An ICT System Fault Analysis Technology Based on Text Classification and Image Recognition

Guodong Li, Jinyi Sun, Xin Guo
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Abstract

Due to the increasing complexity of power grid, SGCC hopes to have a computer-aided decision-making scheme for ICT system fault identification. This paper proposes an ICT fault analysis technology which integrates text classification and image recognition. It aims to solve the problem that only relying on the knowledge reserve and personal experience of a single staff member is often unable to analyze and judge the fault of power grid system. In this paper, the main work is as follows: first, preprocess the ICT fault report data to get more easily recognized structured data by computer; second, build a text recognition model to classify the text content; then build an image recognition model to classify the image content; finally, integrating the text and image classification based on the above content, linear regression is applied to the weight parameters to improve the accuracy and reliability of the classification results. According to the test results of comprehensive test data, the accuracy of the method is good and it can be used as a reliable scheme test.
基于文本分类和图像识别的ICT系统故障分析技术
由于电网日益复杂,SGCC希望有一个计算机辅助的ICT系统故障识别决策方案。提出了一种集文本分类和图像识别于一体的ICT故障分析技术。旨在解决仅依靠单个工作人员的知识储备和个人经验往往无法对电网系统的故障进行分析和判断的问题。本文的主要工作如下:首先,对ICT故障报告数据进行预处理,得到便于计算机识别的结构化数据;其次,建立文本识别模型,对文本内容进行分类;然后建立图像识别模型对图像内容进行分类;最后,结合上述内容对文本和图像进行分类,对权重参数进行线性回归,提高分类结果的准确性和可靠性。综合试验数据的试验结果表明,该方法精度较高,可作为一种可靠的方案试验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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