系统仿真学报 ›› 2024, Vol. 36 ›› Issue (8): 1832-1842.doi: 10.16182/j.issn1004731x.joss.24-0127
• 论文 • 上一篇
谢旭, 邱晓刚, 包亦正, 许凯
收稿日期:
2024-02-02
修回日期:
2024-03-19
出版日期:
2024-08-15
发布日期:
2024-08-19
第一作者简介:
谢旭(1988-),男,副教授,博士,研究方向为建模与仿真、动态数据驱动仿真。
基金资助:
Xie Xu, Qiu Xiaogang, Bao Yizheng, Xu Kai
Received:
2024-02-02
Revised:
2024-03-19
Online:
2024-08-15
Published:
2024-08-19
摘要:
动态数据驱动仿真是一种“模型和数据相结合”的仿真范式,它将真实系统的观测(数据)持续注入仿真(模型),让数据动态地校正仿真(状态、参数),以此来提高基于仿真的估计和预测能力。由于动态数据驱动仿真融合了模型预测和实时观测两方面的信息,因此它能更准确地估计系统状态并预测状态的未来演化。梳理了动态数据驱动仿真的思想起源和基本概念,延伸介绍了“模型和数据相结合”的思想孕育的一系列仿真范式,并辨析了它们之间的联系和区别;介绍了基于粒子滤波的数据同化方法和identical-twin仿真实验方法;从应用场景、模型和数据、数据同化算法、与新技术的融合等4个维度综述了动态数据驱动仿真的研究现状;从仿真模型、观测数据、数据同化、运行效率、应用领域等5个方面对动态数据驱动仿真未来研究方向进行了展望。
中图分类号:
谢旭,邱晓刚,包亦正等 . 动态数据驱动仿真综述[J]. 系统仿真学报, 2024, 36(8): 1832-1842.
Xie Xu,Qiu Xiaogang,Bao Yizheng,et al . Dynamic Data Driven Simulation: An Overview[J]. Journal of System Simulation, 2024, 36(8): 1832-1842.
1 | Long Yuan, Hu Xiaolin. Dynamic Data Driven Simulation with Soft Data[C]//Proceedings of the Symposium on Theory of Modeling & Simulation-DEVS Integrative. San Diego: Society for Computer Simulation International, 2014: 16:1-16. |
2 | Sarwar Azeem. Spatiotemporal Systems: Gradual Variations, Identification, Adaptation and Robustness[D]. Champaign: University of Illinois Urbana-Champaign, 2009. |
3 | Lahoz William A, Schneider Philipp. Data Assimilation: Making Sense of Earth Observation[J]. Frontiers in Environmental Science, 2014, 2: 1-16. |
4 | Treiber Martin, Kesting Arne. Traffic Flow Dynamics: Data, Models and Simulation[M]. Berlin: Springer Berlin Heidelberg, 2013. |
5 | Leduc G. Road Traffic Data: Collection Methods and Applications: JRC47967[R]. [S.l.]: [s.n.], 2008: 1-52. |
6 | Bouttier F, Courtier P. Data Assimilation Concepts and Methods[R]. [S.l.]: [s.n.], 1999: 1-59. |
7 | Darema F. Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements[C]//Computational Science-ICCS 2004. Berlin: Springer Berlin Heidelberg, 2004: 662-669. |
8 | Darema F. Dynamic Data Driven Applications Systems: New Capabilities for Application Simulations and Measurements[C]//Computational Science-ICCS 2005. Berlin: Springer Berlin Heidelberg, 2005: 610-615. |
9 | Hu X. Dynamic Data Driven Simulation[J]. SCS M & S Magazine II, 2011, 1: 16-22. |
10 | 黄柯棣, 邱晓刚, 查亚兵, 等. 建模与仿真技术[M]. 长沙: 国防科技大学出版社, 2010. |
Huang Kedi, Qiu Xiaogang, Zha Yabing, et al. Modeling and Simulation Technology[M]. Changsha: National University of Defense Technology Press, 2010. | |
11 | Zeigler B, Praehofer H, Kim T G. Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems[M]. 2nd ed. [S.l.]: Academic Press, 2000. |
12 | Xie X. Data Assimilation in Discrete Event Simulations[D]. Delft: Delft University of Technology, 2018. |
13 | Bai Fan, Guo Song, Hu Xiaolin. Towards Parameter Estimation in Wildfire Spread Simulation Based on Sequential Monte Carlo Methods[C]//Proceedings of the 44th Annual Simulation Symposium. San Diego: Society for Computer Simulation International, 2011: 159-166. |
14 | Zhang Bo, Zhong Jinghui, Cai Wentong. A Data-driven Approach for Pedestrian Intention Prediction in Large Public Places[C]//Proceedings of the 2022 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. New York: Association for Computing Machinery, 2022: 33-36. |
15 | Huang Yilin, Seck M D, Verbraeck Alexander. Component-based Light-rail Modeling in Discrete Event Systems Specification DEVS[J]. Simulation, 2015, 91(12): 1027-1051. |
16 | Wang Junfeng, Chang Qing, Xiao Guoxian, et al. Data Driven Production Modeling and Simulation of Complex Automobile General Assembly Plant[J]. Computers in Industry, 2011, 62(7): 765-775. |
17 | Aydt H, Turner S J, Cai Wentong, et al. Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology[C]//2008 22nd Workshop on Principles of Advanced and Distributed Simulation. Piscataway: IEEE, 2008: 109-116. |
