摘要
针对深部矿井地质的复杂性与灾害的多样性,开展基于海量数据驱动的深部矿井无人化、智能化监测预警技术与装备研究是提高深部煤矿开采安全保障的有效途径。然而,目前煤矿监测系统间关联性差、数据利用率低、数据深入智能分析不够,导致煤矿防灾措施实施针对性差,可能存在防灾措施不到位或过量的现象。针对煤矿灾害监测预警的需求,提出了煤矿灾害监测预警大数据平台架构,设计了结构数据、非结构数据、时序数据等异构数据的混搭数据存储方案,从数据汇集、数据质量、数据融合、数据指标等方面设计煤矿监测数据治理方法,并提出面向煤矿灾害预警应用的多租户数据集市构建技术,从而支撑煤矿灾害分析应用的多样化数据需求。
In response to the complexity of geology and the diversity of disasters in deep mine,conducting research on unmanned and intelligent monitoring and early warning technology and equipment for deep mine based on massive data driven is an effective way to improve the safety guarantee of deep coal mining.However,currently,the poor correlation between coal mine monitoring systems,low data utilization rate,and insufficient in-depth intelligent analysis of data have led to poor targeted implementation of disaster prevention measures in coal mines,which may result in inadequate or excessive disaster prevention measures.Focusing on the needs of coal mine disaster monitoring and early warning,a big data platform architecture for coal mine disaster monitoring and early warning is proposed,a mixed data storage scheme for structured data,unstructured data,temporal data and other heterogeneous data is designed,a coal mine monitoring data governance method is designed from data collection,data quality,data fusion,data indicators and other aspects,and a multi-tenant data mart construction technology for coal mine disaster warning applications is proposed,supporting the diversified data requirements of the applications of coal mine disaster analysis.
作者
管增伦
金峰
疏礼春
招晖
李尊龙
GUAN Zenglun;JIN Feng;SHU Lichun;ZHAO Hui;LI Zunlong(China National Coal Group Corporation,Chaoyang,Beijing 100120,China;Information Institute of the Ministry of Emergency Management of the PRC,Chaoyang,Beijing 100029,China;Beijing Jingtong Kexin Technology Co.,Ltd.,Chaoyang,Beijing 100000,China)
出处
《中国煤炭》
北大核心
2024年第7期97-104,共8页
China Coal
基金
中煤集团重点科技项目(20221CY001)。
关键词
大数据
数据集成
数据治理
数据集市
煤矿灾害预警
big data
data integration
data governance
data mart
coal mine disaster early warning
