数字孪生战场: 概念、架构与技术

doi:10.12305/j.issn.1001-506X.2025.01.15

摘要/Abstract

摘要：

当前作战仿真模型存在态势理解滞后、物理模型无法更新等问题, 这些问题将影响后续态势推演与决策效果。为此, 提出数字孪生战场的概念, 并建立数字孪生战场的4层架构模型。基于这种模型, 分析其在战场空间的部署方式及寿命周期运用, 探讨其潜在运用价值。同时, 从多源数据采集与处理、孪生战场建模、模型动态更新和态势推演与决策4个视角出发, 讨论数字孪生战场建模所需的关键技术。随着作战仿真模型的运用, 可以帮助指挥机构做出更高效的决策。

关键词: 数字孪生战场, 指挥决策, 多源数据采集, 作战建模, 模型更新, 态势推演

Abstract:

Current operation simulation models suffer from lagging situational understanding and inability to update physical models, which affects the effectiveness of subsequent situation deduction and decision-making. Therefore, the concept of the digital twin battlefield is proposed, and a four-layer architecture model for the digital twin battlefield is established. The deployment of the digital twin battlefield, its life-cycle use, and its potential use value are analyzed based on this model. Additionally, the key technologies required for digital twin battlefield modeling are discussed from four perspectives: multi-source data acquisition and processing, twin battlefield modeling, model dynamic update, and situation deduction and decision-making. By utilizing of this operation simulation model, more efficient decision-making can be made by command organizations.

Key words: digital twin battlefield, command decision-making, multi-source data acquisition, operation modeling, model update, situation deduction

中图分类号:

TP391.9

熊宇涵, 李雄. 数字孪生战场: 概念、架构与技术[J]. 系统工程与电子技术, 2025, 47(1): 141-152.

Yuhan XIONG, Xiong LI. Digital twin battlefield: concepts, architecture, and key technologies[J]. Systems Engineering and Electronics, 2025, 47(1): 141-152.

