A multi-scale characterization three-dimensional morphology construction method and system suitable for coarse-grained soil

Abstract

The present invention discloses a method and system for constructing a multi-scale representation three-dimensional morphology suitable for coarse-grained soil. The method and system create a first point cloud matrix corresponding to the three-dimensional model coordinate data corresponding to the coarse-grained soil by acquiring the three-dimensional model coordinate data corresponding to the coarse-grained soil in real time; analyze and process the corresponding principal components, and generate first representation data corresponding to the coarse-grained soil; generate corresponding point cloud centroid data based on the data in the first point cloud matrix, and generate second representation data corresponding to the coarse-grained soil based on the point cloud centroid data; subdivide and process the triangular faces of the three-dimensional model corresponding to the coarse-grained soil, and construct the corresponding second point cloud matrix; generate third representation data corresponding to the coarse-grained soil; and construct a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time in combination with the representation data; the method can improve the depiction accuracy of the particle morphology, is applicable to soil particles of any shape, and can quickly and intuitively obtain multi-scale representation data of the soil particles.

Description

Technical Field

[0001] The present invention belongs to the technical field of material analysis and processing, and in particular relates to a multi-scale characterization three-dimensional morphology construction method and system suitable for coarse-grained soil. Background Art

[0002] Particle morphology is a crucial factor influencing the mechanical properties of sand, particularly its shear strength, dilatancy, and critical behavior under low stress conditions, as well as its particle crushing behavior under high stress conditions. Therefore, accurately reconstructing the three-dimensional morphology of sand particles and quantifying their morphological characterization parameters are prerequisites for studying the effects of sand morphology.

[0003] Particle morphology is one of the most important characteristics of particles, and its correlation with mechanical properties has received increasing attention. However, due to the limitations of technical means and experimental equipment, most processing methods are still only made for one or two scales of particles. Therefore, the characterization accuracy of particles by traditional processing methods is not ideal. Moreover, this leads to deviations in the analysis results, affecting the guiding significance of particles for practical engineering.

[0004] Therefore, in view of the above technical problems and defects, it is urgent to design and develop a multi-scale characterization three-dimensional morphology construction method and system suitable for coarse-grained soil. Summary of the Invention

[0005] In order to overcome the shortcomings and difficulties of the above-mentioned prior art, the purpose of the present invention is to provide a method, system and platform for constructing a multi-scale three-dimensional morphology suitable for coarse-grained soil, so as to improve the accuracy of the characterization of particle morphology, and be applicable to soil particles of any shape, and can quickly and intuitively obtain multi-scale characterization data of soil particles.

[0006] The first purpose of the present invention is to provide a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil; the second purpose of the present invention is to provide a multi-scale characterization three-dimensional morphology construction system suitable for coarse-grained soil; the third purpose of the present invention is to provide a multi-scale characterization three-dimensional morphology construction platform suitable for coarse-grained soil.

[0007] The first object of the present invention is achieved in that the method comprises the steps of:

[0008] Acquiring three-dimensional model coordinate data corresponding to the coarse-grained soil in real time, and creating a first point cloud matrix corresponding to the three-dimensional model coordinate data; wherein the first point cloud matrix is ​​a point cloud set including coordinate data of all vertices on the surface of the three-dimensional model;

[0009] Analyzing and processing the principal components corresponding to the first point cloud matrix, and generating first characterization data corresponding to the coarse-grained soil; wherein the first characterization data is shape characterization data of the coarse-grained soil;

[0010] generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data; wherein the second characterization data is roundness characterization data of the coarse-grained soil;

[0011] The triangular faces of the three-dimensional model corresponding to the coarse-grained soil are subdivided and processed, and a second point cloud matrix corresponding to the triangular faces is constructed; and third representation data corresponding to the coarse-grained soil is generated based on the data in the second point cloud matrix; wherein the second point cloud matrix is ​​a vertex index matrix of the triangular faces of the three-dimensional model; and the third representation data is surface texture parameter data of the coarse-grained soil;

[0012] The first characterization data, the second characterization data, and the third characterization data are combined to construct a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time.

[0013] Furthermore, the real-time acquisition of three-dimensional model coordinate data corresponding to the coarse-grained soil and the creation of a first point cloud matrix corresponding to the three-dimensional model coordinate data further include:

[0014] A three-dimensional model corresponding to the coarse-grained soil is constructed, and a correspondence between the vertex coordinates of the three-dimensional model and the triangle node index is established; wherein the triangle is a surface triangle corresponding to the three-dimensional model and composed of vertices.

[0015] Furthermore, the analyzing and processing the principal components corresponding to the first point cloud matrix and generating first characterization data corresponding to the coarse-grained soil further includes:

[0016] generating first ratio data corresponding to the coarse-grained soil based on the principal component data corresponding to the first point cloud matrix;

[0017] The first ratio data is acquired in real time, and first characterization data corresponding to the coarse-grained soil is generated according to the first ratio data; wherein the first ratio data is the principal axis length and the aspect ratio of the coarse-grained soil.

[0018] Furthermore, the step of generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data, further includes:

[0019] Generating vector data from the triangle vertices of the coarse-grained soil three-dimensional model to the point cloud centroid in real time based on the point cloud centroid data;

[0020] At least one three-dimensional grid corresponding to the first point cloud matrix is ​​created based on the data in the first point cloud matrix.

[0021] Furthermore, the step of generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data, further includes:

[0022] Obtaining a three-dimensional grid corresponding to the first point cloud matrix, looping through and processing each internal grid point, and generating distance data from the grid point to the triangular grid surface;

[0023] The actual positional relationship between the grid point and the grid surface is determined in real time based on the distance data.

[0024] Furthermore, the subdivision process includes processing triangular faces of the three-dimensional model corresponding to the coarse-grained soil and constructing a second point cloud matrix corresponding to the triangular faces; and generating third characterization data corresponding to the coarse-grained soil based on data in the second point cloud matrix, further comprising:

[0025] Establishing a correspondence between triangular faces and tetrahedrons in a three-dimensional model of coarse-grained soil, and marking and processing the tetrahedrons;

[0026] Original surface data corresponding to the tetrahedron is acquired, unnecessary variable data corresponding to the tetrahedron is cleared, and manifold surface data corresponding to the tetrahedron is generated.

[0027] Furthermore, the subdivision process includes processing triangular faces of the three-dimensional model corresponding to the coarse-grained soil and constructing a second point cloud matrix corresponding to the triangular faces; and generating third characterization data corresponding to the coarse-grained soil based on data in the second point cloud matrix, further comprising:

[0028] Creating a fitting surface corresponding to the edge data of the point cloud of the coarse-grained soil three-dimensional model, and segmenting and processing the fitting surface;

[0029] Based on the fitted surface after segmentation, combined with the discretized grid data corresponding to the fitted surface and the inscribed polygon data corresponding to the segmented surface, fractal dimension data corresponding to the coarse-grained soil is generated in real time;

[0030] After combining the first characterization data, the second characterization data and the third characterization data to construct a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time, the method further includes: visually displaying the multi-scale three-dimensional morphology corresponding to the coarse-grained soil.

[0031] The second object of the present invention is achieved as follows: the system is applied to the multi-scale representation three-dimensional morphology construction method, and the system includes:

[0032] A first data creation unit is configured to acquire three-dimensional model coordinate data corresponding to the coarse-grained soil in real time and create a first point cloud matrix corresponding to the three-dimensional model coordinate data; wherein the first point cloud matrix is ​​a point cloud set including coordinate data of all vertices on the surface of the three-dimensional model;

[0033] a first data processing and generating unit, configured to analyze and process principal components corresponding to the first point cloud matrix, and generate first characterization data corresponding to the coarse-grained soil; wherein the first characterization data is shape characterization data of the coarse-grained soil;

[0034] a first data generating unit, configured to generate point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and to generate second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data; wherein the second characterization data is roundness characterization data of the coarse-grained soil;

[0035] A second data processing and generating unit is configured to segment and process triangular faces of a three-dimensional model corresponding to the coarse-grained soil, and construct a second point cloud matrix corresponding to the triangular faces; and generate third representation data corresponding to the coarse-grained soil based on data in the second point cloud matrix; wherein the second point cloud matrix is ​​a vertex index matrix of the triangular faces of the three-dimensional model; and the third representation data is surface texture parameter data of the coarse-grained soil;

[0036] The second data creation unit is used to combine the first characterization data, the second characterization data and the third characterization data to construct a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time.

