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

Abstract

The application discloses a mine exploitation volume calculation method and device based on a laser radar point cloud, and a medium, relates to the field of mine exploitation, and solves the problem of large calculation error of mine exploitation volume. The method comprises the following steps: collecting and processing laser radar point clouds corresponding to a target mine before and after exploitation; analyzing the laser radar point clouds before and after exploitation, respectively, and sequentially constructing a first triangular network before exploitation and a second triangular network after exploitation corresponding to the target mine; constructing a three-dimensional boundary box corresponding to the target mine before and after exploitation, and setting voxels of the three-dimensional boundary box; classifying the voxels based on 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; and multiplying the mineral exploitation volume by a mineral density to obtain mine exploitation volume of the target mine and performing display output. The application realizes accurate calculation of mine exploitation volume by calculating a mine exploitation volume.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mine exploitation, and particularly relates to a mine exploitation volume calculation method based on a laser radar point cloud, equipment and a medium. BACKGROUND

[0002] The laser radar point cloud is three-dimensional space data collected through a laser radar technology, and is a technology for describing the shape and position of an object or environment; the laser radar system generates a three-dimensional space discrete point set after emitting a laser pulse and receiving a reflected signal, and each point contains accurate coordinates and attributes such as intensity and color; the mine refers to an industrial site for systematic development of underground or surface mineral resources; the mine exploitation volume refers to the volume or mass of the mined ore or rock in a certain period; accurate calculation of the exploitation volume can be used to evaluate the consumption speed of mineral resources and judge the remaining exploitable reserves, and then plan the mine life and mining strategy.

[0003] However, the current calculation of the mine exploitation volume usually adopts a method based on rule grid difference, and the traditional rule grid difference has limitations in simulating and expressing the terrain, resulting in a large deviation in the calculated mine exploitation volume when facing complex terrain.

[0004] Therefore, the application provides a mine exploitation volume calculation method based on a laser radar point cloud, equipment and a medium. SUMMARY

[0005] The application aims to provide a mine exploitation volume calculation method based on a laser radar point cloud, equipment and a medium to solve the problems in the background technology.

[0006] To achieve the above-mentioned purpose, the application adopts the following technical solutions:

[0007] The mine exploitation volume calculation method based on the laser radar point cloud comprises the following steps:

[0008] Step S1, collecting and processing the laser radar point clouds corresponding to the target mine before and after exploitation, respectively;

[0009] Step S2, analyzing the laser radar point clouds before and after exploitation, respectively, and sequentially constructing a first triangular network before exploitation and a second triangular network after exploitation corresponding to the target mine;

[0010] Step S3, constructing a three-dimensional boundary box corresponding to the target mine before and after exploitation, and setting voxels of the three-dimensional boundary box;

[0011] Step S4, classifying the voxels based on the positions of the voxels in the first triangular network and the second triangular network, and calculating the mineral exploitation volume corresponding to the target mine based on the classification result;

[0012] Step S5, the mineral density is multiplied by the mineral exploitation volume to obtain the mining exploitation volume of the target mine and is displayed and output.

[0013] Further, the step S1 comprises the following sub-steps:

[0014] Step S11, the laser radar is arranged at the bottom of the unmanned aerial vehicle, the radiation radius of the laser radar is read, and the radiation range of the laser radar is calculated based on the radiation radius of the laser radar;

[0015] Step S12, the coverage range of the target mine is obtained, and the unmanned aerial vehicle is driven to sail along one side of the target mine until the coverage range of the target mine is sailed out and the direction is reversed; the step is repeatedly executed until the radiation range of the laser radar overlaps the coverage range of the target mine, and the laser radar point cloud of the target mine is obtained;

[0016] Step S13, the invalid point removal and noise reduction are performed on the laser radar point clouds before and after the mining.

[0017] 3. The method for calculating the mining exploitation volume of the mine based on the laser radar point cloud according to claim 1, wherein the step S2 comprises the following sub-steps:

[0018] Step S21, the laser radar point cloud of the target mine before the mining is obtained and is recorded as a first point cloud set, and the elements in the first point cloud set are recorded as first feature points;

[0019] Step S22, the first feature points are numbered as i, and the coordinates of all the first feature points are obtained and are marked as Pi;

[0020] Step S23, the first triangular network of the target mine before the mining is constructed based on the coordinates of the first feature points;

[0021] Step S24, the laser radar point cloud of the target mine after the mining is obtained and is recorded as a second point cloud set, and the elements in the second point cloud set are recorded as second feature points;

[0022] Step S25, the second feature points are numbered as n, and the second triangular network of the target mine after the mining is constructed based on the coordinates of the second feature points.