图/表 8

图1

表1

图2

图3

图4

图5

图6

图7

参考文献 53

4	陶飞, 张萌, 程江峰, 等. 数字孪生车间——一种未来车间运行新模式[J]. 计算机集成制造系统, 2017, 23 (1): 1- 9.
	TAO F , ZHANG M , CHENG J F , et al. Digital twin workshop: a new paradigm for future workshop[J]. Computer Integrated Manufacturing Systems, 2017, 23 (1): 1- 9.
5	周光辉, 邹永成, 张超, 等. 考虑多维误差的复杂装配过程数字孪生建模方法[J]. 计算机集成制造系统, 2023, 29 (6): 1824- 1839.
	ZHOU G H , ZHOU Y C , ZHANG C , et al. Digital twin mode-ling method for complex assembly process considering multidimensional error[J]. Computer Integrated Manufacturing Systems, 2023, 29 (6): 1824- 1839.
6	赵永胜, 赵志勇, 李迎, 等. 基于数字孪生的机身对接精度优化控制方法[J]. 浙江大学学报(工学版), 2023, 57 (5): 883- 891.
	ZHAO Y S , ZHAO Z Y , LI Y , et al. Optimal control method of fuselage docking accuracy based on digital twin[J]. Journal of Zhejiang University(Engineering Science), 2023, 57 (5): 883- 891.
7	LI L H , LEI B B , MAO C L . Digital twin in smart manufacturing[J]. Journal of Industrial Information Integration, 2022, 26, 100289. doi: 10.1016/j.jii.2021.100289
8	杨帆, 吴涛, 廖瑞金, 等. 数字孪生在电力装备领域中的应用与实现方法[J]. 高电压技术, 2021, 47 (5): 1505- 1521.
	YANG F , WU T , LIAO R J , et al. Application and implementation method of digital twin in electric equipment[J]. High Voltage Engineering, 2021, 47 (5): 1505- 1521.
9	赵鹏, 蒲天骄, 王新迎, 等. 面向能源互联网数字孪生的电力物联网关键技术及展望[J]. 中国电机工程学报, 2022, 42 (2): 447- 458.
	ZHAO P , PU T J , WANG X Y , et al. Key technologies and perspectives of power internet of things facing with digital twins of the energy internet[J]. Proceedings of the CSEE, 2022, 42 (2): 447- 457.
10	周连俊, 李群, 殷明慧, 等. 面向风电机组最大功率点跟踪的转矩曲线增益动态优化[J]. 电工技术学报, 2023, 38 (13): 3447- 3458.
	ZHOU L J , LI Q , YIN M H , et al. Torque curve gain dynamic optimization for maximum power point tracking of wind turbines[J]. Transactions of China Electrotechnical Society, 2023, 38 (13): 3447- 3458.
11	雷霆, 孙骞, 王孟轩. 基于5G的智慧应急指挥平台[J]. 指挥与控制学报, 2020, 6 (4): 319- 323.
	LEI T , SUN Q , WANG M X . The 5G-based smart emergency command platform[J]. Journal of Command and Control, 2020, 6 (4): 319- 323.
12	李瑞昌, 唐雲. 数字孪生体牵引应急管理过程整合: 行进中的探索[J]. 中国行政管理, 2022, 448 (10): 30- 38.
	LI R C , TANG Y . Digital twins traction process integration of emergency management: an ongoing exploration[J]. Chinese Public Administration, 2022, 448 (10): 30- 38.
13	GREIEVES M W . Product life cycle management: the new paradigm for enterprises[J]. International Journal of Product Development, 2005, 2 (1/2): 71- 84. doi: 10.1504/IJPD.2005.006669
14	GREIEVES M W . Virtually perfect: driving innovative and lean products through product lifecycle management[M]. Cocoa Beach: Space Coast Press, 2011.
15	GLAESSGEN E, STARGEL D. The digital twin paradigm for future NASA and U.S. air force vehicles[C]//Proc. of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 2012.
16	YU W , PANOS P , BRENT Y , et al. Energy digital twin technology for industrial energy management: classification, challenges and future[J]. Renewable and Sustainable Energy Reviews, 2022, 161, 112407. doi: 10.1016/j.rser.2022.112407
17	KRITZINGER W , KARNER M , TRAAR G , et al. Digital twin in manufacturing: a categorical literature review and classification[J]. IFAC-Papers OnLine, 2018, 51 (11): 1016- 1022. doi: 10.1016/j.ifacol.2018.08.474
18	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): 1- 18.
	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): 1- 18.
19	SCHLEICH B , ANWER N , MATHIEU L , et al. Shaping the digital twin for design and production engineering[J]. Cirp Annals-Manufacturing Technology, 2017, 66 (1): 141- 144. doi: 10.1016/j.cirp.2017.04.040
20	JIA W J , WANG W , ZHANG Z . From simple digital twin to complex digital twin Part Ⅰ: a novel modeling method for multi-scale and multi-scenario digital twin[J]. Advanced Engineering Informatics, 2022, 53, 101706. doi: 10.1016/j.aei.2022.101706
21	WEST T D , MARCY B . Is digital thread/digital twin affordable? A systemic assessment of the cost of DoD's latest manhattan project[J]. Procedia Computer Science, 2017, 114, 47- 56. doi: 10.1016/j.procs.2017.09.003
22	王飞跃. 平行系统方法与复杂系统的管理和控制[J]. 控制与决策, 2004, 19 (5): 485- 489. doi: 10.3321/j.issn:1001-0920.2004.05.002
	WANG F Y . Parallel system methods for management and control of complex systems[J]. Control and Decision, 2004, 19 (5): 485- 489. doi: 10.3321/j.issn:1001-0920.2004.05.002
