基于数字孪生的机场人群智慧管控技术

doi:10.16182/j.issn1004731x.joss.22-0996

摘要/Abstract

摘要：

针对机场人群应急管控和管理智能化的需求，提出基于数字孪生的机场人群智慧管控方案。构建了包含数据层、建模层、功能层和应用层四维度的一体化人群管控系统框架，并对5个重要应用模块进行了探讨和应用效果展示。该方案通过利用数据驱动的人群仿真模型和智能优化算法，实现机场人群状态的动态预测和管控优化，能有效提升机场人群管控的效率和智能化水平，为建设智慧机场提供技术支撑。

关键词: 数字孪生, 人群仿真, 人群智慧管控, 智慧机场, 智能优化算法

Abstract:

Given the need for intelligent emergency control and management of airport crowds, a smart control scheme for airport crowds based on digital twin is proposed. The scheme constructs an integrated crowd control system framework with four dimensions, including digital layer, modeling layer, functional layer, and application layer. It discusses and demonstrates the application effect of five important application modules. By using a data-driven crowd simulation model and intelligent optimization algorithm, the proposed scheme realizes the dynamic prediction and control optimization of the airport crowd status. The scheme can effectively improve the efficiency and intelligence level of airport crowd control and provide technical support for the construction of intelligent airports.

Key words: digital twin, crowd simulation, crowd intelligent management, smart airport, intelligent optimization algorithm

中图分类号:

TP391.9

钟竞辉,林育钿,李稳强等 . 基于数字孪生的机场人群智慧管控技术[J]. 系统仿真学报, 2024, 36(1): 27-38.

Zhong Jinghui,Lin Yutian,Li Wenqiang,et al . Intelligent Airport Crowd Management Technology Based on Digital Twin[J]. Journal of System Simulation, 2024, 36(1): 27-38.

