辽宁工程技术大学学报（自然科学版）2026，Vol.45Issue(2)：242-248,7.DOI:10.11956/j.issn.1008-0562.20250442
面向变电站智能安监的行为识别与时空特征决策方法
Behavior recognition and spatiotemporal feature decision-making method for intelligent safety monitoring of substations
摘要
Abstract
To address the issues of insufficient granularity in feature extraction and limited adaptability to complex video surveillance scenarios in substations inherent in traditional power personnel behavior recognition methods,this study investigates behavior recognition technology tailored to the needs of power operation and maintenance.An end-to-end video behavior recognition framework is adopted to directly model raw surveillance videos,and a key frame extraction method based on spatiotemporal features is designed to improve inference efficiency.A behavior classification decoder is constructed to enhance the discriminative ability for multiple types of operational actions.The experimental results on the real substation operation video dataset show that the proposed method achieves an overall recognition rate of 93.7%,significantly outperforming traditional image recognition methods such as support vector machine(SVM)and multi-layer perceptron(MLP)in both recognition accuracy and processing speed.The research conclusion provides a technical reference for improving the intelligent monitoring ability of power field.关键词
视频监控/行为识别/时空特征/关键帧提取/端到端/电力运维Key words
video surveillance/behavior recognition/spatiotemporal features/keyframe extraction/end-to-end/power operation and maintenance分类
信息技术与安全科学引用本文复制引用
储海东,陈振宇,杜建光,闫华光,陈毅,赵帅..面向变电站智能安监的行为识别与时空特征决策方法[J].辽宁工程技术大学学报（自然科学版）,2026,45(2):242-248,7.基金项目
国家电网公司总部科技项目(5700-202490330A-2-1-ZX) （5700-202490330A-2-1-ZX）
国家自然科学基金项目(62476242) （62476242）
