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Lecture Notes in Networks and Systems 1351 Zheng Xu Saed Alrabaee Octavio Loyola-González Nurul Hidayah Ab Rahman   Editors Cyber Security Intelligence and Analytics The 6th International Conference on Cyber Security Intelligence and Analytics (CSIA 2024), Volume 1

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Lecture Notes in Networks and Systems 1351 Series Editor Janusz Kacprzyk , Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Türkiye Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

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The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and net- works, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sen- sor Networks, Control Systems, Energy Systems, Automotive Systems, Biologi- cal Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world- wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose (aninda.bose@springer.com).

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Zheng Xu · Saed Alrabaee · Octavio Loyola-González · Nurul Hidayah Ab Rahman Editors Cyber Security Intelligence and Analytics The 6th International Conference on Cyber Security Intelligence and Analytics (CSIA 2024), Volume 1

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Editors Zheng Xu School of Computer and Information Engineering Shanghai Polytechnic University Shanghai, China Octavio Loyola-González NTT DATA Madrid, Madrid, Spain Saed Alrabaee United Arab Emirates University Abu Dhabi, Abu Dhabi, United Arab Emirates Nurul Hidayah Ab Rahman Universiti Tun Hussein Onn Malaysia Selangor, Johor, Malaysia ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-88286-9 ISBN 978-3-031-88287-6 (eBook) https://doi.org/10.1007/978-3-031-88287-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland If disposing of this product, please recycle the paper.

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Preface The 6th International Conference on Cyber Security Intelligence and Analytics (CSIA 2024) is an international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary agenda of cyber security, particularly focusing on threat intelligence and analytics and countering cybercrime. Cyber security experts, including those in data analytics, incident response and digital forensics, need to be able to rapidly detect, analyze and defend against a diverse range of cyber threats in near real-time conditions. For example, when a significant amount of data is collected from or generated by different security monitoring solutions, intelligent and next generation big data analytical techniques are necessary to mine, interpret and extract knowledge of these (big) data. Cyber threat intelligence and analytics are among the fastest growing interdisciplinary fields of research bringing together researchers from different fields such as digital forensics, political and security studies, criminology, cyber security, big data analytics, machine learning, etc. to detect, contain and mitigate advanced persistent threats and fight against organized cybercrimes. The 6th International Conference on Cyber Security Intelligence and Analytics (CSIA 2024), building on the previous suc- cesses meeting in Shanghai, China (2023), (online meeting from 2020 to 2022 due to COVID-19), in Wuhu, China (2019), is proud to be in the 6th consecutive conference year. We are organizing the CSIA 2024 at the Mulian Hotel Guangzhou Zhujiang New Town, Guangzhou, China. It will feature a technical program of refereed papers selected by the international program committee, a keynote address. 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. Our special thanks are due also to the editors of the Springer book series “Lecture Notes in Networks and Systems”, Thomas Ditzinger and Praveena Anandhan for their assistance throughout the publication process.

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Organization Steering Committee Chair Kim-Kwang Raymond Choo University of Texas at San Antonio, USA General Chair Zheng Xu Shanghai Polytechnic University, China Program Committee Chairs Saed Alrabaee United Arab Emirate University, UAE Octavio Loyola-González NTT DATA, Spain Nurul Hidayah Ab Rahman Universiti Tun Hussein Onn Malaysia, Malaysia Publication Chairs Juan Du Shanghai University, China Shunxiang Zhang Anhui University of Science & Technology, China Publicity Chairs Neil. Y. Yen University of Aizu, Japan Junchi Yan Shanghai Jiaotong University, China Local Organizing Chairs Ge You Guangdong Innovative Technical College, China Jiaqi Wang Guangdong Innovative Technical College, China Sulin Pang Jinan University, China

