Methods, apparatus, equipment and storage media for determining contact characteristics of rock particles

Abstract

This application relates to the field of rock mechanics, and more particularly to a method, apparatus, device, and storage medium for determining the contact characteristics of rock particles. The method includes: determining a point cloud of the particle surface; determining the spherical harmonic coefficients corresponding to the particle surface; determining a weight function for resampling the particle surface points; resampling the parameter space based on the weight function and a spherical Voronoi partition to obtain a spherical Voronoi seed as new sampling points in the parameter space, resulting in a new mesh topology; substituting the sampling points and spherical harmonic coefficients into the spherical harmonic function for fitting, determining the coordinates of the particle surface points; and obtaining a reconstructed particle surface with a simplified mesh based on the coordinates of the particle surface points and the mesh topology, thereby determining the contact characteristics of the rock particles. This application can meet the needs of discretizing or reconstructing particle surfaces with different mesh characteristics, enabling more efficient and accurate determination of the contact characteristics of rock particles and reducing the risks in engineering applications.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of geotechnical mechanics, and particularly relates to a method and device for determining contact characteristics of rock particles, an equipment and a storage medium. BACKGROUND

[0002] In the discrete element model, for the irregular geotechnical particle surface, the implicit particle surface is usually discretized into a displayed surface node, so that the general contact algorithm (such as the node-to-surface algorithm) can be used for contact judgment and contact force calculation of the particles.

[0003] In the discrete element model, particle surface discretization and particle surface reconstruction can be included. The particle surface discretization refers to that the continuous particle surface is described by a discrete, connected polygonal mesh (such as a triangular mesh) through surface point sampling. The particle surface reconstruction refers to generating a new set of polygonal meshes that meet certain mesh characteristics to replace the original continuous particle surface or polygonal mesh.

[0004] Different application scenarios can require different mesh characteristics of the particle surface. For example, in the scenarios of the accumulation body slope, the rockfill dam, and the riprapping roadbed, the rock particle material is essentially cemented or suspended by the granular medium, and the upper load is borne and transmitted by the granular medium material. In the application scenarios of the gravel cushion, the sand cushion, and the like, the granular material mainly transmits the load, and the relative sliding of the particles is small. In the discrete element simulation based on the node-to-surface contact algorithm in this working condition, the particle surface point cloud and the mesh need to be as evenly distributed as possible to improve the calculation efficiency and stability.

[0005] In the study of the accumulation body slope, the particle shape plays a major role in the engagement between the particles, the relative sliding, and the natural repose angle, and therefore the particle shape is required to retain the main features as much as possible, such as sharp corners, when the discrete element simulation is performed.

[0006] The current particle surface sampling methods include the equiangular mesh method, the icosahedral subdivision method, and the Fibonacci or golden spiral method. The equiangular mesh method will cause a serious dense point near the pole, and the number of sampling points is difficult to control flexibly. The Fibonacci and golden spiral methods can provide a relatively uniform point cloud in the spherical coordinate parameter space, but the change of the polar radius will cause the point cloud density difference of the particle surface, that is, the point cloud is dense near the central point and sparse far from the central point, which is not conducive to quickly and accurately obtaining the mechanical properties and state of the rock particles, and may exist potential dangers in engineering applications. SUMMARY

[0007] Therefore, the embodiments of the present application provide a rock particle contact feature determination method, device, equipment and storage medium to solve the problem that the mechanical properties and state of rock particles cannot be quickly and accurately obtained when the rock particles are discretized and reconstructed in the prior art, which may cause potential danger in engineering applications.

[0008] The first aspect of the embodiments of the present application provides a rock particle contact feature determination method, which comprises:

[0009] Obtaining the particle morphology of the rock particle by scanning to determine the point cloud of the particle surface;

[0010] Substituting the point cloud of the particle surface into a preset spherical harmonic function to determine the corresponding spherical harmonic coefficient of the particle surface;

[0011] Determining a weight function for resampling of the particle surface points, resampling the parameter space according to the weight function in combination with spherical Voronoi tessellation to obtain spherical Voronoi seeds as new sampling points of the parameter space, generating the Delaunay triangulation of the Voronoi seeds by a convex hull algorithm to obtain a new grid topology;

[0012] Substituting the sampling points and the spherical harmonic coefficient into the spherical harmonic function fitting to obtain the radial distance of the fitted sampling points, determining the coordinates of the particle surface points according to the radial distance, and obtaining the reconstructed particle surface after simplifying the grid according to the coordinates of the particle surface points and the grid topology;

[0013] Determining the contact feature of the rock particle according to the reconstructed particle surface.