[0037] Furthermore, the system further includes:

[0038] A visualization module for visualizing the multi-scale three-dimensional morphology corresponding to coarse-grained soil;

[0039] And / or, the first data creation unit further includes: a first data construction module, configured to construct a three-dimensional model corresponding to the coarse-grained soil, and establish a correspondence between vertex coordinates of the three-dimensional model and triangle node indexes; wherein the triangle is a surface triangle corresponding to the three-dimensional model and composed of vertices;

[0040] And / or, the first data processing and generating unit further includes: a first data generating module, configured to generate first ratio data corresponding to the coarse-grained soil based on the principal component data corresponding to the first point cloud matrix; a second data generating module, configured to acquire the first ratio data in real time and generate first characterization data corresponding to the coarse-grained soil based on the first ratio data; wherein the first ratio data is the principal axis length and the aspect ratio of the coarse-grained soil;

[0041] And / or, the first data generation unit further includes: a third data generation module for generating, in real time, vector data from triangle vertices to the point cloud centroid of the coarse-grained soil three-dimensional model based on the point cloud centroid data; a second data construction module for creating, based on the data in the first point cloud matrix, at least one three-dimensional grid corresponding to the first point cloud matrix;

[0042] And / or, the first data generation unit further includes: a fourth data generation module, configured to obtain a three-dimensional grid corresponding to the first point cloud matrix, cyclically traverse and process each internal grid point, and generate distance data from the grid point to the triangular grid surface; a first determination module, configured to determine, in real time based on the distance data, the actual positional relationship between the grid point and the grid surface;

[0043] And / or, the second data processing and generating unit further includes: establishing a marking processing module for establishing a correspondence between triangular faces of the coarse-grained soil three-dimensional model and tetrahedrons, and marking and processing the tetrahedrons; and a clearing and generating module for obtaining original surface data corresponding to the tetrahedrons, clearing and processing unnecessary variable data corresponding to the tetrahedrons, and generating and obtaining manifold surface data corresponding to the tetrahedrons;

[0044] And / or, the second data processing and generating unit further includes: creating a segmentation processing module for creating a fitting surface corresponding to the edge data of the point cloud of the coarse-grained soil three-dimensional model, and segmenting and processing the fitting surface;

[0045] The fifth data generation module is used to generate fractal dimension data corresponding to coarse-grained soil in real time based on the fitting surface after segmentation processing, combined with the discretized grid data corresponding to the fitting surface, and the inscribed polygon data corresponding to the segmented surface.

[0046] The third object of the present invention is achieved as follows: it includes a processor, a memory and a multi-scale characterization three-dimensional morphology construction platform control program suitable for coarse-grained soil; wherein the multi-scale characterization three-dimensional morphology construction platform control program suitable for coarse-grained soil is executed by the processor, the multi-scale characterization three-dimensional morphology construction platform control program suitable for coarse-grained soil is stored in the memory, and the multi-scale characterization three-dimensional morphology construction platform control program suitable for coarse-grained soil implements the multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil.

[0047] The present invention obtains three-dimensional model coordinate data corresponding to coarse-grained soil in real time through a method, and creates a first point cloud matrix corresponding to the three-dimensional model coordinate data; wherein the first point cloud matrix is ​​a point cloud set containing coordinate data of all vertices on the surface of the three-dimensional model;

[0048] Analyzing and processing the principal components corresponding to the first point cloud matrix, and generating first characterization data corresponding to the coarse-grained soil; wherein the first characterization data is shape characterization data of the coarse-grained soil;

[0049] generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data; wherein the second characterization data is roundness characterization data of the coarse-grained soil;

[0050] The triangular faces of the three-dimensional model corresponding to the coarse-grained soil are subdivided and processed, and a second point cloud matrix corresponding to the triangular faces is constructed; and third representation data corresponding to the coarse-grained soil is generated based on the data in the second point cloud matrix; wherein the second point cloud matrix is ​​a vertex index matrix of the triangular faces of the three-dimensional model; and the third representation data is surface texture parameter data of the coarse-grained soil;

[0051] Combining the first characterization data, the second characterization data and the third characterization data, a multi-scale three-dimensional morphology corresponding to coarse-grained soil is constructed in real time; and the system and platform corresponding to the method can improve the accuracy of the characterization of particle morphology, and are applicable to soil particles of any shape, and can quickly and intuitively obtain multi-scale characterization data of soil particles.

[0052] In other words, the current methods for obtaining single-scale and two-scale particle morphological characterization parameters are upgraded to methods for obtaining three-scale characterization parameters, and using the method of the present invention, multi-scale morphological characterization parameters of coarse-grained soil can be intuitively obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 This is a schematic flow chart of a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0055] Figure 2 Schematic diagram of an embodiment of a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0056] Figure 3 Schematic diagram of a process for obtaining the shape parameters of coarse-grained soil, principal axis length and aspect ratio, in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0057] Figure 4 A schematic diagram of a process for obtaining surface area and volume parameters of coarse-grained soil in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0058] Figure 5 A schematic diagram of a process for obtaining roundness characterization parameters of coarse-grained soil in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0059] Figure 6 A schematic diagram of a process flow for extracting model surface parameters of coarse-grained soil in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0060] Figure 7 Schematic diagram of the calculation process of power spectrum density parameters of coarse-grained soil in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0061] Figure 8 A schematic diagram of a fractal dimension calculation process for coarse-grained soil in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0062] Figure 9 Schematic diagram of the surface power spectrum density matrix processing flow of coarse-grained soil in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0063] Figure 10 Schematic diagram of the surface power spectrum density matrix filling process of coarse-grained soil in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0064] Figure 11A schematic diagram of a power spectrum density analysis process of coarse-grained soil in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0065] Figure 12 Schematic diagram of mapping the three-dimensional model data information of coarse-grained soil after scanning on a spatial coordinate system in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil of the present invention;

[0066] Figure 13 A schematic diagram of three-dimensional point cloud information of coarse-grained soil according to a method for constructing a three-dimensional morphology of multi-scale characterization suitable for coarse-grained soil according to the present invention;

[0067] Figure 14 A schematic diagram of mapping the surface texture of coarse-grained soil on a spatial coordinate system in a multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to the present invention;

[0068] Figure 15 This is a schematic diagram of the architecture of a multi-scale characterization and three-dimensional morphology construction system suitable for coarse-grained soil according to the present invention;

[0069] Figure 16 This is a schematic diagram of the architecture of a multi-scale characterization three-dimensional morphology construction platform suitable for coarse-grained soil according to the present invention;

[0070] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0071] In order to better understand the purpose, technical solutions and advantages of the present invention, the present invention is further described below with reference to the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

[0072] The present invention may also be implemented or applied through other different specific examples, and the details in this specification may also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0073] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0074] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. Secondly, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0075] Preferably, the multi-scale characterization and three-dimensional morphology construction method for coarse-grained soil of the present invention is applied to one or more terminals or servers. The terminal is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0076] The terminal can be a computing device such as a desktop computer, notebook, PDA, cloud server, etc. The terminal can interact with the client through a keyboard, mouse, remote control, touchpad, or voice control device.

[0077] The present invention provides a method, system and platform for constructing a multi-scale three-dimensional morphology suitable for coarse-grained soil.

[0078] like Figure 1 , which is a flow chart of a method for constructing a multi-scale three-dimensional morphology suitable for coarse-grained soil provided by an embodiment of the present invention.

[0079] In this embodiment, the multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil can be applied to a terminal or a fixed terminal with a display function. The terminal is not limited to a personal computer, a smart phone, a tablet computer, a desktop computer or an all-in-one computer equipped with a camera, etc.

[0080] The multi-scale representation and three-dimensional morphology construction method for coarse-grained soil can also be applied in a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network, a metropolitan area network, or a local area network. The multi-scale representation and three-dimensional morphology construction method for coarse-grained soil according to the embodiments of the present invention can be executed by a server, a terminal, or both.

[0081] For example, for a terminal that needs to perform multi-scale characterization and three-dimensional morphology construction suitable for coarse-grained soil, the multi-scale characterization and three-dimensional morphology construction function suitable for coarse-grained soil provided by the method of the present invention can be directly integrated on the terminal, or a client for implementing the method of the present invention can be installed. For another example, the method provided by the present invention can also be run on a server or other device in the form of a software development kit (SDK), and an interface for the multi-scale characterization and three-dimensional morphology construction function suitable for coarse-grained soil is provided in the form of the SDK. The terminal or other device can implement the multi-scale characterization and three-dimensional morphology construction function suitable for coarse-grained soil through the provided interface. The present invention is further explained below in conjunction with the accompanying drawings.