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Contents Construction of a Comprehensive Safety Guarantee System for College Students Based on Digital Twin Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Junyan Song Data Resource Value Evaluation Algorithm Based on Fuzzy Theory and Catastrophe Series Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Lei Wang Comparative Study of ARIMA Model and Long Short Term Memory Network (LSTM) in Economic Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Xiwen Wang The Application and Empirical Study of Causality in the Theory Graph . . . . . . . 33 Guijiao He Research on Machining and Simulation Optimization System of Automobile Steering Knuckle Based on Advanced Algorithm . . . . . . . . . . . . . 43 Xiaopeng Chang, Siyu Chen, Xiyu Zhang, Bangcheng Zhang, and Bo Yu Network Partitioning and Demand Characterization for Management of Urban Low Voltage Power Distribution Systems . . . . . . . . . . . . . . . . . . . . . . . . . 56 Haisheng Hong, Yongshu Chen, Zhifang Zhu, Zheng Sun, Jiarui Guo, and Qin Lin Automation and Data Aggregation Algorithm for Low Code Development in Power Grid Mobile Report Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Wenting Wei and Jie Zhang Big Data Analysis and Smart Grid Security Event Monitoring and Response in the Power Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Hongyu Ke, Zhaoyu Zhu, Shuo Yang, Yi Tang, Ning Xu, and Xin He Construction of an Online Autonomous Learning Model Based on Artificial Intelligence ChatGPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Aimin Li and Chenming Yang Application of PMS Graphic Intelligent Recognition and Analysis Based on Contact Diagram Automatic Generation Technology . . . . . . . . . . . . . . . . . . . . . 105 Wei Ma, Qiang Li, Yuan Yao, and Xinkai Chen

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x Contents Application of Genetic Algorithm in Reasonableness Evaluation of Environmental Design Space Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Xiuliang Xi and Jianmei Wei Construction of an Intelligent Recommendation Model for Digital Media Content Based on Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Xiaoning Tang Research on Link Selection and Allocation for IoT Localization Systems Based on an Improved Ant Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Jiong Zhang, Meng Xu, and Liying Wang Cross-Domain Sharing and Privacy Protection Method of Fused Multi-source Data in Visual Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Longjie Zhu, Xuming Fang, Xinlei Yang, and Ming Li Construction of Cold Chain Logistics and Distribution Site Selection System Based on Multi-objective Optimization Model . . . . . . . . . . . . . . . . . . . . . . 162 Hanjie Jia, Hua Jiang, and Manjiang Chen Hole Detection Algorithm Based on Channel Fusion Siamese Network . . . . . . . . 174 Nuan Sun, Chunhe Shi, Yanchao Cui, Yaran Wang, Xiaoying Shen, and Xinru Shao Intelligent Database Triggers Enable Advanced Analysis of Data Recorded in Audit Logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Yongna Li, Cuiping Li, and Zhaoxia Cui Improvement of Principal Component Analysis Algorithm and Its Simulation Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 Ling Zhang Application of Computer Information Technology in Intelligent Analysis and Decision-Making Support of Diagnosis and Treatment Data . . . . . . . . . . . . . . 208 Yan Gao and Yinsong Zhang Security Vulnerability Detection and Defense of Smart Home Systems Based on the Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Zhenghui Zhao and Miao Chen In-Depth Discussion and Thorough Research on High-Availability Data Technology Within the Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Lei Yao

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Contents xi State Estimation and Fault Location of Multi-machine Power System Using Graph Neural Network and Variational Autoencoder . . . . . . . . . . . . . . . . . . 239 Fan Zhang, Mengyan Guo, and Ya Wang Fault Detection and Diagnosis of Ship Circuit Based on Machine Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 Shuyan Liu The Process of Building Color Extraction is Optimized with K-means Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 Jian Liu and Junru Chen Research on Risk Monitoring and Early Warning Technology for Special Disaster Emergency Rescue Site Based on Reformer Model . . . . . . . . . . . . . . . . . 265 Lei Zhang, Yufeng Fan, and Zhenpeng An Research on Intelligent Optimal Scheduling Algorithm for Vehicle Exhaust Emission in Railway Transportation System . . . . . . . . . . . . . . . . . . . . . . . 275 Zixiang Xu, Xiaokai Zhou, and Yishan Wang OLAP Technology Financial Statistics Information Platform Based on Big Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Hanyue Xu Productivity Estimation Based on Optical Remote Sensing Image Spatiotemporal Fusion Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 Jingyi Chu Partial Differential Equation Data Fusion Algorithm Based on D_S Evidence Theory and Fuzzy Mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 Ximei Shi Exploration of the Application of Blockchain Technology in Secure Storage and Sharing of Archival Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Xiaoning Chen Efficient Data Classification and Prediction Using Random Forest (RF) and Gradient Boosting Machine (GBM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Junliang Du, Xiaoyi Wang, Junpeng Chen, Ziyan Zhao, and Yang Zheng Low Carbon Transformation Effect of Logistics Enterprises Based on Adaboost Regression Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 Li Yao

