基于MK-XGBoost的多传感器融合火灾识别技术

消防科学与技术 ›› 2022, Vol. 41 ›› Issue (1): 104-107.

基于MK-XGBoost的多传感器融合火灾识别技术

李晨辉1，2，胡潇尹1，肖铎1，戚伟1

（1.浙大城市学院，浙江杭州 310015；2. 浙江大学控制科学与工程学院，浙江杭州 310027）

出版日期:2022-01-15 发布日期:2022-01-15
作者简介:李晨辉(1972-)，男，浙江温州人，浙江大学控制科学与工程学院硕士，主要从事智慧消防、火灾识别等方面的研究，浙江省杭州市拱墅区48号，310015。

Multi-sensor fire detection technology based on MK-XGBoost algorithm

LI Chen-hui1,2, HU Xiao-yin1, XIAO Duo1, QI Wei1

（1. Zhejiang University City College，Zhejiang Hangzhou 310015，China; 2. College of Control Science and Engineering,Zhejiang University, Zhejiang Hangzhou 310027, China）

Online:2022-01-15 Published:2022-01-15

摘要/Abstract

关键词: 多传感器融合；火灾识别；XGBoost；Mann-Kendall

Abstract: Aiming at the shortcomings of missing alarms, false alarms in single sensor prediction, this paper proposes a MK-XGBoost fire detection technology based on multi-sensor data fusion. By collecting the temperature, smoke concentration and CO concentration in a confined space, a trend factor is generated based on the Mann-Kendall method. The factor is positively related to the intensity of the upward trend. Then the fire data and the trend factor are input into the XGBoost algorithm as features to determine if a fire occurred. The simulation was carried out under the software FDS and MATLAB. Compared with the original algorithms of SVM and XGBoost, the simulation results show that the accuracy of fire detection is 98.0%, and the recognition time is increased by 0.9 s. Therefore, the MK-XGBoost algorithm can effectively improve the accuracy of fire recognition.

Key words: multi -sensor fusion; fire recognition; XGBoost; Mann-Kendall

李晨辉1，2，胡潇尹1，肖铎1，戚伟1. 基于MK-XGBoost的多传感器融合火灾识别技术[J]. 消防科学与技术, 2022, 41(1): 104-107.

LI Chen-hui1,2, HU Xiao-yin1, XIAO Duo1, QI Wei1. Multi-sensor fire detection technology based on MK-XGBoost algorithm[J]. Fire Science and Technology, 2022, 41(1): 104-107.