[0023] Further, the construction process of the first triangular network is specifically as follows:

[0024] Step S231, one of the first feature points i is randomly selected and is converted into a selected feature point, and then the corresponding first feature point in the first point cloud set is randomly removed; the distances between the remaining first feature points in the first point cloud set and the selected feature point are calculated, the first feature point closest to the selected feature point is recorded as a first base point j, and then the corresponding first feature point in the first point cloud set is removed;

[0025] Step S232, connecting the selected feature point and the first base point to obtain the first baseline, adding the coordinates of the selected feature point Pi and the coordinates of the first base point Pj and dividing by two to obtain the midpoint coordinates of the midpoint corresponding to the first baseline;

[0026] Step S233, calculating the distance between the midpoint corresponding to the first baseline and the remaining first feature points in the first point cloud set, and recording the first feature point closest to the midpoint corresponding to the first baseline as the preselected second base point k;

[0027] Step S234, obtaining the first judgment vector by subtracting the coordinates of the selected feature point Pi from the coordinates of the first base point Pj, and obtaining the second judgment vector by subtracting the coordinates of the first base point Pj from the coordinates of the preselected second base point Pk.

[0028] Further, the construction process of the first triangular network further comprises:

[0029] Step S235, the cross product operation of 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, the first feature point second closest to the midpoint corresponding to the first baseline is recorded as the preselected second base point k, and steps S234 and S235 are executed again;

[0030] If the cross product result of the second judgment vector and the first judgment vector is not zero, the preselected second base point is converted into the second base point, and then the first feature point corresponding to the second base point in the first point cloud set is removed; connecting the second base point and the first base point to obtain the second baseline; the selected feature point, the first base point and the second base point form the first triangle;

[0031] Step S236, adding the coordinates of the second base point Pk and the coordinates of the first base point Pj and dividing by two to obtain the coordinates of the midpoint corresponding to the second baseline. Similarly to steps S233-S235, the third base point is calculated to form the second triangle;

[0032] Step S237, based on the coordinates of the first base point, the second base point and the third base point corresponding to the second triangle, the expression of the circumscribed circle corresponding to the second triangle is calculated, the coordinates of the selected feature point are substituted into the expression of the circumscribed circle, if the result is less than or equal to zero, the third base point is deleted, and steps S234-S237 are repeated; if the result is greater than zero, step S238 is entered;

[0033] Step S238, steps S233-S237 are repeatedly executed to construct a plurality of triangles of the target mine before mining, and all triangles of the target mine before mining are combined to obtain the first triangular network.

[0034] Further, the step S3 comprises the following substeps:

[0035] Step S31, obtain the coordinates of all laser radar point clouds of the target mine before and after mining;

[0036] Step S32, identify the minimum value of the horizontal axis coordinates of all laser radar point clouds as Xmin, and identify the maximum value of the horizontal axis coordinates of all laser radar point clouds as Xmax;

[0037] identify the minimum value of the vertical axis coordinates of all laser radar point clouds as Ymin, and identify the maximum value of the vertical axis coordinates of all laser radar point clouds as Ymax;

[0038] identify the minimum value of the vertical axis coordinates of all laser radar point clouds as Zmin, and identify the maximum value of the vertical axis coordinates of all laser radar point clouds as Zmax;

[0039] Step S33, based on the minimum and maximum values corresponding to the horizontal axis coordinates, vertical axis coordinates and vertical axis coordinates, respectively, a three-dimensional boundary box of the target mine before and after mining is constructed;

[0040] Step S34, the length of the three-dimensional boundary box is multiplied by the width and then by the height to obtain the boundary box volume of the three-dimensional boundary box, the number of pre-mining laser radar point clouds is divided by the boundary box volume to obtain the pre-mining point cloud density, and the number of post-mining laser radar point clouds is divided by the boundary box volume to obtain the post-mining point cloud density;

[0041] The post-mining point cloud density and the pre-mining point cloud density are compared, and the value of the larger one is recorded as the calculated point cloud density;

[0042] Step S35, the reciprocal of the calculated point cloud density is taken as the voxel length of the voxel corresponding to the three-dimensional boundary box, a plurality of voxels of the three-dimensional boundary box are constructed based on the voxel length, each voxel is numbered as m, and the coordinates of the center of the voxel are marked as ZBm;

[0043] wherein, the voxel is a cubic unit in three-dimensional space; m=1, 2, …, z, z is a positive integer.

