基于联合学习的电力安全实体抽取及风险预测方法

Methods of electric power safety entity extraction and risk prediction based on the joint learning

摘要

针对电力安全生产方面存在的知识分散和知识获取效率低的问题,提出基于联合学习的电力安全实体抽取及风险预测方法。采用电力安全知识数据模型与电力安全实体标签体系,对电力作业文本进行有效表示。构建基于双向编码器表征(bidirectional encoder representations from transformers, BERT)、双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)、条件随机场(conditional random fields, CRF)的电力安全生产知识联合抽取模型,自动识别电力作业文本中的关键字实体并预测潜在显性风险。测试结果与样例分析证明电力安全生产知识联合抽取模型在电力实体抽取和风险的有效性,为后续电力安全知识图谱的建立提供可靠的技术支撑。

Abstract

Aiming at the problems of dispersed knowledge and inefficient knowledge acquisition in electric power safety production, a joint learning-based method for electric power safety entity extraction and risk prediction is proposed. An electric power safety knowledge data model and a labeling system for electric power safety entities are adopted to effectively represent the text of electric power operations. A joint extraction model of electric power safety production knowledge based on bidirectional encoder representations from transformers (BERT), bidirectional long short-term memory (BiLSTM) and conditional random fields (CRF) is constructed to automatically identify the keyword entities in the text of electric power operations and predict the potential explicit risks. Test results and sample analysis prove the effectiveness of the joint extraction model for electric power work safety knowledge in electric power entity extraction and risk identification, which providing a robust and reliable technical foundation for the subsequent construction of power safety knowledge graphs.

关键词

电力安全 / 实体抽取 / 风险预测 / 联合学习

Key words

electric power safety / entity extraction / risk prediction / joint learning

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基金资助

南方电网科技基金资助项目(031900KC23040017(GDKJXM20230401))