面向数字孪生的动态数据驱动建模与仿真方法

doi:10.3969/j.issn.1001-506X.2020.12.14

摘要/Abstract

摘要：

信息物理系统(cyber-physical system, CPS)是支撑信息化和工业化深度融合的综合技术体系,传统的建模与仿真方法难以满足其应用需求。因此,提出面向数字孪生的动态数据驱动建模与仿真方法,通过随机有限集对CPS中的物理实体和传感器进行数据驱动建模,并使用基于贝叶斯推理的预测与校正过程支持数据驱动的仿真模型运行。该方法能够很好地实现数字孪生机制下虚实结合的仿真运行,并实现数据驱动的CPS仿真模型解算,通过以实利虚的方式有效提高了仿真结果的准确性和可信性。最后,通过水面舰艇作战应用案例,验证了该方法的可行性和有效性。

关键词: 信息物理系统, 动态数据驱动仿真, 随机有限集, 数字孪生

Abstract:

Cyber-physical system (CPS) is a comprehensive technical system that supports the deep integration of information and industrialization. The classical modeling and simulation method fail to satisfy the application requirements of the CPS. Therefore, a digital twin oriented dynamic data driven modeling and simulation method is proposed, and a random finite set (RFS) is used to build the data driven simulation model of physical entities and sensors of CPS. The Bayesian inference based prediction and update method is proposed to run the CPS model. The proposed method can implement the virtual and real combined simulation running for digital twin very well. It can also enable the data driven model running, and improve the accuracy and credibility of simulation results by using the data from the real space. Finally, the feasibility and validity of the proposed method is certified by using the operational application cases of the surface ships.

Key words: cyber-physical system (CPS), dynamic data driven simulation, random finite set (RFS), digital twin

中图分类号:

王鹏, 杨妹, 祝建成, 鞠儒生, 李革. 面向数字孪生的动态数据驱动建模与仿真方法[J]. 系统工程与电子技术, 2020, 42(12): 2779-2786.

Peng WANG, Mei YANG, Jiancheng ZHU, Rusheng JU, Ge LI. Dynamic data driven modeling and simulation method for digital twin[J]. Systems Engineering and Electronics, 2020, 42(12): 2779-2786.

