Ore body three-dimensional modeling method and device

Abstract

The invention discloses an ore body three-dimensional modeling method and device, and relates to the technical field of three-dimensional modeling. The method includes the following steps that geometric feature parameters of an ore body to be monitored are obtained, a three-dimensional boundary model is constructed, and the surface is divided into a plurality of equal-size grids; collecting point cloud data of the topographic surface of the ore body, mapping the point cloud data into a grid, analyzing the point cloud data through different modeling methods, generating a plurality of three-dimensional ore body structure models, and preferably forming a model set; coupling and analyzing the structural complexity, and screening out an information comparison grid; calculating accumulated differences between the point cloud surface features and the model features, and selecting the model with the minimum accumulated difference as a target ore body three-dimensional model; in combination with real-time mining position information, a construction influence range is determined as a monitoring area, the local structure of the target ore body model is dynamically updated by obtaining vibration information and area point cloud data of the monitoring area, the targets of real-time monitoring and dynamic management of the ore body are achieved, and mining safety and efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional modeling, in particular to a method and device for three-dimensional modeling of ore bodies. BACKGROUND

[0002] In the analysis and mining of ore bodies, obtaining the geometric structure characteristics of the ore bodies is a key link to ensure efficient mining and resource utilization. Traditional ore body analysis methods mainly rely on drilling and geophysical prospecting methods. Although these methods can obtain the basic geometric parameters of the ore bodies, they often fail to fully reflect the complex structure and actual distribution of the ore bodies, resulting in certain limitations in subsequent modeling and mining decisions.

[0003] In the prior art, traditional methods mainly rely on drilling information, geophysical prospecting data, and geological modeling tools, which provide a basis for ore body geometric feature modeling and mining decisions. However, with the increasing complexity of ore body geometry and the dynamic changes in mining needs, the existing traditional technology has certain limitations and deficiencies. For example, it cannot effectively reflect the structural complexity of the ore body surface, thereby providing a basis for information comparison in different regions, resulting in low accuracy of the established model and large errors in subsequent analysis. Meanwhile, in the mining stage, there is no real-time location information scheme in the existing technology. By monitoring the influence range of mining construction, the three-dimensional model of the ore body can be dynamically updated to ensure that its structure can reflect the changes in the mining environment in real time.

[0004] In the prior art, the publication number CN117422828A discloses a method and system for three-dimensional modeling of ore body blocks. The method includes calculating the center of gravity coordinates of adjacent ore body block contour lines, performing projection transformation on the adjacent ore body block contour lines and aligning the centers of gravity, scaling the contour lines with the center of gravity coordinates of the adjacent ore body block contour lines as the center, performing encryption processing on the scaled contour lines, and constructing the surface by connecting the contour lines after coordinate restoration and synchronous advancement. This prior art realizes the connection of ore body blocks through geometric center of gravity projection alignment and circumference synchronous advancement, solves the common problems of connection misplacement and intersection, can realize smooth connection of any contour lines between parallel profiles, and further quickly constructs a three-dimensional model of the ore body that meets the objective expectations. However, this scheme cannot detect and maintain the precision of the model in complex terrain, and does not involve the dynamic updating process in the mining process, thus reducing the real-time and effectiveness of the established model.

[0005] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The present application aims to provide a method and device for three-dimensional modeling of ore bodies to solve the problems in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A method for three-dimensional modeling of ore bodies, comprising the following specific steps: Obtaining geometric characteristic parameters of the ore body to be monitored, constructing a three-dimensional boundary model of the ore body based on the geometric parameters, and dividing the surface area of the three-dimensional boundary model of the ore body into a plurality of grids of the same size; Collecting point cloud data of the terrain surface of the ore body to be monitored, mapping the point cloud distribution of the terrain surface into the corresponding grid, analyzing the mapped point cloud data based on different surface reconstruction algorithms and geometric optimization algorithms, obtaining a plurality of three-dimensional ore body structure models, and forming an optimized three-dimensional model set of the ore body; Determining the terrain structure parameters of the grid based on the point cloud distribution in the grid, coupling and analyzing the terrain structure parameters to determine the structural complexity of the grid, and screening information comparison grids based on the structural complexity; Determining the surface structure features of the information comparison grids based on the point cloud data, calculating the cumulative difference of the surface structure features of the information comparison grids and the corresponding surface structure features in each three-dimensional ore body structure model, and taking the three-dimensional ore body structure model corresponding to the minimum cumulative difference as the target ore body three-dimensional model; Determining the influence range of mining construction based on the real-time position information of the ore body to be monitored, taking the influence range of mining construction as a monitoring area, obtaining vibration information of the monitoring area, determining the vibration update area of the target ore body three-dimensional model, collecting point cloud data of the vibration update area again, dynamically updating the structure of the vibration update area corresponding to the target ore body three-dimensional model based on the point cloud data of the vibration update area, and completing the establishment of the three-dimensional model of the ore body.

[0008] Further, the geometric characteristic parameters of the ore body specifically include size information and boundary shape information of the ore body to be monitored, the size information includes the length, width and height of the ore body, and the boundary shape information includes the boundary and boundary contour line of the top surface and bottom surface of the ore body to be monitored. The method for constructing the three-dimensional boundary model of the ore body is as follows: selecting a three-dimensional modeling software, inputting the geometric characteristic parameters into the three-dimensional modeling software, and constructing the three-dimensional boundary model of the ore body. The surface area of the three-dimensional boundary model of the ore body is divided into grids of the same size, wherein the grid division scheme is specifically determined as follows: reading the boundary data of different view planes of the ore body, determining the division range according to the minimum outer bounding box of different view planes of the three-dimensional model of the ore body, setting the area size of the grid, determining the number of grid nodes in each direction according to the division range of the minimum outer bounding box of different view planes and the size of the grid, and completing the grid division of the surface area according to the number of grid nodes.

