摘要
开关柜设备的检修过程繁琐,需要人工手持设备对其进行检测,效率低下。为了提高开关柜设备的运维工作效率,本文将实体抽取技术运用于开关柜设备运维。首先将知网作为语料库爬取相关文献,然后对爬取的文献进行标注和分类,选择了三种实体抽取模型:使用训练集和验证集对模型进行训练和验证、使用测试集对模型的抽取能力进行测试并计算相关评价指标,最后对三种不同的实体抽取模型进行了比较,结果表明,采用BERT预训练模型的关注上下文信息的BERT-BiLSTM-CRF模型F1分数最高,可达到97.24,与BiLSTM-CRF模型和HMM模型相比分别提高了1.13和3.15。
The switch cabinet plays an important role in the power system.However,the maintenance process of the switch cabinet is often tedious,and requires manual handheld devices for detection,is inefficient.The troubleshooting work also puts forward a higher ability requirement for the operation and maintenance personnel.In order to improve the efficiency of operation and maintenance of switch cabinet,this paper studies the application of entity extraction technology to the operation and maintenance of switch cabinet.This paper first uses CNKI as the corpus to crawl relevant documents,then labels and classifies the crawled documents,selects three entity extraction models,uses the training set verification set to train and verify the model,uses the test set to test the model's extraction ability,calculates relevant evaluation indicators,and compares three different entity extraction models,The BERT-BiLSTM-CRF model,which adopts the BERT pre training model and can focus on context information,has the highest F1 score,reaching 97.24,which is 1.13 and 3.15 higher than BiLSTM-CRF model and HMM model respectively.
作者
路士杰
庞先海
董驰
顾朝敏
郑宝良
LU Shijie;PANG Xianhai;DONG Chi;GU Chaomin;ZHENG Baoliang(State Grid Hebei Energy Technology Service Co.,Ltd.,Shijiazhuang 050400,China;State Grid Hebei Electric Power Research Institute,Shijiazhuang 050021,China;Kechang Electric Co.,Baoding 072558,China)
出处
《河北电力技术》
2023年第3期85-89,共5页
Hebei Electric Power
基金
国网河北能源技术服务有限公司科技项目(TSS2021-13)。
关键词
开关柜
智能运维
实体抽取
switch cabinet
intelligent operation and maintenance
entity extraction
