Hydraulic support is the primary equipment used for surrounding rock control at fully mechanized mining faces. The load, location, and attitude of the hydraulic support are important sets of basis data to predict roof disasters. This paper summarized and analyzed the status of coal mine safety accidents and the primary influencing factors of roof disasters. This work also proposed monitoring characteristic parameters of roof disasters based on support posture-load changes, such as the support location and support posture. The data feature decomposition method of the additive model was used with the monitoring load data of the hydraulic support in the Yanghuopan coal mine to effectively extract the trend, cycle period, and residuals, which provided the period weighting characteristics of the longwall face. The autoregressive, long-short term memory, and support vector regression algorithms were used to model and analyze the monitoring data to realize single-point predictions. The seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models were adopted to predict the support cycle load of the hydraulic support. The SARIMA model is shown to be better than the ARIMA model for load predictions in one support cycle, but the prediction effect of these two algorithms over a fracture cycle is poor. Therefore, we proposed a hydraulic support load prediction method based on multiple data cutting and a hydraulic support load template library. The constructed technical framework of the roof disaster intelligent prediction platform is based on this method to perform predictions and early warnings of roof disasters based on the load and posture monitoring information from the hydraulic support.

液压支架是综采工作面围岩控制的主要设备。液压支架的载荷、位置和姿态是预测屋顶灾害的重要基础数据集。总结分析了煤矿安全事故现状及顶板灾害的主要影响因素。本工作还提出了基于支撑姿态-载荷变化的屋顶灾害监测特征参数，如支撑位置和支撑姿态。将加性模型的数据特征分解方法与羊霍盘煤矿水力支架监测负荷数据结合，有效提取趋势、循环周期和残差，提供了长壁工作面的周期加权特征。自回归的长短期记忆，采用支持向量回归算法对监测数据进行建模分析，实现单点预测。采用季节性自回归综合移动平均（SARIMA）和自回归综合移动平均（ARIMA）模型对液压支架的支护循环载荷进行预测。SARIMA模型在一个支撑周期内的载荷预测优于ARIMA模型，但这两种算法在一个断裂周期内的预测效果较差。为此，我们提出了一种基于多数据切割的水力支架载荷预测方法和水力支架载荷模板库。