With the development of deep learning and the explosive growth of data, translation mode has become the first choice for GEC tasks, and Seq2SEq (sequence to sequence) model has been widely used in GEC tasks. In this paper, the most common RNN (RNN) algorithms in the field of NLP are analyzed, but the RNN algorithms have the problems of gradient dispersion or gradient explosion and long-distance dependence. Transformer addresses these two issues by introducing self-attention. This paper uses the encoder structure of Transformer model to build an English grammar (EG) error correction (EC) model and compares it with the other two algorithm models. According to the test results, the proposed model is superior to the other two models in terms of article and verb form EC except for the preposition EC. Keywords Deep learning · English grammar · Grammatical error correction · Transformer network

1 Introduction With the constant promotion and advance of globalization, English has become increasingly important for people to communicate and to deal with international affairs in all respects. For Chinese people, it is easy to make grammatical mistakes when using English because English is not the native language. Such mistakes may lead to misunderstandings sometimes and may create barriers to communication and J. Meng (B) Teaching and Research Institute of Foreign Languages, Bohai University, Jinzhou, China e-mail: [email protected] Z. Wang Harbin Flying Academy, Harbin, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_78

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cooperation internationally. However, thanks to the rapid progression of computer science and artificial intelligence, the issue of making grammatical errors has been solved with accuracy and high speed. Grammatical EC mainly refers to the automatic identification and Correction of Grammatical mistakes in English sentences through computers. The frequently seen Grammatical mistakes include subject-predicate agreement errors, preposition errors and article errors [1, 2]. There are many kinds of errors in EG. Sometimes there are many errors in a sentence, and the judgment of some errors even depends on the context, which makes the research of EG automatic EC algorithm face great difficulties. Traditional EG automatic EC models often adopt rule-based grammatical correction algorithms and statistical category-based grammatical correction algorithms [3]. In the last few years, with the continuous advance of theories related to deep learning, some deep learning theories have also been used in the field of EG EC. Many researchers have improved the English grammatical error correction model based on the traditional statistical classification, introduced the neural network structure to better extract the context information of the target word, and made great progress in the error correction effect. However, there is still less research on the error correction model of depth classification, and there is still room for further exploration of the error correction model based on depth classification [4]. As one of the tasks of Natural Language Processing (NLP), grammatical EC has drawn extensive attention in the last few years with the publication of more and more relevant data sets [5]. NLP researchers at home and abroad have carried out a large amount of work on this, which has greatly promoted the development of this task. The target of grammatical correction assignment is to detect and correct grammar mistakes contained in a given text, including spelling errors, semantic errors and punctuation errors [6]. As a universal language, the study of EG correction by foreign scholars has been advancing continuously. In the early stage, due to the limitation of the poor performance of computers and the small number of relevant data sets, researches mainly focused on the error syntax detection [7]. With the explosive growth of data and the fast advance of deep learning, neural network models shine in various fields, gradually replacing traditional machine learning methods. More and more researchers use Neural Machine Translation (NMT) to deal with grammar EC. Compared with SMT, NMT model performs better in capturing long distance dependencies and global dependencies between words, and corrects text errors as a whole, which is easier to ensure the correctness and fluency of corrected statements. Therefore, the rapid development of NMT model greatly promotes the research on grammar EC task [8]. This paper focuses on automatic EC for syntax errors. Through the application of deep learning technology, the grammatical errors in sentences are automatically analyzed, and a high-performance grammar EC method is established. The encoder structure of Transformer model is used to seize the contextual information of the target word, and the influence of different training data amount on the EC effect is also explored.

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2 Construction of Grammar EC Model Based on Deep Learning 2.1 Recurrent Neural Network RNN is one of the most frequently used models in the field of NLP. In the traditional neural network model, we believe that all inputs and outputs are independent of each other, the neurons between layers are completely connected, and the neurons within each layer are not connected. However, for most NLP tasks, traditional neural network model architecture is not suitable [9]. The idea of RNN network is to use sequence information in the form that RNN network will remember the previous information, that is, the current output of the model also needs the information memorized at the previous time of the model. Generally speaking, the neurons in the hidden layer of RNN network are connected, and the input of RNN network at current time t includes not only the output of the input layer but also the output of the hidden layer at time t-1. Since most NLP tasks have certain dependencies, that is, words are related to each other, RNN network is more compatible with NLP tasks than traditional neural network, which can greatly improve the ability of model sequence modeling [10]. The calculation process of RNN network is as follows: When the RNN network receives the input text xt at the time of input layer t, it does dot product with the weight matrix U to enter the hidden layer. The hidden layer state st is not only related to the input text xt, but also related to the hidden layer state ST-1 at time t-1. St consists of two parts, one is the dot product between the input text Xt and the weight matrix U, the other is the dot product between the hidden layer state ST-1 and the weight matrix W. RNNs model can improve the ability of model sequence modeling and has been used in many natural language processing assignments. However, the backpropagation algorithm of the network needs to consider not only the gradient at the current moment, but also the influence at the previous moment when calculating the gradient, so when the gradient value is too small or too large, it is easy to give rise to the problem of gradient dispersion and gradient explosion. Gradient dispersion or gradient explosion and distance dependence are the main reasons that restrict the development of RNNs. From problem localization to solution, scientists spent several years of research, and finally derived some methods, among which the most typical ones are Long short-term Memory network (LSTM) and Gated Recurrent Unit (GRU). Due to the excellent time series modeling ability and wide use of GRU and LSTM models, LSTM and GRU have become the most frequently used models for NLP tasks.

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2.2 Transformer Network Since the attention mechanism was introduced into sequence to sequence task, the effect of sequence to sequence model has been greatly improved. In the early sequence-to-sequence generation model, Attention mechanism is often introduced into the structure of RNN or convolutional neural network (CNN). These methods have some disadvantages: the sequence-to-sequence generation model based upon CNN and Attention is difficult to compute in parallel, and the sequence generation model based upon RNN and Attention always fails to capture the text information with a long distance. The Transformer model solves this problem well. In this model, the traditional sequential to sequential RNN model is abandoned, and the self-attention mechanism is introduced. The self-attention mechanism can not only help the model capture long distance semantic information, avoid the issue of semantic information loss caused by too long-distance words, but also enable the model to be trained in parallel. Self-attention mechanism is the most important part of Transformer model, which makes Seq2Seq get rid of the problem that Seq2Seq generation model is difficult to train in parallel and cannot capture long distance semantic information. In the selfattention mechanism, each word in a sentence corresponds to three different matrices, namely query vector, key vector and value vector. More concretely, it refers to the information used to calculate the correlation between the current coded word and other words in the sentence, represents the information used to query the correlation between the coded word, and represents the relevant information of the word to be encoded. These three vectors are respectively obtained by the product of the word embedding vector and three different weight matrices. The vector corresponding to each word in a sentence is merged into the form of three matrices, and then the dot product result of query matrix and key matrix is scaled and normalized. The result of sentence processing by self-attention mechanism is obtained by dot product of the normalized result and the value matrix. The process of self-attention mechanism can be expressed by Eq. (1). √ Attention(Q, K , V ) = so f t max(Q K T / dk )V

(1)

In general, DK is the vector dimension value, which is 512 in Transformer, mainly to prevent the softmax function from being pushed into areas with minimal gradients. Transformer does away with traditional RNN or CNN model structures and has achieved great results. Transformer changes the distance of any two positions to 1 through matrix operation, which is conducive to solving the intractable long-distance dependence problem in NLP. Moreover, the parallelism of the model algorithm is very good, which is conducive to speeding up training, and it is a direction with great scientific research potential.

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2.3 EG EC Model In the thesis, a new deep classification model has been put forward. The model first sends the input sentence into the word embedding layer to obtain the corresponding representation, then uses the encoder structure to capture the feature information of the target word context, and finally concatenates the vectors into the MLP for category judgment. The model from bottom to top is word embedding layer, encoder layer, attention layer and MLP layer. The encoder layer is the encoder structure of Transformer model, which contains multiple attention heads. Multi-head attention allows the model to observe the information of different representation subspaces in different locations. In a multi-head attention head, each part of the input representation interacts with the others to better capture contextual information, and long-term dependencies can be better captured when multi-head attention observes different representation subspaces at different locations. Because the traditional gradient descent method needs to traverse all the training data every time the model parameters are updated, the convergence speed of the algorithm is slow when the sample size is large. To solve this problem, SGD was proposed. SGD randomly picks a sample from the training data each time for learning, and the parameter update process is shown in Eq. (2), where η represents the learning rate and J(θ) represents the loss function. SGD is mainly used to learn linear classifiers such as support vector machine and logistic regression. θ = θ − η · ∇ θ J (θ )

(2)

3 Experimental Design and Analysis 3.1 Test Set and Evaluation Index In this paper, CoNLL2014 test set and officially defined R.5 are used as evaluation indicators. The CONLL-2014 test set contains 1312 sentences and 28 types of grammatical errors, which are obtained by two authoritative taggers who are native English speakers independently annotating grammatical errors and providing correct EC methods. Conll-2014 takes F0.5 as the index. F0.5 combines precision (P) with recall. Recall (R), and set the weight of accuracy to be twice that of recall, because it believes that the accuracy of EC is more important than the breadth of EC in GEC scenarios.

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Table 1 Comparison results of article error types

CUUI

Depth context

Our model

P

31.7

45.1

57.3

R

45.9

30.4

31.6

F0.5

32.8

43.2

49.01

3.2 Experimental Environment and Validation Set In this paper, GeForceGTX 2080 in Nvidia GPU is used for model training and prediction. The validation set used in the parameter adjustment phase of the model in this paper is the training set of CONLL-2014. Since the model in this paper is trained on a large number of data sets generated by plain texts on the Internet, and the data source of CoNLL-2014 is not trained specifically, the training set of CoNLL-2014 is used as the verification set in the parameter adjustment stage to obtain the model parameters that best match the test set.

4 Experimental Results and Analysis The paper compares the EC results of three classification models. They are the paper model, the CUUI model and the deep context model, on the three types of errors: article, preposition, and verb form. As is shown in Table 1, the EC results of the three models on article errors are compared. It can be watched from the table that the EC model in this paper is superior to the other two models in syntax correction except that the recall rate is lower than that of CUUI model. As shown in Fig. 1, for the three models on the preposition error types of statistical EC results, it can be seen in terms of the preposition EC, will be less and the other two models, model in this paper may be due to the category of the preposition, the depth of the model is still unable to better master the match rule, this article has yet to be improved in terms of the preposition EC model. As shown in Fig. 2, the EC result statistics of the three models on verb error types show that the model in this paper is superior to the other two models.

5 Conclusions EG correction is a significant research topic in the domain of natural language processing. An excellent grammar correction system can actually benefit thousands of English learners and help to solve many other tasks in natural language processing.

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40 35 30

Index

25 20 15 10 5 0 CUUI

Depth context

Our model

Model P

R

F0.5

Fig. 1 Statistics of preposition EC results of three models

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Index data

50 40 30 20 10 0 CUUI

Depth context

Our model

Model P

R

F0.5

Fig. 2 Statistics of EC results of verb morphology in different models

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In this thesis, a new deep classification EC model is put forward. The encoder structure of Transformer model is used to catch the contextual information of the target word, and the influence of different training data amount on the EC effect is also explored. The deep classification model put forward in the paper has achieved nice results on the three types of error explored. However, despite of the work which has been done in this paper, there are still many shortcomings and room for improvement. The shortcomings are basically in the following aspects: the grammar EC model based on deep classification lacks interpretability, but it still has good EC effect on specific types of grammar errors. Therefore, how to improve the EC effect of deep classification grammar EC model is still a direction worth discussing and exploring.

References 1. Anuradha P, Kumar PK (2017) Novel architecture for fault tolerant parallel FFTs based on EC codes using Vedic algorithm. Advances in computational sciences and technology 10(10):3123–3130 2. Adsa B, Gb A, Sb B et al (2020) Model-based correction algorithm for Fourier transform infrared microscopy measurements of complex tissue-substrate systems. Anal Chim Acta 1103:143–155 3. Mohammadian-Behbahani MR, Saramad S (2018) Pile-up correction algorithm based on successive integration for high count rate medical imaging and radiation spectroscopy. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment 897(JUL.21):1–7 4. Scienti O, Bamber JC, Darambara D (2020) Inclusion of a charge sharing correction algorithm into an X-ray photon counting spectral detector simulation framework. IEEE Transactions on Radiation and Plasma Medical Sciences 99:1–1 5. Jain A, Jain M, Jain G et al (2019) ‘UTTAM’: an efficient spelling correction system for Hindi language based on supervised learning. ACM Transactions on Asian Language Information Processing 18(1):8.1–8.26 6. Safarian C, Ogunfunmi T (2019) The quaternion minimum error entropy algorithm with fiducial point for nonlinear adaptive systems. Signal Processing 163(OCT.):188–200 7. Kwon B, Lee S (2019) Error detection algorithm for Lempel-Ziv-77 compressed data. Journal of Communications and Networks 21(2):100–112 8. Luo S, Wu H et al (2017) A fast beam hardening correction method incorporated in a filtered back-projection based MAP algorithm. Physics in Medicine Biology 62(5):1–1 9. Guttmann-Flury E, Sheng X, Zhang D et al (2019) A new algorithm for blink correction adaptive to inter- and intra-subject variability. Computers in Biology and Medicine 114:103442 10. Aa A, Ena A, Rm B (2019) Low complexity digital background calibration algorithm for the correction of timing mismatch in time-interleaved ADCs—ScienceDirect. Microelectron J 83:117–125

Design of the University Management Information System Based on User Experience Mingxiu Chen

Abstract In the process of using the management information system of the university, it turns out more practical obstacles, such as the difficulties in implementing the compatible operation of various hierarchical systems, the weak information processing and analysis functions, the lack of important information reminder functions, and the ineffective use of the management information system. Thereby afterwards the system need to be developed by unifying the construction standards of the management information system of various departments, sort out the overall management process of colleges and universities, clarify the responsibility for information release at various levels, and design customized functions. In the face of specific problems in development and implementation, such as insufficient allocation of special funds, accelerated upgrading of data volume and data processing, and requirements for continuous upgrading of the system, this paper proposes countermeasures such as building an independent system development team in colleges and universities, focusing on optimizing core information, and paying attention to cost accounting. Keywords University · Management information system · Design

1 Introduction The management information system is an automatic software system capable of information collection, information transmission, data calculation, report printing, prediction and analysis. Management information system not only depends on the underlying support of system software, such as operating system and database, but also depends on other hardware environment, including server equipment, network environment, etc. [1]. Along with cloud computing and mobile Internet, management M. Chen (B) Department of Business, Changchun Humanities and Sciences College, Changchun, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_79

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M. Chen Systems

Departments

Salary query system

Human resources office

Educational administration system

Dean’s office

Scientific research system

Office of academic research

Venue reservation system

School office

Archives management system Human resources office Asset management system

Assets division

Personnel management system

Human resources office

information system is no longer a single C/S or B/S model [2]. Traditional management information systems (Table 1) generally include salary query system, educational administration system, scientific research system, venue reservation system, archives management system, asset management system, personnel management system, etc. These systems generally use a simple server model, and don’t have the characteristics of distributed, multiple data sources [1]. The design with new technologies and users’ new needs is an inevitable trend. Users of management information system include university management, department staff, grass-roots employees and students. Analyzing their specific new needs, and then optimizing and upgrading the current system is one of the prerequisites for the design of university management information system. How to meet the needs of university management for the various needs of department staff, grass-roots employees and students, the design of the updated management information system is the focus of this study. Through the analysis of the requirements of the design, the important challenges that will be encountered in the implementation process are analyzed, and combined with the existing resources and reality of colleges and universities, targeted solutions and suggestions are proposed to achieve the continuous upgrading and optimization of college management information system. Promoting the construction of university management information system through information technology is conducive to improving the efficiency of comprehensive management, achieving effective communication in academic fields both inside and outside the university, and ensuring the modernization of university management [3].

2 Demand Analysis Based on User Experience With using the system, based on the user experience, summarize the main demand problems, including the difficulties in implementing the compatible operation of each grading system, the weakness of information processing and analysis functions, the lack of important information reminder functions, and the ineffective use of the management information system.

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Resource access control

Information system

Teaching Resources

Library Resources

Campus Websites

Fig. 1 The example of current operation

2.1 Difficult in Realizing the Compatible Operation of All Grading Systems In the process of informatization development, each management department only builds departmental systems according to its own actual needs, such as the scientific research management system of the Scientific Research Department, the educational administration management system of the Academic Affairs Department, the personnel management system of the Personnel Department, the asset management system of the Assets Department, and the library’s graphic management system (Fig. 1). When conducting macro statistics, the information in each management system cannot be retrieved by users or universities at one time, it is impossible to obtain data across systems, let alone to analyze data across systems.

2.2 Weak Information Processing and Analysis Functions The existing management information systems of various departments are at the primary stage of information collection, and the functions of information processing and analysis are weak. The university management needs the system to realize the data analysis of the real-time completion of the important indicators of the school year, the progress of the completion of the school year plan, and the progress of the completion of the medium and long-term strategy in terms of functional settings. Department staff need the system to realize the networking of department business and the release of the latest resources and information in functional settings. Grass

728 Table 2 Main functions of the management information systems

M. Chen Users

Functions

University management

Supervision

Departments

Office work

Teachers

Schedule

Students

Reminder

roots employees need the system to realize the data analysis of the real-time completion of important personal indicators in the school year, the progress of the completion of the school year plan, and suggestions on the long-term development of individuals in terms of function settings. On the basis of personal information query, students need to realize the timely understanding of various important information and the processing of management information of student associations.

2.3 Missing Important Information Reminder Function The most important function of information is real-time (Table 2). The query function of the existing department management information system is basically available, but the push function of real-time effective information and the reminder function of important information are missing. The university management also needs to remind the university management of the important agenda in the near future through realtime analysis. Department staff need to be reminded that their shared department responsibilities are reminders of real-time meetings and other information, reminders of agency events, etc. Grass roots employees need to be reminded of daily courses, daily plans, important information, etc. Students need consultation on classes, exams, various activities, etc.

2.4 Obscure Use Effect of Management Information System The important reason for reinvestment is that it has already had an obvious use effect, and it is difficult to achieve this goal with information management only for the purpose of information storage. College management, department staff, grassroots employees and students hope that the system can serve as their second brain to help them effectively complete their work responsibilities and provide adequate information support for their own development. Effective service management is the basis for truly realizing intelligent campus, because the design of the system is ultimately for users, and the system serves users.

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3 Design on Account of User Experience For design of system based on user experience, first, unify the construction standard of each department’s management information system; secondly, comb all management ways of universities; thirdly, clarify the responsibility of information release at all levels; finally, design customized functions.

3.1 Unify the Construction Standard of Management Information System of All Departments First, from a long-term perspective, colleges and universities should formulate the construction standards for the management information system of each department. Each department should complete the development and maintenance of its own management information according to its own development stage, update the information in real time, and propose new requirements for system upgrading to adapt to future development. When formulating a unified construction standard, colleges and universities should fully consider each department and the overall layout of school. Only when they are highly capable of landing can they produce practical long-term value.

3.2 Comb All Management Ways of Universities Process carding should not only be from top to bottom, but also from bottom to top, which can ensure the realization of the middle and high level goals of the school as a whole, while the bottom-up carding can ensure the realization of the low and micro level goals of the school, and can also intentionally supplement the middle and high level goals of the university. Process management is to comb all flowing activities of school. In actual operation, new processes are established through visual interfaces, or new processes are rebuilt by combination, pruning and other methods, so as to provide an efficient process layout platform [1].

3.3 Clarify the Responsibility of Information Release at All Levels Users are not only users of the management information system, but also maintainers of the management information system. It is the responsibility of users to update management information at all levels into the system in real time. This is a systematic

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project, which needs to be completed together from top to bottom and from bottom to top.

3.4 Customized Function Design Customized function design, as an advanced management information system, is an important part of improving user satisfaction. Starting from the different important needs of university management, department staff, grass-roots employees and students, find out the functions that need to be realized most in the use of the system, and customize the functions.

4 Challenges in Implementation The information system based on user experience needs to be constantly developed. The system development of high user experience cannot be separated from the challenges such as the support of funds, the continuous increase of data, and the continuous upgrading of the system.

4.1 Inadequate Allocation of Special Funds With the limited development funds, the funds that can be allocated to the construction of system are not sufficient. They need many investment to various aspects, and they should consider which are urgent and which are secondary, and often it is included in secondary development projects. At the same time, the more powerful the system is, if it is delivered to a special technology company, its development cost is extremely high. Moreover, the technology company mainly focuses on the development of large projects for enterprises, and does not focus on the micro cost in universities. For various reasons, the further system upgrading will be faced with double dilemma in funds and providers.

4.2 Accelerated Upgrading of Data Volume and Data Processing The second major problem faced by the university management information system in the construction process is how to catch up with the data volume and the rapid growing speed of data processing, which is an inevitable challenge for any one. Although

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information system is in a continuous cycle of academic years and semesters, the external information is constantly changing rapidly, which is still a challenge to the overall data volume and data processing ability.

4.3 Requirements for Continuous System Upgrading After using the university management information system for a period of time, it is necessary to upgrade the system. Upgrading requires reinvestment of funds. Only with the guarantee of reinvestment of funds, can the system function continuously meet the changing work needs of users. How to obtain financial support for system upgrading is an important problem faced by the continuous upgrading of the system.

5 Breakthrough of Implementation It is a deliberate exploration to build an independent system development team, optimize core information and pay attention to cost accounting.

5.1 Build an Independent System Development Team in Colleges and Universities Lack of funds is a weakness, but colleges and universities are rich in human resources. The development of human resources of existing computer teachers and students not only provides a practical platform for them, but also provides financial support for their practical training. The building of computer training parks in schools also requires a certain amount of capital investment (Table 3). One investment to meet the two needs is an important attempt to solve the financial problem. Table 3 Main fund support of the management information systems

Funds

Resources

Construction of training platform

University

Construction of management information system Departments Economic output

Projects

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5.2 Focus on Optimizing Core Information In the face of the processing of a large amount of information, the most important thing is to find the core key information. The determination of the core key information is that senior managers extract the key data to ensure the completion of the goals according to the overall development strategy of the school. The continuous updating and optimization of this part of information is an important means to solve the complexity and rapid changes of the system information.

