我国西部矿区因地质条件复杂，煤层顶板水害问题日渐突出，传统水害预警手段难以有效应对这一挑战。为解决水害预警中指标选取、预警方法及标准构建等核心问题，系统开展了顶板水害预警的基础理论研究、关键技术分析及智能化平台建设工作。首先，在传统透明地质模型基础上提出了透明水文地质模型的概念，引入动态更新功能并明确其设计实现思路；其次，基于顶板水害发育“3阶段”机理分析，提出指标选择应遵循约束性、独立性和持续性“3原则”，确定了地表水特征、含水层结构及性质、隔水层结构及性质、采动影响围岩运移特征及水文要素动态变化特性5大类主要影响因素，共从中提取出14项可量化指标构成顶板水害预警指标体系；根据已有事故案例、现场水文观测数据变化规律和相关标准规范，提出由4个预警指标参与的智能水害预警方法，划分出4个等级的预警级别、标准和应对措施；以透明水文地质模型为逻辑底层，内嵌预警指标、方法、标准和多态接口，集成开发了具备3个层级、6个核心功能的顶板水害智能化预警平台，该平台通过触发器和轮询的混合预警机制，实现了多指标综合评价与预警；最后，平台在西部某煤矿进行了为期1 a的示范应用，期间成功发出15次预警信息，其中10次为监测设备异常预警，5次为水害信息预警，所有预警均提前采取了有效应对措施。结果表明：该平台的应用验证了预警指标、方法、标准和平台的有效性与适用性。未来的研究可进一步优化预警模型和数据分析方法，以提升平台的预警精度和响应速度。

Water inrush in western China’s coal seam roofs is increasingly problematic due to complex geological conditions, with traditional warning methods proving ineffective. To address key challenges like indicator selection, warning methods, and standardization, research has focused on early warning theory, critical technologies, and intelligent platform development. First, a transparent hydrogeological model is introduced, building on the traditional geological model and incorporating dynamic updates. Second, based on the three-stage mechanism of water inrush development, indicator selection follows the principles of constraint, independence, and continuity. Five key factors are identified: surface water, aquifer structure and properties, aquiclude structure and properties, rock movement due to mining, and dynamic changes in hydrological elements. From these, 14 quantifiable indicators form the early warning system. Third, an intelligent early warning method is proposed using four indicators, establishing four warning levels, standards, and response measures based on accident cases, hydrological data, and standards. Fourth, an intelligent platform, integrating three layers and six core functions, is developed with a hybrid mechanism of triggers and polling for comprehensive evaluation and early warning. Finally, the platform was applied for a year in a western coal mine, issuing 15 warnings, including 10 for equipment anomalies and 5 for water inrush, all addressed with timely measures. The results validate the platform’s effectiveness. Future improvements will optimize the model and data analysis to enhance warning precision and speed.