[0014] In combination with the first aspect, in a first possible implementation manner of the first aspect, determining a weight function for resampling of the particle surface points, resampling the parameter space according to the weight function in combination with spherical Voronoi tessellation to obtain spherical Voronoi seeds, comprises:

[0015] Determining the weighted centroid of the particle surface polygon;

[0016] Determining the weight function of the particle surface points;

[0017] Resampling the parameter space according to the weighted centroid and the weight function in combination with spherical Voronoi tessellation to obtain spherical Voronoi seeds.

[0018] In combination with the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, determining the weighted centroid of the particle surface polygon comprises:

[0019] Dividing the particle surface polygon into a plurality of triangles according to the particle surface polygon and the polygon centroid assumption value;

[0020] determining a weighted centroid and an area of the divided triangle;

[0021] calculating a weighted centroid of the particle surface polygon according to the centroid and the area of the divided triangle.

[0022] In a third possible implementation manner of the first aspect, in the second possible implementation manner of the first aspect, the determining of the weighted centroid of the divided triangle comprises:

[0023] respectively determining perpendicular lines of three sides of the triangle, determining three intersection points of the three perpendicular lines;

[0024] taking an average of the three intersection points as the weighted centroid of the triangle.

[0025] In a fourth possible implementation manner of the first aspect, in the first possible implementation manner of the first aspect, the determining of the weight function of the particle surface point comprises:

[0026] the weight function of the radial distance of the particle surface point is determined as: where S is a surface area of the particle, N is a number of Voronoi seeds, r i is a radial distance of the particle surface point in a corresponding Voronoi seed.

[0027] or the weight function of the curvature of the surface point is determined as: where N is a number of Voronoi seeds, a is a dimensionless parameter, a = 1, i is an index, μ, σ, respectively represent a mean value, a root mean square, and an expected value in statistical distribution parameters of the surface point related to the surface curvature, and: c i = max{|c i1 |,|c i2 |}, c i represents a maximum value of absolute values of principal curvatures of the surface point in the triangular mesh.

[0028] In a fifth possible implementation manner of the first aspect, in the first possible implementation manner of the first aspect, the re-sampling of the parameter space according to the weighted centroid and the weight function in combination with the spherical Voronoi partition to obtain the spherical Voronoi seed comprises:

[0029] initializing a group of random Voronoi seeds on the particle surface;

[0030] calculating weights of the Voronoi seeds according to positions of the Voronoi seeds and corresponding weight functions;

[0031] establishing a Delaunay triangular partition of the Voronoi seeds through a convex hull algorithm.

[0032] calculating a weighted centroid of the triangle in the triangulation;

[0033] ordering the weighted centroid of the triangle of the Delaunay triangulation of the Voronoi seed, constructing a Voronoi cell corresponding to the Voronoi seed;

[0034] calculating a centroid of the Voronoi cell and a distance error of the Voronoi seed, determining a position of the Voronoi seed according to the error.

[0035] In a sixth possible implementation manner of the first aspect, according to the distance error, the position of the Voronoi seed is determined, including:

[0036] When the error is greater than a predetermined error threshold and the number of iterations is less than a predetermined iteration threshold, the centroid of the Voronoi cell is taken as the Voronoi seed, the centroid of the Voronoi cell is recalculated and the distance error is determined, until the distance error is less than or equal to the predetermined error threshold, or the number of iterations is greater than or equal to the predetermined iteration threshold.

[0037] The second aspect of the embodiment of the present application provides a method for determining a contact feature of a rock particle.

