Research and application of intelligent early warning system for coal mine fires
-
摘要: 目前煤矿火灾监测系统实现了对矿井煤自燃标志性气体、温度、烟雾、火焰等部分指标的单独监测，未对煤矿火灾相关因素进行有效、全面、统一的监测。针对该问题，从内因、外因2个方面分析了煤矿火灾潜在危险因素，提出一种分源分区监测火情态势的方法。内因火灾方面，主要针对较易发生火灾的工作面采空区、密闭采空区及人工自然发火观测点等进行监测；外因火灾方面，主要针对机电硐室及其配电点、带式输送机系统、电缆等方面进行监测。建立了煤矿火灾分源分区监测指标体系，采用人工监测或在线监测的方式定期采集或更新火灾特征参量数据，按数据采集方式及影响程度，将火灾监测指标分为动态指标、静态指标和关联指标。设计了火灾智能预警系统的总体架构和业务流程，采用基于多指标联合逻辑推理的预警方法实现内因火灾预警，采用基于D−S 证据理论的多参量融合预警方法实现外因火灾预警。现场试验结果表明，火灾智能预警系统实现了对矿井火灾的有效监测预警，具有煤矿火灾风险预警“一张图”可视化展示功能，同时具备火灾智能模拟演示功能及避灾路线动态规划功能。Abstract: Currently, the coal mine fire monitoring system has achieved separate monitoring of some indicators such as the iconic gases, temperature, smoke, and flame of coal spontaneous combustion in mines.But the system has not effectively, comprehensively, and uniformly monitored the factors related to coal mine fires. In order to solve this problem, potential risk factors of coal mine fires are analyzed from two aspects: internal and external factors. A method of monitoring fire situation in different sources and areas is proposed. In terms of internal fires, monitoring is mainly carried out on goaf areas, enclosed goaf areas, and artificial natural fire observation points that are prone to fires. In terms of external fires, monitoring is mainly carried out on the mechanical and electrical chambers and their distribution points, belt conveyor systems, cables, and other aspects. A monitoring index system for coal mine fire sources and areas has been established. The system regularly collects or updates fire feature parameter data through manual or online monitoring. According to the data collection method and impact degree, fire monitoring indicators are divided into dynamic indicators, static indicators, and related indicators. The overall architecture and business process of a fire intelligent warning system is designed. The system uses a warning method based on multi index joint logical reasoning to achieve internal fire warning, and uses a multi parameter fusion warning method based on D-S evidence theory to achieve external fire warning. The on-site test results show that the fire intelligent warning system has achieved effective monitoring and warning of mine fires, with a visual display function of a coal mine fire risk warning "one picture". The system has a fire intelligent simulation demonstration function and a dynamic planning function for disaster avoidance routes.
-
-
![]()
图 1 煤矿火灾智能预警系统总体架构
Figure 1. Overall architecture of intelligent early warning system for coal mine fire
![]()
图 2 煤矿火灾智能预警业务流程
Figure 2. Intelligent early warning business process for coal mine fire
![]()
图 3 煤矿火灾智能预警系统“一张图”平台
Figure 3. "One Picture" platform for intelligent early warning system of coal mine fire
表 1 工作面采空区煤自燃气体指标、预警规则及对策措施
Table 1 Gas indicators, warning rules and countermeasures for coal spontaneous combustion in goaf of working face
阶段 CO体积
分数/10−6C2H6 C3H8 状态 安全风险等级 预警级别 对策措施 Ⅰ 0～50 无 无 无自燃隐患 低风险 蓝色预警 注氮等预防性防灭火措施 Ⅱ 50～500 无 无 缓慢氧化 一般风险 黄色预警 应加强监测，采取注氮、灌浆等预防性防灭火措施 Ⅲ 500～1000
