主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

消防科学与技术 ›› 2020, Vol. 39 ›› Issue (10): 1430-1434.

• • 上一篇    下一篇

建筑火灾中人员疏散响应过程推理与评估模型研究

颜峻1 ,疏学明2,胡俊2,邓博誉2   

  1. 1. 中国劳动关系学院安全与职业卫生工程研究所,北京100048;2. 清华大学公共安全研究院,北京100084
  • 出版日期:2020-10-15 发布日期:2020-10-15
  • 作者简介:颜峻(1977-),男,天津人,中国劳动关系学院副教授,主要从事公共安全、建筑防火技术、火灾风险评估及智慧消防等方面的研究,北京市海淀区增光路45号,100048。
  • 基金资助:
    中国劳动关系学院2017 年教育教学改革项目(JG1727);国家自然科学基金项目(71774094)

Research on evacuation response process reasoning and assessment model in building fire

YAN Jun1, SHU Xue-ming2, HU Jun2,DENG Bo-yu2   

  1. (1. Institute of Safety and Occupational Hygiene Engineering,China Institute of Industrial Relations,Beijing 100048, China;2. Department of Engineering Physics, Institute of Public Safety Research, Tsinghua University, Beijing 100084, China
  • Online:2020-10-15 Published:2020-10-15

摘要: 为了预防建筑火灾发生和减少火灾中人员的伤亡,利用消防大数据分析技术和贝叶斯网络分析方法,研究了建筑火灾发展过程及人员疏散响应过程的推理与动态评估方法。通过对建筑消防设施响应状态和火灾中人员心理和行为特征的分析,探讨了火灾阶段划分方法和人员疏散行为的分类,提出了火灾中人员伤亡的轨迹交叉理论,进而构建了基于贝叶斯网络的建筑火灾动态风险和人员疏散安全评估框架。分别探讨了建筑火灾发展、人员疏散响应等2 条研究主线的推理过程,探讨了疏散条件评估过程。研究表明,该模型可通过对建筑特征、消防设施状态信息以及人员响应信息等消防大数据进行融合,实现火灾中人员疏散响应过程推理与动态风险评估,从而提高建筑消防安全管理水平

关键词: 火灾, 疏散, 风险, 动态评估, 贝叶斯网络

Abstract: In order to prevent building fires and reduce casualties in fires, reasoning and dynamic evaluation methods of building fire development process and evacuation response process are studied by using fire big data analysis technology and Bayesian network analysis method. Through the analysis of the response state of building fire fighting facilities and the psychological and behavioral characteristics of occupants in fire, the method of fire stage division and the classification of evacuation behavior are discussed and the trajectory intersection theory of casualties in fire is put forward, and framework on fire dynamic risk and evacuation safety assessment based on Bayesian network is constructed. The reasoning process of building fire development and evacuation response are discussed respectively, and the evacuation condition assessment process is discussed Research shows that the model can fuse large fire data such as building characteristics, fire facility status information and occupant response information, evacuation response process reasoning and dynamic risk assessment model in building fire are realized, so as to improve the level of building fire safety management.

Key words: fire, evacuation behavior, risk dynamic assessment, Bayesian network