[0038] A device for determining a contact feature of a rock particle, the device comprising:

[0039] a point cloud determining unit configured to determine a point cloud of a particle surface by scanning a particle morphology of the rock particle;

[0040] a spherical harmonic coefficient determining unit configured to determine a spherical harmonic coefficient corresponding to the particle surface by substituting the point cloud of the particle surface into a preset spherical harmonic function;

[0041] a grid topology determining unit configured to determine a weight function for resampling of the particle surface points, resample a parameter space according to the weight function and a spherical Voronoi partition to obtain a spherical Voronoi seed as a new sampling point of the parameter space, and generate a Delaunay triangulation of the Voronoi seed by a convex hull algorithm to obtain a new grid topology;

[0042] a reconstructing unit configured to substitute the sampling point and the spherical harmonic coefficient into a spherical harmonic function to fit and obtain a radial distance of the fitted sampling point, determine a coordinate of the particle surface point according to the radial distance, and obtain a reconstructed particle surface after simplification of the grid according to the coordinate of the particle surface point and the grid topology;

[0043] a feature determining unit configured to determine the contact feature of the rock particle according to the reconstructed particle surface.

[0044] The third aspect of the embodiments of the present application provides a device for determining contact characteristics of rock particles, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method according to any one of the first aspect when executing the computer program.

[0045] The fourth aspect of the embodiments of the present application provides a computer readable storage medium, comprising: the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method according to any one of the first aspect.

[0046] Compared with the prior art, the embodiments of the present application have the beneficial effects that: the embodiments of the present application determine the weight function of the particle surface point for resampling, resample the parameter space according to the weight function combined with the spherical Voronoi partition, obtain the spherical Voronoi seed as the new sampling point of the parameter space, generate the Delaunay triangle partition of the Voronoi seed through the convex hull algorithm, obtain the new grid topology, which can be used to obtain any number of surface points, can meet the needs of particle surface discretization or reconstruction of different grid characteristics, is easy to realize the simplification of the high-precision particle surface grid with a large number of point clouds, and can control the number of point clouds and realize the adaptive distribution of the point clouds according to the weight function, so as to more efficiently and accurately determine the contact characteristics of rock particles and reduce the risk of engineering application. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 is an implementation flow diagram of a method for determining contact characteristics of rock particles provided by the embodiments of the present application;

[0049] Figure 2 is a schematic diagram of determining the weighted centroid of a polygon on a particle surface provided by the embodiments of the present application;

[0050] Figure 3 is a schematic diagram of determining the weighted centroid of a triangle provided by the embodiments of the present application;

[0051] Figure 4 is an implementation flow diagram of a method for determining a Voronoi seed provided by the embodiments of the present application;

[0052] Figure 5A schematic diagram of a Voronoi seed and a Voronoi unit provided for embodiments of this application;

[0053] Figure 6 This is a schematic diagram of a device for determining the contact characteristics of rock particles provided in an embodiment of this application;

[0054] Figure 7 This is a schematic diagram of the rock particle contact characteristic determination device provided in the embodiments of this application. Detailed Implementation

[0055] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0056] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0057] Traditional methods for sampling points on particle surfaces include the isometric grid method and the Fibonacci or golden spiral lattice method. The isometric grid method results in severely dense points near the poles, and the number of sampling points is difficult to control flexibly. While the Fibonacci or golden spiral lattice method can provide a relatively uniform point cloud in spherical coordinate parameter space, the variation in the polar radius when mapped to the particle surface leads to differences in point cloud density—that is, the point cloud is denser in areas closer to the center point and sparser in areas farther away.

[0058] In view of this, embodiments of the present invention provide a method for determining the contact characteristics of rock particles based on particle surface discretization and reconstruction using spherical Voronoi, in order to solve the problems caused by existing particle surface discretization and reconstruction methods, such as uneven distribution of particle surface sampling points, difficulty in controlling the number of sampling points, differences in point cloud density after the sampling points are mapped to the particle surface, and adaptive distribution of the mesh.

[0059] like Figure 1 The diagram shown is a schematic representation of the implementation flow of a method for determining the contact characteristics of rock particles according to an embodiment of this application, which is described in detail below:

[0060] In S101, the particle morphology of the rock particles is obtained by scanning, and the point cloud of the particle surface is determined.

[0061] In this application embodiment, soil and rock particle data can be acquired via CT (laser) scanning. Point cloud data of particle surface points can be obtained through image processing. This application can shift the centroid of a single particle to the origin of the Cartesian coordinate system and the spherical coordinate system, so its surface points (x, y, z) can be represented using the spherical coordinate system. The transformation expression between spherical coordinates and Cartesian coordinates is described as follows:

[0062]

[0063] in, This represents the radial distance from a point on the particle surface to the origin of the coordinate system.

