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Article(id=1149733270016933975, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149733267617788430, articleNumber=1003-3033(2024)12-0094-06, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.12.0688, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1725465600000, receivedDateStr=2024-09-05, revisedDate=1730908800000, revisedDateStr=2024-11-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1752047372583, onlineDateStr=2025-07-09, pubDate=1735315200000, pubDateStr=2024-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752047372583, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752047372583, creator=13701087609, updateTime=1752047372583, updator=13701087609, issue=Issue{id=1149733267617788430, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='12', pageStart='1', pageEnd='228', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752047372010, creator=13701087609, updateTime=1756361981736, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1167830052499628941, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149733267617788430, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1167830052499628942, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149733267617788430, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=94, endPage=99, ext={EN=ArticleExt(id=1149733270176317531, articleId=1149733270016933975, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Intelligent analysis of building fire accidents based on knowledge graph, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To provide intelligent and systematic decision support for building safety management，building fire accidents data was collected and summarized. The knowledge graph of building fire accidents was developed to construct a knowledge graph database. Based on the dimensions of time，space，theme，and important entities，the implementation process of the intelligent question-answering system was innovatively presented. Moreover，the intelligent analysis of building fire risk was performed. The results showed that daytime and summer were high-risk periods for building fires. The frequency of building fire accidents in East China was significantly higher than that in other regions，and the fire risk of building fires was higher in electrical and warehouse areas. Reinforced concrete frame structures and factory buildings were more prone to building fires. Most ignition sources were combustible solids，and the main cause of fire accidents was illegal construction behavior.

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为建筑安全管理提供智能化、系统化的决策支持，收集和整理建筑火灾事故数据。构建建筑火灾事故知识图谱，形成建筑火灾领域的知识库；基于时间、空间、主题和重要实体维度，创新展示智能问答系统的实现过程，开展建筑火灾风险智能分析。研究结果表明：白天和夏季属于建筑火灾的高发时段；华东地区的建筑火灾事故发生频率显著高于其他区域，电气、仓库区域建筑火灾风险较高；钢筋混凝土框架结构的建筑和厂房建筑更易发生建筑火灾；起火源大多数为可燃固体，火灾事故的原因主要与违规施工行为有关。

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许 慧 (1988—)，女，河南商丘人，博士，副教授，主要从事工程项目智能安全风险方面的研究。E-mail：。

薛红，讲师。

周启琳，经济师。

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许 慧 (1988—)，女，河南商丘人，博士，副教授，主要从事工程项目智能安全风险方面的研究。E-mail：。

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许 慧 (1988—)，女，河南商丘人，博士，副教授，主要从事工程项目智能安全风险方面的研究。E-mail：。

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薛红，讲师。

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薛红，讲师。

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周启琳，经济师。

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周启琳，经济师。

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companyId=1167742947874316665, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=5 重庆市重大项目服务中心 重庆市区域合作服务中心，重庆 401120)])], figs=[ArticleFig(id=1167742949656895899, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=EN, label=Fig.1, caption=Main entities (concepts)，relationships，and properties in the field of building fire accidents, figureFileSmall=SWnvRrH6xk1ALJ0mB4WogA==, figureFileBig=M61+lLXVupIRd40BjzaQtA==, tableContent=null), ArticleFig(id=1167742949765947804, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=CN, label=图1, caption=建筑火灾事故领域主要实体(概念)、关系和属性, figureFileSmall=SWnvRrH6xk1ALJ0mB4WogA==, figureFileBig=M61+lLXVupIRd40BjzaQtA==, tableContent=null), ArticleFig(id=1167742949870805405, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=EN, label=Fig.2, 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figureFileBig=FeXmFZUNBBKS5U9GrnwaMA==, tableContent=null), ArticleFig(id=1167742950206349729, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=EN, label=Table 1, caption=

Time dimension accident statistics

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时间维度 事故极值统计 事故分布
季节 夏季占事故
总数的27.0%		 春季、夏季、
秋季、冬季
旬 中旬占事故总数的35.0% 上旬、中旬、下旬
时间段 上午、中午和下午发生的
事故占事故总数的57.4%		 上午、中午、下午、
晚上、子夜、凌晨
), ArticleFig(id=1167742950256681378, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=CN, label=表1, caption=

