Coal mine gas prediction method based on deep learning

Abstract

The invention discloses a coal mine gas prediction method based on deep learning. An observable data set oriented to big data analysis is established according to actual production data collected on site; data preparation of high-dimensional gas data is carried out; the method comprises the steps of preprocessing measurement misalignment and missing data, preprocessing a time sequence, normalizingsample data, reducing dimensions and the like. The method comprises the following steps of: selecting a deep neural network-DNN (Deep Neural Network) as a gas data sensing model; based on Keras, a distributed deep learning framework is established, a plurality of machine learning algorithms are integrated, then an automatic machine learning engine is created, a model is trained, testing is completed, and intelligent prediction of coal mine gas is achieved by applying the deep neural network regression model. By means of the method, a more accurate risk pre-judgment basis can be provided for the mining and tunneling process of the coal mine.

Description

technical field [0001] The invention relates to the technical field of coal mine ventilation safety information, in particular to a coal mine gas prediction method based on deep learning. Background technique [0002] Coal mine gas prediction is a prerequisite for the realization of gas accident prevention, and the accuracy of coal mine gas prediction directly affects the practical application of prediction results. Traditional gas prediction methods are restricted by data samples, information processing technology, and the scale of prediction models, so their practicability and accuracy cannot meet the actual needs of the site. With the continuous development of coal mine safety production mechanization, automation and informatization, information related to coal mine underground ventilation safety has gradually accumulated, forming a massive collection of high-dimensional information. However, due to historical and technical reasons, these huge amounts of data are left id...

AI Technical Summary

Problems solved by technology

However, due to historical and technical reasons, these huge amounts of data are left idle or are only used for preliminary retrieval and analysis, and there is no further mining and discovery of a large amount of useful information and objective laws hidden behind them.

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