Mining rock caving simulation system and method based on natural caving method

Abstract

The invention discloses a natural caving method-based mining ore rock caving simulation system and method, relates to the technical field of ore rock caving simulation, and aims to solve the problems that the traditional ore rock caving simulation only focuses on acquisition of macroscopic geological data and rock mechanical parameters in the aspect of data acquisition, and the existing simulation system only focuses on acquisition of macroscopic geological data and rock mechanical parameters in the aspect of analysis methods during microstructure analysis. The algorithm is relatively single and rough, so that the simulation result cannot accurately reflect the real caving condition of the ore rock in the actual mining process. According to the invention, the data acquisition module comprehensively acquires macro and micro data and scientifically processes the macro and micro data, the microstructure analysis module accurately analyzes a microstructure by using multiple advanced algorithms and constructs a prediction model, the macro mechanical analysis module combines multiple algorithms to accurately analyze mechanical behaviors, and the data fusion calculation module ingeniously fuses the data to improve the prediction accuracy. Mining risks are effectively reduced, and safe and efficient mining operation is guaranteed.

Description

Technical Field

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

[0002] In the mining industry, rock caving is a highly efficient and cost-effective method widely used to extract large-scale underground ore bodies. Its core principle is to leverage the ore body's inherent geological conditions and mechanical properties to induce natural rock collapse under certain mining disturbances, thereby enabling ore extraction. However, accurately predicting the process, extent, and timing of rock caving has always been a key challenge in mining engineering.

[0003] Patent publication number CN117709069A discloses a three-dimensional numerical simulation method for metal mine caving, belonging to the technical field of mining methods. This invention can quickly and accurately simulate the caving process and patterns of ore and rock caving, showing a quasi-conical caving pattern. This provides a rational, efficient, and accurate method for studying large-scale ore and rock caving problems on-site.

[0004] Patent publication number CN118194672A discloses a real-time prediction and early warning method for natural cave production capacity. This method aims to provide a comprehensive and innovative system and method to address key issues in mine production forecasting, early warning, and sustainable production. Through real-time modeling, simulation, and verification, it achieves high-precision predictions of mine production capacity, accurately assessing production trends and guiding mining operations.

[0005] Traditional rock collapse simulations have traditionally focused on acquiring macroscopic geological data and rock mechanical parameters. Existing simulation systems employ relatively simplistic and crude algorithms for microstructural analysis, often based on simple strength theory and finite element methods. These algorithms fail to fully consider the complexity of rock mechanical behavior under varying geological environments, nor the coupling relationship between microstructure and macromechanical properties. Consequently, simulation results fail to accurately reflect the actual rock collapse conditions during mining. The resulting poor accuracy and reliability pose significant risks to mining companies when developing mining plans.

[0006] In order to solve the above problems, a rock collapse simulation system and method based on natural caving mining is proposed. Summary of the Invention

[0007] The purpose of the present invention is to provide a system and method for simulating rock collapse based on natural caving mining, which solves the problem in the background technology that there is a large deviation between the collapse simulation and the actual situation.

[0008] To achieve the above object, the present invention provides the following technical solution: a rock collapse simulation system for mining based on natural caving method, comprising: The data acquisition module tests the collected rock samples to obtain macroscopic mechanical parameter data and microstructural data, cleans and classifies the acquired data, and integrates and stores them after standardization. The microstructure analysis module uses X-ray diffraction data processing algorithms to identify mineral composition and content based on the collected microstructure data, and uses image analysis algorithms to determine the crystal structure, microcracks, and pore characteristics. It outputs microstructure feature data and uses multi-scale feature extraction technology to capture the trend of microcracks. Based on the trend of microcracks, a prediction model for the direction and time parameters of the collapse is constructed. The dominant direction is determined through statistical analysis of the microcracks trend data, and the time parameters of the ore collapse are calculated. The macro-mechanical analysis module analyzes the mechanical behavior of rocks under different stress states based on the collected macro-mechanical parameter data, combined with the strength theory algorithm and the finite element algorithm, and outputs the macro-mechanical analysis results; The data fusion calculation module fuses the microstructure characteristic data output by the microstructure analysis module with the macromechanical analysis results output by the macromechanical analysis module to calculate and predict the range and time parameters of ore collapse; The result display module receives the calculation results output by the data fusion calculation module and displays the ore collapse simulation results in a three-dimensional animation.

