《计算机应用》唯一官方网站 ›› 2023, Vol. 43 ›› Issue (3): 986-990.DOI: 10.11772/j.issn.1001-9081.2022010070

• 前沿与综合应用 • 上一篇    

基于粒子滤波的隧道火灾烟气速度估计方法

黄琼1(), 丁兆云2   

  1. 1.中国消防救援学院 基础部,北京 102202
    2.国防科技大学 系统工程学院,长沙 410073
  • 收稿日期:2022-01-19 修回日期:2022-02-26 接受日期:2022-03-08 发布日期:2022-03-22 出版日期:2023-03-10
  • 通讯作者: 黄琼
  • 作者简介:黄琼(1985—),女,江西丰城人,讲师,硕士,主要研究方向:机器学习、安全工程
    丁兆云(1983—),男,湖北荆门人,副教授,博士,主要研究方向:数据挖掘、人工智能。
  • 基金资助:
    国家重点研发计划项目(2020YFC1511600)

Estimation method of tunnel fire smoke velocity based on particle filtering

Qiong HUANG1(), Zhaoyun DING2   

  1. 1.Department of Basic Courses,China Fire and Rescue Institute,Beijing 102202,China
    2.College of Systems Engineering,National University of Defense Technology,Changsha Hunan 410073,China
  • Received:2022-01-19 Revised:2022-02-26 Accepted:2022-03-08 Online:2022-03-22 Published:2023-03-10
  • Contact: Qiong HUANG
  • About author:DING Zhaoyun, born in 1983, Ph. D., associate professor. His research interests include data mining, artificial intelligence.
  • Supported by:
    National Key Research and Development Program of China(2020YFC1511600)

摘要:

针对目前隧道火灾烟气速度测量成本较高、模拟精度较低与较难保证实时性等问题,提出一种基于粒子滤波的隧道火灾烟气速度估计方法。首先建立相关系统状态方程和观测方程,然后利用实时传感器数据获取观测值,最后运用粒子滤波算法实现烟气速度的实时估计。实验结果表明,所提方法的响应时间达到了毫秒级别,基本满足实时性要求,且烟气速度的平均绝对误差(MAE)基本能够控制在真实值的20%以内,具有较高的模拟估计精度,可为消防救援和人员疏散提供有用的关键信息,也可为排烟系统和消防规划策略提供理论依据。

关键词: 粒子滤波, 粒子数, 隧道火灾, 烟气速度, 观测位置

Abstract:

Aiming at the current problems of high cost, low simulation accuracy and difficulty in ensuring real-time performance of tunnel fire smoke velocity measurement, a method for estimating smoke velocity of tunnel fire based on particle filtering was proposed. Firstly, the relevant system state equation and observation equation were established, and then the observation values were obtained by using the real-time sensor data. Finally, the real-time estimation of smoke velocity was realized by using the particle filtering algorithm. Experimental results show that the response time of the proposed method can reach the millisecond level, which can basically meet the real-time requirements; and the Mean Absolute Error (MAE) of smoke velocity can basically be controlled within 20% of the true value with high simulation estimation accuracy. The proposed method can provide useful key information for fire rescue and evacuation, and provide theoretical basis for smoke extraction system and fire planning strategy.

Key words: particle filtering, particle number, tunnel fire, smoke velocity, observation location

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