|本期目录/Table of Contents|

[1]李聪,徐子烜,王雨情,等.城市燃气管网泄漏事故分析知识图谱构建及应用研究*[J].中国安全生产科学技术,2022,18(10):5-12.[doi:10.11731/j.issn.1673-193x.2022.10.001]
 LI Cong,XU Zixuan,WANG Yuqing,et al.Construction and application of knowledge graph for leakage accident analysis of urban gas pipeline network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(10):5-12.[doi:10.11731/j.issn.1673-193x.2022.10.001]
点击复制

城市燃气管网泄漏事故分析知识图谱构建及应用研究*
分享到:

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年10期
页码:
5-12
栏目:
学术论著
出版日期:
2022-10-31

文章信息/Info

Title:
Construction and application of knowledge graph for leakage accident analysis of urban gas pipeline network
文章编号:
1673-193X(2022)-10-0005-08
作者:
李聪徐子烜王雨情许文博杨锐
(1.中国矿业大学(北京) 应急管理与安全工程学院,北京 100083;
2.清华大学 工程物理系公共安全研究院,北京 100084)
Author(s):
LI Cong XU Zixuan WANG Yuqing XU Wenbo YANG Rui
(1.School of Emergency Management and Safety Engineering,China University of Mining and Technology (Beijing),Beijing 100083,China;
2.Institute of Public Safety Research,Department of Engineering Physics,Tsinghua University,Beijing 100084,China)
关键词:
燃气管网燃气泄漏知识图谱模式层神经网络
Keywords:
gas pipeline network gas leakage knowledge graph schema-level neural network
分类号:
X932
DOI:
10.11731/j.issn.1673-193x.2022.10.001
文献标志码:
A
摘要:
为深入认识燃气管网泄漏事故的发生发展机理,提高事故分析预测的自动化、智能化、数字化水平,利用知识图谱对燃气管网泄漏事故进行研究。在事故案例分析的基础上,从人-物-环-管的角度对燃气泄漏过程以及火灾爆炸次生事故的相关实体进行归纳梳理,对实体间的逻辑关系和非逻辑关系进行辨识,并对实体的属性进行分类,进而构建出较为全面的燃气管网泄漏事故知识图谱。在此基础上,搭建BP神经网络模型,基于已知实体或属性状态,预测相关联其他实体或属性的状态。研究结果表明:燃气管网知识图谱能够有效展示燃气管网泄漏事故发展的动态过程及相关要素,结合BP神经网络能够有效预测事故的发展路径及相关状态,从而提高燃气管网泄漏事故的分析预测水平与效率。
Abstract:
In order to fully understand the occurrence and development mechanism of the leakage accident of gas network,and improve the automation,intelligence and digitalization level of accident analysis and prediction,the leakage accident of gas pipeline network were studied by using the knowledge graph.On the basis of accident cases analysis,the related entities of gas leakage process and secondary accidents of fire and explosion were summarized from the perspective of man-material-environment-management.The logical and non-logical relationship between the entities were identified,and then the attributes of entities were classified to construct a more comprehensive knowledge graph of gas pipeline network leakage accidents.Based on this,a BP neural network model was constructed to predict the state of related other entities or attributes based on the known entity or attribute state.The results showed that the knowledge graph of gas pipeline network could effectively demonstrate the dynamic process and relevant elements of the development of gas pipeline network leakage accidents,and the combination of BP neural network could effectively predict the development path and relevant states of the accidents,thus improving the analysis and prediction level and efficiency of gas pipeline network leakage accidents.

参考文献/References:

