Mine mining quantity calculation method and device based on laser radar point cloud and medium

Abstract

The invention discloses a mining quantity calculation method and device based on laser radar point clouds and a medium, relates to the field of mining, and solves the problem that the mining quantity calculation error is large, and the method comprises the steps: collecting and processing the laser radar point clouds corresponding to a target mine before mining and after mining; analyzing the laser radar point clouds before and after mining, and sequentially constructing a first triangular network before mining and a second triangular network after mining corresponding to the target mine; constructing corresponding three-dimensional boundary boxes before and after mining of the target mine, and setting voxels of the three-dimensional boundary boxes; performing classification based on the positions of the voxels in the first triangular network and the second triangular network, and calculating a mineral exploitation volume corresponding to the target mine based on a classification result; the mining volume is multiplied by the mineral density to obtain the mining quantity of the target mine, and the mining quantity is displayed and output. The mining volume is calculated, so that accurate calculation of the mining quantity is realized.

Description

Technical Field

[0001] This invention belongs to the field of mining technology, specifically a method, equipment, and medium for calculating mining output based on lidar point clouds. Background Technology

[0002] LiDAR point cloud is a technology that uses three-dimensional spatial data collected by LiDAR technology to describe the shape and position of objects or environments. A LiDAR system generates a set of discrete three-dimensional points by emitting laser pulses and receiving reflected signals. Each point contains precise coordinates and attributes such as intensity and color. A mine is an industrial site that is systematically developed for the extraction of underground or surface mineral resources. Mine output refers to the volume or mass of ore or rock extracted from a mine within a certain period. Accurately calculating the output can be used to assess the rate of consumption of mineral resources and determine the remaining recoverable reserves, thereby planning the mine's lifespan and mining strategies.

[0003] However, at present, the calculation of mining output usually adopts the method based on regular grid difference. However, when faced with complex terrain, the traditional regular grid difference method has limitations in simulating and representing the terrain, resulting in a large deviation in the calculated mining output. Therefore, this invention proposes a method, equipment, and medium for calculating mining output based on lidar point clouds. Summary of the Invention

[0004] The purpose of this invention is to propose a method, equipment, and medium for calculating mining output based on lidar point clouds, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for calculating mining output based on lidar point clouds, including: Step S1: Collect and process the lidar point cloud data corresponding to the target mine before and after mining. Step S2: Analyze the lidar point clouds before and after mining respectively, and construct the first triangular network before mining and the second triangular network after mining for the target mine in sequence. Step S3: Construct the three-dimensional bounding box of the target mine before and after mining, and set the voxels of the three-dimensional bounding box; Step S4: Classify the voxels based on their positions in the first and second triangular networks, and calculate the mineral mining volume corresponding to the target mine based on the classification results. Step S5: Multiply the mineral extraction volume by the mineral density to obtain the mining output of the target mine and display it.

[0006] Further, step S1 includes the following sub-steps: Step S11: Install a lidar on the bottom of the drone, read the lidar's radiation radius, and calculate the lidar's radiation range based on the lidar's radiation radius. Step S12: Obtain the coverage area of ​​the target mine, sail forward along one side of the target mine until you leave the coverage area of ​​the target mine and turn around; repeat until the radiation range of the lidar overlaps with the coverage area of ​​the target mine to obtain the lidar point cloud of the target mine. Step S13: Perform invalid point removal and noise reduction on the lidar point cloud before and after mining.

[0007] 3. The method for calculating mining output based on lidar point clouds according to claim 1, characterized in that step S2 includes the following sub-steps: Step S21: Obtain the lidar point cloud of the target mine before mining and record it as the first point cloud set, and record the elements in the first point cloud set as the first feature points; Step S22: Number the first feature point as i, obtain the coordinates of all the first feature points and mark them as Pi; Step S23: Construct the first triangular network of the target mine before mining based on the coordinates of the first feature point; Step S24: Obtain the lidar point cloud of the target mine after mining and record it as the second point cloud set, and record the elements in the second point cloud set as the second feature points; Step S25: Number the second feature point as n, and construct the second triangular network of the target mine after mining based on the coordinates of the second feature point.

