Research on Broadband Fault Detection Method for 5G Network of Distributed Smart Grid Based on Deep Learning

Recently, the rapid development of 5G trusted communication technology has greatly contributed to economic and social progress, greatly improving production efficiency. However, the rapidly developing 5G communication technology will inevitably add complexity to the maintenance of network lines and energy scheduling of smart grids. In order to better safeguard the operation and maintenance of 5G communication and energy scheduling of smart grids, reduce operation and maintenance costs, and better ensure the normal scheduling of smart grid systems, this paper conducts in-depth research on the distributed smart grid inspection system of automated unmanned aerial vehicles based on large-scale data and deep learning technology, and applies image super-resolution technology to the system. A real-time fault detection method for distributed smart grid unmanned aerial vehicles based on 5G communication sites and deep learning is proposed. Specifically, the paper mainly builds a network model based on deep convolutional neural networks and attention mechanisms. The attention mechanism can effectively assign more weights to important fault features, which is beneficial for improving the performance of the model. The experimental results show that compared with existing lightweight models, the method proposed in this paper can not only improve the evaluation indicators and accurately determine whether there are faults in 5G stations and power grid dispatch systems, but also reduce the parameters and computational costs of the model. In actual deployment, the method proposed in this article can meet real-time production needs, greatly improving the efficiency of operation and maintenance, while reducing operation and maintenance costs.