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

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...

AI Technical Summary

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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