Abstract: Aiming at the problems of insufficient stability, weak generalization ability, and the influence of equipment configuration in the traditional scheduling method for the detection and scheduling of power metering equipment, a detection and scheduling model named GNN-RL(Graph Neural Network-Reinforcement Learning) is proposed. The model treats the scheduling problem as a Markov decision process. Firstly, the graph structure model of electric energy metering equipment detection and scheduling is constructed. Then, the problem features are extracted through the improved graph neural network and passed to the action selection network to generate decisions. After the scheduling, the model collects feedback information to train the scheduling policy in the reinforcement learning module. In the training phase, GNN-RL optimizes the message passing mechanism, employs a loss function closely related to the scheduling objective, and dynamically adjusts the learning rate. A multi-task learning framework is introduced to deal with task allocation and time scheduling. The experimental results show that GNN-RL has obvious advantages in optimization ability, solution accuracy, and stability, and has great advantages in solving the detection and scheduling problem of energy metering equipment, which significantly improves the efficiency and reliability of problem solving.

Key words: electric metering, graph neural networks, reinforcement learning, disjunctive graph, job shop scheduling