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xii Contents Data Privacy Protection Technology in Digitalization of Power System Security Management: Application of Homomorphic Encryption Scheme . . . . . . 346 Dong Wang, Caihua Liu, Lifei Chen, Junliang Wang, Feng Su, and Xiangyang Li Mobile Communication Network Base Station Deployment Under 5G Technology: A Discussion on the Combination of Genetic Algorithm and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Moxin Zhang, Yimin Wang, and Bingjiao Shi Application of Digital Intelligent Algorithm in the Construction of Internet Cultural Communication Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Xi Chen Application of Multi-model Fusion Deep NLP System in Classification of Brain Tumor Follow-Up Image Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 Jinzhu Yang Construction and Experimental Verification of Automatic Classification Process Based on K-Mer Frequency Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Pengwei Zhu A Strategy to Determine Priorities Among Multiple Goals: Approaches from Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Mucun Xie, Dachao Shang, and Shuyan Zeng Innovative Application of Bayesian Algorithm in Network Security Risk Assessment Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 Haosheng Li, Qingqing Ren, Wei Chen, Yixuan Ma, Qingwang Zhang, and Wanting Lv Power Fault Detection Method Based on Waveform Data and Expert System . . . 425 Tengyue Gui, Weimin Xu, Husong Wang, and Haobin Xu Transportation Network Scheduling System Based on Data Analysis . . . . . . . . . . 435 Shuting Xu Program Structure Defect Localization and Repair Methods in Software Security Reverse Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 Yan Li Application of Data Mining in the Development and Management of Software Engineering in Cloud Computing Platform . . . . . . . . . . . . . . . . . . . . . 453 Qing Tan

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Contents xiii Credit Rating Optimization Model Based on Deep Q-Network . . . . . . . . . . . . . . . 464 Yijiao Fan Network Security Situation Automatic Prediction System Based on Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476 Wenyue Qi Research on Optimization Algorithm of Multi-agent System . . . . . . . . . . . . . . . . . 485 Jieru Wang Time Series Decision Analysis Based on Linear Programming and SARIMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496 Shuai Li, Zeyuan Zhang, and Dongming Jiang Artificial Intelligence-Driven Network Intrusion Detection and Response System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 Haokun Chen, Yiqun Wang, Shangyu Zhai, Wanrong Bai, Zhiqiang Diao, and Dongyang An Identifying Consumer Behavior Patterns from Massive User Transaction Data Based on Data Mining Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519 Qi Wang Hierarchical Scheduling Method of Power Emergency Based on Differential Evolution Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530 Kuiwen Huang, Taiping Yuan, Haowen Yu, Jie Zhu, and Huajun Tang Construction of Movie Knowledge Graph and Design of Recommendation System Based on Movielens Dataset Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 Peng Dong Design and Implementation of a High Concurrency Online Payment Platform Based on Distributed Microservice Architecture . . . . . . . . . . . . . . . . . . . 551 Tianyou Huang Design and Implementation of a General Data Collection System Architecture Based on Relational Database Technology . . . . . . . . . . . . . . . . . . . . . 561 Yuxin Wang System Design and Implementation of Particle Filter Algorithm Combined with Mean Shift in High-Precision Event Camera Positioning . . . . . . . . . . . . . . . . 573 Shu Xu, Erlan Wang, and Haiming Zhang

