An error recognition method for power equipment defect records based on knowledge graph technology

Hui-fang WANG; Zi-quan LIU

doi:10.1631/FITEE.1800260

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An error recognition method for power equipment defect records based on knowledge graph technology

Regular Papers | Updated：2022-05-19

- An error recognition method for power equipment defect records based on knowledge graph technology
- 基于知识图谱技术的电力设备缺陷记录错误识别方法
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 11, Pages: 1564-1577(2019)
- Affiliations：
  
  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  Hui-fang WANG, E-mail: huifangwang@zju.edu.cn
- Funds：
- DOI：10.1631/FITEE.1800260
  CLC： TM76;TP181
- Received：24 April 2018，
  
  Revised：2019-11-12，
  
  Published：2019-11
- Accepted：
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Hui-fang WANG, Zi-quan LIU. An error recognition method for power equipment defect records based on knowledge graph technology[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(11): 1564-1577.
DOI：

Hui-fang WANG, Zi-quan LIU. An error recognition method for power equipment defect records based on knowledge graph technology[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(11): 1564-1577. DOI： 10.1631/FITEE.1800260.

摘要

为实时检测缺陷记录的录入错误，提出一种基于知识图谱技术的电力设备缺陷记录错误识别方法。针对电力缺陷文本特点，通过优化构建知识图谱的一般流程，提出利用现有电力设备缺陷记录语料构建电力设备缺陷知识图谱的方法。然后，基于所构建的知识图谱，针对电力设备缺陷记录的各种错误类型，提出利用图搜索识别缺陷记录错误。最后，对比所提方法和机器学习方法在缺陷记录错误识别上的查准率、查全率、

分数、准确率和效率，并分析影响各种方法错误识别效果的因素。比较结果表明，所提方法在缺陷记录错误识别效果上有明显优势，识别效率满足实时性要求。

Abstract

To recognize errors in the power equipment defect records in real time

we propose an error recognition method based on knowledge graph technology. According to the characteristics of power equipment defect records

a method for constructing a knowledge graph of power equipment defects is presented. Then

a graph search algorithm is employed to recognize different kinds of errors in defect records

based on the knowledge graph of power equipment defects. Finally

an error recognition example in terms of transformer defect records is given

by comparing the precision

recall

-score

accuracy

and efficiency of the proposed method with those of machine learning methods

and the factors influencing the error recognition effects of various methods are analyzed. Results show that the proposed method performs better in error recognition of defect records than machine learning methods

and can satisfy real-time requirements.

关键词

Keywords

references

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