[0008] Furthermore, the construction process of the first triangular network is as follows: Step S231: Arbitrarily select a first feature point i and convert it into a selected feature point. Then, arbitrarily remove the corresponding first feature point from the first point cloud set. Calculate the distance between the remaining first feature points in the first point cloud set and the selected feature point. Record the first feature point closest to the selected feature point as the first base point j. Then, remove the corresponding first feature point from the first point cloud set. Step S232: Connect the selected feature point with the first base point to obtain the first baseline. Add the coordinates Pi of the selected feature point and the coordinates Pj of the first base point, and divide by two to obtain the coordinates of the midpoint of the corresponding midpoint of the first baseline. Step S233: Calculate the distance between the midpoint corresponding to the first baseline and the remaining first feature points in the first point cloud set, and record the first feature point closest to the midpoint corresponding to the first baseline as the pre-selected second base point k; Step S234: Subtract the coordinates Pi of the selected feature point from the coordinates Pj of the first base point to obtain the first judgment vector, and subtract the coordinates Pj of the first base point from the coordinates Pk of the pre-selected second base point to obtain the second judgment vector.

[0009] Furthermore, the construction process of the first triangular network also includes: Step S235: Perform a cross product operation between the second judgment vector and the first judgment vector; if the cross product result of the second judgment vector and the first judgment vector is zero, record the first feature point that is closest to the midpoint corresponding to the first baseline as the pre-selected second base point k, and execute steps S234 and S235 again. If the cross product of the second judgment vector and the first judgment vector is not zero, the pre-selected second base point is transformed into a second base point, and then the first feature point corresponding to the second base point is removed from the first point cloud set; the second base point and the first base point are connected to obtain the second baseline; the feature point, the first base point and the second base point are selected to form the first triangle; Step S236: Add the coordinates of the second base point Pk and the coordinates of the first base point Pj, and divide by two to obtain the coordinates of the midpoint corresponding to the second baseline. Similarly to steps S233-S235, calculate the third base point to form the second triangle. Step S237: Calculate the expression for the circumcircle of the second triangle based on the coordinates of the first, second, and third base points corresponding to the second triangle. Substitute the coordinates of the selected feature points into the expression for the circumcircle. If the result is less than or equal to zero, delete the third base point and repeat steps S234-S237. If the result is greater than zero, proceed to step S238. Step S238: Repeat steps S233-S237 to construct multiple triangles of the target mine before mining, and combine all the triangles of the target mine before mining to obtain the first triangular network.

[0010] Further, step S3 includes the following sub-steps: Step S31: Obtain the coordinates of all lidar point clouds of the target mine before and after mining; Step S32: Identify the minimum value of the horizontal axis coordinate of all LiDAR point clouds and record it as Xmin; Identify the maximum value of the horizontal axis coordinate of all LiDAR point clouds and record it as Xmax. The minimum value of the vertical axis coordinate of all LiDAR point clouds is denoted as Ymin, and the maximum value of the vertical axis coordinate of all LiDAR point clouds is denoted as Ymax. The minimum value of the vertical axis coordinates of all LiDAR point clouds is denoted as Zmin, and the maximum value of the vertical axis coordinates of all LiDAR point clouds is denoted as Zmax. Step S33: Construct the three-dimensional bounding boxes of the target mine before and after mining based on the minimum and maximum values ​​of the horizontal axis coordinate, vertical axis coordinate, and vertical axis coordinate, respectively. Step S34: Multiply the length, width and height of the 3D bounding box to obtain the volume of the 3D bounding box. Divide the number of LiDAR point clouds before mining by the volume of the bounding box to obtain the point cloud density before mining. Divide the number of LiDAR point clouds after mining by the volume of the bounding box to obtain the point cloud density after mining. The point cloud density after mining is compared with the point cloud density before mining, and the one with the larger value is recorded as the calculated point cloud density. Step S35: Take the reciprocal of the calculated point cloud density as the voxel length of the corresponding voxel of the 3D bounding box, construct multiple voxels of the 3D bounding box based on the voxel length, number each voxel as m, and mark the coordinates of the voxel center as ZBm; Where a voxel is a cubic unit in three-dimensional space; m = 1, 2, ..., z, where z is a positive integer.

