Research on Detection Optimization of the 3.0 Base Version of the Metering Automation System Based on Reinforcement Learning Algorithm

In response to the problems of low detection efficiency and insufficient strategy optimization ability of the current base version detection tools in dynamic and complex scenarios, this paper proposes a detection optimization framework based on deep reinforcement learning. Firstly, by constructing a dual-mode dynamic testing model that integrates local detection and online detection, the base version quality assessment process is formalized as a Markov decision process, where the state space covers key indicators such as functional integrity, interface consistency, and storage throughput (refer to the GB/T 25000 standard). Secondly, an intelligent agent based on the Deep Q-Network (DQN) is designed to achieve adaptive optimization of detection dimension selection, feature index priority scheduling, and test case generation. A multi-objective hybrid reward mechanism (R =0.6 defect discovery rate +0.3 throughput gain - 0.1* time cost) is innovatively introduced to balance detection quality and resource consumption in dynamic interaction. Experiments show that this method improves detection efficiency by 37.2% compared to traditional schemes, with a feature index coverage rate of 98.5%, and its robustness in multi-unit detection scenarios is verified in third-party testing. The developed microservice architecture detection tool integrates a reinforcement learning decision interface, supporting real-time visualization of detection progress and automatic generation of electronic reports. This research provides an innovative solution for the full life cycle quality control of smart grid base versions.