中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (S1): 101-107.doi: 10.16265/j.cnki.issn1003-3033.2026.S1.0016

• 安全技术与工程 • 上一篇    下一篇

井工煤矿灾害综合管控平台架构设计

贺忠磊1(), 王海山1, 刘立仁2   

  1. 1 国能数智科技开发(北京)有限公司 智能生产事业部, 北京 100010
    2 陕西德源府谷能源有限公司 三道沟煤矿, 陕西 榆林 719000
  • 收稿日期:2026-02-12 修回日期:2026-05-15 出版日期:2026-06-30
  • 作者简介:

    贺忠磊 (1992—),男,河南新乡人,本科,助理工程师,主要从事煤矿安全风险监测类系统建设工作。E-mail:。

    王海山 高级工程师

Architecture design of comprehensive disaster prevention and control platform for underground coal mines

He Zhonglei1(), Wang Haishan1, Liu Liren2   

  1. 1 Smart Manufacturing Division, CHN Energy Intelligent Technology Development (Beijing) Co., Ltd., Beijing 100010, China
    2 Sandaogou Coal Mine of Shaanxi Deyuan Fugu Energy Co., Ltd., Yulin Shaanxi 719000, China
  • Received:2026-02-12 Revised:2026-05-15 Published:2026-06-30

摘要:

为解决井工煤矿多灾种预警系统相互割裂、信息孤立、响应滞后等问题,研发一套井工煤矿灾害综合管控平台架构,该架构采用“1+5+N”业务框架和分层技术体系,统一接入5大类灾害监测数据,以及通风、排水、人员定位等多个业务子系统,构建趋势预测、推演仿真、多因素风险预警、辅助决策及预测预警等5类智能预警模型库,并进行应用验证。结果表明:该平台采用微服务与分布式计算技术,可实现风险的早期识别、动态研判与超前预警;该架构能够提升监测数据的完整性与融合效率,预报成功率提高35%,时效性提升60%,实现从风险感知到联动管控的全流程贯通。

关键词: 井工煤矿, 灾害综合管控, 平台架构, 智能预警模型, 联动

Abstract:

To address the problems of early warning systems for various disasters in underground coal mines, including fragmentation, isolated information, and slow response, an architecture for a comprehensive disaster prevention and control platform in underground coal mines was developed. The "1+5+N" business framework and a layered technical system were adopted by the architecture. Five types of disaster monitoring data and several business subsystems such as ventilation, drainage, and personnel positioning were integrated. A base for five intelligent early warning models was established, including trend prediction, disaster simulation, multi-factor risk early warning, decision support, and forecast and early warning. An application validation was also carried out. The results indicate that by using microservices and distributed computing, the platform enables early identification, dynamic analysis, and early warning of risks. The architecture effectively improves data integrity and fusion efficiency, with the forecast success rate increasing by 35% and timeliness improving by 60%. A whole-process flow from risk perception to linkage control is successfully realized.

Key words: underground coal mine, comprehensive disaster prevention and control, platform architecture, intelligent early warning model, linkage

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