System and method for simulating rock fall based on natural caving method mining

Abstract

The application discloses a natural caving method-based mining rock caving simulation system and method, relates to the technical field of rock caving simulation, and the traditional rock caving simulation is only focused on obtaining macro geological data and rock mechanics parameters in the aspect of data collection. From the analysis method, the existing simulation system is single and rough in algorithm when analyzing the microstructure, so that the simulation result cannot accurately reflect the real caving condition of the rock in the actual mining process. The macro and micro data are comprehensively obtained and scientifically processed through the data acquisition module, the microstructure analysis module is used to accurately analyze the microstructure and construct a prediction model by using various advanced algorithms, the macro mechanics analysis module is used to accurately analyze the mechanical behavior by combining various algorithms, and the data fusion calculation module is used to skillfully fuse data to improve the prediction accuracy, effectively reduce the mining risk, and ensure that the mining operation is safe and efficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ore rock collapse simulation, in particular to a mining ore rock collapse simulation system and method based on natural caving method. BACKGROUND

[0002] In the field of mining, the natural caving method is widely used in the mining of large-scale ore bodies underground as an efficient and low-cost mining method. The core principle is to use the geological conditions and mechanical properties of the ore body to promote the natural caving of the ore body under certain mining disturbance, thereby realizing the mining of ore. However, accurately predicting the process, range and time of ore rock collapse has always been a key problem in mining engineering.

[0003] To this end, the patent document with publication number CN117709069A discloses a three-dimensional caving numerical simulation method for metal mines, belonging to the technical field of mine exploitation methods. The invention can quickly and accurately simulate the ore rock caving process and caving law, and the caving state is conical. A reasonable, efficient and accurate research method for large-scale ore rock caving problems on site is provided.

[0004] To this end, the patent document with publication number CN118194672A discloses a natural caving method production capacity real-time prediction and early warning method, aiming to provide a comprehensive and innovative system and method to solve key problems in mine production prediction, early warning and sustainable production. Through real-time modeling simulation and verification, high-precision prediction of the production capacity of the mine production process is achieved, which can accurately assess the trend of mineral production and guide mining production.

[0005] Traditional ore rock collapse simulation in data acquisition has only focused on obtaining macroscopic geological data and rock mechanics parameters in the past. From the analysis method, the existing simulation system is relatively single and rough in microstructure analysis, and is mostly based on simple strength theory and finite element algorithm, without fully considering the complexity of rock mechanics behavior under different geological environments and the coupling relationship between microstructure and macroscopic mechanical properties, resulting in that the simulation results cannot accurately reflect the real collapse of ore rock in the actual mining process. The accuracy and reliability of the simulation results are poor, which makes mining enterprises face great risks when formulating mining plans.

[0006] In view of the above problems, a mining ore rock collapse simulation system and method based on natural caving method is proposed. SUMMARY

[0007] The purpose of the present application is to provide a mining ore rock collapse simulation system and method based on natural caving method, which solves the problem of large deviation between collapse simulation and actual situation in the background art.

[0008] To achieve the above object, the present application provides the following technical solutions: a natural caving method mining rock caving simulation system based on, comprising:

[0009] The data acquisition module tests the collected rock samples, obtains macro mechanical parameter data and microstructure data, and cleans and classifies the obtained data, and integrates and stores the data after standardization processing;

[0010] The microstructure analysis module, based on the collected microstructure data, uses an X-ray diffraction data processing algorithm to identify mineral composition and content, uses an image analysis algorithm to determine crystal structure, microcrack and pore characteristics, outputs microstructure characteristic data, uses a multi-scale feature extraction technology to capture microcrack trend, constructs a caving direction and time parameter prediction model based on the microcrack trend, and determines the dominant trend by statistically analyzing the microcrack trend data, and calculates the time parameter of ore caving;

[0011] The macro mechanical analysis module, according to the collected macro mechanical parameter data, combines strength theory algorithm and finite element algorithm to analyze the mechanical behavior of rock under different stress states, and outputs macro mechanical analysis results;

[0012] The data fusion calculation module fuses the microstructure characteristic data output by the microstructure analysis module and the macro mechanical analysis results output by the macro mechanical analysis module, calculates and predicts the range and time parameter of ore caving;

[0013] The result display module receives the calculation results output by the data fusion calculation module, and displays the ore caving simulation results in three-dimensional animation.

