基于MK-XGBoost的多传感器融合火灾识别技术

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

针对单一传感器预测中的漏报、误报的缺点,本文提出了一种基于MK-XGBoost的多传感器数据融合火灾识别技术。该技术通过采集受限空间的温度、烟雾质量浓度、CO体积分数,基于Mann-Kendall检验方法生成趋势因子,该因子与上升趋势呈正相关,后续将火灾数据与趋势因子作为特征,采用XGBoost算法判断火灾是否发生。在软件FDS与MATLAB下进行仿真,并与SVM、XGBoost原算法比较。仿真结果表明:火灾判断的准确率为98.0%,识别时间提高了0.9 s,说明本算法能够有效地提高火灾识别的准确性与时效性。

Aiming at the shortcomings of missing alarms,false alarms in single sensor prediction,this paper proposes a MKXGBoost 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.