数据与计算发展前沿2026，Vol.8Issue(1)：77-90,14.DOI:10.11871/jfdc.issn.2096-742X.2026.01.007
基于贝叶斯网络与强化学习的警务资源动态调度方法
A Dynamic Scheduling Method for Police Resources Based on Bayesian Networks and Reinforcement Learning
摘要
Abstract
[Objective]To address issues with traditional fixed police resource allocation models,which cannot promptly respond to dynamic changes in regional crime risks and lack dynamic synergy optimization across multiple types of police resources,[Methods]this paper proposes a dynam-ic police resource scheduling method based on Bayesian networks and reinforcement learning.The method first uses Bayesian networks to evaluate crime risks in different regions,then em-ploys reinforcement learning algorithms to obtain optimal police resource allocation strategies.To verify the effectiveness of this method,five different resource allocation plans were designed using a district in a large northern city as a case study.[Results]Experimental results show that in the reinforcement learning model,the DQN algorithm achieved the best training performance(with a reward value of 1,755.82).The rein-forcement learning method reduced the expected risk value by 6.68%compared to traditional allocation methods.Non-linear fitting results between resources and risk indicate that resource input within the range of 1.1 to 1.2 times the baseline value yields the optimal cost-benefit ratio.The research results are applicable to the rational al-location of policing resources in the field of urban public security.关键词
贝叶斯网络/强化学习/警务资源调度/犯罪风险评估/DQN算法/非线性回归Key words
bayesian networks/reinforcement learning/police resource allocation/crime risk assessment/DQN algorithm/nonlinear regression引用本文复制引用
刘春龙,马秋平,王润生,胡今鸣,胡啸峰..基于贝叶斯网络与强化学习的警务资源动态调度方法[J].数据与计算发展前沿,2026,8(1):77-90,14.基金项目
国家自然科学基金项目(72174203) （72174203）
