WO2020173030A1 - Smart coal caving method based on real time monitoring of variation in top coal thickness - Google Patents

Abstract

A smart coal caving method based on real time monitoring of variation in top coal thickness, using ultrawide-band radar and laser 3D scanning technology to monitor top coal thickness information in real time and automatically regulate the opening and closing of a coal caving opening, and comprising the following steps: (I) an ultrawide-band radar apparatus (4) calculates a variation in top coal thickness on the basis of a radar echo signal; (II) a laser 3D scanning apparatus (6) calculates a coal caving amount on the basis of a scanned coal flow geometric feature, coal flow speed, and coal caving time at the coal caving opening; (III) a function equation between the top coal thickness variation and the coal caving time is established, and optimal open and close times of the coal caving opening for different coal caving stages are calculated; and (IV) open and close instructions are sent to the coal caving opening by means of a top coal caving controller, thus achieving smart coal caving in a fully-mechanized caving workface. The present method allows for accurate control and strong adaptability in the entire coal caving process, without the need for manual interference, effectively raising coal caving efficiency, and reducing labor intensity.

Description

An intelligent coal caving method based on real-time monitoring of top coal thickness variation Technical field

The invention relates to a coal caving method for a fully mechanized caving face, in particular to an intelligent coal caving method based on real-time monitoring of top coal thickness variation, and belongs to the technical field of fully mechanized caving automatic mining.

Background technique

Coal is my country’s main energy source. Thick coal seams are rich in reserves and mainly adopt fully mechanized caving mining methods. The national "13th Five-Year Plan" lists "accelerating the development and application of unmanned coal mining technology" as a key project in the energy field. State Administration of Work Safety The “mechanized replacement of personnel, automated reduction of personnel” scientific and technological strengthening security special action has pointed out the direction for scientific and technological innovation in the coal industry, and the realization of intelligent fully-mechanized caving mining in thick coal seams will become an important development trend.

In the process of fully mechanized caving mining, one of the urgent problems to be solved is how to correctly determine the best opening and closing time of the coal caving mouth, which is a technical problem that needs to be solved urgently to match the intelligent fully mechanized caving mining. At present, there are two main methods for the opening and closing time of the coal caving opening. One is that the coal caving worker determines the opening and closing of the coal caving opening through vision and hearing, and experience accumulation, which will lead to over discharge or under discharge. When this happens, a large amount of coal or gangue is mixed, which wastes resources and increases the cost of later washing. At the same time, the coal caving process will generate a large amount of dust and gas, which poses a huge threat to the life and health of coal caving operators. The second is to apply the electro-hydraulic control system to realize the opening and closing of the coal caving opening. The basic method is to embed the preset coal caving program in the electro-hydraulic control system, and realize the caving according to the preset coal caving action and coal caving time. Opening and closing of coal mouth. However, the thickness of the coal seam in the working face varies greatly, whether it is between adjacent supports or in the strike length direction. Therefore, there is a big defect in completing the opening and closing of the caving mouth in advance according to the unified coal caving procedure of all the supports of the working face. Due to the change of the thickness of the top coal at any time, the opening and closing time of the caving mouth also changes accordingly. If the closing time remains the same, it will inevitably lead to over-discharge or under-discharge in the process of caving coal, resulting in a decline in coal quality or loss of resources. Therefore, real-time automatic adjustment of the opening and closing time of the coal caving opening according to the change of the top coal thickness to realize adaptive coal caving is the key technology for realizing intelligent coal caving in a real sense.

Summary of the invention

Technical problem: The purpose of the present invention is to overcome the deficiencies in the prior art, and provide a real-time monitoring system based on the change of top coal thickness, which is convenient to use, can effectively improve the efficiency of top coal caving, and reduce the labor intensity of coal caving workers. Intelligent coal caving method.