5.3 Focus on Cost Accounting Scientific cost accounting is more conducive to scientific decision-making. It is better for decision-makers to experience the powerful functions and effectiveness of the management information system by reasonably calculating the actual use value and benefits generated by the optimized management information system and clarifying the output input ratio. The Information system also increases the value of an organization’s competitive advantage and facility the users in the decision making processes [4]. At the same time, make full use of the convenience of the Internet, such as moving servers to the “cloud” and using virtualization technology to allocate and manage computing and storage resources as a whole [5].

6 Conclusions There are difficulties in achieving compatible operation of systems at all levels, weak information processing and analysis functions, lack of important information reminder functions, and unsatisfactory use effect. We need to unify the construction standards of the management information system of all departments, comb all management ways of universities, clarify the responsibilities of information release at all levels, and design customized functions to develop the system. When meeting insufficient allocation of special funds, accelerated upgrading of data volume and data processing, and continuous upgrading of the system, we can try to establish an independent system development team, focus on core information optimization, focus on cost accounting and other countermeasures. In a word, the continuous optimization of university management information system is to constantly upgrade and update the system based on user needs, and the system design based on user needs can more closely meet the needs of users. At the same time, in the face of various challenges, universities dare to constantly try, explore and break through is the inevitable choice for development. The standards and

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policies are also important [4]. The design-reality gap assessment of selected information communication technology [6], how schools and universities gain competitive advantages from Management Information System [7], the implementation of practicum activities in daily lectures [8], and the premise for the formation of a project management system at university [9] also need to be considered. Acknowledgements This work was supported by the Ministry of Education’s collaborative education project: the application of English thinking training in business English audio-visual and oral courses (project number: 202,101,147,005); The “Demonstration Course” project of ideological and political teaching reform in Changchun Humanities College: the “Ideological and Political Course” demonstration project of administrative office English; In 2021, Jilin Provincial Department of Education’s project of cooperation between production and education: exploration of curriculum teaching reform to enhance innovation and entrepreneurship.

References 1. Yang Y (2021) The construction path of university management information system from a multi-dimensional perspective. Fujian Computer 37(04):59–61 2. Chunzhi Z, Hong W (2001) MIS system design based on C/S and B/W/S mixed mode. Computer Application Research 18(10):123–125 3. Xu H (2020) Building China’s university smart campus management information system. Informatization Construction 12:63 4. Sudianto L, Simon P (2021) Development application of a quality assurance management information system for Paulus Indonesia Christian University. IOP Conference Series: Materials Science and Engineering 1088(1) 5. Junzhou L, Jiahui J, Aibo S et al (2011) Cloud computing: architecture and key technologies. Journal of Communications 32(7):3–21 6. Omohwovo OE, Olatokun WM, Ojutawo IR, Okocha JO (2021) Information and communication technology projects and management information system in Nigerian universities: a design reality approach. Libr Philos Pract 2021:2021 7. Burhanuddin (2021) Management information system and its competitive advantages for school and university organization in the new world order. In: 7th international conference on education and technology (ICET 2021), p 601 8. Eskaluspita AY (2020) ISO 27001:2013 for laboratory management information system at school of applied science Telkom university. IOP Conference Series: Materials Science and Engineering 879(1) 9. Chekotilo EY, Abramkina DA, Kichigina OY (2020) Financial resources management information system of flagship university projects. In: 2nd international scientific and practical conference “modern management trends and the digital economy: from regional development to global economic growth” (MTDE 2020)

Design of Dynamic Monitoring System for Cold Chain Logistics Vehicles Based on Internet Digital Intelligence Technology Yingling Du, Zongbao Sun, and Jinxia Li

Abstract With the continuous development of the Internet, the logistics industry has also grown rapidly. In people’s life, we can often see many companies transporting their required products directly to nearby cities or suburban areas, which requires a cold chain logistics monitoring system to be set up in these places. This often requires cold chain equipment to complete these tasks and provide a certain amount of time to the destination consumer for this purpose, but because China has not yet established a perfect monitoring and query system and related technical standards as well as the lack of real-time dynamic monitoring of fresh food, resulting in its development being affected or even causing serious consequences. Therefore, in order to understand the dynamics of food in the whole supply chain timely and accurately, we designed a real-time monitoring system for cold chain logistics vehicles based on Internet digital intelligence technology, which can realize the tracking and supervision of fresh food. Keywords Internet digital intelligence technology · Cold chain logistics vehicles · Dynamic monitoring system design

1 Introduction With the development of social economy, people put forward higher requirements for the logistics industry, not only to provide relevant services from the production side but also to the supply chain management direction, which makes the cold chain logistics monitoring system occupy an increasingly important position in the market competition, and its monitoring system plays an increasingly important role in the Y. Du · J. Li Industry College of Cold Chain Logistics and Supply Chain, Shandong Institute of Commerce and Technology, Jinan, China Z. Sun (B) Inzone Group Co., Ltd., Jinan, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_80

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whole logistics process. The cold chain logistics has the characteristics of high cost and low efficiency, which requires us to carry out real-time dynamic monitoring of the cold chain transport vehicles in order to timely find and deal with related problems. At present, most of the enterprises in China still use the traditional static tracking and control mode to track and adjust the information of product quality, supply and demand and inventory, which is a large workload and low efficiency. The lack of dynamic monitoring technology leads to the inability to grasp the changes of consumers’ demand for goods and the inability to understand the changes of market demand in time, thus causing the production and supply to be uncoordinated and unable to realize the balance of supply and demand [1].

2 The Current Situation of Internet Digital Intelligence Technology In recent years, the development speed of China’s Internet numerical intelligence technology is very rapid. At present, many domestic manufacturing enterprises mainly adopt manual inspection and control for cold chain logistics monitoring, and realize real-time monitoring of cold chain logistics transportation by means of manual patrol and analysis to monitor product quality. Although China has initially established a numerical control data collection system, there are still many problems in intelligent vehicle tracking and dynamic monitoring, for example, it cannot automatically locate the vehicle condition information, it cannot analyze the vehicle operation status in time and make feedback processing, and it cannot quickly and effectively obtain the changes of the temperature and the idle ratio in the vehicle in the current time period. This paper proposes an improved cold chain logistics monitoring system, which can collect and analyze real-time data during the operation of the vehicle, and realize automatic measurement and control of the location information and driving status through GPS positioning technology.

3 Overall Design of Cold Chain Logistics Vehicle Dynamic Monitoring System Cold chain logistics vehicle dynamic monitoring system is based on Internet data transmission technology, which uses advanced communication network and GPS positioning tracking technology to integrate the traditional temperature and humidity information collection to the computer processing center for intelligent management. The system stores real-time data in the cold storage management system and displays it through a visual intercom device, and monitors it in real time through a GPS positioning tracking module, so that the temperature, humidity and other information can be automatically collected. This cold chain logistics vehicle dynamic monitoring

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system has the advantages of low cost, high reliability and high efficiency, which can effectively realize the dynamic real-time monitoring of cold chain logistics vehicles, thus enhancing the overall social and economic benefits [2].

3.1 Topology Analysis Based Modeling Feature Extraction The cold chain logistics vehicle monitoring and dispatching system directly serves the suppliers and sellers, realizes real-time monitoring in the process of cold chain logistics transportation, and automatically dispatches the vehicles through the network technology. The system is based on Internet digitization and GIS (Geographic Information System). It uses sensor technology to complete data information collection and processing, and then uses GPRS technology to upload to the network server. Through the network server, the cold chain logistics process is monitored in real time, and the operating parameters of vehicles and related equipment are automatically adjusted according to their actual needs to achieve the goal of dynamic balance control. Finally, the collected data is stored in the local database of the server after unified processing, which facilitates the real-time dynamic tracking and monitoring of vehicles in the process of cold chain logistics, so that the cold chain storage and distribution plan can be adjusted and optimized in time to improve the efficiency of the whole supply chain. The data source of the monitoring part of the cold chain logistics vehicle monitoring and dispatching system is the vehicle terminal, while the processing of noninternet data is done by the cold chain logistics vehicles and related enterprises, and the system can also control them effectively after realizing the real-time monitoring function. The system uses PHP script language, combined with Baidu map, to realize the display of temperature and humidity of the goods in the transport vehicle as well as geographical information, and real-time monitoring according to the set route [3]. The system has a very high practical value in the dynamic monitoring, scheduling and control of cold chain logistics vehicles. It can effectively help enterprises to realize the collection and management of information such as the number and temperature of vehicles, and it can also improve transportation efficiency and reduce operating costs. The functional diagram of the cold chain logistics monitoring system is shown in Fig. 1.

3.2 B/S Architecture In order to realize the real-time monitoring of the cold chain, a set of perfect and effective operation management system must be established to achieve the efficient and safe completion of each logistics link, which can ensure the efficient operation of the whole supply chain system. B/S architecture is a new network structure designed based on distributed database construction and self-organization technology. It is

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Fig. 1 Cold chain logistics monitoring system functions

centered on a distributed storage system, and through dynamic monitoring and supervision of the resources of each node in the cold chain logistics network, it realizes real-time collection of information on temperature and speed of the whole supply chain network. Its biggest feature is that it can share data and carry out unified scheduling and control processing in the shortest time [4]. It also has the advantages of strong fault tolerance and openness, and it can realize dynamic monitoring and management for different nodes of cold storage, sorting equipment and transportation vehicles, thus improving the real-time and accuracy of cold chain logistics monitoring.

3.3 Hardware Data Interface Design Multi-threading. Nowadays, Windows operating systems can run multiple programs at the same time and in different pages, which leads to poor timeliness of data exchange, slow response time and high transmission cost due to network topology and communication protocols on the same information flow. The cold chain logistics monitoring system based on the Internet digital intelligence technology can detect the dynamic status of vehicles in real time and display it on the server, which makes it easy for managers to manage and control the whole supply process, so that the temperature information and circulation of each node can be effectively and quickly transmitted to the decision makers, thus improving the efficiency of the whole supply chain, and at the same time, this can also avoid the problems caused by poor timeliness and slow response of information exchange. It can also avoid unnecessary losses due to poor information exchange timeliness, slow response time and other problems.

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Since any individual thread has its own CPU execution time slice. Therefore, having a thread that can dynamically record in real time throughout the cold chain logistics monitoring system allows for quick detection of all possible problematic nodes and bottleneck moments. Each thread does not exist independently, e.g., primary and secondary threads can complete communication with each other and between work and user interface threads, and each thread has a special data storage entry. In the cold chain logistics monitoring system, if the data information of all possible problem nodes and bottleneck moments can be recorded in real time and quickly located to the key, the whole process control can be completed [5]. Communication protocol. It is not only a simple transmission protocol, but also an interactive application. The purpose of the cold chain logistics vehicle monitoring and dispatching system is to monitor the real-time information of the cold chain truck and provide real-time information for the cold chain logistics in the Internet digital intelligence network. Using advanced RF technology, GPRS wireless communication network and GPS global positioning system, the cargo data captured in each location of the cold chain transport vehicle is analyzed and its status is adjusted accordingly according to the time point. In this paper, Java script is written to listen to the fixed port of the server, read the data uploaded by the data collectors and enter them into the database. Considering that there will be multiple sets of data collectors, the Java script uses multi-threading technology to implement a mechanism that supports multiple data collectors to connect to the network server at the same time [6]. The hardware data interface workflow diagram is shown in Fig. 2.

Fig. 2 Hardware data interface workflow diagram

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3.4 Map Positioning Design In the cold chain logistics monitoring system, due to its special characteristics, realtime positioning of the tracked cargo information is a very important and complex and difficult problem. In order to solve this problem, this paper proposes a model based on the combination of map transformation function and GPS global satellite positioning technology. Using this method, the current position of the monitored vehicles can be quickly and accurately fed back to the relevant departments and provide more services to users, and the cold chain tracking and control can be realized by analyzing the dynamic changes of the real-time monitoring nodes in the process of cold chain logistics. GPS (Global Positioning Satellite Technology) is a navigation information technology with a wide range of applications and high accuracy. It uses radio waves as a carrier to measure and track the movement speed of targets. With the deepening of economic globalization and the coming of the Internet era, GIS system has been growing rapidly and widely used in various industries, such as transportation and logistics management, e-government and related industry control. In addition, GPS also has the function of remote communication, it can monitor and locate the vehicle comprehensively, and can track the vehicle in real time. Cold chain logistics monitoring system is a kind of low temperature management mode in the supply chain environment, with transportation, procurement and sales as the core, which can realize the information flow and material flow from suppliers to users in the whole process [7].

4 The Establishment of Cold Chain Logistics Vehicle Scheduling Model The dynamic monitoring of cold chain logistics vehicles refers to the use of digital intelligence technology to collect data such as delivery time and temperature, and then query the operation status in the current environment in the database and display it on the column management platform. At present, the analysis of vehicle dynamic scheduling problems based on Internet digital intelligence technology mainly uses the process-oriented control (CPS) algorithm: by tracking and monitoring the current state and recording the relevant parameters, so as to achieve an intuitive understanding and mastery of the system’s operating status and temperature change trends, and can provide timely feedback to the user, so that the system can automatically adjust the cold chain logistics This will facilitate the system to automatically adjust the temperature, humidity and other parameters in the process of cold chain logistics, so that it can control the vehicle within a reasonable range of use. After analyzing the whole process of cold chain logistics transportation, we can see that there are four main parts that can generate costs: the fixed cost of the transport vehicle, the cost of energy loss to maintain the temperature in the car, the cost of penalty for not

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reaching the destination within the specified time, and the cost of cargo loss caused by the loss of goods during the transportation process [8]. Transportation vehicle fixed costs include vehicle fixed costs and transportation process costs. Among them, the vehicle fixed cost refers to the cold chain logistics enterprises in the vehicle monitoring investment costs, operating costs and personnel management fees, etc. Assume that the supply center has m vehicles for C customer service cost of each vehicle is fixed and equal to r m (m = 1, 2, 3...M). So the vehicle fixed cost as shown in formula (1); transportation process cost refers to the cold chain logistics enterprises in the vehicle monitoring investment costs and operating costs, the cost is related to the distance traveled by the vehicle, the specific calculation as shown in formula (2). c11 =

M 

r m = m R0

(1)

m=1

c12 =

C M  C  

U Si j ximj

(2)

m=1 i=0 j=0

Cooling cost of cold chain truck. The cooling cost of cold chain logistics truck mainly comes from the cost of maintaining the constant temperature in the car, which includes the fluctuation of car temperature, vehicle speed and mileage, as well as the cost of deterioration of goods during transportation, as shown in formula (3). M c2 = λ1

C C

m=1

m j=0 U Si j x i j

i=0

v

+ λ2

m  C 

x kj p mj

(3)

m=1 j=1

Cost of loss of goods during transportation. The cost of loss of goods during transportation refers to the loss of value due to damage, deterioration and other effects of goods in transit due to the long delivery distance [9, 10]. c3 = q

M  C 

(ρ1 timj + ρ2 t j )ximj

(4)

m=1 j=0

After the previous analysis of the whole transportation process, so as to derive the cost of the whole cold chain logistics transportation process, so the scheduling model of cold chain logistics vehicles is shown in formula (5). MinT = c11 + c12 + c2 + c3

(5)

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5 System Testing The monitoring system of cold chain items plays a crucial role in the productization process. In the cold chain logistics monitoring system, the products are monitored dynamically in real time by temperature sensors, GPS positioning and vehicle location sensing devices. In addition, the low-power design completes the cold chain monitoring task for 7 days under the condition of limited battery power, which meets the working time requirements of the cold chain and effectively reduces the cost of cold chain logistics. At the same time, the system has to use the internal FLASH to backup the data of these 7 days, through this 7 days monitoring, it can record and process the temperature change and status information of the products in the whole process accurately and in real time. According to the GPRS base station database provided by the mobile, the effective GPS coordinates of the base station points can be obtained, and through this information, the real-time monitoring of temperature changes and status in the cold chain logistics process can be realized, which meets the customer’s needs for dynamic product monitoring.

6 Conclusion To sum up, with the popularity and development of Internet digital intelligence, the logistics industry is also progressing. In order to improve the temperature control requirements during the transportation of perishable products such as cold chain foods, the monitoring system needs to be supported by higher level technology in terms of real-time and accuracy. At present, what is used in China is GPS monitoring as the core using RF chip EMS as a data processing tool to complete the collection and exchange of information from the origin of the product to the consumer market, and then the collected information is transmitted to the production enterprises, distributors and other nodes through GPS signals, so as to achieve real-time monitoring of the temperature of the product during transportation. This can improve the efficiency of cold chain logistics, reduce food loss and ensure the interests of consumers. The future development direction of cold chain logistics is inevitably to achieve full monitoring, dynamic management and intelligent control, so as to improve the operational efficiency of the entire supply chain and reduce operating costs. Acknowledgements This work was supported by National teaching innovation team projectInnovation and Practice of Reform in Team Teacher Education and Teaching in Professional Field of Modern Logistics and Supply Chain in Vocational Colleges in the New Era (ZH2021010201), and it was also supported by the project from Shandong Institute of Commerce and Technology-Research and Practice of Integrated Education Mode of BlueSword Production Training Base (A201).

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References 1. Fahrni ML, Ismail IAN, Refi DM et al (2022) Management of COVID-19 vaccines cold chain logistics: a scoping review. Journal of Pharmaceutical Policy and Practice 1:15–17 2. Monti G, Fumagalli F, Quaranta G et al (2018) A permanent wireless dynamic monitoring system for the Colosseum in Rome. Journal of Structural Integrity and Maintenance 2:23–25 3. Yang K, Zhang X, Shi Q et al (2017) A monitoring system for cold chain logistics vehicle based on Beidou satellites. In: Proceedings of 2017 6th international conference on advanced materials and computer science (ICAMCS 2017), pp 300–305 4. Xiong G, Wang Z, Zheng L et al (2021) Product development of an extended cold chain logistics vehicle. Smart Systems and Green Energy 1:33 5. Ren Q-S, Fang K, Yang X-T et al (2022) Ensuring the quality of meat in cold chain logistics: a comprehensive review. Trends in Food Science Technology 118–119 6. Oliveira G, Magalhães F, Cunha Á et al (2017) Dynamic monitoring system for utility-scale wind turbines: damage detection and fatigue assessment. J Civ Struct Heal Monit 5:15–17 7. Lim MK, Li Y, Song X (2021) Exploring customer satisfaction in cold chain logistics using a text mining approach. Industrial Management Data Systems 119–121 8. Karen R (2020) Paths to progress for women in cold chain logistics. Refrigerated Frozen Foods 12–13 9. Pereira S, Magalhães F, Gomes J, Lemos J et al (2017) Installation and results from the first 6 months of operation of the dynamic monitoring system of Baixo Sabor arch dam. Procedia Engineering 188–199 10. Chaitanoo N, Ongkunaruk P, Leingpibul D (2020) The use of physical simulation to evaluate thermal properties of food containers in cold chain logistics. IOP Conference Series: Materials Science and Engineering 1:773

Cloud Computing Information System Security Monitoring Under Artificial Intelligence Technology Cuijin Lao and Shen Qin

Abstract Cloud computing (CC) represents the trend of IT field towards the road of intensification, scale and specialization, and is a profound change that is happening in the IT field. It greatly improves the efficiency of using various resources, but also brings great impact and challenges to the security and privacy protection of users’ information assets. In this paper, we take a dam cloud computing information system as an example, use Revit as a platform, combine Visual Studio and C# language, secondary development of Revit, and establish a dam safety monitoring BIM IS. The monitoring data management module completes the interaction between Revit and database through Visual Studio and C# language, and adopts two ways of monitoring instrument 3D model and command button to realize the association between each monitoring item and monitoring data; the monitoring data analysis module embeds the mathematical model established by the research into the system to realize the modeling prediction of dam safety monitoring data. The research results show that the development of the dam cloud platform safety monitoring BIM IS with the functions of dam structure management, monitoring instrument management, monitoring data management and monitoring data analysis is of positive significance to improve the efficiency of dam safety monitoring and promote the development of dam safety monitoring technology. Keywords Artificial intelligence technology · Cloud computing · Information systems · Security monitoring

C. Lao · S. Qin (B) Liuzhou City Vocational College, Liuzhou 545000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_81

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1 Introduction With the further penetration of network in human’s daily life, network data is exploding, the traditional network is highly dependent on hardware, and its performance cannot be fully utilized, flexible scheduling cannot be made, and the limited investment can no longer carry massive data, so virtualization technology and cloud computing emerge as the times require. Nowadays, major IT companies, while developing CC-related technologies within their enterprises, have also deepened the research of CC into major universities and invested heavily in it, which shows that “cloud” has become a trend. However, due to its weak security links, most of them are deployed in the way of private clouds and even many data-sensitive enterprises are deterred from it, so how to achieve effective SM of CC information system (IS) and provide corresponding crisis management solutions has become a hot topic. In this paper, the SM of CC IS is studied based on AIT. Although the research and papers on CC security have shown a linear increase in recent years, after comparative analysis, it is easy to find that most scholars and researchers in the research on CC security countermeasures focus on purely technical attacks, without considering the role played by human factors and organizational management factors in the security of CC services, and without providing users with effective and operational solutions from their perspective. This paper is innovative in the sense that it takes into account the technical and organizational factors that play a role in the security of CC services. The innovation of this paper is to combine technical means, personnel management and organizational management within CC service providers with proactive information security protection measures that can be taken by users, and to combine them with artificial intelligence technologies for analysis and discussion [1, 2]. This paper focuses on the development of dam cloud computing platform safety monitoring IS based on AIT and the establishment of prediction model. Through probe collection, real-time monitoring, early warning prevention and control, intelligent defense, exception isolation, audit disposal and other means, combined with big data analysis, real-time situation awareness, cloud platform centralized management and automatic processing are realized, using Revit as the piggyback platform of the system, combining Visual Studio and C# language for secondary development of Revit to complete the construction of dam safety monitoring BIM IS. The research route of using Revit, the mainstream modeling software of BIM, as the development platform, combined with the software Visual Studio and C# language to establish the BIM IS for dam safety monitoring is proposed; the 3D model of the dam and monitoring instruments is constructed using Revit. The characteristics of commonly used safety monitoring mathematical models are studied and analyzed [3, 4].

