China Safety Science Journal ›› 2022, Vol. 32 ›› Issue (4): 155-162.doi: 10.16265/j.cnki.issn1003-3033.2022.04.023

• Public safety • Previous Articles     Next Articles

Dynamic fire risk indexes for stadiums from perspective of big data

LU Ying1,2(), ZHAO Zhipan1, JIANG Xuepeng1,2, WU Jindong3, FAN Xiaopeng1   

  1. 1 School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan Hubei 430081, China
    2 Hubei Provincial Industrial Safety Engineering Technology Research Center, Wuhan Hubei 430081, China
    3 Wuhan University of Technology and Optical Science Co., Ltd., Wuhan Hubei 430070, China
  • Received:2022-01-18 Revised:2022-03-25 Online:2022-04-28 Published:2022-10-28

Abstract:

In order to solve problems that static indicators are more frequently used in fire risk assessment of stadiums, while dynamic indicators are not clear, and internet of things monitoring data required for dynamic assessment is diverse and complex, characteristics of 48 kinds of internet of things monitoring data such as fire host and fire tank liquid level were analyzed, and a quantifiable dynamic index system was constructed, including fault location percentage of fire hosts and difference between actual and standard liquid level. Then, an data set based on monitoring data of 27 stadiums was established, 48 indicators were screened and optimized using random forest algorithm, and development and optimization of dynamic fire risk assessment indicators were studied. The results show that when the 9-dimensional features with the lowest importance are deleted, mean square error reaches the minimum of 0.05, and optimal dynamic fire risk assessment index system for stadiums is obtained.

Key words: big data, stadium, dynamic fire risk, fire risk assessment, random forest algorithm