Technical solution: In order to achieve the above purpose, the intelligent coal caving method based on real-time monitoring of top coal thickness variation of the present invention uses ultra-wideband radar device and laser three-dimensional scanning device to monitor top coal thickness information in real time, and automatically according to the top coal thickness variation To adjust the opening and closing time of the coal caving outlet, the specific steps include:

(1) Install the ultra-wideband radar device on the top beam of the hydraulic support near the middle and rear position. The ultra-wideband radar device includes a transmitter, a receiver and a control machine; the hydraulic support includes: a top beam, a pole, and a shield Beam, tail beam, base, hydraulic support controller and electro-hydraulic servo control system. Among them, the transmitting device transmits the ultra-wideband radar signal into the top coal seam, and the receiving device receives the echo signal after the ultra-wideband radar signal is reflected by the coal and rock interface and performs de-noising processing. According to the control machine, the radar signal is recorded from transmission to reception. Time to get the propagation time of the ultra-wideband radar signal in the coal seam;

计算出顶煤厚度，并采用时域有限差分方法来提高顶煤厚度的计算精度， (2) Real-time acquisition of the propagation time of ultra-wideband radar signals in the coal seam, according to the formula

Calculate the thickness of the top coal, and use the finite difference time domain method to improve the calculation accuracy of the thickness of the top coal,

In the formula: L is the thickness of the top coal, C is the propagation speed of electromagnetic waves in vacuum, ΔT is the propagation time of the signal in the coal seam, and ε is the relative permittivity;

(3) Install the laser three-dimensional scanning device on the hydraulic support close to the tail beam and aim at the coal caving opening of the hydraulic support. After the coal caving starts, the laser three-dimensional scanning device continuously scans the coal flow information discharged from the coal caving opening in real time to obtain the coal Three-dimensional geometric characteristics of the flow surface and coal flow velocity;

(4) Using the three-dimensional geometric characteristics of the coal flow surface and coal flow velocity collected by laser three-dimensional scanning, combined with the coal caving time to calculate the actual coal caving amount at the coal caving mouth;

(5) According to the top coal thickness calculated in step 2 and the actual coal caving amount calculated in step 4, the radial basis neural network is used to establish the corresponding relationship equation between the change of the top coal thickness and the coal caving time;

(6) Combining the established correspondence equation and the variation of the top coal thickness, calculate the best opening and closing time of the coal caving port in each coal caving stage;

(7) The obtained optimal opening and closing time of the coal caving opening is transmitted to the hydraulic support controller, and the hydraulic support controller sends opening and closing instructions to the coal caving opening through the electro-hydraulic servo control system to realize the intelligent caving of the fully mechanized caving face coal.

In the step 1, denoising processing is performed on the received ultra-wideband radar signal, specifically: wavelet denoising, deconvolution adaptive denoising and partial differential denoising.

In step 3 described in 3, data processing is performed on the three-dimensional geometric features of the coal flow surface scanned by the laser, specifically a point cloud denoising method based on a multi-dimensional tree.

In the step 5, a radial basis neural network is used to establish a neural network model with top coal thickness, coal flow geometric characteristics, coal flow velocity, and coal caving volume as input and top coal flow time parameters as output.

Beneficial effects: Due to the adoption of the above technical solution, the present invention has the following advantages compared with the prior art:

(1) The present invention automatically adjusts the opening and closing time of the caving opening according to the change of top coal thickness in real time to realize adaptive coal caving, which effectively eliminates the caving caused by factors such as the change of top coal thickness at any time, poor caving regularity, and complicated control variables. Over-discharge or under-discharge occurs in the coal process. Therefore, the present invention has strong adaptability and can effectively control the opening and closing time of the coal caving outlet;

