系统仿真学报 ›› 2024, Vol. 36 ›› Issue (11): 2654-2661.doi: 10.16182/j.issn1004731x.joss.23-0947

• 研究论文 • 上一篇    

一种基于兵棋推演的作战目标体系关键节点识别方法

张永夫1, 刘洋2, 袁禾3   

  1. 1.联合作战学院,河北 石家庄 050084
    2.联合作战学院,北京 100091
    3.解放军77611部队,西藏 拉萨 850001
  • 收稿日期:2023-07-27 修回日期:2023-11-01 出版日期:2024-11-13 发布日期:2024-11-19
  • 第一作者简介:张永夫(1996-),男,助工,硕士,研究方向为运筹分析与军事智能决策。

A Method for Key Node Identification in Operational Target System Based on War Gaming

Zhang Yongfu1, Liu Yang2, Yuan He3   

  1. 1.Joint Operation College, Shijiazhuang 050084, China
    2.Joint Operation College, Beijing 100091, China
    3.PLA 77611 Troops, Lhasa 850001, China
  • Received:2023-07-27 Revised:2023-11-01 Online:2024-11-13 Published:2024-11-19

摘要:

作战目标体系关键节点识别是作战指挥决策的重要依据。针对当前作战目标体系关键节点识别缺乏战役级动态对抗环境下的实验验证,借助大型计算机兵棋系统推演所得的数据,以一体化防空网为例,构建具备大规模实体和复杂交互关系的作战目标体系复杂网络模型;结合兵棋数据特点,综合作战目标价值特性和网络结构特性,体现体系贡献率,提出作战目标价值指标体系,采用深度强化学习框架——FINDER框架识别作战目标体系复杂网络模型的关键节点。通过兵棋推演对抗的方式,观察将FINDER框架识别的关键节点移除后体系效能的变化,并与人工推演的体系效能变化对比,结果验证了所提方法的有效性。

关键词: 作战目标体系, 关键节点识别, 复杂网络, 兵棋, 深度强化学习

Abstract:

The identification of key nodes in an operational target system is an important basis for combat command decision-making. Due to the lack of experimental verification of key node identification in the current operational target system in a campaign-level dynamic confrontation environment, a complex network model of operational target system with large-scale entities and complex interaction relationship was constructed by taking integrated air defense network as an example, with the help of the data derived from the large joint war gaming; the characteristics of wargame data were considered, and the value characteristics of combat targets and network structure characteristics were integrated, embodying the contribution rate of the system. The value index system of operational targets was proposed, and the deep reinforcement learning framework-FINDER framework was adopted to identify the key nodes of the complex network model of the operational target system. By means of war gaming, the change in system effectiveness after removing the key nodes identified by the FINDER framework was observed and compared with the change in system effectiveness deduced manually. The results verified the effectiveness of the proposed method.

Key words: operational target system, key node identification, complex network, war gaming, deep reinforcement learning

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