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xiv Contents Research on Optimization of Visual Space Fractal Design Algorithm Based on Fractal Geometry and Complex Network Theory . . . . . . . . . . . . . . . . . . 585 Huimin Chen, Zhenting Li, and Junlin Zhou Binary Logistic Model of Smart Tourism Based on Data Information System . . . 596 Danhong Chen, Lei Zhao, Yining Zhuang, Meilin Zhang, Yu Sun, and Xin Liu Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607

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Construction of a Comprehensive Safety Guarantee System for College Students Based on Digital Twin Technology Junyan Song(B) Shandong Business Institute, Shandong, China 57997092@qq.com Abstract. Campus security incidents in colleges and universities are sudden and urgent in nature. Once they occur, they will have a great negative impact on the safety of college students and social stability. To address this problem, this paper proposes to build a comprehensive safety protection system for college students based on digital twin technology. The system first builds a digital twin model of the campus through 3D modeling technology, and then deploys IoT devices to collect video streams, entry and exit records, and emergency information on the campus. The collected data is transmitted to the big data center and efficiently stored and analyzed using cloud computing technology. On this basis, an intelligent early warning system is developed using support vector machines to predict and warn of potential security risks, while also building an emergency response system. The system also includes virtual safety education and training modules developed using digital twin technology, and regularly organizes emergency drills to test and optimize emergency response processes. Finally, through the suggestion box and online feedback system, we collect opinions and suggestions from students and faculty on the safety assurance system, and optimize and upgrade the safety assurance system based on the feedback and safety incident analysis results. After the comprehensive safety guarantee system for college students was established, the number of safety incidents decreased by about 52.2%, and the average student injury rate dropped from 1.735% to 0.54%. This paper confirms the actual con- tribution of the system in reducing safety incidents and lowering student injury rates, demonstrates the effective application of digital twin technology in campus safety management, and provides colleges and universities with a more intelligent and systematic safety assurance solution. Keywords: Comprehensive Support System · Digital Twin Technology · Intelligent Early Warning · Emergency Response 1 Introduction Higher education institutions should not only provide students with knowledge and skills, but also ensure their safety and health during their stay at school. As an emerging tool, digital twin technology provides a new solution for campus safety management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 Z. Xu et al. (Eds.): CSIA 2024, LNNS 1351, pp. 1–12, 2025. https://doi.org/10.1007/978-3-031-88287-6_1

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2 J. Song This paper analyzes the application of digital twin technology in the field of campus safety, and also verifies the effectiveness of this technology in reducing safety incidents and reducing student injury rates through comparative experiments. This paper con- structs and evaluates a comprehensive security assurance system based on digital twin technology, providing new ideas and tools for university safety management. The paper is structured as follows: first, the research background and the current application status of digital twin technology in the field of campus safety are introduced; then the method of building a comprehensive safety protection system for college stu- dents and the overall architecture of the system are explained; and the experimental design is presented and the number of safety incidents and student injury rates before and after the construction of the protection system are compared; finally, the research findings are summarized and the potential and future development direction of digital twin technology in campus safety management are discussed. 2 Related Work In today’s rapidly developing society, safety education management has become an important issue in the field of education. Based on a brief overview of related work, Su [1] comprehensively analyzed the problems existing in safety education management and proposed corresponding innovative optimization measures based on actual condi- tions, in order to provide all-round protection for students’ learning and life on the basis of improving the current work situation. Xie [2] conducted a comprehensive analysis of student safety education management from the perspective of new media, and combined existing problems and the characteristics of information dissemination on new media platforms to explore countermeasures for the efficient implementation of student safety education management. Xi and Cao [3] took safety as the starting point and deeply ana- lyzed the safety hazards and common legal risks faced by college students in their daily life, study and social life, providing college students with a comprehensive and system- atic set of safety knowledge and legal guidance. Based on the fact that some students have weak safety awareness, Fu and Cheng [4] found that public safety education can improve college students’ skills in dealing with natural disasters, emergencies and man- made disasters, and enable them to have corresponding emergency response capabilities in emergency situations, thereby reducing casualties and property losses and ensuring the safety and stability of university campuses. Zhang et al. [5] conducted a survey and analysis on the current status of students’ fire safety awareness and found that students currently have weak fire safety awareness, lack of fire safety knowledge and insufficient fire emergency response capabilities. They analyzed the hidden dangers and causes of fires in student dormitories and formulated corresponding fire prevention measures to improve students’ fire safety awareness and the fire safety level of student dormitories and prevent fires. Bhandal et al. [6] evaluated the application of digital twin technology in opera- tions and supply chain management and identified the trends and value potential of this emerging research area. Khan et al. [7] provided a comprehensive overview of the con- cepts, classifications, challenges, and opportunities of digital twins for wireless systems. Aloqaily et al. [8] explored the integration of digital twins and advanced intelligent tech- nologies to realize the metaverse. Mihai et al. [9] found that digital twin technology can