[0011] Further, step S4 includes the following sub-steps: Step S41: Obtain the first triangular network and the second triangular network corresponding to the target mine, and obtain the three-dimensional bounding box and voxel corresponding to the target mine; Step S42: Import the first triangular network and the second triangular network into the 3D bounding box, and read the coordinates of the voxel centers corresponding to multiple voxels; Step S43: Select any voxel and project the voxel center vertically according to the coordinates. If the projection of the voxel falls outside both the first triangular network and the second triangular network, then discard the corresponding voxel. Conversely, if a voxel falls within the first triangular network, the triangle surrounding the voxel is designated as the first discriminant triangle; if a voxel falls within the second triangular network, the triangle surrounding the voxel is designated as the second discriminant triangle. Step S44: For the first discriminant triangle, read the three vertices A(X1, Y1, Z1), B(X2, Y2, Z2), and C(X3, Y3, Z3); subtract the coordinates of point A from the coordinates of point B to obtain the first vector; subtract the coordinates of point A from the coordinates of point C to obtain the second vector; multiply the first vector by the second vector to obtain the normal vector FX(nx, ny, nz) of the plane equation. The equation of the plane containing the first discriminant triangle is determined based on the coordinates of the three vertices and the normal vector. The specific plane equation is as follows: In the formula, X, Y, and Z are independent variables, representing the x-coordinate, y-coordinate, and z-coordinate of any point, respectively.

[0012] Furthermore, step S4 also includes the following sub-steps: Step S45: Obtain the coordinates ZBm(Xm, Ym, Zm) of the voxel center. Calculate the distance JYm from the voxel center to the corresponding triangular plane using the following formula: ; Step S46: If JYm is greater than zero, discard the corresponding voxel, indicating that the voxel center is located above the first triangular network; if JYm is equal to zero, record the corresponding voxel as the boundary voxel; if JYm is less than zero, record the corresponding voxel as the voxel to be discriminated, indicating that the voxel is located below the first triangular network. Step S47, similar to steps S44-S45, calculate the distance JEm from the center of the voxel to be judged to the corresponding triangular plane of the second triangular network; Step S48: If JEm is less than zero, discard the corresponding voxel, indicating that the voxel center is located below the second triangular network; if JEm is equal to zero, record the corresponding voxel as the boundary voxel; if JEm is greater than zero, record the corresponding voxel as the mining area voxel. Step S49: Count the number of voxels KC in the mining area and the number of boundary voxels BJ. Calculate the corresponding mineral mining volume TJ of the target mine using the following formula: TJ = KC × TS + 0.5 × BJ × TS; where TS is the volume of the voxel.

[0013] The present invention also provides an electronic device, the electronic device comprising: A memory that stores a computer program; The processor is communicatively connected to the memory and implements the above-described method when the computer program is executed by the processor.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention first collects and processes lidar point clouds corresponding to the target mine before and after mining; then, it analyzes the lidar point clouds before and after mining respectively, and sequentially constructs a first triangular network corresponding to the target mine before mining and a second triangular network corresponding to the target mine after mining; this invention achieves the description of the surface features of the target mine by constructing the first triangular network corresponding to the target mine before mining and the second triangular network corresponding to the target mine after mining.

[0016] 2. This invention constructs a three-dimensional bounding box corresponding to the target mine before and after mining, and sets the voxels of the three-dimensional bounding box; then, it classifies the voxels based on their positions in the first and second triangular networks, and calculates the mining volume corresponding to the target mine based on the classification results; finally, it obtains the mining output of the target mine by multiplying the mining volume by the mineral density and displays it; this invention achieves accurate calculation of mining output by calculating the mining volume. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of lidar point cloud acquisition in this invention; Figure 3 This is a flowchart illustrating the construction process of the triangular network in this invention. Figure 4 This is a schematic diagram of voxels in a three-dimensional bounding box in this invention; Figure 5 This is a schematic diagram of the voxel classification region in this invention; Figure 6 This is a structural block diagram of the electronic device in this invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figures 1-5 As shown, the technical solution provided by this invention is as follows: a method for calculating mining volume based on lidar point clouds. First, lidar point clouds of the target mine before and after mining are obtained. The lidar point clouds are processed to construct triangular networks of the target mine before and after mining. Then, the mining range of the target mine is obtained and a three-dimensional bounding box is constructed. The three-dimensional bounding box is divided into multiple voxels. The voxels are classified according to their positions in the first and second triangular networks. Based on the classification results, the mining volume of the target mine is calculated, thereby achieving accurate calculation of mining volume.