[0064] The radial distance Described as a spherical harmonic series, it can be expressed as:

[0065]

[0066] Where n is the degree, m is the order, N is the cutoff degree, and a n,m For spherical harmonic coefficients, It is a spherical harmonic function, and its specific form is:

[0067]

[0068] in, For the accompanying Legendre function, c n,m The normalization constant is given by the following formula:

[0069] In S102, the point cloud of the particle surface is substituted into a preset spherical harmonic function to determine the spherical harmonic coefficients corresponding to the particle surface.

[0070] For particle surface reconstruction, given the point cloud describing the particle surface in S101... This will allow the point cloud to be in Substituting the spherical harmonics sequentially achievable The matrix is ​​solved by solving the following system of linear equations:

[0071] The spherical harmonic coefficient α corresponding to the point on the particle surface can be obtained. n,m In this context, the subscript i represents the number of a point on the particle surface, and the superscript represents the one-dimensional form of the two-dimensional numbers n and m. That is, the N-degree spherical harmonic series contains a total of (n+1) such numbers. 2 The term is given by n, where n is the degree, m is the order, and N is the truncation degree.

[0072] In S103, a weight function for resampling particle surface points is determined. Based on the weight function and the spherical Voronoi partition, the parameter space is resampled to obtain a spherical Voronoi seed as a new sampling point in the parameter space. The Delaunay triangulation of the Voronoi seed is generated by the convex hull algorithm to obtain a new mesh topology.

[0073] In this embodiment of the application, the step of determining the Voronoi seed may include:

[0074] 1031, determine the weighted centroid of the polygon on the particle surface.

[0075] Before determining the weighted centroid of the polygon on the particle surface, this application can, as follows: Figure 2 As shown, the particle surface polygon is first divided into multiple triangles based on the assumed values ​​of the polygon's centroid and the polygon's surface polygon. For example... Figure 2 In the polygon of the particle surface, O' V Assuming the initial value of the centroid, the centroid can be connected to the endpoints of each edge to form multiple triangles corresponding to the polygon on the particle surface.

[0076] After determining the triangles corresponding to the polygons on the particle surface, the weighted centroid of each triangle can be obtained, as shown in the following example. Figure 3 As shown, given a triangle with vertices A, B, and C, its weighted centroid O satisfies:

[0077] d w (x O ,x A ,w A )=d w (x O ,x B ,w B )=d w (x O ,x C ,w C )

[0078] Where, x A x B x C and x O These are the coordinates of vertices A, B, and C, and the weighted centroid O, respectively; w is the weight at each vertex; and d is the weight of the vertices. w The weighted distance function can be expressed as:

[0079] d w (x O ,x A ,w A )=|x A -x O | 2 -w A

[0080] Here, the symbol |x| represents the Euclidean distance. For certain combinations of weight values, the triangular weighted centroid defined above may not exist. Therefore, this work uses an approximate triangular weighted centroid, the calculation steps of which are as follows:

[0081] 1) Assume that the weighted midpoints of the sides of the triangle are O1, O2, and O3, O4, and O5, respectively. AB O BC and O AC They respectively satisfy:

[0082]

[0083] 2) Draw three perpendicular lines passing through the weighted midpoint of each edge and perpendicular to the corresponding edge, and calculate the three intersection points I of the pairwise intersecting perpendicular lines. A I B I C ,See Figure 3 .

[0084] 3) Calculate the average of the three vertices of the inner triangle as the weighted centroid O of the larger triangle. i .

[0085] 4) Normalize the weighted centroid of the triangle using its Euclidean distance, and then project it onto the unit sphere.

[0086] like Figure 2 As shown, the centroid of the spherical polygon is calculated as follows:

[0087]

[0088] Where A i Triangle V i V i+1 O' V The area of ​​the plane (Euclidean) O' V The initial assumed value for the centroid can be set to the arithmetic mean of all vertices of the spherical polygon; Triangle V i V i+1 O' V The centroid; the subscript i represents the index, and N is the number of vertices. Furthermore, The distance is normalized using its Euclidean distance to project onto a unit sphere. It's important to note that using triangular distances and plane areas instead of precise great circle distances and spherical areas simplifies the calculation of the weighted centroids of triangles and spherical polygons. With a large number of Voronoi seeds, the error caused by this simplification is negligible.