[0009] Further, the point cloud acquisition device with set device parameters is used to scan the ore body terrain surface multiple times to obtain point cloud data of the terrain surface of the ore body to be monitored, the device parameters including resolution, scanning range and angle, the point cloud data of the terrain surface of the ore body to be monitored obtained by each scanning is subjected to denoising processing, and the terrain point cloud data retained after the denoising processing is subjected to point cloud registration to register the point clouds obtained by different scanning to form a unified point cloud data set. The logic of constructing the preferred ore body three-dimensional model set is that: a plurality of surface reconstruction algorithms are used to analyze the mapped point cloud data one by one to generate a plurality of preliminary three-dimensional surface models, and different geometric optimization methods are used to optimize each preliminary three-dimensional surface model to obtain a plurality of three-dimensional ore body structure models to form the preferred ore body three-dimensional model set; the surface reconstruction algorithms include Delaunay triangulation, Alpha shape algorithm, least square method surface fitting, Poisson surface reconstruction and Marching Cubes algorithm; and the geometric optimization methods include smoothing optimization, vertex simplification optimization, resampling optimization and boundary reconstruction optimization.

[0010] Further, the terrain structure parameters in each grid on the ore body surface are characterized by the point cloud distribution of different grids on the ore body surface, the terrain structure parameters including surface structure curvature, relief roughness and point cloud density, wherein the surface structure curvature is characterized by point cloud data coordinates, and the formula is: ; In the formula, is the surface structure curvature of the i-th grid, is the structure curvature of the p-th point in the i-th grid, wherein p is the index of the point in the grid, , is the total number of points in the grid; The formula for calculating the relief roughness is: ; In the formula, is the vertical coordinate of the p-th point, is the relief roughness of the i-th grid, is the average value of the vertical coordinates of the points in the i-th grid.

[0011] Further, the formula for characterizing the structure complexity in different grids on the ore body surface is: ; In the formula, is the structure complexity of the i-th grid, is the normalized value of the surface structure curvature of the i-th grid, is the normalized value of the relief roughness of the i-th grid, The normalized value of the i-th grid point cloud density is denoted as The information comparison grid is screened based on the structural complexity, and the logic for screening the information comparison grid is as follows: a structural complexity threshold is set, the structural complexity is compared with the structural complexity threshold, and the screening is performed according to the comparison result: When the structural complexity is less than the structural complexity threshold, it is determined that the terrain change of the grid does not need to be monitored, and the grid is not recorded as an information comparison grid; When the structural complexity is greater than the structural complexity threshold, it is determined that the terrain change of the grid is complex, and the grid is recorded as an information comparison grid; The structural complexity threshold is denoted as

[0012] Further, the surface structure feature includes the average normal change rate and the average vertical coordinate value of the target region, and the formula for calculating the normal change rate based on the point cloud data is: ; In the formula, is the average normal vector of the q-th target region in the j-th information comparison grid, is the average normal vector of the h-th corresponding neighborhood of the q-th target region, is the length of the average normal vector of the q-th target region, is the length of the average normal vector of the h-th corresponding neighborhood of the q-th target region, is the average normal change rate of the q-th target region in the j-th information comparison grid; where q is the index of the randomly selected target region in the information comparison grid, j is the index of the information comparison grid, and h is the index of the neighborhood of the target region; The average normal change rate and the average vertical coordinate value of the corresponding region in the three-dimensional ore body structure model are analyzed, the difference is calculated, and the difference of all randomly selected target regions is accumulated, and the formula for calculating the difference is: ; In the formula, is the accumulated difference, , are the average normal change rate and the average vertical coordinate value of the corresponding region of the q-th target region in the j-th information comparison grid in the three-dimensional ore body structure model, is the average vertical coordinate value of the q-th target region in the j-th information comparison grid, is the total number of information comparison grids; The three-dimensional ore body structure model with the smallest accumulated difference is selected as the target ore body three-dimensional model.

[0013] Further, the real-time position information of the ore body to be analyzed is mined to determine the mining construction influence range, wherein the specific formula for calculating the mining construction influence range is: ; In the formula, is the mining construction influence distance, is the influence distance reference value, is the mining depth, is the mining depth influence coefficient, is the rock-soil strength influence coefficient, is the rock-soil compressive strength of the monitored ore body, is the rock-soil compressive strength reference value; The current mining construction site is taken as the center, and the mining construction influence distance is taken as the radius to determine the mining construction influence range, and the vibration monitoring unit is arranged in the grid divided in the mining construction influence range to monitor the vibration information of the region, wherein the vibration information includes the vibration frequency and the vibration amplitude; The vibration updating area of the target ore body three-dimensional model is determined, and the specific logic is as follows: If the vibration frequency and the vibration amplitude , it is judged that the grid does not need to be updated; Otherwise, the grid is dynamically updated as the vibration updating area of the target ore body three-dimensional model; The structure of the target ore body three-dimensional model is dynamically updated through the surface structure characteristics of the vibration updating area, and the specific method is: the point cloud data of the vibration updating area is collected again, and the structure of the vibration updating area is dynamically updated by using the model construction method corresponding to the target ore body three-dimensional model, so that the local update of the structure of the target ore body three-dimensional model is realized.