(2) The intelligent coal caving method based on the real-time monitoring of top coal thickness variation provided by the present invention, the ultra-wideband radar technology can obtain top coal thickness information in real time, and the laser three-dimensional scanning technology can monitor the coal caving amount at the coal caving mouth in real time; and Based on this, a nonlinear function equation between the variation of the top coal thickness and the caving time is established, and the optimal opening and closing time of each caving opening in each coal caving stage is calculated. Therefore, the present invention can be more objective and accurate To determine the best opening and closing time of the coal caving port, the control of the entire coal caving process is relatively accurate and the effect is significant;

(3) The intelligent coal caving method based on the real-time monitoring of the change of the top coal thickness provided by the present invention, because the caving port has the optimal opening and closing time in each coal caving stage after obtaining the best opening and closing time of the coal caving stage. The liquid control system issues instructions, and the electro-hydraulic control system realizes the closing and opening of the coal caving opening. Therefore, the present invention can realize the entire coal caving opening and closing process without manual intervention, effectively improving the efficiency of coal caving and reducing coal caving workers. Labor intensity.

Description of the drawings

Fig. 1 is a flow chart of the opening and closing of caving openings based on real-time monitoring of the change in top coal thickness of the present invention.

Figure 2 is a diagram of the topological structure of the radial basis neural network of the present invention.

Fig. 3 is a schematic diagram of the arrangement of the ultra-wideband radar device and the laser three-dimensional scanning device of the present invention on the hydraulic support.

In the picture: 1-top beam; 2-pole; 3-shield beam; 4-ultra-wideband radar device; 5-tail beam; 6-laser three-dimensional scanning device; 7-base.

detailed description

The present invention will be further described below in conjunction with the embodiments in the drawings:

As shown in Figure 1, the intelligent coal caving method based on the real-time monitoring of the top coal thickness variation of the present invention uses the ultra-wideband radar device 4 and the laser three-dimensional scanning device 6 to monitor the top coal thickness information in real time, and automatically according to the top coal thickness variation To adjust the opening and closing time of the coal caving outlet, the specific steps include:

(1) Install the ultra-wideband radar device on the top beam 1 of the hydraulic support near the middle and rear position. The ultra-wideband radar device 4 includes a transmitting device, a receiving device and a control machine connected in sequence; the hydraulic support includes a top beam 1. Vertical pole 2, shield beam 3, tail beam 5, base 7, controller and electro-hydraulic servo control system, top beam 1 is set on vertical pole 2, vertical pole 2 is set on base 7, and shield beam 3 is set on Between the top beam 1 and the base 7, the tail beam 5 is connected to the rear of the top beam 1. The controller and electro-hydraulic servo control system have different installation positions according to different hydraulic support product models. Transmit the ultra-wideband radar signal greater than 0.25 into the top coal seam through the transmitting device. The receiving device receives the echo signal reflected by the ultra-wideband radar signal through the coal-rock interface and performs de-noising processing. According to the control machine, the radar signal is recorded from transmission to The time used for receiving is the propagation time of the ultra-wideband radar signal in the coal seam; the denoising processing of the received ultra-wideband radar signal is specifically: wavelet denoising, deconvolution adaptive denoising and partial differential denoising .

计算出顶煤厚度，并采用时域有限差分方法来提高顶煤厚度的计算精度， (2) Real-time acquisition of the propagation time of ultra-wideband radar signals in the coal seam, according to the formula

Calculate the thickness of the top coal, and use the finite difference time domain method to improve the calculation accuracy of the thickness of the top coal,

In the formula: L is the thickness of the top coal, C is the propagation velocity of electromagnetic waves in vacuum, ΔT is the propagation time of the signal in the coal seam, and ε is the relative permittivity;

(3) Install the laser three-dimensional scanning device 6 at the coal caving opening of the hydraulic support near the tail beam and aligned with the hydraulic support. After the coal caving starts, the laser three-dimensional scanning device 6 continuously scans the coal flow information discharged from the coal caving opening in real time to obtain Three-dimensional geometric characteristics of the coal flow surface and coal flow velocity; data processing is performed on the point cloud data obtained from the three-dimensional geometric characteristics of the coal flow surface scanned by the laser, specifically a point cloud denoising method based on multi-dimensional (K-Demension, KD) tree.

