Intelligent identification algorithm for safety risks of transmission line maintenance operations based on deep learning

In order to achieve the possibility and probability of discovering the violation of operation tasks through previous data, this paper proposes an intelligent identification algorithm for safety risk of transmission line maintenance operations based on deep learning. Firstly, a large amount of transmission line maintenance operation image data is collected, secondly, the size of image chunks is determined by using a priori information and experiments to reduce the number of layers of convolution and reduce the computing time. Finally, a standard library of image samples of transmission lines is successfully constructed, which achieves the standardised collection of inspection defect samples with key features of multi-source data and the standardised management of transmission equipment information, unified labelling transformation and efficient storage and retrieval of defect information. The experimental results show that real-time monitoring and early warning of potential safety risks in transmission line maintenance operations can be achieved through the deep learning-based intelligent identification algorithm for safety risks in transmission line maintenance operations, thus improving the safety and efficiency of maintenance operations.