[0089] 1032, Determine the weighting function for the points on the particle surface.

[0090] To adapt to the needs of different grid features, two weighting functions based on surface features to control the distribution of sampling points are proposed as follows:

[0091] 1) The weighting formula based on the radial distance between surface points is written as:

[0092]

[0093] In the formula, S is the surface area of ​​the particle, N is the number of Voronoi seeds, and r i denoted as the radial distance from a point on the particle surface to the corresponding Voronoi seed.

[0094] 2) The weighting formula based on the surface point curvature is written as:

[0095]

[0096] Where N is the number of Voronoi seeds, and α is a dimensionless parameter (α = 1 in this paper). i is the subscript index, μ, σ, Let represent the mean, root variance, and expected value of the statistical distribution parameters of surface points related to surface curvature, respectively. The surface point curvature has two values; the one with the largest absolute value is taken. Each parameter is obtained from the following formula:

[0097] c i =max{|c i1 |,|c i2 |}, c i

[0098] This represents the maximum absolute value of the principal curvature at a point in the triangular mesh.

[0099] 1033. Based on the weighted centroid and the weight function, and combined with the spherical Voronoi partitioning, the parameter space is resampled to obtain the spherical Voronoi seed.

[0100] After determining the weighted centroids of the triangle and the spherical polygon, the weighted Voronoi seed can be determined, such as... Figure 4 As shown, it may include:

[0101] 1) such as Figure 5 As shown, a set of random Voronoi seeds can be initialized, as well as parameters such as the weight function w, tolerance error e, and maximum number of iterations c = 100.

[0102] 2) Calculate the weight of the Voronoi seed based on the current position of the Voronoi seed and the selected weight function.

[0103] 3) Use the convex hull algorithm to construct the Delaunay triangulation of the Voronoi seed.

[0104] 4) Calculate the weighted centroid (center) of each triangle in the triangulation.

[0105] 5) For each Voronoi seed, collect the weighted centroids of all Delaunay triangles containing it, and sort them in a predetermined direction, such as counterclockwise or clockwise, as follows: Figure 5 As shown, construct the corresponding Voronoi unit;

[0106] 6) Calculate the centroid (center) of each Voronoi unit and calculate the distance error between the Voronoi seed and the Voronoi unit centroid. If the distance error is greater than a predetermined error threshold and the number of iterations is less than a predetermined number of iterations threshold, return to 2) to continue the calculation.

[0107] 7) If the distance error is less than or equal to a predetermined error threshold, or the number of iterations is greater than or equal to a predetermined number of iterations threshold, then output the current Voronoi seed as a new sampling point in the parameter space.

[0108] In S104, the sampling points and spherical harmonic coefficients are substituted into the spherical harmonic function for fitting to obtain the radial distance of the fitted sampling points. The coordinates of the particle surface points are determined based on the radial distance. The reconstructed particle surface after simplifying the mesh is obtained based on the coordinates of the particle surface points and the mesh topology.

[0109] Using the sampling points obtained above spherical harmonic coefficient a n,m Substituting the spherical harmonic function, we obtain the radial distance r of the fitted sampling points by fitting the spherical harmonic function, and then convert the spherical coordinates of the surface points... Convert to Cartesian coordinates (x, y, z). Combine the surface point coordinates with the new topology to obtain the reconstructed particle surface after simplifying the mesh.

[0110] In S105, the contact characteristics of the rock particles are determined based on the reconstructed particle surface.

[0111] The contact characteristics may include contact force calculation or contact judgment, etc.

[0112] When determining the contact characteristics of rock particles based on the reconstructed particle surface, this scheme uses a weighted spherical centroid Voronoi sphere to adaptively distribute the sampling points. This can be used to obtain any number of surface points. By providing a flexible method to control the local density of the point cloud, it meets the needs of particle surface discretization or reconstruction with different mesh characteristics. This facilitates the simplification of high-precision particle surface meshes with a large number of point clouds, while simultaneously controlling the number of point clouds and achieving adaptive point cloud distribution based on a weighting function.