18 | Kamrani Farzad, Ayani Rassul. Using On-line Simulation for Adaptive Path Planning of UAVs[C]//11th IEEE International Symposium on Distributed Simulation and Real-Time Applications (DS-RT'07). Piscataway: IEEE, 2007: 167-174. |
19 | 段伟. 平行仿真的内涵、发展与应用[J]. 指挥与控制学报, 2019, 5(2): 82-86. |
Duan Wei. Parallel Simulation: Motivation, Concept and Application[J]. Journal of Command and Control, 2019, 5(2): 82-86. | |
20 | 王会霞. 平行仿真技术研究[J]. 航天控制, 2016, 34(6): 64-67. |
Wang Huixia. Research on Parallel Simulation Technology[J]. Aerospace Control, 2016, 34(6): 64-67. | |
21 | 窦林涛, 初阳, 周玉芳, 等. 平行仿真技术在指控系统中的应用构想[J]. 指挥控制与仿真, 2017, 39(1): 62-69. |
Dou Lintao, Chu Yang, Zhou Yufang, et al. Conception of the Application of Parallel Simulation Technology in Command and Control System[J]. Command Control & Simulation, 2017, 39(1): 62-69. | |
22 | Grieves M, Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems[M]//Franz-Josef Kahlen, Flumerfelt S, Anabela Alves. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. Cham: Springer International Publishing, 2017: 85-113. |
23 | 张霖. 关于数字孪生的冷思考及其背后的建模和仿真技术[J]. 系统仿真学报, 2020, 32(4): 1-10. |
Zhang Lin. Cold Thinking about the Digital Twin and the Modeling and Simulation Techniques Behind It[J]. Journal of System Simulation, 2020, 32(4): 1-10. | |
24 | Liu Mengnan, Fang Shuiliang, Dong Huiyue, et al. Review of Digital Twin About Concepts, Technologies, and Industrial Applications[J]. Journal of Manufacturing Systems, 2021, 58, Part B: 346-361. |
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 | 陶飞, 张贺, 戚庆林, 等. 数字孪生十问:分析与思考[J]. 计算机集成制造系统, 2020, 26(1): 1-17. |
Tao Fei, Zhang He, Qi Qinglin, et al. Ten Questions Towards Digital Twin: Analysis and Thinking[J]. Computer Integrated Manufacturing Systems, 2020, 26(1): 1-17. | |
27 | Nichols N K. Data Assimilation: Aims and Basic Concepts[C]//Data Assimilation for the Earth System. Dordrecht: Springer Netherlands, 2003: 9-20. |
28 | Ide K, Courtier P, Ghil M, et al. Unified Notation for Data Assimilation : Operational, Sequential and Variational[J]. Journal of the Meteorological Society of Japan. Ser. II, 1997, 75(1B): 181-189. |
29 | Wu Wanshu, Purser R J, Parrish D F. Three-dimensional Variational Analysis with Spatially Inhomogeneous Covariances[J]. Monthly Weather Review, 2022, 130(12): 2905-2916. |
30 | Lorenc A C, Rawlins F. Why does 4D-var beat 3D-Var?[J]. Quarterly Journal of the Royal Meteorological Society, 2005, 131(613): 3247-3257. |
31 | Arulampalam M S, Maskell S, Gordon N, et al. A Tutorial on Particle Filters for Online Nonlinear/non-gaussian Bayesian Tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188. |
32 | Gillijns S, Mendoza O B, Chandrasekar J, et al. What is the Ensemble Kalman Filter and How Well Does It Work?[C]//Proceedings of the 2006 American Control Conference. Piscataway: IEEE, 2006: 4448-4453. |
33 | Evensen Geir. The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation[J]. Ocean Dynamics, 2003, 53(4): 343-367. |
34 | Djuric P M, Kotecha J H, Zhang J, et al. Particle Filtering[J]. IEEE Signal Processing Magazine, 2003, 20(5): 19-38. |
35 | Peter Jan van Leeuwen. Particle Filtering in Geophysical Systems[J]. Monthly Weather Review, 2009, 137(12): 4089-4114. |
36 | Xie X, Verbraeck Alexander. A Particle Filter-based Data Assimilation Framework for Discrete Event Simulations[J]. Simulation, 2019, 95(11): 1027-1053. |
37 | Xue Haidong, Gu Feng, Hu Xiaolin. Data Assimilation Using Sequential Monte Carlo Methods in Wildfire Spread Simulation[J]. ACM Transactions on Modeling and Computer Simulation, 2012, 22(4): 23. |
38 | Hu Xiaolin. Dynamic Data-driven Simulation: Real-time Data for Dynamic System Analysis and Prediction[M]. Singapore: World Scientific, 2023. |