[0082] like Figures 1-14 As shown, the present invention provides a method for constructing a multi-scale three-dimensional morphology suitable for coarse-grained soil, wherein the method comprises the following steps:

[0083] S01. Acquire three-dimensional model coordinate data corresponding to coarse-grained soil in real time, and create a first point cloud matrix corresponding to the three-dimensional model coordinate data; wherein the first point cloud matrix is ​​a point cloud set including coordinate data of all vertices on the surface of the three-dimensional model;

[0084] S02. Analyze and process the principal components corresponding to the first point cloud matrix, and generate first characterization data corresponding to the coarse-grained soil; wherein the first characterization data is shape characterization data of the coarse-grained soil;

[0085] S03. Generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data; wherein the second characterization data is roundness characterization data of the coarse-grained soil;

[0086] S04. Segmenting and processing the triangular faces of the three-dimensional model corresponding to the coarse-grained soil, and constructing a second point cloud matrix corresponding to the triangular faces; generating third representation data corresponding to the coarse-grained soil based on the data in the second point cloud matrix; wherein the second point cloud matrix is ​​a vertex index matrix of the triangular faces of the three-dimensional model; and the third representation data is surface texture parameter data of the coarse-grained soil;

[0087] S05. Combining the first characterization data, the second characterization data, and the third characterization data, constructing a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time.

[0088] The method further comprises: acquiring three-dimensional model coordinate data corresponding to the coarse-grained soil in real time, and creating a first point cloud matrix corresponding to the three-dimensional model coordinate data;

[0089] S011. Construct a three-dimensional model corresponding to coarse-grained soil, and establish a corresponding relationship between the vertex coordinates of the three-dimensional model and the triangle node index; wherein the triangle is a surface triangle corresponding to the three-dimensional model and composed of vertices.

[0090] The analyzing and processing the principal components corresponding to the first point cloud matrix and generating first characterization data corresponding to the coarse-grained soil further includes:

[0091] S021. Generating first ratio data corresponding to the coarse-grained soil based on the principal component data corresponding to the first point cloud matrix;

[0092] S022. Acquire the first ratio data in real time, and generate first characterization data corresponding to the coarse-grained soil based on the first ratio data; wherein the first ratio data is the principal axis length and the aspect ratio of the coarse-grained soil.

[0093] The method further comprises: generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data.

[0094] S031. Generating vector data from the triangle vertices of the coarse-grained soil three-dimensional model to the point cloud centroid in real time based on the point cloud centroid data;

[0095] S032. Create at least one three-dimensional grid corresponding to the first point cloud matrix based on the data in the first point cloud matrix.

[0096] The method further comprises: generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data.

[0097] S033. Obtain a three-dimensional grid corresponding to the first point cloud matrix, traverse and process each internal grid point in a loop, and generate distance data from the grid point to the triangular grid surface;

[0098] S034. Determine in real time the actual positional relationship between the grid point and the grid surface based on the distance data.

[0099] The method further comprises: dividing the triangular faces of the three-dimensional model corresponding to the coarse-grained soil and constructing a second point cloud matrix corresponding to the triangular faces; and generating third characterization data corresponding to the coarse-grained soil based on the data in the second point cloud matrix.

[0100] S041. Establishing a correspondence between triangular faces and tetrahedrons in a three-dimensional model of coarse-grained soil, and marking and processing the tetrahedrons;

[0101] S042: Acquire original surface data corresponding to the tetrahedron, clear unnecessary variable data corresponding to the tetrahedron, and generate manifold surface data corresponding to the tetrahedron.

[0102] The method further includes: dividing the triangular faces of the three-dimensional model corresponding to the coarse-grained soil and constructing a second point cloud matrix corresponding to the triangular faces; and generating third characterization data corresponding to the coarse-grained soil based on data in the second point cloud matrix.

[0103] S043, creating a fitting surface corresponding to the edge data of the point cloud of the coarse-grained soil three-dimensional model, and segmenting and processing the fitting surface;

[0104] S044. Based on the segmented fitting surface, combined with discretized grid data corresponding to the fitting surface and inscribed polygon data corresponding to the segmented surface, fractal dimension data corresponding to the coarse-grained soil is generated in real time.

[0105] After combining the first characterization data, the second characterization data, and the third characterization data to construct a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time, the method further includes:

[0106] S06. Visualize the multi-scale three-dimensional morphology corresponding to coarse-grained soil.

[0107] Specifically, in an embodiment of the present invention, a method for obtaining multi-scale characterization parameters of coarse-grained soil is provided, and the method for obtaining multi-scale characterization parameters of coarse-grained soil comprises: obtaining an STL model file containing geometric data of coarse-grained soil through a 3D scanner, and using a built-in sub-function of MATLAB software to read the coordinate information of the three-dimensional model of coarse-grained soil from the STL file; calling a sub-function named f_getAR2 (f_getAR2 is the abbreviation of get Aspect Ratiod, which means obtaining the aspect ratio of the particles. It is recommended that this sub-function be written in MATLAB and expressed in English. The following reference to f_getAR2 all indicates this meaning and will not be explained in the same way) to obtain the principal axis length and aspect ratio of the particles; calling a sub-function named f_getSpCompact (f_getSpCompact is the abbreviation of get Sphericity and The abbreviation of Compact, which means to obtain the sphericity of the three-dimensional model. This sub-function is recommended to be written in MATLAB, so it is expressed in English. The f_getAR2 mentioned below all means this meaning and will not be explained in the same way. The sub-function is used to calculate the sphericity, total volume, total surface area, and compactness of the particles; the shape analysis results obtained from the sub-function are stored in a file named form (form is a structure, which is a data structure used to store and organize different types of data in MATLAB.It allows you to combine multiple related data items into a single variable, so as to conveniently manage and access these data. The following form refers to this meaning and will not be explained in the same way. groudness is the abbreviation of roundness, which means calculating the roundness-related parameters of the particles. It is recommended to write this sub-function in MATLAB, so it is expressed in English. The following reference to f_getroundness2 refers to this meaning and will not be explained in the same way again); perform principal component analysis (PCA) to obtain the principal components of the particles, and call a sub-function named MyRobustCrust (in the fields of computer graphics and geometry processing, "Crust" usually refers to an algorithm used to generate a triangular mesh of a surface from point cloud data, and "Robust" means that the algorithm has a certain robustness or robustness and can still produce effective results when facing irregular or noisy data. It is recommended to write this sub-function in MATLAB, so it is expressed in English. The following reference to MyRobustCrust refers to this meaning and will not be explained in the same way again) to find a face with pN (assuming pN is a specific point, that is, a vertex on the particle) as a vertex; call f_getSurfTex (f_getSurfTex is get Surface Texture is the abbreviation of Texture, which means obtaining the surface texture parameters of the particles. This sub-function is recommended to be written in MATLAB, so it is expressed in English. The following reference to f_getSurfTex represents this meaning and will not be explained in the same way. It is used to calculate the surface texture characteristics of the particles; the built-in sub-function of MATLAB is used to output the shape, roundness, and surface texture parameters of the soil particles, and the calculation results are visualized.

[0108] The coordinate information of the three-dimensional model of coarse-grained soil is obtained, and the coordinate information of the three-dimensional model of coarse-grained soil includes: in the STL file, the surface of the three-dimensional model of coarse-grained soil is usually composed of triangles. Each triangle is composed of three vertices, and the coordinate information of these vertices can be found in the STL file. In MATLAB, when the STL file is read using the stlread function (in MATLAB, STL files can be read through a function called stlread. This function can extract the vertices and normals of triangle fragments from the STL file), it stores the vertex coordinates of the model in p and the index of the triangle in t; p (points) is a matrix containing the coordinates of all vertices on the surface of the model. Each row represents a vertex and contains three columns, representing the x, y and z coordinates respectively. t (triangles) is a matrix, each row of which represents a triangle. Each element is the index corresponding to the vertex in p. For example, if a row of t is [1,2,3], then the three vertices of this triangle are p(1,:), p(2,:), and p(3,:); in this way, the coordinates in p and the indices in t establish a relationship, and the corresponding vertex coordinates can be found through the index to describe the surface shape of the three-dimensional model. This representation method is a common three-dimensional model representation method, which is particularly suitable for meshes composed of triangles.

[0109] The steps of obtaining coordinate information for the 3D model of coarse-grained soil and calling a subfunction named f_getAR2 (f_getAR2 is the abbreviation for getAspectRatiod, meaning to obtain the particle aspect ratio. This subfunction is recommended to be written in MATLAB, so it is expressed in English. All references to f_getAR2 below refer to this meaning and will not be repeated here) to calculate the particle's principal axis length and aspect ratio include: function[L,EL,FL]=f_getAR2(p): Define a function f_getAR2 that accepts a parameter p, which is an n*3 matrix representing a 3D point cloud. The function outputs a length vector L and two length ratios EL and FL. L is a vector containing the lengths of the point cloud along the three principal axes. EL is the ratio of the point cloud's length along the second principal axis to the length along the first principal axis, i.e., the aspect ratio. FL is the ratio of the point cloud's length along the third principal axis to the length along the second principal axis. [~,score,~]=pca(p); Perform principal component analysis on the point cloud data using the MATLAB pca function. This function returns three values, which contain the projection of the point cloud along the principal axes.