时间维度事故统计

, figureFileSmall=null, figureFileBig=null, tableContent=
时间维度 事故极值统计 事故分布
季节 夏季占事故
总数的27.0%		 春季、夏季、
秋季、冬季
旬 中旬占事故总数的35.0% 上旬、中旬、下旬
时间段 上午、中午和下午发生的
事故占事故总数的57.4%		 上午、中午、下午、
晚上、子夜、凌晨
), ArticleFig(id=1167742950344761763, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=EN, label=Table 2, caption=

Number and content presentation of time dimension related path problem

, figureFileSmall=null, figureFileBig=null, tableContent=
分析类别 多维度分析
查询分析 问题数量 13个
问题举例及
实体关系		 春季发生的事故
有哪些(发生季节-事故)
统计分析 问题数量 13个
问题举例及
实体关系		 上旬发生的事故有多少起(发生日期-事故)
混合分析 问题数量 1 118个
问题举例及
实体关系		 上午因电气设备故障发生的事故有哪些(发生时间-事故原因-事故)
), ArticleFig(id=1167742950403482020, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=CN, label=表2, caption=

时间维度关联路径问题数量及内容展示

, figureFileSmall=null, figureFileBig=null, tableContent=
分析类别 多维度分析
查询分析 问题数量 13个
问题举例及
实体关系		 春季发生的事故
有哪些(发生季节-事故)
统计分析 问题数量 13个
问题举例及
实体关系		 上旬发生的事故有多少起(发生日期-事故)
混合分析 问题数量 1 118个
问题举例及
实体关系		 上午因电气设备故障发生的事故有哪些(发生时间-事故原因-事故)
), ArticleFig(id=1167742950466396581, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=EN, label=Table 3, caption=

Spatial dimension accident statistics

, figureFileSmall=null, figureFileBig=null, tableContent=
空间维度 事故极值统计 事故分布
事故地域 华东地区占事故
总数的36.2%		 东北地区、华北地区、华中地区、华东地区、华南地区、西南地区、西北地区
事故区域 仓库区域占
事故总数的
36.4%		 公共区域(大厅、楼梯间等)、仓库区域(仓间等)、居住区域(住宅、宾馆等)等
), ArticleFig(id=1167742950533505446, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=CN, label=表3, caption=

空间维度事故统计

, figureFileSmall=null, figureFileBig=null, tableContent=
空间维度 事故极值统计 事故分布
事故地域 华东地区占事故
总数的36.2%		 东北地区、华北地区、华中地区、华东地区、华南地区、西南地区、西北地区
事故区域 仓库区域占
事故总数的
36.4%		 公共区域(大厅、楼梯间等)、仓库区域(仓间等)、居住区域(住宅、宾馆等)等
), ArticleFig(id=1167742950600614311, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=EN, label=Table 4, caption=

Number and content presentation of spatial dimension related path problem

, figureFileSmall=null, figureFileBig=null, tableContent=
分析类别 多维度分析
查询分析 问题数量 14个
问题举例及
实体关系		 发生在华东地区的事故有哪些(事故地域-事故)
统计分析 问题数量 14个
问题举例及
实体关系		 发生在仓库区域的事故有多少起(事故区域-事故)
混合分析 问题数量 1 176个
问题举例及
实体关系		 华东地区公共区域的事故有哪些(事故地域-事故区域-事故)
), ArticleFig(id=1167742950718054824, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=CN, label=表4, caption=

空间维度关联路径问题数量及内容展示

, figureFileSmall=null, figureFileBig=null, tableContent=
分析类别 多维度分析
查询分析 问题数量 14个
问题举例及
实体关系		 发生在华东地区的事故有哪些(事故地域-事故)
统计分析 问题数量 14个
问题举例及
实体关系		 发生在仓库区域的事故有多少起(事故区域-事故)
混合分析 问题数量 1 176个
问题举例及
实体关系		 华东地区公共区域的事故有哪些(事故地域-事故区域-事故)
), ArticleFig(id=1167742950793552297, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=EN, label=Table 5, caption=

