Active identification and detection of external operator safety risk factors for smart grid based on deep learning

PENG Fang; LIU Tiantian; LU Weilong; PAN Jianhong; REN Junda

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Active identification and detection of external operator safety risk factors for smart grid based on deep learning

更新时间：2025-11-15

- Active identification and detection of external operator safety risk factors for smart grid based on deep learning
- Vol. 62, Issue 7, (2025)
- 作者机构：
  
  1. 国家电网有限公司大数据中心
  2. 福建亿榕信息技术有限公司
  3. 国网吉林省电力有限公司
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2025
- 稿件说明：
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PENG Fang, LIU Tiantian, LU Weilong, et al. Active identification and detection of external operator safety risk factors for smart grid based on deep learning[J]. 2025, 62(7).
DOI：

PENG Fang, LIU Tiantian, LU Weilong, et al. Active identification and detection of external operator safety risk factors for smart grid based on deep learning[J]. 2025, 62(7). DOI：

摘要

针对电网外部施工、作业过程中作业人员未正确佩戴安全帽和安全带等装备以及违规吸烟等行为所带来的安全风险问题，文章基于YOLOv7深度学习模型研究了电网作业人员自身安全风险因素检测识别系统。文中设计了智能电网人员安全风险因素在线监测系统架构，在此基础上分析了YOLOv7模型的结构及应用，最后基于改进的人员风险行为因素数据集对所述方法进行了分析验证。实验结果表明，相比于前代YOLO类目标检测模型，YOLOv7在电网作业人员行为风险因素的监测中具有更高的检测效率，能够更好地满足智能电网监测系统对作业人员行为风险因素的实时监测需求。

Abstract

Aiming at the safety risk problems caused by the incorrect wearing of helmets

safety belts and illegal smoking of operators during the construction of power grid

this paper designs a detection and identification system for the safety risk caused by operators based on YOLOv7 deep learning model. This paper designs the framework of the online monitoring system for personnel safety risk factors of smart grid

analyzes the structure and application of YOLOv7 model on this basis

and finally analyzes and verifies the method based on the improved data set of personnel risk behavior factors. The experimental results show that compared with the previous generations of YOLO model

YOLOv7 has higher detection efficiency and speed in the monitoring of behavioral risk factors of operators

which can better meet the real-time detection requirements of the smart grid monitoring system on safety risk factors of operators.

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