[0110] The steps of calculating the length vector L and the two length ratios EL and FL include: L = zeros(3,1); creating a column vector L containing three elements to store the lengths of the point cloud in the three principal axis directions. L(1) = max(score(:,1))-min(score(:,1)); calculating the length of the point cloud in the first principal axis direction, that is, the range on this axis in the projected coordinates. L(2) = max(score(:,2))-min(score(:,2)); calculating the length of the point cloud in the second principal axis direction. L(3) = max(score(:,3))-min(score(:,3)); calculating the length of the point cloud in the third principal axis direction. L = sort(L,'descend'); sorting the length vector L in descending order for subsequent use. a = L(1); b = L(2); c = L(3); assigning the three elements in the sorted length vector to variables a, b, and c respectively for subsequent calculations. EL = b / a;: Calculates the length ratio of the point cloud along the second principal axis, i.e., the aspect ratio. FL = c / b;: Calculates the length ratio of the point cloud along the third principal axis.

[0111] The steps of obtaining coordinate information of the coarse-grained soil 3D model and calling a subfunction named f_getSpCompact (f_getSpCompact is the abbreviation of get Sphericity and Compact, meaning to obtain the sphericity of the 3D model. This subfunction is recommended to be written in MATLAB and is therefore expressed in English. All references to f_getAR2 below represent this meaning and will not be explained in the same way) to calculate the sphericity (sphericity), total volume (totalVolume), total surface area (totalArea), and convexity (convexity) of the 3D model include: function[S,totalVolume,totalArea,Co]=f_getSpCompact(p,t): A function f_getSpCompact(p,t) is defined, which accepts two input parameters: p represents the vertex coordinates and t represents the triangle node. The output is sphericity S, total volume totalVolume, total surface area totalArea, and convexity Co.

[0112] The total volume and total surface area of ​​the coarse-grained soil are obtained, a function named f_getVolSA(TRI,P) is called to calculate the total volume and total surface area of ​​the particles, and then these values ​​are used to calculate the roundness and convexity. The steps of calculating the roundness and convexity include: Centroid = [sum(X) / n,sum(Y) / n,sum(Z) / n], where n represents the number of vertices of the particle, the coordinates of X, Y and Z are extracted from the vertex matrix P, and the center of mass of the point cloud is calculated. for i = 1: size(TRI, 1); U = [X(TRI(i, 1)), Y(TRI(i, 1)), Z(TRI(i, 1))]; V = [X(TRI(i, 2)), Y(TRI(i, 2)), Z(TRI(i, 2))]; W = [X(TRI(i, 3)), Y(TRI(i, 3)), Z(TRI(i, 3))], where U, V, and W represent the coordinates of the three vertices of the triangle. Inside the loop, extract the coordinates of each triangle's three vertices from the vertex matrix P. A = VU; B = WU; C = cross(A, B); normC = norm(C); Calculate the vectors of the two triangle sides, namely vector A and vector B. Compute the cross product to obtain the normal vector C of the triangle parallelogram. Then calculate the norm of C, which is the area of ​​the parallelogram. a = 0.5 * normC; area = area + a; Using the area formula for a parallelogram, add the area a of each triangle to the total surface area. Sidea = U - Centroid; Sideb = V - Centroid; Sidec = W - Centroid; Cros = cross(Sidea, Sideb); Calculate the vectors from the triangle vertices to the center of mass. vol = vol + abs(Sidec * Cros') / 6; Using the formula for calculating the volume of a tetrahedron, add the volume of each tetrahedron to the total volume vol. Calculate the volume contribution of each triangle on a given surface and sum these contributions to ultimately determine the volume of the entire enclosed surface. This method can be used to measure the compactness of particles or objects in three-dimensional space, as more compact objects have more triangles on their surface and their volume contribution is greater.

[0113] The steps of calling a sub-function named f_getroundness2 (f_getroundness2 is the abbreviation of get roundness, which means calculating the roundness-related parameters of the particles. It is recommended to write this sub-function in MATLAB, so it is expressed in English. All f_getroundness2 mentioned below means this meaning and will not be explained in the same way) to calculate the roundness-related parameters of the particles include: defining a MATLAB function f_getroundness2, which accepts vertex p, face t and parameter No as input and returns a structure out; using the reducepatch function (reducepatch function is a built-in function of MATLAB. This function is used to reduce the number of triangular meshes, thereby reducing the complexity of the model. It can be used to simplify the computational burden when displaying or processing large three-dimensional models.) to reduce the complexity of the triangular mesh and retain a specified number of faces, that is, the number specified by parameter No; extracting X, Y and Z coordinates from the vertex matrix p to generate a three-dimensional mesh of size n_interp^3, which contains points representing the interior of the particle. These points are stored in the matrix points (in MATLAB, you can set a matrix to store point data information and name it points); initialize a zero vector with the number of non-zero elements to store the minimum distance of each internal grid point to the surface. Loop through each internal grid point, calculate its minimum distance to the triangular mesh surface, and store it in disp_map (in MATLAB, you can set a column vector and name it disp_map to store the minimum distance of each internal grid point to the triangular mesh surface. Its size is the same as the number of rows of the points matrix, that is, it has the same number of elements as the number of internal grid points. Each element of disp_map stores the minimum distance of the corresponding internal grid point to the triangular mesh surface.). If the point is outside the mesh, the distance is set to zero; find the maximum value and the corresponding index in disp_map, and then use the index to find the coordinates of the point corresponding to the maximum value in points, and use it as the center of the largest inscribed sphere; calculate the curvature at each point on the triangular mesh. k1 is the minimum principal curvature, k2 is the maximum principal curvature, km is the mean curvature, and kg is the Gaussian curvature. The roundness corresponding to four different curvatures (k1, k2, km, and kg) is calculated. This function counts the number of each curvature at points with a radius less than R_ins and calculates the average roundness. The four roundness values ​​are combined into a vector, Roundness. The result is stored in a structure and used as the function output. This structure includes the center of the maximum inscribed sphere (center), the maximum inscribed sphere radius (R_ins), the four roundness values ​​(Roundness), the original vertex (p), the original face (t), and the curvature information (Curvature).

[0114] Call a sub-function named MyRobustCrust (in the fields of computer graphics and geometry processing, "Crust" usually refers to an algorithm for generating a triangular mesh of a surface from point cloud data, and "Robust" means that the algorithm has a certain robustness or robustness and can still produce valid results when facing irregular or noisy data. This sub-function is recommended to be written in MATLAB, so it is expressed in English. The following reference to MyRobustCrust indicates this meaning and will not be explained in the same way). The steps for finding a face with pN as vertices include: using the Delaunayn function (Delaunayn function is the Delaunay function, which is a built-in function of MATLAB) to perform Delaunay triangulation to obtain the vertex index matrix of the triangular face; establishing a connection relationship between triangles and tetrahedrons; calculating the center of the tetrahedron circumscribed circle and its radius; marking the inner and outer tetrahedrons; extracting the original surface from the marked tetrahedrons and clearing variables that are no longer needed; and extracting the manifold surface.

[0115] The subroutine named f_getSurfTex (f_getSurfTex is short for get Surface Texture, meaning to obtain the surface texture parameters of the particles. This subroutine is recommended to be written in MATLAB, so it is expressed in English. All references to f_getSurfTex below refer to this meaning and will not be repeated). The steps for calculating the surface texture characteristics of the particles include: detecting the edges of the 3D point cloud on the xy plane and fitting the edge data into a surface using linear interpolation; splitting the surface into two parts based on the relationship between the height and the fitted surface, and calculating a discretized mesh; calculating the largest inscribed square of the split surface, extracting the square area used for PSD (Power Spectral Density) calculation, and subtracting the average value; and calculating the power spectral density (PSD) of each square area and other PSD-related parameters. PSD-related parameters, such as the fractal dimension, reflect the complexity and irregularity of the surface. For particle surfaces, a higher fractal dimension indicates a more complex and rougher surface.

