Optimization of Large Equipment Maintenance Resource Scheduling based on Reinforcement Learning and Knowledge Graph

The scheduling of maintenance resources for large-scale equipment faces challenges such as strong task dynamics, complex constraint conditions, and high knowledge dependence. Traditional scheduling methods are difficult to achieve multi-objective real-time optimization. This article proposes an intelligent scheduling optimization method that integrates knowledge graph and reinforcement learning. Firstly, construct a knowledge graph in the field of large-scale equipment maintenance, structurally store equipment failure modes, maintenance processes, resource constraints, and historical scheduling cases, and form a prior knowledge base for scheduling decisions. On this basis, a reinforcement learning scheduling model based on Deep Q-Network （DQN） is designed, which combines maintenance tasks, resource states, and knowledge graph embedding features as a state space to minimize maintenance completion time, resource load balancing, and emergency task response delay as multi-objective reward functions. Through simulation training and testing in typical large-scale equipment maintenance scenarios, the results show that this method reduces the average scheduling cycle by 18.6% and improves resource utilization by 12.3% compared to traditional heuristic algorithms and single reinforcement learning methods. The introduction of knowledge graphs effectively alleviates the exploration difficulty of reinforcement learning under sparse rewards and improves the generalization ability of scheduling strategies. This article provides a new idea for intelligent scheduling of maintenance resources for large-scale equipment.