Three-dimensional-point-cloud-data-based prevention method of coal mine roof disaster

Abstract

The invention discloses a three-dimensional-point-cloud-data-based prevention method of a coal mine roof disaster. System equipment comprises a ground analysis device arranged on the ground, an underground center station, a plurality of three-dimensional laser scanners having same parameters, and a three-dimensional point cloud data processing chip, wherein the center station, the plurality of three-dimensional laser scanners, and the three-dimensional point cloud data processing chip are arranged underground. All underground devices are intrinsically safe ones. According to the method, the possibility of roof disaster occurrence is analyzed based on the point cloud data difference. The coal mine roof disaster risk is predicted by using a method of analyzing three-dimensional point cloud data. The structure is simple; the devices are easy to arranged; the adaptability and recognition rate are high; calculation is simple; the operation efficiency is high; manual intervention is reduced; the low cost is low and the adaptability of the system is high; and hardware and software can be upgraded or repaired conveniently. A three-dimensional voxel filtering algorithm is employ; because of high stability and reliability of the algorithm, data compression, being filtering, can be carried out on the collected three-dimensional point cloud data of the roof rapidly and effectively and the searching can be accelerated greatly. On the basis of a three-dimensional normal distribution transform algorithm, rapid convergence can be realized and thus problems of poor registration convergence and frequent occurrence of local optimum of the common algorithm of the three-dimensional point cloud data can be solved; and the time is saved and complexity of the common algorithm can be reduced. The method can be applied to monitoring of the roof falling disaster in a complex environment; the monitoring precision and efficiency can be improved effectively; and great convenience is provided for safety production.

Description

technical field [0001] The invention relates to roof disaster prevention in underground coal mines, in particular to a roof disaster prevention system based on three-dimensional point cloud data, and belongs to the field of virtual reality technology. Background technique [0002] Coal is the main energy source in our country, but our country's geological environment is complex and basic information is scarce. In the process of coal mining, roof disaster is one of the five major disasters in coal mines, and its occurrence probability ranks first. With the improvement of the mechanization level of domestic coal mines, the advancing speed of the working face has also been significantly increased, which can reach more than ten meters a day, or even more. issues of support. At present, the support of the cut hole is based on the support of the adjacent mining roadway, so there is often a problem of insufficient support. However, due to the relatively large cross-sectional area...

AI Technical Summary

Problems solved by technology

However, the theoretical research on the stability of the roof of the roadway mainly uses the limit equilibrium theory to analyze the caving conditions of the roof rock mass, but in the actual application process, there are mainly the following problems: the boundary conditions in the theoretical calculation are difficult to determine; it is difficult to pass the theoretical calculation To determine the failure mode of the roof rock formation, the field application has certain limitations

At present, although there are many types of instruments developed for roadway roof monitoring, there are shortcomings in the monitoring process such as low monitoring density, unreasonable detection cycle, insufficient detection of surrounding rock depth, and low detection accuracy of instruments.

Moreover, although there are gray models, nonlinear regression models, numerical simulation experiments, etc. in the prediction models of the risk of roof fall, they are all based on the results of previous on-site measurements as information, and these models are analyzed based on time series , and the actual situation on the site is complex and changeable, so the prediction accuracy of these models is not very high, so it is necessary to establish a more complete prediction model for the dangerous area of ​​the roof fall

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