Thematic dimension accident statistics

, figureFileSmall=null, figureFileBig=null, tableContent=
主题维度 事故极值统计/ % 事故分布
建筑结构 45.9(钢筋混凝土
框架结构)		 钢筋混凝土框架结构、钢架结构、砖木结构、砖混框架结构、锌铁硼棚、木质结构、彩钢结构
建筑用途 56.0(厂房) 商业、住宅、厂房、综合
事故阶段 88.2(运营) 施工、运营
起火物质 35.7(可燃固体) 可燃固体、可燃液体、可燃气体、可燃金属、自燃物质、非金属粉尘和纤维
事故等级 46.3(一般事故) 特别重大事故、重大事故、较大事故、一般事故
事故原因 35.5(违规违
章施工作业)		 电气设备故障、明火的使用不当、可燃气泄漏、违规使用电气、违规违章施工作业、自然灾害
), ArticleFig(id=1167742950860661162, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=CN, label=表5, caption=

主题维度事故统计

, figureFileSmall=null, figureFileBig=null, tableContent=
主题维度 事故极值统计/ % 事故分布
建筑结构 45.9(钢筋混凝土
框架结构)		 钢筋混凝土框架结构、钢架结构、砖木结构、砖混框架结构、锌铁硼棚、木质结构、彩钢结构
建筑用途 56.0(厂房) 商业、住宅、厂房、综合
事故阶段 88.2(运营) 施工、运营
起火物质 35.7(可燃固体) 可燃固体、可燃液体、可燃气体、可燃金属、自燃物质、非金属粉尘和纤维
事故等级 46.3(一般事故) 特别重大事故、重大事故、较大事故、一般事故
事故原因 35.5(违规违
章施工作业)		 电气设备故障、明火的使用不当、可燃气泄漏、违规使用电气、违规违章施工作业、自然灾害
), ArticleFig(id=1167742950965518763, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=EN, label=Table 6, caption=

Number and content presentation of thematic dimension related path problem

, figureFileSmall=null, figureFileBig=null, tableContent=
分析类别 多维度分析
查询分析 问题数量 29个
问题举例及
实体关系		 发生在钢筋混凝土框架结构的事故有哪些(建筑结构-事故)
统计分析 问题数量 29个
问题举例及
实体关系		 由未熄灭烟头点火源引起的事故有多少起(起火物质-事故)
混合分析 问题数量 1 566个
问题举例及
实体关系		 由明火的使用不当引起的一般事故有哪些(事故原因-事故等级-事故)
), ArticleFig(id=1167742951032627628, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149733270016933975, language=CN, label=表6, caption=

主题维度关联路径问题数量及内容展示

, figureFileSmall=null, figureFileBig=null, tableContent=
分析类别 多维度分析
查询分析 问题数量 29个
问题举例及
实体关系		 发生在钢筋混凝土框架结构的事故有哪些(建筑结构-事故)
统计分析 问题数量 29个
问题举例及
实体关系		 由未熄灭烟头点火源引起的事故有多少起(起火物质-事故)
混合分析 问题数量 1 566个
问题举例及
实体关系		 由明火的使用不当引起的一般事故有哪些(事故原因-事故等级-事故)
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基于知识图谱的建筑火灾事故智能分析
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许慧 1, 2 , 蒋玫 3 , 薛红 4 , 周启琳 5
中国安全科学学报 | 安全工程技术 2024,34(12): 94-99
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中国安全科学学报 | 安全工程技术 2024, 34(12): 94-99
基于知识图谱的建筑火灾事故智能分析
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许慧1, 2 , 蒋玫3, 薛红4, 周启琳5
作者信息
  • 1 重庆邮电大学 经济管理学院，重庆 400065
  • 2 重庆邮电大学大数据智能计算重点实验室，重庆 400065
  • 3 重庆邮电大学 理学院，重庆 400065
  • 4 山东大学 管理学院，山东 济南 250100
  • 5 重庆市重大项目服务中心 重庆市区域合作服务中心，重庆 401120