图/表 7

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参考文献 56

1	张雷. GIS+BIM在数字孪生机场建设中的应用[J]. 工程技术研究, 2021, 6(6): 12-14.
2	李欣, 刘秀, 万欣欣. 数字孪生应用及安全发展综述[J]. 系统仿真学报, 2019, 31(3): 385-392.
	Li Xin, Liu Xiu, Wan Xinxin. Overview of Digital Twins Application and safe Development[J]. Journal of System Simulation, 2019, 31(3): 385-392.
3	杨建伟. 3D协同可视化在智慧机场的应用与研究[J]. 中国设备工程, 2021(19): 64-66.
4	孙凌. 数字孪生机场建设的思考[J]. 中国高新科技, 2021(14): 149-150.
	Sun Ling. Considerations on the Construction of Digital Twin Airport[J]. China High and New Technology, 2021(14): 149-150.
5	刘潇翔, 汤亮, 曾海波, 等. 航天控制系统基于数字孪生的智慧设计仿真[J]. 系统仿真学报, 2019, 31(3): 377-384.
	Liu Xiaoxiang, Tang Liang, Zeng Haibo, et al. Smart Design and Simulation of Aerospace Control System Based on Digital Twin[J]. Journal of System Simulation, 2019, 31(3): 377-384.
6	吴宏刚. 智慧民航的构想和探索[J]. 网信军民融合, 2021(7): 41-43.
7	Grieves M. Digital Twin: Manufacturing Excellence through Virtual Factory Replication[EB/OL]. [2022-08-14]. .
8	Grieves M, Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems[M]. Cham: Springer International Publishing, 2017: 85-113.
9	Zhong Jinghui, Li Dongrui, Huang Zhixing, et al. Data-driven Crowd Modeling Techniques: A Survey[J]. ACM Transactions on Modeling and Computer Simulation, 2022, 32(1): 4.
10	Wolinski D, Guy S J, Olivier A H, et al. Parameter Estimation and Comparative Evaluation of Crowd Simulations[J]. Computer Graphics Forum, 2014, 33(2): 303-312.
11	Zhao Mingbi, Stephen John Turner, Cai Wentong. A Data-driven Crowd Simulation Model Based on Clustering and Classification[C]//Proceedings of the 2013 IEEE/ACM 17th International Symposium on Distributed Simulation and Real Time Applications. Piscataway, NJ, USA: IEEE, 2013: 125-134.
12	Zhong Jinghui, Cai Wentong, Luo Linbo, et al. Learning Behavior Patterns from Video: A Data-driven Framework for Agent-based Crowd Modeling[C]//Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2015: 801-809.
13	Lerner Alon, Chrysanthou Yiorgos, Lischinski Dani. Crowds by Example[J]. Computer Graphics Forum, 2007, 26(3): 655-664.
14	Chen Yimeng, Wu Cheng-Lung, Lung Lau Pong, et al. Airport Passenger Shopping Modeling and Simulation: Targeting Distance Impacts[C]//2019 Winter Simulation Conference (WSC). Piscataway, NJ, USA: IEEE, 2019: 524-535.
15	Zhong Jinghui, Cheng Tiantian, Liu Weili, et al. An Evolutionary Guardrail Layout Design Framework for Crowd Control in Subway Stations[J]. IEEE Transactions on Computational Social Systems, 2023, 10(1): 297-310.
16	Park Andrew J, Zouaghi Houssem, Tsang Herbert H. Crowd Control Strategy Framework Using Real-time 3D Simulations[C]//2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). Piscataway, NJ, USA: IEEE, 2018: 253-258.
17	Jur van den Berg, Guy S J, Lin Ming, et al. Reciprocal n-Body Collision Avoidance[C]//Robotics Research. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011: 3-19.
18	Feliciani Claudio, Shimura Kenichiro, Yanagisawa Daichi, et al. Study on the Efficacy of Crowd Control and Information Provision Through a Simple Cellular Automata Model[C]//Cellular Automata. Cham: Springer International Publishing, 2018: 470-480.
19	邵荃, 唐志星, 孙海燕, 等. 基于视频图像分析的旅客群体性事件预警方法[J]. 解放军理工大学学报(自然科学版), 2013, 14(5): 501-506.
	Shao Quan, Tang Zhixing, Sun Haiyan, et al. Mass Events Warning in Terminal Based on Video Surveillance[J]. Journal of PLA University of Science and Technology(Natural Science Edition), 2013, 14(5): 501-506.
20	Zheng Yujun, Sheng Weiguo, Sun Xingming, et al. Airline Passenger Profiling Based on Fuzzy Deep Machine Learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(12): 2911-2923.
21	张龙财, 余美君. 航站楼到港旅客应急疏散仿真优化[J]. 综合运输, 2021, 43(10): 83-89.
	Zhang Longcai, Yu Meijun. Simulation and Majorization of the Terminal Arrival Emergency Evacuation[J]. China Transportation Review, 2021, 43(10): 83-89.
22	陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18.
	Tao Fei, Liu Weiran, Liu Jianhua, et al. Digital Twin and Its Potential Application Exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18.
23	刘大同, 郭凯, 王本宽, 等. 数字孪生技术综述与展望[J]. 仪器仪表学报, 2018, 39(11): 1-10.
	Liu Datong, Guo Kai, Wang Benkuan, et al. Summary and Perspective Survey on Digital Twin Technology[J]. Chinese Journal of Scientific Instrument, 2018, 39(11): 1-10.
24	Qi Qinglin, Tao Fei, Hu Tianliang, et al. Enabling Technologies and Tools for Digital Twin[J]. Journal of Manufacturing Systems, 2021, 58, Part B: 3-21.
25	杨林瑶, 陈思远, 王晓, 等. 数字孪生与平行系统:发展现状、对比及展望[J]. 自动化学报, 2019, 45(11): 2001-2031.
	Yang Linyao, Chen Siyuan, Wang Xiao, et al. Digital Twins and Parallel Systems: State of the Art, Comparisons and Prospect[J]. Acta Automatica Sinica, 2019, 45(11): 2001-2031.
26	Schroeder Greyce N, Steinmetz Charles, Carlos E Pereira, et al. Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange[J]. IFAC-PapersOnLine, 2016, 49(30): 12-17.
27	Lien S Y, Cheng S M, Shih S Y, et al. Radio Resource Management for QoS Guarantees in Cyber-physical Systems[J]. IEEE Transactions on Parallel and Distributed Systems, 2012, 23(9): 1752-1761.
28	Amarjeet Singh Yumnam, Chaitanya Sreeram Y, Shaik Abdul Naeem. Overview: Weblog Mining, Privacy Issues and Application of Web Log Mining[C]//2014 International Conference on Computing for Sustainable Global Development (INDIACom). Piscataway, NJ, USA: IEEE, 2014: 638-641.