[1]宁波市燃气协会.全国燃气事故分析报告(2021年·第二季度报告暨半年综述)[EB/OL].(2021-08-05)[2022-04-02].http://nbga.org.cn/GaNewsDetail.aspx?id=102657.
[2]黄忠宏,帅健,徐后佳,等.城镇燃气管道完整性管理效能评价方法[J].中国安全生产科学技术,2021,17(S1):165-171. HUANG Zhonghong,SHUAI Jian,XU Houjia,et al.Efficiency evaluation method on integrity management of urban gas pipeline[J].Journal of Safety Science and Technology,2021,17(S1):165-171.
[3]刘海云,韩晓松,翟振岗,等.基于复杂网络的燃气管线破裂灾害链风险分析[J].中国安全生产科学技术,2020,16(9):37-42. LIU Haiyun,HAN Xiaosong,ZHAI Zhengang,et al.Risk analysis on rupture disaster chain of gas pipeline based on complex network[J].Journal of Safety Science and Technology,2020,16(9):37-42.
[4]ALIBEK K,FAISAL K,YANG M,et al.Gas leakage detection using spatial and temporal neural network model[J].Process Safety and Environmental Protection,2022,160:968-975.
[5]王文和,刘林精,董传富,等.城市埋地燃气管道泄漏火灾致因耦合分析[J].消防科学与技术,2019,38(3):430-433. WANG Wenhe,LIU Linjing,DONG Chuanfu,et al.Coupling analysis of causes of leakage fire inurban buried gas pipeline[J].Fire Science and Technology,2019,38(3):430-433.
[6]SHAILESH C,HUNG N,ANNIE N.Evaluating critical gas pipeline crossings for freight truck routes[J].Case Studies on Transport Policy,2019,7(4):680-688.
[7]郭榕,杨群,刘绍翰,等.电网故障处置知识图谱构建研究与应用[J].电网技术,2021,45(6):2092-2100. GUO Rong,YANG Qun,LIU Shaohan,et al.Construction and application of power grid fault handing knowledge graph[J].Power System Technology,2021,45(6):2092-2100.
[8]李永卉,周树斌,周宇婷,等.基于图数据库Neo4j的宋代镇江诗词知识图谱构建研究[J].大学图书馆学报,2021,39(2):52-61. LI Yonghui,ZHOU Shubin,ZHOU Yuting,et al.Research and implementation on knowledge graph of Zhenjing Poetry in Song Dynasty based on graph database Neo4j[J].Journal of Academic Library,2021,39(2):52-61.
[9]朱庆,王所智,丁雨淋,等.铁路隧道钻爆法施工智能管理的安全质量进度知识图谱构建方法[J].武汉大学学报(信息科学版),2021,46(3):353-359. ZHU Qing,WANG Suozhi,DING Yulin,et al.Construction method of “Safety-Quality-Schedule” knowledge graph for intelligent management of drilling and blasting construction of railway tunnels[J].Geomatics and Information Science of Wuhan University,2021,46(3):353-359.
[10]蒋长红.带有安全审计和负载均衡的分布式数据存储系统[D].成都:电子科技大学,2018.
[11]SINGH J,SINGH S,SINGH P J.Investigation on wall thickness reduction of hydropower pipeline underwent to erosion-corrosion process[J].Engineering Failure Analysis,2021,127:105504.
[12]CHEN M Y,LI N,MU H L.Assessment of a low-carbon natural gas storage network using the FLP model:a case study within China-Russia natural gas pipeline east line’s coverage[J].Journal of Natural Gas Science and Engineering,2021,96:104246.
[13]陆雄文.管理学大辞典[M].上海:上海辞书出版社,2013:363-364.
[14]KHAN S,LIU X F,SHAKIL K A,et al.A survey on scholarly data:from big data perspective[J].Information Processing and Management,2017,53(4):923-944.
[15]LI F,WANG W H,XU J,et al.A CAST-based causal analysis of the catastrophic underground pipeline gas explosion in Taiwan [J].Engineering Failure Analysis,2020,108:104343.
[16]武晓旭,龚孔成,贾明涛.煤矿事故预测的指数平滑——BP神经网络混合模型研究[J].中国安全生产科学技术,2014,10(9):165-169. WU Xiaoxu,GONG Kongcheng,JIA Mingtao.Research on mixed model of exponential smoothing method and BP neural network for accident forecasting in coal mine[J].Journal of Safety Science and Technology,2014,10(9):165-169.
[17]吴鑫,赵红霞,罗筱毓,等.基于BP神经网络的岩石损伤声发射事件源定位研究[J].中国安全生产科学技术,2021,17(8):36-42. WU Xin,ZHAO Hongxia,LUO Xiaoyu,et al.Study on acoustic emission event source location of rock damage based on BP neural network[J].Journal of Safety Science and Technology,2021,17(8):36-42.

相似文献/References:

[1]杨春生.突发事件现场警戒区域的研究*[J].中国安全生产科学技术,2009,5(6):167.
 YANG Chun sheng.Study on the emergency incident cordon zones[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2009,5(10):167.
[2]顾一波,霍宇芒.基于贝叶斯网络的地震次生燃气管道泄漏事件链构建[J].中国安全生产科学技术,2016,12(7):134.[doi:10.11731/j.issn.1673-193x.2016.07.024]
 GU Yibo,HUO Yumang.Construction of event chain for secondary gas pipeline leakage induced by earthquake based on Bayesian network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(10):134.[doi:10.11731/j.issn.1673-193x.2016.07.024]
[3]虞丹阳,玉建军,靳新迪.2种基于模式识别的环状燃气管网泄漏检测方法[J].中国安全生产科学技术,2017,13(1):187.[doi:10.11731/j.issn.1673-193x.2017.01.031]
 YU Danyang,YU Jianjun,JIN Xindi.Study on two kinds of leakage detection methods for loop gas pipeline network based on pattern recognition[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(10):187.[doi:10.11731/j.issn.1673-193x.2017.01.031]
[4]玉建军,王帅,郭敏,等.基于相似理论的燃气管网水力工况分析[J].中国安全生产科学技术,2018,14(7):128.[doi:10.11731/j.issn.1673-193x.2018.07.019]
 YU Jianjun,WANG Shuai,GUO Min,et al.Study on Hydraulic Operation Condition of Gas Pipeline Network Based on Similarity Theory[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(10):128.[doi:10.11731/j.issn.1673-193x.2018.07.019]
[5]吴建松,原帅琪,蔡继涛,等.基于OpenFOAM的综合管廊舱内燃气泄漏扩散数值模拟[J].中国安全生产科学技术,2020,16(2):168.[doi:10.11731/j.issn.1673-193x.2020.02.027]
 WU Jiansong,YUAN Shuaiqi,CAI Jitao,et al.Numerical simulation of gas leakage and dispersion in utility tunnel compartment based on OpenFOAM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(10):168.[doi:10.11731/j.issn.1673-193x.2020.02.027]
[6]高保彬,任闯难,刘彦伟,等.挡气板对综合管廊燃气泄漏扩散影响规律研究*[J].中国安全生产科学技术,2021,17(7):35.[doi:10.11731/j.issn.1673-193x.2021.07.006]
 GAO Baobin,REN Chuangnan,LIU Yanwei,et al.Study on influence laws of gas baffle on gas leakage and diffusion in utility tunnel[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(10):35.[doi:10.11731/j.issn.1673-193x.2021.07.006]

备注/Memo

备注/Memo:
收稿日期: 2022-04-27
* 基金项目: 国家重点研发计划项目(2021YFF0600403);国家自然科学基金项目(U2033206);中央高校基本科研业务经费项目(2022YQAQ05)
作者简介: 李聪,博士,讲师,主要研究方向为燃气管网泄漏及火灾爆炸机理、高高原及航空器火灾动力学、森林火灾风险评估及灭火技术、低温燃料动力学及火行为等。
更新日期/Last Update: 2022-11-13