[0021] In this invention, the method for calculating the mining output is as follows: Step S1: Collect and process the lidar point cloud data corresponding to the target mine before and after mining. In this invention, step S1 includes the following sub-steps: Step S11: Install a lidar on the bottom of the drone, read the lidar's radiation radius, and calculate the lidar's radiation range based on the lidar's radiation radius. Step S12, as follows Figure 2 As shown, obtain the coverage area of ​​the target mine, sail forward along one side of the target mine until you leave the coverage area of ​​the target mine and turn around; repeat the process until the radiation range of the lidar overlaps with the coverage area of ​​the target mine to obtain the lidar point cloud of the target mine. Step S13: Perform invalid point removal and noise reduction on the lidar point cloud before and after mining; Among them, invalid point removal and noise reduction are relatively mature existing technologies, and will not be described in detail in this embodiment.

[0022] Step S2: Analyze the lidar point clouds before and after mining respectively, and construct the first triangular network before mining and the second triangular network after mining for the target mine in sequence. In this invention, step S2 includes the following sub-steps: Step S21: Obtain the lidar point cloud of the target mine before mining and record it as the first point cloud set, and record the elements in the first point cloud set as the first feature points; Step S22: Number the first feature point as i, obtain the coordinates of all the first feature points and mark them as Pi; Step S23: Construct the first triangular network of the target mine before mining based on the coordinates of the first feature point; In this invention, please refer to Figure 3 As shown, the construction process of the first triangular network is as follows: Step S231: Arbitrarily select a first feature point i and convert it into a selected feature point. Then, arbitrarily remove the corresponding first feature point from the first point cloud set. Calculate the distance between the remaining first feature points in the first point cloud set and the selected feature point. Record the first feature point closest to the selected feature point as the first base point j. Then, remove the corresponding first feature point from the first point cloud set. Step S232: Connect the selected feature point with the first base point to obtain the first baseline. Add the coordinates Pi of the selected feature point and the coordinates Pj of the first base point, and divide by two to obtain the coordinates of the midpoint of the corresponding midpoint of the first baseline. Step S233: Calculate the distance between the midpoint corresponding to the first baseline and the remaining first feature points in the first point cloud set, and record the first feature point closest to the midpoint corresponding to the first baseline as the pre-selected second base point k; Step S234: Subtract the coordinates Pi of the selected feature point from the coordinates Pj of the first base point to obtain the first judgment vector; subtract the coordinates Pj of the first base point from the coordinates Pk of the pre-selected second base point to obtain the second judgment vector. Step S235: Perform a cross product operation between the second judgment vector and the first judgment vector; if the cross product result of the second judgment vector and the first judgment vector is zero, record the first feature point that is closest to the midpoint corresponding to the first baseline as the pre-selected second base point k, and execute steps S234 and S235 again. If the cross product result is zero, it means that the pre-selected second base point, the first base point, and the selected feature point are collinear. If the cross product of the second judgment vector and the first judgment vector is not zero, the pre-selected second base point is transformed into a second base point, and then the first feature point corresponding to the second base point is removed from the first point cloud set; the second base point and the first base point are connected to obtain the second baseline; the feature point, the first base point and the second base point are selected to form the first triangle; Step S236: Add the coordinates of the second base point Pk and the coordinates of the first base point Pj, and divide by two to obtain the coordinates of the midpoint corresponding to the second baseline. Similarly to steps S233-S235, calculate the third base point to form the second triangle. Step S237: Calculate the expression for the circumcircle of the second triangle based on the coordinates of the first, second, and third base points corresponding to the second triangle. Substitute the coordinates of the selected feature points into the expression for the circumcircle. If the result is less than or equal to zero, delete the third base point and repeat steps S234-S237. If the result is greater than zero, proceed to step S238. It should be noted that a result less than zero indicates that the selected feature point is located inside the circumcircle of the second triangle, while a result equal to zero indicates that the selected feature point is located on the circumference of the circumcircle of the second triangle. Step S238: Repeat steps S233-S237 to construct multiple triangles of the target mine before mining, and combine all the triangles of the target mine before mining to obtain the first triangular network; wherein, the first triangular network is used to describe the surface features of the target mine before mining. It should be noted that this invention uses multiple interconnected triangles to obtain a triangular network corresponding to the target mine, and then uses the triangular network to describe the surface features corresponding to the target mine; compared with the traditional method of describing surface features using regular grids, the triangular network will provide a more detailed description of surface features. For example, in areas with dramatic terrain undulations, triangular networks generate dense small triangles to capture details, while traditional regular networks use a fixed-space distribution of nodes, with each grid cell being the same size. In the latter case, for detailed areas, the grid is too sparse to capture abrupt changes in terrain, leading to data distortion. Step S24: Obtain the lidar point cloud of the target mine after mining and record it as the second point cloud set, and record the elements in the second point cloud set as the second feature points; Step S25: Number the second feature point as n, and construct the second triangular network of the target mine after mining based on the coordinates of the second feature point; It should be noted that the construction process of the second triangular network is exactly the same as that of the first triangular network, and will not be repeated here.