（默认）无 无 自热加速氧化 较大风险 橙色预警 应加强注氮、注浆防灭火措施的时间和工程量 有 − Ⅳ ＞1000
（默认）有 有 激烈氧化 重大风险 红色预警 封闭火区，对该区域封闭处理，
继续采取注氮、灌浆等防灭火措施有 有 
下载: 导出CSV
表 2 工作面回风流煤自燃气体指标、预警规则及对策措施
Table 2 Gas indicators, warning rules and countermeasures for coal spontaneous combustion in the return air flow of the working face
阶段 CO体积分数/10−6 C2H6 C3H8 状态 安全风险等级 预警级别 对策措施 Ⅰ 0～24 无 无 无自燃隐患 低风险 蓝色预警 注氮等预防性防灭火措施 Ⅱ 24～100 无 无 缓慢氧化 一般风险 黄色预警 应加强监测，采取注氮、灌浆等预防性防灭火措施 Ⅲ ＞100
（默认）无 无 自热加速氧化 较大风险 橙色预警 应加强注氮、注浆防灭火措施的时间和工程量 有 无 Ⅳ ＞100
（默认）有 有 激烈氧化 重大风险 红色预警 封闭火区，对该区域封闭处理，
继续采取注氮、灌浆等防灭火措施有 有
下载: 导出CSV
表 3 基于 D−S 证据理论的多参量融合预警规则及对策措施
Table 3 Multi parameter fusion warning rules and countermeasures based on D-S evidence theory
阶段 起火概率Pfire 安全风险等级 预警级别 对策措施 Ⅰ 0＜Pfire＜0.25 低风险 蓝色预警 喷淋喷粉等预防性防灭火措施 Ⅱ 0.25≤Pfire＜0.5 一般风险 黄色预警 应加强监测，采取喷淋、喷粉等预防性防灭火措施 Ⅲ 0.5≤Pfire＜0.75 较大风险 橙色预警 应加强喷淋、喷粉等防灭火措施的时间和工程量 Ⅳ 0.75≤Pfire＜1 重大风险 红色预警 封闭火区，对该区域封闭处理，继续采取注喷淋、喷粉等防灭火措施
下载: 导出CSV
表 4 煤矿火灾预警跟踪考察结果
Table 4 Results of coal mine fire warning tracking and inspection
考察区域 实际危险次数 蓝色预警次数 黄色预警次数 橙色预警次数 红色预警次数 预警准确率/% 综采工作面 33 29 1 0 0 90.90 密闭采空区 0 0 0 0 0 − 人工自然发火观测点 0 0 0 0 0 − 北二盘区4−2煤变电所 1 1 0 0 0 100.00 主斜井一部胶带 3 2 0 0 0 66.00 安全监控系统监测点 0 0 0 0 0 −
下载: 导出CSV
-
[1] 邓军,文虎,张辛亥,等. 煤田火灾防治理论与技术[M]. 徐州:中国矿业大学出版社,2014. DENG Jun,WEN Hu,ZHANG Xinhai,et al. Coal field fire prevention theory and technology[M]. Xuzhou:China University of Mining & Technology Press,2014.
[2] 邓军,肖旸,张辛亥,等. 煤火灾害防治技术的研究与应用[J]. 煤矿安全,2012,43(增刊1):58-61. DENG Jun,XIAO Yang,ZHANG Xinhai,et al. Research and application of coal fire disaster prevention and control technology[J]. Safety in Coal Mines,2012,43(S1):58-61.
[3] 梁运涛,侯贤军,罗海珠,等. 我国煤矿火灾防治现状及发展对策[J]. 煤炭科学技术,2016,44(6):1-6,13. LIANG Yuntao,HOU Xianjun,LUO Haizhu,et al. Development countermeasures and current situation of coal mine fire prevention & extinguishing in China[J]. Coal Science and Technology,2016,44(6):1-6,13.
[4] 孙继平,孙雁宇. 矿井火灾监测与趋势预测方法研究[J]. 工矿自动化,2019,45(3):1-4. SUN Jiping,SUN Yanyu. Research on methods of mine fire monitoring and trend prediction[J]. Industry and Mine Automation,2019,45(3):1-4.
[5] 国家发展改革委,国家能源局,应急管理部,等. 关于印发《关于加快煤矿智能化发展的指导意见》的通知[EB/OL]. [2023-06-10]. https://www.gov.cn/zhengce/zhengceku/2020-03/05/content_5487081.htm. National Development and Reform Commission,National Energy Administration,Ministry of Emergency Management,et al. Notice on printing and distributing The guiding opinions on speeding up the intelligent development of coal mines[EB/OL]. [2023-06-10]. http://www.gov.cn/zhengce/zhengceku/2020-03/05/content_5487081.htm.
[6] 国家能源局,国家矿山安全监察局. 国家能源局国家矿山安全监察局关于印发《煤矿智能化建设指南(2021年版)》的通知[EB/OL]. [2023-06-10]. http://www.gov.cn/zhengce/zhengceku/2021-06/19/content_5619502.htm. National Energy Administration,National Mine Safety Administration. Notice on issuing the Guide to Intelligent Construction in Coal Mines(2021 Edition) [EB/OL]. [2023-06-10]. http://www.gov.cn/zhengce/zhengceku/2021-06/19/content_5619502.htm.
