A Reliability Prediction Method for Power Communication Network Based on Node Importance

To ensure the connectivity and service quality of the power communication network and reduce the hazards caused by node failures, a reliability prediction method of the power communication network based on node importance is proposed. First, to integrate the dynamic and static characteristics of nodes, the service importance calculation method is studied from the perspective of dynamic services, and the GAT-based node topology importance evaluation algorithm is studied from the perspective of static topology. Then, a comprehensive node importance calculation method based on FAHP is proposed to fuse the dynamic and static importance to achieve a more comprehensive node importance evaluation. Next, a Bi-LSTM-based reliability prediction method for the power communication network is proposed. To predict the reliability of the network more accurately, the method combines node importance, extracts valid fields and formulary mapping for real-time monitoring data, and fuses multi-dimension data based on information entropy technology to enhance the generalization capability of the Bi-LSTM prediction model. The experimental results show that the proposed node evaluation algorithm can accurately identify the important nodes that have a greater impact on the network reliability compared with the existing literature. Meanwhile, the proposed prediction algorithm can accurately predict network reliability and keep abreast of the changing trend of network reliability.