Research on the Robustness of Behavior Recognition Model in Power Safety Monitoring Based on Neural Network Algorithm

With the continuous development of power systems and the deepening of power safety supervision work, it is particularly important to effectively identify and monitor the behavior of power safety supervision personnel. This research aims to improve the accuracy and real-time performance of personnel behavior recognition in the field of electric power safety supervision through advanced neural network technology. First, this study conducts an in-depth analysis of the common behaviors of electric power safety supervisors, including daily inspections, accident handling, equipment maintenance and other tasks. On this basis, a behavior recognition model based on neural network algorithm is proposed. This model combines deep learning and pattern recognition methods and can accurately classify various behaviors of power safety supervisors. Secondly, a large amount of field data was used for training and testing in the study to ensure the robustness and generalization ability of the model. Furthermore, this study focuses on the real-time and adaptability of the algorithm. Taking into account the particularity of power safety supervision, by adjusting the structure and parameters of the neural network, the algorithm can perform behavior recognition more efficiently in real-time scenarios, and the training of neural networks optimizes the performance of the algorithm. Experiments have proven that the proposed behavior recognition model based on neural network algorithm has high accuracy and robustness in classifying human behavior.