[0023] Step S3: Construct the three-dimensional bounding box of the target mine before and after mining, and set the voxels of the three-dimensional bounding box; In this invention, step S3 includes the following sub-steps: Step S31: Obtain the coordinates of all lidar point clouds of the target mine before and after mining; Step S32: Identify the minimum value of the horizontal axis coordinate of all LiDAR point clouds and record it as Xmin; Identify the maximum value of the horizontal axis coordinate of all LiDAR point clouds and record it as Xmax. The minimum value of the vertical axis coordinate of all LiDAR point clouds is denoted as Ymin, and the maximum value of the vertical axis coordinate of all LiDAR point clouds is denoted as Ymax. The minimum value of the vertical axis coordinates of all LiDAR point clouds is denoted as Zmin, and the maximum value of the vertical axis coordinates of all LiDAR point clouds is denoted as Zmax. Step S33: Construct the three-dimensional bounding boxes of the target mine before and after mining based on the minimum and maximum values ​​of the horizontal axis coordinate, vertical axis coordinate, and vertical axis coordinate, respectively. It should be noted that in the actual process of constructing a 3D bounding box, you can choose to expand it by g units in all directions, that is, construct the 3D bounding box with Xmin-g, Xmax+g, Ymin-g, Ymax+g, Zmin-g, Zmax+g. Step S34: Multiply the length, width and height of the 3D bounding box to obtain the volume of the 3D bounding box. Divide the number of LiDAR point clouds before mining by the volume of the bounding box to obtain the point cloud density before mining. Divide the number of LiDAR point clouds after mining by the volume of the bounding box to obtain the point cloud density after mining. The point cloud density after mining is compared with the point cloud density before mining, and the one with the larger value is recorded as the calculated point cloud density. Step S35, as Figure 4 As shown, the reciprocal of the calculated point cloud density is taken as the voxel length of the corresponding voxel of the 3D bounding box. Multiple voxels of the 3D bounding box are constructed based on the voxel length, and each voxel is numbered m. The coordinates of the voxel center are marked as ZBm. Where a voxel is a cubic unit in three-dimensional space; m = 1, 2, ..., z, where z is a positive integer.

[0024] Step S4: Classify the voxels based on their positions in the first and second triangular networks, and calculate the mineral mining volume corresponding to the target mine based on the classification results. In this invention, step S4 includes the following sub-steps: Step S41: Obtain the first triangular network and the second triangular network corresponding to the target mine, and obtain the three-dimensional bounding box and voxel corresponding to the target mine; Step S42: Import the first triangular network and the second triangular network into the 3D bounding box, and read the coordinates of the voxel centers corresponding to multiple voxels; Step S43: Select any voxel and project the voxel center vertically according to the coordinates. If the projection of the voxel falls outside both the first triangular network and the second triangular network, then discard the corresponding voxel. Conversely, if a voxel falls within the first triangular network, the triangle surrounding the voxel is designated as the first discriminant triangle; if a voxel falls within the second triangular network, the triangle surrounding the voxel is designated as the second discriminant triangle. Step S44: For the first discriminant triangle, read the three vertices A(X1, Y1, Z1), B(X2, Y2, Z2), and C(X3, Y3, Z3); subtract the coordinates of point A from the coordinates of point B to obtain the first vector; subtract the coordinates of point A from the coordinates of point C to obtain the second vector; multiply the first vector by the second vector to obtain the normal vector FX(nx, ny, nz) of the plane equation. The equation of the plane containing the first discriminant triangle is determined based on the coordinates of the three vertices and the normal vector. The specific plane equation is as follows: In the formula, X, Y, and Z are independent variables, representing the x-coordinate, y-coordinate, and z-coordinate of any point, respectively. Step S45: Obtain the coordinates ZBm(Xm, Ym, Zm) of the voxel center. Calculate the distance JYm from the voxel center to the corresponding triangular plane using the following formula: ; Step S46: If JYm is greater than zero, discard the corresponding voxel, indicating that the voxel center is located above the first triangular network; if JYm is equal to zero, record the corresponding voxel as the boundary voxel; if JYm is less than zero, record the corresponding voxel as the voxel to be discriminated, indicating that the voxel is located below the first triangular network. Step S47, similar to steps S44-S45, calculate the distance JEm from the center of the voxel to be judged to the corresponding triangular plane of the second triangular network; Step S48: If JEm is less than zero, discard the corresponding voxel, indicating that the voxel center is located below the second triangular network; if JEm is equal to zero, record the corresponding voxel as the boundary voxel; if JEm is greater than zero, record the corresponding voxel as the mining area voxel. like Figure 5 As shown, voxels located within the discard area are discarded, voxels located within the mining area are recorded as mining area voxels; voxels located above the first triangular network and the second triangular network are recorded as boundary voxels. Step S49: Count the number of voxels KC in the mining area and the number of boundary voxels BJ. Calculate the corresponding mineral mining volume TJ of the target mine using the following formula: TJ = KC × TS + 0.5 × BJ × TS; where TS is the volume of the voxel.

