航空学报 > 2025, Vol. 46 Issue (1): 330615-330615   doi: 10.7527/S1000-6893.2024.30615

舰载机保障作业自适应批量匹配决策方法

刘广1,2, 王华3,4,5, 林友芳1,2, 贺硕3,4,5, 李亚飞3,4,5(), 徐明亮3,4,5   

  1. 1.北京交通大学 计算机科学与技术学院,北京 100091
    2.交通数据分析与挖掘北京市重点实验室,北京 100044
    3.郑州大学 计算机与人工智能学院,郑州 450001
    4.智能集群系统教育部工程研究中心,郑州 450001
    5.国家超级计算郑州中心,郑州 450001
  • 收稿日期:2024-04-28 修回日期:2024-05-20 接受日期:2024-06-20 出版日期:2025-01-15 发布日期:2024-07-12
  • 通讯作者: 李亚飞 E-mail:ieyfli@zzu.edu.cn
  • 基金资助:
    国家自然科学基金(62372416);河南省自然科学基金(242300421215);国家重点研发计划(2021YFB3301504)

Adaptive batch matching decision method for carrier-based aircraft support operations

Guang LIU1,2, Hua WANG3,4,5, Youfang LIN1,2, Shuo HE3,4,5, Yafei LI3,4,5(), Mingliang XU3,4,5   

  1. 1.School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100091,China
    2.Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing 100044,China
    3.School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China
    4.Engineering Research Center of Intelligent Swarm Systems,Ministry of Education,Zhengzhou 450001,China
    5.National Supercomputing Center in Zhengzhou,Zhengzhou 450001,China
  • Received:2024-04-28 Revised:2024-05-20 Accepted:2024-06-20 Online:2025-01-15 Published:2024-07-12
  • Contact: Yafei LI E-mail:ieyfli@zzu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(62372416);Henan Natural Science Foundation(242300421215);National Key Research and Development Program of China(2021YFB3301504)

摘要:

衡量航空母舰作战性能的关键指标是舰载机出动架次率,其高低取决于舰载机的保障阵位匹配策略。现有工作主要采用序列匹配和批量匹配方法为舰载机匹配保障阵位,但均存在一定的局限性,难以兼顾保障作业阵位匹配的实时性和质量。面对复杂时变的作业环境,确定合理的保障作业匹配策略变得十分困难。因此,在批量匹配方法的基础上,提出了一种新的舰载机保障作业自适应批量匹配决策方法。首先,通过构建多维环境状态编码的强化学习方法求解较优的时间窗划分策略。然后,在每个时间窗内应用高效率的批量匹配算法,以求解保障作业与保障阵位的最佳匹配方案。基于公开的尼米兹号航母数据进行的多组模拟实验结果表明,本文方法能够有效应对保障环境的动态变化,在满足实时性需求的前提下能够快速求解出高质量的保障作业阵位分配方案。

关键词: 舰载机, 航空保障, 实时调度, 强化学习, 自适应决策

Abstract:

The key indicator for measuring the combat performance of an aircraft carrier is the sortie rate of carrier-based aircraft, which depends on the support station matching strategy of carrier-based aircraft. Existing works mainly use sequence matching and batch matching methods to match suitable stations for carrier-based aircraft. However, both methods have certain limitations, and it is difficult for the methods to ensure both real-timeliness and quality of station matching at the same time. Facing the complex and time-varying support environment, it becomes extremely difficult to determine a reasonable support operation matching strategy. In this paper, we propose a novel adaptive batch matching decision-making method for carrier-based aircraft support operations based on the batch matching method. First, the optimal time window division strategy is solved by constructing a reinforcement learning method for multi-dimensional environmental state encoding. Then, a highly efficient batch matching algorithm is applied within each time window to find the best matching solution for support operations and support stations. The results of multiple sets of simulation experiments based on the publicly available Nimitz aircraft carrier data show that our proposed method can effectively respond to dynamic changes in the support environment, and can quickly solve high-quality support operation assignment plans while meeting real-time requirements.

Key words: carrier-based aircraft, support operation, real-time scheduling, reinforcement learning, adaptive decision

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