为保障我国受水害威胁矿井的安全生产，加快煤矿水害预测预警技术的智能化进程，提高煤矿水害预测预警效果，基于国内水害机理与预警领域的研究现状，对煤矿水害智能预警系统建设过程中存在的预警系统建设水平与数据接入标准不统一、水害大数据信息的分类与时空匹配管理复杂、水害大数据信息的智能处理分析不足、预警及智能决策信息发布的时效性差4类技术问题进行了详细论述。针对4类技术问题，从预警系统资源整合及数据驱动角度出发，将水害预警资源分为信息采集资源与计算资源，将水害预警大数据信息分为静态本源信息与动态监测信息，将数据处理分为基础地质模型数据处理、数值处理与计算模拟及信息融合数据处理，将煤矿灾害预警分为监测参数预警、指标分级预警、智能模型预警，提出并剖析了煤矿水害智能预警系统关键技术架构。提出满足技术需求系统软件服务架构，给出了基础设施层、数据资源层、应用支撑层、业务应用层、用户展现层的软件服务方案。结合水害预警建设流程提出了针对水害监测数据的门控循环单元算法预警模型，给出了预警模型的网络结构，研究了预警模型的前向计算、反向传播计算、权重梯度计算方法，分析了不同类型感知数据接入、存储、编码、模型的分类、智能深度学习模型的构建测试、预警信息发布的技术路径，为煤矿水害智能预警系统智能化建设提供借鉴。

In order to ensure the safe production of mine threatened by water hazard, speed up the intelligent process of mine water hazard prediction and early warning technology, and improve the effect of mine water hazard prediction and early warning, based on the research status of water hazard mechanism and monitoring and early warning at home and abroad, four types of key technical issues for constructing water hazard monitoring and intelligent early warning systems are analyzed. The complexity of early warning requirements and data access standards, the classification and spatio-temporal matching of multi-source heterogeneous big data information, the intelligent processing and analysis of water hazard big data information, and the timeliness of early warning and intelligent decision information release are discussed in detail. From the perspective of early warning system resource integration and data drive, water hazard warning resources are divided into information collection resources and computing resources, water hazard warning big data information is divided into static source information and dynamic monitoring information, and data processing is divided into basic geological model data processing, numerical processing and Computational simulation and information fusion data processing divide coal mine disaster early warning into primary monitoring parameter early warning, intermediate index grading early warning, and advanced intelligent model early warning. The key technical architecture of an intelligent warning system for coal mine water hazards is proposed and analyzed. A software service architecture that meets the technical requirements is proposed, including infrastructure layer, data resource layer, application support layer, business application layer, and user presentation layer. Based on the water hazard warning construction process, a Gated Recurrent Unit algorithm warning model for water hazard monitoring data is proposed, and the network structure of the warning model is given. The forward calculation, backward propagation calculation, and weight gradient calculation methods of the warning model are studied. The classification of different types of perception data access, storage, encoding, models, construction and testing of intelligent deep learning models, and technical paths for warning information release are analyzed. It provides a reference for the intelligent construction of coal mine water hazard early warning.