[0116] That is, the first aspect of the present invention provides a method for obtaining shape characterization parameters of coarse-grained soil, the method comprising: reading coordinate information of a three-dimensional model of coarse-grained soil from a scanned STL file, including a vertex coordinate matrix P and a triangle node index t; converting the vertex coordinate matrix P of the three-dimensional model into a three-dimensional point cloud matrix, performing principal component analysis on the point cloud matrix data, and obtaining projections of the point cloud in the three principal axis directions; calculating the length and aspect ratio of the point cloud data in the three principal axis directions of the particles, thereby obtaining shape characterization parameters of the particles;

[0117] The second aspect of the present invention provides a method for obtaining roundness characterization parameters of coarse-grained soil, which comprises: extracting X, Y, and Z coordinates from a vertex matrix P and calculating the centroid of a point cloud; extracting the coordinates of the three vertices of each triangle from the vertex matrix P; calculating the vectors of the two triangle sides, namely, vector A and vector B. Calculating the cross product to obtain the normal vector C of the triangle parallelogram. Then, calculating the modulus of C, namely, the area of ​​the parallelogram; adding the area a of each triangle to the total surface area area according to the area formula of the parallelogram; calculating the vector from the triangle vertex to the centroid; and adding the volume of each tetrahedron to the total volume vol according to the volume formula of the tetrahedron. The volume contribution of each triangle on a given surface is calculated, and these contributions are accumulated to finally obtain the volume of the entire closed surface. This method can be used to measure the compactness of particles or objects in three-dimensional space, as more compact objects have more triangles on their surface and contribute more to the volume. Extract the X, Y, and Z coordinates from the vertex matrix p to generate a three-dimensional mesh of size n_interp^3, which contains the points representing the interior of the particle. These points are stored in the matrix points. Initialize a zero vector with the number of nonzero elements to store the minimum distance from each interior mesh point to the surface. Loop through each interior mesh point, calculate its minimum distance to the triangular mesh surface, and store it. If the point is outside the mesh, the distance is set to zero. Find the maximum value and the corresponding index in the stored data, then use this index to find the coordinates of the point corresponding to the maximum value in points and use it as the center of the largest inscribed sphere. Calculate the curvature at each point on the triangular mesh. k1 is the minimum principal curvature, k2 is the maximum principal curvature, km is the mean curvature, and kg is the Gaussian curvature. Calculate the circularity corresponding to four different curvatures (k1, k2, km, kg). It counts the number of each type of curvature at points with a radius less than R_ins, calculates the average of the four roundness values, and combines them into a vector called Roundness. The result is stored in the structure out as the output of the function. This includes the center of the maximum inscribed sphere (center), the radius of the maximum inscribed sphere (R_ins), the four roundness values ​​(Roundness), the original vertex p, the original face t, and the curvature information (Curvature).

[0118] The third aspect of the present invention provides a method for obtaining surface texture parameter characterization parameters of coarse-grained soil, which comprises: triangulating a three-dimensional model to obtain a vertex index matrix tetr of a triangular face; establishing a connection relationship between a triangle and a tetrahedron, calling a CC function, and calculating the center of the tetrahedron circumscribed circle and its radius; marking the inner and outer tetrahedrons, extracting the original surface from the marked tetrahedrons, clearing the variables that are no longer needed, and extracting the manifold surface; detecting the edge of a 3D point cloud on an xy plane, and fitting the edge data into a surface using linear interpolation; dividing the surface into two parts according to the relationship between the height and the fitted surface, and calculating a discretized grid; calculating the maximum inscribed square of the surface after segmentation, extracting the square area for PSD calculation, and subtracting the average value; calculating the power spectral density (PSD) of each square area and other PSD-related parameters, wherein PSD-related parameters such as fractal dimension reflect the complexity and irregularity of the surface. For a particle surface, the higher the fractal dimension, the more complex and rough the surface.

[0119] like Figure 2 As shown, a schematic diagram of the implementation process of a multi-scale parameter acquisition method for coarse-grained soil provided by an embodiment of the present invention; in step S101, the coordinate information of the three-dimensional model of coarse-grained soil is read. The surface of the three-dimensional model of coarse-grained soil in the STL file is usually composed of triangles, each triangle is composed of three vertices, and the coordinate information of these vertices can be found in the STL file. In MATLAB, when the stlread function is used to read the STL file, it stores the vertex coordinates of the model in p and the index of the triangle in t. In step S201, principal component analysis is performed on the point cloud data. Principal Component Analysis (PCA) is a commonly used dimensionality reduction and data compression technology, and it is also a method for understanding data structure. Its goal is to map the original data to a new coordinate system through linear transformation so as to better express the variance of the data in the new coordinate system.

[0120] The following are the main mathematical principles used by PCA: Covariance Matrix: For a given data set, first calculate the covariance matrix of the data. The covariance matrix describes the degree of correlation between different dimensions. Eigenvalue Decomposition: Perform eigenvalue decomposition on the covariance matrix. Eigenvalue decomposition decomposes the covariance matrix into eigenvectors and corresponding eigenvalues. Select principal components: The eigenvalue represents the variance of the data in the direction of each eigenvector. Select the eigenvectors corresponding to the first k largest eigenvalues. These eigenvectors form the basis of the new coordinate system, which is called the principal components.

[0121] Projection: Project the original data onto the selected principal components. This projection results in a representation of the original data in the new coordinate system. Dimensionality reduction: If the number of selected principal components is less than the dimensionality of the original data, dimensionality reduction can be achieved by removing less important principal components. Dimensionality reduction helps reduce data complexity and noise. Through these steps, PCA provides a way to better represent data variability in the new coordinate system, thereby reducing redundant information in the data.

[0122] In step S202, a matrix is ​​obtained through PCA analysis, and each column of the matrix corresponds to a principal component score, and these principal component scores represent the projection of each data point in the principal component direction. In step S203, the range of the projection value in each principal axis direction is calculated, that is, the maximum value minus the minimum value, to obtain the length in the principal axis direction. In step S204, the length vector L is sorted in descending order. In this way, the first element in L is the largest length in the principal axis direction, the second element is the second largest length in the principal axis direction, and the third element is the smallest length in the principal axis direction. In step S205, the three elements in the sorted length vector are extracted, representing the lengths of the principal components in the three directions respectively, and two length-width-height ratios are calculated: EL represents the ratio of the length of the second longest principal component to the length of the longest principal component. In step S206, the aspect ratio is calculated: FL=c / b represents the ratio of the length of the shortest principal component to the length of the second longest principal component.

[0123] In step S301, the X, Y, and Z coordinates are extracted from the vertex matrix P. The center of the vertex coordinates is the centroid of the point cloud. In steps S302, S303, S304, S305, S306, and S307, the area of ​​each triangle is calculated using a cross product, and the volume of the tetrahedron formed by the three vertices connected to the centroid is calculated using the centroid. Finally, the total surface area and total volume are accumulated.

[0124] In step S401, the three columns of the vertex coordinate matrix p are extracted as vectors X, Y, and Z, respectively. In step S402, a zero vector with the number of non-zero elements is initialized to store the minimum distance from each internal grid point to the surface. In step S403, the Euclidean distance formula is used to calculate the shortest distance to the surface. The Euclidean distance is the straight-line distance between two points in space. For points (x1, y1, z1) and (x2, y2, z2), the Euclidean distance d is calculated as:

[0125]

[0126] In step S404, the maximum value and the corresponding index of the values ​​calculated in step S403 are found, and then the coordinates of the point corresponding to the maximum value are found using the index, which is used as the center of the maximum inscribed sphere. The maximum inscribed sphere radius Rins is obtained by finding the shortest distance to the surface from the internal mesh point of the surface and taking the maximum value. In step S405, the minimum principal curvature K1, the maximum principal curvature K2, and the average curvature K at each point on the triangular mesh surface are calculated. m , Gaussian curvature K g , the specific calculation method is detailed in the following formulas (2)(3)(4)(5).

[0127] The principal curvature is obtained by calculating the divergence and curl of the surface normal vector field. Assume that the normal vector of the surface is N, and the tangent vectors on the surface are Tu and Ty, where u and v are the parameters of the surface. The minimum and maximum principal curvatures k1 and k2 are calculated as follows:

[0128]

[0129]

[0130] in, is the divergence of the tangent vector, is the curl of the tangent vector;

[0131] Mean curvature Km; the mean curvature is the average of the minimum and maximum principal curvatures, and the calculation formula is:

[0132]

[0133] Gaussian curvature K g : Gaussian curvature is the product of the minimum and maximum principal curvatures, and is calculated as:

[0134] K g =K1.K2 (5)

[0135] These calculations involve concepts such as the tangent vector, normal vector, gradient, divergence, and curl on a surface, as well as the corresponding vector operations. In numerical computations, differential methods are often used to estimate gradients, divergences, and curl, and then calculate curvature. Such calculations are very useful for understanding the local geometric properties of a surface.

[0136] In step S406, the maximum inscribed sphere radius R calculated in step S404 is used ins The minimum principal curvature K1, the maximum principal curvature K2, and the average curvature K at each point calculated in step S405 are m , Gaussian curvature K g , calculate the roundness index for each curvature.

[0137] For each curvature Ki, the absolute value of its reciprocal is calculated, indicating that the larger the reciprocal of the curvature, the smaller the curvature and the flatter the surface. The absolute values ​​of these reciprocals are then weighted averaged to obtain the roundness index for each curvature.

[0138] The specific calculation formula is as follows: For K1:

[0139]

[0140] Where N1 is the number of points on the surface where the absolute value of the reciprocal of the curvature K1 is less than the maximum inscribed sphere radius. For K2:

[0141]

[0142] Where N2 is the number of points on the surface where the absolute value of the reciprocal of the curvature K2 is less than the maximum inscribed sphere radius. m :

[0143]

[0144] Among them, N m is the curvature K of the surface m The number of points whose reciprocal absolute value is less than the maximum inscribed sphere radius. g :

[0145]

[0146] These roundness indices reflect the flatness of the surface in a local area by taking a weighted average of the inverse of the curvature.