[0014] Preferably, the data acquisition module calculates the elastic modulus to obtain the macro mechanical parameter data: , wherein is the elastic modulus, is the pressure applied to the collected sample, is the cross-sectional area of the sample, is the length change after being stressed, is the original length.

[0015] Preferably, the X-ray diffraction crystal structure analysis calculation formula is: , wherein is the interplanar spacing of the crystal, is the incident angle, is the diffraction order, is the wavelength of X-ray, and the interplanar spacing is calculated by measuring the diffraction angle , so as to determine the crystal structure and further infer the mineral composition;

[0016] The diffraction peak intensity of the mineral phase is calculated based on the crystal structure model , whose formula is , wherein is the Lorentz polarization factor, is the structure factor, which determines the content of each mineral in the ore sample.

[0017] Preferably, a multi-scale feature extraction technique is used to comprehensively capture the trend of micro-cracks, width variation, and connectivity, a Gaussian pyramid algorithm is used to construct different scale images, and the first layer image is obtained by layer image The second layer image is obtained by layer image The third layer image is obtained by

[0018] , wherein is the Gaussian kernel function coefficient, and the edge detection algorithm is used on different scale images to extract the crack edge. For the detected crack edge point , the tangent direction of the adjacent points is calculated to determine the trend of the crack.

[0019] Image analysis, given the ratio of pixels to actual size , the pixel width of the micro-crack in the image is measured as , then the actual crack width is .

[0020] Preferably, the number of crack trend data is , each crack trend angle is , the angle range is divided into several intervals, the number of cracks in each interval is counted, and the elastic modulus of the rock stratum is used to predict the collapse direction.

[0021] Preferably, the rock damage calculation formula for calculating the collapse time parameter is: , wherein is the damage variable change rate, is the plastic strain rate, is the equivalent stress, and are material constants.

[0022] Preferably, in the macro-mechanical analysis module, the rock model is divided into a grid, a four-node quadrilateral element is selected, and the adaptive grid densification technique is used to automatically increase the grid density in the stress concentration area according to the geometry of the rock and the stress distribution.

[0023] Preferably, in the data fusion calculation module, when the microstructure feature data and the macro-mechanical analysis results are fused, the data is processed to remove redundant information and retain the main components of the ore collapse prediction, and a support vector machine algorithm is used to construct a prediction model.

[0024] Preferably, the result display module displays the ore caving simulation in three-dimensional animation, sets different time steps, calculates the position and shape change of the ore at each time step, and provides user interaction functions.

[0025] The application method of the ore caving simulation system based on the natural caving method mining rock first determines the target mining area and plans the data collection scheme, then uses the data collection module to collect rock samples, obtains and processes macroscopic mechanical parameters and microscopic structure data; secondly, the microscopic structure analysis module identifies the mineral composition and content, determines the crystal and microscopic crack characteristics, and constructs the caving direction and time parameter prediction model; at the same time, the macroscopic mechanical analysis module analyzes the macroscopic mechanical behavior combined with the strength theory and the finite element algorithm, and the data fusion calculation module fuses the results of the two modules to calculate and predict the ore caving range and time parameters; finally, the result display module displays the results in three-dimensional animation.

[0026] Compared with the prior art, the beneficial effects of the present application are as follows:

[0027] 1、The ore caving simulation system and method based on the natural caving method mining rock provided by the present application comprehensively obtains macroscopic and microscopic data through the data collection module and processes them scientifically, the microscopic structure analysis module uses various advanced algorithms to accurately analyze the microscopic structure and construct a prediction model, the macroscopic mechanical analysis module accurately analyzes the mechanical behavior combined with various algorithms, the data fusion calculation module skillfully fuses the data to improve the prediction accuracy, and the result display module presents the results in three-dimensional animation. The system and method significantly improve the accuracy and reliability of ore caving prediction, provide strong support for mining scheme development, effectively reduce mining risks, and ensure safe and efficient mining operation, which has extremely important practical value and innovative significance in mining engineering. BRIEF DESCRIPTION OF DRAWINGS

[0028] Fig. 1 The overall flowchart of the present application is shown in the figure;

[0029] Fig. 2 The flowchart structure of the present application is shown in the figure. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0031] In order to further understand the content of the present application, the present application will be described in detail with reference to the drawings.