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参考文献 34

1	许少伦, 严正, 张良, 等. 信息物理融合系统的特性、架构及研究挑战[J]. 计算机应用, 2013, 33 (S2): 1- 5, 45.
	XU S L , YAN Z , ZHANG L , et al. Cyber physical system: features, architecture, and research challenges[J]. Journal of Computer Applications, 2013, 33 (S2): 1- 5, 45.
2	GARIBALDO F , REBECCHI E . Cyber-physical system[J]. AI & Society, 2018, 33, 299- 311.
3	王中杰, 谢璐璐. 信息物理融合系统研究综述[J]. 自动化学报, 2011, 37 (10): 1157- 1166.
	WANG Z J , XIE L L . Cyber-physical systems: a survey[J]. Acta Automatica Sinica, 2011, 37 (10): 1157- 1166.
4	李伯虎, 张霖, 任磊, 等. 云制造典型特征、关键技术与应用[J]. 计算机集成制造系统, 2012, 18 (7): 1345- 1356.
	LI B H , ZHANG L , REN L , et al. Typical characteristics, technologies and applications of cloud manufacturing[J]. Computer Integrated Manufacturing Systems, 2012, 18 (7): 1345- 1356.
5	HEHENBERGER P , VOGEL-HEUSER B , BRADLEY D , et al. Design, modelling, simulation and integration of cyber physical systems: methods and applications[J]. Computers in Industry, 2016, 82 (C): 273- 289.
6	BONCI A , PIRANI M , LONGHI S . Tiny cyber-physical systems for performance improvement in the factory of the future[J]. IEEE Trans.on Industrial Informatics, 2019, 15 (3): 1598- 1608. doi: 10.1109/TII.2018.2855747
7	REN T . Simulation of cyber-physical systems of systems: some research areas computational understanding, awareness, and wisdom[J]. Journal of System Simulation, 2018, 30 (2): 363- 372.
8	ELSHENAWY M , ABDULHAI B , DARIEBYB M , et al. Towards a service-oriented cyber-physical systems of systems for smart city mobility applications[J]. Future Generations Computer Systems, 2018, 79 (2): 575- 587.
9	QI Q L , TAO F . Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison[J]. IEEE Access, 2018, 6, 3585- 3593. doi: 10.1109/ACCESS.2018.2793265
10	TAO F , CHENG J F , QI Q L , et al. Digital twin-driven product design, manufacturing and service with big data[J]. The International Journal of Advanced Manufacturing Technology, 2017, 94, 3563- 3576.
11	陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25 (1): 5- 22.
	TAO F , LIU W R , ZHANG M , et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25 (1): 5- 22.
12	HAAG S , ANDERL R . Digital twin-proof of concept[J]. Manufacturing Letters, 2018, 15 (B): 64- 66.
13	UHLEMANN H J T , LEHMANN C , STEINHILPER R . The digital twin: realizing the cyber physical production system for industry 4.0[J]. Procedia Cirp, 2017, 61, 335- 340. doi: 10.1016/j.procir.2016.11.152
14	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 F , LIU W R , LIU J H , et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24 (1): 1- 18.
15	TAO F , ZHANG H , LIU A , et al. Digital twin in industry: state-of-the-art[J]. IEEE Trans.on Industrial Informatics, 2019, 15 (4): 2405- 2413. doi: 10.1109/TII.2018.2873186
16	TAO F , QI Q L . Make more digital twins[J]. Nature, 2019, 573 (7775): 490- 491. doi: 10.1038/d41586-019-02849-1
17	KARVE P M , GUO Y L , LKAPUSUZOGLU B , et al. Digital twin approach for damage-tolerant mission planning under uncertainty[J]. Engineering France Mechanics, 2020, 225, 106766. doi: 10.1016/j.engfracmech.2019.106766
18	FRANCISCO A , MOHAMMADI N , TAYLOR J E , et al. Smart city digital twin-enabled energy management: toward real-time urban building energy benchmarking[J]. Journal of Management in Engineering, 2020, 36 (2): 04019045. doi: 10.1061/(ASCE)ME.1943-5479.0000741
19	ZHENG Y , YANG S , CHENG H C . An application framework for digital twin and its case study[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10 (3): 1141- 1153. doi: 10.1007/s12652-018-0911-3
20	LI Y P , SHU D W , SHI F , et al. A multi-rate co-simulation of combined phasor-domain and time-domain models for large-scale wind farms[J]. IEEE Trans.on Energy Conversion, 2020, 35 (1): 324- 335. doi: 10.1109/TEC.2019.2936574
21	DYLAN P, ANDREAS G. Adaptive resolution control in distributed cyber physical system simulation[C]//Proc.of the Winter Simulation Conference, 2016: 1487-1498.
22	CHENG J F , ZHANG H , TAO F , et al. DT-Ⅱ: digital twin enhanced industrial internet reference framework towards smart manufacturing[J]. Robotics and Computer Integrated Manufacturing, 2020, 62, 101881. doi: 10.1016/j.rcim.2019.101881
23	WANG P , YANG M , PENG Y , et al. Sensor control in anti-submarine warfare——a digital twin and random finite sets based approach[J]. Entropy, 2019, 21 (8): 767. doi: 10.3390/e21080767
24	DAREMA F. Dynamic data driven applications systems: a new paradigm for application simulations and measurements[C]//Proc.of the International Conference on Computational Science, 2004: 662-669.
25	DAREMA F. Dynamic data driven applications systems: new capabilities for application simulations and measurements[C]//Proc.of the International Conference on Computational Science, 2005: 610-615.
26	WANG M H , HU X L . Data assimilation in agent based simulation of smart environments using particle filters[J]. Simulation Modelling Practice & Theory, 2015, 56 (8): 36- 54.
27	GOODMAN I R , MAHLER R P , NGUYEN H T . Mathema-tics of data fusion[M]. Dordrecht: Springer, 1997.
28	MAHLER R . Multitarget Bayes filtering via first-order multitarget moments[J]. IEEE Trans.on Aerospace Electronic Systems, 2003, 39 (4): 1152- 1178. doi: 10.1109/TAES.2003.1261119
29	冯新喜, 蒲磊, 孔云波, 等. 基于随机有限集理论的多扩展目标跟踪技术综述[J]. 空军工程大学学报(自然科学版), 2016, 17 (3): 93- 99.
	FENG X X , PU L , KONG Y B , et al. A survey of multiple extended targets tracking techniques based on FISST[J]. Journal of Air Force Engineering University(Natural Science Edition), 2016, 17 (3): 93- 99.
30	MAHLER R P S . Statistical multisource multitarget information fusion[M]. Norwood: Artech House, 2007.
31	彭华甫, 黄高明, 田威. 随机有限集理论及其在多目标跟踪中的应用和实现[J]. 控制与决策, 2019, 34 (2): 225- 232.
	PENG H F , HUANG G M , TIAN W , et al. Random finite set: theory, application and implementation for multi-target tracking[J]. Control and Decision, 2019, 34 (2): 225- 232.
32	WANG P , LI G , PENG Y , et al. Random finite set based parameter estimation algorithm for identifying stochastic systems[J]. Entropy, 2018, 20 (8): 569. doi: 10.3390/e20080569
33	XUE H D , GU F , HU X L . Data assimilation using sequential Monte Carlo methods in wildfire spread simulation[J]. ACM Trans.on Modeling and Computer Simulation, 2012, 22 (4): 23.
34	SCHUHMACHER D , VO B T , VO B N . A consistent metric for performance evaluation of multi-object filters[J]. IEEE Trans.on Signal Processing, 2008, 56 (8): 3447- 3457. doi: 10.1109/TSP.2008.920469

舰船	真实的转弯角速率/(rad/s)	偏差的转弯角速率/(rad/s)	开始时间/s	结束时间/s
舰船1	0.005 8	0.009 2	1	100
舰船2	0.005 8	0.009 2	1	80
舰船3	0.008 7	0.013 7	1	90
舰船4	0.008 7	0.013 7	30	100
舰船5	0.005 8	0.009 2	35	100
母船	0.004 4	0.006 9	1	50

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