[0147] In step S501, Delaunay triangulation is performed using the delaunayn function. Delaunay triangulation is a geometric algorithm based on a discrete point set, which aims to generate a non-overlapping triangular mesh with good properties. This algorithm is based on some important mathematical principles and geometric properties:

[0148] 1. Definition of a Delaunay triangle: The key concept in Delaunay triangulation is the "Delaunay triangle." For a given set of points, a triangle is a Delaunay triangle if and only if its circumcircle contains no other points in the set. This ensures that the generated triangular mesh does not contain any sharp or overly steep angles, thereby improving mesh quality.

[0149] 2. Properties of the circumcircle: The circumcircle of a Delaunay triangle has an important property: every point in the point set lies on or is on the circumcircle. This property is the key to Delaunay triangulation.

[0150] 3. Delaunay triangulation construction: The Delaunay triangulation construction algorithm is primarily implemented through an incremental approach. Specifically, it starts with a single triangle and gradually adds new points, ensuring that the properties of the Delaunay triangle are maintained each time. As new points are added, existing triangles are adjusted, even flipping some edges, to maintain the properties of the Delaunay triangle.

[0151] The algorithm implementation of Delaunay triangulation involves some basic geometric and algebraic formulas, as well as some properties related to the circumcircle. The following are some mathematical formulas that may be used in the implementation:

[0152] 1. The radius and center of the circumscribed circle

[0153] The calculation formula for the circumcircle radius R and the center (h, k) of the Delaunay triangle is:

[0154]

[0155]

[0156]

[0157] Among them, a, b, c are the side lengths of the triangle, s is the semi-perimeter, A is the area of ​​the triangle; h is the horizontal coordinate of the center of the circumscribed circle; k is the vertical coordinate of the center of the circumscribed circle.

[0158] 2. Distance from a point to a line; the formula for calculating the distance from a point (x0, y0) to the line Ax+By+C=0 is:

[0159]

[0160] 3. Counterclockwise check: used to check whether the three points are arranged in a counterclockwise direction. The cross product formula can be used:

[0161] cross=(x2-x1)(y3-y1)-(y2-y1)(x3-x1) (14)

[0162] If cross>0, then points P1, P2, and P3 are in a counterclockwise direction.

[0163] 4. Edge flipping: When adding new points to a Delaunay triangle mesh, an edge flipping operation is required. This edge flipping operation involves the circumscribed circle property.

[0164] In step S502 , the connectivity data from tetrahedron to triangle and from triangle to tetrahedron is obtained so that the relevant information can be efficiently searched and updated during the execution of the algorithm.

[0165] In step S503, calculating the circumcenter and radius of the tetrahedron involves some basic principles of geometry and algebra. The following are the general steps and mathematical formulas for calculating the circumcenter and radius, assuming the coordinates of the vertices of the tetrahedron are P1(x1, y1, z1), P2(x2, y2, z2), P3(x3, y3, z3), and P4(x4, y4, z4).

[0166] 1. Calculate the volume of a tetrahedron:

[0167]

[0168] 2. Calculate the coordinates of the circumscribed sphere center:

[0169]

[0170]

[0171]

[0172] 3. Calculate the radius of the circumscribed sphere:

[0173]

[0174] The derivation of these formulas involves calculating determinants, where the elements of the determinant are the coordinates of the vertices of the tetrahedron. Calculating these parameters can be used for applications such as determining whether a tetrahedron contains a point and generating Delaunay triangulations.

[0175] In steps S504 and S505, the tetrahedron is marked as internal or external according to the circumscribed sphere center and radius, and an initial set of surfaces is generated. In step S506, the surface of the given point cloud is reconstructed, and the reconstructed triangle facet set and normal vector are output. In step S601, the edge of the 3D point cloud is detected on the xy plane, and the edge data is fitted into a surface using linear interpolation. In step S602, the surface is divided into two parts according to the relationship between the height and the fitted surface, and the discretized grid is calculated. In step S603, the maximum inscribed square of the surface after segmentation is calculated, the square area for PSD calculation is extracted, and the average value is subtracted. In step S604, the power spectral density (PSD) and other PSD-related parameters of each square area are calculated. PSD-related parameters such as fractal dimension reflect the complexity and irregularity of the surface.

[0176] In step S701, the surface height data H is obtained and read in ASCII format and represented as H(x,y), where x and y are grid coordinates. The lateral resolution np is obtained. The cutoff length R is obtained. In step S702, q0 = pi / R*2; q0 is the cutoff frequency, which is calculated by the cutoff length R. In step S703, the surface data size is adjusted to generate grid coordinates. In step S704, the power spectral density of the surface height data is calculated. In step S705, the root mean square (RMS) value corresponding to the power spectral density is calculated. In step S706, the fractal dimension D is calculated. In step S801, the surface height data is represented as H(x,y). The number of discrete points is represented as np. In step S802, a two-dimensional discrete Fourier transform is performed on the surface height data, and the result is normalized by dividing it by (2*pi)^2*np^2 to obtain the frequency domain Hm.

[0177] The two-dimensional discrete Fourier transform (2D DFT) is a method that converts a discrete two-dimensional spatial domain signal into a frequency domain representation. For the input matrix H, its two-dimensional discrete Fourier transform is defined as follows:

[0178]

[0179] H(u,v) is the complex result represented in the frequency domain. h(x,y) is the input signal in the spatial domain. M and N are the number of rows and columns of the input signal, respectively. u and v are the coordinates in the frequency domain.

[0180] In step S803, the power spectrum density matrix C is normalized and calculated by multiplying by (2*pi)^2*(1 / M / np)*(1 / N / np); in step S804, the power spectrum density matrix C is symmetricized, and the symmetric part is retained. In step S805, the frequency vectors q_x and q_y are calculated by dividing the angular frequency by the horizontal and vertical resolution np and the number of rows and columns of the data for subsequent power spectrum density analysis. Calculating the frequency values ​​along the x and y directions involves extracting specific frequency information from the frequency domain representation of the Fourier transform. Here, it is assumed that u represents the frequency in the x direction and v represents the frequency in the y direction.

[0181] For an M×N input signal, the frequency can be calculated as follows:

[0182] 1. Calculate the frequency coordinates of U

[0183]

[0184] Here, pixel size along xpixel size along x represents the pixel size in the x direction.

[0185] 2. Calculate the frequency coordinates of V

[0186]

[0187] Here, pixel size along ypixel size along y indicates the pixel size in the y direction.

[0188] In step S901 , surface height data H is obtained, and the power spectrum density matrix C obtained in step S804 and the lateral resolution np and cutoff frequency q0 obtained in step S805 are input.

[0189] In step S902, variables are initialized, and the power spectrum density is angularly averaged considering the surface isotropy. The specific steps are as follows:

[0190] 1. Angle average of frequency C(u,v)

[0191]

[0192] Here, θ is an angle representing the components of C in different directions.

[0193] 2. Iteration in the calculation process

[0194] For each frequency bin C(u,v), Cbar(u,v) is calculated in a loop over mx and my that traverses the entire two-dimensional range of the signal.

[0195] 3. Calculation of frequency component amplitude m'

[0196] m′ is defined as the amplitude of the frequency component in the frequency domain, that is,

[0197]

[0198] 4. Condition judgment and storage

[0199] In the conditional judgment, if m' is close to the predetermined value i, the value of Cbar(u,v) is stored in the matrix Cs. This matrix is ​​used to store the components of Cbar in different directions.

[0200] 5. Final summation result

[0201] Sum the non-zero elements in Cs to get Cm(u,v). This value represents the angular average power spectral density of C on the frequency components u and v.

[0202] 6. Calculate Cm(u,v).i and Cm(u,v).i3; calculate the product of the average power spectral density of these angles and store them in Cm_i and Cm_i_3 respectively. Get the cutoff wavelength entered by the user; in step S903, the Pythagorean theorem of the right triangle is used. For each pair of horizontal and vertical coordinates (mx,my), their corresponding frequency m' in the frequency space is calculated. In step S904, filter out the items related to the specified frequency i and perform matrix filling; in step S905, set an arithmetic progression from 1 to N / 2, which contains a series of frequency values ​​from the minimum frequency to the maximum frequency, for subsequent power spectral density analysis. In step S1001, prepare the logarithm-log data of the frequency qL and the power spectral density Cm in the format required for fitting; in step S1002, define the fitting model as the linear model a·x+b. In step S1003, set the options for nonlinear least squares fitting, including the setting of the starting point. In step S1004, the slope and intercept of the fitting result are extracted, and the fitting curve is calculated; in step S1005, the fractal dimension is calculated according to the formula D=(8+slope) / 2.

