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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.

The power operation site typically faces high voltages, strong currents, and other environmental hazards. Safety fence as a boundary for power operation is of great importance to ensure the safety of operators. The preparation of operation fence is temporary and random, and the risk identification is a complex problem that involves a dynamic interaction between individuals and fence. The existing methods of determining the safety area based on prior knowledge are insufficient. Therefore, this paper proposes a risk identification method based on neighborhood correlation networks and vector calculation. In this method, firstly, the key targets of fence operation are detected by the target detection method. Then, considering that the fence operation target has the characteristics of flat shape and small size; the region proposal network by guided anchoring (GA-RPN) and neighborhood-related network module is introduced to improve the detection accuracy. Finally, the vector calculation criterion is used to judge whether the operator is in the safety fence area, to realize the risk identification of the operation fence overrun. Experiments are conducted on available power fence operation datasets, and the obtained results showed that the proposed method presents better performance than the classical target detection methods.

The Unmanned Aerial Vehicle (UAV) inspection mode has been gradually implemented in the power system. The UAV inspection image is checked by the target detection technology, but there is no high-precision target detection algorithm as the technical support. In this regard, this paper proposes a target detection algorithm based on the improved RetinaNet which is suitable for transmission lines defect detection. In this algorithm, the shortcomings of the RetinaNet anchor frame extraction mechanism based on Apriori are corrected. At the same time, the number and size of anchor frames are redesigned by using the improved K-means + + algorithm, so that the anchor frame of the improved algorithm gets the highest average IoU (Intersection over Union) value, which matches the actual size of the transmission line defects. Then, in RetinaNet, the feature pyramid network based on DenseNet is built as the backbone network to improve the model accuracy and make the model lighter. The improved model is trained and tested by using the data set of transmission line defects for validation. The results show that the proposed method has advantages and effectiveness in the detection of transmission line defects, and meets the requirements of intelligent inspection in terms of accuracy.