[0009] Preferably, the elastic modulus is calculated in the data acquisition module to obtain macroscopic mechanical parameter data: ,in is the elastic modulus, The pressure applied to the sample collection. To measure the cross-sectional area of ​​the sample, is the length change after the force is applied, is the original length.

[0010] Preferably, the X-ray diffraction crystal structure analysis calculation formula is: ,in is the interplanar spacing of the crystal, is the angle of incidence, is the diffraction order, is the wavelength of the X-ray, and by measuring the diffraction angle , calculate the interplanar spacing to determine the crystal structure and infer the mineral composition; Calculation of diffraction peak intensity of mineral phases based on crystal structure models , whose formula is ,in is the Lorentz polarization factor, is the structural factor to determine the content of each mineral in the ore rock sample.

[0011] Preferably, a multi-scale feature extraction technique is used to fully capture the trend, width change, and connectivity of micro cracks, and a Gaussian pyramid algorithm is used to construct images of different scales. Layer Image By Layer Image Obtained by the following formula: ,in is the Gaussian kernel function coefficient. On images of different scales, the edge detection algorithm is used to extract the crack edge. For the detected crack edge points , by calculating the tangent direction of adjacent points, the direction of the crack is determined; Image analysis, the ratio of known pixels to actual size is , the pixel width of the micro crack in the image is measured to be , then the actual crack width for .

[0012] Preferably, the number of fracture direction data is , the strike angle of each crack is , divide the angle range into several intervals, count the number of cracks in each interval, and calculate the elastic modulus of the rock layer based on the , predict the collapse direction.

[0013] Optimally, the collapse time parameter calculation and rock damage calculation formula are: ,in is the damage variable change rate, is the plastic strain rate, is the equivalent stress, and is the material constant.

[0014] Preferably, in the macro-mechanical analysis module, the rock model is meshed, four-node quadrilateral elements are selected, and the mesh density is automatically increased in the stress concentration area through adaptive mesh encryption technology according to the geometric shape and stress distribution of the rock.

[0015] Preferably, in the data fusion calculation module, when the microstructure characteristic data is fused with the macroscopic mechanical analysis results, the data is subjected to dimensionality reduction processing to remove redundant information in the data, retain the main components of ore collapse prediction, and construct a prediction model through the support vector machine algorithm.

[0016] Preferably, the result display module displays the ore collapse simulation in a three-dimensional animation, sets different time steps, calculates the position and shape changes of the ore in each time step, and provides user interaction functions.

[0017] The application method of the rock collapse simulation system based on the natural caving mining method is to first determine the target mining area and plan the data collection plan, then use the data acquisition module to collect rock samples, obtain and process macro-mechanical parameters and micro-structure data; secondly, the micro-structure analysis module identifies the mineral composition and content, determines the crystal and micro-crack characteristics, and constructs a prediction model for the collapse direction and time parameters; at the same time, the macro-mechanical analysis module combines strength theory and finite element algorithm to analyze the macro-mechanical behavior, and 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.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention provides a rockfall simulation system and method for natural caving mining. The data acquisition module comprehensively acquires and scientifically processes macro and micro data. The microstructure analysis module uses multiple advanced algorithms to accurately analyze microstructures and construct prediction models. The macromechanical analysis module combines multiple algorithms to precisely analyze mechanical behavior. The data fusion calculation module cleverly integrates data to improve prediction accuracy. The results display module intuitively presents results through 3D animation. This system and method significantly improves the accuracy and reliability of rockfall predictions, provides strong support for mining plan formulation, effectively reduces mining risks, and ensures safe and efficient mining operations. It has extremely important practical value and innovative significance in mining engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is the overall flow chart of the present invention; Figure 2 It is a schematic diagram of the process framework structure of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] In order to further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings.

