矿井水情监测与水害风险预警平台设计与实现

连会青, 徐斌, 田振焘, 刘德民, 杨艺, 潘光义, 王瑞

连会青, 徐斌, 田振焘, 刘德民, 杨艺, 潘光义, 王瑞. 矿井水情监测与水害风险预警平台设计与实现[J]. 煤田地质与勘探, 2021, 49(1): 198-207. DOI: 10.3969/j.issn.1001-1986.2021.01.021
引用本文: 连会青, 徐斌, 田振焘, 刘德民, 杨艺, 潘光义, 王瑞. 矿井水情监测与水害风险预警平台设计与实现[J]. 煤田地质与勘探, 2021, 49(1): 198-207. DOI: 10.3969/j.issn.1001-1986.2021.01.021
LIAN Huiqing, XU Bin, TIAN Zhentao, LIU Demin, YANG Yi, PAN Guangyi, WANG Rui. Design and implementation of mine water hazard monitoring and early warning platform[J]. COAL GEOLOGY & EXPLORATION, 2021, 49(1): 198-207. DOI: 10.3969/j.issn.1001-1986.2021.01.021
Citation: LIAN Huiqing, XU Bin, TIAN Zhentao, LIU Demin, YANG Yi, PAN Guangyi, WANG Rui. Design and implementation of mine water hazard monitoring and early warning platform[J]. COAL GEOLOGY & EXPLORATION, 2021, 49(1): 198-207. DOI: 10.3969/j.issn.1001-1986.2021.01.021

 

矿井水情监测与水害风险预警平台设计与实现

基金项目: 

国家重大研发计划课题(2017YFC0804108);国家自然科学基金项目(51774136);河北省自然科学基金重点项目(D2017508099)

详细信息
    作者简介:

    连会青,1975年生,女,山西忻州人,博士,教授,从事矿井水害防治工作.E-mail:2366884370@qq.com

    通讯作者:

    徐斌,1988年生,男,湖南邵阳人,博士,讲师,从事地质灾害及其防治对策研究工作.E-mail:jinzigaofeng@126.com

  • 中图分类号: TD745

Design and implementation of mine water hazard monitoring and early warning platform

  • 摘要: 任何矿井水害事故均会显现不同的预兆,为夯实水害智能预警基础,明晰了感知、辨识、评估、预测及相互逻辑关系的系统建设内涵,针对不同水害类型,根据其突水机理不同,设计三大类多模式水害典型场景,建立相应突水判据,提出了确定性理论精确预测与包括大数据及深度学习在内的非确定性趋势推测两类预测方法,为智能预警系统的预测预报、预警准则及阈值设置奠定理论基础。以陕西省彬长矿区亭南矿为例,建立了动态信息、静态信息及关联信息的指标体系,将地面水文动态监测单元、井下水情环境监测单元以及采掘工作面采动动态监测单元集成,构建原位采集和突水要素预兆感知系统,实施基于关键层电性参数动态监测、关键部位单点或多点多参数监测联合布置的突水前兆信息精准获取方案,采用确定性模拟模型和非确定性智能模型,实现水害预测预警功能,基于多源数据融合和空间联动分析技术,预警系统实现了井上下全空间水害风险预警“一张图”的可视化展示。实践表明,监测预警平台理论基础扎实,预测预警效果显著。
    Abstract: Any mine water hazard will show different omens. In order to consolidate the foundation of intelligent early warning of water disaster, the connotation of system construction of perception, identification, evaluation, prediction and the interlogical relation are clarified. The mechanism of water inrush is varred for different types of water hazards, we designed three types of multi-mode water inrush typical scenarios and established the corresponding water inrush criteria. In addition, so we proposed two kinds of forecasting methods, namely the precise prediction of deterministic theory and the non-deterministic trend projections including big data and deep learning, which laid a solid theoretical foundation for the prediction, warning criteria and threshold setting of the intelligent early warning system. Taking Tingnan Coal Mine of Binchang mining area in Shaanxi Province as an example, we established an index system of dynamic information, static information and related information, which integrated the ground hydrological dynamic monitoring unit, underground hydrological environment monitoring unit and mining dynamic monitoring unit of mining face, and built an in-situ acquisition and water-inrush element prediction perception system. Based on the dynamic monitoring of electrical parameters of key layers and the joint arrangement of single point or multi-point and multi-parameter monitoring of key parts, the accurate acquisition scheme of water inrush precursor information was implemented. The deterministic simulation model and the non-deterministic intelligent model were adopted to realize the prediction and early warning function of water disasters. According to the multi-source data fusion and spatial linkage analysis technology, the early warning system realized the visual display of "one map" of the whole space water disaster early warning on surface and underground. The results prove that the monitoring and early warning platform has a solid theoretical foundation, and the prediction and early warning effect is remarkable.
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  • 收稿日期:  2020-11-14
  • 修回日期:  2021-01-01
  • 发布日期:  2021-02-24

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