[0113] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0114] Figure 6 This is a schematic diagram of a device for determining the contact characteristics of rock particles provided in an embodiment of this application, as shown below. Figure 6 As shown, the device includes:

[0115] Point cloud determination unit 601 is used to obtain the particle morphology of the rock particles by scanning and determine the point cloud on the particle surface.

[0116] The spherical harmonic coefficient determination unit 602 is used to substitute the point cloud of the particle surface into a preset spherical harmonic function to determine the spherical harmonic coefficient corresponding to the particle surface.

[0117] Mesh topology determination unit 603 is used to determine the weight function for resampling of particle surface points. Based on the weight function and the spherical Voronoi partition, the parameter space is resampled to obtain the spherical Voronoi seed as a new sampling point in the parameter space. The DeLorean triangulation of the Voronoi seed is generated by the convex hull algorithm to obtain a new mesh topology.

[0118] The reconstruction unit 604 is used to substitute the sampling points and spherical harmonic coefficients into the spherical harmonic function for fitting, to obtain the radial distance of the fitted sampling points, to determine the coordinates of the particle surface points based on the radial distance, and to obtain the reconstructed particle surface after simplifying the mesh based on the coordinates of the particle surface points and the mesh topology.

[0119] Feature determination unit 605 is used to determine the contact features of the rock particles based on the reconstructed particle surface.

[0120] Figure 6 The device for determining the contact characteristics of rock particles shown is, with Figure 1 The method for determining the contact characteristics of the rock particles shown corresponds to this.

[0121] Figure 7 This is a schematic diagram of a device for determining the contact characteristics of rock particles according to an embodiment of this application. Figure 7As shown, the rock particle contact feature determination device 7 of this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70, such as a program for determining the contact features of rock particles. When the processor 70 executes the computer program 72, it implements the steps in the various method embodiments for determining the contact features of rock particles described above. Alternatively, when the processor 70 executes the computer program 72, it implements the functions of each module / unit in the various device embodiments described above.

[0122] For example, the computer program 72 may be divided into one or more modules / units, which are stored in the memory 71 and executed by the processor 70 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 72 in the rock particle contact characteristic determination device 7.

[0123] The device for determining the contact characteristics of the rock particles may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that... Figure 7 This is merely an example of the rock particle contact feature determination device 7 and does not constitute a limitation on the rock particle contact feature determination device 7. It may include more or fewer components than shown, or combine certain components, or different components. For example, the rock particle contact feature determination device may also include input / output devices, network access devices, buses, etc.

[0124] The processor 70 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0125] The memory 71 can be an internal storage unit of the rock particle contact feature determination device 7, such as a hard disk or memory of the rock particle contact feature determination device 7. The memory 71 can also be an external storage device of the rock particle contact feature determination device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the rock particle contact feature determination device 7. Furthermore, the memory 71 can include both internal storage units and external storage devices of the rock particle contact feature determination device 7. The memory 71 is used to store the computer program and other programs and data required by the rock particle contact feature determination device. The memory 71 can also be used to temporarily store data that has been output or will be output.

[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0127] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0128] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0129] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0130] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0131] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0132] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0133] The above-described 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, and should all be included within the protection scope of this application.

Claims

1. A method of determining a contact feature of rock particles, characterized by, The method comprises: Obtaining a point cloud of a particle surface of the rock particle by scanning a particle morphology of the rock particle; Substituting the point cloud of the particle surface into a preset spherical harmonic function to determine spherical harmonic coefficients corresponding to the particle surface; Determining a weight function for resampling of the particle surface points, and resampling a parameter space according to the weight function in combination with spherical Voronoi partitioning to obtain spherical Voronoi seeds as new sampling points of the parameter space, and generating a Delaunay triangulation of the Voronoi seeds by a convex hull algorithm to obtain a new grid topology; Substituting the sampling points and the spherical harmonic coefficients into the spherical harmonic function fitting to obtain a radial distance of the fitted sampling points, determining coordinates of the particle surface points according to the radial distance, and obtaining a simplified grid reconstructed particle surface according to the coordinates of the particle surface points and the grid topology; Determining a contact feature of the rock particle according to the reconstructed particle surface.