[0022] Combine Figure 1-Figure 2 The rock collapse simulation system based on natural caving mining of the present invention comprises: The data acquisition module tests the collected rock samples, obtains macroscopic mechanical parameter data and microscopic structural data, and cleans and classifies the acquired data. When the data acquisition module cleans the data, it uses a statistically based outlier detection method. For macroscopic mechanical parameter data and microscopic structural data, the mean value of the data is set to , the standard deviation is , will deviate from the mean by more than The data points are judged as outliers and eliminated. At the same time, the data smoothing algorithm is used to process the collected continuous data to reduce the impact of data fluctuations on subsequent analysis. When one of the data points in the measurement data of the rock elastic modulus is too different from the surrounding data points, it can be identified and removed through outlier detection. Then, in the classification process, the macroscopic mechanical parameters are divided into strength and deformation types, and the microstructural data are divided into mineral composition and microcracks. The standardization process enables data from different sources and magnitudes to be compared and analyzed on the same scale. In this way, in the subsequent fusion calculation, the analysis deviation caused by the difference in data magnitude can be avoided, and the accuracy of the simulation can be improved.

[0023] Ensure data reliability, integrate and store data after standardized processing; In the data acquisition module, the elastic modulus is calculated to obtain macroscopic mechanical parameter data: ,in is the elastic modulus, The pressure applied to the sample collection. To measure the cross-sectional area of ​​the sample, is the length change after the force is applied, is the original length, and then analyze the X-ray diffraction crystal structure and calculate the formula: ,in is the interplanar spacing of the crystal, is the angle of incidence, is the diffraction order, is the wavelength of the X-ray, and by measuring the diffraction angle , calculate the interplanar spacing to determine the crystal structure and infer the mineral composition; Calculation of diffraction peak intensity of mineral phases based on crystal structure models , whose formula is ,in is the Lorentz polarization factor, is the structural factor to determine the content of each mineral in the ore rock sample.

[0024] The microstructure analysis module uses X-ray diffraction data processing algorithms to identify mineral composition and content based on the collected microstructure data, and uses image analysis algorithms to determine the crystal structure, microcracks, and pore characteristics. It outputs microstructure feature data and uses multi-scale feature extraction technology to capture the trend of microcracks. Based on the trend of microcracks, a prediction model for the direction and time parameters of the collapse is constructed. The dominant direction is determined through statistical analysis of the microcracks trend data, and the time parameters of the ore collapse are calculated. In the trend of micro cracks, multi-scale feature extraction technology is used to fully capture the trend, width change and connectivity of micro cracks, and Gaussian pyramid algorithm is used to construct images of different scales. Layer Image By Layer Image Obtained by the following formula: ,in is the Gaussian kernel function coefficient, where after normalization Examples of Gaussian function coefficients are as follows: Then the filtered image is sampled alternately row and column, that is, , thus we get layer image, where For the layer image, and , on images of different scales, the edge detection algorithm is used to extract the crack edge, and the detected crack edge points By calculating the tangent direction of adjacent points, the direction of the crack is determined. On each layer of the image, the edge detection algorithm is used to extract the crack edge, and the least squares method is used to fit the straight line to determine the local direction of the crack. For complex crack networks, the graph theory algorithm is used to analyze the connectivity and branching of the cracks and construct a crack network graph, in which the nodes represent the intersections or endpoints of the cracks and the edges represent the crack segments. By calculating the topological characteristics of the network graph, the structural characteristics of the crack network are deeply understood. Secondly, the number and length of cracks with different directions are counted and the crack direction rose is drawn. for , The length of the cracks The calculation formula is as follows: , for the three points collected The cracks are composed of ;in are the coordinates of the first crack edge point, Indicates the The coordinates of the edge points of each crack are drawn in polar coordinates, with different angle intervals as polar angle directions and the number or length of cracks in each interval as polar diameter length.

[0025] Image analysis, the ratio of known pixels to actual size is , the pixel width of the micro crack in the image is measured to be , then the actual crack width for , the number of fracture direction data is , the strike angle of each crack is , divide the angle range into several intervals, count the number of cracks in each interval, and calculate the elastic modulus of the rock layer based on the , predict the collapse direction, calculate the collapse time parameters after obtaining the collapse direction, and the rock damage calculation formula is: ,in is the damage variable change rate, is the plastic strain rate, is the equivalent stress, and is the material constant.