    • 许 慧 (1988—)，女，河南商丘人，博士，副教授，主要从事工程项目智能安全风险方面的研究。E-mail：。

    薛红，讲师。

    周启琳，经济师。

Intelligent analysis of building fire accidents based on knowledge graph
Hui XU1, 2 , Mei JIANG3, Hong XUE4, Qilin ZHOU5
Affiliations
  • 1 College of Economics and Management，Chongqing University of Posts and Telecommunications，Chongqing 400065，China
  • 2 Key Laboratory of Big Data Intelligent Computing，Chongqing University of Posts and Telecommunications，Chongqing 400065，China
  • 3 College of Science，Chongqing University of Posts and Telecommunications，Chongqing 400065，China
  • 4 School of Management，Shandong University，Jinan Shandong 250100，China
  • 5 Chongqing Major Projects Service Centre，Chongqing Regional Cooperation Service Centre，Chongqing 401120，China
出版时间: 2024-12-28 doi: 10.16265/j.cnki.issn1003-3033.2024.12.0688
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摘要
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为建筑安全管理提供智能化、系统化的决策支持，收集和整理建筑火灾事故数据。构建建筑火灾事故知识图谱，形成建筑火灾领域的知识库；基于时间、空间、主题和重要实体维度，创新展示智能问答系统的实现过程，开展建筑火灾风险智能分析。研究结果表明：白天和夏季属于建筑火灾的高发时段；华东地区的建筑火灾事故发生频率显著高于其他区域，电气、仓库区域建筑火灾风险较高；钢筋混凝土框架结构的建筑和厂房建筑更易发生建筑火灾；起火源大多数为可燃固体，火灾事故的原因主要与违规施工行为有关。

知识图谱  /  建筑火灾事故  /  智能分析  /  安全管理  /  图数据库

To provide intelligent and systematic decision support for building safety management，building fire accidents data was collected and summarized. The knowledge graph of building fire accidents was developed to construct a knowledge graph database. Based on the dimensions of time，space，theme，and important entities，the implementation process of the intelligent question-answering system was innovatively presented. Moreover，the intelligent analysis of building fire risk was performed. The results showed that daytime and summer were high-risk periods for building fires. The frequency of building fire accidents in East China was significantly higher than that in other regions，and the fire risk of building fires was higher in electrical and warehouse areas. Reinforced concrete frame structures and factory buildings were more prone to building fires. Most ignition sources were combustible solids，and the main cause of fire accidents was illegal construction behavior.

