基于多流映射-动态验证的多平台协同任务链博弈建模与资源优化

doi:10.12305/j.issn.1001-506X.2026.03.20

摘要/Abstract

摘要：

针对传统任务链验证存在动态交互与信息不确定性建模不足的问题，提出一种解决方案。首先，构建“分解-抽象-验证”方法论体系，结构化分解作战流程为模块化单元，从实体、信息、规则3个维度提取要素并参数化，通过ExtendSim建立动态验证环境。然后，设计五流协同映射模型，将作战活动抽象为执行流（时序）、情报流（交互）、物质流（资源）、属性流（状态）、决策流（指控）的多维耦合体系。最后，开发闭环建模框架，实现作战基元参数化（耗时/概率/资源）、复杂规则模块化编程及多层流程嵌套。仿真结果表明，所提方法能够体现作战的信息活动规律，可为多平台协同任务链评估提供思路和方法。

关键词: 任务链, 电子对抗, 建模仿真, ExtendSim

Abstract:

In view of lack of dynamic interaction and information uncertainty modeling in traditional task chain verification, a solution scheme is proposed. Firstly, construct the “decomposition-abstraction-verification” methodology system, structurally decompose the operational process into modular units, extract and parameterize the elements from the three dimensions of entities, information and rules, and establish a dynamic verification environment through ExtendSim. Then, a five flow collaborative mapping model is designed to abstract operational activities into a multi-dimensional coupling system of execution flow （time sequence）, intelligence flow （interaction）, material flow （resources）, attribute flow （state） and decision flow （command and control）. Finally, develop a closed-loop modeling framework to realize parameterization of operational primitives （time-consuming / probability / resources）, modular programming of complex rules and multi-layer process nesting. Simulation results show that the proposed method can reflect the operational information activity law, and provide ideals and method for the evaluation of multi-platform collaborative task chain.

Key words: task chain, electronic countermeasure, modeling and simulation, ExtendSim

中图分类号:

E 0-03

李争, 何明浩, 胡乔林. 基于多流映射-动态验证的多平台协同任务链博弈建模与资源优化[J]. 系统工程与电子技术, 2026, 48(3): 946-961.

Zheng LI, Minghao HE, Qiaolin HU. Modeling and resource optimization of multi-platform collaborative task chaingame based on multi-stream mapping-dynamic verification[J]. Systems Engineering and Electronics, 2026, 48(3): 946-961.