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2 CC IS Security Analysis Cloud computing brings a flexible, fast, efficient and small money experience for all kinds of businesses. It also poses new challenges to network security and service security under the cloud computing environment. The security of the cloud computing environment determines whether key businesses are suitable for migration to the cloud computing platform.

2.1 Types and Characteristics of Information in CC Environment Digital information under CC is the same as traditional digital information types, including: numerical information resources, image information resources, multimedia information resources, and text information resources. The digital information under CC environment has all the characteristics of traditional digital information and adds new features brought by the new environment of CC as follows. Private in nature. Traditional information resources are not restricted by time, space, or language and can be retrieved by people, but the digital information in the CC environment all belong to individual users and have exclusivity; low controllability [5]. The characteristics of CC services determine that the core of information processing is on the server side, although private cloud servers can be controlled by users themselves, public cloud servers are not completely controlled by users themselves. and for the information transmitted to the public ECS, users cannot know the process of information processing, and this sensitive information that concerns users is like being put into a black box, and users are not very clear about the processing of public cloud information, and for this reason, different terms and regulations may apply to the data, for example, data that is protected in China stored in a server in the United States may not be protected by the laws of the country where it is stored, which poses a great potential problem [6].

2.2 Information Security Problems Faced by Users Since users mainly provide data to service providers for processing when using public cloud services of cloud computing, several user privacy issues arise in the information between users and service providers in this process as follows. Access: The user has the right to know what personal information of the user is stored and obtained by the service provider while using the CC service, such as the user’s birthday, age, family status, contact information, etc., and the user has the right to request the service provider to delete the personal information.

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Compliance: Users have the right to know the requirements and details of information privacy regulations in the compliance, and how the off-site migration of user data in CC services (mainly cross-border migration) will affect the details of personal information privacy in the compliance [7, 8]. Retention and Destruction: When a subscriber cancels a service, does the CC service provider retain the subscriber’s information, and if so for how long, how does the CC provider destroy the personal information after the retention phase, how does it ensure that the information is destroyed and how does it ensure that the information has been properly destroyed and is not used by other subscribers [9].

2.3 Information Security Issues Faced by CC Service Providers 2.3.1

Infrastructure Security Issues

Infrastructure security for CC includes security issues at the network, host, and application levels. When analyzing CC security from the network layer, public and private clouds should be fully considered. In the private CC service mode, the CC service provider will deploy a dedicated extranet according to the user, and its network layer risk will be much reduced. The existing web applications basically have security flaws such as incomplete access verification mechanisms, logical errors in program design, and so on. Hackers will constantly scan these web applications to master these vulnerabilities for intellectual property theft and cyber fraud. This not only poses a threat to CC service providers, but also increases the risk and loss to users. For CC service providers, the web applications set up on public cloud platforms must withstand the test of Trojan horses, viruses and hacking attacks [10].

2.3.2

Data Security Issues

Data security issues in cloud computing environment mainly include the following aspects: Data integrity, which refers to the protection of system information or information processing from intentional or unintentional unauthorized modifications, means that the state of the user’s information should be the same as the user expects. With CC services, the open-source model provided by service providers and changes to network firewall rules can pose threats that are not easily detectable. Confidentiality, it means that the information is private and should not be disclosed. Threats to data confidentiality include password cracking attacks by hackers, unauthorized and inappropriate access control, malicious code and worms.

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Availability, availability ensures that the system and data are accessible to users when needed, in principle, without control of time and space factors, and that users can use digital information whenever conditions allow. For availability, the service provider should provide a 7 by 24 service [11]. Therefore, the goal of protecting user information security in the cloud computing environment mainly include the following aspects: Data integrity and confidentiality. Data integrity and confidentiality is a unique property that always accompanies information security and is the main research of information security. Generally speaking, it means that unauthorized users do not have the right to obtain and change information. For traditional paper-based information, the manager only needs to protect the entity of the document and prohibit access to unauthorized persons to complete the purpose [12]. Availability. Availability means that the owner of digital information can get the right to use the information in time when the need for the information arises. Availability is a new requirement for information security protection in the information in the network era, and it is also an information security requirement that must be met by CC. Controllability. Controllability refers to the adoption of corresponding technical and management means to achieve the supervision of digital information and to achieve the legal management of digital information.

2.4 Problems in Cloud Computing Platform At present, the main problems faced by the cloud computing platform are: how to reasonably and effectively separate the internal network of the second tier cloud computing platform; How to timely discover and effectively isolate malicious code and Trojan viruses inside the cloud computing platform; How to effectively monitor the deployment of virtual machines and network traffic in the cloud computing platform; How to realize automatic and visual O&M deployment; How to audit and evaluate the internal security of the cloud computing platform. To solve these problems, it is urgent to build a linkage big data platform, a cloud computing network monitoring system and a cloud computing situational awareness system. The Vxlan technology is used to reasonably partition the internal network of the second tier cloud computing platform. By deploying virtual firewalls, VWAF, bastion machines, equipped with physical device probes and traffic probes, real-time information collection is achieved to visualize traffic and applications, achieve unified management of security policies, achieve threat detection and security isolation between virtual machines, and achieve audit and traceability of network attacks.

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3 AIT 3.1 Neural Network Model Artificial neural networks are computational models that mimic the structure and function of biological neural networks to process information, and are very complex nonlinear dynamical systems with powerful self-learning, self-organizing and selfadaptive capabilities. The biological nervous system consists of nerve cells and neurons, and each neuron can process external information independently.

3.2 BP Neural Network Model Design Influence factor analysis. A good mathematical model should be able to reveal the inner law between the dependent variable and the influencing factor, so the choice of the main influencing factor and the form of the factor is very important. Safety monitoring studies and experience show that for homogeneous earth dams, reservoir water level is an important factor affecting the dam and should be added to the modeling as an influencing factor, commonly in the form of a power of water level, etc. Rainfall also has a significant impact on dam deformation and seepage monitoring. The influence factor of rainfall R can be taken in the form of average rainfall, or the previous rainfall and, such as the first 3 days of rainfall and R1–3 , the first 5 days of rainfall and R1–5 . Normalization of samples. The monitoring data collected from different monitoring projects represent different physical meanings and have different magnitudes, and the variability between the data is large, which can easily lead to errors in the model and affect the analysis effect, therefore, it is necessary to normalize the data samples. The normalization of the sample can be done by using the maximum and minimum normalization methods to normalize the data so that the data after normalization of the sample all fall within the interval [0,1], and the normalization formula is as follows. X' =

X − X min X max − X min

(1)

where Xmax and Xmin denote the maximum and minimum values in the sample data, respectively. The samples can also be normalized using the premnmx function so that the data all fall within the interval [0.1,0.9], with the following normalization formula. X' =

0.8(X − X min ) + 0.1 X max − X min

(2)

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3.3 Determination of the Number of Neurons in the Hidden Layer The number of neurons in the hidden layer directly affects the training effect of BP neural network. Too many neurons will lead to overfitting and increase the number of iterations, which will not only prolong the training time, but also increase the error of the network and affect the subsequent prediction analysis; too few neurons, the training effect of neural network is poor, with little fault tolerance and poor fitting effect. Therefore, we need to initially determine the number of neurons in the hidden layer based on engineering practice and empirical formula (2). r=

√ n+m+α

(3)

where r is the number of neurons in the hidden layer, m is the number of neurons in the output layer, n is the number of neurons in the input layer, and α is an integer between 1 and 10.

4 Research on SM of CC IS with AIT 4.1 System Structure and Function Module Layering This paper takes dam safety monitoring IS based on cloud computing platform as an example for research and analysis, using Revit as the secondary development platform, combined with Visual Studio and C# programming language. Using Revit platform to establish a 3D BIM model of the dam and monitoring instruments, it is easy to understand and master the main structure of the dam and the deployment of monitoring instruments; establish Access database, realize the interaction between Revit and database, and manage the monitoring data efficiently; establish a mathematical model in Visual Studio software, and embed the model into the dam safety monitoring; realize the Predictive analysis of the monitoring data. The whole system is divided into cloud platform foundation layer, cloud platform virtual layer, cloud platform monitoring layer, cloud platform data layer, cloud platform service layer, and cloud platform application layer. Figure 1 shows the cloud platform system structure. (1) Cloud platform infrastructure layer: provide the platform with infrastructure, network equipment, storage equipment, servers, etc. required for operation. (2) Cloud platform virtual layer: This layer includes virtual servers, virtual network adapters, virtual switches, virtual firewalls, etc. It integrates virtual computing resources, virtual network devices, and virtual storage devices, pools resources through virtualization technology, and realizes the functions of resource allocation on demand, rapid service deployment, and so on.

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Fig. 1 Cloud platform system structure

(3) Cloud platform monitoring layer: consists of data sensing devices and network security devices. Among them, data sensing devices include sensors, cameras, weather stations, water and rain monitoring stations, patrol detectors, etc.; Network security equipment includes firewall, bastion machine, honeypot, traffic cleaning, load balancer, intrusion detection, intrusion prevention, WAF, etc. It is used to sense, collect and monitor relevant physical quantities, environmental quantities and network traffic. (4) Cloud platform data layer: it adopts distributed storage, mainly storing monitoring data, model data, computing data, geographic data, log data, system model data, etc.

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Fig. 2 Detection model block diagram

(5) Cloud platform service layer: including monitoring center, computing center, authority center, message center, situation awareness system, etc. It is the link connecting the cloud platform data layer and application layer. (6) Cloud platform application layer: It mainly includes four modules: dam structure management, monitoring instrument management, monitoring data management and monitoring data analysis, each of which is interconnected by Visual Studio and C# language. Figure 2 shows the monitoring model.

4.2 Dam Structure Management Module The dam structure management module is based on the 3D solid model of the dam in Revit software to realize the 2D and 3D visualization of the dam, through the basic editing functions such as scaling, moving and hiding the model, presenting the dam structure from multiple perspectives such as overall, local and section of the dam, and grasping the global and detailed situation of the main structure of the dam and the surrounding terrain from multiple views. The module can also quickly and easily generate drawings of the dam project to facilitate other management tasks. Monitoring data management module: Monitoring data management module uses database to manage monitoring data, realizes the interaction between Revit and database through Visual Studio and C# language, and uses two ways of command button and instrument 3D model to realize the association between each monitoring item and monitoring data. Monitoring data analysis module: The monitoring data analysis module uses Visual Studio and C# language to embed the mathematical model established by the study into the dam safety monitoring BIM IS to model and analyze the dam safety monitoring data and predict the dam operation condition.

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Fig. 3 Classification of elements

4.3 Overview of Graph Elements As the carrier of model information, drawing element is one of the most basic element types of Revit and the basis of BIM modeling. In Revit, elements are divided into three categories: model elements, datum elements and view-specific elements, and the three types of elements can also be subdivided into five subcategories, as shown in Fig. 3. Model elements are the elements that have been set in the system, including the main model elements and model components elements. The main model elements are the most commonly used elements in the modeling process; model components are the subsidiary parts of the project, which are added separately in the late modeling stage and can be flexibly designed and modified according to the actual project. The axis network, elevation, and reference plane included in the base element class together form the base design plane of Revit modeling, which makes the built model conform to the standard specification. The view-specific elements class includes two subclasses: annotated elements and detailed elements. The annotated elements are used to mark the model dimensions and annotate the model information, etc. When the dimensions and information of the model elements are modified in Revit, the corresponding annotated elements will automatically update the parameters to improve the modeling efficiency and independent of each other.

5 Conclusions The dam safety monitoring BIM IS constructed in this paper has improved the efficiency and effectiveness of dam safety monitoring and data analysis to a certain extent, but there are still some later improvements that can be made: in terms of

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monitoring data analysis, this paper does not consider the joint analysis between different monitoring projects and different monitoring points, and only models a single measurement point, which can be considered later to introduce a multi-point monitoring model in the system In the future, we can consider introducing multipoint monitoring models into the system to improve the effect of predictive analysis of monitoring data. The research results of safety monitoring indicators can be considered to be introduced into the system to increase its early warning function. With the rapid development of Internet and computer technology, the system can also consider combining with other BIM software for function expansion. The SM of CC IS based on AIT needs further in-depth research.

References 1. Bukhalev VA, Skrynnikov AA, Boldinov VA (2022) Game minimax control of automatic system bandwidth under information counteraction. Autom Remote control 83(2):282–290 2. Muaaz M, Chelli A, Gerdes MW, Pätzold M (2022) Wi-sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health IS. Ann des Télécommunications 77(3–4):163–175 3. Pawar P, Parolia N, Shinde S, Edoh TO, Singh M (2022) eHealthChain—a blockchain-based personal health information management system. Ann des Télécommunications 77(1–2):33–45 4. Sergei K, Alexander K, Polina E, Nikita A (2022) Factoring ethics in management algorithms for municipal information-AI. Ethics 2(1):145–156 5. Oliveira-Dias D, Moyano-Fuentes J, Maqueira-Marín JM (2022) Understanding the relationships between information technology and lean and agile supply chain strategies: a systematic literature review. Ann Oper Res. 312(2):973–1005 6. Tang P, Yao Z, Luan J, Xiao J (2022) How information presentation formats influence usage behaviour of course management systems: flow diagram navigation versus menu navigation. Behav Inf Technol 41(2):383–400 7. Chang V, Kacsuk P, Wills GB, Behringer R (2022) Call for special issue papers: big data and the internet-of-things in complex IS: selections from IoTBDS 2022 and COMPLEXIS 2022: deadline for manuscript submission: September 30, 2022. Big Data 10(2):93–94 8. Schütte R, Ahlemann F, Becker J, Legner C, Lehrer C, Wiesche M, Richter G (2022) Quo Vadis IS research in times of digitalization? Bus Inf Syst Eng 64(4):529–540 9. Spiekermann S, Krasnova H, Hinz O, Baumann A, Benlian A, Gimpel H, Heimbach I, Köster A, Maedche A, Niehaves B, Risius M, Trenz M (2022) Values and ethics in IS. Bus Inf Syst Eng 64(2):247–264 10. Deepu TS, Ravi V (2022) Modelling of interrelationships amongst enterprise and interenterprise IS barriers affecting digitalization in electronics supply chain. Bus Process Manag J 28(1):178–207 11. Elia G, Margherita A, Massaro A, Vacca A (2022) Adoption of open innovation in the COVID19 emergency: developing a process. Bus Process Manag J 28(2):419–441 12. Shen X, Lu Y, Zhang Y, Liu X, Zhang L (2022) An innovative data integrity verification scheme in the internet of things assisted information exchange in transportation systems. Clust Comput. 25(3):1791–1803

Optimization of Parametric Park Landscape Design Based on Grasshopper Module Platform Yi Fu and Chensong Wang

Abstract Parametric design is widely used in various industries. This paper, based on the analysis of Grasshopper analog number platform in park landscape model production cases, points out the feasibility and practicability of parametric application in landscape design. Compared with the traditional modeling method, it has the advantages of modular, visual and dynamic model design. Keywords Parameterization · Parameter visualization · Grasshopper

1 Grasshopper Parameter Analog Platform Introduction Parametric design is widely used in all walks of life, in the late twentieth century because of its accurate computing power was applied to the field of industrial design and architecture, but parametric due to its own professional lead to high learning difficulty [1]. With Rhino + Grasshopper as the module platform, the development of the parameterized platform has solved many difficulties in architecture and engineering design, and more and more interior and landscape design began to introduce Grasshopper plug-in. In traditional design, designers need to constantly analyze it. New tools to quickly capture, select, analyze, and display the information needed for urban design. Therefore, visualization tools must be fully featured with ease of use, real-time performance, multivariate data integration and fusion [2]. The Grasshopper plug-in solves this problem through the visualizing interface of the parameters. As long as the designer changes the design parameters slightly, he can optimize them according to certain rules to get the best results. A parameterized model is a new product. As a place for the general public to rest, play and entertain activities, parks also play an important role in scientific and cultural activities, but compared with this, they play an important role as the green lungs in the urban ecosystem. Hardware Y. Fu · C. Wang (B) School of design and art, Shenyang Jianzhu University, Shenyang, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_82

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Fig. 1 The Grasshopper module composition

facilities are relatively perfect, and the public green space environment is good. In recent years, China attaches great importance to urban environment construction, and the most important measure is to continuously increase the support for park environment construction. As to speak, China’s current park construction is in the booming period, got the encouragement and support of people from all walks of life, on the one hand, improve the pace of the urban modernization and quality, improve the appearance of the city at the same time, on the other hand, the single landscape model repeated processing and modification also increase the details of the workload of the designer [3], many problems need to be solved. The Grasshopper Modulus platform is a visual programming plug-in of Rhino, which provides the user with the operation of parameter control visualization through the mode of node combination links. Because of its modular mode and dynamic real-time interaction, it brings users a convenient and quick parameter visualization experience [4]. The Grasshopper computing module is mainly composed of two modules, as shown in Fig. 1: the operator and the parameters. The modular composition mode is more convenient for beginners to learn. Even if you do not understand the algorithm and code behind it, you can also connect the operator. It reflects the trend of AI parametric application. Grasshopper elements used to process information include logic operations and operations that include digit operations and geometric operations including vectors, space, space, space, and delivery. Logical operations involve mathematical formulas and vectors, and use geometric operators as carriers. On an independent operation, the input and output of multiple parameters can be realized. On the left is the input of the parameters, and on the right is the output of the parameters, as shown in Figs. 2, 3.

2 Design Mode of Visualization In the era of big data, the process of data analysis is often inseparable from the cooperation and complementary advantages of machines and people. More than

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Fig. 2 Operator distribution figure

Fig. 3 Input and output diagram of the operator parameters

80% of the information we get from the outside world comes from the visual system [5]. Grasshopper’s software design uses an intuitive graphical interface, as shown in Fig. 4, which can clearly show the operation of each step, and each step has a corresponding calculation step. Such a programming method not only facilitates the user’s learning and organization, but also can find the problem in the shortest time. In entially from left to right, each graph has a different encoding and running rule. One operation is done through the operator’s connection head, while in the interface, the gray and gray operators indicate that the resulting results are shaded.

Fig. 4 The grasshopper production page

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The process of parametric design is the process of constraint specification, constraint solution and constraint satisfaction [6]. The design activity is essentially to build the constraint model and conduct the constraint solution of by extracting the effective constraints. Therefore, the data parameterization of the model, the first step of the first to data constraints, constraints are the core of the technology, this step can be regarded as the process of constraint satisfaction. Designers typically target functional, structural, and manufacturing constraints as design objectives and map them into specific geometric topologies, translating them into geometric constraints.

3 Parametric Design Process Based on the Grasshopper 3.1 Modeling of Visual Analysis In this chapter, a design optimization analysis of a Marine park landscape is based on the Grasshopper parametric design process, and is designed through the Tyson polygon modulus nodes. Compared with the traditional polygon modeling method, this scheme can realize the parametric modeling through two basic methods: program drive method and size drive method. The shape of the landscape refers to the growth shape and characteristics of coral reefs. Landscape designs generally exist in a dynamic place of living behavior, and their form should have a high degree of harmony with the environment where it is placed. Therefore, the design mission of the landscape form is to have the continuity and coordination with the conditions of the site and the people in the space, so as to achieve the positive interaction between people, the environment and landscape [7]. The parametric design controls the shape of the model with the enlightening results of morphological research in mathematics. Many research results in mathematics can show the essence of the subset phenomenon of organizational relations between objects, which is the expression of conceptualizing laws. In the traditional polygon modeling, designers shape a fixed model according to their own modeling ability, which is not conducive to the later adjustment and modification. When the parametric results are applied to the model design, it can create a more visual artistic expression form, which can better reflect the advantages in the later adjustment and modification. This scheme is made by using voronoi algorithm according to the advance conception. Voronoi algorithm floor plan and derivatives are shown in Fig. 5.

3.2 Modeling the Node-Drive Settings When using the program-driven method for modeling, we should first analyze the characteristics of the graph geometry model, so as to get the main parameters and

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Fig. 5 Voronoi algorithm floor plan and derivatives

the mathematical relationship between the various dimensions, which is incorporated into the program [8]. Enter the required parameters in the program, and the program determines the other relevant size values through the mathematical relationship between the various dimensions, and finally determines the whole model. Size drive method is an extension of the program drive method, the basic principle is first through the application to generate the basic diagram involved, then in this figure with a series of identifiers to identify all dimensions, these dimensions in programming by the program or interactive input, after the user interaction select a certain entity modification is completed, the application once update graphics, will satisfy the user given constraints or relational [9]. Generally speaking, the parameters can be divided into two major categories, one is the invariant parameters used as constraints, and the other is the parameters that can be adjusted according to the morphology in a certain range [10]. After the parametric model is made, the model can be observed by adjusting the variable parameters to find out the form that meets the requirements. Figure 6 is a screenshot of the parameter adjustment interface of the landscape. The driver sets two parameters as variable parameters, respectively, the two-dimensional random point distribution of the Tyson polygon, and the hollow edge parameters. Different values have different form results, intuitive interaction and controllability, more convenient for designers to control and adjust the product shape.

3.3 Modeling Node Parameter Setting The main body shape of the landscape model can analyze the diversity of the parametric model dynamically, as shown in Fig. 7. The model should be a modeling state that follows the artistic composition technique and meets the public cognition. From the dynamic analysis of the model, four forms are selected for comparison. From the perspective of parameter adjustment, the overall feeling of the first model is too thick, and the hollow out is large, which does not conform to the public’s cognition of the coral reef. The thickness of the second model is too thin, which cannot well meet the distribution of the random hollow out. In the third model, the value of the random

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Fig. 6 Parameterized model node driver

distribution of 2D points is too large, and too much hollow out is not conducive to the distribution and process production, and the connection is more guaranteed, and it is not suitable for the construction. The number of two-dimensional random point parameters is set as 50–350, and as the number of two-dimensional random points increases, the denser the hollow becomes. When the two-dimensional random point is selected, that is, the twodimensional random point is a fixed and constant value, and the next step is to

Fig. 7 The dynamics of the model

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Fig. 8 The final morphology of the model

dynamically select the parameters of the hollow edge. The higher the value of the hollow edge value, the wider the edge, and vice versa. The following is a dynamic analysis of the adjustable parameters of the model, through dynamic observation, to derive the most satisfactory morphology. According to the above dynamic analysis, the fourth model form can be determined as the final design form, that is, when the two-dimensional random point is about 250, and when the hollow edge is 15, the shape, hollow distribution and connection can meet the design and production requirements, as shown in Fig. 8.

