社区户内燃气泄漏动态预警模型

doi:10.16265/j.cnki.issn1003-3033.2022.03.012

中国安全科学学报 ›› 2022, Vol. 32 ›› Issue (3): 90-97.doi: 10.16265/j.cnki.issn1003-3033.2022.03.012

社区户内燃气泄漏动态预警模型

李超¹^,²(), 邓小宝¹^,², 史运涛¹^,², 孙德辉¹^,², 焦彦宗¹^,²

¹北方工业大学电气与控制工程学院,北京 100144
²北方工业大学现场总线技术及自动化北京市重点实验室,北京 100144

收稿日期:2021-12-20 修回日期:2022-02-17 出版日期:2022-08-23 发布日期:2022-09-28

作者简介:

李超 (1986—),男,山东临沂人,硕士,高级实验师,主要从事大数据与人工智能、工业互联网等方面的研究。E-mail: lichao@ncut.edu.cn。
李超高级实验师, 史运涛教授, 孙德辉教授,

基金资助:
国家重点研发计划项目(2018YFC0809700); 国家重点研发计划项目(2018YFC0807000)

Dynamic early warning model of household gas leakage in communities

LI Chao¹^,²(), DENG Xiaobao¹^,², SHI Yuntao¹^,², SUN Dehui¹^,², JIAO Yanzong¹^,²

¹College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
²Beijing Key Laboratory of Field Bus Technology and Automation, North China University of Technology, Beijing 100144, China

Received:2021-12-20 Revised:2022-02-17 Online:2022-08-23 Published:2022-09-28

摘要/Abstract

摘要：

为提升社区户内燃气泄漏动态预警的能力,构建一种社区户内燃气泄漏动态预警模型。首先,利用无线传感器网络采集社区户内燃气数据,通过智能网关实现数据上云的功能;其次,在云平台中利用模糊控制算法优化随机森林算法的输入,减少重要度较低特征的干扰,并将优化后的数据作为随机森林算法的输入,以泄漏等级作为输出,建立模糊-随机森林模型;然后,开发可视化模块,以显示社区户内燃气泄漏的等级;最后,根据北京某社区燃气历史数据,在实验室条件下进行仿真模拟,验证该模型的有效性。结果表明:该模型可有效提升社区户内燃气泄漏的在线监测和动态预警的能力,与其他算法相比,模糊-随机森林算法在发现早期微小泄漏方面表现更优。

关键词: 社区户内, 燃气泄漏, 动态预警, 在线监测, 模糊控制算法, 随机森林算法

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

李超, 邓小宝, 史运涛, 孙德辉, 焦彦宗. 社区户内燃气泄漏动态预警模型[J]. 中国安全科学学报, 2022, 32(3): 90-97.

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.