图/表 23

图1

表1

图2

表2

图3

图4

图5

表3

图6

表4

图7

图8

图9

图10

表5

表6

图11

图12

图13

表7

图14

表8

图15

参考文献 31

1	DONNEL S D, LUNDAY B J, BOARDMAN N T. Analysis of a distributed command-and-control algorithm to implement mosaic warfare[J]. Military Operations Research, 2024, 29 (1): 5- 30. doi: 10.5711/1082598329105
2	WANG K X, ZHAO T D, YUAN Y, et al. A new multi-layer performance analysis of unmanned system-of-systems within IoT[J]. Reliability Engineering & System Safety, 2025, 259, 110953. doi: 10.1016/j.ress.2025.110953
3	KOUR R, KARIM R, DERSIN P. Modelling cybersecurity strategies with game theory and cyber kill chain[EB/OL]. [2024-10-20]. https://doi.org/10.1007/s13198-025-02733-4.
4	SADLEK L, YAMIN M M, ČELEDA P, et al. Severity-based triage of cybersecurity incidents using kill chain attack graphs[J]. Journal of Information Security and Applications, 2025, 89, 103956. doi: 10.1016/j.jisa.2024.103956
5	李兴华, 于永生, 孟真, 等. 从杀伤链看无人智能装备在俄乌冲突中的运用[J]. 指挥控制与仿真, 2024, 46(5): 6–12.
	LI X H, YU Y S, MENG Z, et al The application of unmanned intelligent equipment in the Russia-Ukraine conflict from the perspective of the kill chain [J] Command, Control, and Simulation, 2024, 46(5): 6–12.
6	YAO K, HUANG S. Simulation technology and analysis of military simulation training[C]//Proc. of the 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, 2021.
7	WANG Q B, ZHANG Q C, ZUO M, et al. A entity relation extraction model with enhanced position attention in food domain[J]. Neural Processing Letters, 2022, 54 (2): 1449- 1464. doi: 10.1007/s11063-021-10690-9
8	JI B, XU H, YU J, et al. A two-phase paradigm for joint entity-relation extraction[J]. Computers, Materials and Continua, 2022, 74 (1): 1303- 1318. doi: 10.32604/cmc.2023.032168
9	MESLOUB K, INNAL F, DUCQ Y. Emergency response plan modeling using IDEF0 and BPMN approaches[J]. International Journal of Safety & Security Engineering, 2023, 13 (2): 375- 384. doi: 10.18280/ijsse.130220
10	HUANG J H, YIN J S, WANG S S. A conversion method from IDEF3 to stochastic behavior tree[C]//Proc. of the 35th Chinese Control and Decision Conference, 2023: 4925–4927.
11	KIM C H, WESTON R H, HODGSON A, et al. The complementary use of IDEF and UML modelling approaches[J]. Computers in Industry, 2003, 50 (1): 35- 56. doi: 10.1016/S0166-3615(02)00145-8
12	LUO J L, YI S J, LIN Z X, et al. Petri-net-based deep reinforcement learning for real-time scheduling of automated manufacturing systems[J]. Journal of Manufacturing Systems, 2024, 74, 995- 1008. doi: 10.1016/j.jmsy.2024.05.006
13	BATHAEIAN N S, KAMRANI H, SAKHAEI-NIA M. LCPN: a method for modelling ladder diagrams by colored Petri nets and its use in race detection[EB/OL]. [2024-10-20]. https://doi.org/10.1007/s41870-024-02210-4.
14	KISS G, BAKUCZ P. Fuzzy petri nets for traffic node reliability[J]. Sensors, 2024, 24 (19): 6337. doi: 10.3390/s24196337
15	OPRANESCU V, IONITA A D. Review of cyber-physical systems modeling with UML, SysML and MARTE[J]. IEEE Access, 2025, 13, 47132- 47145. doi: 10.1109/ACCESS.2025.3551117
16	CHU C Y, YANG W K, CHEN Y J. Dynamic fault tree generation and quantitative analysis of system reliability for embedded systems based on SysML models[J]. Sensors, 2024, 24 (18): 6021. doi: 10.3390/s24186021
17	ZHANG Q, LIU J H, CHEN X. Multidisciplinary reliability design optimization modeling based on SysML[J]. Applied Sciences, 2024, 14 (17): 7558. doi: 10.3390/app14177558
18	TORRE D, GENERO M, LABICHE Y, et al. How consistency is handled in model-driven software engineering and UML: an expert opinion survey[J]. Software Quality Journal, 2023, 31 (1): 1- 54. doi: 10.1007/s11219-022-09585-2
19	MAHARDIKA F, MERANI S G, SUSENO A T. Application of extreme programming methods to the design of employee salary information systems[J]. Blend Science Engineering Journal, 2024, 2 (3): 204- 217.
20	YUAN Z C, HU X D, ZHANG G, et al. Integration of UML class diagrams based on semantics and structure[J]. International Journal of Software Engineering and Knowledge Engineering, 2024, 34 (1): 1281- 1321.
21	AGHAMOHAMMADPOUR A, MAHDIPOUR E, ATTARZADEH I. Architecting threat hunting system based on the DODAF framework[J]. The Journal of Supercomputing, 2023, 79 (4): 4215- 4242. doi: 10.1007/s11227-022-04808-6
22	KHOOSHIAN N, YARIZANGANEH M, ALHAEI H. A reflection on the appropriacy of the implementation of the DoDAF information architecture framework as an organizational information architecture for universities: the case for shiraz university[J]. Journal of Studies in Library and Information Science, 2024, 16 (4): 1- 22.
23	ZHENG H X, LIU X, WU J H, et al. The on-orbit mission analysis of OTV based on DoDAF[J]. Aircraft Engineering and Aerospace Technology, 2021, 93 (6): 937- 945. doi: 10.1108/AEAT-03-2020-0062
24	WITZKI A, SIEVERT A, KÜPER K, et al. Generic military simulation of a complex workplace: a new tool for applied research in military settings[J]. Military Psychology, 2024, 36 (1): 114- 124. doi: 10.1080/08995605.2021.2000319
25	SOH Y W. Engineering resilience into the marine expeditionary units resupply system through military foraging[D]. Monterey, California: Naval Postgraduate School, 2017.
26	DU D, LIU T T, GUO C. Analysis of container terminal handling system based on petri net and ExtendSim[J]. Promet-Traffic & Transportation, 2023, 35 (1): 87- 105. doi: 10.7307/ptt.v35i1.4196
27	赵雪梦, 任天助, 方哲梅. 基于多模型的体系架构迭代设计方法[J]. 系统仿真学报, 2025, 37 (6): 1512- 1521.
	ZHAO X M, REN T Z, FANG Z M. Iterative design method for architecture based on multiple models[J]. Journal of System Simulation, 2025, 37 (6): 1512- 1521.
28	葛冰峰, 夏博远, 杨志伟, 等. ExtendSim模型与数据驱动的指控流程建模与分析[J]. 系统工程与电子技术, 2020, 42(5): 1063–1072.
	GE B F, XIA B Y, YANG Z W, et al. ExtendSim model and data driven accusation process modeling and analysis[J] Systems Engineering and Electronics, 2020, 42(5): 1063–1072.
29	田旭光, 吴龙涛, 张成名, 等. 基于ExtendSim平台的装备战场抢修仿真研究[J]. 现代防御技术, 2025, 53(5): 191–199.
	TIAN X G, WU L T, ZHANG C M, et al. Research on equipment battlefield repair simulation based on ExtendeSim platform [J]. 2025, 53(5): 191–199.
30	梁甲慧, 张策, 王寿鹏. 要地防空作战火力配置ExtendSim仿真分析[J]. 舰船电子工程, 2021, 41 (1): 53- 57，108.
	LIANG J H, ZHANG C, WANG S P. ExtendSim simulation analysis of firepower configuration for key air defense operations[J]. Ship Electronic Engineering, 2021, 41 (1): 53- 57，108.
31	刘冠邦, 张昕, 郑明. 美军空战场杀伤链发展与启示[J]. 指挥信息系统与技术, 2020, 11(4): 10–14.
	LIU G B, ZHANG X, ZHENG M. The development and inspiration of the us air battlefield kill chain[J] Command Information Systems and Technology, 2020, 11(4): 10–14.