[0026] By calculating the tangent direction of adjacent points to determine the crack direction, it can fully utilize the local information of the crack edge points and reflect the changes in the crack direction at different locations in real time. In the overall fitting method, it can capture the local fluctuations and turning points of the crack direction in more detail, and has better adaptability to complex and changeable crack morphologies. In mining projects, the collapse of ore rocks is often closely related to the direction of cracks. Along the direction of cracks, ore rocks are more likely to be damaged and collapse. Therefore, the accurate crack direction data obtained can provide key guidance for mining design and construction, reasonably plan the mining sequence, determine the layout direction of support structures, etc., thereby improving the safety and efficiency of mining operations and reducing mining costs and risks. In addition, the local direction of cracks determined by fitting straight lines can provide an important basis for macroscopic analysis of the overall stability of ore rocks. When conducting ore rock stability assessment and collapse prediction, the local direction information can be integrated to construct an overall direction model of the internal crack system of the ore rock. 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 the formulation of scientific and reasonable mining plans and safety measures, and ensuring the smooth progress of mine production and the safety of personnel and equipment.

[0027] The macro-mechanical analysis module analyzes the mechanical behavior of rocks under different stress states based on the collected macro-mechanical parameter data, combined with the strength theory algorithm and the finite element algorithm, and outputs the macro-mechanical analysis results. In the macro-mechanical analysis module, the rock model is meshed and four-node quadrilateral elements are selected. According to the geometric shape and stress distribution of the rock, the grid density is automatically increased in the stress concentration area through adaptive grid encryption technology. When meshing the rock model, in addition to selecting four-node quadrilateral elements, different unit types and sizes are used in different areas according to the heterogeneity and anisotropy characteristics of the rock. In areas with complex rock structures 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 solution process, an iterative solver is used and reasonable convergence criteria are set, such as the energy error is less than a certain threshold or the displacement increment is less than a given value, to improve the calculation efficiency and accuracy and ensure that the macro-mechanical analysis results can accurately reflect the actual mechanical behavior of the ore rock.

[0028] The data fusion calculation module fuses the microstructural feature data output by the microstructural analysis module with the macromechanical analysis results output by the macromechanical analysis module to calculate and predict the range and time parameters of ore collapse. In the data fusion calculation module, when fusing the microstructural feature data with the macromechanical analysis results, the data is subjected to dimensionality reduction processing to remove redundant information in the data, retain the main components of ore collapse prediction, and construct a prediction model using the support vector machine algorithm; The result display module receives the calculation results output by the data fusion calculation module and displays the ore collapse simulation results in a three-dimensional animation. The result display module displays the ore collapse simulation in a three-dimensional animation, sets different time steps, calculates the position and shape changes of the ore in each time step, and provides user interaction functions.

[0029] The application method of the rock collapse simulation system based on the natural caving mining method is as follows: first, the target mining area is determined and the data collection plan is planned. Then, the data acquisition module is used to collect rock samples, obtain and process macroscopic mechanical parameters and microstructural data; secondly, the microstructural analysis module identifies the mineral composition and content, determines the crystal and microcrack characteristics, and constructs a prediction model for the collapse direction and time parameters; at the same time, the macroscopic mechanical analysis module combines strength theory and finite element algorithm to analyze the macroscopic mechanical behavior, and 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 a three-dimensional animation. According to the actual mining area's topography and ore body distribution, a three-dimensional model is created and assigned corresponding materials and textures. In the process of simulating ore collapse, according to the calculated ore position and morphological changes, the animation keyframe technology is used to accurately set the ore motion state at each time step, enhance the visualization effect, enable mining engineers and decision makers to observe the dynamic process of rock collapse more intuitively, and help to deeply understand the mechanical behavior and collapse mechanism of ore rock.

