Research on intelligent hazard early warning architecture and key technologies for coal mine
-
摘要: 我国已初步实现对煤矿瓦斯、火灾、水害、顶板、粉尘五大灾害的监测报警或预警，但智能化水平较低，不具备自我分析和决策能力。在智慧矿山概念框架下，阐述了煤矿灾害智能预警的内涵，提出了灾害智能预警的感知数据精准化、预警模型智能化、预警防灾协同化、应急决策高效化4个方面的特征。设计了煤矿灾害智能预警总体架构：由感知控制层、传输层、存储分析层和应用层4个层级组成，可实现对各类灾害的智能预警和智能管控；采用统一标准、统一采集、统一存储、统一分析、统一展现的数据处理原则，实现灾害智能预警多源异构数据共享与深度挖掘利用，从而解决数据孤岛、数据烟囱等问题。基于煤矿灾害智能预警总体架构，设计了灾害智能预警业务流程，为灾害智能预警设计提供参考。总结了煤矿灾害智能预警关键技术，包括瓦斯、火灾、水害、顶板、粉尘精准监测预警技术和灾害融合智能预警技术，分析了各关键技术难点及发展方向。以青龙寺煤矿灾害智能预警平台为实例，展示了灾害智能预警技术在监测监控、灾害预警、应急救援、分级管控等方面的应用效果。提出应深入研究灾害精准感知技术及装备、多场耦合致灾机理、预警模型自学习自适应技术，以实现高级阶段的灾害智能化预警。Abstract: The monitoring alarming or early warning of five major hazards in coal mines are initially achieved in China. The hazards include gas, fire, water damage, roof and dust. However, the level of intelligence is relatively low, and it does not have the capability to self-analyzing and making decisions. Under the framework of the concept of intelligent mines, the connotation of intelligent hazard early warning for coal mine is elaborated. The four features of intelligent hazard early warning are proposed: accurate perception data, intelligent early warning models, collaborative early warning and hazard prevention, and efficient emergency decision-making. The overall architecture of intelligent hazard early warning for coal mine is designed. It consists of four layers: perception control layer, transmission layer, storage analysis layer, and application layer. It can achieve intelligent early warning and control of various hazards. It adopts the data processing principles of unified standards, unified collection, unified storage, unified analysis, and unified presentation. It can achieve multi-source heterogeneous data sharing and deep mining utilization for intelligent hazard early warning, so as to solve problems such as isolated data island and data chimney. Based on the overall architecture of intelligent hazard early warning for coal mine, an intelligent hazard early warning business process has been designed to provide reference for intelligent hazard early warning design. The key technologies of intelligent hazard early warning of coal mine are summarized. The key technologies include precise monitoring and early warning of gas, fire, water damage, roof and dust, and intelligent hazard fusion early warning technology. The difficulties and development directions of each key technology are analyzed. Taking intelligent hazard early warning platform of Qinglongsi Coal Mine as an example, the application effects of intelligent hazard early warning technology in monitoring, hazard early warning, emergency rescue and hierarchical control are demonstrated. It is proposed to conduct in-depth research on precise hazard perception technology and equipment, multi field coupling hazard mechanism, and self-learning and adaptive technology of warning models to achieve intelligent hazard early warning in advanced stages.
-
-
![]()
图 1 灾害智能预警总体架构
Figure 1. Architecture of intelligent hazard early warning for coal mine
-
[1] 罗香玉,李嘉楠,郎丁. 智慧矿山基本内涵、核心问题与关键技术[J]. 工矿自动化,2019,45(9):61-64. LUO Xiangyu,LI Jianan,LANG Ding. Basic connotation,core problems and key technologies of wisdom mine[J]. Industry and Mine Automation,2019,45(9):61-64.
[2] 王国法,庞义辉,任怀伟. 智慧矿山技术体系研究与发展路径[J]. 金属矿山,2022(5):1-9. WANG Guofa,PANG Yihui,REN Huaiwei. Research and development path of smart mine technology system[J]. Metal Mine,2022(5):1-9.
[3] 袁亮. 我国煤炭工业安全科学技术创新与发展[J]. 煤矿安全,2015,46(增刊1):5-11. YUAN Liang. Innovation and development of safety science and technology in coal industry of China[J]. Safety in Coal Mines,2015,46(S1):5-11.