[0014] The application also provides a kind of ore body three-dimensional modeling device, the ore body three-dimensional modeling device is used to execute the above-mentioned ore body three-dimensional modeling method, including: Topographic division analysis module, for obtaining the geometric characteristic parameters of the ore body to be monitored, constructing the ore body three-dimensional boundary model based on the geometric parameters, and dividing the surface area of the ore body three-dimensional boundary model into a plurality of same size grids; Surface feature mapping module, for collecting point cloud data of the topographic surface of the ore body to be monitored, and mapping the point cloud distribution of the topographic surface into the corresponding grid, analyzing the mapped point cloud data based on different surface reconstruction algorithms and geometric optimization algorithms to obtain a plurality of three-dimensional ore body structure models, forming an optimized ore body three-dimensional model set; The fine analysis and contrast determination module is used for determining the terrain structure parameters of the grid based on the point cloud distribution in the grid, coupling the terrain structure parameters to determine the structure complexity of the grid, and screening out the information contrast grid based on the structure complexity; The three-dimensional model establishment module is used for determining the surface structure features of the information contrast grid based on the point cloud data, and calculating the cumulative difference of the surface structure features with the corresponding surface structure features in each three-dimensional ore body structure model, so that the three-dimensional ore body structure model corresponding to the minimum cumulative difference is taken as the target ore body three-dimensional model. The dynamic updating and correction module is used for determining the mining construction influence range based on the real-time position information of the ore body to be monitored, taking the mining construction influence range as the monitoring area, obtaining the vibration information of the monitoring area, determining the vibration updating area of the target ore body three-dimensional model, collecting the point cloud data of the vibration updating area again, and dynamically updating the structure of the vibration updating area corresponding to the target ore body three-dimensional model based on the point cloud data of the vibration updating area, so as to complete the establishment of the ore body three-dimensional model.

[0015] Compared with the prior art, the present application has the following advantages: Firstly, by obtaining the geometric feature parameters of the ore body to be monitored, a three-dimensional boundary model is constructed, which can comprehensively reflect the shape and structure features of the ore body. Combined with the minimum area target of multiple division schemes and non-grid areas, the grid division scheme is effectively optimized, the accuracy of the model in detail processing is ensured, and the complex geometric shape of the ore body can be more realistically presented.

[0016] In addition, the present application obtains the structure complexity of different grids on the surface of the ore body by coupling the surface structure parameters. The introduction of this feature enables the representative grids to be selected by comparison and the information contrast area to be formed in the process of model establishment; the fineness of the established model is ensured through the information contrast area, the applicability and effectiveness of the selected model in actual mining are ensured, and the calculation resources are saved.

[0017] Finally, the present application includes the acquisition of real-time position information and the dynamic updating of the model. Through the vibration information in the monitoring area, the structure features of the target ore body three-dimensional model are adjusted in time, the real-time monitoring and dynamic management of the ore body are realized, and the mining safety and efficiency are improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 It is a whole method flowchart of the present application; Figure 2 It is a structure complexity mapping diagram of different grids; Figure 3 It is a model and point cloud grid area normal change rate contrast column chart; Figure 4 It is a whole device structure schematic diagram of the present application. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to specific embodiments.

[0020] It should be noted that, unless otherwise defined, technical terms or scientific terms used in the present application should be understood as their common meanings to those having ordinary skills in the art to which the present application pertains. The terms "first", "second", and similar terms used in the present application do not denote any order, quantity, or importance, but are only used to distinguish different components. The terms "comprising" or "including" and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connected" or "linked" and similar terms do not mean physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like only represent relative positional relationships, which can change when the absolute positions of the described objects change.

[0021] Embodiments Please refer to Figure 1 The present application provides a technical solution: A three-dimensional modeling method of ore body, the specific steps comprising: Step 1: Obtain the geometric characteristic parameters of the ore body to be monitored, construct a three-dimensional boundary model of the ore body based on the geometric parameters, and divide the surface area of the three-dimensional boundary model of the ore body into a plurality of grids of the same size.

[0022] The geometric characteristic parameters of the ore body specifically include size information and boundary shape information of the ore body to be monitored, the size information includes length, width and height of the ore body, and the boundary shape information includes boundary and boundary contour line of the top surface and bottom surface of the ore body to be monitored.

[0023] The size information of the ore body is mainly used to describe the spatial range and size of the ore body, including the length, width and height of the ore body. These parameters can be obtained by the following methods: drilling measurement, by laying drilling holes, obtaining the depth, thickness and boundary crossing point data of the ore body, which can directly obtain the size information of the underground ore body, especially suitable for the measurement of the thickness (height) of the ore body, but the drilling layout is limited by the cost and geological complexity.

[0024] Geological mapping, through the geological mapping of the ground surface, determines the surface distribution range of the ore body, and is suitable for obtaining the surface size information of the ore body; three-dimensional scanning and remote sensing technology, using laser scanning (LiDAR), unmanned aerial vehicle remote sensing and other technologies to obtain three-dimensional terrain data of the surface of the ore body, laser scanning or unmanned aerial vehicle image collection is carried out on the surface area of the ore body, the resolution is high, and the surface range of the ore body can be quickly obtained.

[0025] The boundary shape information of the ore body refers to the surface shape, boundary contour, and specific distribution characteristics of the top surface and the bottom surface of the ore body. Specifically, the following methods can be used: drill hole data is not only used to obtain the size of the ore body, but also used to infer the boundary shape information of the ore body. The starting and ending points of the drill hole passing through the ore body, such as the intersection of the top surface and the bottom surface, are extracted. A plurality of drill hole point data are combined to form boundary point cloud data of the ore body. The surface scanning and analysis method of the open pit ore body, using laser scanning (LiDAR) or unmanned aerial vehicle technology to directly scan the surface of the ore body, generates the actual shape of the top surface of the ore body.

