Research And Application Of Water Damage Early Warning System For Sandstone Roof In Fully Mechanized Caving Face

With the westward shift of mining center of gravity and the increase of mining depth and thickness of coal sean,the process of water inrush of sandstone roof becomes more complicated,and the water damage accident is difficult to predict which affects the safe mining process of coal mine.It is urgent to analyze the mechanism of water inrush in sandstone roof of coal face,grasp the trend before and after water inrush in time,and establish a water inrush warning model,so as to reduce the loss caused by water damage.Firstly,this paper analyzes the data changes before and after water inrush in current coal mine monitoring indexes,clarifies the key index system affecting water damage,and decides to take aquifer level,water temperature,open channel flow and mine pressure as the main research object.Due to the large fluctuation of open channel flow and mine pressure in the actual monitoring process,it is difficult to define the early warning threshold.Therefore,this paper establishes the GRU neural network model to predict and analyze the key indicators,so as to improve the early warning perception and ensure the safety of coal mine production.In order to improve the accuracy of flow prediction,the open channel flow prediction model combining attention mechanism and GRU neural network is improved.According to the characteristics of strong nonlinearity and fluctuation of ore pressure sequence,a multi-feature extraction ore pressure prediction model based on complementary mode decomposition and Inception network is proposed to achieve accurate prediction of ore pressure.The experimental results show that the fitting degree of the improved flow prediction model is improved by 21.7%compared with the traditional GRU neural network,and the fitting degree of the mine pressure prediction model is improved by 43.3%compared with the GRU neural network,which proves the effectiveness of the model.Secondly,this paper analyzes the abnormal situation of each early warning index on the basis of prediction and divides the threshold level.Logistic regression method was used to fit the multi-factor water inrush warning function,calculate the risk degree of water inrush,design the whole warning process,and build the roof water inrush warning model.The accuracy rate of the simulation experiment reaches 94%,which indicates the effectiveness of the early warning model and provides countermeasures for the prevention and control of roof water.Finally,the hydrological monitoring and early warning system is built in this paper,which adopts the mode of one screen and multiple information display to analyze hydrological monitoring data in real time and predict and early warning information visually.The system is developed by Vue+Flask+Sql Server framework,realizing a one-stop platform from data collection and cleaning to model tuning and packaging and interface design...