摘要
电力设备缺陷文本包含大量设备缺陷历史信息,从文本中精确辨识缺陷信息,可提供对于设备的故障率建模和健康状态评价问题的有效指导。引入依存句法分析技术,提出"左贪心"出栈规则和基于神经网络的依存关系状态转移分析模型,实现了电力设备实际缺陷文本和缺陷分类标准文本的依存句法树构建,并结合缺陷文本特点提出了电力设备依存句法树的剪枝、切分和重构方法。同时,提出了基于依存关系的树匹配算法,实现实际缺陷和标准缺陷依存句法树的匹配。以主变压器缺陷文本为例,研究了基于依存句法分析的缺陷信息辨识方法的可行性和有效性。结果表明,所提方法相比于其他树匹配算法和语义相似度计算方法在效率和准确性上有明显提升。
Power equipment defect text contains a lot of historical defect information of the equipment.The accurate information identification from defect text can provide effective guidance for equipment failure rate modeling and health status evaluation.By introducing dependency parsing technology,“left-greedy”out stack rule and neural network based dependency state transition analysis model are proposed.This paper implements the dependency-syntax-tree construction of actual defect text and defect classification standard text of power equipment,and proposes a method of pruning,segmentation and reconstruction of power equipment dependency syntax tree using the characteristics of the defect text.Meanwhile,dependency relationship based tree matching algorithm is proposed to match actual defects with standard defect dependency syntax trees.The feasibility and effectiveness of defect information identification method based on dependency syntax analysis is illustrated with the example of main transformer defect text.The results show that the efficiency and accuracy of the proposed method are improved substantially compared with other tree matching algorithms and semantic similarity computation methods.
作者
邵冠宇
王慧芳
吴向宏
陆金龙
李建红
何奔腾
SHAO Guanyu;WANG Huifang;WU Xianghong;LU Jinlong;LI Jianhong;HE Benteng(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;Zhejiang Huayun Information Technology Co.,Ltd.,Hangzhou 310012,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2020年第12期178-185,共8页
Automation of Electric Power Systems
关键词
文本挖掘
电力设备
依存句法分析
依存句法树匹配
信息辨识
text mining
power equipment
dependency parsing
dependency syntax tree matching
information identification