领域	典型研究	应用潜力
概念建模	文献[27]：体系架构迭代设计	作战体系架构构建与迭代
军事推演	文献[28]：联合作战背景下的指控流程建模	指控流程的建模与分析
火力配置	文献[29]：要地防空作战火力配置	指导要地防空作战
后勤保障	文献[30]：装备战场抢修	装备维修保障流程重构

名称	参数分布	消耗资源	注释
目标探测	三角（30，40，90）s	1	任务链探测目标所需时间
目标定位跟踪	三角（1，5，180）s	1	任务链定位跟踪目标所需时间
目标瞄准	三角（0.5，1，45）s	1	任务链瞄准目标所需时间
目标攻击	三角（200，280，300）s	1	任务链攻击目标所需时间
目标属性	（0.75, 0.25）	—	创建75%的一般目标，25%的重点目标
发现概率	0.95	—	目标被发现的概率
定位跟踪概率	0.98	—	目标被定位跟踪的概率
瞄准概率	0.95	—	目标被瞄准的概率
命中概率	0.98	—	目标被命中的概率
杀伤概率	0.9	—	目标被杀伤的概率

活动名称	参数分布/s	消耗资源	注释
目标预警	三角（5，6，10）	1	对对方打击预警所需时间
作战准备	三角（60，90，120）	5	电子对抗行动准备所需时间
情报处理	三角（30，50，90）	2	二级指挥单元情报预处理时间
上级决策	三角（10，20，30）	1	一级指挥单元决策时间
下达命令	三角（60，70，100）	1	二级指挥单元向三级单元下达命令时间
电子防护	三角（5，8，10）	1	实施电子防护行动所需的时间
电子干扰	三角（40，50，60）	3	实施电子干扰行动所需的时间

参数类别	参数名称	参数设置
仿真基础设置	仿真终止时间/s	3 600
目标创建时间间隔	泊松分布（λ=60）	期望值=方差=60
指控资源配置	一级指控资源数量	5
	二级指控资源数量	10
	三级指控资源数量	15
电子对抗效果	电子干扰成功命中率降低/%	40
	电子防护成功命中率降低/%	50
	干扰失败时命中率影响/%	0

类别	行动前	行动后
消灭数量	45	34
未被发现数量	2	2
未被定位跟踪数量	2	2
未被瞄准数量	2	2
逃离数量	4	15
消灭率/%	81.8	61.81