[0044] Further, the step S4 comprises the following sub-steps:

[0045] Step S41, obtain the first triangular network and the second triangular network corresponding to the target mine, and obtain the three-dimensional boundary box and the voxel corresponding to the target mine;

[0046] Step S42, import the first triangular network and the second triangular network into the three-dimensional boundary box, and read the coordinates of the center of the voxel corresponding to a plurality of voxels;

[0047] Step S43, arbitrarily select a voxel, vertically project the center of the voxel corresponding to the voxel according to the coordinates, and if the projection of the voxel falls outside the first triangular network and the second triangular network at the same time, discard the corresponding voxel;

[0048] Conversely, if the voxel falls in the first triangular network, the triangle surrounding the voxel is recorded as the first discriminant triangle; if the voxel falls in the second triangular network, the triangle surrounding the voxel is recorded as the second discriminant triangle;

[0049] In step S44, for the first discriminant triangle, the three vertices A (X1, Y1, Z1), B (X2, Y2, Z2), and C (X3, Y3, Z3) of the first discriminant triangle are read; the coordinates of point B are subtracted from the coordinates of point A to obtain a first vector; the coordinates of point C are subtracted from the coordinates of point A to obtain a second vector, and the first vector is multiplied by the second vector to obtain a normal vector FX (nx, ny, nz) of the plane equation;

[0050] The plane equation of the triangular plane where the first discriminant triangle is located is determined based on the coordinates of the three vertices and the normal vector, and the plane equation is specifically:

[0051] In the formula, X, Y, and Z are independent variables, representing the horizontal coordinate, vertical coordinate, and vertical coordinate of any point, respectively.

[0052] Further, the step S4 further includes the following sub-steps:

[0053] In step S45, the coordinates ZBm (Xm, Ym, Zm) of the voxel center corresponding to the voxel are obtained, and the distance JYm from the voxel center to the corresponding triangular plane is calculated by the formula as follows:

[0054]

[0055] In step S46, if JYm is greater than zero, the corresponding voxel is discarded, indicating that the voxel center is located above the first triangular network; if JYm is equal to zero, the corresponding voxel is recorded as a boundary voxel; if JYm is less than zero, the corresponding voxel is recorded as a to-be-judged voxel, indicating that the voxel is located below the first triangular network;

[0056] In step S47, the distance JEm from the voxel center corresponding to the to-be-judged voxel to the triangular plane corresponding to the second triangular network is calculated in the same way as steps S44-S45.

[0057] In step S48, if JEm is less than zero, the corresponding voxel is discarded, indicating that the voxel center is located below the second triangular network; if JEm is equal to zero, the corresponding voxel is recorded as a boundary voxel; if JEm is greater than zero, the corresponding voxel is recorded as a mining area voxel;

[0058] In step S49, the number KC of the mining area voxels and the number BJ of the boundary voxels are counted, and the target mine corresponding mineral mining volume TJ is calculated by the formula as follows: ​

[0059] TJ = KC x TS + 0.5 x BJ x TS; in the formula, TS is the volume of the voxel.

[0060] The application also provides an electronic device, which comprises:

[0061] a memory, which stores a computer program;

[0062] a processor, which is connected in communication with the memory, and when the computer program is executed by the processor, the above method is realized.

[0063] The application also provides a computer readable storage medium, which stores a computer program, and when the program is executed by a processor, the above method is realized.

[0064] As described above, due to the adoption of the above technical solutions, the application has the following beneficial effects:

[0065] 1. Firstly, the application collects and processes laser radar point clouds corresponding to the target mine before and after mining, respectively; then, the laser radar point clouds before and after mining are analyzed, and a first triangular network corresponding to the target mine before mining and a second triangular network corresponding to the target mine after mining are sequentially constructed; the first triangular network and the second triangular network are constructed to realize the description of the surface features of the target mine.

[0066] 2. The application constructs a three-dimensional boundary box corresponding to the target mine before and after mining, sets voxels of the three-dimensional boundary box, classifies the voxels based on their positions in the first triangular network and the second triangular network, calculates the mining volume of the target mine based on the classification results, and finally calculates and displays the mining amount of the target mine by multiplying the mining volume by the density of the mineral. BRIEF DESCRIPTION OF DRAWINGS

[0067] For the convenience of those skilled in the art, the application will be further described below with reference to the accompanying drawings.