图/表 10

图1

表1

图2

表2

图3

图4

图5

图6

图7

图8

参考文献 16

[1]	王春雪, 吕淑然. 城市燃气管道泄漏致灾混合概率风险评估研究[J]. 中国安全科学学报, 2016, 26(12):146-151.
	WANG Chunxue, LYU Shuran. Mixture probabilistic model based assessment of risk of disaster induced by urban gas pipeline leakage[J]. China Safety Science Journal, 2016, 26(12):146-151.
[2]	杨永胜, 钟少波, 余致辰, 等. 一种城市燃气管网泄漏风险动态计算模型[J]. 中国安全科学学报, 2018, 28(1):167-172. doi: 10.16265/j.cnki.issn1003-3033.2018.01.028
	YANG Yongsheng, ZHONG Shaobo, YU Zhichen, et al. A model for dynamic risk calculation of urban gas pipeline[J]. China Safety Science Journal, 2018, 28(1):167-172. doi: 10.16265/j.cnki.issn1003-3033.2018.01.028
[3]	LI Xinhong, HAN Ziyue, ZHANG Renren, et al. An integrated methodology to manage risk factors of aging urban oil and gas pipelines[J]. Journal of Loss Prevention in the Process Industries, 2020, 66:DOI:10.1016/j.jlp.2020.104154.
[4]	黄平, 吴子谦, 袁梦琦, 等. 基于实时监测数据的城镇窨井可燃气体泄漏特性分析[J]. 安全与环境学报, 2019, 19(2):569-575.
	HUANG Ping, WU Ziqian, YUAN Mengqi, et al. Analysis of gas leakage features of urban inspection wells based on real-time monitoring data[J]. Journal of Safety and Environment, 2019, 19(2):569-575.
[5]	刘永涛, 刘佳, 李玉华, 等. 基于SIM900A的语音燃气报警装置设计与实现[J]. 现代电子技术, 2017, 40(3):96-99.
	LIU Yongtao, LIU Jia, LI Yuhua, et al. Design and implementation of voice gas alarm device based on SIM900A[J]. Modern Electronics Technique, 2017, 40(3):96-99.
[6]	HOU Longfei, QIAN Xinming, DU Bingxuan, et al. Optimization of the gas leak monitoring points distributed underground[J]. Journal of Loss Prevention in the Process Industries, 2017, 47:95-103. doi: 10.1016/j.jlp.2017.02.031
[7]	林海飞, 高帆, 严敏, 等. 煤层瓦斯含量PSO-BP神经网络预测模型及其应用[J]. 中国安全科学学报, 2020, 30(9):80-87.
	LIN Haifei, GAO Fan, YAN Min, et al. Study on PSO-BP neural network prediction method of coal seam gas content and its application[J]. China Safety Science Journal, 2020, 30(9):80-87.
[8]	郑晓亮, 来文豪, 薛生. MI和SVM算法在煤与瓦斯突出预测中的应用[J]. 中国安全科学学报, 2021, 31(1):75-80. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.011
	ZHENG Xiaoliang, LAI Wenhao, XUE Sheng. Application of MI and SVM in coal and gas outburst prediction[J]. China Safety Science Journal, 2021, 31(1):75-80. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.011
[9]	王新颖, 张瑞程, 张惠然, 等. Keras在燃气管道阀门故障诊断中的应用[J]. 消防科学与技术, 2020, 39(4):541-546.
	WANG Xinying, ZHANG Ruicheng, ZHANG Huiran, et al. Application of Keras in gas pipeline valve fault diagnosis[J]. Fire Science and Technology, 2020, 39(4):541-546.
[10]	钱恒, 虞慧群, 范贵生. 基于增量式随机森林的燃气负荷预测方法[J]. 华东理工大学学报:自然科学版, 2019, 45(1):133-139.
	QIAN Heng, YU Huiqun, FAN Guisheng. A gas consumption prediction method based on incremental random forest regression algorithm[J]. Journal of East China University of Science and Technology, 2019, 45(1):133-139.
[11]	ABDULLA M, HERZALLAH R. Probabilistic multiple model neural network based leak detection system: experimental study[J]. Journal of Loss Prevention in the Process Industries, 2015, 36:30-38. doi: 10.1016/j.jlp.2015.05.009
[12]	王智勇, 缑俊, 郭勇, 等. 燃气管道泄漏引发的温度变化研究[J]. 电子科技大学学报, 2015, 44(2):306-310.
	WANG Zhiyong, GOU Jun, GUO Yong, et al. Investigation on the temperature shift from fuel pipeline leakage[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(2):306-310.
[13]	周向阳, 贾媛. 航空遥感惯性稳定平台模糊/PID复合控制[J]. 仪器仪表学报, 2016, 37(11):2545-2554.
	ZHOU Xiangyang, JIA Yuan. Fuzzy/PID compound control for inertially stabilized platform in airborne remote sensing[J]. Chinese Journal of Scientific Instrument, 2016, 37(11):2 545-2 554.
[14]	PRECUP R E, HELLENDOORN H. A survey on industrial applications of fuzzy control[J]. Computers in Industry, 2011, 62(3):213-266. doi: 10.1016/j.compind.2010.10.001
[15]	XIE Chengyu, CHAO Lei, QIN Yaguang, et al. Using a stochastic forest prediction model to predict the hazardous gas concentration in a one-way roadway[J]. AIP Advances, 2020, 10(11):DOI:10.1063/5.0025594.
[16]	徐娟, 陈时桢, 何烊剑, 等. 基于模糊PID的平衡头自适应控制策略研究[J]. 电子测量与仪器学报, 2016, 30(6):895-902.
	XU Juan, CHEN Shizhen, HE Yangjian, et al. Research on self-satisfaction control strategy of balancing head based on fuzzy-PID[J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(6):895-902.

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

社区户内燃气泄漏动态预警模型

Dynamic early warning model of household gas leakage in communities

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 16

相关文章 5

编辑推荐

Metrics

本文评价

[1]	曾纪涵, 章光, 吴浩, 胡少华. 改进POT模型下尾矿坝综合预警值确定方法[J]. 中国安全科学学报, 2022, 32(5): 134-139.
[2]	卢颖, 赵志攀, 姜学鹏, 吴锦东, 范小鹏. 大数据视域下体育场馆动态火灾风险指标研究[J]. 中国安全科学学报, 2022, 32(4): 155-162.
[3]	冯巩, 夏元友, 王智德, 严敏嘉. 基于位移信息融合的露天矿边坡动态预警方法[J]. 中国安全科学学报, 2022, 32(3): 116-122.
[4]	田丰，李钫. 基于HHT的局部放电在线监测与电缆安全的相关性研究[J]. 中国安全科学学报, 2013, 23(7): 38-.
[5]	杨禹华，钟震宇，蔡康旭. 基于模糊模式识别的瓦斯含量指标异常预警技术[J]. 中国安全科学学报, 2007, 17(9): 172-.