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清华大学学报(自然科学版)  2022, Vol. 62 Issue (6): 1081-1087    DOI: 10.16511/j.cnki.qhdxxb.2022.22.017
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恶劣环境条件下海外天然气管道站场事故演化知识图谱建模及预警方法
陈传刚, 胡瑾秋, 韩子从, 陈怡玥, 肖尚蕊
中国石油大学(北京) 安全与海洋工程学院, 北京 102249
Knowledge graph based early warning method for accident evolution in natural gas pipeline station abroad for harsh environmental conditions
CHEN Chuangang, HU Jinqiu, HAN Zicong, CHEN Yiyue, XIAO Shangrui
College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
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摘要 近年来恶劣环境条件(雷电、风、雨)频发,给长输油气管道尤其是海外油气管道的长期安全运行带来了重大威胁。海外油气管道的运行与应急救援面临着恶劣环境条件下的管道风险事故数据不足、跨国协同较为困难等问题。为解决当前管道事故预警模型过于依赖现场运行数据或事故相关数据的情况,该文利用恶劣环境条件下的天然气管道站场有限的事故文本数据,提出了一种基于知识图谱的站场事故演化预警模型。该模型采用双向长短期记忆网络-条件随机场算法(Bi-LSTM-CRF)对站场事故文本进行因果关系抽取,并加入了文本特征以增强抽取效果,然后利用Neo4j图数据库根据因果关系抽取的结果,建立了恶劣环境条件下海外天然气管道站场事故演化知识图谱。结果表明:相较于传统长输管道站场事故预警方法,该文所提出的基于知识图谱的站场事故预警模型不仅能够实现站场事故的预警,还能够实现对事故的路径预测以及事故应急决策推荐。这证明了该预警模型不仅可用性好,还能够有效地帮助海外天然气管道站场安全管理人员进行准确的风险控制与事故预防。
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陈传刚
胡瑾秋
韩子从
陈怡玥
肖尚蕊
关键词 恶劣环境条件天然气管道站场双向长短期记忆网络-条件随机场(Bi-LSTM-CRF)算法知识图谱事故预警路径预测    
Abstract:In recent years, harsh environmental conditions (lightning, wind and rain) have posed significant threats to the safe operation of long oil and gas pipelines, especially in oil and gas pipelines in other countries. The operation and emergency responses for oil and gas pipelines abroad have faced various problems such as insufficient pipeline risk accident data and difficult cross-border coordination during harsh environmental conditions. Current pipeline accident early warning models rely too much on field data or accident-related data. Thus, this paper presents a station accident risk evolution early warning method based on a knowledge graph that uses a small amount of accident report data from natural gas pipeline stations for harsh environmental conditions. The method uses a bidirectional long short-term memory-conditional random field algorithm (Bi-LSTM-CRF) to extract the causal relationships from station accident reports, with a Neo4j graph database then used to establish the knowledge graph for accident risk evolution in natural gas pipeline stations abroad experiencing harsh environmental conditions. The results show that this knowledge graph based station accident risk early warning method not only provides earlier warnings of station accidents than traditional station accident early warning methods for long pipelines, but also predicts the accident path with recommended accident responses. The results show that this early warning method can effectively help safety management personnel in natural gas pipeline stations abroad provide better risk control and accident prevention.
Key wordsharsh environmental condition    natural gas pipeline station    bidirectional long short-term memory-conditional random field (Bi-LSTM-CRF) algorithm    knowledge graph    accident early warning    path prediction
收稿日期: 2021-12-22      出版日期: 2022-05-06
基金资助:国家自然科学基金项目(52074323);中石油战略合作科技专项(ZLZX2020-05-02);中国石油大学(北京)科研基金项目(ZX20200137)
通讯作者: 胡瑾秋,教授,E-mail:hujq@cup.edu.cn      E-mail: hujq@cup.edu.cn
作者简介: 陈传刚(1990-),男,博士研究生。
引用本文:   
陈传刚, 胡瑾秋, 韩子从, 陈怡玥, 肖尚蕊. 恶劣环境条件下海外天然气管道站场事故演化知识图谱建模及预警方法[J]. 清华大学学报(自然科学版), 2022, 62(6): 1081-1087.
CHEN Chuangang, HU Jinqiu, HAN Zicong, CHEN Yiyue, XIAO Shangrui. Knowledge graph based early warning method for accident evolution in natural gas pipeline station abroad for harsh environmental conditions. Journal of Tsinghua University(Science and Technology), 2022, 62(6): 1081-1087.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2022.22.017  或          http://jst.tsinghuajournals.com/CN/Y2022/V62/I6/1081
  
  
  
  
  
  
  
  
  
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