|本期目录/Table of Contents|

[1]徐坚强,刘小勇,苏燕飞,等.基于贝叶斯网络的建筑火灾动态风险评估方法研究[J].中国安全生产科学技术,2019,15(2):138-144.[doi:10.11731/j.issn.1673-193x.2019.02.022]
 XU Jianqiang,LIU Xiaoyong,SU Yanfei,et al.Study on dynamic risk assessment method of building fire based on Bayesian network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(2):138-144.[doi:10.11731/j.issn.1673-193x.2019.02.022]
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基于贝叶斯网络的建筑火灾动态风险评估方法研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
15
期数:
2019年2期
页码:
138-144
栏目:
职业安全卫生管理与技术
出版日期:
2019-02-28

文章信息/Info

Title:
Study on dynamic risk assessment method of building fire based on Bayesian network
文章编号:
1673-193X(2019)-02-0138-07
作者:
徐坚强12 刘小勇2 苏燕飞23 黄潜生23
(1.安徽建筑大学 土木工程学院,安徽 合肥 230601;
2.清华大学合肥公共安全研究院,安徽 合肥 230601;
3.辰安天泽智联技术有限责任公司,安徽 合肥 230601)
Author(s):
XU Jianqiang12 LIU Xiaoyong2 SU Yanfei23 HUANG Qiansheng23
(1. College of Civil Engineering, Anhui Jianzhu University, Hefei Anhui 230601, China;
2. Hefei Institute for Public Safety Research, Tsinghua University, Hefei Anhui 230601, China;
3.Global Safety Tanzer Technology Co.,Ltd,Hefei Anhui, 230601)
关键词:
动态风险评估物联网大数据贝叶斯网络建筑火灾
Keywords:
dynamic risk assessment internet of things big data bayesian network building fire
分类号:
X913
DOI:
10.11731/j.issn.1673-193x.2019.02.022
文献标志码:
A
摘要:
为提高建筑火灾风险评估的准确性,建立1种智能化的动态风险评估方法。针对具体建筑的风险评估,以物联网技术为基础,构建智能消防监测系统,在建筑日常使用过程中通过动态风险评估,实现火灾风险要素的实时监测、数据传输,充分发挥大数据、云计算的支撑作用,将贝叶斯网络方法引入火灾风险定量评估过程,构建火灾动态风险评估模型;结合具体的应用实例,分析不确定因素对风险评估结果的影响。研究结果表明:基于贝叶斯网络的动态风险评估方法能较准确地反映建筑火灾风险的可能性,达到实时监测、动态评估的效果。
Abstract:
In order to improve the accuracy of risk assessment on the building fire, an intelligent dynamic risk assessment method was established. Aiming at the risk assessment of specific buildings, an intelligent fire monitoring system was built based on the technology of Internet of Things. The realtime monitoring and data transmission of fire risk elements were realized through the dynamic risk assessment in the daily usage process of buildings, and the dynamic risk assessment model of fire was constructed by introducing the Bayesian network method into the quantitative risk assessment process of fire with fully playing the supporting role of big data and cloud computing. Combined with the specific application example, the influence of uncertain factors on the results of risk assessment was analyzed in detail. The results showed that the dynamic risk assessment method based on Bayesian network could reflect the possibility of building fire risk more accurately, and achieve the effect of realtime monitoring and assessment.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2018-10-30
基金项目: 国家十三五重点研发计划项目 (2018YFC0810603);国家应急管理部消防救援局项目(2018XFCX25)
作者简介: 徐坚强,硕士研究生,主要研究方向为公共安全、消防社会化。
通信作者: 刘小勇,博士,高级工程师,主要研究方向为公共安全。
更新日期/Last Update: 2019-03-06