[0203] To achieve the above object, the present invention also provides a multi-scale characterization three-dimensional morphology construction system suitable for coarse-grained soil, the system is applied to the multi-scale characterization three-dimensional morphology construction method, such as Figure 15 As shown, the system includes:

[0204] A first data creation unit is configured to acquire three-dimensional model coordinate data corresponding to the coarse-grained soil in real time and create a first point cloud matrix corresponding to the three-dimensional model coordinate data; wherein the first point cloud matrix is ​​a point cloud set including coordinate data of all vertices on the surface of the three-dimensional model;

[0205] a first data processing and generating unit, configured to analyze and process principal components corresponding to the first point cloud matrix, and generate first characterization data corresponding to the coarse-grained soil; wherein the first characterization data is shape characterization data of the coarse-grained soil;

[0206] a first data generating unit, configured to generate point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and to generate second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data; wherein the second characterization data is roundness characterization data of the coarse-grained soil;

[0207] A second data processing and generating unit is configured to segment and process triangular faces of a three-dimensional model corresponding to the coarse-grained soil, and construct a second point cloud matrix corresponding to the triangular faces; and generate third representation data corresponding to the coarse-grained soil based on data in the second point cloud matrix; wherein the second point cloud matrix is ​​a vertex index matrix of the triangular faces of the three-dimensional model; and the third representation data is surface texture parameter data of the coarse-grained soil;

[0208] The second data creation unit is used to combine the first characterization data, the second characterization data and the third characterization data to construct a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time.

[0209] The system further includes: a visualization display module for visually displaying a multi-scale three-dimensional morphology corresponding to the coarse-grained soil;

[0210] And / or, the first data creation unit further includes: a first data construction module, configured to construct a three-dimensional model corresponding to the coarse-grained soil, and establish a correspondence between vertex coordinates of the three-dimensional model and triangle node indexes; wherein the triangle is a surface triangle corresponding to the three-dimensional model and composed of vertices;

[0211] And / or, the first data processing and generating unit further includes: a first data generating module, configured to generate first ratio data corresponding to the coarse-grained soil based on the principal component data corresponding to the first point cloud matrix; a second data generating module, configured to acquire the first ratio data in real time and generate first characterization data corresponding to the coarse-grained soil based on the first ratio data; wherein the first ratio data is the principal axis length and the aspect ratio of the coarse-grained soil;

[0212] And / or, the first data generation unit further includes: a third data generation module for generating, in real time, vector data from triangle vertices to the point cloud centroid of the coarse-grained soil three-dimensional model based on the point cloud centroid data; a second data construction module for creating, based on the data in the first point cloud matrix, at least one three-dimensional grid corresponding to the first point cloud matrix;

[0213] And / or, the first data generation unit further includes: a fourth data generation module, configured to obtain a three-dimensional grid corresponding to the first point cloud matrix, cyclically traverse and process each internal grid point, and generate distance data from the grid point to the triangular grid surface; a first determination module, configured to determine, in real time based on the distance data, the actual positional relationship between the grid point and the grid surface;

[0214] And / or, the second data processing and generating unit further includes: establishing a marking processing module for establishing a correspondence between triangular faces of the coarse-grained soil three-dimensional model and tetrahedrons, and marking and processing the tetrahedrons; and a clearing and generating module for obtaining original surface data corresponding to the tetrahedrons, clearing and processing unnecessary variable data corresponding to the tetrahedrons, and generating and obtaining manifold surface data corresponding to the tetrahedrons;

[0215] And / or, the second data processing and generation unit also includes: creating a segmentation processing module, which is used to create a fitting surface corresponding to the edge data of the point cloud of the coarse-grained soil three-dimensional model, and segmenting and processing the fitting surface; a fifth data generation module, which is used to generate fractal dimension data corresponding to the coarse-grained soil in real time based on the fitting surface after segmentation processing, in combination with the discretized grid data corresponding to the fitting surface, and the inscribed polygon data corresponding to the segmented surface.

[0216] In the system solution embodiment of the present invention, the method steps involved in the multi-scale characterization three-dimensional morphological construction suitable for coarse-grained soil have been described above in detail. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, which will not be repeated here.

[0217] To achieve the above objectives, the present invention also provides a multi-scale characterization three-dimensional morphology construction platform suitable for coarse-grained soil, such as Figure 16 As shown, it includes a processor, a memory, and a control program for a multi-scale characterization 3D morphology construction platform suitable for coarse-grained soil; wherein, the processor executes the control program for the multi-scale characterization 3D morphology construction platform suitable for coarse-grained soil, the control program for the multi-scale characterization 3D morphology construction platform suitable for coarse-grained soil is stored in the memory, and the control program for the multi-scale characterization 3D morphology construction platform suitable for coarse-grained soil implements the steps of the multi-scale characterization 3D morphology construction method suitable for coarse-grained soil. For example:

[0218] S01. Acquire three-dimensional model coordinate data corresponding to coarse-grained soil in real time, and create a first point cloud matrix corresponding to the three-dimensional model coordinate data; wherein the first point cloud matrix is ​​a point cloud set including coordinate data of all vertices on the surface of the three-dimensional model;

[0219] S02. Analyze and process the principal components corresponding to the first point cloud matrix, and generate first characterization data corresponding to the coarse-grained soil; wherein the first characterization data is shape characterization data of the coarse-grained soil;

[0220] S03. Generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data; wherein the second characterization data is roundness characterization data of the coarse-grained soil;

[0221] S04. Segmenting and processing the triangular faces of the three-dimensional model corresponding to the coarse-grained soil, and constructing a second point cloud matrix corresponding to the triangular faces; generating third representation data corresponding to the coarse-grained soil based on the data in the second point cloud matrix; wherein the second point cloud matrix is ​​a vertex index matrix of the triangular faces of the three-dimensional model; and the third representation data is surface texture parameter data of the coarse-grained soil;

[0222] S05. Combining the first characterization data, the second characterization data, and the third characterization data, constructing a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time.

[0223] The method further comprises: acquiring three-dimensional model coordinate data corresponding to the coarse-grained soil in real time, and creating a first point cloud matrix corresponding to the three-dimensional model coordinate data;

[0224] S011. Construct a three-dimensional model corresponding to coarse-grained soil, and establish a corresponding relationship between the vertex coordinates of the three-dimensional model and the triangle node index; wherein the triangle is a surface triangle corresponding to the three-dimensional model and composed of vertices.

[0225] The specific details of the steps have been explained above and will not be repeated here.

[0226] In an embodiment of the present invention, the built-in processor of the multi-scale characterization and three-dimensional morphology construction platform for coarse-grained soil can be composed of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor utilizes various interfaces and circuits to connect various components, and executes or executes programs or units stored in memory, as well as calls data stored in memory, to perform various functions and process data for the multi-scale characterization and three-dimensional morphology construction platform for coarse-grained soil.

[0227] The memory is used to store program codes and various data. It is installed in a multi-scale characterization three-dimensional morphology construction platform suitable for coarse-grained soil, and realizes high-speed and automatic access to programs or data during operation.

[0228] The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electronically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0229] The present invention uses a method to obtain three-dimensional model coordinate data corresponding to coarse-grained soil in real time, and create a first point cloud matrix corresponding to the three-dimensional model coordinate data; wherein the first point cloud matrix is ​​a point cloud set containing coordinate data of all vertices on the surface of the three-dimensional model; analyze and process the principal components corresponding to the first point cloud matrix, and generate first characterization data corresponding to the coarse-grained soil; wherein the first characterization data is shape characterization data of the coarse-grained soil; based on the data in the first point cloud matrix, generate point cloud centroid data corresponding to the first point cloud matrix, and based on the point cloud centroid data, generate second characterization data corresponding to the coarse-grained soil; wherein the second characterization data is roundness characterization data of the coarse-grained soil; The triangular faces of the three-dimensional model corresponding to the coarse-grained soil are subdivided and processed, and a second point cloud matrix corresponding to the triangular faces is constructed; based on the data in the second point cloud matrix, third characterization data corresponding to the coarse-grained soil are generated; wherein, the second point cloud matrix is ​​the vertex index matrix of the triangular faces of the three-dimensional model; the third characterization data is the surface texture parameter data of the coarse-grained soil; combining the first characterization data, the second characterization data and the third characterization data, a multi-scale three-dimensional morphology corresponding to the coarse-grained soil is constructed in real time; and the system and platform corresponding to the method can improve the accuracy of the characterization of the particle morphology, and are applicable to soil particles of any shape, and can quickly and intuitively obtain multi-scale characterization data of soil particles.

[0230] In other words, the current methods for obtaining single-scale and two-scale particle morphological characterization parameters are upgraded to methods for obtaining three-scale characterization parameters, and using the method of the present invention, multi-scale morphological characterization parameters of coarse-grained soil can be intuitively obtained.