[7] 仲晓星,王建涛,周昆. 矿井煤自燃监测预警技术研究现状及智能化发展趋势[J]. 工矿自动化,2021,47(9):7-17. ZHONG Xiaoxing,WANG Jiantao,ZHOU Kun. Monitoring and early warning technology of coal spontaneous combustion in coal mines:research status and intelligent development trends[J]. Industry and Mine Automation,2021,47(9):7-17.
[8] 车辉,邢慧芬,樊玉琦,等. 基于大数据的火灾智能预警系统[J]. 计算机系统应用,2020,29(10):120-126. CHE Hui,XING Huifen,FAN Yuqi,et al. Fire intelligent early warning system based on big data[J]. Computer Systems & Applications,2020,29(10):120-126.
[9] 曹一凡. 基于物联网的火灾监测预警系统研究[D]. 唐山:华北理工大学,2020. CAO Yifan. Research on the Iot based fire monitoring and early warning system[D]. Tangshan:North China University of Science and Technology,2020.
[10] 郭庆. 采空区煤自燃预警技术及应用研究[D]. 徐州:中国矿业大学,2021. GUO Qing. Research on early warning technology and application of coal spontaneous combustion in mined areas[D]. Xuzhou:China University of Mining and Technology,2021.
[11] 陈晓晶. 基于“云−边−端”协同的煤矿火灾智能化防控体系建设[J]. 煤炭科学技术,2022,50(12):136-143. CHEN Xiaojing. Construction of intelligent prevention and control of coal mine fire based on "cloud-edge-end" cooperation[J]. Coal Science and Technology,2022,50(12):136-143.
[12] 徐磊,李希建. 基于大数据的矿井灾害预警模型[J]. 煤矿安全,2018,49(3):98-101. XU Lei,LI Xijian. Mine disaster warning model based on big data[J]. Safety in Coal Mines,2018,49(3):98-101.
[13] 岳宁芳,金彦,孙明福,等. 基于多指标气体的煤自燃进程分级预警研究[J]. 安全与环境学报,2020,20(6):2139-2146. YUE Ningfang,JIN Yan,SUN Mingfu,et al. Multi-staged warning system for controlling the coal spontaneous combustion based on the various index gases[J]. Journal of Safety and Environment,2020,20(6):2139-2146.
[14] 丁震,李浩荡,张庆华. 煤矿灾害智能预警架构及关键技术研究[J]. 工矿自动化,2023,49(4):15-22. DING Zhen,LI Haodang,ZHANG Qinghua. Research on intelligent hazard early warning architecture and key technologies for coal mine[J]. Journal of Mine Automation,2023,49(4):15-22.
[15] 张庆华,马国龙. 我国煤矿重大灾害预警技术现状及智能化发展展望[J]. 智能矿山,2020,1(1):52-62. ZHANG Qinghua,MA Guolong. Status and intelligent development prospect of coal mine major disaster early-warning technology in China[J]. Journal of Intelligent Mine,2020,1(1):52-62.
[16] 李明建. 煤矿多灾种融合预警技术与装备[J]. 智能矿山,2022,3(7):156-157. LI Mingjian. Multi disaster integrated early warning technology and equipment for coal mines[J]. Journal of Intelligent Mine,2022,3(7):156-157.
[17] 李杰. 青龙煤矿煤自燃无线监测预警技术研究[D]. 西安:西安科技大学,2020. LI Jie. Study on wireless monitoring and early warning technology of coal spontaneous combustion in Qinglong Coal Mine[D]. Xi'an:Xi'an University of Science and Technology,2020.
[18] 程永新. 煤矿带式输送机火灾光纤传感检测技术研究[J]. 煤炭科学技术,2019,47(2):131-135. CHENG Yongxin. Technology research on optical fiber sensing detection for belt conveyor fire in coal mine[J]. Coal Science and Technology,2019,47(2):131-135.
[19] 赵双斌. 煤矿机电硐室温度监测与预警功能实现[J]. 煤炭与化工,2022,45(7):86-88. ZHAO Shuangbin. The realization and effect evaluation of temperature monitoring and early warning function of coal mine electromechanical chamber[J]. Coal and Chemical Industry,2022,45(7):86-88.
