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作 者:高德荃 刘广旭 冯宝 赵子岩 张媛 GAO Dequan;LIU Guangxu;FENG Bao;ZHAO Ziyan;ZHANG Yuan(State Grid Information&Telecommunication Brach,Xicheng District,Beijing 100761,China;NARI Group Co.,Ltd.,Nanjing 211106,Jiangsu Province,China)
机构地区:[1]国家电网有限公司信息通信分公司,北京市西城区100761 [2]南瑞集团有限公司,江苏省南京市211106
出 处:《电力信息与通信技术》2023年第9期60-66,共7页Electric Power Information and Communication Technology
基 金:国家电网有限公司信息通信分公司科技项目资助“电力信息通信智慧运行关键场景技术研究与应用”(52993919000G)。
摘 要:SDH光传输系统是支撑大电网安全运行的关键电力通信设施,当前对于实时严重告警的排查主要依赖通信调度人员的经验开展关联分析,该模式存在排查速度慢、业务协同差等不足。为了解决以上问题,提出了一种面向SDH光传输系统告警的领域知识图谱构建方法。针对告警文本中实体嵌套和边界难以确定的问题,构建BERT+Bi-LSTM+CNN模型进行命名实体识别。采用Multi-Attention+TextRNN模型进行关系抽取,并基于GCN+Self-Attention进行知识图谱融合。以某线路的通信设备告警文本为实验对象,采用所提出的方法进行知识抽取和融合,实体识别准确率达到91.42%,知识融合的hits@1能达到38.1%,验证了针对SDH光传输系统告警场景的领域知识图谱构建方法的有效性。The SDH optical transmission system is a key power communication facility that supports the safe operation of large power grids.Currently,the troubleshooting of real-time serious alarms mainly relies on the experience of communication dispatchers to carry out correlation analysis.The mode has some shortcomings such as slow troubleshooting speed and poor business coordination.In order to solve the above problems,a domain knowledge graph construction method for SDH optical transmission system alarm is proposed.Aiming at the problem that entity nesting and boundary are difficult to determine in alarm text,the BERT+Bi-LSTM+CNN model is constructed for named entity recognition.the Multi-Attention+TextRNN model is used for relation extraction,and the knowledge graph fusion is performed based on GCN+Self-Attention.Taking the communication alarm text of a certain SDH line as the experimental object,the proposed method is used to knowledge extraction and fusion,the entity recognition accuracy rate reaches 91.42%,the hits@1 of knowledge fusion can reach 38.1%,which verifies the effectiveness of the domain knowledge graph construction method for SDH optical transmission system alarm.
关 键 词:SDH光传输系统 告警排查 知识抽取 知识融合 BERT TextRNN
分 类 号:TN915.853[电子电信—通信与信息系统]
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