Analysis of General Middleware Fault Prediction Model under Log Mining Technology

Authors

  • Fei Wu, Ting Li, Fucai Luo, Shulin Wu, Chuanqi Xiao

Abstract

In order to study of general middleware failure prediction model, a fault prediction model is constructed in the general middleware, the big data intelligent analysis method is used, the prediction process and feasibility of the model are analyzed, and the integrated network management system and the application in the field of information management system of electric power are explored under log mining technology. The results show that the Apriori-LTIS fault prediction algorithm is designed by analyzing the log processing module and its mining steps. The algorithm has short analysis time, high accuracy and recall rate, and good feasibility. It is applied to the integrated network management system and the electric power information management field, the fault location is located quickly, the accuracy rate is high, the error rate is low, the work efficiency of the system is improved, and the service life of the system is extended. The results show that the fault prediction model is of great significance to the safe and stable operation of the system. The error problems fed back in the operation process can provide more valuable information for the operation and maintenance personnel and provide reference for the future study of the fault prediction model.

Published

2020-03-31

Issue

Section

Articles