[0068] Figure 1 The application is a method flowchart;

[0069] Figure 2 The application is a schematic diagram of the collection of laser radar point clouds;

[0070] Figure 3 The application is a triangular network construction flowchart;

[0071] Figure 4 The application is a schematic diagram of voxels in a three-dimensional boundary box;

[0072] Figure 5 This is a schematic diagram of the voxel classification region in this invention;

[0073] Figure 6 This is a structural block diagram of the electronic device in this invention. Detailed Implementation

[0074] 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.

[0075] 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.

[0076] In this invention, the method for calculating the mining output is as follows:

[0077] Step S1: Collect and process the lidar point cloud data corresponding to the target mine before and after mining.

[0078] In this invention, step S1 includes the following sub-steps:

[0079] 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.

[0080] 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.

[0081] Step S13: Perform invalid point removal and noise reduction on the lidar point cloud before and after mining;

[0082] Among them, invalid point removal and noise reduction are relatively mature existing technologies, and will not be described in detail in this embodiment.

[0083] Step S2, respectively, before and after mining laser radar point cloud analysis, in turn, to build the first triangular network corresponding to the target mine before mining and the second triangular network after mining;

[0084] In the application, the step S2 comprises the following sub-steps:

[0085] Step S21, obtain the laser radar point cloud of the target mine before mining and mark it as the first point cloud set, and mark the elements in the first point cloud set as the first feature points;

[0086] Step S22, the first feature point is numbered as i, and the coordinates of all first feature points are obtained and marked as Pi;

[0087] Step S23, the first triangular network of the target mine before mining is constructed based on the coordinates of the first feature points;

[0088] In the application, please refer to Figure 3 The construction process of the first triangular network is as follows:

[0089] Step S231, an arbitrary first feature point i is selected as a selected feature point, and then the corresponding first feature point in the first point cloud set is removed; the distance between the remaining first feature points in the first point cloud set and the selected feature point is calculated, and the first feature point closest to the selected feature point is marked as the first base point j, and then the corresponding first feature point in the first point cloud set is removed;

[0090] Step S232, connecting the selected feature point and the first base point to obtain the first baseline, adding the coordinates of the selected feature point Pi and the coordinates of the first base point Pj and dividing by two to obtain the midpoint coordinates of the midpoint corresponding to the first baseline;

[0091] 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 mark the first feature point closest to the midpoint corresponding to the first baseline as the preselected second base point k;

[0092] Step S234, subtract the coordinates of the selected feature point Pi from the coordinates of the first base point Pj to obtain the first judgment vector, and subtract the coordinates of the first base point Pj from the coordinates of the preselected second base point Pk to obtain the second judgment vector;

[0093] Step S235, the second judgment vector and the first judgment vector are cross multiplied; if the cross product of the second judgment vector and the first judgment vector is zero, the first feature point second closest to the midpoint corresponding to the first baseline is marked as the preselected second base point k, and steps S234 and S235 are executed again;

[0094] If the cross product result of the second judgment vector and the first judgment vector is not zero, the preselected second base point is converted into the second base point, and then the first feature point corresponding to the second base point in the first point cloud set is removed; the second base line is obtained by connecting the second base point and the first base point; the first triangle is formed by selecting the feature point, the first base point and the second base point;

[0095] If the cross product result of the second judgment vector and the first judgment vector is not zero, the preselected second base point is converted into the second base point, and then the first feature point corresponding to the second base point in the first point cloud set is removed; the second base line is obtained by connecting the second base point and the first base point; the first triangle is formed by selecting the feature point, the first base point and the second base point;

[0096] In step S236, the coordinates of the second base point Pk and the coordinates of the first base point Pj are added and divided by two to obtain the coordinates of the midpoint of the second base line. Similarly, steps S233-S235 are used to calculate the third base point and form the second triangle.

[0097] In step S237, the expression of the circumscribed circle corresponding to the second triangle is calculated based on the coordinates of the first base point, the second base point and the third base point. The coordinates of the selected feature point are substituted into the expression of the circumscribed circle. If the result is less than or equal to zero, the third base point is deleted, and steps S234-S237 are repeated. If the result is greater than zero, step S238 is entered.

