电力文本数据挖掘现状及挑战
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摘要
文本数据是电力大数据的重要组成部分,对其进行有效挖掘是智能电网深入、全面发展的需要。在目前已有研究成果的基础上,对电力领域文本数据挖掘的现状及挑战进行了深入剖析。首先分析了文本挖掘技术的发展过程及存在难题;接着重点分析了电力文本数据挖掘的关键技术及其研究现状,包括文本预处理技术、文本表示方法以及数据挖掘方法;然后以电力设备缺陷文本为对象,介绍了文本挖掘技术在电力领域的应用,包括缺陷文本质量的提升与保证、缺陷文本严重程度自动分类、缺陷发生部件及程度的自动提取、缺陷文本检索、基于缺陷文本的电力设备健康状态评价等,可为其他类型电力文本的挖掘提供参考;最后,探讨了电力文本挖掘面临的挑战以及未来发展方向。
Text data is an important part of power big data. It is the need for deep and comprehensive development of smart grid to explore text data effectively. Based on the existing research results, the current status and challenges of text data mining in the power field are deeply analyzed. Firstly, the development process and difficulties of text mining technology are analyzed. Then the key technologies and research status in the process of power text data mining are focused on, including text preprocessing technique, text representation method, and data mining method. Based on the current power equipment defect texts, the paper introduces the application of text mining technology in the power field, including improvement and guarantee of defect text quality, automatic classification of defect text severity, automatic extraction of defect occurrence components and degrees, defect text retrieval, power device health status evaluation based on defect text and so forth. These applications provide reference for mining other types of power texts. Finally, the challenges and future development directions of power text mining are discussed.
Text data is an important part of power big data. It is the need for deep and comprehensive development of smart grid to explore text data effectively. Based on the existing research results, the current status and challenges of text data mining in the power field are deeply analyzed. Firstly, the development process and difficulties of text mining technology are analyzed. Then the key technologies and research status in the process of power text data mining are focused on, including text preprocessing technique, text representation method, and data mining method. Based on the current power equipment defect texts, the paper introduces the application of text mining technology in the power field, including improvement and guarantee of defect text quality, automatic classification of defect text severity, automatic extraction of defect occurrence components and degrees, defect text retrieval, power device health status evaluation based on defect text and so forth. These applications provide reference for mining other types of power texts. Finally, the challenges and future development directions of power text mining are discussed.
引文
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[15]曹靖,陈陆燊,邱剑,等.基于语义框架的电网缺陷文本挖掘技术及其应用[J].电网技术,2017,41(2):637-643.
[16]刘梓权,王慧芳.基于知识图谱技术的电力设备缺陷记录检索方法[J].电力系统自动化,2018,42(14):158-164.
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[18]邵冠宇,王慧芳,何奔腾.电网设备缺陷文本的质量评价与提升方法[J/OL].电网技术:1-9[2019-03-05].https://doi.org/10.13335/j.1000-3673.pst.2018.0266.
[19]WANG H F,LIU Z Q.An Error Recognition Method for Power Equipment Defect Records Based on Knowledge Graph Technology[J/OL].Frontiers of Information Technology&Electronic Engineering.http://www.jzus.zju.edu.cn/iparticle.php?doi=10.1631/FITEE.1800260.
[20]马润泽,王龙响,余佳文,等.考虑历史缺陷文本信息的断路器状态评价研究[J].机电工程,2015,32(10):1375-1379.
[21]刘梓权,王慧芳,曹靖,等.基于卷积神经网络的电力设备缺陷文本分类模型研究[J].电网技术,2018,42(2):644-651.
[2]苗新,张东霞,孙德栋.在配电网中应用大数据的机遇与挑战[J].电网技术,2015,39(11):3122-3127.
[3]彭小圣,邓迪元,程时杰,等.面向智能电网应用的电力大数据关键技术[J].中国电机工程学报,2015,35(3):503-511.
[4]中国电机工程学会信息化专委会.中国电力大数据发展白皮书[R].北京:中国电机工程学会,2013.
[5]YU X H,XUE Y S.Smart Grids:A Cyber-Physical Sys tems Perspective[J].Proceedings of IEEE,2016,104(5):1085-1070.
[6]费思源.大数据技术在配电网中的应用综述[J].中国电机工程学报,2018,38(1):85-96.
[7]余容,李光强,尹健.微博文本挖掘研究综述[J].情报探索,2017(5):97-103.
[8]王伟,孙玉霞,齐庆杰,等.基于BiGRU-Attention神经网络的文本情感分类模型[J/OL].计算机应用研究:1-10[2019-03-05].http://kns.cnki.net/kcms/detail/51.1196.TP.20181011.1246.010.html.
[9]陈江涛,张金隆,张亚军.在线商品评论有用性影响因素研究:基于文本语义视角[J].图书情报工作,2012,56(10):119-123.
[10]张小花.基于文本分类技术的垃圾邮件过滤研究[D].合肥:安徽大学,2017.
[11]包振栋.基于生物医学文本挖掘的蛋白质间相互作用关系抽取方法的研究[D].咸阳:西北农林科技大学,2017.
[12]周亮,胡坤鹏,唐硕,等.基于文本挖掘方法的突发事件与医学救援装备关联研究[J].中国医疗设备,2018,33(3):154-158.
[13]邱剑.电力中文文本数据挖掘技术及其在可靠性中的应用研究[D].杭州:浙江大学,2016.
[14]邱剑,王慧芳,应高亮,等.文本信息挖掘技术及其在断路器全寿命状态评价中的应用[J].电力系统自动化,2016,40(6):107-112.
[15]曹靖,陈陆燊,邱剑,等.基于语义框架的电网缺陷文本挖掘技术及其应用[J].电网技术,2017,41(2):637-643.
[16]刘梓权,王慧芳.基于知识图谱技术的电力设备缺陷记录检索方法[J].电力系统自动化,2018,42(14):158-164.
[17]鞠平,周孝信,等.“智能电网+”研究综述[J].电力自动化设备,2018,38(5):2-11.
[18]邵冠宇,王慧芳,何奔腾.电网设备缺陷文本的质量评价与提升方法[J/OL].电网技术:1-9[2019-03-05].https://doi.org/10.13335/j.1000-3673.pst.2018.0266.
[19]WANG H F,LIU Z Q.An Error Recognition Method for Power Equipment Defect Records Based on Knowledge Graph Technology[J/OL].Frontiers of Information Technology&Electronic Engineering.http://www.jzus.zju.edu.cn/iparticle.php?doi=10.1631/FITEE.1800260.
[20]马润泽,王龙响,余佳文,等.考虑历史缺陷文本信息的断路器状态评价研究[J].机电工程,2015,32(10):1375-1379.
[21]刘梓权,王慧芳,曹靖,等.基于卷积神经网络的电力设备缺陷文本分类模型研究[J].电网技术,2018,42(2):644-651.