Masonry beam deformation monitoring system and monitoring and forewarning method based on Hopfield neural network

Abstract

The invention discloses a masonry beam deformation monitoring system and monitoring and forewarning method based on a Hopfield neural network. The masonry beam deformation monitoring system comprisesa grating optical fiber sensor arranged at each monitoring point in a roadway and an optical fiber grating demodulator arranged on a well and in communication connection with the grating optical fibersensors through a downhole monitoring system platform. The signal output end of the optical fiber grating demodulator is connected with a Hopfield neural network processing system, and the Hopfield neural network processing system and a storage searching server conduct information interaction. By adopting the masonry beam deformation monitoring system and monitoring and forewarning method, multi-point and multi-parameter monitoring is achieved; the grating optical fiber sensor are assembled to collect data, mining optical fibers are adopted for transmission, so that the capability of resisting electromagnetic interference is high, and operation is stable; and the data are processed based on the Hopfield neural network processing system, and a data platform is shared, so that parameters can be optimized, the forewarning and alarming threshold value can be further adjusted according to the deformation situation of the downhole roadway, and thus the efficacy of the monitoring system is improved greatly.

Description

technical field [0001] The invention belongs to the technical field of data monitoring, and relates to an underground roadway deformation monitoring system, in particular to a Hopfield neural network-based masonry beam deformation monitoring system and a monitoring and early warning method. Background technique [0002] In recent years, with the continuous innovation of coal mining technology, the driving speed of shafts has also been continuously increased, and safety considerations have become the top priority in geotechnical engineering, tunnel engineering, slope engineering and coal mine engineering. As the depth continues to increase, various geological disasters caused by mine pressure in the roadway also occur frequently, such as the damage of masonry beams leading to the deformation of the roof triangular block area, and the excessive hardness of the key seam coal causes a large amount of original rock stress in the rock mass. , The release during mining leads to roc...

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Problems solved by technology

[0002] In recent years, with the continuous innovation of coal mining technology, the driving speed of shafts has also been continuously increased, and safety considerations have become the top priority in geotechnical engineering, tunnel engineering, slope engineering and coal mine engineering. As the depth continues to increase, various geological disasters caused by mine pressure in the roadway also occur frequently, such as the damage of masonry beams leading to the deformation of the roof triangular block area, and the excessive hardness of the key seam coal causes a large amount of original rock stress in the rock mass. , The release during mining leads to rock burst and coal burst, and the roadside and roof collapse bring huge roadway maintenance work, which increases the auxiliary repair cost of the roadway, and also brings many safety hazards

Therefore, it is necessary to establish a long-term masonry beam deformation monitoring system to effectively reduce and prevent the occurrence of safety accidents caused by roadway deformation and damage. However, the design software used in the underground monitoring system can only perform simple statistical monitoring. Data, causing a large amount of data to accumulate on the server, the optimization of support parameters is often a large amount of statistical data and then modified according to manual traditional experience, this often takes some unimportant data into the scope of modification, and the efficiency is greatly reduced

A large amount of statistical data will also cause parameter modification to be unable to be refined, and there is no correlation between the data, which reduces the possibility of prediction and early warning, and greatly reduces the effectiveness of the downhole monitoring system

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