4 Conclusions The paper analyzes the specific steps and thinking sequence of parametric design through Grasshopper parameter analog number platform and parametric design process. The results show that the landscape design according to the parametric design process is feasible and practical. Compared with the traditional polygon modeling method, the parametric park landscape design of Grosshopper plug-in has the advantages of modular, visual and dynamic model design. The model production is more beautiful, and the later modification and adjustment is more convenient. Parameterized design will be the future trend of landscape design, but the parametric design process needs to be further improved, which requires more practice and efforts of designers.

References 1. Abdelaal M, Amtsberg F, Becher M, Duque ER, Kannenberg F, Calepso AS et al (2022) Visualization for architecture, engineering, and construction: shaping the future of our built world. IEEE Computer Graphics and Applications 88–95

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2. Anderson C (2008) The petabyte age: because more isn’t just more–more is different. Wired Magazine, pp 106–120 3. Zhi HZ, Yi MX, Bang Z (2011) An innovative design based on CAD environment. Advanced Materials Research 166–169 4. Sun B, Huang S (2019) Realizing product serialization by Grasshopper parametric design. IOP Conference Series: Materials Science and Engineering 5. Card SK, Mackinlay JD, Shneiderman B (2019) Readings in information visualization: using vision to think. MorganKaufmann Publishers, San Francisco, p 125 6. Tian Y, Miao Y, Yu Y, Zhang Z (2019) Parametric design method based on Grasshopper and shoe last bottom pattern moulding characteristics. In: Proceedings of 2019 4th international conference on mechanical, manufacturing, pp 139–146 7. Negendahl K, Barholm-Hansen A, Andersen R (2022) Parametric stock flow modelling of historical building typologies. Buildings 68–76 8. Kim D, Chai YH (2020) Improving presence in real-time architectural visualization. Cogent Arts and Humanitiespp 139–146 9. Yang Y, Sun W, Su G (2022) A novel support-vector-machine-based Grasshopper optimization algorithm for structural reliability analysis. Buildings 86–91 10. Rodrigo BA, Anderson AC, da Silva AM, da Silva JVL, Nivaldo A (2015) Assessment of orbital volume in frontofacial advancements. Journal of Craniofacial Surgery 843–848

Energy Consumption Trend Analysis Based on Energy Elastic Consumption Coefficient Method Under the Background of Carbon Dual Control Ding Chen, Chun Li, Weidong Zhong, Wei Liu, and Yan Yan

Abstract Based on the general background of the transformation from energy consumption dual control to carbon dual control, this paper takes S City, a southern coastal city, as an example, analyzes its energy consumption, economic development and energy transformation route, and sets up two different development scenarios for energy consumption in S City during 2022–2030 by combining the current policy guidance. The elastic coefficient method combined with multiple linear regression is used to predict the future energy consumption and its change trend. The forecast results show that the current energy transformation development route is relatively in line with the requirements of low-carbon development. Keywords Energy consumption trends · Elastic energy consumption coefficient · Big data on energy · Carbon double control

1 Introduction Energy is an momentous guarantee for the tenable growth of economy and society, but with the increasing energy crisis and environmental pollution, higher requirements are put forward for the pattern of energy consumption. In recent years, China has gradually shifted from “dual control” of total energy use and intensity to “dual control” of total carbon emission and strength, and more than 80% of carbon emissions come from energy consumption [1, 2]. Both the total amount and consumption structure of energy use will have a great influence on the carbon emission index.

D. Chen · C. Li · W. Zhong · W. Liu State Grid Zhejiang Electric Power Co., Ltd, Jiaxing Power Supply Company, Jiaxing 314000, Zhejiang, China Y. Yan (B) School of Economics and Management, Northeast Electric Power University, Jilin 132000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_83

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Therefore, while requiring efficient energy use, it will pay more attention to cleanliness, highlight the policy orientation of controlling fossil energy consumption, improve overall efficiency through end-consumption electrification, and promote the optimization of energy consumption structure by vigorously developing renewable energy and improving energy systems that complement each other with multiple energy sources [3]. In this context, the prediction of future energy consumption trends by mining energy big data is of great significance for grasping future energy demand trends, developing secure and efficient energy strategies, and facilitating the goal of “carbon peak carbon neutrality”.

2 Analysis of the Basic Situation of S City 2.1 Social Development S City is located in the eastern coastal area with relatively developed economy, the economic development data and demographic data are shown in Table 1. Table 1 GDP and its growth rate of S City from 2006 to 2020 Year

GDP (100 million Yuan)

Rate of growth (%)

Population (ten thousand)

Rate of growth (%)

2006

1354.18

13.9

408.8

2.07

2007

1606.66

14.5

420.6

2.89

2008

1816.6

10.9

424.3

0.88

2009

1917.33

9.7

431.8

1.77

2010

2357.22

14

450.5

4.33

2011

2698.81

10.8

463.9

2.97

2012

2909.65

8.9

473.5

2.07

2013

3234.34

9.4

480.2

1.41

2014

3493.97

7.7

486.5

1.31

2015

3696.62

7.2

493.6

1.46

2016

3979.04

7.2

501.4

1.58

2017

4500.26

8

512.6

2.23

2018

5018.38

7.7

523.1

2.05

2019

5423.58

7.1

533.5

1.99

2020

5509.52

3.5

541.1

1.42

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Table 2 Energy consumption in S City from 2006 to 2020 Year

Natural gas (100 million cubic meters)

Raw coal (10,000 tons)

Total social electricity consumption (10,000 KWH)

Total energy consumption (10,000 tons of standard coal)

2006

0.82

1303.94

1,810,517.00

1230.65

2007

1.25

1498.11

2,128,890.00

1406.80

2008

1.22

1489.46

2,317,449.00

1463.24

2009

1.32

1516.92

2,508,587.00

1492.87

2010

2.19

1576.75

2,905,219.00

1633.56

2011

2.97

1668.37

3,279,051.00

1659.68

2012

3.26

1619.09

3,514,240.00

1682.85

2013

3.83

1592.70

3,854,512.00

1718.16

2014

5.28

1557.32

3,964,500.00

1788.07

2015

5.38

1552.10

4,133,516.00

1888.00

2016

9.27

1831.92

4,530,971.00

1944.44

2017

11.02

1878.05

4,839,624.00

1977.63

2018

14.18

1870.70

5,220,618.00

1997.88

2019

16.25

1801.12

5,380,258.00

2147.50

2020

16.00

1715.31

5,490,962.00

2181.22

2.2 Energy Service Condition As show in Table 2, the data of total energy use illustrate that the total energy consumption has continued to increase since 2006, but the increase rate fluctuated slightly after 2015, showing a trend of fluctuation and decline; The main endconsumption of primary energy includes raw coal and natural gas. Because the terminal consumption of crude oil in the historical data analysis is very low, it is not analyzed here; Terminal consumption of secondary energy mainly includes the data of electricity consumption, you can see that the terminal use of electricity grows rapidly. As for the energy consumption per unit of GDP, the 2020 figure is only 45% of that of 2006, indicating that great progress has been made in the dual-control work of energy consumption. The specific data are shown in Fig. 1.

2.3 Policy Constraints Taking the 14th Five-Year Plan and energy plan of S City and its province as the main reference and considering energy production projects under construction and

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Fig. 1 Energy consumption per unit GDP and its decline rate in S City from 2006 to 2020

already built but not put into production, Integrate planning constraint or prospective indicator data.

3 Prediction Model Based on Elasticity Coefficient of Energy Consumption Changes in energy use are influenced by many external important factors, including economy, population, energy efficiency and policy orientation [4]. In this paper, combined with the characteristics of energy consumption data of S City and different development scenarios under current policy guidance, a combined prediction model is constructed by combining the energy consumption elastic coefficient method and regression analysis [5], which can effectively improve the prediction accuracy of a single model and expand the application scope of a single model [6, 7]. According to the principle of importance, the predicted change trend analysis of energy consumption includes the main terminal consumption of primary energy in S city and the electricity use of the whole society, and the main consumption of primary energy includes raw coal and natural gas. According to the principle of energy consumption statistics, for energy that is used repeatedly, the consumption is calculated only once.

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3.1 Division of Development Stages According to the current energy policies and related planning, this paper divides the future energy economic development scenarios of S City into the following two categories: (1) Unconstrained development scenarios Economic construction will be the focus of development, and the green and lowcarbon transformation of energy and the economy will not be considered. The only constraint is the binding target in the 14th Five-Year Plan for energy. In this development scenario, economic development and population growth are the most important influences on energy supply, so GDP and population are taken as the main factors affecting energy supply changes in the construction of the model. (2) Green development Scenario That is, at the same time of development, we should consider energy conservation and low-carbon and energy transformation, and continue to enhance the proportion of clean energy in the energy supply structure, so as to realize the goal of enduring economic growth, optimization of energy structure and coupling and coordination of environmental protection. Under this development scenario, for the ultimate goal of transformation, policy indicators such as the 14th Five-Year Plan for energy should be incorporated into it according to policy expectations. With the promotion of technological progress and structural optimization, energy utilization efficiency will be improved to some extent, and energy supply intensity and energy supply structure will be changed to some extent. Therefore, in the construction of the model, apart from GDP and population, energy consumption per unit GDP is also added into the main influencing factors.

3.2 Parameter Setting According to the economic targets set, the forecast targets of the 14th Five-Year Plan of S City and the GDP growth in the past six years are taken into consideration, and the GDP data of 2021 is taken as the base. As shown in Table 3. For the prediction of population growth, the main influencing factors include birth rate, death rate and natural growth rate. According to the common model of Table 3 Forecast data of S City in 2022–2030 Year

2022

2023

2024

2025

2026

2027

2028

2029

2030

GDP 6895.18 7456.51 8015.75 9000.00 9675.00 10,400.63 11,180.67 11,963.32 12,800.75 (One hundred million yuan)

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Table 4 Population forecast data of S City in 2022–2030 Year

2022

2023

2024

2025

2026

2027

2028

2029

2030

Population (Ten thousand people)

561.91

572.20

582.48

592.72

602.94

613.13

623.28

633.40

643.47

2028

2029

Table 5 Forecast value of index of S City in 2021–2030 Year

2021

2022

2023

2024

2025

2026

2027

2030

Energy consumption per unit GDP (t standard coal/ten thousand yuan)

0.42

0.40

0.39

0.38

0.37

0.36

0.35

0.34

0.34

0.33

Reduction rate of energy consumption per unit (%)

− 3.70

− 3.50

− 2.72

− 2.80

− 4.55

− 2.02

− 2.02

− 2.02

− 2.02

− 2.02

population forecasting in China, the population of the starting year is taken as the base. As shown in Table 4. In this paper, the calculation caliber was converted to the regional scope, combined with the reduction requirements of unit energy consumption in S city, and estimated according to the historical data and its decline rate. It is known that in 2022, S city strives to achieve a reduction of 3.5% in energy consumption per unit of GDP and control the total growth rate within 2.3%. Meanwhile, based on the relevant policy documents of the province, it is required that the index should be 14.5% lower than that in 2020 by 2025. The predicted result is shown in Table 5. Based on the comprehensive consideration of the consumption elasticity coefficient of domestic developed areas, the future economic and social development situation and energy demand change trend of S city [8], the energy consumption elasticity coefficient of S city is predicted to be between 0.30 and 0.40 in 2022 to 2025 under different scenario. As the elasticity coefficient is greatly affected by the historical data, The coefficients of 2026–2030 are set as 0.40 and 0.35 under two development scenarios.

3.3 Model Building Based on the above analysis, it is necessary to consider the fractional incremental energy contribution value (CVSI) [9, 10] and build a combined model through

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multiple linear regression when predicting single energy consumption. The incremental contribution value by energy type refers to the contribution of different kinds of energy in the elasticity coefficient of energy consumption, the calculation formula is as follows: Ci =

Ei e E

(1)

where, C i represents the contribution value of each energy increment; ΔE i represents each energy consumption increment, ΔE represents the growing in total energy use, e is the elastic coefficient of energy use; Therefore, the incremental contribution value of a single category of energy is equal to the elastic coefficient of energy use minus the incremental contribution value of other energy sources except the category of energy. The general form of multiple linear regression model is: Y = β0 + β1 X1 + β2 X2 + L + βj Xj + L + βk Xk + u

(2)

In the formula above, k is the number of explanatory variables; β j (j = 1, 2, …, k) is the regression coefficient; u is the random error after removing the influence of k independent variables on Y. The matrix expression of n random equations is: Y = Xβ + u

(3)

With 2020 as the base year and various parameters under the two development scenarios, the above sub-energy incremental contribution prediction method and multiple linear regression model are combined. In other words, when predicting the contribution value of a certain category of energy, regression analysis is adopted to forecast the consumption demand of raw coal, natural gas and electricity in Jiaxing City in 2022–2025 and 2030.

3.4 Predict the Outcome (1) Unconstrained development scenarios In this scenario, only GDP and population are considered to calculate the incremental contribution value of each energy component. Set GDP as x1, population as x2, contribution of raw coal as y11, contribution of natural gas as y12 and contribution of electric power as y13. According to the formula, SPSS software was used to calculate the multiple linear prediction model of each energy as: y11 = 0.023x1 − 0.247x2 + 173.175 y12 = −0.085x1 + 5.745x2 − 829.635

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y13 = 392.619x1 + 16336.414x2 − 5346003.877 The test results are shown in Tables 6, 7 and 8. The test results are as follows: The R2 of each model established are 0.978, 0.928 and 0.998, and the regression fitting effect is good; At the significance level of α = 0.05, the probability sig (regression equation significance) corresponding to the t statistic of each independent variable was lower than 0.05, and the T-test passed; The probability sig values (regression coefficient significance) corresponding to the F statistic are 0.00, 0.01 and 0.00 respectively, it is less than 0.05, so the F test passes. The final forecast result is shown in Table 9: (2) Green development Scenario The green development model adds energy consumption per unit of GDP on the basis of the unconstrained development scenario. Set GDP as x1, population as x2, energy consumption per unit GDP as × 3, contribution of raw coal as y21, contribution of natural gas as y22, and contribution of electric power as y23. According to the formula, SPSS software was used to calculate the multiple linear prediction model of each energy as: y21 = 0.027x1 − 0.18x2 − 565.979x3 + 1971.551 y22 = 0.002x1 + 0.169x2 + 27.202x3 − 96.900 y23 = 763.839x1 − 3260.517x2 − 1871970.130x3 + 3918911.537 The test results are shown in Tables 10, 11 and 12: The test results are as follows: the R2 of each model established are 0.982, 0.932 and 0.999, show that the regression fitting effect is good; At the significance level of α = 0.05, the probability sig (regression equation significance) corresponding to the t statistic of each independent variable was lower than 0.05, and the T-test passed; Table 6 Summary of unconstrained development models Types of energy

R

R2

Adjust R2

Durbin Watson

Natural gas

0.978

0.957

0.950

1.456

Raw coal

0.928

0.947

0.946

1.584

Total social electricity consumption

0.998

0.997

0.996

1.871

Table 7 Unconstrained development of multiple linear regression coefficients Inspection Natural gas Constant GDP

Raw coal

Total social electricity consumption

Population Constant GDP

Population Constant GDP

Population

T

1.878

4.680 − 2.198

− 1.595

− 1.566 1.787

− 4.452

4.231 5.170

sig

0.05

0.00

0.05

0.045 0.014

0.01

0.01

0.047

0.00

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Table 8 Unconstrained development of multiple linear regression F statistics Inspection

Natural gas

Raw coal

Total social electricity consumption

F

143.115

14.200

1967.220

0.00

0.01

sig

0.00

The probability sig value (regression coefficient significance) corresponding to the F statistic is 0.00, 0.002, 0.00 respectively, it is less than 0.05, so the F test passes. The final forecast result is shown in Table 13. Under the green development scenario, there is little difference between the forecast results in 2025 and the data predicted in The 14th Five-Year Plan for Energy of S City. Because when the elasticity coefficient of energy consumption is 0.35, the reduction rate refers to the binding index of energy plan, which meets the requirements of reducing energy consumption index under the current background of double control of energy and carbon. The forecast results of raw coal consumption also increase first and then decrease, the proportion of natural gas will further increase, and the energy consumption structure will be gradually optimized. Under the unconstrained development scenario, predicted results when energy consumption is greater than 0.35. When GDP growth is the same, the forecast results show different trends. Natural gas, raw coal and electric power all maintain a continuous growth trend, except for natural gas, they are all higher than the prediction result of 0.35 coefficient, and the energy consumption structure has little change.

4 Conclusion According to the forecast results of energy consumption under the above two different development scenarios, it can be seen that under the current policy-oriented green development scenario, constrained by policy indicators such as the 14th Five-Year Plan for energy, the proportion of raw coal consumption terminals is expected to continue to decline, while natural gas and electricity are expected to develop rapidly, with the proportion of power terminals rising from 52 to 62%. Overall, the total terminal coal consumption may show a downward trend, power consumption continues to grow, the proportion of clean energy keep improving, the elasticity coefficient of energy consumption decreases, and the energy consumption structure will be upgraded. It will be promoted by technological progress and structural optimization, so as to gain economic growth, energy structure optimization and environmental protection coupling and coordination. Therefore, it is suggested that S City continue to adhere to policy orientation, promote the end-consumption electrification, and practice the path of clean energy supply structure.

20.99

2027 22.60

2028 24.24

2029 25.89

2030 27.57

274.15

276.90

279.65

283.85

286.58

289.40

292.34

295.26

298.30

6,262,500 6,596,426.49 7,012,224.80 7,426,491.01 8,177,446.27 8,673,522.24 9,208,092.11 9,784,072.60 10,361,896.36 10,981,440.07

19.42

2026

Electricity (10,000 KWH)

17.90

2025

272.86

16.50

2024

Raw coal (10,000 tons)

15.09

2023

11.68

2022

Natural gas (100 million cubic meters)

Energy 2021 consumption

Table 9 Forecast of energy consumption in 2022–2030 in unconstrained development scenario

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0.982 0.932 0.999

Natural gas

Raw coal

Total social electricity consumption

R

Types of energy

Table 10 Summary of green development models

0.998

0.952

0.964

R2

0.997

0.950

0.955

Adjust R2

1.827

1.104

1.199

Durbin Watson

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− 1.254

0.02

T

sig

0.02

Raw coal

0.04

0.015

1.794 0.05

0.328 0.45

0.14

0.04

0.640

0.03

0.881

0.05

0.002

3.987 − 0.341

0.05

− 2.145

Population Energy consumption

Total social electricity consumption Population Energy Constant GDP consumption

− 0.014 0.106

Population Energy Constant GDP consumption

1.018 0.516

Constant GDP

Inspection Natural gas

Table 11 Multiple linear regression coefficient of green development

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8.982 0.002

106.060

0.00

F

sig

Raw coal

Natural gas

Inspection

Table 12 Multiple linear regression F statistics of green development

0.00

1676.120

Total social electricity consumption

Energy Consumption Trend Analysis Based on Energy Elastic … 777

275.30

19.23

2025

271.59

23.21

2026

271.79

25.69

2027

271.31

28.42

2028

270.12

31.42

2029

268.85

34.43

2030

266.86

37.71

6,262,500 6,540,780.73 6,929,329.82 7,316,711.75 7,870,558.67 8,302,525.21 8,753,849.98 9,225,969.07 9,698,483.33 10,191,830.37

273.58

17.31

2024

Electricity (10,000 KWH)

271.87

15.38

2023

259.87

2022

Raw coal (10,000 tons)

Natural gas 11.12 (100 million cubic meters)

Energy 2021 consumption

Table 13 Green development scenario Forecast of energy consumption in 2022–2030

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Energy Consumption Trend Analysis Based on Energy Elastic …

779

Acknowledgements Fund project: State Grid Zhejiang Electric Power Co., Ltd. provincial industrial unit science and technology project “Research on key technologies of renewable energy and electrification development path deduction taking into account carbon trading and carbon emissions under the dual carbon background” (project No.: 2022-KJLH-SJ-018).

References 1. Ming Z, Yongqi Y, Lihua W et al (2016) The power industry reform in China 2015: policies, evaluations and solutions. Renew Sustain Energy Rev 57:94–110 2. Lisha Y, Boqiang L (2016) Carbon dioxide-emission in China’s power industry: evidence and policy implications. Renew Sustain Energy Rev 60:258–267 3. Li Y, Ji Q, Wang Z, Xiong Z, Zhan S, Yang Y, Hao Y (2021) Environment, green energy mismatch, industrial intelligence and economics growth: theory and empirical evidence from China. Development and Sustainability, pp 1–32 4. Hussain A, Rahman M, Memon JA (2016) Forecasting electricity consumption in Pakistan: the way forward. Energy Policy 90:73–80 5. Fumo N, Biswas MAR (2015) Regression analysis for prediction of residential energy consumption. Renew Sustain Energy Rev 47:332–343 6. Huiru Z, Haoran Z, Sen G (2016) Using GM(1,1) optimized by MFO with rolling mechanism to forecast the electricity consumption of inner Mongolia. Applied Sciences-Basel 6(1):20 7. Jain RK, Smith KM, Culligan PJ, Taylor JE (2014) Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy 168–178 8. Ye J, Dang Y, Li B (2018) Grey-Markov prediction model based on background value optimization and central-point triangular whitenization weight function. Communications in Nonlinear Science and Numerical 320–330 9. Zhang M, Guo H, Sun M et al (2022) A novel flexible grey multivariable model and its application in forecasting energy consumption in China. Energy 239:122441 10. Zhao H, Lifeng W (2020) Forecasting the non-renewable energy consumption by an adjacent accumulation grey model. J Clean Prod 275:124113

The Development and Application of the New Art Deco Style in Poster Design Under the Perspective of Artificial Intelligence Bingnan Pang and Tiantian Chen

Abstract The important impact of the development of artificial intelligence in the field of humanities is to deepen the understanding of human beings. New Art Deco as a design style emphasizing creativity and individuality has the core content of which is to use decoration as a means to closely follow the functional needs as the goal. This thesis focuses on the evolution of New Art Deco in modern poster design trends, design techniques, and style changes. The thesis then studies the development and application of New Art Deco in modern poster design in the context of artificial intelligence. Keywords New art deco · Poster design · Post-modernism · Artificial intelligence

1 Introduction The formation of every style of the times is based on the transformation of production methods. Due to the influence of modernism, the traditional decorative style is always characterized by simple and colorful geometric shapes [1]. In the era of artificial intelligence, New Art Deco style not only retains the beauty of the sharp lines of the traditional decorative style, but it also combines various different elements, adds more humane colors, and emphasizes a high degree of aesthetic effect.

