Dynamic early warning model of household gas leakage in communities

doi:10.16265/j.cnki.issn1003-3033.2022.03.012

Abstract

Abstract:

In order to improve gas leakage warning performance for community safety, a dynamic early warning model for household gas leakage was proposed. Firstly, indoor gas data of each home in the community were collected by using wireless sensor network, and uploaded to the cloud by smart gateway. Secondly, inputs of random forest algorithm were optimized by utilizing fuzzy control algorithm to reduce interference of features with lower importance on the cloud platform, based on which a fuzzy-random forest model was established with optimized data as input of random forest algorithm and leakage grade as output. Then, a visual module was developed to present gas leakage grade of each home. Finally, the model's effectiveness was verified through simulation test under lab conditions based on historical gas data collected form a certain community in Beijing. The results show that this model can effectively improve ability of online monitoring and dynamic early warning of gas leaks in the community. Compared with other algorithms, the fuzzy-random forest algorithm shows better performance in detecting early small leaks.

Key words: community household, gas leakage, dynamic early-warning, online monitoring, fuzzy control algorithm, random forest algorithm

LI Chao, DENG Xiaobao, SHI Yuntao, SUN Dehui, JIAO Yanzong. Dynamic early warning model of household gas leakage in communities[J]. China Safety Science Journal, 2022, 32(3): 90-97.

Figures/Tables 10

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编号	参数	测量范围
T₁	可燃气体体积分数/%	0~0.5
T₂	加臭剂质量浓度/(mg·m^-3)	0~50
T₃	温度/℃	-55~100
T₄	管道压力/Pa	0~2 000
T₅	管道流量/(m³·h^-1)	0~5
T₆	用气量/m³	0~3

编号	等级	状态
0	低	燃气泄漏量极少或传感器波动,危险性极低
1	较低	燃气泄漏量较少,危险性较低
2	中	具有一定燃气泄漏量,危险性中等
3	较高	燃气泄漏量较多,危险性较高
4	高	燃气泄漏量极多,危险性极高