[0030] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0031] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The rock collapse simulation system based on natural caving method is characterized by: include: The data acquisition module tests the collected rock samples to obtain macroscopic mechanical parameter data and microstructural data, cleans and classifies the acquired data, and integrates and stores them after standardization. The microstructure analysis module uses X-ray diffraction data processing algorithms to identify mineral composition and content based on the collected microstructure data, and uses image analysis algorithms to determine the crystal structure, microcracks, and pore characteristics. It outputs microstructure feature data and uses multi-scale feature extraction technology to capture the trend of microcracks. Based on the trend of microcracks, a prediction model for the direction and time parameters of the collapse is constructed. The dominant direction is determined through statistical analysis of the microcracks trend data, and the time parameters of the ore collapse are calculated. The macro-mechanical analysis module analyzes the mechanical behavior of rocks under different stress states based on the collected macro-mechanical parameter data, combined with the strength theory algorithm and the finite element algorithm, and outputs the macro-mechanical analysis results; The data fusion calculation module fuses the microstructure characteristic data output by the microstructure analysis module with the macromechanical analysis results output by the macromechanical analysis module to calculate and predict the range and time parameters of ore collapse; The result display module 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 rock collapse simulation system based on natural caving mining according to claim 1 is characterized by: In the data acquisition module, the elastic modulus is calculated to obtain macroscopic mechanical parameter data: ,in is the elastic modulus, The pressure applied to the sample collection. To measure the cross-sectional area of ​​the sample, is the length change after the force is applied, is the original length.

3. The rock collapse simulation system based on natural caving mining according to claim 1 is characterized in that: X-ray diffraction crystal structure analysis calculation formula: ,in is the interplanar spacing of the crystal, is the angle of incidence, is the diffraction order, is the wavelength of the X-ray, and by measuring the diffraction angle , calculate the interplanar spacing to determine the crystal structure and infer the mineral composition; Calculation of diffraction peak intensity of mineral phases based on crystal structure models , whose formula is ,in is the Lorentz polarization factor, is the structural factor to determine the content of each mineral in the ore rock sample.

4. The rock collapse simulation system based on natural caving mining according to claim 1 is characterized by: The multi-scale feature extraction technology is used to fully capture the trend, width change and connectivity of micro cracks, and the Gaussian pyramid algorithm is used to construct images of different scales. Layer Image By Layer Image Obtained by the following formula: ,in is the Gaussian kernel function coefficient. On images of different scales, the edge detection algorithm is used to extract the crack edge. For the detected crack edge points , by calculating the tangent direction of adjacent points, the direction of the crack is determined; Image analysis, the ratio of known pixels to actual size is , the pixel width of the micro crack in the image is measured to be , then the actual crack width for .

5. The rock collapse simulation system based on natural caving mining according to claim 4 is characterized in that: The number of fracture direction data is , the strike angle of each crack is , divide the angle range into several intervals, count the number of cracks in each interval, and calculate the elastic modulus of the rock layer based on the , predict the collapse direction.

6. The rock collapse simulation system based on natural caving mining according to claim 5 is characterized in that: Calculation of collapse time parameters and rock damage calculation formula: ,in is the damage variable change rate, is the plastic strain rate, is the equivalent stress, and is the material constant.

7. The rock collapse simulation system based on natural caving mining according to claim 1 is characterized by: In the macro-mechanical analysis module, the rock model is meshed, and four-node quadrilateral elements are selected. According to the geometric shape and stress distribution of the rock, the mesh density is automatically increased in the stress concentration area through adaptive mesh encryption technology.

8. The rock collapse simulation system based on natural caving mining according to claim 1 is characterized by: In the data fusion calculation module, when the microstructure characteristic data is fused with the macroscopic mechanical analysis results, the data is subjected to dimensionality reduction processing to remove redundant information in the data, retain the main components of ore collapse prediction, and construct a prediction model through the support vector machine algorithm.

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

10. The application method of the rock collapse simulation system based on natural caving mining according to any one of claims 1 to 9, characterized in that: First, the target mining area is determined and a data collection plan is planned. Then, the data acquisition module is used to collect rock samples, obtain and process macroscopic mechanical parameters and microstructural data. Secondly, the microstructural analysis module identifies the mineral composition and content, determines the characteristics of crystals and microcracks, and constructs a prediction model for the collapse direction and time parameters. At the same time, the macroscopic mechanical analysis module combines strength theory with finite element algorithm to analyze the macroscopic mechanical behavior. The data fusion calculation module combines the results of the two modules to calculate and predict the range and time parameters of ore collapse. Finally, the result display module displays the results in 3D animation.

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