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<p>To cope with complex dynamic patrol environments and maximize the benefits of regional social security patrols, this study abstracts patrol areas as a graph model and constructs a network diagram of the social security patrol area. Then, an initial configuration method for intelligent patrol agents based on greedy algorithms is proposed, and game theory was introduced to construct an "attacker defender" game patrol model. The policy set and payoff function are defined, and a generalized geometric coverage algorithm is designed to solve the policy set of both parties in the model, thereby obtaining the optimal patrol strategy for intelligent patrol agents. The results indicated that the initial configuration method of intelligent patrol agents based on greedy algorithm reduced the global average idle time of patrol agents to about 200&#xa0;s and 765&#xa0;s respectively for the initial deployment of road networks A and B. Under the same number of patrol agents, the global average coverage rate increased to over 80% faster. In addition, the revenue of the GGC algorithm based on multi-linear programming for solving the A and B game patrol models was (15.0, -30.0) and (21.1, -38.2), respectively, which were superior to other strategies. The above data indicates that the research method can improve patrol efficiency and comprehensive coverage, providing strong support for optimizing social security patrol strategies.</p>

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Optimization of social security patrol strategy based on graph theory and GGC algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月6日

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  • Jian Wang

摘要

To cope with complex dynamic patrol environments and maximize the benefits of regional social security patrols, this study abstracts patrol areas as a graph model and constructs a network diagram of the social security patrol area. Then, an initial configuration method for intelligent patrol agents based on greedy algorithms is proposed, and game theory was introduced to construct an "attacker defender" game patrol model. The policy set and payoff function are defined, and a generalized geometric coverage algorithm is designed to solve the policy set of both parties in the model, thereby obtaining the optimal patrol strategy for intelligent patrol agents. The results indicated that the initial configuration method of intelligent patrol agents based on greedy algorithm reduced the global average idle time of patrol agents to about 200 s and 765 s respectively for the initial deployment of road networks A and B. Under the same number of patrol agents, the global average coverage rate increased to over 80% faster. In addition, the revenue of the GGC algorithm based on multi-linear programming for solving the A and B game patrol models was (15.0, -30.0) and (21.1, -38.2), respectively, which were superior to other strategies. The above data indicates that the research method can improve patrol efficiency and comprehensive coverage, providing strong support for optimizing social security patrol strategies.