39 | Hu Xiaolin, Wu Peisheng. A Data Assimilation Framework for Discrete Event Simulations[J]. ACM Transactions on Modeling and Computer Simulation, 2019, 29(3): 17. |
40 | Ciuffo B, Punzo V, Montanino M. The Calibration of Traffic Simulation Models. Report on the Assessment of Different Goodness of Fit Measures and Optimization Algorithms. MULTITUDE Project-COST Action TU0903: EUR25188[R]. [S.l.]: European Commission-Joint Research Centre, 2012: 1-84. |
41 | Gu Feng. Dynamic Data Driven Application System for Wildfire Spread Simulation[D]. Atlanta: Georgia State University, 2010. |
42 | Ntaimo L, Hu Xiaolin, Sun Yi. DEVS-FIRE: Towards an Integrated Simulation Environment for Surface Wildfire Spread and Containment[J]. Simulation, 2008, 84(4): 137-155. |
43 | Hu Xiaolin, Sun Yi, Ntaimo L. DEVS-FIRE: Design and Application of Formal Discrete Event Wildfire Spread and Suppression Models[J]. Simulation, 2012, 88(3): 259-279. |
44 | Wang Minghao, Hu Xiaolin. Data Assimilation in Agent Based Simulation of Smart Environments Using Particle Filters[J]. Simulation Modelling Practice and Theory, 2015, 56: 36-54. |
45 | Yan Xuefeng, Gu Feng, Hu Xiaolin, et al. Dynamic Data Driven Event Reconstruction for Traffic Simulation Using Sequential Monte Carlo Methods[C]//2013 Winter Simulations Conference (WSC). Piscataway: IEEE, 2013: 2042-2053. |
46 | Xie X, van Lint Hans, Verbraeck Alexander. A Generic Data Assimilation Framework for Vehicle Trajectory Reconstruction on Signalized Urban Arterials Using Particle Filters[J]. Transportation Research Part C: Emerging Technologies, 2018, 92: 364-391. |
47 | Wang Song, Xie Xu, Ju Rusheng. A Mesoscopic Traffic Data Assimilation Framework for Vehicle Density Estimation on Urban Traffic Networks Based on Particle Filters[J]. Entropy, 2019, 21(4): 358. |
48 | Xue Haidong, Hu Xiaolin. An Effective Proposal Distribution for Sequential Monte Carlo Methods-based Wildfire Data Assimilation[C]//2013 Winter Simulations Conference (WSC). Piscataway: IEEE, 2013: 1938-1949. |
49 | Bolic M, Djuric P M, Hong Sangjin. Resampling Algorithms and Architectures for Distributed Particle Filters[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2442-2450. |
50 | Bai Fan, Gu Feng, Hu Xiaolin, et al. Particle Routing in Distributed Particle Filters for Large-scale Spatial Temporal Systems[J]. IEEE Transactions on Parallel and Distributed Systems, 2016, 27(2): 481-493. |
51 | Al-Fuqaha A, Guizani Mohsen, Mohammadi M, et al. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications[J]. IEEE Communications Surveys & Tutorials, 2015, 17(4): 2347-2376. |
52 | Shi Weisong, Cao Jie, Zhang Quan, et al. Edge Computing: Vision and Challenges[J]. IEEE Internet of Things Journal, 2016, 3(5): 637-646. |
53 | Shi Weisong, Dustdar Schahram. The Promise of Edge Computing[J]. Computer, 2016, 49(5): 78-81. |
54 | Mao Yuyi, You Changsheng, Zhang Jun, et al. A Survey on Mobile Edge Computing: The Communication Perspective[J]. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2322-2358. |
55 | Xie X, Xu K. Distributed Dynamic Data Driven Simulations: Basic Idea and an Illustration Example[C]//2023 IEEE/ACM 27th International Symposium on Distributed Simulation and Real Time Applications (DS-RT). Piscataway: IEEE, 2023: 105-108. |
56 | Blasch E, Ravela S, Aved A. Handbook of Dynamic Data Driven Applications Systems[M]. Cham: Springer International Publishing, 2018. |
57 | Ma Yuqing, Xie Xu, Chen Hailiang. MaMiH: A New Data Assimilation Framework Based on Macro-micro Hierarchical Simulation Model[C]//Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023). Bellingham: SPIE, 2023: 127912L. |
58 | Huang Yilin, Xie Xu, Cho Y, et al. Particle Filter-based Data Assimilation in Dynamic Data-driven Simulation: Sensitivity Analysis of Three Critical Experimental Conditions[J]. Simulation, 2023, 99(4): 403-415. |
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