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Coal and gas outburst prediction method based on sparse inverse covariance

A gas outburst and prediction method technology, applied in the field of coal mine safety production, can solve the problems of difficult coal and gas data representation, low calculation efficiency, and low prediction accuracy

Active Publication Date: 2019-09-10
HEFEI UNIV OF TECH
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Problems solved by technology

[0006] The present invention provides a coal and gas outburst prediction method based on sparse inverse covariance in order to overcome the shortcomings of existing technologies, such as difficulty in characterization of coal and gas outburst data, low calculation efficiency, and low prediction accuracy, in order to quickly and accurately Predict coal and gas outstanding problems accurately and improve prediction accuracy and efficiency

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  • Coal and gas outburst prediction method based on sparse inverse covariance
  • Coal and gas outburst prediction method based on sparse inverse covariance
  • Coal and gas outburst prediction method based on sparse inverse covariance

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Embodiment Construction

[0044] refer to figure 1 , in this embodiment, a coal and gas outburst prediction method based on sparse inverse covariance is carried out as follows:

[0045] Step 1: Obtain a set of coal and gas outburst data as training samples. The data in the coal mine monitoring system are all collected by sensors. Correspondingly, the coal and gas outburst data are also collected by many sensors in different time periods. Therefore, coal and gas outburst data have multivariate and time attributes, which is called multivariate time series data in technical terms. A group of training samples obtained in the present embodiment is formed by coal and gas outburst feature data T={T 1 , T 2 ,...,T i ,...,T N} and classification label data Y={y 1 ,y 2 ,...,y i ,···,y N} composition, where, T i represents the i-th coal and gas outburst feature data, and T i =[T i 1 , T i 2 ,...,T i p ...,T i D ],T i p Represents the i-th coal and gas outburst feature data T i In the p-th di...

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Abstract

The invention discloses a coal and gas outburst prediction method based on sparse inverse covariance. The coal and gas outburst prediction method comprises the following steps: 1, processing coal andgas outburst data into multivariable time sequence data; 2, constructing a model-based method to represent multivariable coal and gas outburst time sequence data; 3, initializing a sparse inverse covariance of each category in the coal and gas outburst data set by using a Graphal Lasso method; 4, optimizing the sparse inverse covariance by using LogDet divergence; and 5, selecting a classifier, namely a maximum likelihood classifier, which is matched with the model to carry out classification prediction on the coal and gas outburst test data. According to the method, the problem of representation of the coal and gas outburst data and the problem of sparsity of the inverse covariance representing each category of the coal and gas outburst are solved, so that the coal and gas outburst data can be classified quickly and accurately.

Description

technical field [0001] The invention relates to the field of coal mine safety production, in particular to a coal and gas outburst prediction method based on sparse inverse covariance. Background technique [0002] Coal is the main energy source and important raw material in the development of my country's national economy. However, the safety situation of my country's coal production is still very severe. Mine gas, coal dust, fire, flood and roof accidents are the five natural disasters in coal mines, among which gas is the number one "killer" of coal mines, and coal and gas outburst are the most frequently occurring gas disaster accidents and the number of people injured is large One of the typical dynamic disasters. Therefore, it is of great practical significance to quickly and accurately predict coal and gas outburst, which can not only improve the safety of coal mine production, but also generate huge economic and social benefits. [0003] At present, various sensors...

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Application Information

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IPC IPC(8): G06K9/62G06F17/16
CPCG06F17/16G06F18/2136G06F18/241G06F18/214
Inventor 吕俊伟胡学钢李培培廖建兴
Owner HEFEI UNIV OF TECH
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