Research on intelligent identification method of distribution grid operation safety risk based on semantic feature parsing

Abstract

Identifying safety risks in distribution networks is of great significance for ensuring the safety of personnel and the stable operation of the distribution system. Existing research on safety risk identification in distribution network operations mainly focuses on personnel irregular dress detection and dynamic unsafe behavior identification. However, the actual operation scenario of the distribution network involves a complex process of multi-element interaction and integration of personnel, tools, and equipment machinery, where the risk of violations is often hidden within the intricate web of interactions. For this reason, this paper focuses on the problem of violation identification of human-object interaction relations in distribution network operation scenarios and proposes a violation risk identification method based on multiple interaction relations. The method firstly extracts the features of the distribution network operation image by convolutional neural network resnet101, then introduces the coding-decoding structure to re-encode the feature vectors to get the feature vectors with different interactions, and at the same time, utilizes the conditional filtering module to improve the convergence speed of the structure, and utilizes the Residual Information Exchange Module and the multi-layer mlp structure to discriminate the interaction pairs of multiple relationships, and finally takes the ladder climbing operation scenario as an example for the experimental validation. The experimental results showed that the proposed method can realize the accurate identification of human-object interaction relationships and violation risk, and has strong practical application value.