[4] 宁小亮. 基于多源信息融合的煤与瓦斯突出动态预警模型[J]. 矿业安全与环保,2020,47(3):1-5,16. NING Xiaoliang. Dynamic early warning model of coal and gas outburst based on multi-source information fusion[J]. Mining Safety & Environmental Protection,2020,47(3):1-5,16.
[5] 李明建,赵旭生,谈国文,等. 区域煤矿瓦斯灾害风险预警数据采集技术研究[J]. 工矿自动化,2020,46(7):57-63. LI Mingjian,ZHAO Xusheng,TAN Guowen,et al. Research on gas disaster risk early warning data collection technology for regional coal mines[J]. Industry and Mine Automation,2020,46(7):57-63.
[6] 姚亚虎. 基于瓦斯地质特征的多参量突出预警技术在新建煤矿的应用[J]. 矿业安全与环保,2021,48(3):94-99. YAO Yahu. Application of multi-parameter outburst warning technology based on gas geological characte-ristics in Xinjian Coal Mine[J]. Mining Safety & Environmental Protection,2021,48(3):94-99.
[7] 易欣,胡震,王伟峰,等. 煤自然发火智能监测与早期预警关键技术[J]. 煤矿安全,2022,53(9):31-37. YI Xin,HU Zhen,WANG Weifeng,et al. Key technologies for intelligent monitoring and early warning of coal spontaneous combustion[J]. Safety in Coal Mines,2022,53(9):31-37.
[8] 靳德武. 我国煤矿水害防治技术新进展及其方法论思考[J]. 煤炭科学技术,2017,45(5):141-147. JIN Dewu. New development of water disaster prevention and control technology in China coal mine and consideration on methodology[J]. Coal Science and Technology,2017,45(5):141-147.
[9] 谭云亮,张明,徐强,等. 坚硬顶板型冲击地压发生机理及监测预警研究[J]. 煤炭科学技术,2019,47(1):166-172. TAN Yunliang,ZHANG Ming,XU Qiang,et al. Study on occurrence mechanism and monitoring and early warning of rock burst caused by hard roof[J]. Coal Science and Technology,2019,47(1):166-172.
[10] 郭奋超,李腾龙,马威,等. 大采高工作面高压喷雾降尘技术研究及应用[J]. 矿业安全与环保,2021,48(5):92-95. GUO Fenchao,LI Tenglong,MA Wei,et al. Research and application of high pressure spray dust removal technology for fully mechanized mining face with large mining height[J]. Mining Safety & Environmental Protection,2021,48(5):92-95.
[11] 莫金明, 马威. 大采高综采面负压除尘微雾净化装置应用研究[J/OL]. 煤炭学报: 1-12[2022-08-15]. DOI: 10.13225/j.cnki.jccs.2021.2045. MO Jinming, MA Wei. Negative-pressure dust removal and micro-mist purification device for fully-mechanised mining faces with a large mining height[J/OL]. Journal of China Coal Society: 1-12[2022-08-15]. DOI: 10.13225/j.cnki.jccs.2021.2045.
[12] 吴群英,蒋林,王国法,等. 智慧矿山顶层架构设计及其关键技术[J]. 煤炭科学技术,2020,48(7):80-91. WU Qunying,JIANG Lin,WANG Guofa,et al. Top-level architecture design and key technologies of smart mine[J]. Coal Science and Technology,2020,48(7):80-91.
[13] 王国法,杜毅博,任怀伟,等. 智能化煤矿顶层设计研究与实践[J]. 煤炭学报,2020,45(6):1909-1924. WANG Guofa,DU Yibo,REN Huaiwei,et al. Top level design and practice of smart coal mines[J]. Journal of China Coal Society,2020,45(6):1909-1924.
[14] 李树刚,马莉,杨守国. 互联网+煤矿安全信息化关键技术及应用构架[J]. 煤炭科学技术,2016,44(7):34-40. LI Shugang,MA Li,YANG Shouguo. Key technology and application framework of internet plus mine safety informationization[J]. Coal Science and Technology,2016,44(7):34-40.
[15] 张科利,王建文,曹豪. 互联网+煤矿开采大数据技术研究与实践[J]. 煤炭科学技术,2016,44(7):123-128. ZHANG Keli,WANG Jianwen,CAO Hao. Study and practice on big data technology of internet plus coal mining[J]. Coal Science and Technology,2016,44(7):123-128.
