Auxiliary method for early warning of coal mine water inrush disaster based on neural network

A kind of neural network and coal mine technology, applied in mining equipment, mining equipment, earthwork drilling and mining, etc., can solve the problems of failing to reach the warning level, not being able to give early warning in time, and prone to safety accidents

Inactive Publication Date: 2020-02-28
XIAN UNIV OF SCI & TECH
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Problems solved by technology

[0004] In the existing prediction methods, water inrush disasters are often directly judged, and the content of the judgment often needs to reach a certain level of early warning before early warning, resulting in too many water inrush disasters in coal mines and the water inrush level reaches When the warning level is not reached, the early warning cannot be carried out in time, resulting in poor warning effect and prone to safety accidents

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  • Auxiliary method for early warning of coal mine water inrush disaster based on neural network

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[0023] In order to make the above objects, features and advantages of the present invention more comprehensible, specific implementations of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0024] In the following description, a lot of specific details are set forth in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described here, and those skilled in the art can do it without departing from the meaning of the present invention. Similarly generalized, the present invention is therefore not limited by the specific embodiments disclosed below.

[0025] Secondly, the present invention is described in detail in conjunction with schematic diagrams. When describing the implementation of the present invention in detail, for the convenience of explanation, the cross-sectional view showing the device structure will not be partially enlarged accor...

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Abstract

The invention belongs to the technical field of coal mine water inrush early warning, and particularly relates to an auxiliary method for early warning of a coal mine water inrush disaster based on aneural network. The method comprises the specific step I of finding monitoring points, and arranging monitoring equipment; the step II of linking the monitoring equipment, and setting procedures; thestep III of outputting the set procedures into a controller, and linking the controller to the monitoring equipment; the step IV of starting the equipment for early warning of water inrush, and comparing set values according to the monitored water inrush level and the number of the water inrush positions; the step V of selecting the alarm level according to the comparison information, performing real-time monitoring and linkage through a plurality of monitoring points, and comparing with the set values according to the linkage information for obtaining the early warning level; and giving an alarm in different modes according to the early warning level. The situation that multiple water inrush disasters in a small range occur simultaneously and consequently an early warning system is triggered can be avoided effectively.

Description

technical field [0001] The invention relates to the technical field of early warning of water inrush in coal mines, in particular to an auxiliary method for early warning of water inrush disasters in coal mines based on a neural network. Background technique [0002] The early warning of water inrush in coal mines mainly studies the mechanism of water inrush and analyzes the previous water inrush accidents, summarizes the risk factors and main links that induce water inrush accidents, analyzes the most likely causes of water inrush, and determines a set of suitable water inrush accidents. The index system to solve the water inrush problem, and adopt a deep learning method to identify, analyze, evaluate, and judge the risk of water inrush accidents, and determine the level of water inrush risk based on the data of historical water inrush accidents. [0003] Existing water inrush prediction methods include backpropagation neural network (BPNN), support vector machine (SVM) and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): E21F17/18
CPCE21F17/18
Inventor 桑亚群李占利张昭昭牟琦
Owner XIAN UNIV OF SCI & TECH
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