[0026] The method for constructing the three-dimensional boundary model of the ore body is: selecting a three-dimensional modeling software, inputting the geometric characteristic parameters into the three-dimensional modeling software, and constructing the three-dimensional boundary model of the ore body; The three-dimensional boundary model of the ore body needs to use professional modeling software. The following is a commonly used three-dimensional geological modeling software: Leapfrog, which is designed for geological modeling and supports rapid generation of three-dimensional models of ore bodies; Surpac, which provides powerful three-dimensional visualization and modeling functions, supports mine design, resource estimation, etc. It can directly process drill hole data and geological profiles to generate wireframe or mesh models of ore bodies; it also includes Micromine and Datamine, etc.; The geometric characteristic parameters are imported into the selected software, the imported data are checked, and the spatial consistency is ensured. The depth points of the top surface and the bottom surface of the ore body are extracted as point cloud data. The boundary contour points of the ore body are extracted from the surface measurement data. The boundary contour points of the ore body are fitted into smooth contour lines. The top surface, the bottom surface and the boundary contour lines are combined to construct a wireframe model of the ore body, and a three-dimensional boundary model of the ore body is formed.

[0027] The surface area of the three-dimensional boundary model of the ore body is divided into grids of the same size, wherein the grid division scheme specifically includes: reading the boundary data of different view planes of the ore body, determining the division range according to the minimum outer bounding box of different view planes of the three-dimensional model of the ore body, setting the area size of the grid, determining the number of grid nodes in each direction according to the division range and the grid size of the minimum outer bounding box of different view planes, and completing the grid division of the surface area according to the number of grid nodes.

[0028] Step 2: Collect point cloud data of the terrain surface of the ore body to be monitored, and map the point cloud distribution of the terrain surface into the corresponding grid, analyze the mapped point cloud data based on different surface reconstruction algorithms and geometric optimization algorithms, obtain a plurality of three-dimensional ore body structure models, and form an optimized three-dimensional ore body model set.

[0029] The point cloud acquisition device with set device parameters is used to scan the ore body terrain surface multiple times to obtain point cloud data of the terrain surface of the ore body to be monitored, the device parameters including resolution, scanning range and angle, the point cloud data of the terrain surface of the ore body to be monitored obtained by each scanning is subjected to denoising processing, and the terrain point cloud data retained after the denoising processing is subjected to point cloud registration, the point clouds obtained by different scanning are registered to form a unified point cloud data set. The logic for constructing the optimized three-dimensional ore body model set is that a plurality of surface reconstruction algorithms are used to analyze the mapped point cloud data one by one to generate a plurality of preliminary three-dimensional surface models, and different geometric optimization methods are used to optimize each preliminary three-dimensional surface model to obtain a plurality of three-dimensional ore body structure models and form an optimized three-dimensional ore body model set; the surface reconstruction algorithms include Delaunay triangulation, Alpha shape algorithm, least square method surface fitting, Poisson surface reconstruction and Marching Cubes algorithm; the geometric optimization methods include smoothing optimization, vertex simplification optimization, resampling optimization and boundary reconstruction optimization.

[0030] A plurality of preliminary three-dimensional ore body surface models are generated one by one by applying a plurality of surface reconstruction algorithms to the preprocessed point cloud data. The selection of the surface reconstruction algorithm will affect the applicability and accuracy of the model, and a suitable algorithm is selected according to the characteristics of the ore body, the Delaunay triangulation algorithm constructs a triangular mesh according to the point cloud data to connect all points into triangular facets to generate a surface, the Poisson surface reconstruction algorithm generates a smooth three-dimensional surface based on the point cloud normal vector information, the Alpha shape algorithm generates a convex hull of the point cloud by adjusting the parameters, identifies the boundary of the point cloud, and reconstructs the ore body surface, the least square method surface fitting fits an optimal smooth surface according to the point cloud data by using the least square method to minimize the error between the point cloud and the surface, the Poisson surface reconstruction generates a smooth surface according to the point cloud data and the normal vector, and the Marching Cubes algorithm discretizes the point cloud data into a three-dimensional grid, generates an isosurface by processing each grid cell one by one, and finally constructs a three-dimensional surface.

[0031] In order to improve the accuracy and rationality of the three-dimensional ore body structure model, it is usually necessary to optimize the initially generated three-dimensional surface model, mainly including the following steps: smoothing processing, eliminating the noise and too sharp surface of the model, making the model more smooth, using Gaussian smoothing, Laplace smoothing algorithm, adjusting the area with large surface curvature change; vertex simplification optimization, reducing the number of surface polygons, reducing the model complexity, improving the calculation efficiency; resampling optimization, adjusting the grid vertex distribution without destroying the geometric shape, uniformizing the vertex density, using uniform sampling algorithm, redistributing the vertices on the model surface; boundary reconstruction optimization, repairing the model boundary, making the boundary more clear and reasonable.

[0032] Through the combination of different surface reconstruction and geometric optimization, a complete three-dimensional ore body structure model is finally generated.

[0033] Step 3: Determine the topographic structure parameters of the grid based on the point cloud distribution within the grid, couple the analysis of the topographic structure parameters to determine the structural complexity of the grid, and select the information comparison grid based on the structural complexity.