[16] 李爽,薛广哲,方新秋,等. 煤矿智能化安全保障体系及关键技术[J]. 煤炭学报,2020,45(6):2320-2330. LI Shuang,XUE Guangzhe,FANG Xinqiu. et al. Coal mine intelligent safety system and key technologies[J]. Journal of China Coal Society,2020,45(6):2320-2330.
[17] 宁小亮. 基于大数据的煤与瓦斯突出预警技术[J]. 矿业安全与环保,2020,47(4):51-56. NING Xiaoliang. Early warning technology of coal and gas outburst based on big data[J]. Mining Safety & Environmental Protection,2020,47(4):51-56.
[18] 金声尧,贾澎涛,王鹏,等. 高应力围岩灾变信息采集及智能预警[J]. 矿业研究与开发,2021,41(6):170-175. JIN Shengyao,JIA Pengtao,WANG Peng,et al. Information collection and intelligent warning for high stress surrounding rock disaster[J]. Mining Research and Development,2021,41(6):170-175.
[19] 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.
[20] 杜毅博, 赵国瑞, 巩师鑫. 智能化煤矿大数据平台架构及数据处理关键技术研究[J]. 煤炭科学技术, 2020, 48（7）: 177-185 DU Yibo, ZHAO Guorui, GONG Shixin. Study on big data platform architecture of intelligent coal mine and key technologies of data processing[J]. Coal Science and Technology, 2020, 48（7）: 177-185.
[21] 刘鹏,景江波,魏卉子,等. 基于时空约束的瓦斯事故知识库构建及预警推理[J]. 煤炭科学技术,2020,48(7):262-273. LIU Peng,JING Jiangbo,WEI Huizi,et al. Construction of knowledge base and early warning inference of gas accident based on time-space constraints[J]. Coal Science and Technology,2020,48(7):262-273.
[22] 邓军,李贝,王凯,等. 我国煤火灾害防治技术研究现状及展望[J]. 煤炭科学技术,2016,44(10):1-7,101. DENG Jun,LI Bei,WANG Kai,et al. Research status and outlook on prevention and control technology of coal fire disaster in China[J]. Coal Science and Technology,2016,44(10):1-7,101.
[23] 刘忠全,李启发. 神华集团公司煤矿水害防治技术[J]. 煤炭工程,2017,49(增刊2):22-25. LIU Zhongquan,LI Qifa. Water disaster controlling technology in coal mines of Shenhua Group[J]. Coal Engineering,2017,49(S2):22-25.
[24] 靳德武,乔伟,李鹏,等. 煤矿防治水智能化技术与装备研究现状及展望[J]. 煤炭科学技术,2019,47(3):10-17. JIN Dewu,QIAO Wei,LI Peng,et al. Research status and prospects on intelligent technology and equipment for mine water hazard prevention and control[J]. Coal Science and Technology,2019,47(3):10-17.
[25] 乔伟,靳德武,王皓,等. 基于云服务的煤矿水害监测大数据智能预警平台构建[J]. 煤炭学报,2020,45(7):2619-2627. QIAO Wei,JIN Dewu,WANG Hao,et al. Development of big data intelligent early warning platform for coal mine water hazard monitoring based on cloud service[J]. Journal of China Coal Society,2020,45(7):2619-2627.
[26] 李德文,隋金君,刘国庆,等. 中国煤矿粉尘危害防治技术现状及发展方向[J]. 矿业安全与环保,2019,46(6):1-7,13. LI Dewen,SUI Jinjun,LIU Guoqing,et al. Technical status and development direction of coal mine dust hazard prevention and control technology in China[J]. Mining Safety & Environmental Protection,2019,46(6):1-7,13.
-
期刊类型引用(3)
1. 郭清杰，张伟. 煤炭计质计量智能管控关键技术的研究与应用. 煤炭加工与综合利用. 2022(09): 17-20+25 . 
百度学术
2. 徐方廷，黄锐，王秉. 三元空间融合视阈下的安全态势感知模型研究. 中国安全生产科学技术. 2022(11): 5-11 . 百度学术
3. 陈鹏，江欢，陈泳奇，梅梦雅，章俊伟. 服务于公共安全风险态势感知的冬奥会信息资源体系研究. 现代信息科技. 2021(12): 36-40 . 百度学术
其他类型引用(1)
下载:
计量
- 文章访问数: 1440
- HTML全文浏览量: 80
- PDF下载量: 196
- 被引次数: 4