23	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 L Y , CHEN S Y , WANG X , et al. Digital twins and para-llel systems: state of the art, comparisons and prospect[J]. Acta Automatica Sinica, 2019, 45 (11): 2001- 2031.
24	毛子泉, 高家隆, 龚建兴, 等. 虚实结合仿真在军事领域的应用综述[J]. 系统仿真学报, 2023, 35 (11): 2289- 2311.
	MAO Z Q , GAO J L , GONG J X , et al. Application of virtual-real simulation in military field[J]. Journal of System Simulation, 2023, 35 (11): 2289- 2311.
25	工业4.0研究院. 数字孪生国防白皮书[EB/OL]. [2023-12-02]. http://www.innovation4.cn/library/r54964.
	CHINA ACADEMY OF INDUSTRIES 4.0. Digital twin defense whitepaper[EB/OL]. [2023-12-02]. http://www.innovation4.cn/library/r54964.
1	PERKINS D G . Multi-domain battle: the advent of twenty-first century war[J]. Military Review, 2017, 97 (6): 8- 13.
2	向南, 豆亚杰, 姜江, 等. 马赛克战概念下作战模块应急重构自主决策[J]. 指挥与控制学报, 2020, 6 (3): 223- 228. doi: 10.3969/j.issn.2096-0204.2020.03.0223
	XIANG N , DOU Y J , JIANG J , et al. Autonomous emergency decision-making of combat module under mosaic warfare[J]. Journal of Command and Control, 2020, 6 (3): 223- 228. doi: 10.3969/j.issn.2096-0204.2020.03.0223
3	王旭东, 陈奡, 宦国杨, 等. 面向作战指挥的数字孪生应用[J]. 指挥信息系统与技术, 2021, 12 (6): 26- 32.
	WANG X D , CHEN A , HUAN G Y , et al. Application of digital twin for command and control[J]. Command Information System and Technology, 2021, 12 (6): 26- 32.
26	何丽娜. 美国空军数字化战略的实施分析与启示[J]. 战术导弹技术, 2022, (5): 99- 105.
	HE L N . Implementation of USAF digitalization strategy and its implications[J]. Tactical Missile Technology, 2022, (5): 99- 105.
27	吴云超, 傅琛, 张宁馨. 面向数字孪生战场的智能体建模框架构建[J]. 指挥信息系统与技术, 2022, 13 (4): 19-25, 31.
	WU Y C , FU C , ZHANG N X . Construction of agent modeling framework for digital twin battlefield[J]. Command Information System and Technology, 2022, 13 (4): 19-25, 31.
28	纪广, 郝建国, 张振伟. 面向无人机作战的虚拟孪生系统设计方案[J]. 兵工学报, 2022, 43 (8): 1902- 1912.
	JI G , HAO J G , ZHANG Z W . Design scheme of virtual twin system for UAV combat[J]. Acta Armamentarii, 2022, 43 (8): 1902- 1912.
29	潘琦, 马志强. 马赛克战研究发展综述[J]. 中国电子科学研究院学报, 2021, 16 (7): 728- 736. doi: 10.3969/j.issn.1673-5692.2021.07.015
	PAN Q , MA Z Q . Research and development of mosaic warfare[J]. Journal of China Academy of Electronics and Information Technology, 2021, 16 (7): 728- 736. doi: 10.3969/j.issn.1673-5692.2021.07.015
30	JAMES M T, HAMID R S. Leveraging digital twins to enhance performance of IoT in disadvantaged networks[C]//Proc. of the International Wireless Communications and Mobile Computing Conference, 2020: 1303-1308.
31	WANG P , YANG M , ZHU J C , et al. Digital twin-enabled online battlefield learning with random finite sets[J]. Computational Intelligence and Neuroscience, 2021, 2021, 5582241. doi: 10.1155/2021/5582241
32	方伟光, 聂兆伟, 刘宸宁, 等. 数字孪生驱动的武器装备智能保障技术研究[J]. 系统工程与电子技术, 2023, 45 (4): 1247- 1260. doi: 10.12305/j.issn.1001-506X.2023.04.35
	FANG W G , NIE Z W , LIU C N , et al. Research on digital twin driven intelligent weaponry support technology[J]. Systems Engineering and Electronics, 2023, 45 (4): 1247- 1260. doi: 10.12305/j.issn.1001-506X.2023.04.35
33	夏景演, 黄如意, 陈祝云, 等. 孪生数据与特征增强融合驱动的装备小样本诊断方法[J]. 中国科学: 技术科学, 2023, 53 (7): 1202- 1213.
	XIA J Y , HUANG RY , CHEN Z Y , et al. Intelligent fault diagnosis method using small fault samples driven by digital data and feature enhancement[J]. SCIENTIA SINICA Technologica, 2023, 53 (7): 1202- 1213.
34	舒明敏, 曾艳丽, 黄川林, 等. 面向未来智能化作战仿真的新型装备建模架构[J]. 电子信息对抗技术, 2022, 37 (5): 60- 65. doi: 10.3969/j.issn.1674-2230.2022.05.013
	SHU M M , ZENG Y L , HUANG C L , et al. New equipment modeling architecture for future intelligent combat simulation[J]. Electronic Information Warfare Technology, 2022, 37 (5): 60- 65. doi: 10.3969/j.issn.1674-2230.2022.05.013
35	刘帅, 张宏江. 基于MAXSim的装备体系作战仿真系统架构设计[J]. 计算机仿真, 2021, 38 (4): 5-8, 15. doi: 10.3969/j.issn.1006-9348.2021.04.002
	LIU S , ZHANG H J . Architecture design of equipment system operational simulation system based on MAXSim[J]. Computer Simulation, 2021, 38 (4): 5-8, 15. doi: 10.3969/j.issn.1006-9348.2021.04.002
36	CHEN J , GAO X D , RONG J , et al. A situation awareness assessment method based on fuzzy cognitive maps[J]. Journal of Systems Engineering and Electronics, 2022, 33 (5): 1108- 1122. doi: 10.23919/JSEE.2022.000108