[0034] The point cloud distribution of different grids on the ore body surface is used to characterize the topographic structure parameters of each grid on the ore body surface, including surface structure curvature, relief roughness and point cloud density. The surface structure curvature is characterized by point cloud data coordinates, and the formula is: ; In the formula, is the surface structure curvature of the i-th grid, is the structure curvature of the p-th point in the i-th grid, where p is the index of the point in the grid, , is the total number of points in the grid; It should be noted that the structure curvature of the p-th point in the i-th grid needs to be calculated by the three-dimensional coordinates of the point cloud data. The surface curvature is used to describe the bending degree of the local surface of the point cloud, which reflects the geometric characteristics of the local topography of the ore body surface. The specific steps include selecting a local neighborhood: determining the neighborhood points of the p-th point by K-neighbor or radius search, fitting a local surface: using a quadratic surface fitting method to describe the local geometric shape of the p-th point, calculating the principal curvature based on the derivative of the fitted surface, and obtaining the Gaussian curvature as the structure curvature of the p-th point in the i-th grid.

[0035] The formula for calculating the relief roughness is: ; In the formula, is the vertical coordinate of the p-th point,​​ is the fluctuation roughness of the i-th grid, is the average value of the vertical coordinates of the points in the i-th grid.

[0036] The formula for characterizing the structural complexity of different grids on the surface of the ore body is: ; In the formula, is the structural complexity of the i-th grid, is the normalized value of the surface structure curvature of the i-th grid, is the normalized value of the fluctuation roughness of the i-th grid, is the normalized value of the point cloud density of the i-th grid.

[0037] It is worth noting that the structural complexity of the i-th grid is used to characterize the structural complexity of different grids on the surface of the ore body, and comprehensively reflects the geometric and distribution characteristics of the local grid on the surface of the ore body, where The larger the value of

[0038] where is the normalized value of the surface structure curvature of the i-th grid, which represents the bending degree of the local geometric shape of the grid surface. The greater the curvature, the more complex the grid surface, for example, there are more protrusions, depressions, and wrinkles, and the surface morphology changes dramatically, so is proportional to The square root of the curvature operation prevents the excessive contribution of high curvature to the structural complexity index. The influence of curvature on complexity is nonlinear, and the square root operation is more in line with the law that the surface complexity gradually tends to saturation with the increase of curvature in reality.

[0039] is the normalized value of the fluctuation roughness of the i-th grid. Roughness describes the change in height difference of surface points, reflecting the degree of surface fluctuation. The influence of fluctuation roughness on complexity is nonlinear: when the roughness is small, its change has a greater contribution to structural complexity; when the roughness is high, its influence gradually tends to saturation, and the use of the logarithmic function better simulates this relationship.

[0040] It is the normalized value of the point cloud density of the i-th grid. The point cloud density describes the distribution density of data points within the grid and indirectly reflects the complexity of the local structure of the surface. When the same point cloud acquisition device with set device parameters is used to acquire surface structure, when the surface has more details, such as wrinkles, cracks, pits or high surface roughness, the device needs to use a higher point density to capture these complex features. Therefore, the higher the point cloud density, the more drastic the geometric changes of the surface structure and the higher the complexity of the local area.

[0041] Information comparison grids are selected based on structural complexity. The specific logic for selecting these grids is as follows: a structural complexity threshold is set, and the structural complexity is compared to this threshold. Based on the comparison results, the grids are then selected. When the structural complexity When it is determined that the terrain change of the grid does not require monitoring, the grid is not recorded as an information comparison grid. Structural complexity When the terrain of a grid is deemed to be complex, it is designated as an information comparison grid. The structural complexity threshold is used. The specific settings should be based on expert experience.

[0042] Step 4: Based on point cloud data, determine the surface structure features of the information comparison grid, and calculate the cumulative difference between the grid and the corresponding surface structure features in each 3D ore body structure model. The 3D ore body structure model corresponding to the minimum cumulative difference is taken as the target ore body 3D model.

[0043] The surface structure features include the region's average normal change rate and the region's average vertical coordinate value. The formula used to calculate the normal change rate based on point cloud data is: ; In the formula, Let be the average normal vector of the q-th target region within the j-th information comparison grid. Let be the average normal vector of the h-th corresponding neighborhood of the q-th target region. This represents the magnitude of the average normal vector of the q-th target region. Let be the average normal vector magnitude of the h-th corresponding neighborhood of the q-th target region. The average rate of change of the normal to the q-th target region within the grid is compared to the j-th piece of information. The average normal vector of the target region is obtained by weighted average of the normal vectors of the facets constituting the target region, and the corresponding neighborhood of the target region is specifically set to include a fixed radius neighborhood, with the center of the target region as the center of the circle, and a fixed radius is set, and all regions within the radius range except the target region are equally divided to form a plurality of corresponding neighborhoods.

[0044] Wherein q is the index of the randomly selected target region in the information comparison grid, j is the index of the information comparison grid, and h is the index of the corresponding neighborhood. The normal line change rate and the vertical coordinate value of the corresponding region in the three-dimensional ore body structure model are analyzed, the difference is calculated, and the difference of all randomly selected target regions is accumulated, and the formula for calculating is: ; In the formula, is the accumulated difference, 、 are the average normal line change rate and the average vertical coordinate value of the corresponding region of the qth target region in the jth information comparison grid in the three-dimensional ore body structure model, is the average vertical coordinate value of the qth target region in the jth information comparison grid, is the total number of information comparison grids.

