基于双深度Q网络的电力物联网自动化测试策略动态优化方法

顾志明; 李博; 代泽林; 朱梦梦; 张立; 保明康

doi:10.11959/j.issn.1000-0801.DXKX260018

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基于双深度Q网络的电力物联网自动化测试策略动态优化方法

更新时间：2026-05-19

- 基于双深度Q网络的电力物联网自动化测试策略动态优化方法
- Dynamic Optimization Method for Automated Testing Strategy of Power Internet of Things Based on Double Deep Q-Network
- 电信科学 2026年
- 作者机构：
  
  1.云南电网有限责任公司电力科学研究院，云南昆明 650217
  云南省绿色能源与数字电力量测及控保重点实验室，云南昆明 650217
  3.云南电网有限责任公司，云南昆明650011
- 作者简介：
  
  顾志明（1996—），男，硕士研究生，工程师，研究方向：物联网与智能量测及品控检测技术，E-mail：673294117@qq.com；
  李博，高级工程师，研究方向：电能计量，E-mail：49923387@qq.com。
- 基金信息：
  
  云南电网公司科技项目, 名称：电力物联传感器及终端设备检测平台建设与评价体系方法研究(YNKJXM20240410)
- DOI：10.11959/j.issn.1000-0801.DXKX260018
  中图分类号：
- 收稿：2026-01-08，
  
  修回：2026-02-21，
  
  录用：2026-05-18，
- 稿件说明：
移动端阅览
顾志明, 李博, 代泽林, 等. 基于双深度Q网络的电力物联网自动化测试策略动态优化方法[J/OL]. 电信科学, 2026.

Gu Zhiming, Li Bo, Dai Zelin, et al. Dynamic Optimization Method for Automated Testing Strategy of Power Internet of Things Based on Double Deep Q-Network[J/OL]. Telecommunications Science, 2026.
顾志明, 李博, 代泽林, 等. 基于双深度Q网络的电力物联网自动化测试策略动态优化方法[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260018.

Gu Zhiming, Li Bo, Dai Zelin, et al. Dynamic Optimization Method for Automated Testing Strategy of Power Internet of Things Based on Double Deep Q-Network[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260018.

摘要

本文提出一种基于深度强化学习的电力物联网自动化测试策略优化方法，核心采用双深度 Q 网络（Double Deep Q-Network，DDQN）架构，通过目标网络与经验回放机制提升策略学习的稳定性与收敛效率。该方法依托双深度 Q 网络的决策能力，使智能体在与电力物联网环境的实时交互中，基于奖励反馈动态调整测试动作（如测试用例选择、参数配置优化），实现对电力物联网设备和系统的高效覆盖测试。实验结果表明，相较于传统随机策略与顺控策略，基于双深度 Q 网络的优化方法在智能电表、继电保护装置等典型设备测试中，缺陷检测率平均超 90%，测试覆盖率稳定在 93% 以上，且平均测试时间缩短 3-7 秒；在高负载、故障注入等动态场景下仍保持优异性能，有效应对电力物联网环境的不确定性，为电力物联网的稳定运行提供有力技术保障。

Abstract

This paper proposes an optimization method for automated testing strategies in the Power Internet of Things (Power IoT) based on deep reinforcement learning

with the core adoption of a Double Deep Q-Network (DDQN) architecture. Through the target network and experience replay mechanism

the stability and convergence efficiency of strategy learning are enhanced. Relying on the decision-making capability of the DDQN

this method enables the intelligent agent to dynamically adjust testing actions (such as test case selection and parameter configuration optimization) based on reward feedback during real-time interaction with the Power IoT environment

thereby achieving efficient coverage testing of Power IoT devices and systems. Experimental results show that

compared with traditional random strategies and sequential control strategies

the optimization method based on DDQN achieves an average defect detection rate of over 90% and a stable test coverage rate of more than 93% in the testing of typical devices such as smart electricity meters and relay protection devices. Additionally

the average testing time is shortened by 3-7 seconds. The method still maintains excellent performance in dynamic scenarios such as high load and fault injection

effectively addressing the uncertainties of the Power IoT environment and providing strong technical support for the stable operation of the Power IoT.

关键词

Keywords

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