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作 者:齐子琛 胡玉玲 万雨瑞[1,2] 卓亮 Qi Zichen;Hu Yuling;Wan Yurui;Zhuo Liang(Institute of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 102616,China;Beijing Key Laboratory of Intelligent Processing for Building Big Data,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;PetroChina Kunlun Gas Co.Ltd.of Chuzhou,Anhui Chuzhou 239400,China)
机构地区:[1]北京建筑大学电气与信息工程学院,北京102616 [2]北京建筑大学建筑大数据智能处理方法研究北京市重点实验室,北京100044 [3]滁州中石油昆仑燃气有限公司,安徽滁州239400
出 处:《消防科学与技术》2023年第5期718-723,共6页Fire Science and Technology
摘 要:目前,燃气事故应急处置预案大多以文本形式存储,处置人员需要通过查阅大量文本确定相应的处置措施,难以满足应急事件处置的快速性与时效性需求。为了克服时效性差的缺点,及提高以文本形式存储的燃气应急处理预案的可复用性与可扩展性,构建了基于燃气事故应急处置知识图谱的辅助决策框架,采用“自顶向下”的方式构建知识图谱。采取BERTBiLSTM-CRF模型对燃气事故的背景信息进行实体抽取,采取结合语义角色标注与依存句法分析的方法对燃气事故处置方法和处置信息进行实体-关系三元组抽取,最终在Neo4j图数据库当中存储与显示构建的燃气事故应急处置知识图谱。研究可为燃气事故应急处置辅助决策提供有效支持。Currently,most of the gas accident emergency response plans are stored in the form of text,and the on-site disposal personnel need to check a large number of texts to determine the corresponding disposal measures,which can hardly meet the rapidity and timeliness of emergency response.In order to overcome the shortcomings of poor timeliness and improve the reusability and flexibility of the gas emergency response plan stored in the form of text,an auxiliary decision-making framework based on the knowledge graph of gas accident emergency response was built,using a"top-down"approach.BERT-BiLSTM-CRF method is used to extract the background information of gas accident cases,and a method combining semantic role labeling with dependency parsing is proposed to build entity-relationship triad.The extracted gas emergency disposal entities and relational knowledge are finally stored and displayed in the Neo4j graph database.This study can provide effective support for auxiliary decision-making of gas accident emergency disposal.
关 键 词:燃气应急处置 知识图谱 BERT-BiLSTM-CRF模型 依存句法分析 语义角色标注
分 类 号:X913[环境科学与工程—安全科学] TU996.8[建筑科学—供热、供燃气、通风及空调工程]
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