吉林大学学报（信息科学版）2025，Vol.43Issue(5)：988-998,11.
基于GNN和RL的电能计量设备检测调度问题求解
Solving Algorithms of Detection Scheduling for Electric Metering Equipment Based on GNN and RL
摘要
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.关键词
电能计量/图神经网络/强化学习/析取图/车间调度Key words
electric metering/graph neural networks/reinforcement learning/disjunctive graph/job shop scheduling分类
计算机与自动化引用本文复制引用
杨思洁,杨依睿,刘思,陈欢军,徐韬,孔德政,窦全胜..基于GNN和RL的电能计量设备检测调度问题求解[J].吉林大学学报（信息科学版）,2025,43(5):988-998,11.基金项目
国家自然科学基金资助项目(62341605) （62341605）
国家电网公司科技基金资助项目(5700-202219210A-1-1-ZN) （5700-202219210A-1-1-ZN）