37	SHEN G Q , LEI L , LI Z L , et al. Deep reinforcement learning for flocking motion of multi-UAV systems: learn from a digital twin[J]. IEEE Internet of Things Journal, 2022, 9 (13): 11141- 11153. doi: 10.1109/JIOT.2021.3127873
38	LEE E B K , VAN B D L , BICKFORD J F . Digital twin-enabled decision support in mission engineering and route planning[J]. Systems, 2021, 9 (4): 82. doi: 10.3390/systems9040082
39	陈黎, 李芳芳, 冯清江, 等. 一种基于OODA-A环的防空体系及其作战时效分析[J]. 指挥与控制学报, 2021, 7 (4): 383- 388. doi: 10.3969/j.issn.2096-0204.2021.04.0383
	CHEN L , LI F F , FENG Q J , et al. Combat timeliness analysis of air defense system-of-systems based on OODA-A loop[J]. Journal of Command and Control, 2021, 7 (4): 383- 388. doi: 10.3969/j.issn.2096-0204.2021.04.0383
40	LAN Y S , DENG K B , MAO S J , et al. Adaptive evolvement of information age C4ISR structure[J]. Journal of Systems Engineering and Electronics, 2015, 26 (2): 301- 316. doi: 10.1109/JSEE.2015.00036
41	郑少秋, 韩立斌, 王静, 等. 军事大数据概念内涵、发展挑战与技术实践[J]. 指挥与控制学, 2023, 9 (5): 508- 516. doi: 10.3969/j.issn.2096-0204.2023.05.0508
	ZHENG S Q , HAN L B , WANG J , et al. The conceptual connotation, development challenges and technical practices of military big data[J]. Journal of Command and Control, 2023, 9 (5): 508- 516. doi: 10.3969/j.issn.2096-0204.2023.05.0508
42	石崇林. 基于数据挖掘的兵棋推演数据分析方法研究[D]. 长沙: 国防科学技术大学, 2014.
	SHI C L. Research of wargaming data analysis methods based on data mining[D]. Changsha: National University of Defense Technology, 2014.
43	PAN Y , ZHANG L M . A BIM-data mining integrated digital twin framework for advanced project management[J]. Automation in Construction, 2021, 124, 103564. doi: 10.1016/j.autcon.2021.103564
44	李文俊, 杨学强, 杜家兴. 基于数据中台的装备保障数据集成[J]. 系统工程与电子技术, 2020, 42 (6): 1317- 1323. doi: 10.3969/j.issn.1001-506X.2020.06.15
	LI W J , YANG X Q , DU J X . Equipment support data integration based on ODPS[J]. Systems Engineering and Electronics, 2020, 42 (6): 1317- 1323. doi: 10.3969/j.issn.1001-506X.2020.06.15
45	SHI J Y , PAN Z Y , JIANG L , et al. An ontology-based metho-dology to establish city information model of digital twin city by merging BIM, GIS and IoT[J]. Advanced Engineering Informatics, 2023, 57, 102114. doi: 10.1016/j.aei.2023.102114
46	任璐英, 王庆国, 马倩, 等. 基于元模型的复杂产品虚拟样机建模方法研究[J]. 系统工程与电子技术, 2022, 44 (5): 1609- 1614. doi: 10.12305/j.issn.1001-506X.2022.05.22
	REN L Y , WANG Q G , MA Q , et al. Modeling method study for virtual prototyping of complex products based on metamodel[J]. Systems Engineering and Electronics, 2022, 44 (5): 1609- 1614. doi: 10.12305/j.issn.1001-506X.2022.05.22
47	LI H , PINTO G , PISCITELLI M S , et al. Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset[J]. Engineering Applications of Artificial Intelligence, 2024, 130, 107701. doi: 10.1016/j.engappai.2023.107701
48	GHOSH A K , ULLAH A S , TETI R , et al. Developing sensor signal-based digital twins for intelligent machine tools[J]. Journal of Industrial Information Integration, 2021, 24, 100242. doi: 10.1016/j.jii.2021.100242
49	PRIYANKA E B , THANGAVEL S , GAO X Z , et al. Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques[J]. Journal of Industrial Information Integration, 2022, 26, 100272. doi: 10.1016/j.jii.2021.100272
50	VILLALONGA A , NEGRI E , BISCARDO G , et al. A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins[J]. Annual Reviews in Control, 2021, 51, 357- 373. doi: 10.1016/j.arcontrol.2021.04.008
51	张梦钰, 豆亚杰, 陈子夷, 等. 深度强化学习及其在军事领域中的应用综述[J]. 系统工程与电子技术, 2024, 46 (4): 1297- 1308. doi: 10.12305/j.issn.1001-506X.2024.04.18
	ZHANG M Y , DOU Y J , CHEN Z Y , et al. Deep reinforcement learning and its applications in military field[J]. Systems Engineering and Electronics, 2024, 46 (4): 1297- 1308. doi: 10.12305/j.issn.1001-506X.2024.04.18
52	程恺, 陈刚, 余晓晗, 等. 知识牵引与数据驱动的兵棋AI设计及关键技术[J]. 系统工程与电子技术, 2021, 43 (10): 2911- 2917. doi: 10.12305/j.issn.1001-506X.2021.10.26
	CHENG K , CHEN G , YU X H , et al. Knowledge traction and data-driven wargame AI design and key technologies[J]. Systems Engineering and Electronics, 2021, 43 (10): 2911- 2917. doi: 10.12305/j.issn.1001-506X.2021.10.26
53	王宇鹏, 朱诗兵, 李长青. 基于平行系统和机器学习的作战试验鉴定框架设计[J]. 指挥与控制学报, 2022, 8 (3): 311- 317.
	WANG Y P , ZHU S B , LI C Q . Operational test and evaluation framework based on parallel system and machine learning[J]. Journal of Command and Control, 2022, 8 (3): 311- 317.