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Construction of a Comprehensive Safety Guarantee System 3 be used for single entities, end-to-end services, and multiple services, and can realize the analysis, design, and real-time monitoring and control of IoT services to achieve cost- effective and resource-optimized operations. Mendi et al. [10] explored the application of digital twin technology in the military field, especially in fault diagnosis and health monitoring of satellite systems. The above research not only covers the theoretical basis and practical strategies of safety education, but also demonstrates the potential of dig- ital twin technology in improving the effectiveness of safety education and optimizing safety management. This study explores how to integrate digital twin technology into the safety education and management of college students. By building a comprehen- sive safety protection system for college students, it is expected to simulate and predict potential safety risks, provide safety education and emergency response training, and optimize the safety management process. This will not only enhance students’ safety awareness and self-protection capabilities, but will also provide colleges and universities with a more intelligent and systematic security solution. 3 Methods 3.1 Construction of Digital Twin Model Three-dimensional modeling uses computer programs to model physical systems in the real world into three-dimensional models. 3D modeling enables interactive experience of virtual scenes, provides visual and immersive interactive experience, and enables intu- itive understanding and control of virtual scenes. In the comprehensive campus security system, 3D modeling technology creates a high-precision, high-fidelity model of the campus, including the campus’s building structure, transportation system, and security facilities. Through three-dimensional modeling, the virtual-real integration of physi- cal and information dimensions is achieved and the full reproduction of the operating system is completed. The digital campus building structure, transportation system and safety facilities are the basis for building a digital twin model [11]. Transforming the physical entity of the campus into a digital model, including the precise collection of indoor structural properties and graphic information of buildings. By using drone map- ping and office software to process data, a detailed 3D model of the digital campus is constructed. These models include buildings, as well as transportation systems within the campus such as roads, sidewalks, and parking lots, as well as security facilities such as surveillance cameras, emergency alarm buttons, and access control systems. The dig- ital process integrates data from campus security systems such as video surveillance systems, electronic patrol systems, and checkpoint systems to provide a picture of the campus’ security situation monitoring, achieving comprehensive monitoring of “people, vehicles, land, events, and objects” across the entire campus. 3.2 IoT Device Deployment The deployment of high-definition cameras is an important part of achieving real-time monitoring and intelligent early warning mechanisms. By installing high-definition video surveillance equipment in various teaching buildings, main roads and corners

