Coal mine working face mine pressure data prediction method based on reverse propagation neural network

Abstract

The invention discloses a coal mine working face mine pressure data prediction method based on a reverse propagation neural network. According to the method, roof mine pressure period weighting step pitch and period weighting strength parameters collected by a mined working face and a mined working face of a coal mine, and burial depth, burial depth elevation change rate, coal seam thickness, coalseam thickness change rate, mining thickness, coal seam inclination angle, coal seam inclination angle change rate, immediate roof thickness, basic roof thickness and inclination length of corresponding positions are utilized; and propulsion speed parameters are trained in the reverse propagation neural network, and the trained reverse propagation neural network can predict roof mine pressure data of any position of any working face according to requirements. Compared with traditional mine pressure calculation, the method has the advantages of high prediction accuracy, simplicity, high efficiency and the like. Meanwhile, a new thought combined with the artificial intelligence technology is provided for working face mine pressure data prediction.

Description

technical field [0001] The invention belongs to the field of mine pressure in coal mines, and in particular relates to a method for predicting mine pressure data in coal mine working faces based on a reverse propagation neural network. Background technique [0002] As the core of coal mining theory, mine pressure and its appearance law are the top priority of coal mine research. In the study of mine pressure, the analysis and calculation of the pressure step distance and the pressure intensity of the roof of the working face are often the focus and difficulty of the research. Existing theories mostly use elastic-plastic theory to calculate the roof pressure, but the error is often large. The pressure step distance and pressure intensity of the roof of the working face are mostly in a nonlinear relationship with mining factors, and it is often difficult to fit an ideal functional relationship. [0003] In recent years, artificial intelligence technology has been developed a...

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Problems solved by technology

[0004] In order to overcome the practical problem of the large calculation error of the pressure step distance and the pressure force of the roof of the working face in the prior art, the purpose of the present invention is to provide a method for predicting the mine pressure data of the coal mine working face based on the backpropagation neural network

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