2. The method of claim 1, wherein, Determining a weight function for resampling of the particle surface points, and resampling a parameter space according to the weight function in combination with spherical Voronoi partitioning to obtain spherical Voronoi seeds, comprises: Determining a weighted centroid of the particle surface polygon; Determining the weight function of the particle surface points; Resampling the parameter space according to the weighted centroid and the weight function in combination with spherical Voronoi partitioning to obtain the spherical Voronoi seeds.

3. The method of claim 2, wherein, Determining a weighted centroid of the particle surface polygon, comprises: Dividing the particle surface polygon into a plurality of triangles according to the particle surface polygon and a polygon centroid assumed value; Determining a weighted centroid and an area of the divided triangle; Calculating the weighted centroid of the particle surface polygon according to the centroid and the area of the divided triangle.

4. The method of claim 3, wherein, Determining a weighted centroid of the divided triangle, comprises: Respectively determining perpendicular lines of three sides of the triangle to determine three intersection points of the three perpendicular lines; Taking an average of the three intersection points of the three perpendicular lines as the weighted centroid of the triangle.

5. The method of claim 2, wherein, Determining the weight function of the particle surface points, comprises: A weight function of the radial distance of the particle surface point is determined as: Wherein, S is the surface area of the particle, N is the number of Voronoi seeds, r i is the radial distance of the particle surface point in the corresponding Voronoi seed; Or, the weight function for determining the curvature of the surface point is: where N is the number of Voronoi seeds, a is a dimensionless parameter, a = 1, i is a subscript index, μ,σ, respectively represent the mean, root mean square, and expected value in the statistical distribution parameters of the surface point related to the surface curvature, c i represents the maximum value of the absolute value of the principal curvature of the surface point in the triangular mesh.

6. The method of claim 2, wherein, Resampling the parameter space according to the weighted centroid and the weight function in combination with spherical Voronoi partitioning to obtain the spherical Voronoi seeds, comprises: Initializing a group of random Voronoi seeds on the particle surface; Calculating weights of the Voronoi seeds according to positions of the Voronoi seeds and corresponding weight functions; Establishing a Delaunay triangulation of the Voronoi seeds by a convex hull algorithm; Calculating weighted centroids of triangles in the triangulation; Sorting the weighted centroids of the triangles of the Delaunay triangulation of the Voronoi seeds to construct Voronoi cells corresponding to the Voronoi seeds; Calculating a centroid of the Voronoi cell and a distance error of the Voronoi seeds, and determining positions of the Voronoi seeds according to the error.

7. The method of claim 6, wherein, Determining positions of the Voronoi seeds according to the distance error, comprises: When the error is greater than a predetermined error threshold and the number of iterations is less than a predetermined iteration threshold, the centroid of the Voronoi cell is taken as the Voronoi seed, the centroid of the Voronoi cell is recalculated and the distance error is determined, until the distance error is less than or equal to the predetermined error threshold, or the number of iterations is greater than or equal to the predetermined iteration threshold.

8. An apparatus for determining a contact feature of rock particles, characterized by, The device comprises: A point cloud determination unit configured to determine a point cloud of a particle surface by scanning a particle morphology of the rock particle; A spherical harmonic coefficient determination unit configured to substitute the point cloud of the particle surface into a preset spherical harmonic function to determine a spherical harmonic coefficient corresponding to the particle surface; A grid topology determination unit configured to determine a weight function for resampling of the particle surface points, resample a parameter space according to the weight function in combination with spherical Voronoi partitioning to obtain a spherical Voronoi seed as a new sampling point of the parameter space, generate a Delaunay triangulation of the Voronoi seed by a convex hull algorithm, and obtain a new grid topology; A reconstruction unit configured to substitute the sampling point and the spherical harmonic coefficient into a spherical harmonic function fitting to obtain a radial distance of the fitted sampling point, determine a coordinate of the particle surface point according to the radial distance, and obtain a reconstructed particle surface after grid simplification according to the coordinate of the particle surface point and the grid topology; A feature determination unit configured to determine a contact feature of the rock particle according to the reconstructed particle surface.

9. A rock particle contact feature determination device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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