B. Pang · T. Chen (B) School of Animation, Hainan College of Software Technology, Qionghai, Hainan Province, China e-mail: [email protected] B. Pang Faculty of Art and Design, Universiti Teknologi MARA, Kota Bharu, Kelantan, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_84

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2 Origin of New Art Deco New Art Deco, which can also be called Post-modernism, is actually the predecessor of Modernism, the philosophical view of which is “Mechanical Aesthetics” [2]. This aesthetic theory has produced a number of outstanding masters of modernist design, such as Walter Gropius and Laszlo Moholy-Nagy of the German Bauhaus School [3], as well as Louis Sullivan and Frank Lloyd Wright of the American Chicago School of Architecture. These men have made indelible contributions to design and design education in the first and middle half of the twentieth century. Mechanical Aesthetics started mankind’s modern industrial civilization, but its adherence to functionalism led to the excessive pursuit of the economy as well as the adoption of standardization and mass production. Mechanics and technology are completely contradictory to people’s innate desire to dress up and be fashionable. It greatly ignores the human nature of the desire for decoration. At any age, the urge to decorate is always present [2]. It seeks to transform a single function into beauty and to enhance the value of a dull and monotonous entity by giving it meaning [4]. To do this, people are willing to work on even the smallest of lines. Although mankind could not abandon all the conveniences brought by the development of modern science and production technology, more and more people began to doubt that material wealth could replace spiritual food, and a new wave of decorative was born. The long-suppressed “decorative impulse” in human nature began to awaken, along with the strong desire brought by the renewed pursuit of this impulse which can be said to be the fundamental trigger and original impetus for the emergence of New Art Deco.

3 The Evolution of New Art Deco in Poster Design Trends In the poster design trend, the evolution from Art Deco to New Art Deco can be briefly described in the following four periods, as show in Fig. 1.

Fig. 1 Poster design style development

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3.1 Advocating Handicraft, Resurrecting Tradition, and Delicate Style—The Period of the Pre-industrial and Arts and Crafts Movements In order to satisfy the psychology of the upper classes to show their status and pursue vanity, this period saw the emergence of pretentious and artificial fashions in design [5]. The Baroque style of the eighteenth century, characterized by geometric symmetry, symbolic layout, and elaborate ornamentation, and the Victorian style of the nineteenth century had distinct class characteristics.

3.2 Emphasis on Nature, Promotion of Decoration, Eclecticism—The Period of the Art Nouveau and Art Deco Movements The Art Nouveau movement originated at the turn of the century in Europe and the United States, and was one of the most widely influential decorative art movements in the twentieth century other than the modernist design style which covers the fields of architecture, product, and graphic design [6]. It played a key role in carrying on the tradition and modern design. Art Nouveau styles differed greatly from country to country, but the pursuit of decoration and the exploration of new styles were common to the designers of all countries involved in this movement, but in general, the spirit was poor and the methods were old-fashioned, as show in Fig. 2.

Fig. 2 March 3 Ethnic Cultural Festival, 2021, by Bingnan Pang & Tiantian Chen

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Fig. 3 Campus Job Fair, 2021, by Bingnan Pang and Tiantian Chen

3.3 Against Decoration, Emphasizing Rationality, and Doing More with Less—The Period of the Modernist and Internationalist Movements The emergence of a new design style in the early twentieth century could not be separated from the development of industrial technology, which not only promoted the emancipation of contemporary ideology, but also led to the exploration of modern art [7]. The internationalism in the design field during this period replaced a wide variety of design styles, which had no room for progress and fully showed that they were outdated and obsolete, and had to be replaced by a new design style, as show in Fig. 3.

3.4 Deco, Classical Fashion, and Human Freedom—The Period of the Post-Modernist The period after the 1960s, called “post-modern”, was characterized by the ephemeral and volatile nature of the media and the explosion of information. Post-modernism originated from Modernism which is a rebellion against the pure rationality of modernism and functionalism. This is especially seen the in the formalism of international style, and it is a reflection and answer to the negative effects of industrial civilization [8]. Post-modernism adheres to the principle of human-centered design which emphasizes the dominance of human experience in design. People do not want to be slaves of reason and machines, so they must face reality and keep up with to draw nutrients and inspiration from reality [9], New Art Deco was born, and all kinds of designs after the 1970s until now can be summarized as the Post-modern period.

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Fig. 4 Joint job fairs in large and medium-sized cities, 2021, by Bingnan Pang and Tiantian Chen

4 Stylistic Changes of New Art Deco in Modern Poster Design 4.1 New Wave This style is arguably the most direct successor and opponent of Art Deco, It adopts a capital unadorned line style as decoration, uses a thick stepped scale to arrange the layout, and vividly uses space and font diversity to change the thickness of the traditional font’s strokes and reorganize the illustration elements and other patchwork. In the composition of the law and the form of a bold breakthrough, so that the entire poster screen is dynamic with more decorative fun and visual impact [10], as show in Fig. 4.

4.2 Hand-Drawn Style and Artistic Advertising After Modernism adopted photography and other “mechanical” means as the main graphic language for a long time, the re-adoption of “hand-drawing”, the most primitive and intimate method of expression, became the strongest desire of contemporary design [11]. Abandoning mechanical rationality or precision, the “traces” of human

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Fig. 5 March 3 Ethnic Cultural Festival, 2021, by Bingnan Pang and Tiantian Chen

hands were reproduced in the poster images. The “traces” of human hands are reproduced in the poster images which are popular in the global design world, as show in Fig. 5. In addition to this, many master painters also joined the ranks of designing posters and other printed materials, making this field at that time present a hundred different scenes of competition.

4.3 Localism After the end of World War II, culture and design developed rapidly with the rising political and economic status of many countries. The various design styles extended from the local traditional culture began to come to the world stage and gain the attention of all. Since the 1980s, Chinese graphic design has also shown a great change. This has generated a large number of excellent poster design works. These works have a large number of traditional Chinese elements absorbed and combined with modern design techniques to show a broader perspective of poster design works on the recognition and application of Chinese characteristics of culture. These fully reflect the new aesthetic concept of modern society as well as its unique design style and influence [11]. Also, these works have received the attention of the international design community.

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4.4 Changing Times Since the 1990s, with the development and popularization of computer technology and later the development and perfection of personal computers, computers have become an essential design tool and means for designers. With the help of computers, designers can choose better poster design techniques and production means that can make a variety of modifications to the poster design plan in a short period of time greatly improving the work efficiency. Designers can also quickly put forward, optimize, and improve the design plan in an instant. In stepping into the twenty-first century, today’s AI technology development, whether it is color matching, typography and composition or other skills, can easily be learned though AI. In addition to the visual weight of the user, it can also be learned through big data collection of user operations, AI seems to have been omnipotent. The latest information shows that Ali P4 level AI designer Lubanner can already complete the design of 8000 posters per second, as show in Fig. 6.

5 New Art Deco in Contemporary Poster Design New Art Deco style poster design highly emphasizes the decorative picture. It both inherits the essence of traditional culture and focus on the design of the historical context, but it also accepts the essence of modern civilization [12], promotes the natural and elegant interest in life, and sets the traditional and modern integration of classical and fashion in one. It completely breaks the fetters of the functionalist design concept. Due to the diverse forms of expression, poster design presents a different style of A new look.

Fig. 6 AI Design-LUBAN visual generation schematic

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Fig. 7 The sea is full of rivers, 2018, by Bingnan Pang and Sheng Liu

5.1 Nostalgic Style Fashion Vintage Many advanced countries also do not hesitate to borrow and absorb the excellent national traditional culture of other countries to expand and enrich themselves. Chinese characters and auspicious patterns such as dragons and phoenixes are used by Western designers as the best elements for hunting and fighting. For example, China’s Olympic Games poster design uses oracle bones and auspicious clouds as decorative elements which fully demonstrates this point, as show in Fig. 7.

5.2 Bionic Natural Decoration Nature is an inexhaustible source of beauty for designers. The beautiful forms of people, animals, plants, and landscapes are all rich explicit resources, while the decorative nutrients extracted and drawn from the natural view and aesthetics cultivated and derived from the unique natural growth environment of human beings are also used as invisible resources. The designers use the colors and patterns of nature on the poster design or font structure and participate in the embellishment of the main image to more reflect the soothing and elegant life, as show in Fig. 8.

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Fig. 8 Trip to Hainan, 2018, by Bingnan Pang and Dongsheng Shi

5.3 Avant-garde Style Fashionable Decoration In keeping up with the times, the timely application of contemporary representative new elements, new technologies, and new techniques cannot only effectively get rid of the same old, outdated face of poster design, while injecting the atmosphere of the times and generate new vitality, but also to achieve the effect of icing on the cake, as show in Fig. 9. With the improvement of the domestic design level, the new decorative is not only applied in poster design, but it also has a significant impact on various other design-related industries.

Fig. 9 30th Anniversary, 2018, by Bingnan Pang and Zhuoting Ma

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Fig. 10 Building Hainan together, 2018, by Bingnan Pang and Shanru Yu

5.4 Accent-style Decorative Tendencies Different times, classes, and image requirements for poster design, creates a different scope of use. In fact, since ancient times, human beings have the idiom story of “drawing the dragon’s eye”. Human ancestors also decorated and recorded their lives by painting on the rock wall. Twenty-first century human beings are well aware of the importance of “poster design” as one of the means of information transmission. With the importance of artificial intelligence in this era of map reading, mankind has taken great pains to perfect the application of the new decorative, many decorative techniques, and decorative elements which are becoming the favourite of a new generation of designers, as show in Fig. 10. With the development of minimalism to the extreme, the New Art Deco has began to seek a new way out, not only to present a streamlined line at the same time very vital, but as an emergence of many new waves of modern commercial posters as the New Art Deco wave in the field of poster design scatters new waves.

6 Reflections on the New Art Deco Poster Design In the growing development of artificial intelligence technology today, the New Art Deco design of the poster, with dynamic, trendy, flair, and other elements, convey more individual aesthetic ideas which are elegant, tasteful, practical, and contains interests which are extremely rich in cultural connotations.

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In front of the New Art Deco style, avant-garde and nostalgic have been a perfect fusion of poster design without too many rules and regulations, which has its unique artistic influence to impress many discerning eyes. Here, the bright colors are boldly used combining different elements, different textures, and different shapes at will to outline the individual picture. This is not only retains the traditional design style in the smooth lines of beauty and the expression of the composite texture, but it adds more respect for the “human” [13], modern simplicity and still maintains the visual language of “less and more”. The modern minimalism still maintains the visual language of “less and more”, but the concept is beyond the pure and even dogmatic model. Therefore, the simplicity is no longer cold. There is freedom of thought in the ethereal, and there is a pleasing fluidity in the concise which also gives people unlimited imagination when you define a style.

7 Conclusion In summary, in the vision of the age of artificial intelligence, artistic style will transcends space to express itself in the purest and truest way of New Art Deco. This is just as the theme which has been advocated by New Art Deco—let decoration never be vulgar.

References 1. Gumulya D, Narima S (2020) design exploration inspired by the 1925 art deco style with morphological chart. In: Proceeding international conference on multimedia, architecture, and design, vol 1 2. El Hadi HAEM, Abdel Salam SH, Abu Saif FM (2021) Combining organic and geometrical aspects of (art nouveau) and (art deco) in textile printing wall hanging contemporary designs. International Design Journal 11(6):243–255 3. Rewa N (2020) April in Paris: theatricality, modernism, and politics at the 1925 art deco expo by Irene R. Makaryk. Univ Tor Q 89(3):621–622 4. Lena JC, Pachucki MC (2013) The sincerest form of flattery: innovation, repetition, and status in an art movement. Poetics 41(3):236–264 5. Olin M (2020) Ornament and European modernism: from art practice to art history, pp 185–187 6. Finn M (2020) Alphonse Mucha: art nouveau/nouvelle femme. Art Inquiries 18(1):77–82 7. Darling E, Fair A, Kelly J (2021) Beyond Bauhaus: modernism in Britain, 1933–1966, pp 496–497 8. Zeller S (2021) Centering the periphery: reassessing Swiss graphic design through the prism of regional characteristics. Des Issues 37(1):64–75 9. Shales E (2022) ‘Beautiful, plain objects like [SKF] ball bearings’: the enigma of aestheticizing anonymity in ‘machine art’ and modernist logotypes. Journal of Design History 10. Gumulya D et al (2021) Form giving design exploration inspired by the 1980s memphis design with morphological chart analysis. Journal Dimensi Seni Rupa dan Desain 17(2):217–231 11. Chen T, Pang B, Dai M (2021) Exploring the application of photography in graphic design. Journal of Physics: Conference Series. 1881(2)

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12. Moore B (2020) Walter Benjamin, advertising, and the utopian moment in modernist literature. Modernism/Modernity 27(4):769–790 13. Dai M et al (2021) Application of 3D digital modeling technology in the display of marine fish science in the South China sea. Journal of Physics: Conference Series 1881(3)

Product Feature Modeling Based on Graphics and Image Fusion Chaoran Tong and Shi Yang

Abstract With the development of graphics and image fusion technology, product feature modeling has become an important part of people’s improvement of quality of life. This paper combines image-based rendering techniques and geometry-based rendering techniques to describe indoor virtual scenes. Since the virtual indoor space can only be used to describe a part of the indoor environment, and in order to improve the interactivity of the virtual scene, the system provides an import function of the physical model. Finally, this paper introduces the concept of time into the system, providing two methods of real-time rendering and non-real-time rendering to handle different rendering requirements, providing animation and interactive roaming to make it have the characteristics of virtual environment. The system approximates the monitoring environment by simple modeling, multiple mapping methods and importing models, and has strong practicability, good real-time performance and display effect. Keywords Graphic image fusion · Panoramic roaming · Environment mapping · Product feature modeling · Monitoring

1 Introduction Feature-based product models can express product shape, management, technology, materials and precision features, facilitate efficient exchange of data, facilitate the exchange and sharing of design information, manufacturing information, installation information, maintenance information, etc., thus providing a good product information sharing [1]. Feature modeling techniques treat features as basic information C. Tong Guangzhou University, Guangzhou, Guangdong, China S. Yang (B) Guangdong University of Education, Guangzhou, Guangdong, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_85

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elements, describing products as sets of information consisting of several features with defined engineering and functional attributes. In recent years, feature technology has made great progress [2]. The object-oriented organization, visualization and operability technology research of features has entered the practical stage, and many commercial software such as Pro/engineer, Solidworks and other parameter modeling software, CATIA and other surface design software have appeared. Standard for the Ex-change of Product Model Data (STEP) is the core of the Integrated Product Information Model (IPIM) [3]. Based on the importance of graphics and image fusion techniques, many research teams have conducted research. Oyewole proposed an enhanced product image classification architecture, which has components integrated with data acquisition preprocessing, feature extraction, dimension reduction and machine learning methods. The core of these components is the fusion algorithm based on feature vectors. The algorithm aims to obtain dimension reduced features from the histogram of color image representative features based on directional gradient, and has high classification accuracy [4]. Sadr believes that Clifford algebra has created a new perspective for image processing. In order to achieve effective integration of graphics and image processing. He proposed a hardware framework based on Clifford algebraic algorithm and used it in image segmentation. Compared with software method, the image processing speed of this framework is 25 times faster [5]. Singh believes that the most promising field of image processing today is graphic image fusion technology. This technology can cover better data than each source image without adding any artifacts. In order to verify its practicability, he considered three elements, including spatial domain fusion methods, different transform domain technologies and performance evaluation indicators of graphic and image fusion [6]. Wiley discussed the development of graphics and image fusion technology from the perspective of computer vision theory, and explained that the development of computer vision is mainly related to graphics and image fusion with the latest technology overview and theoretical concepts. This graphic and image fusion technology adopts a multi range application method through massive data analysis, which can help users analyze images to obtain necessary data information, and can also be used to understand relevant events, describe information, and design beautiful graphics [7]. Image-based image fusion technology is still in the development stage. Therefore, finding a way to combine these two technologies and balance the use of these two technologies in the system is a very important issue. This paper first introduces a simple graphics-based and image-based rendering system; uses a distributed Web server to accelerate the panoramic roaming of crime scenes, and compares it with other panoramic implementations; then describes the multi-rendering environment of OpenGL on MFC The design of the work; explains how to combine the graphicsbased rendering technology with the image-based rendering technology, and gives the rendering effect of this method; finally, this paper introduces the implementation of the animation system. The research in this paper shows that the graphic image fusion technology combines the graphic image information to describe the product and jointly complete the modeling and surface design of the product, which is not only feasible, but also necessary for the design and manufacture of the product.

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2 Method 2.1 VRML Technology VRML means “virtual reality modeling language.” VRML is designed to enjoy realtime 3D images on the Web. The VRML browser is both a plugin, a helper application, and a standalone application. This allows VRML applications to be separated from 3D modeling and animation applications. VRML provides 60 degrees of freedom, allowing users to move in three or three directions, while also creating hyperlinks to other 3D spaces. The server provides VRML files and supports images, videos, sounds and other resources, and the client downloads files that he wishes to make network access. The virtual domain described by the file is accessed interactively through a VRML browser on the local platform. Platform independence is achieved because the browser is provided by the local platform [8].

2.2 Hybrid Technology Based on Graphics and Image Fusion Based on the view grading strategy, the graphic image fusion rendering method can flexibly adapt to the performance conditions of the hardware environment. Image rendering is a strategy for implementing large-scale scene rendering in space [9]. When the hardware performance is good, a wider range of regions of interest can be set up for a better experience; conversely, when the hardware performance is relatively low, setting a smaller range of regions of interest can ultimately achieve a complete presentation of the scene. Figure 1 is a result of a hybrid technology based on graphic image fusion [10, 11].

3 Experiment 3.1 Scene Loading Speed Comparison Test A total of five scenes with different file sizes were tested, and their panoramic images were 680, 1020, 1954, 2550, and 3566 KB. The VRML script size was 18 KB, and the number of tests was 50. For comparison with other panoramic roaming systems, this article also tested the same scenario described by QuickTimeVR on a centralized web server. Since QuickTimeVR stores scene information and panoramic images in the same file, it cannot be distributed on the distributed web server described herein. The file sizes are 548, 968, 1441, 2009, and 2776 KB. If we need to use a component to control the loading of all scenes or pass parameter data between scenes, we need to mark the node where the component is located as a “resident node” so that it is not

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Fig. 1 Mixed technology results based on graphic image fusion

automatically destroyed during scene switching, resident memory. The experimental results are shown in Fig. 2.

3.2 Virtual Scene Information Extraction In the structure of the information integration platform, the client implements the processing of the information in the database system through the application development interface and the XML processor, mainly realizes the specific operation of the shared information, and the database management system generates the product design data, the three-dimensional model and the like. Various information. When designing the information model to integrate the platform structure, the basic functions of the platform should be guaranteed and have certain flexibility. Figure 3 is a virtual scene view of a panoramic image.

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Fig. 2 Virtual panorama scene loading time

Fig. 3 Virtual scene graph of the panoramic image

4 Result When you click the File Open menu command, the application framework activates the File Open dialog box, allowing the user to specify the name of the file to open. The program then automatically calls the Serialize function of the document class to complete the loading of the data and calls the OnDraw function of the view class to provide the opportunity to redraw the window. Create a document class object when the application starts, and also create a framework class object and a view class object. This is specific to MFCDoc/View, which always generates document class objects, framework class objects, and class objects whenever a document is generated. They serve the document three-in-one. For a document class object, there can be multiple view class objects associated with it, and for a view class object, only one document class object is associated. Its debugging success interface is shown in Fig. 4.

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Fig. 4 Document-view-animator structure debugging successful interface

5 Discussion By studying VRML technology, we have mastered the special advantages of VRML technology and determined the specific methods and means to implement virtual experiments and virtual laboratories. Using the 3D modeling software 3DSMAX and VRML interface, the 3D model is first built in the dedicated modeling software (3DSMAX) and then converted to the VRML file format. Utilize VRML technology features: five-way, multi-perception, stereo effect, dynamic display, script function, etc., create scene files with intersection function in VRML format. Using the advantages of the JavaScript language in web applications, using JavaScript code to achieve various interactive actions during the experiment, to achieve real results. Using the browser/server architecture and HTML related technology, a remote experimental teaching system platform was built. Finally, the system provides a way to roam a reconstructed virtual scene. Record the path of the user’s real-time roaming virtual scene, and then make the non-real-time rendering method improve the drawing quality while acquiring the scene at a specific moment. Roaming animations can also use a variety of view encodings.

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6 Conclusion In image-based rendering techniques, whether the final expression of the input image or the image scene abstraction can significantly improve the accuracy of the virtual scene display and reduce the amount of information that needs to be stored. This paper first extracts description information from a single-view virtual scene and describes the panoramic scene from a higher level. Then use VRML technology to achieve virtual scene roaming in B/S mode. First, the development and application of virtual experiments and virtual laboratories are described. The establishment of virtual laboratories is an indispensable part of the realization of digital circuit distance learning. This paper studies the virtual experiment methods and methods based on VRML language and 3DSMAX language. According to the design and interaction of the digital circuit virtual laboratory, the functional requirements, performance requirements and module structure of the network virtual laboratory are discussed using VRML/3DSMAX solutions and technologies. The 3D experimental scene modeling method and VRML scene optimization strategy are given. Although the research in this paper is based on the theory of graphics and image fusion technology, and has made a more profound research on the construction of product feature model, and has certain achievements and practicality, there are still some areas to be improved and perfected in this paper. This test only compares the field loading speed and virtual scene information extraction, and does not consider the impact of other aspects of performance. Therefore, the universality of the conclusion needs to be further verified and deepened. In the future research, this paper will continue to move towards this problem, and constantly improve the test conditions to provide more objective and accurate reference.