[0098] It should be noted that if the result is less than zero, it means that the selected feature point is inside the circumscribed circle corresponding to the second triangle, and if the result is equal to zero, it means that the selected feature point is on the circumference of the circumscribed circle corresponding to the second triangle.

[0099] In step S238, steps S233-S237 are repeated to construct multiple triangles of the target mine before mining, and a first triangular network is obtained by combining all the triangles of the target mine before mining. The first triangular network is used to describe the surface features of the target mine before mining.

[0100] It should be noted that the present application uses multiple interconnected triangles to form a triangular network corresponding to the target mine, and then describes the surface features of the target mine through the triangular network. Compared with the traditional method of describing the surface features by regular grid, the triangular network is more detailed in describing the surface features.

[0101] For example, in areas with dramatic terrain changes, the triangular network will generate dense small triangles to capture details, while the traditional regular network uses fixed spacing nodes, and each grid element has the same size. The details cannot be captured due to the sparseness of the grid, resulting in distorted data.

[0102] In step S24, the laser radar point cloud of the target mine after mining is obtained and recorded as a second point cloud set, and the elements in the second point cloud set are recorded as second feature points.

[0103] Step S25, the second feature point is numbered as n, and a second triangular network of the target mine after mining is constructed based on the coordinates of the second feature points;

[0104] It should be noted that the construction process of the second triangular network is completely same as the construction process of the first triangular network, and will not be repeated here.

[0105] Step S3, a three-dimensional boundary box of the target mine before and after mining is constructed, and the voxels of the three-dimensional boundary box are set;

[0106] In the present application, the step S3 comprises the following sub-steps:

[0107] Step S31, the coordinates of all laser radar point clouds of the target mine before and after mining are obtained;

[0108] Step S32, the minimum value of the horizontal axis coordinates of all laser radar point clouds is identified as Xmin, and the maximum value of the horizontal axis coordinates of all laser radar point clouds is identified as Xmax;

[0109] The minimum value of the vertical axis coordinates of all laser radar point clouds is identified as Ymin, and the maximum value of the vertical axis coordinates of all laser radar point clouds is identified as Ymax;

[0110] The minimum value of the vertical axis coordinates of all laser radar point clouds is identified as Zmin, and the maximum value of the vertical axis coordinates of all laser radar point clouds is identified as Zmax;

[0111] Step S33, the three-dimensional boundary box of the target mine before and after mining is constructed based on the minimum and maximum values of the horizontal axis coordinates, the vertical axis coordinates and the vertical axis coordinates, respectively;

[0112] It should be noted that in the actual construction process of the three-dimensional boundary box, g unit lengths can be selected to expand to the surrounding, that is, the three-dimensional boundary box is constructed by Xmin-g, Xmax+g, Ymin-g, Ymax+g, Zmin-g and Zmax+g;

[0113] Step S34, the length of the three-dimensional boundary box is multiplied by the width and then by the height to obtain the boundary box volume of the three-dimensional boundary box, the number of the laser radar point clouds before mining is divided by the boundary box volume to obtain the point cloud density before mining, and the number of the laser radar point clouds after mining is divided by the boundary box volume to obtain the point cloud density after mining;

[0114] The point cloud density after mining and the point cloud density before mining are compared, and the value of the larger one is recorded as the calculated point cloud density;

[0115] Step S35, as Figure 4As shown, the reciprocal of the point cloud density is calculated as the voxel length of the voxel corresponding to the three-dimensional bounding box, the voxels of the plurality of three-dimensional bounding boxes are constructed by the voxel length, each voxel is numbered as m, and the coordinates of the voxel center are marked as ZBm.

[0116] wherein the voxel is a cubic unit in three-dimensional space; m=1, 2, …, z, and z is a positive integer.

[0117] Step S4, based on the position of the voxel in the first triangular network and the second triangular network, classification is carried out, and the mining volume of the target mine is calculated based on the classification result;

[0118] In the present application, the step S4 comprises the following sub-steps:

[0119] Step S41, the first triangular network and the second triangular network corresponding to the target mine are obtained, and the three-dimensional bounding box and the voxel corresponding to the target mine are obtained;

[0120] Step S42, the first triangular network and the second triangular network are imported into the three-dimensional bounding box, and the coordinates of the voxel center corresponding to the plurality of voxels are read;

[0121] Step S43, an arbitrary voxel is selected, the voxel center corresponding to the voxel is vertically projected according to the coordinates, if the projection of the voxel falls outside the first triangular network and the second triangular network at the same time, the corresponding voxel is discarded;

