基于自然语言处理的变电站主设备缺陷文本挖掘特征选择算法

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-20 DOI:10.1049/cps2.12079
Xiaoqing Mai, Tianhu Zhang, Changwu Hu, Yan Zhang
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引用次数: 0

摘要

变电站主设备缺陷信息的相关文本特征空间维度和特征权重较高,难以准确选择挖掘特征。采用自然语言处理(NLP)中短期神经网络模型实现日志中缺陷信息文本特征词的分割。在提取分类较多的主变设备缺陷信息文本特征形成特征空间后,设计 TF-IDF 算法计算文本关键词的重要性权重,判断缺陷信息文本特征词汇的关键性,准确定位缺陷信息文本特征,实现缺陷信息文本特征挖掘。实验表明,该算法对海量变电站主设备日志信息的特定词分割具有较高的精度。
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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Feature selection algorithm for substation main equipment defect text mining based on natural language processing

The dimension of relevant text feature space and feature weight of substation main equipment defect information is high, so it is difficult to accurately select mining features. The Natural Language Processing (NLP) medium and short-term neural network model is used to realise the defect information text feature word segmentation in the log. After extracting the text features of defect information of main substation equipment with high categories to form the feature space; the TF-IDF algorithm is designed to calculate the importance weight of text keywords, judge the criticality of defect information text feature vocabulary, accurately locate defect information text features, and realise defect information text feature mining. Experiments show that the algorithm has high precision for specific word segmentation of massive substation main equipment log information.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
期刊最新文献
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