References 1. Dai Y, Li Y, Liu L-J (2019) Structuring description for product image data with multilabel. Sensing and Imaging 20(1):1–19 2. Musa MA, Bashir I (2020) Image processing techniques in computer vision: an important step for product visual inspection. International Journal of Science for Global Sustainability 6(3):7–7 3. Kaur H, Koundal D, Kadyan V (2021) Image fusion techniques: a survey. Archives of computational methods in Engineering 28(7):4425–4447 4. Oyewole SA, Olugbara OO (2018) Product image classification using Eigen colour feature with ensemble machine learning. Egyptian Informatics Journal 19(2):83–100 5. Sadr A, Orouji N (2019) Clifford algebra’s geometric product properties in image-processing and its efficient implementation. Iranian Journal of Electrical and Electronic Engineering 15(2):182–188 6. Singh S, Mittal N, Singh H (2021) Review of various image fusion algorithms and image fusion performance metric. Archives of Computational Methods in Engineering 28(5):3645–3659 7. Wiley V, Lucas T (2018) Computer vision and image processing: a paper review. International Journal of Artificial Intelligence Research 2(1):29–36 8. Rabiser D (2018) Multi-purpose, multi-level feature modeling of large-scale industrial software systems. Softw Syst Model 17(3):913–938

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9. Singh P (2021) A new wavelet-based multi-focus image fusion technique using method noise and anisotropic diffusion for real-time surveillance application. J Real-Time Image Proc 18(4):1051–1068 10. Vargas E (2018) Spectral image fusion from compressive measurements. IEEE Trans Image Process 28(5):2271–2282 11. Buades A (2020) Backlit images enhancement using global tone mappings and image fusion. IET Image Proc 14(2):211–219

Blockchain-Based Car Networking Data Privacy Security Assessment Model Puqing Wang and K. L. Hemalatha

Abstract In recent years, vehicle-mounted ad hoc networks (vanets) have been regarded as the infrastructure of intelligent transportation systems, which can improve traffic efficiency and ensure the safety of vehicles and pedestrians. As a part of the Internet, the application value and research significance of the Internet of Vehicles technology are beyond doubt. The issue of privacy protection has always been a concern. For this reason, it is necessary to evaluate its privacy security. This article mainly uses the experimental method and the comparative method to discuss the experimental data of this article in groups. Experimental data shows that the accuracy of data based on blockchain reaches more than 90%, while the accuracy of non-blockchain data is lower. This shows that blockchain technology plays a great role in data security. Keywords Blockchain data · Internet of Vehicles · Privacy and security · Evaluation model

1 Introduction In order to promote cooperation between vehicles and share valuable driving information, two kinds of communication are established in the vehicle ad hoc network. Although the main security services of the vehicular ad hoc network have been in-depth research in other fields, the research results in these fields can provide a secure communication channel to resist external attacks, but the trust management and privacy issues have not been well resolved [1]. P. Wang (B) Jiangxi University of Applied Science, Nanchang, Jiangxi, China e-mail: [email protected] K. L. Hemalatha Department of Information Science and Engineering, Sri Krishna Institute of Technology, Bangalore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_86

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There are many theoretical results of research on the data privacy security evaluation model of the Internet of Vehicles based on the blockchain. For example, Tang Chunming explored blockchain technology and cryptographic technology in order to solve the problem of information insecurity brought about by the traditional centralized model of the Internet of Vehicles. Chattaraj said that with the birth of the blockchain concept, the scope of this technology has been expanded to cover different fields such as finance and logistics [2]. Some people also proposed a data security communication model based on the blockchain of the Internet of Vehicles node in response to the high risk of data security in the Internet of Vehicles [3, 4]. Therefore, this article intends to study the blockchain technology and design the evaluation model for the data security of the Internet of Vehicles. This article first studies the Internet of Vehicles, and elaborates its basic concepts and potential safety hazards. Next, describe the blockchain. Then analyzed the data security model of cloud computing + blockchain. Finally, the relevant results are obtained by designing the evaluation model and conducting experimental tests.

2 Blockchain-Based Car Networking Data Privacy Security Assessment Model 2.1 Internet of Vehicles Since the introduction of the “Internet+” concept, all aspects of life have been quietly changing. As a traditional manufacturing industry, automobiles seem to have very little connection with the Internet and Internet thought, but the emergence of “Internet+” has really spawned the automobile industry [5, 6]. Understanding the Internet of Vehicles can start from three aspects: The first layer (end system): The end system is the smart sensor of the car, which collects and records the smart information of the vehicle, and perceives the driving situation and the surrounding environment. The second layer (management system): Solve the networking of vehicles and vehicles (V2V), vehicles and roads (V2R), vehicles and networks (V2I), and vehicles and people (V2H), forming a unit of public and private networks at the same time. The third layer (cloud system): The Internet of Vehicles is a cloud-based vehicle operation information platform. The green chain includes ITS, emergency vehicles, auto repair and auto parts, company vehicle management and manufacturing, emergency services, mobile Internet, etc. A large amount of information from multiple sources requires cloud computing [7, 8].

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The Internet of Vehicles can ensure the distance between cars, reduce the risk of collisions, help car owners navigate in real time, and improve traffic operation efficiency. (1) Security hazards caused by man-made intrusion Remotely control the vehicle. Many third parties obtain vehicle operating data through OBD, and notify vehicle owners through information such as driving record prompts. RFID security issues of electronic tags. Electronic RFID tags used in vehicles are mainly used for remote identification, and currently have nothing to do with the car itself. The hidden dangers include the loss and theft of RFID memory data and the confidentiality of the vehicle owner’s lane data. Since there is no uniform standard for keyless systems, many keyless systems are not safe enough, and there are great security risks. One of the most important technologies in the Internet of Vehicles is sensor technology. The data collected by various smart sensors needs to be transmitted to the cloud server. These personal data may be attacked and stolen. Incomplete data storage methods have led to the loss of personal data in many ways. The vehicle network system usually stores the location, trajectory or other private personal information of the owner’s vehicle. The data cloud security challenge of the Internet of Vehicles. Mobile cloud is large in scale, diverse in variety and relatively low cost, which also poses challenges to the identity verification and access rights of users and operators [9, 10]. (2) The hidden dangers brought by the network structure of the Internet of Vehicles The Internet of Vehicles consists of three layers: perception layer, network layer and application layer. Each layer is divided into working phases to meet the functional requirements of Internet of Vehicles. The perception layer is mainly used to supplement the acquisition and collection of vehicle information, perceive the real environment and convert it into data. There are many types and numbers of sensors applied to the detection layer due to different detection functions (such as GPS data, vehicle speed, tire pressure, etc.). The data received from the sensor is passed to the onboard processor for unified processing. This process involves several security issues of data fusion. Due to different network types, the network layer itself will be subject to various network attacks. My network created by various network technologies has an imperfect integration mechanism, which can easily lead to loopholes in the Internet of Vehicles. The application layer is mainly the aggregation and application layer of vehicle information. Application layer security includes three aspects: service environment, service access and service platform.

2.2 Blockchain Since the development of the Internet, the birth of every new technology has fundamentally changed people’s lifestyles. Today, a new technology is booming: blockchain. Not only the information chain, but also the value chain, this has led

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more and more people to realize the blockchain technology, understand its principles and apply them in practice. The core of blockchain technology is to solve the problem of trust cost; decentralization and disintermediation are the core concepts of blockchain technology. Blockchain despises all old ideas that limit our thinking, and will destroy management methods and centralized models that control transaction execution. Blockchain relaxes the rein of trust. Blockchain’s automated script code system can help users create advanced smart contracts, currencies or other decentralized applications. A block refers to a data block, and each data block contains a stack of network transaction information, which is used to verify the validity of the information and generate the next block. Blockchain is a chain connected by blocks in time series. Just like the idiom solitaire, adjacent idioms must be connected in order to form an idiom chain. The blockchain in the blockchain is divided into three types. The public chain is a blockchain that participates in the consensus process, that is, anyone can become a node, and any node can freely join and leave. Network and participate in reading and writing chain data. Consortium chain refers to a blockchain jointly managed by multiple institutions. Each organization or institution manages one or more nodes, and its data can only be read, written, and sent by different institutions in the system. In general, consortium chains are usually used for transactions and settlements between organizations. A private chain refers to a blockchain that requires permission to join a node. The write permissions of each node in the private chain are strictly controlled, and the read permissions can also be selectively opened to the outside world as needed. Private channels are generally suitable for internal business applications and financial scenarios, such as internal data management and audits of certain institutions. Ant Financial is the leading index of private channel applications. The characteristics of the blockchain are that the underlying P2P network adds encryption and security technologies to solve the problem of data encoding and erasure. Second, the electronic transaction applications it produces, such as Bitcoin, exhibit the characteristics of online electronic currency transactions.

2.3 Cloud Computing + Blockchain Internet of Vehicles Data Security Model Attackers inside the ad hoc vehicle network can not only obtain private information by analyzing all the messages broadcast on the network, and easily track the location of other vehicles, but they can also tamper with messages, which poses a considerable risk to network road traffic safety. The trust management system can help the vehicle determine whether the received message is trustworthy. In a centralized system, all execution processes are carried out on a central server, which will cause a large delay and is not suitable for the high-quality service requirements of the on-board self-organizing network. In a decentralized system, the trust management system is

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usually deployed in the road unit (rsu), but rsu can be maliciously attacked. Attackers will maliciously manipulate the vehicle reputation information stored in rsu, which is an important problem that needs to be solved. Some in-vehicle infotainment (IVI) systems are vulnerable to remote hacker attacks, which means that all vehicles equipped with these systems will be affected. Hackers can use WiFi and USB connections to invade the car system to control hardware devices, and even use the car navigation system to record to find the exact location of the vehicle and update it automatically at any time. With the continuous progress of the intelligence and interconnection of the Internet of Vehicles, there are endless safety incidents of the Internet of Vehicles. Wirelessly connect the vehicle wireless access point to perform vehicle system security inspection, enter the vehicle entertainment system, perform remote attack simulation, conduct entertainment system risk assessment, check system vulnerabilities, and propose targeted remedial measures. Internet access in the car is a matter of life safety for the driver. Today’s vehicle systems are extremely vulnerable to attacks. They have no intuitive knowledge of the safety of their own systems, nor do they have system safety warnings. Unauthorized personnel can enter the vehicle system through technical means; actively promote the safety research and standard formulation of the Internet of Vehicles. Differential data protection ensures that whether an entity is included in the data set has almost no impact on the final published query results. In other words, when querying these two records, the odds ratio for the same query to produce the same results for the two records is close to 1. So: Pr[s(c) ∈ ts ] ≤ fµ Pr[s(c' ) ∈ ts ]

(1)

The smaller the value of µ, the higher the level of privacy protection. Many differential privacy methods add controlled noise to reduce the sensitivity of query results. The Laplacian mechanism increases the Laplacian noise.  pdf Q sc1 (a) =  pdf Q sc2 (a) =

p p

 =

noise( p − g(c1 )) noise( p − g(c2 ))

(2)

Among them, Qs (a) is a continuous random variable. Differential privacy is a privacy framework formed to protect this type of statistical database from anonymity technology. First of all, the privacy of the Internet of Vehicles is more important than the privacy of the mobile Internet. There are currently various models and methods for the security protection of vehicle Internet data. We propose a basic model of data security for the Internet of Vehicles. The model integrates various blockchain technologies and cloud computing technologies to create a car networking model. The basic idea is to transfer some security-related functions and data of the entire Internet of Vehicles to the blockchain and use cloud resources to protect them.

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3 Test of the Data Privacy Security Evaluation Model for the Internet of Vehicles 3.1 Test Purpose In order to ensure that the vehicle-mounted mobile devices constructed in this article can be accessed without infringing on the personal information and resource security of other users and other participants, it is necessary to protect their privacy rights from infringement. At the same time, it is also hoped that the communication speed between the vehicle system and the wireless network can be increased through this system, thereby enhancing the ability of the entire city’s intelligent transportation system to protect users in terms of safety, thereby promoting the development of urban intelligent transportation.

3.2 Construction of the Evaluation Model The data privacy security protection model designed in this paper includes the embedded storage and access control strategy of the blockchain, distributed node and multi-path communication technology, and its application scenarios. In this system, user identity authentication is carried out through the embedded micro-domain perception module. Use firewall technology to realize intrusion detection such as antiattack. Use the card password management function to record and monitor malicious accounts and illegal behaviors. The architecture of this article is divided into three parts, namely data privacy security protection, blockchain technology and embedded system deployment. The details are shown in Fig. 1. This paper is based on blockchain technology, from the perspective of data security, to study the impact of access rights and usage methods between different nodes on privacy protection. It can effectively guarantee the legitimate rights and interests of users while protecting their personal information from being illegally intercepted and maliciously tampered with. This article uses a combination of hierarchical encryption and distributed storage to design. At the same time, taking into account the unexpected situations that may occur during the operation of the system, a block and modular management mode is adopted.

3.3 Test Indicators In this test, the collected car networking data will be classified, and then the four most common sets of car networking data will be selected for experimental analysis. Among them, the first two groups are data of different styles of cars that apply

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Distributed control system Blockchain technology

Embedded Systems

Firewall Evaluation server

Fig. 1 Data privacy security assessment model for the internet of vehicles

blockchain technology, and the last two groups are data of different styles of cars that don’t apply blockchain technology. The analysis of the results of this security assessment is to consider its security from multiple dimensions when designing the data privacy protection model. Data openness and credibility are expressed by calculating whether there is any leakage of information such as user login account numbers and passwords. The feasibility and stability of the algorithm, including calculation methods, physical equipment, storage methods, and communication channels, take into account the user’s personal sensitivity, and within a certain range, the privacy protection performance indicators can be measured. Avoid accidental factors that lead to the leakage of data confidentiality.

4 Analysis of Test Results 4.1 Analysis of the Accuracy of the Evaluation Model Through experimental exploration, this article analyzes its grouping and accuracy, credibility and stability, and obtains the data results in Table 1. In Table 1, the data privacy and security of the Internet of Vehicles can basically be guaranteed. As shown in Fig. 2, we can find that the accuracy, credibility and stability of the data privacy security assessment of the Internet of Vehicles based on the blockchain are higher than those of the non-blockchain data. And the data of different models will be different. In general, the blockchain technology plays a relatively large role in the data of the Internet of Vehicles.

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Table 1 Assessment model accuracy analysis

Stability

Accuracy (%)

Reliability (%)

Stability (%)

Group 1

93

91

92

Group 2

95

92

94

Group 3

89

90

89

Group 4

87

89

86

Reliability

Accuracy

86%

Group 4

87%

89%

Groups

89% Group 3

89%

Group 2

90%

92%

94% 95%

92% Group 1 80%

91% 85%

90%

93% 95%

100%

Rate Fig. 2 Assessment model accuracy analysis

5 Conclusion With the rapid development of Internet technology, sharing information and data has become a trend. This personal privacy may be used illegally or improperly. Therefore, how to protect user identity security is also one of the problems. When compressing and protecting data, care should be taken to ensure the privacy of the communicating parties. This paper builds a privacy and security protection framework based on blockchain. In the system architecture, considering the existence of multiple hierarchical structures of the Internet of Vehicles platform, a layered distributed protocol is used to realize the communication and interaction between the various layers. Acknowledgements Science and Technology Research Project of Jiangxi Provincial Department of Education under Grant (No. GJJ191100).

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References 1. Garg S, Singh A, Aujla GS et al (2020) A probabilistic data structures-based anomaly detection scheme for software-defined internet of vehicles. IEEE Trans Intell Transp Syst 99:1–10 2. Chattaraj D, Bera B, Das AK et al (2021) Block-CLAP: blockchain-assisted certificateless key agreement protocol for internet of vehicles in smart transportation. IEEE Trans Veh Technol 99:1 3. Knieps G (2019) Internet of Things, big data and the economics of networked vehicles. Telecommun Policy 43(2):171–181 4. Pokhrel SR (2021) Software defined internet of vehicles for automation and orchestration. IEEE Trans Intell Transp Syst 99:1–10 5. Ghane S, Jolfaei A, Kulik L et al (2020) Preserving privacy in the internet of connected vehicles. IEEE Trans Intell Transp Syst 99:1–10 6. Choi CS, Baccelli F, Veciana GD (2019) Modeling and analysis of data harvesting architecture based on unmanned aerial vehicles. IEEE Trans Wirel Commun 99:1 7. Bagga P, Das AK, Wazid M et al (2021) On the design of mutual authentication and key agreement protocol in internet of vehicles-enabled intelligent transportation system. IEEE Trans Veh Technol 99:1 8. Manogaran G, Saravanan V, Hsu CH (2021) Information-centric content management framework for software defined internet of vehicles towards application specific services. IEEE Trans Intell Transp Syst 99:1–9 9. Kwon D, Kim J, Mohaisen DA et al (2020) Self-adaptive power control with deep reinforcement learning for millimeter-wave Internet-of-vehicles video caching. J Commun Netw 22(4):326– 337 10. Rehman HU, Ghani A, Zubair M et al (2019) IPS: incentive and punishment scheme for omitting selfishness in the Internet of Vehicles (IoV). IEEE Access 7(1):109026–109037

Research on Multi-modal Human–Computer Interaction Products Under the Background of Artificial Intelligence Shuo Li

Abstract Today’s society is in an era of data flooding, data and life are inseparable. Under the impact of big data, traditional products are gradually moving towards easy-to-use and multi-functional intelligence. Multi-modality is extremely suitable for applications in information recognition, artificial intelligence, human–computer interaction and other related fields, and will achieve better performance and effects than other traditional methods. Multi-modal information processing technology can better analyze the information flow between people and products. This article briefly analyzes the inevitable connection between multi-modality and intelligent products in the era of big data, and explores the necessity and significance of intelligent design concepts in product design. Keywords Big data · Multi-modal · Artificial intelligence · Information

1 Research Background and Significance In today’s society, the term artificial intelligence continues to appear in all areas of life, and is considered by most people to be the direction of future world development [1]. However, the development of artificial intelligence is not as beautiful and smooth as people expected. The term “artificial mental retardation” is popular in the Internet recently because the existing technology is not enough to make real intelligence. Artificial intelligence is unable to effectively understand the information sent by people due to its lack of corresponding social attributes and creativity. Compared with human intelligence, it is essentially different. It is currently only used to replace part of human labor. In layman’s terms, the inspiration of artificial intelligence perception, learning, reasoning and action comes from the human senses [2]. Information in the S. Li (B) Shenyang Institute of Technology, Fushun, Liaoning, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_87

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real world generally appears in the form of multi-modality. The concept of multimodality originally originated in the field of computer information interaction. People found that the information expression methods displayed by some media could not fully express the various information actually encountered., So the more specific concept of modal is introduced. Modal refers to the way people interact with the external environment through their own senses, which is essentially the interaction of information between humans and the environment, while multi-modal refers to the interaction between multiple senses. With the continuous improvement of living standards, people’s demand for smart products is not limited to being usable, In some areas, artificial intelligence is more efficient than humans [3]. Multi-modality is a bridge connecting people and machines. The key to the development of artificial intelligence in the future.

2 Research Status At present, the most successful life smart products in China are smart speakers, led by Xiaodu, Tmall Elf and Xiaoai (in Fig. 1). The three brands are genetically different and each has its own strengths: Xiao Ai is backed by Xiaomi’s Mijia ecological smart home ecology, so it is inherently cost-effective and smart home genes. At the same time, Mijia is also the best smart home brand on the domestic consumer side. The producer of Tmall Genie originated from Alibaba and has a natural ecommerce gene. From the function to the user interface, there are elements of its product Taobao everywhere. Xiaodu originated from Baidu, and Baidu is known for its search engine and artificial intelligence, so Xiaodu has a wealth of Q&A resources and is extremely interactive. The three manufacturers have incorporated their own elements and industry advantages into the design of their smart speakers. After investigation, people prefer the interaction with smart speakers compared to other factors. Take voice interaction as an example. This is not only the recognition of pronunciation, but also the analysis of discourse logic. If a user wants to watch a movie of a certain celebrity, it is usually necessary to accurately mention each keyword, such as: “I want to watch Peng Yuyan’s movie”. However, as long as you express a general meaning with Xiaodu smart speakers, such as: “Xiaodu Xiaodu, come to Peng Yuyan”, it can automatically recommend Peng Yuyan-related film and television dramas. The reason is that Baidu’s artificial intelligence itself is known for its strong learning ability, so it is very prominent in this aspect, and it is more favored by consumers.

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Fig. 1 Intelligent speakers

3 Analysis of the Necessity of Multi-modal Information in the Development of Artificial Intelligence The sum of the human brain’s perceptual system is a complete multi-modal learning system. It can be seen that multi-modal learning is the key to the development of artificial intelligence. The following examples illustrate the importance of the combination of multi-modality and artificial intelligence. The GPT-3 model has been selected as one of the “Top Ten Breakthrough Technologies” of MIT Technology Review in 2021. However, it is reported that the use of cloud-hosted GPT-3 instance chatbots has found a new “method” to reduce the work of doctors-suggesting that patients commit suicide (in Fig. 2). The French manufacturer inferred: “AI’s weird response and unstable nature make it unsuitable for contacting patients.” The emotions that artificial intelligence lacks are the bottleneck of its current development. Differences in social background will affect emotional labeling. This will directly affect the correctness of the interpretation of the obtained multi-modal data [4]. The robot must be able to combine various data such as the patient’s physical condition and mental state in order to correctly judge the patient’s needs. To make smart products have the same ability to perceive beauty as humans, it is necessary to use multi-modal human–computer interaction in the field of artificial intelligence to analyze multiple sources of information. AI often causes mixed emotions in people who come into contact with it [5]. As a tool to assist humans in completing a series of activities, smart products must fully understand human information; similarly, the key for people to

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Fig. 2 The GPT-3 chatbot

evaluate the quality of smart products is also whether the feedback of smart products to humans meets the original wishes.