[20] 王国法,任怀伟,庞义辉,等. 煤矿智能化(初级阶段)技术体系研究与工程进展[J]. 煤炭科学技术,2020,48(7):1-27. WANG Guofa,REN Huaiwei,PANG Yihui,et al. Research and engineering progress of intelligent coal mine technical system in early stages[J]. Coal Science and Technology,2020,48(7):1-27.
[21] 谭波,邵壮壮,郭岩,等. 基于指标气体关联分析的煤自燃分级预警研究[J]. 中国安全科学学报,2021,31(2):33-39. TAN Bo,SHAO Zhuangzhuang,GUO Yan,et al. Research on grading and early warning of coal spontaneous combustion based on correlation analysis of index gas[J]. China Safety Science Journal,2021,31(2):33-39.
[22] 邓军,肖旸,陈晓坤,等. 矿井火灾多源信息融合预警方法的研究[J]. 采矿与安全工程学报,2011,28(4):638-643. DOI: 10.3969/j.issn.1673-3363.2011.04.026 DENG Jun,XIAO Yang,CHEN Xiaokun,et al. Study on early warning method of multi-source information fusion for coal mine fire[J]. Journal of Mining & Safety Engineering,2011,28(4):638-643. DOI: 10.3969/j.issn.1673-3363.2011.04.026
[23] 洪向共,钟地长,赵庆敏. 基于多传感器融合的陆空两栖机器人移动控制系统设计[J]. 科学技术与工程,2020,20(8):3103-3108. HONG Xianggong,ZHONG Dichang,ZHAO Qingmin. Design of mobile control system for air-ground amphibious robot based on multi-sensor fusion[J]. Science Technology and Engineering,2020,20(8):3103-3108.
[24] 颜云华,金炜东. 基于多传感器信息融合的列车转向架机械故障诊断方法[J]. 计算机应用与软件,2020,37(8):48-51. DOI: 10.3969/j.issn.1000-386x.2020.08.009 YAN Yunhua,JIN Weidong. Mechanical fault diagnosis method of train bogie based on multi-sensor information fusion[J]. Computer Applications and Software,2020,37(8):48-51. DOI: 10.3969/j.issn.1000-386x.2020.08.009
[25] 王嫒娜,李英顺,贺喆. D−S证据理论融合粗糙集的火控系统状态评估[J]. 控制工程,2020,27(12):2176-2184. WANG Aina,LI Yingshun,HE Zhe. State evaluation of fire control system based on fusion of D-S evidence theory and rough set[J]. Control Engineering of China,2020,27(12):2176-2184.
[26] 王俊松,李建林. D−S证据理论改进方案综述[J]. 信息化研究,2011,37(6):4-7. WANG Junsong,LI Jianlin. Overview of D-S evidence theory modification[J]. Informatization Research,2011,37(6):4-7.
[27] 杨呈永,刘佳祎. 基于物联网节点加权的D−S证据理论数据融合算法[J]. 桂林理工大学学报,2019,39(3):731-736. YANG Chengyong,LIU Jiayi. Data fusion algorithm based on weighted D-S evidence theory in Internet of things[J]. Journal of Guilin University of Technology,2019,39(3):731-736.
[28] 叶瑾,许枫,杨娟,等. 一种基于多传感器的复合量测IMM−EKF数据融合算法[J]. 电子学报,2020,48(12):2326-2330. YE Jin,XU Feng,YANG Juan,et al. A composite measurement IMM-EKF data fusion algorithm based on multi-sensor[J]. Acta Electronica Sinica,2020,48(12):2326-2330.
[29] 韩丙光,赵子源,刘建,等. 基于多传感器信息融合的电缆火灾预警建模与仿真[J]. 电子设计工程,2022,30(10):150-154. HAN Bingguang,ZHAO Ziyuan,LIU Jian,et al. Modeling and simulation of cable fire warning based on multi-sensor information fusion[J]. Electronic Design Engineering,2022,30(10):150-154.
-
期刊类型引用(2)
1. 张巍，李彬. 机车车轮滚动接触疲劳特性及减缓方法. 机械设计与制造. 2024(11): 290-295 . 
百度学术
2. 蔡玉强，朱东升. 基于动力学仿真的高速曲柄压力机曲轴疲劳寿命分析. 工程设计学报. 2017(06): 680-686 . 百度学术
其他类型引用(2)
计量
- 文章访问数: 930
- HTML全文浏览量: 417
- PDF下载量: 162
- 被引次数: 4