knowledge graph  /  building fire accident  /  intelligent analysis  /  safety management  /  graph database
许慧, 蒋玫, 薛红, 周启琳. 基于知识图谱的建筑火灾事故智能分析. 中国安全科学学报, 2024 , 34 (12) : 94 -99 . DOI: 10.16265/j.cnki.issn1003-3033.2024.12.0688
Hui XU, Mei JIANG, Hong XUE, Qilin ZHOU. Intelligent analysis of building fire accidents based on knowledge graph[J]. China Safety Science Journal, 2024 , 34 (12) : 94 -99 . DOI: 10.16265/j.cnki.issn1003-3033.2024.12.0688
正文
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0 引言
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建筑火灾是我国火灾的主要类型之一。仅2023年上半年，全国接报火灾55万起，其中建筑火灾占48.7%[1]。建筑火灾事故管理依赖历史事故数据，通过案例采集、筛选和加工可确保信息的有效性。
建筑火灾事故分析方面，关城等[2]基于“2-4”模型揭示了个人违规、监管不力和设计缺陷是火灾的主要直接原因；HASOFER[3]利用建筑消防安全系统性能评估模型分析了公寓建筑火灾风险；张文辉等[4]采用哲学和数学方法系统分析城市火灾风险，在安全管理领域有效提升了风险评估精度。2016年我国消防队伍正式提出未来必须全面推进智慧消防建设，提升社会火灾整体防控能力[5]。在智慧消防领域，2012年美国国家标准技术研究院启动智慧消防项目，推动消防工作模式的革新[6]。英国利用数据库高效管理和分析数据，快速评估了城市火灾风险并处理火灾隐患[7]。李杰等[8]利用知识图谱技术初步绘制了安全科学图数据库。知识图谱技术能够有效整合和分析大量数据[9]，运用自然语言处理及文本分析技术，形成建筑火灾图数据库，以支持复杂查询和逻辑推理的实施。
综上所述，目前针对建筑火灾事故的研究主要集中于事故风险分析和事故致因研究，采用传统方法且案例数据分布不均，而以建筑火灾事故案例数据为对象的相关研究尚缺乏，建筑火灾事故间的联系梳理也不完善。因此，笔者拟利用知识图谱技术构建建筑火灾事故图数据库，进行时间、空间、主题及重要实体等多维度智能分析和可视化展示，以期为建筑火灾的预防、风险评估及建筑安全管理等提供科学的决策支持。
1 建筑火灾事故知识图谱构建
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1.1 数据来源及数据分析
1.1.1 数据来源
由于网站数据呈现多样化，涵盖众多广告和无关信息，且信息结构化不足，数据采集难度较大，因此，选择八爪鱼采集器抓取数据。八爪鱼采集器能够模拟人类的网页浏览方式，提取来自各种网站和网页的大量标准化数据，从而帮助用户实现数据的自动采集、处理和标准化。参考《建筑设计防火规范》(2018年版)(GB50016—2014)，得到有关建筑物的关键词：厂房、仓库、民用建筑等，通过网络爬虫技术从安全管理网站[10]、国家消防救援局网站[11]采集包含关键词的事故案例数据。具体过程包括批量生成目标网址，并配置相应的采集流程，以提取事故数据的标题、发布时间、网址和部分文本信息，一共获取2 640条事故案例。
1.1.2 数据分析
八爪鱼软件采集到的数据中包含大量不规范或无关的信息，通过编写Python脚本遍历初始数据文件，再结合人工筛选初步整理数据信息。删除视频类和图片类网页数据、与建筑火灾事故主题无关的文档数据和网页信息、国外建筑火灾事故数据信息和重复数据后得到1 164条初步事故数据。针对初步事故数据的标题和网址，人工采集建筑火灾事故的文本信息，主要内容包括事故的标题、事故时间、事故发生的经过等。同时，删除12类实体信息不完整的事故数据，最终收集到423起建筑火灾事故数据作为研究样本。
1.2 知识图谱构建框架
1.2.1 知识抽取
1) 实体抽取。指从文本或数据源中识别出命名实体并分类的过程。利用自然语言处理技术和人工修正[12]，识别出12类共5 076个实体。
2) 关系抽取。指从文本中抽取出2个或多个实体之间的语义关系。基于规则的关系抽取，先分析依存句法，然后应用人工定义的规则形成三元组数据，并通过扩展规则来提炼语义关系[13]。抽取出13类共5 499个实体关系。
3) 属性抽取。指从文本中抽取描述实体特征的信息。通过考虑实体和属性的语义相容性、差异性等问题，定义14类共计5 922个事故属性信息。
建筑火灾事故领域主要实体(概念)、关系和属性如图1所示。
1.2.2 知识存储
利用Neo4j图数据库存储建筑火灾事故信息，以图的形式提升知识质量和可用性[14]。Neo4j是开源的图数据库，通过Cypher查询语言实现对数据的直观操作和分析[15]。构建建筑火灾事故知识图谱(部分)如图2所示。
1.2.3 知识可视化
1) 实体信息查询。知识图谱实体信息查询是对节点相关信息的检索[16]。通过详细描述实体特征来构建事故画像，集中展示事故的各项关键信息，点击关联节点提供深入的实体关系查询。
2) 关系信息查询。根据实体内容总结出13类关系。
3) 分类信息查询。分类查询是对每类实体中不同实体数量进行查询，通过节点的密集程度或关系量，获取事故分布情况。