[0122] On the contrary, if the voxel falls in the first triangular network, the triangle enclosing the voxel is marked as the first discriminant triangle; if the voxel falls in the second triangular network, the triangle enclosing the voxel is marked as the second discriminant triangle;

[0123] Step S44, for the first discriminant triangle, the three vertices A (X1, Y1, Z1), B (X2, Y2, Z2), and C (X3, Y3, Z3) of the first discriminant triangle are read; the coordinates of the B point are subtracted from the coordinates of the A point to obtain a first vector; the coordinates of the C point are subtracted from the coordinates of the A point to obtain a second vector, and the first vector is multiplied by the second vector to obtain a normal vector FX (nx, ny, nz) of the plane equation;

[0124] The plane equation of the triangular plane where the first discriminant triangle is located is determined based on the coordinates of the three vertices and the normal vector, and the plane equation is specifically:

[0125] ; in the formula, X, Y, and Z are independent variables, representing the horizontal coordinate, the vertical coordinate, and the vertical coordinate of an arbitrary point, respectively;

[0126] Step S45, the coordinates ZBm (Xm, Ym, Zm) of the voxel center corresponding to the voxel are obtained, and the distance JYm from the voxel center to the corresponding triangular plane is calculated by the formula, and the formula is specifically as follows:

[0127] ;

[0128] 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.

[0129] 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;

[0130] 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.

[0131] 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.

[0132] 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:

[0133] TJ = KC × TS + 0.5 × BJ × TS; where TS is the volume of the voxel.

[0134] Step S5: Multiply the mineral extraction volume by the mineral density to obtain the mining output of the target mine and display it.

[0135] Example 2: Figure 6As shown, the embodiment provides an electronic device, which can include a processor, a communications interface, a memory and a communications bus, wherein the processor, the communications interface and the memory complete the communication with each other through the communications bus. The processor can call the logical instructions in the memory to execute the mine exploitation volume calculation method based on the laser radar point cloud, which includes: collecting and processing the laser radar point cloud corresponding to the target mine before and after mining respectively; analyzing the laser radar point cloud before and after mining respectively, and sequentially constructing the first triangular network before mining and the second triangular network after mining corresponding to the target mine; constructing the three-dimensional boundary box corresponding to the target mine before and after mining, and setting the voxel of the three-dimensional boundary box; classifying based on the position of the voxel in the first triangular network and the second triangular network, and calculating the mineral exploitation volume corresponding to the target mine based on the classification result; and multiplying the mineral exploitation volume by the mineral density to obtain the mine exploitation volume of the target mine and performing display output.

[0136] In addition, the logical instructions in the memory described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0137] Embodiment 3: The application also provides a computer program product, which comprises a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the above-described method for calculating the mining volume of a mine based on a laser radar point cloud, the method comprising: collecting and processing laser radar point clouds corresponding to a target mine before and after mining, respectively; analyzing the laser radar point clouds before and after mining, respectively, and sequentially constructing a first triangular network before mining and a second triangular network after mining corresponding to the target mine; constructing a three-dimensional boundary box corresponding to the target mine before and after mining, and setting voxels of the three-dimensional boundary box; classifying the voxels based on their positions in the first triangular network and the second triangular network, and calculating a mining volume of a mineral corresponding to the target mine based on the classification result; and multiplying the mining volume of the mineral by a mineral density to obtain a mining volume of the target mine and outputting the mining volume.

[0138] Embodiment 4: The application also provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the above-described method for calculating the mining volume of a mine based on a laser radar point cloud, the method comprising: collecting and processing laser radar point clouds corresponding to a target mine before and after mining, respectively; analyzing the laser radar point clouds before and after mining, respectively, and sequentially constructing a first triangular network before mining and a second triangular network after mining corresponding to the target mine; constructing a three-dimensional boundary box corresponding to the target mine before and after mining, and setting voxels of the three-dimensional boundary box; classifying the voxels based on their positions in the first triangular network and the second triangular network, and calculating a mining volume of a mineral corresponding to the target mine based on the classification result; and multiplying the mining volume of the mineral by a mineral density to obtain a mining volume of the target mine and outputting the mining volume.

[0139] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0140] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and the necessary general hardware platform from the above description of the embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that contributes to the technical solutions can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0141] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present 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 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. 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; 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. 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 1-6 when the computer program is executed by the processor.

8. 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 6.

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