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4 J. Song of the campus, all-round monitoring of the campus can be achieved. These cameras not only provide real-time video streams, but also perform behavior recognition and anomaly detection through video analysis, thereby promptly identifying potential security issues. Deploying an intelligent access control system to strengthen access management on cam- pus, ensure that only authorized personnel can enter specific areas, and provide safer and more convenient access control. The intelligent access control system is combined with the digital twin model to achieve real-time monitoring and analysis of the flow of people on campus, thereby improving the efficiency and effectiveness of campus secu- rity management. The emergency alarm button provides a quick response mechanism. In an emergency, pressing the emergency alarm button will immediately send out a sig- nal for help. The button is equipped with a two-way voice intercom function, allowing on-site personnel to have real-time conversations with the alarm center. The emergency alarm button can also be combined with the digital twin model to achieve rapid position- ing and response to emergency events and improve the campus’ emergency response capabilities. 3.3 Comprehensive Safety Guarantee System for College Students The comprehensive security protection system for college students is a comprehensive and systematic security management framework that deeply integrates digital twin tech- nology, the Internet of Things, big data analysis, and cloud computing technologies. It includes intelligent warning systems, emergency response systems, virtual security education and training, and decision support systems, aiming to build an intelligent and responsive campus security environment. The system works collaboratively to prevent and respond to various campus security incidents and improve the safety level of students. Figure 1 shows the security system architecture: Fig. 1. The comprehensive security system architecture for college students 3.3.1 Development of Intelligent Early Warning System Support vector machine (SVM) achieves classification by finding the maximum marginal boundary between different categories. In digital twin technology, SVM identifies pat- terns and abnormal behaviors from a large amount of collected data to predict and iden- tify potential security risks. By analyzing campus surveillance video streams, SVM can identify illegal intrusions, abnormal gatherings and other behaviors and trigger warnings.

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Construction of a Comprehensive Safety Guarantee System 5 By analyzing data such as the flow of people on campus and abnormal behavior, the system identifies safety hazards and immediately sends warning notifications to the security management team to ensure that response measures are taken quickly. This early warning mechanism not only improves the response speed to campus security threats, but also intervenes before security incidents occur to reduce losses. In the SVM model, the identification of abnormal behavior can be further refined into the following optimization problem: min w,b,ξ 1 2 ||w||2 + C n∑ i=1 ξi (1) subject to yi(w · xi + b) ≥ 1 − ξi, ξi ≥ 0, i = 1, ...n (2) W is the normal vector of the hyperplane in SVM, b is the bias term in the SVM model, ξi is the slack variable for the i-th data point, which is used to handle data points that cannot strictly satisfy the hard margin condition, C is a regularization parameter that controls the trade-off between the margin width and the classification error, and n is the total number of data points in the dataset. xi is the feature vector of the i-th data point, and yi is the category label of the i-th data point. 3.3.2 Construction of Emergency Response System The formulation of emergency handling processes and plans is the basis of the emergency response system. By integrating public safety data, analyzing and judging incidents, locating and alerting suspicious incidents, accurate deployment and online dispatch of police forces can be achieved. At the same time, relying on video fusion and feedback from the processing process, the time from discovering problems to solving them can be shortened, and a three-dimensional public safety prevention and control system based on the organic combination of terminal perception and three-dimensional scenes can be established. In addition, by upgrading the text plan to a visual form, it becomes a new type of emergency plan with the characteristics of operability, visualization, quantifiability, easy management, and easy sharing. When an accident occurs, multiple departments will coordinate their operations, quickly issue execution plans and provide special support with one click, so as to quickly locate the accident, complete the accident handling, and restore the campus to normal status. Through digital twin technology, a series of virtual emergency drills were carried out, covering response strategies for various man-made emergencies such as terrorist attacks and school bullying [12, 13]. These drills enhance the ability of teachers and students to respond to emergencies. They also allow campus administrators to continuously optimize actual emergency response plans based on feedback from simulation results, striving to minimize casualties and property losses when real accidents occur. The digital twin platform uses a visual interface to display the location and status of various emergency resources in real time, queries the inventory of available emergency resources based on location data and sensor devices, provides underlying data support for emergency response plans for emergencies, and provides command, dispatch, and efficient resource allocation guarantees.