[0032] In combination withFigs. 1-2 The natural caving method-based mining rock caving simulation system of the present application comprises:

[0033] The data acquisition module tests the collected rock samples, obtains macro mechanical parameter data and microstructure data, and cleans and classifies the obtained data. When the data is cleaned in the data acquisition module, an abnormal value detection method based on statistics is adopted. For the macro mechanical parameter data and the microstructure data, the mean value of the data is set to , the standard deviation is , and the data points deviating from the mean value by more than

[0034] are determined as abnormal values and are removed. Meanwhile, a data smoothing algorithm is used to process the collected continuous data, reducing the influence of data fluctuations on subsequent analysis.

[0035] When measuring the elastic modulus of the rock, if one data point differs greatly from the surrounding data points, it can be identified and removed through abnormal value detection. Then in the classification process, the macro mechanical parameters are divided into strength and deformation categories, and the microstructure data are divided into mineral composition and microcracks. Standardization processing enables data of different sources and magnitudes to be compared and analyzed on the same scale, avoiding analysis bias caused by data magnitude differences and improving the accuracy of simulation.

[0036] The diffraction peak intensity of the mineral phase is calculated based on the crystal structure model , and the formula is , wherein is the Lorentz polarization factor, is the structure factor, and the content of each mineral in the rock sample is determined.

[0037] ​​​​​​​​The microstructure analysis module, based on the collected microstructure data, uses an X-ray diffraction data processing algorithm to identify mineral composition and content, uses an image analysis algorithm to determine crystal structure, micro-fissure and pore characteristics, outputs microstructure characteristic data, uses multi-scale feature extraction technology to capture micro-fissure trend, constructs a collapse direction and time parameter prediction model based on the micro-fissure trend, and determines the dominant trend by statistically analyzing the micro-fissure trend data, and calculates the time parameter of ore collapse;

[0038] In the trend of micro-fissure, multi-scale feature extraction technology is used to comprehensively capture the trend, width change and connectivity of micro-fissure, and a Gaussian pyramid algorithm is used to construct different scale images. In the first layer image The first layer image is obtained by the following formula:

[0039] , wherein is the Gaussian kernel function coefficient, and after normalization The Gaussian function coefficient is as follows: Then, the filtered image is sampled by every other row and column, that is , and thus the first layer image is obtained, wherein is the first layer image, and , on different scale images, an edge detection algorithm is used to extract fissure edges, for the detected fissure edge points , the trend of the fissure is determined by calculating the tangent direction of adjacent points, in each layer image, the edge detection algorithm is used to extract the fissure edge, and the least square method is used to fit a straight line to determine the local trend of the fissure, for a complex fissure network, a graph theory algorithm is used to analyze the connectivity and branching of the fissure, and a fissure network graph is constructed, wherein the nodes represent the intersection points or end points of the fissure, and the edges represent the fissure segments, by calculating the topological features of the network graph, the structural characteristics of the fissure network are deeply understood, and then the number and length of fissures with different trends are counted, and a fissure trend rose is drawn.

[0040] For a fissure composed of , , the length of the fissure is calculated by the following formula:

[0041] For a fissure composed of three points , the length of the fissure is calculated by the following formula: , wherein is the coordinate of the first fissure edge point, The coordinates of the edge points of the fissure are plotted in polar coordinates, with different angle intervals as the polar angle direction and the number or length of the fissures in each interval as the polar radius length.

[0042] Image analysis, the ratio of known pixels to actual size is , the pixel width of the microscopic fissure in the image is measured as , the actual fissure width is , the number of fissure orientation data is , and each fissure orientation angle is , the angle range is divided into several intervals, the number of fissures in each interval is counted, the elastic modulus of the rock stratum is , the collapse direction is predicted, and the rock damage calculation formula is obtained after the collapse time parameter is calculated: , wherein is the damage variable change rate, is the plastic strain rate, is the equivalent stress, and are material constants.

[0043] The fissure orientation is determined by calculating the tangent direction of adjacent points, which can fully utilize the local information of the fissure edge points and reflect the orientation change of the fissure at different positions in real time. In the overall fitting method, it can capture the local fluctuations and turns of the fissure orientation more meticulously and has better adaptability to complex and variable fissure shapes.

