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

消防科学与技术 ›› 2023, Vol. 42 ›› Issue (10): 1370-1373.

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基于VR的商业综合体火灾中人员疏散逃生心理与行为研究

张孝春1, 韩书阳1, 徐岳鹏1, 陈钦佩2,3,4   

  1. （1. 广东工业大学 环境科学与工程学院，广东 广州 510006；2. 应急管理部天津消防研究所，天津 300381；3. 工业与公共建筑火灾防控技术应急管理部重点实验室，天津 300381；4. 天津市消防安全技术重点实验室，天津 300381）
  • 出版日期:2023-10-15 发布日期:2023-10-15
  • 作者简介:张孝春(1987- ),男,山东济南人,广东工业大学安全工程系副主任,副教授,主要从事火灾动力学理论与防治基础、安全评估等研究,广东省广州市广州大学城外环西路100号广东工业大学工学三号馆,510006。
  • 基金资助:
    基金项目：国家自然科学基金面上项目（52276108）；广州市基础研究计划基础与应用基础研究项目（202201010555）；广州市哲学社会科学发展“十四五”规划2022年度羊城青年学人课题（2022GZQN28）

Research on evacuation and escape psychology and behavior of personnel in commercial complex fire scenarios based on VR technology

Zhang Xiaochun1, Han Shuyang1, Xu Yuepeng1, Chen Qinpei2,3,4   

  1. (1. School of Environmental Science and Engineering,Guangdong University of Technology, Guangdong Guangzhou 510006, China; 2. Tianjin Fire Science and Technology Research Institute of MEM, Tianjin 300381, China; 3. Laboratory of Fire Protection Technology for Industry and Public Building, Ministry of Emergency Management, Tianjin 300381, China; 4. Tianjin Key Laboratory of Fire Safety Technology, Tianjin 300381, China)
  • Online:2023-10-15 Published:2023-10-15

摘要： 随着经济发展，各大城市商业综合体数量不断增加。由于商业综合体建筑面积较大，内部结构复杂，一旦发生火灾，多数人员不熟悉安全出口路径，不利于疏散逃生。为了研究火灾发生时人员的疏散心理与行为，利用Blender和Unity 3D构建大型商业综合体VR火灾场景，结合监测试验对象的心率变化、逃生时间以及是否正确逃生3个维度，利用机器学习对试验数据进行K-means聚类分析。该研究旨在对人员疏散逃生心理与行为进行分类分析，为增强人员的疏散安全意识，完善消防疏散能力安全教育提供一定帮助。

关键词: VR技术, 机器学习, K-means聚类分析

Abstract: With the economic development, the number of commercial complexes in major cities is increasing, and shopping and entertainment in commercial complexes are favored by consumers. Due to the large building area and complex internal structure of the commercial complex, in case of fire, most people are not familiar with the safety exit path, which is not conducive to evacuation. In order to study the evacuation psychology and behavior of people in the event of a fire, this study used Blender and Unity 3D to construct a VR fire scene of a large commercial complex, combined with the monitoring of subjects' heart rate change, escape time and the correct escape, carried out K-means clustering analysis on the experimental data by machine learning. The purpose of this study is to classify and analyze the evacuation psychology and behavior of personnel, and at the same time to enhance the evacuation safety awareness of personnel and improve the safety education of fire evacuation ability.

Key words: VR technology, machine learning;K- means cluster analysis