[0045] It should be noted that the accumulated difference is used to represent the accumulation of the normal line change rate difference and the vertical coordinate difference of the selected target region in the three-dimensional ore body structure model, The larger the value of the accumulated difference is, the greater the difference between the three-dimensional ore body structure model and the actual monitored ore body structure is.

[0046] The normal line change rate reflects the degree of change of the local shape of the three-dimensional ore body surface, directly describes the geometric complexity of the surface in the region, and the difference between the normal line change rates of the point cloud data and the model can be calculated to evaluate the accuracy of the geometric characteristics of the model. The vertical coordinate value directly reflects the consistency of the ore body model and the point cloud data in the height direction, and the height difference The calculation of the accumulated difference is to evaluate the deviation of the model and the actual point cloud data in the spatial position, and by accumulating the normal line change rate difference and the vertical coordinate difference of all target regions, the overall deviation of the entire ore body model and the point cloud data in the geometric structure and position can be obtained.

[0047] The three-dimensional ore body structure model with the smallest accumulated difference is selected as the target ore body three-dimensional model.

[0048] Step 5: Based on the real-time position information of the ore body to be monitored, the influence range of the mining construction is determined, the influence range of the mining construction is taken as the monitoring area, the vibration information of the monitoring area is obtained, the vibration updating area of the target ore body three-dimensional model is determined, the point cloud data of the vibration updating area is collected again, and the vibration updating area structure corresponding to the target ore body three-dimensional model is dynamically updated based on the point cloud data of the vibration updating area, so as to complete the establishment of the ore body three-dimensional model.

[0049] Based on the real-time position information of the ore body to be analyzed, the influence range of the mining construction is determined, and the specific formula for calculating the influence range of the mining construction is: ; In the formula, is the influence distance of the mining construction, is the reference value of the influence distance, is the mining depth, is the mining depth influence coefficient, is the rock-soil strength influence coefficient, is the rock-soil compressive strength of the ore body to be monitored, is the reference value of the rock-soil compressive strength, which can be obtained by the reference data corresponding to the rock-soil of the ore body to be monitored; It should be noted that is a standard influence range, which is usually given based on empirical data or model simulation results, representing the influence range of the mining construction under certain standard conditions, such as specific mining depth and rock-soil strength. The influence range is dynamically adjusted according to the actual mining conditions by correcting .

[0050] Mining depth is one of the key factors affecting the influence range of the construction. Generally, the deeper the mining depth, the greater the disturbance to the surrounding rock-soil, and the wider the influence range. The square root form indicates that the greater the depth, the smaller the influence increment per unit depth, preventing excessive correction. adjusts the contribution of depth to the influence range, and the specific value is related to geological conditions, construction methods, etc., which is set according to expert experience.

[0051] Rock-soil compressive strength is a key factor for the ore body and surrounding rock mass to resist deformation or destruction. When the rock-soil strength is high, the rock mass is more stable, and the influence range will decrease. When the rock-soil strength is low, the rock mass is more prone to deformation or destruction, and the influence range will increase. adjusts the contribution of rock-soil strength variation to the influence range, and the specific value is closely related to the properties of the rock mass and the geological environment, which is set according to expert experience.

[0052] The current mining construction site is taken as the center, and the mining construction influence distance is taken as the radius to determine the mining construction influence range The vibration monitoring unit is arranged in the grid divided in the mining construction influence range, and the area vibration information is monitored, the vibration information including vibration frequency and vibration amplitude; wherein the grid divided in the mining construction influence range can be set to different sizes according to the size of the mining construction influence range, so as to improve the vibration monitoring capacity and save the calculation resources.

[0053] The vibration updating area of the target ore body three-dimensional model is determined, and the logic is as follows: If the vibration frequency is less than 0.5 times the vibration frequency of the target ore body three-dimensional model, and the vibration amplitude is less than 0.5 times the vibration amplitude of the target ore body three-dimensional model, it is judged that the grid does not need to be updated. If the vibration frequency is greater than 1.5 times the vibration frequency of the target ore body three-dimensional model, and the vibration amplitude is greater than 1.5 times the vibration amplitude of the target ore body three-dimensional model, the grid is dynamically updated as the vibration updating area of the target ore body three-dimensional model. If the vibration frequency is greater than 1.5 times the vibration frequency of the target ore body three-dimensional model, and the vibration amplitude is greater than 1.5 times the vibration amplitude of the target ore body three-dimensional model, the grid is dynamically updated as the vibration updating area of the target ore body three-dimensional model. The surface structure characteristics of the vibration updating area are used to dynamically update the structure of the target ore body three-dimensional model, and the specific method is as follows: the point cloud data of the vibration updating area is collected again, and the structure of the vibration updating area is dynamically updated by using the model construction method corresponding to the target ore body three-dimensional model, so as to realize the local update of the structure of the target ore body three-dimensional model.

