Deep Learning Model Based Behavioural Recognition Technology for Electricity Operators and Its Safety Guardianship Analysis

Abstract

Abstract This study leverages the Openpose system to capture skeletal key points of electric power operators, simplifying network complexity by sharing convolutional layers during the ReLU activation phase. We introduce a graph convolutional network (GCN) to model these skeletal sequences, creating a spatio-temporal deep learning approach for behavior recognition. Tested on a relevant dataset, our Openpose-GCN network demonstrates stability with a training loss of 0.11 after 700 iterations, achieves over 90% accuracy in recognizing operator actions and behaviors, and maintains a recognition error below 0.003 for operations with varying risk levels. These findings underscore the potential of our approach to enhance electric power operation safety through real-time risk warning and control.