[0025] Step S5: Multiply the mineral extraction volume by the mineral density to obtain the mining output of the target mine and display it.

[0026] Example 2: As Figure 6 As shown, this embodiment provides an electronic device, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The processor can call logical instructions in the memory to execute a method for calculating mining output based on LiDAR point clouds. This method includes: acquiring and processing LiDAR point clouds corresponding to the target mine before and after mining; analyzing the LiDAR point clouds before and after mining, and sequentially constructing a first triangular network before mining and a second triangular network after mining corresponding to the target mine; constructing three-dimensional bounding boxes corresponding to the target mine before and after mining, and setting the voxels of the three-dimensional bounding boxes; classifying the voxels based on their positions in the first and second triangular networks, and calculating the mining volume corresponding to the target mine based on the classification results; multiplying the mining volume by the mineral density to obtain the mining output of the target mine and displaying it.

[0027] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0028] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the method for calculating the mining volume based on lidar point clouds provided by the above methods. The method includes: collecting and processing lidar point clouds corresponding to the target mine before and after mining; analyzing the lidar point clouds before and after mining respectively, and sequentially constructing a first triangular network and a second triangular network corresponding to the target mine before mining; constructing a three-dimensional bounding box corresponding to the target mine before and after mining, and setting the voxels of the three-dimensional bounding box; classifying the voxels based on their positions in the first and second triangular networks, and calculating the mining volume corresponding to the target mine based on the classification results; multiplying the mining volume by the mineral density to obtain the mining volume of the target mine and displaying it.

[0029] Example 4: This application also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program performs the above-described methods for calculating mining volume based on lidar point clouds. The method includes: collecting and processing lidar point clouds corresponding to the target mine before and after mining; analyzing the lidar point clouds before and after mining, and sequentially constructing a first triangular network and a second triangular network corresponding to the target mine before mining; constructing three-dimensional bounding boxes corresponding to the target mine before and after mining, and setting the voxels of the three-dimensional bounding boxes; classifying the voxels based on their positions in the first and second triangular networks, and calculating the mining volume corresponding to the target mine based on the classification results; multiplying the mining volume by the mineral density to obtain the mining volume of the target mine and displaying it.

[0030] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0031] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for calculating mining output based on lidar point clouds, characterized in that, The methods include: Step S1: Collect and process the lidar point cloud data corresponding to the target mine before and after mining. Step S2: Analyze the lidar point clouds before and after mining respectively, and construct the first triangular network before mining and the second triangular network after mining for the target mine in sequence. Step S3: Construct the three-dimensional bounding box of the target mine before and after mining, and set the voxels of the three-dimensional bounding box; Step S4: Classify the voxels based on their positions in the first and second triangular networks, and calculate the mineral mining volume corresponding to the target mine based on the classification results. Step S5: Multiply the mineral extraction volume by the mineral density to obtain the mining output of the target mine and display it.