4 Research on the Human–Computer Interaction Mode of Smart Products In the field of smart products, with the development of computer technology, strong technical support is provided for the development of smart products, enabling them to have more complete interaction methods and richer functions. Human–computer interaction is one of the core functions of smart products and a bridge between people and products. A man–machine interface with reasonable control methods and friendly interaction methods will allow users to have a better interactive experience, and will also allow smart products to better serve users [6]. At present, the commonly used human–computer interaction methods for smart products are mainly as follows:

4.1 Touch Screen Interface Control At present, researchers use mobile phone applications to control smart products. It is very practical to integrate the current situation of the era of widespread use of smart phones. In large-scale products, a touch screen is also installed, and users directly use the touch screen to operate. The design of the man–machine interaction mode of the operation interface needs to allow the user to quickly understand and master

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Fig. 3 Touch Screen Interface Control

the operation of the interface, and to ensure the operation efficiency of the user [7, 8] (in Fig. 3).

4.2 Gesture Control Gesture is a common way of expressing information in people’s daily life, and it can also be used as a way of human–computer interaction for smart products. When the gesture sensor is applied to a smart product, the recognized gesture will be output in the mode of a cursor, and it will be recognized according to a variety of gestures configured inside the sensor (in Fig. 4). However, the gesture recognition of smart products will increase the complexity of the calculation, and it is not yet perfect to the extent that it can be installed. At the same time, in actual use, if the user’s hand movement changes due to environmental interference, the gesture interaction system recognizes The accuracy rate will be greatly reduced.

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Fig. 4 Gesture recognition

4.3 Voice Control Now a variety of smart products will be installed with voice recognition modules, which are used in inconvenient or unnecessary use scenarios (in Fig. 5). Traditional embedded platform processing limitations, its performance and effects are relatively simple, but the settings are simple and pre-configured voice modes, so the voice recognition effect is not ideal. As an open artificial intelligence platform with intelligent voice interaction as the core, IFlytek’s voice recognition technology has developed rapidly and is widely used in smart products, making voice control products convenient and practical. However, in a noisy environment, the accuracy of speech recognition will be slightly affected, and for some users with accents or dialects, the accuracy of voice interaction is not too high.

4.4 Other Control Methods In addition, there are also control methods such as infrared sensing and eye recognition to control the product (in Fig. 6). These methods are innovative and challenging. Faced with the different needs of people, researchers will develop a variety of human– computer interaction interfaces. Because of their different research purposes and methods, the use of smart products will not be the same. Holstein et al. designed a smart glasses through which teachers can see the learning status of the whole class

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Fig. 5 Voice recognition

and each student in time. In general, with the diversification of life and the individualization of needs, smart products with single-human–computer interaction can no longer meet the needs of most people [9]. Therefore, compared with existing products, multi-modal human–computer interaction intelligent products can better meet the needs of people in today’s era.

Fig. 6 Eye recognition

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5 Prospects of Artificial Intelligence Multi-modal Human–Computer Interaction Products In recent years, artificial intelligence products based on multi-modal information fusion have received extensive attention from domestic and foreign researchers, and related research has also made great progress. But from the perspective of practical applications, how to reasonably apply artificial intelligence to actual production and life is still a very challenging problem. In the past, people had a very single purpose for obtaining information, that is, use. For example, obtaining market information was for the launch of new products. Once these direct goals are achieved, this information will be forgotten. Now it is different. The value of information nowadays is not only reflected in its original purpose of use, and the derivative value of some information may exceed its own. Although artificial intelligence is constantly improving, it still lacks social attributes, creative ability, values, depth of thinking and other issues. Smart products can complete most of the work, which makes human labor easier. But the very small part of the work that is left to be completed by humans is also very important, and is the prerequisite for successful, accurate and intelligent human– computer interaction. As Kevin Kelly said in his speech, the victory of AlphaGo does not mean that artificial intelligence has defeated humans. It is essentially the outstanding performance of human–computer interaction [10]. This is the best result of human–computer interaction [11].

6 Concluding Remarks In layman’s terms, the existing artificial intelligence is unable to create machines that truly reason and solve problems. These machines seem to be intelligent but do not really possess intelligence, nor do they have autonomous consciousness. For existing artificial intelligence, only humans actively input information and questions it will execute and answer, and will not actively think about problems or actively learn. Because they are not set up by programmers from the beginning, the huge response database ensures that they can answer your questions or respond after receiving relevant information. The development of science and technology has destined that people and computers are inseparable. Both information and data are an indispensable part of the future world. Multi-modality is an open research field with a natural interdisciplinary nature, allowing different disciplines and different theories. Perspective research, its core human–computer interaction part is exactly the key theoretical support required by artificial intelligence. Artificial intelligence uses multi-modal information interaction methods to receive information sent by people in all aspects, so as to make better judgments. As a designer, he should also meet the needs of the development of the times, flexibly combine new technologies with traditional products, and cannot build a car behind closed doors, otherwise the most beautiful products will lose their usefulness. In short, in human–computer

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interaction, it is more necessary to present these all-round multi-modal information to artificial intelligence. Only in this way can a truly intelligent artificial intelligence be constructed.

References 1. Zhang, Dafoe (2019) Artificial intelligence: American attitudes and trends. Technical report Center for the Governance of AI. Future of Humanity Institute, University of Oxford 2. Johnson PC, Laurell C et al (2022) Digital innovation and the effects of artificial intelligence on firms research and development—automation or augmentation, exploration or exploitation? Technol Forecast Social Change 179:121636. https://doi.org/10.1016/j.techfore.2022.121636 3. Alkatheiri MS (2022) Artificial intelligence assisted improved human-computer interactions for computer systems. Comp Electr Eng 101:107950. ISSN 0045-7906.https://doi.org/10.1016/ j.compeleceng.2022.107950 4. Makhija A, Richards D, de Haan J et al (2018) The influence of gender, personality cognitive and affective student engagement on academic engagement in educational virtual worlds. In: International conference on artificial intelligence in education. Springer, Cham, pp 297–310 5. Broadbent (2017) Interactions with robots: The truths we reveal about ourselves. Ann Rev Psychol 68:627–652. https://doi.org/10.1146/annurev-psych-010416-043958 6. Law T, Scheutz M (2021) Chapter 2—Trust: recent concepts and evaluations in humanrobot interaction. In: Trust in human-robot interaction. Academic Press, pp 27–57. ISBN 9780128194720, https://doi.org/10.1016/B978-0-12-819472-0.00002-2 7. Klaudia T et al. Attitudes to AI among high school students: understanding distrust towards humans will not help us understand distrust towards AI 8. O’Brien, Roll I, Kampen A, Davoudi N (2021) Rethinking (Dis) engagement in humancomputer interaction. Comp Human Behav 128:107109. ISSN 0747-5632. https://doi.org/10. 1016/j.chb.2021.107109 9. Holstein K, McLaren BM, Aleven V (2018) Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In: International conference on artificial intelligence in education. Springer, Cham, pp 154–168 10. Mittal S, Kahn M, Romero D (2011) Smart manufacturing: characteristics. technologies and enabling factors. J Eng Manuf 233(5):1342–1361 11. Kelly K (2019) How AI can bring a second industrial revolution [EB/OL] [10 July 2019]. https:// www.ted.com/talks/kevin_kelly_how_ai_can_bring_on_a_second_industrial_revolution

Frame Design Based on Machine Learning Sports Result Prediction Xiaodan Yang and B. P. Upendra Roy

Abstract Machine learning is a multi-field cross-discipline, which includes probabilistic analysis, statistics, estimation process, word analysis, algorithm optimization process and many other functions. This article aims to study how machine learning technology, as a technology specially used for training computers, predicts the results of sports, and discusses in depth how to design a reasonable framework for sports data based on machine learning. This paper proposes a clustering analysis algorithm to deeply study the accuracy of sports results prediction in machine learning tasks, as well as its application methods and application fields. At the same time, this paper also proposes the K-mean algorithm and T test to detect experimental data, in order to obtain the most accurate experimental results, thereby promoting the widespread use of machine learning in the prediction of sports results. The experimental results of this paper show that in the sports industry, the accuracy of machine learning-based sports results testing is continuously improving, from 78 to 85%, and at this stage it has reached 91%. We believe that in the near future, with the continuous development of technology, the prediction of sports results based on machine learning will be used in more and more sports tests. Keywords Machine learning · Sports industry · Sports result prediction · Neural network

X. Yang (B) Haojing College of Shaanxi University of Science and Technology, Xi’an, Shaanxi, China e-mail: [email protected] B. P. Upendra Roy Department of Electronics and Communication Engineering, Channabasaveswara Institute of Technology, Gubbi, Tumkur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_88

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1 Introduction In recent years, with the advancement of big data, the application and promotion of big data in the field of machine learning has accelerated. Technology-based technology can effectively complete machine learning, machine learning technology has developed into a very hot research topic and plays a decisive role in big data. Big data and big data analysis technologies are technologies that use existing computer system templates to convert big data or big data collected by computers into useful information. Use larger data, better machine training effects, higher accuracy, more identifiable content, and reduce violations and incompatibility. Many scholars at home and abroad have conducted in-depth research on the research topic of frame design based on machine learning sports result prediction. Jin’s article uses the advantages of machine learning in data analysis and feature mining in dragon boat training. A safety mode control model of dragon boat sports physical training based on machine learning is proposed [1]. Thabtah proposed a new intelligent machine learning framework for predicting the results of NBA games, aiming to discover influential feature sets that affect the results of NBA games. We want to determine whether machine learning methods are suitable for using historical data (previous games) to predict the outcome of an NBA game, and what are the important factors that affect the outcome of the game [2]. Karaosmanoglu proposed a new method based on the use of Convolutional Neural Networks (CNN) to visually predict solutions to electromagnetic problems. The construction and training of the CNN model allows the surface current images obtained in the early stages of the iterative solution to be used to predict the final (convergent) solution image. Numerical experiments show that, compared with the corresponding input image, the predicted image contains significantly better visual details [3]. Liu’s article aims to solve the problem of lagging behind the methods and means of volleyball technical prediction in our country. Through field visits, it is found that the analysis and research methods of technical and tactics in Chinese volleyball practice are relatively backward, which has affected the rapid development of Chinese volleyball to a certain extent. Therefore, it is a necessary and urgent task to realize the reform of my country’s volleyball technical and tactical analysis methods and means [4]. Herold believes that machine learning is a relatively new concept in football, and little is known about its usefulness in determining the performance indicators that determine the outcome of the game [5]. Although the above-mentioned scholars have a very comprehensive understanding of the concepts and methods of machine learning, they have not well combined machine learning with the frame design of sports result prediction. Therefore, this article will build on the basis of the predecessors. Carry out in-depth research on the frame design based on machine learning sports result prediction, and explore the application law of machine learning in the field of sports, in order to better promote the continuous development of the sports industry and make an outlook for future development.

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2 Frame Design Based on Machine Learning Sports Result Prediction Method 2.1 Machine Learning The purpose of machine learning classification is to predict quality variables by simulating a separation model based on the training data system, and then use the model to predict the value of the test data variables [6, 7]. This type of data processing is called supervised learning, because the level of data processing refers to the class variables during modeling, and it can be divided into two modes: supervised learning and unsupervised learning. Collaborative training is an important teaching method for collaborative training services. Because there is no research on basic knowledge and no design data, it is difficult to provide working techniques to design and optimize collaborative training services. The design of collaborative training projects requires an understanding of the types of science and the internal relationships within the scientific field in training activities [8, 9]. The design of machine learning is an accurate and reasonable design based on the real learning situation [10, 11], which ensures the stability between training facilities and teaching methods. But in practice, machine learning is also “practical” rather than “designed”.

2.2 Prediction of Sports Results Predicting the outcome of a game is an important tool in sports and requires accurate prediction. Traditionally, the corresponding results are predicted through statistics and statistical models. These models are often confirmed by domain experts; due to the uniqueness of the characteristics associated with small games of different sports, the results of different studies in this application cannot generally be directly compared [12, 13]. Sports training is a physical exercise plan and training method adopted to achieve established physical education goals. Due to the high repetitiveness, repetitiveness and characteristics of sports training, it also increases the chance of sports harming sports [14, 15].

2.3 Algorithm of Frame Design Based on Machine Learning for Sports Result Prediction Cluster analysis, also known as data segmentation [16, 17], is a tool for data system analysis. It comes from many fields such as mathematics, computer science and economics, and is a very important algorithm. Cluster analysis analyzes the data

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based on the similarity with classification, and then collects hidden knowledge and information in each category of data analysis data [18]. T test is a non-parametric test method for two types of problems, using the t distribution method to derive the probability of variance. When using the T-test method for classification, the T value of each component is calculated. The process is as follows: − x2

xˆ1 ∑ n1

T =/

i=1

n 1 +n 2 −2

∑

i n i (Y i

2

×

(1)

n 1 +n 2 n 1 ×n 2

− Y total ) · ∑

∑

j (Yi j

i − Y )2

(2)

According to the above formula, k is the number of groups and N is the total number of observations, which can be obtained: ∑

− Y total)2 k−1 ∑ ∑ 2 wss i j (Yi j − Y ) = wmss = N−k N −k bss = k−1

i n i (Y i

(3)

(4)

3 Experiments Based on Machine Learning Sports Result Prediction Based on various principles of machine learning, this paper proposes a structured experimental method to predict sports athletes’ sports results. The prediction method helps to obtain the best results of sports data and a given data set, as shown in Fig. 1. Machine learning has a wide range of uses. While predicting the results of sports data, it can also predict the muscle or external injuries of basketball players, as shown in Table 1. Table 2 shows the relationship between the cause of injury and the predicted outcome. Sports injuries exist objectively in sports training. Therefore, physical education teachers should pay great attention to sports injuries, learn injury prediction techniques, and efficiently use machine learning techniques to detect students’ sports results [19, 20]. At the same time, they should lead Students recognize sports injuries and form awareness of sports injuries, as shown in Fig. 2. Machine learning tasks can make a more accurate prediction of sports results. The accuracy of sports training prediction results is closely related to many factors, including athletes’ physical fitness, weather factors, type of equipment, etc. Different

Frame Design Based on Machine Learning Sports Result Prediction

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Business understanding Sports playing

Transform Database Data preparation

Deploy

Data Reading

Modeling Evaluation Fig. 1 Motion result prediction framework design

Table 1 Predict the occurrence of sports injuries due to stealing the ball FMS score

Damage occurs

FMS < 14

7

No damage occurred

Total

Injury incidence (%)

10

17

35.30

FMS > 14

10

41

51

11.02

Total

15

30

45

15.14

Table 2 Injury monitoring for athletes Code

Age

FMS score

Is it symmetrical

Time-lost

New injury site

1

18

19

Yes

One week

Left knee

2

20

20

No

One week

Left ankle

3

25

16

Yes

Two week

Waist

factors will also lead to different prediction accuracy. The specific manifestations are shown in the Fig. 3.

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Fig. 2 The importance of sports injury awareness

Value

SLAM OLS SDA UAD

0

20

40

60

Type Predictive technology

Value

SLAM OLS SDA UAD 0

20

40

60

Type

4 Discuss Many influencing factors of physical activities on college students, physical education teachers may not be able to fully meet the educational requirements of modern students. With the continuous in-depth research on information technology, sports technology and artificial intelligence, many physical education teachers and sports research experts have produced a large number of college sports practice prediction models. Intensive training is widely regarded as one of the most popular techniques in machine learning in recent years.

5 Conclusion With the steady improvement of living standards, the material life of students has increased, and the lack of exercise has caused the physical quality of some students

Frame Design Based on Machine Learning Sports Result Prediction

Value

Well

Not bad

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Worse

55 50 45 40 35 30 25 20 0

1

2

3

4

5

Types

Value

Well

Not bad

Worse

60 55 50 45 40 35 30 25 20 0

1

2

3

4

5

Types Fig. 3 Different factors affecting forecast results

to decline to varying degrees. Student physical education is an important way to improve the physical condition of students. Predicting students’ sports performance can help the gymnasium service management department to formulate appropriate training activities and formulate the most in-depth training process. How to develop predictive models for high-performance sports has attracted many high school and university physical education departments. Sport training is an important factor to improve students’ physical fitness. The sports prediction mechanism based on machine learning can well predict the physical condition of students during exercise and the acceptable exercise intensity, which is convenient for teachers to make different sports learning plans for different students.

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References 1. Yin J, Wang X et al (2019) Study on safety mode of dragon boat sports physical fitness training based on machine learning—ScienceDirect. Saf Sci 120(C):1–5 2. Thabtah F, Zhang L, Abdelhamid N (2019) NBA game result prediction using feature analysis and machine learning. Ann Data Sci 6(1):103–116 3. Karaosmanoglu B, Ergul O (2019) Visual result prediction in electromagnetic simulations using machine learning. IEEE Ant Wirel Propag Lett 99:1 4. Liu Q, Liu Q (2021) Prediction of volleyball competition using machine learning and edge intelligence. Mob Inf Syst 2021(1):1–8 5. Herold M, Goes F, Nopp S et al (2019) Machine learning in men’s professional football: current applications and future directions for improving attacking play. Int J Sports Sci Coach 14(6):798–817 6. Ganesan A, Murugan H (2020) English football prediction using machine learning classifiers. Int J Pure Appl Math 118(22):533–536 7. Shan Y, Mai Y (2020) Research on sports fitness management based on blockchain and Internet of Things. EURASIP J Wirel Commun Netw 2020(1):1–13 8. Li G, Zhang C (2019) Research on static image recognition of sports based on machine learning. J Intell Fuzzy Syst 37(12):1–11 9. Liu T, Zheng Z (2020) Negotiation assistant bot of pricing prediction based on machine learning. Int J Intell Sci 10(2):9–21 10. Rozen R, Weihs D (2021) Machine-learning provides patient-specific prediction of metastatic risk based on innovative mechanobiology assay. Ann Biomed Eng 49(7):1–10 11. Wang X, Zhang D, Asthana A et al (2021) Design of English hierarchical online test system based on machine learning. J Intell Syst 30(1):793–807 12. Xu W, Xiong W, Shao Z et al (2021) Analysis of effectiveness and performance prediction of sports flipped classroom teaching based on neural networks. Sci Program 2021(3):1–7 13. Ajitha P, Sivasangari A, Rajkumar RI et al (2020) Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms. J Intell Fuzzy Syst 40(1):1–9 14. Lee JS, Lee H (2019) Developing a pedestrian satisfaction prediction model based on machine learning algorithms. J Korea Plann Asso 54(3):106–118 15. Kim J (2019) A solar power prediction scheme based on machine learning algorithm from weather forecasts. J Korean Inst Inf Technol 17(9):83–89 16. Wang Y, Chattaraman V, Kim H et al (2017) Predicting purchase decisions based on spatiotemporal functional MRI features using machine learning. IEEE Trans Auton Ment Dev 7(3):248–255 17. Sankar M, Sathidevi PS (2020) Design of MELPe-based variable-bit-rate speech coding with Mel scale approach using low-order linear prediction filter and representing excitation signal using glottal closure instants. Arab J Sci Eng 45(3):1785–1801 18. Venkatesh KK, Strauss R, Grotegut C et al (2020) 256: Machine learning-based prediction models for postpartum hemorrhage. Am J Obstet Gynecol 222(1):S175–S176 19. Khalil K, Eldash O, Kumar A et al (2020) Machine learning-based approach for hardware faults prediction. IEEE Trans Circ Syst I: Regular Papers 99:1–13 20. Atahan-Evrenk S, Atalay FB (2019) Prediction of intramolecular reorganization energy using machine learning. J Phys Chem A 123(36):7855–7863

Vehicle Detection Algorithm Based on Video Image Processing in Intelligent Transportation System Hongliang Guan

Abstract With the rapid development of the automobile industry, the problem of traffic congestion has become increasingly prominent. In order to reduce traffic accidents, improve road transportation efficiency and ensure road traffic safety, there is an urgent need for intelligent processing of vehicle real-time dynamic detection research. In this paper, the research of vehicle detection algorithm based on video image processing in the intelligent transportation system is aimed at real-time detection of vehicles through the algorithm to ensure driving safety. This article mainly uses experimental method and comparative method to analyze the application of several different algorithms in detection. The experimental results show that the minimum feature vector of the background difference method in vehicle detection is 32, but the highest accuracy is 98.4%. This shows that the background difference method is very useful in image processing. Keywords Intelligent processing · Traffic system · Video image · Vehicle detection

1 Introduction In the field of traffic management, vehicle detection is an important means to solve road congestion, frequent accidents and improve the efficiency of road traffic operation. In order to meet the needs of urban modernization and informatization development and increase the level of road network capacity to deal with emergencies, a set of video image processing methods for vehicle detection has become an important part of the design of intelligent transportation systems. There are many theoretical results of vehicle detection algorithms based on video image processing in intelligent transportation systems. For example, Chen Ke H. Guan (B) Jilin Railway Technology College, Jilin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_89

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addresses the inefficiency and poor operability of current video-based vehicle speed measurement methods that require manual calibration, and proposes a method to automatically perform important parameters such as focal length, inclination angle, and distance to the ground for typical road monitoring [1]. Su Guimin said that the detection and tracking of vehicle targets is a key technology for intelligent transportation. Existing vehicle detection and tracking algorithms have different performances, and it is difficult to meet the real-time and high-precision requirements of video traffic monitoring at the same time [2]. Aiming at the problem of vehicle video recognition, Li Suicang proposed a vehicle video recognition algorithm model based on LSTM [3]. Therefore, this article proposes a vehicle detection algorithm for video image processing on the basis of previous studies, which is of practical significance. This article first studies the intelligent transportation system. Then the video traffic detection technology is explained. After analyzing the basic knowledge of pattern recognition, a detection method for moving targets is proposed. Finally, the vehicle detection algorithm of video image processing is compared and researched through experiments, and the data results are obtained.