Data processing is performed on the 3D point cloud data of the coal flow surface scanned by laser, specifically the point cloud denoising method based on KD tree, which mainly uses statistical principles to calculate the mean and standard deviation of the distance between a point and each point in the neighborhood, and set The best threshold interval, if the distance of a point in the neighborhood is not in this interval, the point is considered a noise point.

k值可根据点云模型的大小来决定；计算

内每个点到当前点P的欧氏距离D＝{d ₁,d ₂,...,d _k}；计算距离D的均值μ和标准差σ；最后判断如果某个点的距离d _i不在区间μ±ασ中，则将其对应的点去除；α的取值根据邻域的大小来决定。其中P为激光扫描点云集，N为KD树点云模型，k为点云模型大小，α为标准差系数，D为点云之间的距离，μ是均值和σ是标准差； The specific process is: establish a KD tree for the coal flow point cloud P of the coal caving mouth scanned by the laser, traverse the point cloud P, and find it through the KD tree

The k value can be determined according to the size of the point cloud model; calculation

The Euclidean distance D={d ₁ ,d ₂ ,...,d _k } between each point in the current point P; calculate the mean μ and standard deviation σ of the distance D; finally judge if the distance d _{i of} a certain point If it is not in the interval μ±ασ, the corresponding point is removed; the value of α is determined according to the size of the neighborhood. Where P is the laser scanning point cloud set, N is the KD tree point cloud model, k is the point cloud model size, α is the standard deviation coefficient, D is the distance between the point clouds, μ is the mean and σ is the standard deviation;

(4) Using the three-dimensional geometric characteristics of the coal flow surface and the coal flow velocity collected by the laser three-dimensional scanning device 6 in step 3, combined with the coal caving time to calculate the actual coal caving volume;

(5) According to the top coal thickness calculated in step 2 and the actual coal caving amount calculated in step 4, the radial basis neural network is used to establish the correspondence between the change in the top coal thickness and the coal caving time Relation equation; as shown in Figure 2, establish a radial basis neural network model with top coal thickness, coal flow geometric characteristics, coal flow velocity, coal caving volume and other factors as input, and top coal flow time parameters as output , Its input and output relationship is:

Where: t is the node center of the hidden layer, and σ is the standard deviation of the basis function in the hidden layer;

The method for determining the standard deviation σ of the basis function and the network weight ω in the hidden layer of the radial basis function neural network is as follows:

Where: d _max is the maximum distance between the selected centers, n is the number of hidden nodes, G is the output of the hidden layer, and d is the expected output of the output layer;

Use radial basis neural network to establish a neural network model with top coal thickness, coal flow geometric characteristics, coal flow velocity, and coal caving volume as input and top coal flow time parameters as output;

是隐含层基函数，ω是网络权值。 x _M represents the coal caving parameters of the M th group (top coal thickness, coal flow geometric characteristics, coal flow velocity, and caving coal amount), y _j represents the top coal flow time corresponding to the M th group at time j.

Is the hidden layer basis function, and ω is the network weight.

(6) Combining the non-linear function equation established in step 5 and the change of the top coal thickness monitored in step 1, accurately calculate the best of the three stages of coal caving in the gangue stage, gangue mixed caving stage and coal caving stage Opening and closing time

(7) The optimal opening and closing time of the coal caving opening obtained in step 6 is transmitted to the hydraulic support controller, and the hydraulic support controller sends opening and closing instructions to the coal caving opening through the electro-hydraulic servo control system to realize the control of the fully mechanized caving face Intelligent coal caving.