[0044] In mining engineering, the collapse of ore rock is often closely related to the orientation of fissures. Along the direction of fissure orientation, ore rock is more likely to be damaged and collapsed. Therefore, by obtaining accurate fissure orientation data, key guidance can be provided for mining design and construction, such as reasonable planning of mining sequence and determination of the arrangement direction of support structure, thereby improving the safety and efficiency of mining operations and reducing mining costs and risks. In addition, the local orientation of fissures determined by fitting a straight line can provide important basis for analyzing the overall stability of ore rock. When evaluating the stability of ore rock and predicting collapse, integrating local orientation information can construct a model of the overall orientation of the fissure system inside the ore rock, and then combined with mechanical analysis and other means, the stress distribution and deformation of the ore rock under different mining conditions can be more accurately predicted, providing strong support for formulating scientific and reasonable mining schemes and safety measures, and ensuring the smooth progress of mine production and the safety of personnel and equipment.

[0045] The macro-mechanical analysis module analyzes the mechanical behavior of the rock under different stress states according to the collected macro-mechanical parameter data, combines the strength theory algorithm and the finite element algorithm, and outputs the macro-mechanical analysis result. In the macro-mechanical analysis module, the rock model is meshed, four-node quadrilateral elements are selected, and the grid density is automatically increased in the stress concentration area through the adaptive grid refinement technology according to the stress distribution and the geometric shape of the rock. When meshing the rock model, in addition to selecting four-node quadrilateral elements, different element types and sizes are used in different areas according to the anisotropy and heterogeneity of the rock. In the area with complex rock structure or stress concentration, eight-node hexahedral elements are used. The grid density is automatically adjusted according to the stress gradient and error estimation in the calculation process. In the solving process, an iterative solver is used, and reasonable convergence criteria are set, such as energy error less than a certain threshold or displacement increment less than a given value, to improve the calculation efficiency and accuracy, and ensure that the macro-mechanical analysis result can accurately reflect the actual mechanical behavior of the ore rock.

[0046] The data fusion calculation module fuses the microstructure feature data output by the microstructure analysis module and the macro-mechanical analysis result output by the macro-mechanical analysis module, calculates and predicts the range and time parameters of the ore collapse. In the data fusion calculation module, when fusing the microstructure feature data and the macro-mechanical analysis result, the data is dimensionally reduced to remove redundant information and retain the main components of the ore collapse prediction. A prediction model is constructed through a support vector machine algorithm.

[0047] The result display module receives the calculation result output by the data fusion calculation module and displays the ore collapse simulation result in a three-dimensional animation. The result display module sets different time steps to calculate the position and shape change of the ore at each time step when displaying the ore collapse simulation in a three-dimensional animation, and provides user interaction functions.

[0048] The application method of the rock collapse simulation system based on the natural caving method mining first determines the target mining area and plans the data collection scheme, then uses the data collection module to collect rock samples, obtains and processes the macro mechanical parameters and microstructure data; secondly, the microstructure analysis module identifies the mineral composition and content, determines the crystal and micro crack characteristics, and constructs the collapse direction and time parameter prediction model; at the same time, the macro mechanical analysis module analyzes the macro mechanical behavior combined with the strength theory and the finite element algorithm, the data fusion calculation module fuses the results of the two modules to calculate and predict the ore collapse range and time parameters; finally, the result display module displays the results in three-dimensional animation, according to the actual terrain and ore body distribution of the mining area, creates a three-dimensional model and gives the corresponding material and texture, in the simulation of the ore collapse process, according to the calculated position and shape change of the ore, using the key frame technology of animation, accurately setting the motion state of the ore at each time step, enhancing the visualization effect, so that mining engineers and decision makers can more intuitively observe the dynamic process of rock collapse, which helps to deeply understand the mechanical behavior and collapse mechanism of the rock.

[0049] It should be noted that, in this document, the terms such as first and second are used merely to distinguish one entity or operation from another, and do not necessarily require or imply that these entities or operations exist in any such actual relationship or order. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0050] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A system for simulating the caving of ore and rock based on natural caving mining, characterized in that, Comprise: a data acquisition module, which tests collected rock samples to obtain macroscopic mechanical parameter data and microscopic structure data, and cleans and classifies the obtained data, and integrates and stores the data after standardization processing; a microscopic structure analysis module, which, based on the collected microscopic structure data, identifies mineral composition and content by using an X-ray diffraction data processing algorithm, determines crystal structure, microscopic crack and pore characteristics by using an image analysis algorithm, outputs microscopic structure characteristic data, captures microscopic crack trend by using a multi-scale feature extraction technology, constructs a collapse direction and time parameter prediction model based on the microscopic crack trend, and determines a dominant trend by statistically analyzing the microscopic crack trend data, and calculates a time parameter of ore collapse; a macroscopic mechanical analysis module, which, according to the collected macroscopic mechanical parameter data, analyzes the mechanical behavior of rock under different stress states by combining a strength theory algorithm and a finite element algorithm, and outputs macroscopic mechanical analysis results; a data fusion calculation module, which fuses the microscopic structure characteristic data output by the microscopic structure analysis module and the macroscopic mechanical analysis results output by the macroscopic mechanical analysis module, calculates and predicts the range and time parameter of ore collapse; a result display module, which receives the calculation results output by the data fusion calculation module, and displays the ore collapse simulation results in a three-dimensional animation.