2 Vehicle Detection Algorithm Based on Video Image Processing in Intelligent Transportation System 2.1 Intelligent Transportation System With the rapid development of computer, communication and control technology, significant progress has been made in traffic management. Technologies such as traffic monitoring, traffic control, information collection and transmission have begun to take shape in transportation, but they also exposed many problems such as low transportation efficiency and high energy consumption [4, 5]. The development of intelligent transportation system is based on the combination of traditional technology and modern information technology, based on computers, using advanced vehicle detection and positioning technology, video image processing and other new means to achieve real-time control of parking lots. With the development of science and technology, vehicle automatic detection technology has also made great progress. Intelligent transportation systems mainly use video image processing and voice recognition to realize real-time monitoring and management of road traffic safety and road environment information. The key technology of vehicle detection in traffic video is digital image processing, which involves computers, optical imaging and other related fields. In the traditional video acquisition and transmission process, the camera motions to shoot the road surface and monitors the changes in vehicle speed and traffic flow in real time to command and control the entire road section. The camera is installed with a camera to obtain road condition information. This method requires a large amount of lane data collection and storage to realize monitoring and management functions. In the intelligent transportation

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system based on video analysis technology, basic tasks such as vehicle positioning, tracking and cruising are mainly realized through image processing algorithms [6, 7]. The purpose of vehicle detection is to realize the collection of information such as the driving state and direction on the road and the distance traveled in the intelligent transportation system. The vehicle monitoring system can obtain road traffic, lane location and traffic volume, so as to provide a basis for formulating safe and effective driving routes. Video image processing technology as an advanced, practical, rapid and efficient development and is widely used in the field of road traffic management is that the license plate recognition technology and computer vision research methods are closely related, and it can perform real-time traffic flow. Detect and analyze and make judgments on traffic conditions. Vehicle detection is a key part of the intelligent transportation system. It plays an important role in the entire traffic management system and plays a decisive role in affecting road traffic and safety protection [8, 9].

2.2 Video Traffic Detection Technology Video traffic detection is a technology that uses traffic cameras to detect road conditions, uses digital image processing technology to identify target vehicles on the road, and extracts relevant traffic flow parameters for further processing. Digital image processing, also known as computer image processing, refers to the process of converting image signals into digital signals and processing them with a computer. In the vehicle detection system, image processing is an important link, which can also be said to be operations such as scanning and transforming the collected pictures. Video analysis requires preprocessing technology to convert it into a digital signal and then transmit it to a computer. Due to the complex and changeable road environment and the inability to directly obtain the large amount of information in the captured image, and there are interference factors (such as noise and background), it may also lead to incorrect judgments and even cause accidents, which will be introduced in the vehicle detection process. A certain amount of noise and shadows affect the relationship between the target and surrounding objects in the picture. The purpose of vehicle detection is to find the target and determine the direction in the video image. Therefore, preprocessing the image can improve the positioning speed and accuracy. The traffic flow is extracted by a series of algorithms such as steering and acceleration after the vehicle information is captured by the camera. Due to the long use time of the video vehicle detection system, environmental factors and problems in practical applications need to be considered when processing images. Therefore, this paper adopts the method based on gray-scale image to realize the target vehicle detection [10, 11]. In the field of transportation, video detection technology with image processing functions has played an important role in traffic control and supervision such as vehicle detection, vehicle tracking, and vehicle accident management [12]. Gaussian noise refers to a type of noise whose probability density function is Gaussian distribution. The probability density function expression of Gaussian noise

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is: P(a) = √

1 2θ π

f −(a−e)

2

/2θ 2

(1)

Among them, e household represents the average value or expectation of the gray value a, and θ represents the standard deviation of a. The probability density function expression of Rayleigh noise is:  P(a) =

0, ax y

(2)

Among them, variance of the probability density θ 2 can be obtained √ the mean 2e andy(4−π) by e = x + π y/4 and θ = 4 .

2.3 Detection of Moving Targets Vehicle detection system is an important part of intelligent traffic management. In modern traffic management systems, video image processing technology as a new, advanced and effective auxiliary means has been widely used in various fields. Vehicle detection system is an important part of intelligent traffic control. Video image processing technology is also receiving more and more attention. Vehicle identification plays a pivotal role in intelligent traffic control systems. During the entire traffic process, road information can be recorded, analyzed and fed back in real time. The sequence of video images is captured by the camera and processed for its output. Video image processing technology has been widely used as an emerging computer vision technology, digital photogrammetric method, computer science and so on. It is mainly used to solve the problem of complex and changeable and unable to directly identify the moving target or the change of the moving route. The camera can also pick up the road reflection signal, and then transform it into a computer for analysis and calculation through the vehicle detection system. Vehicle trajectory tracking uses video image processing technology on traffic flow, road signs and markings and other road sections to display the changes in the direction and speed of the vehicle in each detection area on the computer. Vehicle detection is to obtain real-time dynamic information of traffic flow through sensors (including cameras) in video images. The moving target tracking system has the advantages of high value, strong reliability and robustness. Vehicle detection methods based on background subtraction generally require a large amount of complex hardware support to successfully recognize and locate moving objects. In the intelligent video traffic detection system, the detection of moving vehicles has an important impact on the reception of traffic information, the analysis and

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detection of road traffic conditions and problems, and the coordinated control of traffic areas. Edge detection is an important part of image segmentation and the basis for determining the image boundary. Therefore, it is necessary to discuss and analyze the classic edge detection operators. In vehicle detection, the determination of moving targets is a very important issue, because vehicle tracking is the analysis and processing of images. Under normal circumstances, we will use methods based on background optical flow, statistical average or weighted average to achieve. The background difference method is based on the principle that the camera position and the background are relatively fixed. First select an image that can represent the background in real time, and then subtract the background image from the current frame image that contains the vehicle to obtain an image that only contains the target vehicle. The algorithm works well when the background image is static and there is no obvious change. But in the video detection system, the background of the image is a gradual process. The passage of vehicles or other objects, changes in lighting, shaking of leaves and occlusion of buildings will all affect the background, so realtime capturing and updating of the background is the most important priority of this part of the work.

3 Experimental Prototype System and Result Analysis 3.1 System Software and Hardware Environment Combine optimized SVM and Gabor parameters to detect vehicles during the day, use classification accuracy, penalty factors and the number of support vectors to construct fitness functions, and use genetic niche algorithm to find SVM and Gabor Optimize parameters to get the best training model and the best Gabor filter Device. MATLAB series software is a high-tech visual development environment, mainly used for scientific computing and interactive programming. OpenCV is a cross-platform computer vision library based on BSD (open source) license and can run on Linux, Windows and Mac OS operating systems. The details are shown in Fig. 1: (1) Hardware environment: Microcomputer: CPU frequency is 2.50 GHz, memory size is 1G. Canon camera: used to shoot on-vehicle road video. (2) Software environment: Operating system: Windows XP operating system.

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CV processing and vision algorithms

MLL statistical classifier

HighGUI image and video input and output

Drawing function, XML support

Fig. 1 OpenCV structure module

3.2 Design of Testing Experiment The pattern includes different types of vehicles with different colors. The system implementation is divided into two parts: The generation of the hypothetical vehicle area is implemented in VC++6.0 using OpenCV vision library functions. The optimization of SVM and Gabor parameters is implemented using the libsvm toolkit under the matlab7.0 environment.

3.3 Experimental Prototype System In the daytime vehicle detection experiment, the experimental data is first divided into a training set and a test set, and then appropriate parameters are coded for each individual to construct a Gabor filter and SVM model.

4 Experimental Results and Analysis 4.1 Average Experimental Accuracy of Each Optimization Method For several different algorithms such as Gabor, PCA, SVM and background difference method, the average experimental accuracy obtained is shown in Table 1. The

Vehicle Detection Algorithm Based on Video Image Processing … Table 1 Average experimental accuracy of each optimization method

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Eigenvector dimension

Average accuracy

Gabor

53

95.2

PCA

86

75.8

SVM

46

96.5

Background difference method

32

98.4

Eigenvector dimension

Average accuracy

120

86 75.8

Precision

80 60

98.4

96.5

95.2

100

53

46

40

32

20 0 Gabor

PCA

SVM

Background difference method

Methods

Fig. 2 Average experimental accuracy of each optimization method

number of feature quantities of the daytime vehicle detection method proposed in this paper is only 32, and the average experimental accuracy can reach 98.4%. As shown in Fig. 2, we can see that the number of feature vectors of Gabor is 53, and the accuracy is 95.2%. PCA has the largest number of feature vectors, reaching 86, but its accuracy is the smallest, only 75.8%. The number of feature vectors of SVM is 46, and the accuracy reaches 96.5%.

5 Conclusion With the increase in the number of vehicles, traffic problems have become more and more serious, and traffic congestion has become more and more obvious. Therefore, in order to relieve the pressure caused by urban roads and improve road traffic capacity has become a goal of urban construction. In order to solve this problem, we propose video detection technology to help alleviate traffic congestion intelligently. Vehicle detection is an important part of the intelligent transportation system, and its level of

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development is constantly improving, but there are still many problems. Therefore, the research in this article can only provide a reference, and further in-depth research is needed. Acknowledgements Innovation and practice of pre-qualified personnel training mode for railway locomotive and vehicle manufacturing and Maintenance specialty, Jilin Higher Education Association (JGJX2019D705).

References 1. Chen K (2017) Automatic camera calibration method for automatic detection of vehicle speed in video. Comput Appl 037(008):2307–2312, 2333 2. Su G (2018) Vehicle detection and tracking based on traffic video and deep learning 1. Urban Utilities 005(004):72–76, 88 3. Li S, Chen F (2018) A video vehicle detection algorithm based on LSTM. Microcomput Appl 037(007):54–57 4. Zheng J, Xiao et al (2017) An adaptive vehicle detection algorithm based on magnetic sensors in intelligent transportation systems. Ad-hoc Sensor Wirel Netw 36(1/4):211–232 5. Wang E, Yong LI, Wang Y et al (2020) Vehicle key information detection algorithm based on improved SSD. IEICE Trans Fundam Electron Commun Comput Sci E103.A(5):769–779 6. Heng D, Cheng H et al (2018) Vehicle detection and classification using model-based and fuzzy logic approaches. Transp Res Rec 1935(1):154–162 7. Chen C, Liu B, Wan S et al (2020) An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Trans Intell Transp Syst PP(99):1–13 8. Wang Y, Ban X, Wanga H et al (2019) Detection and classification of moving vehicle from video using multiple spatio-temporal features. IEEE Access PP(99):1–1 9. Joseph D, Crabtree et al (2000) Dedicated short-range communications technology for freeway incident detection: performance assessment based on traffic simulation data. Transp Res Rec 2000(1):59–69 10. Wang H, Zhao Y (2020) A front water recognition method based on image data for off-road intelligent vehicle. J Adv Transp 2020(7):1–14 11. Liu X, Zhang Z (2021) A vision-based target detection, tracking, and positioning algorithm for unmanned aerial vehicle. Wirel Commun Mob Comput 2021(7):1–12 12. Taher HB, Hashem KM, Sajet FA (2018) Proposed method for road detection and following boundaries. J Theor Appl Inf Technol 96(18):6106–6116

Path Planning of Autonomous Vehicles Based on Deep Learning Technology Yangyong Liu

Abstract With the continuous improvement of the intelligent level of unmanned vehicles, its operating range is becoming wider and wider, the road traffic environment it faces is becoming more and more complex, and the possibility of various uncertain events is also increasing. It is of great significance to study the global path planning that adapts to the real traffic network. The purpose of this paper is to study the simulation experiment of autonomous vehicle path planning under deep learning technology. The background and significance of the research on unmanned driving strategy, the current development status of autonomous driving technology in the world, and the current development status of deep learning technology are introduced. The requirements of the control cycle duration when the car is running at high speed. The BIT* algorithm is improved, and the NBIT* algorithm is proposed, which greatly improves the planning speed when dealing with a large number of path planning tasks. The path planning of the 200 images in the NBIT* algorithm test set takes 1.61 s in total. Keywords Deep learning · Unmanned driving · Path planning · Simulation experiment

1 Introduction With the continuous progress and development of human civilization, people’s daily means of transportation have also changed from traditional livestock to today’s vehicles such as cars and planes [1]. Due to the improvement of people’s living and production standards, the demand for vehicles is not only satisfied with transportation, but improving the intelligent level of vehicles is also one of the major needs Y. Liu (B) College of Intelligent Manufacturing and Automobile, Chongqing Vocational College of Transportation, Chongqing 402247, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Atiquzzaman et al. (eds.), Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City—Volume 1, Lecture Notes on Data Engineering and Communications Technologies 167, https://doi.org/10.1007/978-981-99-0880-6_90

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of today’s society [2]. Highly intelligent unmanned vehicles are of great significance to scientific exploration in today’s society, and every unmanned field detection mission must require advanced intelligent unmanned vehicles [3]. In the unmanned field exploration mission, the unmanned vehicle needs to move on the complex and bumpy road surface. It must carry out as many scientific exploration activities as possible while ensuring its own safety, so it must have good environmental observation capabilities. And autonomous obstacle avoidance ability, which requires the unmanned vehicle to have the ability of environmental perception, path planning and control itself [4]. Path planning and driverless vehicles are booming, and Yudin D. A. provides a new approach to training intelligent agents based on the integration of reinforcement learning and computer vision to simulate the behavior of driverless vehicles. Using the complete visual information about road intersections obtained from aerial photos, we investigate the automatic detection of all road agents using various deep neural network architectures (YOLOv3, Faster R-CNN, RetinaNet, Cascade R-CNN, Mask R-). Relative position [5]. Lima J. conducts in-depth training of autonomous vehicle services through orbital simulations to identify paths that provide maximum safety (no impact) and minimize space reduction. A method has been developed to create simulation regions to analyze the performance of the proposed model at different problem levels. The determination method used is based on using multiple layers of a multi-line perceptron network, where parameters and hyperparameters are determined by finding a grid. These models are based on game maps generated by their training process [6]. Vigorously developing unmanned vehicles is of great practical significance for alleviating and even solving problems such as traffic safety and traffic congestion [7]. In this paper, we study the unmanned path planning algorithm. Under the guidance of the global path planning, the vehicle can obtain the current road condition information through the sensor, and use the local path planning system to generate a collision-free local driving path. In the process of path planning, whether the obtained path reasonably avoids the current obstacles, whether the kinematic constraints of the vehicle itself are taken into account, and whether the path search is efficient, are the key issues it faces. The purpose of this paper’s research on path planning is to solve the problem that under the constraints of vehicle kinematics, unmanned vehicles can obtain a more reasonable obstacle avoidance driving scheme on the local path encountering obstacles, and ensure the high efficiency of path search and the path Real and feasible.

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2 Research on Simulation of Unmanned Vehicle Path Planning Based on Deep Learning Technology 2.1 Deep Learning Deep learning is an idea that contrasts with traditional machine learning algorithms [8]. In a deep study, there are one or more hidden layers between the input stage and the output stage, and at each layer except the input stage, the connection between the layers gives the dimension of each unit [9]. After the statistics flow from the input to the output, the error derivative is calculated inversely from the output layer to each hidden layer, and the slope is distributed to the input plane according to the derivative rule, thereby maintaining the information dimension and increasing the function loss. Furthermore, this type of neural network is called a deep neural network or multilayer perceptron (MLP), which corresponds to a set point in a single production system [10].

2.2 Unmanned Driving The “DeepDriving” unmanned vehicle adopts an open layered architecture, and each system and layer has a clearly defined interface, which facilitates the collaborative work of software and hardware products developed by different manufacturers, and also reduces the coupling degree of systems at all levels [11]. Conducive to the parallel development and research of different technical teams. Lightweight communication marshalling tools are used for data communication transmission between systems to ensure the integrity and real-time nature of data transmission [12].

2.3 Path Planning An optimal index is required in path planning, usually the shortest path, the lowest fuel consumption, etc. Through the path planning algorithm, it is necessary to provide a collision-free path. Under the premise of satisfying the optimal index, the path should also satisfy the dynamic and kinematic constraints of the car. Path planning methods are widely used in many fields, such as aerospace vehicles, mobile robots, logistics and distribution, communication transmission, vehicle path planning and so on. For the unmanned vehicle path planning system, if the environmental information is known, an electronic map is established in the unmanned vehicle navigation coordinate system, and the planning based on the electronic map is called global path planning; the surrounding environment information is unknown, and the vision of the vehicle is required. The sensor perceives the environment in real time and builds a local map. However, unlike the robot path planning problem, the

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robot usually performs path planning in a small environment, while the driving environment of the driverless car is complex and the driving route is long. Only relying on the sensor to perceive the surrounding environment for local path planning will cause The calculation efficiency is low, the security is reduced, and it is very likely that the specified end position cannot be reached. Therefore, it is necessary to establish a global unmanned vehicle navigation map for global planning.

3 Investigation and Research on Simulation of Unmanned Vehicle Path Planning Based on Deep Learning Technology 3.1 Simulation and Experiment In order to verify the effectiveness of the proposed NBIT* algorithm, we build a joint simulation test platform of Matlab and Carsim. Set the reference trajectory as double line shift and the reference speed as 40 km/h. After the simulation and verification of the validity, the C++ control program was written, and the unmanned vehicle “MC” was used for field testing in the unmanned driving test site of the university. The unmanned vehicle platform mainly includes 6 major modules, specifically: based on the underlying data fusion multi-sensor perception system; central system based on planning and multi-objective dynamic optimization; control system based on advanced control algorithm, vehicle control architecture and implementation; basic support system based on database, underlying research, machine learning, etc.; based on closed roads and open roads, the experimental road system of intelligent transformation and the evaluation system based on the four-dimensional evaluation model of “quality-performance-intelligence-safety”.

3.2 Datasets In the process of making the dataset, two different starting points and two different ending points are selected in pairs. The entire dataset has a total of 3000 pieces of 125 × 125 pixel size, which are intercepted from the raster map, added with starting points and ending points, and processed. The images and their corresponding label files, of which 2000 images are used as the training set, 900 images are used as the validation set, and the remaining 100 images are used for the final test. The training environment includes TeslaV100S-PCIE-32 GB graphics card, ubuntu16.04, tensorflow1.14, cuda10.0, etc. The length of the label is set to 40.

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3.3 Algorithm Improvement Aiming at the problem that the BIT* algorithm is slow to solve the optimal path, takes too much time, this paper adopts the deep learning method to overcome this shortcoming, and improves the BIT* algorithm. Based on the proposed NBIT* algorithm. The edge filling method of each layer of the deep learning model of the NBIT* algorithm is “same”, which means that the filling is automatic, and the convolution kernel size K is not taken into account when the edge filling is performed. Use • to represent rounding up, and the output size of the edge filling method of “same”, as shown in formula 1:  n input (1) n out put S where ninput : input size; noutput : output size; S: step size. The edge filling method whose mode is “valid” indicates that no filling is performed, and the output size of this edge filling method is shown in Eq. 2:  n out put

n input − K + 1 S

 (2)

The fully connected layer of the deep learning model of the NBIT* algorithm has three layers, and the number of neural unit nodes in these three layers is 1024, 1024 and 80 respectively. Each convolutional layer is followed by a ReLU activation function, and the parameter initialization methods of the convolutional layer and the fully connected layer are “he_normal”.

4 Analysis and Research on Path Planning Simulation of Unmanned Vehicles Under Deep Learning Technology 4.1 NBIT* Algorithm Test After training, the latest model saved in the training process is used to test the test set images. The two paths planned by the NBIT* algorithm from the test results are shown in Fig. 1. The green point in the figure is the starting point, the blue point is the end point, and the thin black line is the path predicted by the NBIT* algorithm. The starting point of the path in the two figures is (38, 53), and the end point is (321.267). The thin black line in the figure is the path predicted by the deep learning model of the NBIT* algorithm. Comparing a and b, it can be seen that under the condition that the starting point and the ending point are unchanged, when the obstacles and the environment change, the NBIT* algorithm can still plan a near-optimal path.

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Fig. 1 The path diagram planned by the NBIT* algorithm

This shows that the NBIT* algorithm has a certain dynamic performance to adapt to obstacles and environmental changes. It is worth noting that since the path predicted by the deep learning model of the NBIT* algorithm cannot be 100% accurate, sometimes the path predicted by the deep learning model of the NBIT* algorithm slightly intersects with obstacles. However, since the obstacles have been inflated and a safe distance has been reserved when the dataset is generated in this paper, the path predicted by the deep learning model of the NBIT* algorithm can still be regarded as a feasible and near-optimal path.

4.2 Comparison of Path Planning Before and After Improvement The BIT* algorithm and the NBIT* algorithm are located in the same area, and the same starting point and end point can be seen in the raster NBIT* algorithm and the BIT* algorithm, and the best possible path can be planned. The proposal time of the BIT* algorithm as a function of the number of patents is shown in Fig. 2. which is the change curve of the planning time of the BIT* algorithm under the same environment as the NBIT* algorithm and the same starting and ending conditions. *The planning time of the algorithm increases with the number of iterations, but the path planning of the 200 images in the NBIT* algorithm test set takes a total of 1.61 s, which is much lower than the path planning time of the BIT* algorithm, as shown in Table 1. It can be seen that the planning speed of the NBIT* algorithm is significantly improved compared to the BIT* algorithm, especially when there are a large number of planning tasks, the speed advantage of the NBIT* algorithm is very obvious.

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BIT* algorithm

843

NBIT* algorithm

10 Total time spent on path planning/s

9 8 7 6 5 4 3 2 1 0 200

400

600

800

1000

Number of iterations/test set

Fig. 2 Comparison of path planning time between NBIT* algorithm and BIT* algorithm

Table 1 Comparison of path planning time between NBIT* algorithm and BIT* algorithm

Number of iterations/test BIT* algorithm NBIT* algorithm set 200

2.32

1.61

400

4.12

2.22

600

6.01

3.32

800

7.88

4.88

1000

8.78

5.45

5 Conclusions Path planning is an important guarantee for the unmanned vehicle to safely complete the task goal. The unmanned vehicle autonomously plans an optimal obstacle avoidance path between the starting point and the end point of the task according to the perception of the surrounding environment by its on-board sensors. Able to make rational decisions in complex, obstacle-filled environmental spaces. This paper summarizes the previous research results, points out the problems that need to be solved, and proposes corresponding solutions. A path planning algorithm NBIT* based on deep learning is proposed, which improves the progressive optimal path planning algorithm BIT*. The test results show that the NBIT* algorithm can plan

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a near-optimal path like the BIT* algorithm, and the NBIT* algorithm can not only adapt to the changes of obstacles and the environment, but also adapt to the changes, and has good dynamic performance. Certain practicality to adapt to different external conditions. Most importantly, when dealing with a large number of path planning tasks, the average path planning time of the NBIT* algorithm is much lower than that of the original BIT* algorithm.

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