[0054] Please refer to Figure 4 The application also provides a kind of ore body three-dimensional modeling device, which is used to execute the above-mentioned ore body three-dimensional modeling method, comprising: A terrain division analysis module is used to obtain the geometric characteristic parameters of the ore body to be monitored, construct a three-dimensional boundary model of the ore body based on the geometric parameters, and divide the surface area of the three-dimensional boundary model of the ore body into a plurality of grids of the same size. A surface feature mapping module is used to collect point cloud data of the surface of the terrain of the ore body to be monitored, map the point cloud distribution of the surface of the terrain into the corresponding grid, analyze the mapped point cloud data based on different surface reconstruction algorithms and geometric optimization algorithms, obtain a plurality of three-dimensional ore body structure models, and form an optimized three-dimensional ore body model set. A fine analysis and comparison determination module is used to determine the terrain structure parameters of the grid based on the point cloud distribution in the grid, analyze the terrain structure parameters to determine the structure complexity of the grid, and select information comparison grids based on the structure complexity. A three-dimensional model establishment module is used to determine the surface structure characteristics of the information comparison grid based on the point cloud data, calculate the cumulative difference between the surface structure characteristics and the corresponding surface structure characteristics in each three-dimensional ore body structure model, and take the three-dimensional ore body structure model corresponding to the minimum cumulative difference as the target ore body three-dimensional model. ​The dynamic updating correction module is used for determining a mining construction influence range with real-time position information of the ore body to be mined, taking the mining construction influence range as a monitoring area, obtaining vibration information of the monitoring area, determining a vibration updating area of the target ore body three-dimensional model, collecting point cloud data of the vibration updating area again, and dynamically updating a vibration updating area structure corresponding to the target ore body three-dimensional model based on the point cloud data of the vibration updating area, so as to complete establishment of the ore body three-dimensional model.

[0055] The above formulas are all dimensionless values calculated, the formula is obtained by collecting a large amount of data to simulate a formula of the nearest real situation, and the preset parameters in the formula are set by a person skilled in the art according to actual conditions.

[0056] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software methods depends on the specific application and design constraints of the technical solutions.

[0057] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, which can be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0058] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for three-dimensional modeling of ore bodies, characterized in that, The specific steps include: Obtain the geometric feature parameters of the ore body to be monitored, construct a three-dimensional boundary model of the ore body based on the geometric parameters, and divide the surface area of ​​the three-dimensional boundary model of the ore body into several grids of the same size; Point cloud data of the topographic surface of the ore body to be monitored is collected, and the point cloud distribution of the topographic surface is mapped to the corresponding grid. Based on different surface reconstruction algorithms and geometric optimization algorithms, the mapped point cloud data is analyzed to obtain several three-dimensional ore body structure models and form a set of preferred ore body three-dimensional models. The terrain structure parameters of the grid are determined based on the point cloud distribution within the grid. The terrain structure parameters are coupled and analyzed to determine the structural complexity of the grid. Information comparison grids are selected based on the structural complexity. The surface structure features of the information comparison grid are determined based on point cloud data, and the cumulative difference between the grid and the corresponding surface structure features in each three-dimensional ore body structure model is calculated. The three-dimensional ore body structure model corresponding to the minimum cumulative difference is taken as the target ore body three-dimensional model. Using the real-time location information of the ore body to be monitored, the impact range of mining operations is determined. The impact range of mining operations is used as the monitoring area. Vibration information of the monitoring area is obtained, and the vibration update area of ​​the target ore body's three-dimensional model is determined. Point cloud data of the vibration update area is collected again. Based on the point cloud data of the vibration update area, the structure of the vibration update area corresponding to the target ore body's three-dimensional model is dynamically updated to complete the establishment of the ore body's three-dimensional model.

2. The method for three-dimensional modeling of ore bodies according to claim 1, characterized in that: The geometric feature parameters of the ore body specifically include the size information and boundary morphology information of the ore body to be monitored. The size information includes the length, width and height of the ore body, and the boundary morphology information includes the top and bottom boundaries and the boundary outline of the ore body to be monitored. The method for constructing a three-dimensional boundary model of the ore body is as follows: select three-dimensional modeling software, input the geometric feature parameters into the three-dimensional modeling software, and construct a three-dimensional boundary model of the ore body; The surface area of ​​the ore body's three-dimensional boundary model is divided into grids of the same size. The specific steps of determining the grid division scheme include: reading the boundary data of different view faces of the ore body; determining the division range based on the minimum bounding box of the different view faces of the ore body's three-dimensional model; setting the area size of the grid; determining the number of grid nodes in each direction based on the division range of the minimum bounding box of the different view faces and the grid size; and completing the grid division of the surface area based on the number of grid nodes.

3. The method for three-dimensional modeling of ore bodies according to claim 2, characterized in that: The surface of the ore body is scanned multiple times using a point cloud acquisition device with pre-set parameters to obtain point cloud data of the surface of the ore body to be monitored. The device parameters include resolution, scanning range and angle. The point cloud data of the surface of the ore body to be monitored obtained from each scan is denoised. The point cloud data of the terrain after denoising is registered. The point clouds obtained from different scans are registered to form a unified point cloud dataset. The logic for constructing the preferred ore body 3D model set is as follows: Multiple surface reconstruction algorithms are used to analyze the mapped point cloud data one by one, generating multiple preliminary 3D surface models. Different geometric optimization methods are then used to optimize each preliminary 3D surface model, resulting in several 3D ore body structure models, forming the preferred ore body 3D model set. The surface reconstruction algorithms include Delaunay triangulation, Alpha shape algorithm, least squares surface fitting, Poisson surface reconstruction, and MarchingCubes algorithm. The geometric optimization methods include smoothing optimization, vertex simplification optimization, resampling optimization, and boundary reconstruction optimization.

4. The method for three-dimensional modeling of ore bodies according to claim 1, characterized in that: The topographic structure parameters within each grid on the ore body surface are characterized by the point cloud distribution of different grids. These parameters include surface curvature, undulation roughness, and point cloud density. The surface curvature is characterized by point cloud data coordinates, based on the following formula: ； In the formula, Let be the surface curvature of the i-th mesh. Let be the structural curvature of the p-th point within the i-th grid, where p is the index of the point within the grid. , This represents the total number of points within the grid. The specific formula used to calculate the surface roughness is as follows: ； In the formula, Let p be the vertical coordinate of the p-th point. Let be the roughness of the i-th grid. It is the average value of the vertical coordinates of all points in the i-th grid.