2. The natural caving method based ore rock caving simulation system according to claim 1, characterized in that: The data acquisition module calculates the elastic modulus to obtain macro mechanical parameter data: wherein E is the elastic modulus, P is the pressure applied to the sample, A is the cross-sectional area of the sample, ΔL is the length change after being stressed, L0 is the original length.

3. The natural caving method based ore rock caving simulation system according to claim 1, characterized in that: Ray diffraction crystal structure analysis calculation formula: Wherein is the interplanar spacing of the crystal, is the incident angle, is the diffraction order, is the wavelength of X-ray, by measuring the diffraction angle , the interplanar spacing is calculated, so as to determine the crystal structure, and then infer the mineral composition; Based on the crystal structure model, the diffraction peak intensity of the mineral phase is calculated , whose formula is , wherein is the Lorentz polarization factor, is the structure factor, and the content of each mineral in the ore rock sample is determined.

4. The natural caving method based ore rock caving simulation system according to claim 1, characterized in that: By using multi-scale feature extraction technology, the trend of microcracks, width variation and connectivity are comprehensively captured. Different scale images are constructed by using Gaussian pyramid algorithm, and the first layer image is obtained by using the following formula: layer image The second layer image is obtained by using the following formula: layer image By using the following formula: wherein are Gaussian kernel function coefficients, on different scale images, the crack edge is extracted by using an edge detection algorithm, and for the detected crack edge points the strike of the crack is determined by calculating the tangent direction of adjacent points; Image analysis, knowing the scale of pixels to actual size , the pixel width of the microfracture in the image is measured to be , then the actual fracture width is .

5. The natural caving method based ore rock caving simulation system according to claim 4, characterized in that: The number of fracture strike data is Each fracture strike angle is Divide the angle range into several intervals, count the number of fractures in each interval, and predict the collapse direction based on the elastic modulus of the rock stratum. ​ 6. The natural caving method based ore rock caving simulation system according to claim 5, characterized in that: Caving time parameter calculation, rock damage calculation formula: wherein is the damage variable change rate, is the plastic strain rate, is the equivalent stress, and is a material constant.

7. The natural caving method based ore rock caving simulation system according to claim 1, characterized in that: In the macroscopic mechanical analysis module, the rock model is meshed, a four-node quadrilateral element is selected, and the mesh density is automatically increased in the stress concentration area by using an adaptive mesh densification technology according to the geometric shape and stress distribution of the rock.

8. The natural caving method based ore rock caving simulation system according to claim 1, characterized in that: In the data fusion calculation module, the data is reduced in dimension when the microscopic structure characteristic data and the macroscopic mechanical analysis results are fused, the redundant information in the data is removed, the main components of ore collapse prediction are retained, and a prediction model is constructed by using a support vector machine algorithm.

9. The natural caving method based ore rock caving simulation system according to claim 1, characterized in that: The result display module displays the ore collapse simulation in a three-dimensional animation, sets different time steps, calculates the position and shape change of the ore at each time step, and provides user interaction functions.

10. Use of a natural caving method based ore rock caving simulation system according to any one of claims 1 - 9, characterized in that: First, the target mining area is determined and the data acquisition scheme is planned, then the rock samples are collected by using the data acquisition module to obtain and process the macroscopic mechanical parameter and microscopic structure data; second, the microscopic structure analysis module identifies mineral composition and content, determines crystal and microscopic crack characteristics, and constructs a collapse direction and time parameter prediction model; meanwhile, the macroscopic mechanical analysis module analyzes the macroscopic mechanical behavior by combining the strength theory and the finite element algorithm, the data fusion calculation module fuses the results of the two modules to calculate and predict the range and time parameter of ore collapse; finally, the result display module displays the results in a three-dimensional animation.

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