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6 J. Song 3.3.3 Virtual Safety Education and Training By creating a virtual safety education environment and simulating various emergency situations, students can experience and learn how to deal with safety incidents without actual risks. This virtual education method improves students’ safety awareness and self-protection ability. For example, laboratory safety emergency drills that combine VR virtual reality with actual operations can enhance laboratory safety emergency response capabilities. At the same time, schools use digital twin smart campus models to conduct virtual emergency drills, covering response strategies for various man-made emergencies such as terrorist attacks and school bullying [14]. These drills have improved the ability of teachers and students to respond to emergencies, and have also helped to continuously optimize actual emergency response plans, striving to minimize casualties and property losses when real accidents occur. 3.3.4 Decision Support System The mean clustering algorithm analyzes the security incident data of different areas on campus, identifies high-risk areas, and provides decision support for resource opti- mization allocation. The digital twin smart campus platform analyzes the weak links in campus security management, identifies high-risk areas and time periods within the campus, and then guides the rational adjustment of security resource layout to ensure the accurate deployment and efficient operation of security forces [15]. K-At the same time, digital twin technology integrates and visualizes various data on campus. By building a digital twin model, various facilities and equipment on campus are digitally presented, and the real-time performance and alarm data of the equipment are displayed, achieving a panoramic perception of the campus security situation. 3.4 Data Collection and Processing Data collection begins with various sensors and monitoring devices deployed on campus, which collect real-time information about the environment and infrastructure such as temperature, humidity, and energy consumption. These data are transmitted through switches, routers, firewalls and other network devices in the campus network as well as cloud service interfaces, as shown in Table 1. Once the data is transferred to the big data center in the data transmission layer, the data processing and storage layer starts working. In this layer, the data is cleaned, transformed and aggregated, and the time series processing of the data supports real-time monitoring and historical data analysis. The processed data is stored in the database and used in subsequent analysis and query. Cloud computing technology provides powerful data processing and analysis capa- bilities, updates the status of digital models in real time, and provides users with a visual display interface. Cloud computing technology supports efficient data storage and analy- sis, ensuring the real-time and accuracy of data processing. Cloud computing technology can be used to achieve real-time monitoring and early warning of campus safety, opti- mization of energy use and energy saving, and predictive maintenance of facilities and equipment.

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Construction of a Comprehensive Safety Guarantee System 7 Table 1. Campus monitoring data Sequence Number Collection Point Sensor Type Data Type Sample Data 1 Academic Building A Temperature Sensor Temperature (°C) 23.5,23.7,23.6,… (Continuous Data) 2 Academic Building A Humidity Sensor Humidity (%) 55,56,55,… (Continuous Data) 3 Dormitory Building B Energy Monitor Electricity Consumption (kWh) 120, 125, 130,… (Hourly Accumulation) 4 Library Temperature Sensor Temperature (°C) 22.0,22.2, 22.1,… (Every 10 min) 5 Library Humidity Sensor Humidity (%) 60,59,60,… (Every 10 min) 6 Gymnasium Light Intensity Sensor Light Intensity (lx) 500, 520, 510,… (Every 30 min) 7 Canteen CO2 Concentration Sensor CO2 Concentration (ppm) 800,780,790,… (Every 15 min) 4 Results and Discussion 4.1 Warning Accuracy of Intelligent Warning System In practical applications, the intelligent warning system can timely identify potential safety risks based on the data provided by the digital twin model. For example, when the population density in a certain area on campus exceeds a preset threshold, the system can immediately issue an early warning, prompting relevant departments to take evacuation measures to avoid safety accidents such as stampedes. In addition, the system also predicts and warns of equipment failures on campus, detects whether there are any abnormalities in the equipment in advance, and reduces safety accidents caused by equipment failures. The intelligent early warning system uses the digital twin model to collect data such as personnel flow and environmental changes in these areas in real time. Figure 2 shows the accuracy of the early warning system in 20 different universities. As shown in Fig. 2, the warning accuracy of the intelligent early warning system in different universities is above 95.3%, with the highest reaching 99.8%. These data results further confirm the key role of digital twin technology in the intelligent early warning system. In the experiment, the system is able to accurately capture and analyze data streams from various sensors and monitoring devices, quickly identify possible security threats, and issue timely warnings. When a fire occurs, the system analyzes data from smoke sensors and temperature sensors to accurately predict the location of the fire before it spreads.