智能决策在军事体系工程的研究综述

doi:10.12305/j.issn.1001-506X.2025.08.16

Abstract

Abstract:

Intelligence is an important direction of future military development, and intelligent decision, as an important means of intelligence, profoundly affects the military’s combat system of systems construction and weapon system of systems development planning. Firstly, this paper gives the technical connotation definition of intelligent decision, summarizes from the perspectives of machine learning, operations optimization, data/knowledge-driven, and system support, elaborates the definition and structure of military system of systems engineering, sorts out the intelligent decision process in the field of military system of systems engineering, summarizes the current application status of intelligent decision in military system of systems requirements, system of systems design, system of systems modeling, system of systems evaluation, system of systems management. Finally, the future outlook of the application of intelligent decision technology in military system of systems engineering is given, to provide some reference value for military intelligence development work.

Key words: intelligent decision, military system of systems engineering, optimization methods, artificial intelligence

CLC Number:

V 217

Jikai WANG, Yajie DOU, Jing LI, Yijun DONG, Jiang JIANG, Yuejin TAN. Review on intelligent decision in the military system of systems engineering research[J]. Systems Engineering and Electronics, 2025, 47(8): 2581-2599.

Figures/Tables 14

Table 1

Fig.1

Table 2

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Table 4

Summary of the intelligent decision literature"

决策分类	文献	创新点	应用技术
机器学习	文献[85]	机器学习系统需求建模和决策选择框架，训练数据与算法支持	ML
	文献[86]	设计基于确定性深度确定性策略梯度算法的决策网络，探索认知雷达与智能干扰机的竞争关系	DRL
	文献[87]	设计两级决策树方法，确定不同工业流程在分布式资源平台（distributed resource platforms，DRPs）中的参与能力，并考虑客户各种特性和能力	DT
	文献[88]	关联规则学习和加权网络分析来分析每个决策结果的特征类别权重，应用加权朴素贝叶斯模型分配不同的权重	DTM
	文献[89]	提出一种ML驱动的决策方法，用于求解数据不完整和大量决策属性的大规模问题	SVM
运筹优化	文献[90]	提出模糊行为投资组合决策模型，求解组合收益的可能性均值	模糊理论
	文献[91]	语言直觉模糊数引入到损失函数中，提出基于单一优化模型的阈值确定方法来导出三支决策（three-way decision，3WD）	3WD
	文献[92]	构建不完全信息条件下基于专家信任网络的多属性群决策方法	专家信任网络
	文献[93]	改进算子增强非支配排序遗传算法Ⅲ（non-dominated sorting genetic algorithm Ⅲ，NSGA-Ⅲ）获得高质量解集和跳出局部最优解的能力，求出Pareto最优解集	NSGA-Ⅲ、多准则妥协解排序法（višekriterijumsko kompromisno rangiranje，VOKOR）
数据&知识驱动	文献[94]	通过实现定量决策模型和蒙特卡罗仿真，提出数据驱动的可靠性维修过程，BIM和GIS集成以支持RCM数据的获取和更新	数据驱动
	文献[95]	提出多模态知识表示方法，构建知识驱动的DSS	知识驱动
	文献[96]	梳理知识与数据协同驱动决策方法，从知识与数据的架构级协同、算法级协同两个层面对典型方法进行分类	知识数据协同驱动
系统支持	文献[97]	提出一种基于数据驱动技术（case based reasoning，CBR）和模型驱动技术（rule based reasoning，RBR）的IDSS方法，用于环境系统的控制、监督和决策支持	IDSS
系统支持	文献[98]	提出一种用于自适应交易策略的混合DSS，将基于规则的系统与DRL相结合，通过学习人类专业知识进行自我改进	规则推理和DRL技术

Table 4

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Table 5

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Fig.7

Fig.8

Table 6

Application of ID in military field SoSE"