29	Biesinger Florian, Meike Davis, Kraß Benedikt, et al. A Case Study for a Digital Twin of Body-in-white Production Systems General Concept for Automated Updating of Planning Projects in the Digital Factory[C]//2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA). Piscataway, NJ, USA: IEEE, 2018: 19-26.
30	王巍, 刘永生, 廖军, 等. 数字孪生关键技术及体系架构[J]. 邮电设计技术, 2021(8): 10-14.
	Wang Wei, Liu Yongsheng, Liao Jun, et al. Key Technology and Architecture of Digital Twin[J]. Designing Techniques of Posts and Telecommunications, 2021(8): 10-14.
31	Li Yixian, Zhong Jinghui. HAS-EA: A Fast Parallel Surrogate-assisted Evolutionary Algorithm[J]. Memetic Computing, 2023, 15(1): 103-115.
32	Li Xiaoyong, Yuan Jie, Ma Huadong, et al. Fast and Parallel Trust Computing Scheme Based on Big Data Analysis for Collaboration Cloud Service[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(8): 1917-1931.
33	赵慕铭. 面向视频监控场景的人群计数算法研究[D]. 上海: 上海交通大学, 2020.
	Zhao Muming. Crowd Counting in Surveillance Videos[D]. Shanghai: Shanghai Jiao Tong University, 2020.
34	Li Bo, Huang Hongbo, Zhang Ang, et al. Approaches on Crowd Counting and Density Estimation: A Review[J]. Pattern Analysis and Applications, 2021, 24(3): 853-874.
35	Wu Wu, Nevatia R. Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors[C]//Tenth IEEE International Conference on Computer Vision (ICCV'05). Piscataway, NJ, USA: IEEE, 2005: 90-97.
36	Viola P, Jones M J. Robust Real-time Face Detection[J]. International Journal of Computer Vision, 2004, 57(2): 137-154.
37	Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Piscataway, NJ, USA: IEEE, 2005: 886-893.
38	Su Feng, Fang Gu, Zou Jujia. Human Detection Using a Combination of Face, Head and Shoulder Detectors[C]//2016 IEEE Region 10 Conference (TENCON). Piscataway, NJ, USA: IEEE, 2016: 842-845.
39	Ji Tao, Liu Leibo, Zhu Wenping, et al. Fast and Efficient Integration of Human Upper-body Detection and Orientation Estimation in RGB-D Video[C]//2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN). Piscataway, NJ, USA: IEEE, 2017: 1178-1181.
40	Zhang Yingying, Zhou Desen, Chen Siqin, et al. Single-image Crowd Counting via Multi-column Convolutional Neural Network[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2016: 589-597.
41	Yuan Lu, Zhang Hao, Xu Ming, et al. A Multiscale CNN Framework for Wireless Technique Classification in Internet of Things[J]. IEEE Internet of Things Journal, 2022, 9(12): 10366-10367.
42	Cao Xinkun, Wang Zhipeng, Zhao Yanyun, et al. Scale Aggregation Network for Accurate and Efficient Crowd Counting[C]//Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 757-773.
43	Subramaniam Abhishek, Shah Vatsal G, Tibrewala Preet D, et al. Crowd Count Estimator Application using CSRNet[C]//2021 International Conference on Intelligent Technologies (CONIT). Piscataway, NJ, USA: IEEE, 2021: 1-6.
44	张震宇, 董丹慧, 冯曙明, 等. 基于高斯混合模型的电力仓库视频运动目标检测[J]. 计算机与数字工程, 2021, 49(8): 1580-1583.
	Zhang Zhenyu, Dong Danhui, Feng Shuming, et al. Video Moving Target Detection in Power Warehouse Based on Gaussian Mixture Model[J]. Computer and Digital Engineering, 2021, 49(8): 1580-1583.
45	Yan Mengen, Li Lianghai, Chen Haochuan. A VideoSAR Moving Target Detection Method Based on GMM[C]//2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML). Piscataway, NJ, USA: IEEE, 2021: 64-69.
46	卢振坤, 刘胜, 钟乐, 等. 人群计数研究综述[J]. 计算机工程与应用, 2022, 58(11): 33-46.
	Lu Zhenkun, Liu Sheng, Zhong Le, et al. Survey on Research of Crowd Counting[J]. Computer Engineering and Applications, 2022, 58(11): 33-46.
47	Shekhar S, Williams B M. Adaptive Seasonal Time Series Models for Forecasting Short-term Traffic Flow[J]. Transportation Research Record, 2007, 2024(1): 116-125.
48	Lippi Marco, Bertini Matteo, Frasconi Paolo. Short-term Traffic Flow Forecasting: An Experimental Comparison of Time-series Analysis and Supervised Learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(2): 871-882.
49	Zheng Jiangchuan, Ni L M. Time-dependent Trajectory Regression on Road Networks Via Multi-task Learning[C]//Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA: AAAI Press, 2013: 1048-1055.
50	Zhang Junbo, Zheng Yu, Qi Dekang, et al. DNN-based Prediction Model for Spatio-temporal Data[C]//Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: Association for Computing Machinery, 2016: 92.
51	Wang Senzhang, Cao Jiannong, Chen Hao, et al. SeqST-GAN: Seq2Seq Generative Adversarial Nets for Multi-step Urban Crowd Flow Prediction[J]. ACM Transactions on Spatial Algorithms and Systems, 2020, 6(4): 22.
52	Shi Xingjian, Chen Zhourong, Wang Hao, et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2015: 802-810.
53	Wang Senzhang, Miao Hao, Chen Hao, et al. Multi-task Adversarial Spatial-temporal Networks for Crowd Flow Prediction[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York, NY, USA: Association for Computing Machinery, 2020: 1555-1564.
54	Zhong Jinghui, Li Dongrui, Cai Wentong, et al. Automatic Crowd Navigation Path Planning in Public Scenes Through Multiobjective Differential Evolution[J/OL]. IEEE Transactions on Computational Social Systems. (2022-11-7) [2022-12-04]. .
55	Ester Martin, Kriegel Hans-Peter, Sander Jörg, et al. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Palo Alto, CA, USA: AAAI Press, 1996: 226-231.
56	Chen Yewang, Zhou Lida, Pei Songwen, et al. KNN-BLOCK DBSCAN: Fast Clustering for Large-scale Data[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(6): 3939-3953.

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