2 “时间-空间-主题”维度智能分析
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基于知识图谱技术展示建筑火灾事故智能问答系统的实现过程，进行维度智能分析。智能问答系统通过定期更新数据源，确保问答效果的时效性和可靠性[17]。
2.1 时间维度分析
2.1.1 时间维度极值分析
时间维度分析包括发生时间、发生日期和发生季节分析，见表1。可以看出，夏季是建筑火灾事故发生最多的季节，占事故总数的27.0%;中旬的事故发生率较高，占事故总数的35.0%;白天(上午、中午、下午)的事故为243件，占事故总数的57.4%。
2.1.2 时间维度智能问答设计
通过查询、统计和混合分析，揭示建筑火灾事故实体之间的复杂联系。在混合分析中，智能问答可生成1 118个具体的问答。事故智能问答展示见表2。
根据匹配、条件等命令建立多层次、多级别的事故智能信息查询问答信息，从而获取建筑火灾事故在不同时间维度下的表现和差异，构建建筑火灾发生的实体关联图[18]。以查询“夏季由自燃物质引起的建筑火灾有哪些?”为例，返回实体及关联如图3所示，单击任一事故节点可查看事故属性和分布，双击则显示事故与其他实体的关联。
2.2 空间维度分析
2.2.1 空间维度极值分析
利用统计学及关联路径分析方法描述建筑火灾的空间分布特征，确定建筑火灾事故的空间分布，见表3。
2.2.2 空间维度智能问答设计
设计基于空间维度的智能分析，包括查询、统计和混合分析。在混合分析中，智能问答可生成1 176个具体问答，深度探索建筑火灾事故实体在空间维度上的复杂关联与影响。事故智能问答见表4。
2.3 主题维度分析
2.3.1 主题维度极值分析
主题维度分析包括建筑结构、建筑用途、事故阶段、起火物质、事故原因和事故等级分析，见表5。
2.3.2 主题维度智能问答设计
在主题维度下进行查询、统计和混合分析。在混合分析中，智能问答生成1 566个具体的问答。建立事故智能问答见表6。
2.4 重要实体维度分析
通过对时间、空间和主题3个维度的综合分析，确定建筑火灾发生的重要实体：事故原因和起火物质。结合知识图谱查询统计和智能分析，精准提取相关信息，形成结构化的回答。通过在知识图谱中利用Cypher语言的相关命令，建立查询统计语句，针对起火物质，例如查询“建筑火灾中由可燃固体引起的事故有多少起”，统计结果为129起。通过执行查询语句，得出起火物质中，可燃固体为重要的影响因素。
针对事故原因数量统计，例如查询“建筑火灾中因违规违章施工作业引起的事故有多少起”，查询统计结果为150起。通过执行查询语句，得出事故原因中，违规违章施工作业、电气设备故障和明火的使用不当为重要的影响因素。
3 结论
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1) 建筑火灾事故的发生具有一定的时间、空间、主题的特点。夏季是建筑火灾事故发生的高发期，占比达到27%;在白天发生建筑火灾事故的占比最高，为57.4%;华东地区建筑火灾事故占总数最高，为36.2%;仓库区域成为建筑火灾事故高发地点，占事故总数的36.4%。
2) 建筑结构中的钢筋混凝土框架结构占总数最高，为45.9%;厂房作为建筑用途中最容易发生火灾的类别，占事故总数的56.0%;起火物质中可燃固体表现出较为突出的危险性，占事故总数最高，为35.7%;违规违章施工作业火灾风险最大。
基金
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  • 国家社会科学基金西部项目资助(22XGL013)
  • 重庆市教委科学技术研究计划项目重点项目(KJZD-K202400603)
  • 重庆市社会科学规划中特理论重点项目(2024ZTZD14)
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2024年第34卷第12期
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doi: 10.16265/j.cnki.issn1003-3033.2024.12.0688
  • 接收时间：2024-09-05
  • 首发时间：2025-07-09
  • 出版时间：2024-12-28
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  • 收稿日期：2024-09-05
  • 修回日期：2024-11-07
基金
国家社会科学基金西部项目资助(22XGL013)
重庆市教委科学技术研究计划项目重点项目(KJZD-K202400603)
重庆市社会科学规划中特理论重点项目(2024ZTZD14)
作者信息
    1 重庆邮电大学 经济管理学院，重庆 400065
    2 重庆邮电大学大数据智能计算重点实验室，重庆 400065
    3 重庆邮电大学 理学院，重庆 400065
    4 山东大学 管理学院，山东 济南 250100
    5 重庆市重大项目服务中心 重庆市区域合作服务中心，重庆 401120
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/CN/10.16265/j.cnki.issn1003-3033.2024.12.0688
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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