体系流程	文献	应用方法	具体问题
体系需求	文献[143]	知识管理与自动化决策支持系统	需求管理与能力开发
	文献[144]	系统工程“V”模型，用户故事概念	作战体系能力需求开发
	文献[145]	元模型，需求本体构建方法	需求开发与验证
	文献[146]	概念演示，信息系统	军事需求论证
	文献[147]	粒子群算法，基于模糊偏好的多属性决策	兵力需求优化
	文献[148]	启发式算法，D-S证据推断方法	需求冲突检测
	文献[149]	DRL技术，多目标优化算法	作战概念需求分析
体系设计	文献[150]	Actor-Critic模型，强化学习，马尔科夫决策过程	无人体系智能体
	文献[151]	作战辅助决策构建，体系流程，	作战系统辅助
	文献[152]	系统架构设计	作战系统需求与设计
	文献[155]	物联网系统辅助决策，不确定信息处理	作战模拟决策
	文献[156]	灰色熵DEMATEL-VIKOR，双边匹配模型	复杂装备协同设计
	文献[157]	灰靶决策，图示评审技术，贝叶斯决策	作战体系装备组合设计
体系建模	文献[160]	DoDAF多视图建模与SOA结合，组合决策	作战体系建模
	文献[161]	依赖网络模型，体系重心度量方法	网络信息体系建模
	文献[162]	属性映射关系，人机协同	作战体系结构设计
	文献[163]	适变价值生产单元模型，有机融合的云流化指控	指控架构设计
	文献[164]	博弈对抗、多视图建模	海上搜救体系建模
	文献[165]	DoDAF与SysML，作战环	无人作战体系建模
	文献[166]	DL技术，认知决策模型	无人集群作战建模
体系评估	文献[168]	GERT	装备体系效能评估
	文献[169]	基于预聚类主动半监督学习的评估方法	作战体系决策评估
	文献[170]	直觉模糊集，加权影响非线性测量系统	装备体系效能评估
	文献[174]	模糊贝叶斯网络	装备体系贡献率评估
	文献[175]	作战时序网络	装备体系能力评估
	文献[176]	功能依赖网络	体系韧性量化评估
	文献[177]	复杂网络理论，基于拓扑结构的体系贡献率评估方法	体系贡献率评估
	文献[178]	军事后勤网络规划系统，Markov-Bootstrap方法	后勤保障评估
体系管理	文献[179]	自适应动态规划优化目标，梯度信息优化奖励函数	武器-目标分配
	文献[180]	多属性对策间资源分配博弈论模型，多迭代分析	资源优化分配
	文献[181]	资源最优共享模型，确定性与随机性对比	资源优化分配
	文献[182]	组合决策分析，专家意见和成本效益分析，智能算法	武器规划问题

Table 6

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内容部分	相关研究
问题模型	证据理论、粗糙集、概率推理、贝叶斯网络、因果分析
算法构建	KD技术、ML技术、DL技术
系统支持	专家系统、人机协同、边缘计算

角度	TD	ID
情报活动	数据样本少，信息不全面	各渠道获取数字化信息，碎片化信息知识生成
设计活动	决策分析不全面，决策结论主观性	全局考虑，综合集成，寻优计算，适应动态变化，协同交互
抉择活动	环境适应性差	考虑环境的不确定性，决策可持续演化
实施活动	人为推动决策，决策时间长	全面规范，流程可追溯，即时决策

ID分类	方法原理	优势	不足
机器学习	利用DL、ML等技术进行分析，预测，优选	寻找参数的关联性，全局统筹	有一定的数据依赖性
运筹优化	利用数理知识，结合计算机工具去研究复杂的优化决策问题，注重模型构建	模型可逻辑描述，结果解释性强	依赖于求解器与决策引擎
数据/知识驱动	利用KD，KG等技术，来处理数据，生成知识，辅助决策分析	可以充分利用碎片化信息	专家意志难以量化
系统支持	基于决策支持系统，运用问题求解中的经验与知识，以人机交互的方式提供决策	决策具有即时性，决策效率高，人机结合，易于操作	决策偏于形式化，属于满意解而非最优解

决策方法	方法特点	适用情景	应用情况
TOPSIS	优劣解距离法，采用与正理想解相对值来排序，计算简单直观，强调利益属性	评价对象得分，其各个指标值已知，多属性决策	军事资源配置[120−121]、体系设计[122]
ELECTRE I、II、Ⅲ	淘汰选择法，级别高于关系	针对属性权重已知、属性值模糊，多属性决策	体系评估[123]
TODIM	基于前景理论，考虑决策心理	主观交互性，风险分析，多准则决策	体系结构[124]
VIKOR	折衷妥协解法，排序稳定性强，可信度高	决策偏好信息不确定，测量单位不同，多准则决策	体系评估[125]
3WD	三支决策，符合人类认知，回避不确定信息	应用范围广，多属性决策	体系评估[126]
PROMETHEE	无需评价指标无量纲和规范化处理，评价结果客观，科学	快速排序需求，软件辅助，多属性决策	军事资源配置[127]

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Review on intelligent decision in the military system of systems engineering research

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