5. The method for three-dimensional modeling of ore bodies according to claim 4, characterized in that: The formula used to characterize the structural complexity within different grids on the ore body surface is: ； In the formula, The structural complexity of the i-th grid is... This represents the normalized value of the surface curvature of the i-th mesh. This represents the normalized value of the roughness of the i-th mesh undulation. This is the normalized value of the point cloud density of the i-th grid. Information comparison grids are selected based on structural complexity. The specific logic for selecting these grids is as follows: a structural complexity threshold is set, and the structural complexity is compared to this threshold. Based on the comparison results, the grids are then selected. When the structural complexity When it is determined that the terrain change of the grid does not require monitoring, the grid is not recorded as an information comparison grid. Structural complexity When the terrain of a grid is deemed to be complex, it is designated as an information comparison grid. For structurally complex thresholds.

6. The method for three-dimensional modeling of ore bodies according to claim 1, characterized in that: The surface structure features include the average rate of change of the normal and the average vertical coordinate value of the target area. The formula used to calculate the rate of change of the normal based on point cloud data is: ； In the formula, Let be the average normal vector of the q-th target region within the j-th information comparison grid. Let be the average normal vector of the h-th corresponding neighborhood of the q-th target region. This represents the magnitude of the average normal vector of the q-th target region. Let be the average normal vector magnitude of the h-th corresponding neighborhood of the q-th target region. The average rate of change of the normal to the q-th target region within the grid is compared to the j-th piece of information. Where q is the index of the randomly selected target region within the information comparison grid, j is the index of the information comparison grid, and h is the neighborhood index of the target region; In the 3D ore body structure model, the average normal variation rate and average vertical coordinate value of the corresponding region are analyzed, their differences are calculated, and the differences of all randomly selected target regions are accumulated. The specific formula used for the calculation is as follows: ； In the formula, To accumulate the differences, , These represent the average rate of change of normal and the average vertical coordinate value of the region corresponding to the q-th target region within the grid compared to the j-th information in the 3D ore body structure model. Let be the average vertical coordinate value of the q-th target region within the j-th information comparison grid. The total number of information comparison grids; The three-dimensional ore body structure model with the smallest cumulative difference is selected as the target ore body three-dimensional model.

7. A coal mine monitoring method based on video images according to claim 6, characterized in that: To analyze the real-time location information of the ore body to be mined, the impact range of mining operations is determined. The specific formula used to calculate the impact range of mining operations is as follows: ； In the formula, Due to the distance affected by mining operations, To affect the distance reference value, For mining depth, The factor representing the influence of mining depth is... The influence coefficient of soil and rock strength. To measure the compressive strength of the soil and rock in the ore body to be monitored, This is a reference value for the compressive strength of soil and rock. Using the current mining and construction site as the center, and the distance affected by the mining and construction as the radius... Using a radius, the impact range of mining operations is determined. Vibration monitoring units are set up in the grid within the impact range to monitor regional vibration information, including vibration frequency and vibration amplitude. The vibration update region of the three-dimensional model of the target ore body is determined based on the following logic: If the vibration frequency And vibration amplitude When this happens, it is determined that the grid does not need to be updated; Conversely, the grid is dynamically updated to the vibration update region of the target ore body's 3D model; By using the surface structure features of the vibration update area, the structure of the target ore body's 3D model is dynamically updated. Specifically, the point cloud data of the vibration update area is collected again, and the structure of the vibration update area is dynamically updated using the model construction method corresponding to the target ore body's 3D model, thereby achieving local updates to the structure of the target ore body's 3D model.

8. A three-dimensional modeling device for ore bodies, characterized in that: The aforementioned ore body three-dimensional modeling device is used to execute the ore body three-dimensional modeling method according to any one of claims 1-7, comprising: The terrain division and analysis module is used to obtain the geometric feature parameters of the ore body to be monitored, construct a three-dimensional boundary model of the ore body based on the geometric parameters, and divide the surface area of ​​the three-dimensional boundary model of the ore body into several grids of the same size. The surface feature mapping module is used to collect point cloud data of the topographic surface of the ore body to be monitored, and map the point cloud distribution of the topographic surface to the corresponding grid. Based on different surface reconstruction algorithms and geometric optimization algorithms, the mapped point cloud data is analyzed to obtain several three-dimensional ore body structure models, forming a set of preferred ore body three-dimensional models. The fine analysis and comparison module is used to determine the terrain structure parameters of the grid based on the point cloud distribution within the grid, coupled analysis of the terrain structure parameters to determine the structural complexity of the grid, and filter out information comparison grids based on the structural complexity. The 3D model building module is used to determine the surface structure features of the information comparison grid based on point cloud data, and calculate the cumulative difference between the grid and the corresponding surface structure features in each 3D ore body structure model. The 3D ore body structure model corresponding to the minimum cumulative difference is taken as the target ore body 3D model. The dynamic update and correction module is used to determine the impact range of mining operations based on the real-time location information of the ore body to be monitored, take the impact range of mining operations as the monitoring area, acquire vibration information of the monitoring area, determine the vibration update area of ​​the target ore body 3D model, collect point cloud data of the vibration update area again, and dynamically update the vibration update area structure of the target ore body 3D model based on the point cloud data of the vibration update area to complete the establishment of the ore body 3D model.

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