Figure 3 is a schematic diagram of the arrangement of the ultra-wideband radar device 4 and the laser three-dimensional scanning device 6 on the hydraulic support; the ultra-wideband radar device 4 is installed on the top beam of the hydraulic support near the middle and rear part, and transmits the ultra-wideband radar signal to the top coal seam through the transmitter The receiving device receives the echo signal reflected by the ultra-wideband radar signal through the coal-rock interface; the laser three-dimensional scanning device 6 is installed on the base of the hydraulic support, close to the tail beam and aligned with the coal caving opening of the hydraulic support, after the coal caving starts , The laser three-dimensional scanning device 6 continuously scans the coal flow information discharged from the coal caving port in real time.

Claims (4)

An intelligent coal caving method based on real-time monitoring of top coal thickness variation, which is characterized in that the ultra-wideband radar device and laser three-dimensional scanning device are used to monitor the top coal thickness information in real time, and the coal caving opening is automatically adjusted according to the top coal thickness variation. Closing time, the specific steps include: (1) Install the ultra-wideband radar device on the top beam of the hydraulic support near the middle and rear position. The ultra-wideband radar device includes a transmitting device, a receiving device and a control machine; the hydraulic support includes: a top beam, a pole, Shield beam, tail beam, base, hydraulic support controller and electro-hydraulic servo control system. Among them, the transmitting device transmits the ultra-wideband radar signal into the top coal seam, and the receiving device receives the ultra-wideband radar signal reflected by the coal and rock interface. Wave signal and perform denoising processing, and get the propagation time of ultra-wideband radar signal in the coal seam according to the time taken by the controller to record the radar signal from transmission to reception; (2) Real-time acquisition of the propagation time of ultra-wideband radar signals in the coal seam, according to the formula

Calculate the thickness of the top coal, and use the finite difference time domain method to improve the calculation accuracy of the thickness of the top coal, In the formula: L is the thickness of the top coal, C is the propagation speed of electromagnetic waves in vacuum, ΔT is the propagation time of the signal in the coal seam, and ε is the relative permittivity; (3) Install the laser three-dimensional scanning device on the hydraulic support close to the tail beam and aim at the coal caving opening of the hydraulic support. After the coal caving starts, the laser three-dimensional scanning device continuously scans the coal flow information discharged from the coal caving opening in real time to obtain the coal Three-dimensional geometric characteristics of the flow surface and coal flow velocity; (4) Using the three-dimensional geometric characteristics of the coal flow surface and coal flow velocity collected by laser three-dimensional scanning, combined with the coal caving time to calculate the actual coal caving amount at the coal caving mouth; (5) According to the top coal thickness calculated in step 2 and the actual coal caving amount calculated in step 4, the radial basis neural network is used to establish the corresponding relationship equation between the change of the top coal thickness and the coal caving time; (6) Combining the established correspondence equation and the variation of the top coal thickness, calculate the best opening and closing time of the coal caving port in each coal caving stage; (7) The obtained optimal opening and closing time of the coal caving opening is transmitted to the hydraulic support controller, and the hydraulic support controller sends opening and closing instructions to the coal caving opening through the electro-hydraulic servo control system to realize the intelligent caving of the fully mechanized caving face coal. The intelligent coal caving method based on real-time monitoring of top coal thickness variation according to claim 1, characterized in that: in said step 1, denoising processing on the received ultra-wideband radar signal is specifically: Wavelet denoising, deconvolution adaptive denoising and partial differential denoising. The intelligent coal caving method based on real-time monitoring of the change in top coal thickness according to claim 1, characterized in that: in the step 3, data processing is performed on the three-dimensional geometric characteristics of the coal flow surface scanned by the laser, specifically based on Point cloud denoising method of multidimensional (K-Demension, KD) tree. The intelligent coal caving method based on real-time monitoring of top coal thickness variation according to claim 1, characterized in that: in said step 5, a radial basis neural network is used to establish the top coal thickness, coal flow geometric characteristics, A neural network model with coal flow velocity and coal caving volume as input and top coal flow time parameter as output.

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