2. The method for calculating mining output based on lidar point clouds according to claim 1, characterized in that, Step S1 includes the following sub-steps: Step S11: Install a lidar on the bottom of the drone, read the lidar's radiation radius, and calculate the lidar's radiation range based on the lidar's radiation radius. Step S12: Obtain the coverage area of ​​the target mine, sail forward along one side of the target mine until you leave the coverage area of ​​the target mine and turn around; repeat until the radiation range of the lidar overlaps with the coverage area of ​​the target mine to obtain the lidar point cloud of the target mine. Step S13: Perform invalid point removal and noise reduction on the lidar point cloud before and after mining.

3. The method for calculating mining output based on lidar point clouds according to claim 1, characterized in that, Step S2 includes the following sub-steps: Step S21: Obtain the lidar point cloud of the target mine before mining and record it as the first point cloud set, and record the elements in the first point cloud set as the first feature points; Step S22: Number the first feature point as i, obtain the coordinates of all the first feature points and mark them as Pi; Step S23: Construct the first triangular network of the target mine before mining based on the coordinates of the first feature point; Step S24: Obtain the lidar point cloud of the target mine after mining and record it as the second point cloud set, and record the elements in the second point cloud set as the second feature points; Step S25: Number the second feature point as n, and construct the second triangular network of the target mine after mining based on the coordinates of the second feature point.

4. The method for calculating mining output based on lidar point clouds according to claim 3, characterized in that, The construction process of the first triangular network is as follows: Step S231: Arbitrarily select a first feature point i and convert it into a selected feature point. Then, arbitrarily remove the corresponding first feature point from the first point cloud set. Calculate the distance between the remaining first feature points in the first point cloud set and the selected feature point. Record the first feature point closest to the selected feature point as the first base point j. Then, remove the corresponding first feature point from the first point cloud set. Step S232: Connect the selected feature point with the first base point to obtain the first baseline. Add the coordinates Pi of the selected feature point and the coordinates Pj of the first base point, and divide by two to obtain the coordinates of the midpoint of the corresponding midpoint of the first baseline. Step S233: Calculate the distance between the midpoint corresponding to the first baseline and the remaining first feature points in the first point cloud set, and record the first feature point closest to the midpoint corresponding to the first baseline as the pre-selected second base point k; Step S234: Subtract the coordinates Pi of the selected feature point from the coordinates Pj of the first base point to obtain the first judgment vector, and subtract the coordinates Pj of the first base point from the coordinates Pk of the pre-selected second base point to obtain the second judgment vector.

5. The method for calculating mining output based on lidar point clouds according to claim 4, characterized in that, The construction process of the first triangular network also includes: Step S235: Perform a cross product operation between the second judgment vector and the first judgment vector; if the cross product result of the second judgment vector and the first judgment vector is zero, record the first feature point that is closest to the midpoint corresponding to the first baseline as the pre-selected second base point k, and execute steps S234 and S235 again. If the cross product of the second judgment vector and the first judgment vector is not zero, the pre-selected second base point is transformed into a second base point, and then the first feature point corresponding to the second base point is removed from the first point cloud set; the second base point and the first base point are connected to obtain the second baseline; the feature point, the first base point and the second base point are selected to form the first triangle; Step S236: Add the coordinates of the second base point Pk and the coordinates of the first base point Pj, and divide by two to obtain the coordinates of the midpoint corresponding to the second baseline. Similarly to steps S233-S235, calculate the third base point to form the second triangle. Step S237: Calculate the expression for the circumcircle of the second triangle based on the coordinates of the first, second, and third base points corresponding to the second triangle. Substitute the coordinates of the selected feature points into the expression for the circumcircle. If the result is less than or equal to zero, delete the third base point and repeat steps S234-S237. If the result is greater than zero, proceed to step S238. Step S238: Repeat steps S233-S237 to construct multiple triangles of the target mine before mining, and combine all the triangles of the target mine before mining to obtain the first triangular network.

