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Method for predicting coal and gas outburst danger of heading end by using post-blasting gas concentration

A gas concentration and gas outburst technology, which is applied in earth-moving drilling, mining equipment, instruments, etc., can solve problems such as poor prediction effect, inability to carry out effective learning and modeling, and achieve low cost, simple and reliable data acquisition, and abundant samples. Effect

Active Publication Date: 2021-12-03
UNIV OF SCI & TECH BEIJING
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The invention provides a method for predicting the risk of coal and gas outburst at the head of the tunnel by using the gas concentration after blasting, so as to solve the technical problem that the existing technology cannot carry out effective learning and modeling, and the prediction effect is poor

Method used

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  • Method for predicting coal and gas outburst danger of heading end by using post-blasting gas concentration
  • Method for predicting coal and gas outburst danger of heading end by using post-blasting gas concentration
  • Method for predicting coal and gas outburst danger of heading end by using post-blasting gas concentration

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Effect test

no. 1 example

[0055] Abnormal gas gushing is one of the important precursors of coal and gas outburst. A large amount of data is stored in the security monitoring system. Therefore, based on the monitoring data, the post-blast gas concentration data can be obtained, relevant features can be extracted, and algorithms such as convolutional neural networks can be used to establish an outburst risk prediction model to realize dynamic and continuous local outburst risk prediction, so as to achieve accurate prediction of outburst risk. the goal of. Based on this, this embodiment starts from the gas concentration monitoring data in the safety monitoring system of the tunneling face, and provides a method for predicting the risk of coal and gas outburst at the tunneling head by using the gas concentration after blasting. This method can be realized by electronic equipment. An electronic device may be a terminal or a server. The execution flow of this method is as follows figure 1 shown, includin...

no. 2 example

[0077] Abnormal gas gushing is one of the important precursors of coal and gas outburst. A large amount of data is stored in the security monitoring system. Therefore, based on the monitoring data, the post-blast gas concentration data can be obtained, relevant features can be extracted, and algorithms such as convolutional neural networks can be used to establish an outburst risk prediction model to realize dynamic and continuous local outburst risk prediction, so as to achieve accurate prediction of outburst risk. the goal of. Based on this, this embodiment starts from the gas concentration monitoring data in the safety monitoring system of the tunneling face, and provides a method for predicting the risk of coal and gas outburst at the tunneling head by using the gas concentration after blasting. This method can be realized by electronic equipment. An electronic device may be a terminal or a server. The execution flow of this method is as follows figure 2 shown, includi...

no. 3 example

[0160] This embodiment provides an electronic device, which includes a processor and a memory; at least one instruction is stored in the memory, and the instruction is loaded and executed by the processor, so as to implement the method of the first embodiment.

[0161] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) and one or more memories, wherein at least one instruction is stored in the memory, so The above instruction is loaded by the processor and executes the above method.

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Abstract

The invention discloses a method for predicting coal and gas outburst danger of a heading end by using post-blasting gas concentration, which comprises the following steps of: acquiring gas concentration monitoring data, identifying wave crest data not lower than a preset threshold value, and taking a sequence consisting of gas concentrations in a preset duration before and after a wave crest including the wave crest as a gas concentration abnormal sequence; adopting a weighted K-nearest neighbor algorithm to classify the gas concentration anomaly sequence; aiming at the gas concentration abnormal sequence classified as blasting operation, automatically extracting blasting time and corresponding gas concentration data by adopting first-order differential convolution operation, and acquiring post-blasting gas concentration data; and on the basis of the post-blasting gas concentration data, predicting the outburst danger degree of the heading head by adopting a convolutional neural network. According to the method, effective identification of abnormal gas concentration of the coal mine driving working face, accurate extraction of gas emission characteristics after blasting and continuous dynamic prediction of outburst danger can be realized.

Description

technical field [0001] The invention relates to the technical field of monitoring and early warning of coal and gas outburst, in particular to a method for predicting the risk of coal and gas outburst at a tunneling head by using the concentration of gas after blasting. Background technique [0002] Coal and gas outburst is one of the most destructive and harmful dynamic disasters in coal mining. Outburst disasters seriously threaten the safe production of coal mines and the personal safety of mine workers. As a key step in the "four-in-one" comprehensive prevention and control system, outburst risk prediction has been widely implemented to reduce the number of outbursts during coal roadway excavation. The development of machine learning and deep learning technology provides the possibility for coal and gas outburst prediction and effective avoidance of outburst disasters. [0003] However, in the current method of using machine learning and deep learning technology for out...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06N3/04E21F17/18E21F17/00
CPCE21F17/18E21F17/00G06N3/045G06F18/2135G06F18/24147
Inventor 宋大钊彭玉杰王洪磊邱黎明
Owner UNIV OF SCI & TECH BEIJING
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