[0231] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for constructing a multi-scale three-dimensional morphology suitable for coarse-grained soil, characterized by: The method comprises the steps of: Acquiring three-dimensional model coordinate data corresponding to the coarse-grained soil in real time, and creating a first point cloud matrix corresponding to the three-dimensional model coordinate data; wherein the first point cloud matrix is ​​a point cloud set including coordinate data of all vertices on the surface of the three-dimensional model; Analyzing and processing the principal components corresponding to the first point cloud matrix, and generating first characterization data corresponding to the coarse-grained soil; wherein the first characterization data is shape characterization data of the coarse-grained soil; generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data; wherein the second characterization data is roundness characterization data of the coarse-grained soil; The method further comprises the steps of: subdividing the triangular faces of the three-dimensional model corresponding to the coarse-grained soil, and constructing a second point cloud matrix corresponding to the triangular faces; generating third characterization data corresponding to the coarse-grained soil based on the data in the second point cloud matrix; wherein the second point cloud matrix is ​​a vertex index matrix of the triangular faces of the three-dimensional model; and the third characterization data is surface texture parameter data of the coarse-grained soil; and further comprising the steps of: establishing a correspondence between the triangular faces of the three-dimensional model of the coarse-grained soil and a tetrahedron, and marking the tetrahedron; obtaining original surface data corresponding to the tetrahedron, clearing unnecessary variable data corresponding to the tetrahedron, and generating manifold surface data corresponding to the tetrahedron; The first characterization data, the second characterization data, and the third characterization data are combined to construct a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time.

2. A multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil according to claim 1, characterized in that: The method further comprises: acquiring three-dimensional model coordinate data corresponding to the coarse-grained soil in real time, and creating a first point cloud matrix corresponding to the three-dimensional model coordinate data; A three-dimensional model corresponding to the coarse-grained soil is constructed, and a correspondence between the vertex coordinates of the three-dimensional model and the triangle node index is established; wherein the triangle is a surface triangle corresponding to the three-dimensional model and composed of vertices.

3. The method for constructing a multi-scale three-dimensional morphology of coarse-grained soil according to claim 1, characterized in that: The analyzing and processing the principal components corresponding to the first point cloud matrix and generating first characterization data corresponding to the coarse-grained soil further includes: generating first ratio data corresponding to the coarse-grained soil based on the principal component data corresponding to the first point cloud matrix; The first ratio data is acquired in real time, and first characterization data corresponding to the coarse-grained soil is generated according to the first ratio data; wherein the first ratio data is the principal axis length and the aspect ratio of the coarse-grained soil.

4. The method for constructing a multi-scale three-dimensional morphology of coarse-grained soil according to claim 1, characterized in that: The method further comprises: generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data. Generating vector data from the triangle vertices of the coarse-grained soil three-dimensional model to the point cloud centroid in real time based on the point cloud centroid data; At least one three-dimensional grid corresponding to the first point cloud matrix is ​​created based on the data in the first point cloud matrix.

5. A method for constructing a multi-scale three-dimensional morphology suitable for coarse-grained soil according to claim 1 or 4, characterized in that: The method further comprises: generating point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and generating second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data. Obtaining a three-dimensional grid corresponding to the first point cloud matrix, looping through and processing each internal grid point, and generating distance data from the grid point to the triangular grid surface; The actual positional relationship between the grid point and the grid surface is determined in real time based on the distance data.

6. The method for constructing a multi-scale three-dimensional morphology of coarse-grained soil according to claim 1, characterized in that: The triangular surface of the three-dimensional model corresponding to the coarse-grained soil is processed and a second point cloud matrix corresponding to the triangular surface is constructed; Generating third characterization data corresponding to the coarse-grained soil according to the data in the second point cloud matrix further includes: Creating a fitting surface corresponding to the edge data of the point cloud of the coarse-grained soil three-dimensional model, and segmenting and processing the fitting surface; Based on the fitted surface after segmentation, combined with the discretized grid data corresponding to the fitted surface and the inscribed polygon data corresponding to the segmented surface, fractal dimension data corresponding to the coarse-grained soil is generated in real time; After combining the first characterization data, the second characterization data, and the third characterization data to construct a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time, the method further includes: Visualize the multi-scale three-dimensional morphology corresponding to coarse-grained soils.

7. A multi-scale characterization three-dimensional morphology construction system suitable for coarse-grained soil, characterized by: The system is applied to the multi-scale representation three-dimensional morphology construction method according to any one of claims 1 to 6, and the system comprises: A first data creation unit is configured to acquire three-dimensional model coordinate data corresponding to the coarse-grained soil in real time and create a first point cloud matrix corresponding to the three-dimensional model coordinate data; wherein the first point cloud matrix is ​​a point cloud set including coordinate data of all vertices on the surface of the three-dimensional model; a first data processing and generating unit, configured to analyze and process principal components corresponding to the first point cloud matrix, and generate first characterization data corresponding to the coarse-grained soil; wherein the first characterization data is shape characterization data of the coarse-grained soil; a first data generating unit, configured to generate point cloud centroid data corresponding to the first point cloud matrix based on the data in the first point cloud matrix, and to generate second characterization data corresponding to the coarse-grained soil based on the point cloud centroid data; wherein the second characterization data is roundness characterization data of the coarse-grained soil; A second data processing and generating unit is configured to segment and process triangular faces of a three-dimensional model corresponding to the coarse-grained soil, and construct a second point cloud matrix corresponding to the triangular faces; and generate third representation data corresponding to the coarse-grained soil based on data in the second point cloud matrix; wherein the second point cloud matrix is ​​a vertex index matrix of the triangular faces of the three-dimensional model; and the third representation data is surface texture parameter data of the coarse-grained soil; The second data creation unit is used to combine the first characterization data, the second characterization data and the third characterization data to construct a multi-scale three-dimensional morphology corresponding to the coarse-grained soil in real time.

8. The multi-scale characterization three-dimensional morphology construction system suitable for coarse-grained soil according to claim 7, characterized in that: The system further comprises: A visualization module for visualizing the multi-scale three-dimensional morphology corresponding to coarse-grained soil; And / or, the first data creation unit further includes: A first data construction module is used to construct a three-dimensional model corresponding to the coarse-grained soil and establish a correspondence between vertex coordinates of the three-dimensional model and triangle node indexes; wherein the triangle is a surface triangle corresponding to the three-dimensional model and composed of vertices; And / or, the first data processing and generating unit further includes: a first data generating module, configured to generate first ratio data corresponding to the coarse-grained soil based on the principal component data corresponding to the first point cloud matrix; a second data generating module, configured to acquire the first ratio data in real time and generate first characterization data corresponding to the coarse-grained soil based on the first ratio data; wherein the first ratio data is the principal axis length and the aspect ratio of the coarse-grained soil; And / or, the first data generating unit further includes: A third data generation module is used to generate vector data from the triangle vertices of the coarse-grained soil three-dimensional model to the point cloud centroid in real time based on the point cloud centroid data; a second data construction module, configured to create at least one three-dimensional grid corresponding to the first point cloud matrix based on the data in the first point cloud matrix; And / or, the first data generating unit further includes: a fourth data generation module, configured to obtain a three-dimensional mesh corresponding to the first point cloud matrix, cyclically process each internal mesh point, and generate distance data from the mesh point to the triangular mesh surface; A first determination module is used to determine the actual position relationship between the grid point and the grid surface in real time based on the distance data; And / or, the second data processing and generating unit further includes: Establishing a marking processing module for establishing a correspondence between triangular faces and tetrahedrons of a three-dimensional model of coarse-grained soil, and marking and processing the tetrahedrons; a clearing and processing generating module, configured to obtain original surface data corresponding to the tetrahedron, clear unnecessary variable data corresponding to the tetrahedron, and generate manifold surface data corresponding to the tetrahedron; And / or, the second data processing and generating unit further includes: Creating a segmentation processing module for creating a fitting surface corresponding to the edge data of the point cloud of the coarse-grained soil three-dimensional model, and segmenting and processing the fitting surface; The fifth data generation module is used to generate fractal dimension data corresponding to coarse-grained soil in real time based on the fitting surface after segmentation processing, combined with the discretized grid data corresponding to the fitting surface, and the inscribed polygon data corresponding to the segmented surface.

9. A multi-scale characterization three-dimensional morphology construction platform suitable for coarse-grained soil, characterized by: It includes a processor, a memory, and a multi-scale characterization three-dimensional morphology construction platform control program suitable for coarse-grained soil; wherein, the multi-scale characterization three-dimensional morphology construction platform control program suitable for coarse-grained soil is executed by the processor, and the multi-scale characterization three-dimensional morphology construction platform control program suitable for coarse-grained soil is stored in the memory, and the multi-scale characterization three-dimensional morphology construction platform control program suitable for coarse-grained soil implements the multi-scale characterization three-dimensional morphology construction method suitable for coarse-grained soil as described in any one of claims 1 to 6.

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