6. The method for calculating mining output based on lidar point clouds according to claim 1, characterized in that, Step S3 includes the following sub-steps: Step S31: Obtain the coordinates of all lidar point clouds of the target mine before and after mining; Step S32: Identify the minimum value of the horizontal axis coordinate of all LiDAR point clouds and record it as Xmin; Identify the maximum value of the horizontal axis coordinate of all LiDAR point clouds and record it as Xmax. The minimum value of the vertical axis coordinate of all LiDAR point clouds is denoted as Ymin, and the maximum value of the vertical axis coordinate of all LiDAR point clouds is denoted as Ymax. The minimum value of the vertical axis coordinates of all LiDAR point clouds is denoted as Zmin, and the maximum value of the vertical axis coordinates of all LiDAR point clouds is denoted as Zmax. Step S33: Construct the three-dimensional bounding boxes of the target mine before and after mining based on the minimum and maximum values ​​of the horizontal axis coordinate, vertical axis coordinate, and vertical axis coordinate, respectively. Step S34: Multiply the length, width and height of the 3D bounding box to obtain the volume of the 3D bounding box. Divide the number of LiDAR point clouds before mining by the volume of the bounding box to obtain the point cloud density before mining. Divide the number of LiDAR point clouds after mining by the volume of the bounding box to obtain the point cloud density after mining. The point cloud density after mining is compared with the point cloud density before mining, and the one with the larger value is recorded as the calculated point cloud density. Step S35: Take the reciprocal of the calculated point cloud density as the voxel length of the corresponding voxel of the 3D bounding box, construct multiple voxels of the 3D bounding box based on the voxel length, number each voxel as m, and mark the coordinates of the voxel center as ZBm; Where a voxel is a cubic unit in three-dimensional space; m = 1, 2, ..., z, where z is a positive integer.

7. The method for calculating mining output based on lidar point clouds according to claim 1, characterized in that, Step S4 includes the following sub-steps: Step S41: Obtain the first triangular network and the second triangular network corresponding to the target mine, and obtain the three-dimensional bounding box and voxel corresponding to the target mine; Step S42: Import the first triangular network and the second triangular network into the 3D bounding box, and read the coordinates of the voxel centers corresponding to multiple voxels; Step S43: Select any voxel and project the voxel center vertically according to the coordinates. If the projection of the voxel falls outside both the first triangular network and the second triangular network, then discard the corresponding voxel. Conversely, if a voxel falls within the first triangular network, the triangle surrounding the voxel is designated as the first discriminant triangle; if a voxel falls within the second triangular network, the triangle surrounding the voxel is designated as the second discriminant triangle. Step S44: For the first discriminant triangle, read the three vertices A(X1, Y1, Z1), B(X2, Y2, Z2), and C(X3, Y3, Z3); subtract the coordinates of point A from the coordinates of point B to obtain the first vector; subtract the coordinates of point A from the coordinates of point C to obtain the second vector; multiply the first vector by the second vector to obtain the normal vector FX(nx, ny, nz) of the plane equation. The equation of the plane containing the first discriminant triangle is determined based on the coordinates of the three vertices and the normal vector. The specific plane equation is as follows: In the formula, X, Y, and Z are independent variables, representing the x-coordinate, y-coordinate, and z-coordinate of any point, respectively.

8. The method for calculating mining output based on lidar point clouds according to claim 7, characterized in that, Step S4 further includes the following sub-steps: Step S45: Obtain the coordinates ZBm(Xm, Ym, Zm) of the voxel center. Calculate the distance JYm from the voxel center to the corresponding triangular plane using the following formula: ； Step S46: If JYm is greater than zero, discard the corresponding voxel, indicating that the voxel center is located on the first triangular network. If JYm equals zero, the corresponding voxel is recorded as a boundary voxel; if JYm is less than zero, the corresponding voxel is recorded as a voxel to be discriminated, indicating that the voxel is located below the first triangular network. Step S47, similar to steps S44-S45, calculate the distance JEm from the center of the voxel to be judged to the corresponding triangular plane of the second triangular network; Step S48: If JEm is less than zero, discard the corresponding voxel, indicating that the voxel center is located below the second triangular network; If JEm equals zero, then the corresponding voxel is recorded as the boundary voxel; If JEm is greater than zero, the corresponding voxel is recorded as the mining area voxel; Step S49: Count the number of voxels KC in the mining area and the number of boundary voxels BJ. Calculate the corresponding mineral mining volume TJ of the target mine using the following formula: TJ = KC × TS + 0.5 × BJ × TS; where TS is the volume of the voxel.

9. An electronic device, characterized in that, The electronic device includes: A memory that stores a computer program; A processor, communicatively connected to the memory, implements the method described in any one of claims 1-8 when the computer program is executed by the processor.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method of any one of claims 1 to 8.

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