ICT system fault analysis method integrating text classification and image recognition

Abstract

The invention discloses an ICT system fault analysis method integrating text classification and image recognition, and belongs to the technical field of fault analysis based on neural network learning. The method comprises the steps that fault data of customer service records are collected, the data are manually preprocessed including two parallel processes of text classification and image recognition, and finally classifier classification needs to be carried out in the two processes; then, through fault judgment, a fault analysis model is established for fault analysis, and initial data is returned for manual preprocessing after model updating. According to the invention, reasonable allocation of ICT system resources is realized; huge pressure brought to customer service operation and maintenance by the increasing number of ICT systems is relieved, the problems that knowledge cannot be shared, internal resources cannot be efficiently coordinated and orderly operated and the like due to the fact that state grid ICT customer service only depends on personal knowledge reserve and experience at the present stage are solved, and the intelligent level of ICT operation and maintenance isimproved.

Description

technical field [0001] The invention belongs to the technical field of neural network learning for fault analysis, in particular to an ICT system fault analysis method that integrates text classification and image recognition. Background technique [0002] With the increasing complexity of the power grid system, it is often impossible to analyze and judge the faults of the power grid system only by relying on the knowledge reserve and personal experience of a single staff member. Therefore, system failure analysis technology is also an important direction of computer-aided decision-making system research. Fault analysis and judgment technology is currently mainly realized through two methods: constructing a fault information database and performing fault analysis based on machine learning. [0003] The method of building a fault information database is usually to complete the storage of fault information by building a knowledge graph. The concept of knowledge graph was fir...

AI Technical Summary

Problems solved by technology

However, this method also has many disadvantages

As a specific application of machine learning algorithms, its difficulties and shortcomings mainly lie in the data. First of all, this method has high requirements for the amount of historical data, and a large amount of historical data represents higher training accuracy; secondly, the quality of historical data is also related to the final classification. The results are closely related, which depends on the knowledge level of the data recorder for the fault at that time; finally, this method needs to classify the fault in advance, and the classification standard is based on the experience of man-made fault handling, and the classification should not be too much or too little. It is also a major difficulty of this method

Images