维度	数字孪生战场模型	兵棋推演模型	任务状态图模型
仿真对象	各类型指挥作战单位与环境	各类型指挥作战单位与环境	各类型作战单位
虚拟实体	基于历史数据构建的离线仿真模型, 能够通过实时数据进行在线修正	基于历史数据构建的离线仿真模型	无
数据传输	在虚拟实体运行全过程中, 通过数据层, 对战场各项数据进行的实时采集, 重视采集时效性	仿真推演前对战场数据进行统一输入	无
推演方法	根据作战目标与实时态势信息, 在孪生模型中将算法、模型与数据相结合, 进行在线模拟推演	对初始目标进行设定, 根据模型信息进行推演模拟	对作战任务的执行过程、状态变化和相互关系进行模拟与推演

[1]	方澄, 路稳, 姬菁颖, 宋玉蒙, 梁斐菲, 罗志伟. 基于外观相似性更新的相关滤波跟踪算法[J]. 系统工程与电子技术, 2022, 44(1): 117-126.
[2]	张睿文, 宋笔锋, 裴扬, 石帅. 基于ABMS的飞机拦截作战效能评估方法[J]. 系统工程与电子技术, 2018, 40(2): 322-329.
[3]	蒲玮, 李雄. 基于Agent行动图的作战建模方法[J]. 系统工程与电子技术, 2017, 39(4): 795-805.
[4]	张晓雪, 刘刚, 罗爱民, 罗雪山. 基于对象Petri网的作战行动方案开发方法[J]. Journal of Systems Engineering and Electronics, 2012, 34(10): 2058-2063.
[5]	周翔翔，姚佩阳，王欣，谢必昌. 基于图论的作战指挥决策群组划分算法[J]. Journal of Systems Engineering and Electronics, 2011, 33(3): 575-580.