A three-dimensional terrain geological modeling method and device based on a material point method simulation

Abstract

The application provides a three-dimensional terrain geological modeling method and device based on material point method simulation, and relates to the technical field of geotechnical engineering numerical simulation, and comprises the following steps: S1, extracting elevation data of a three-dimensional terrain geological region model; S2, converting the elevation data into three different type model component data and processing; S3, based on the generated model component data, discretizing the model into material points to generate a material point model; S4, importing the generated material point model into a material point method calculation program to perform material point method simulation, and obtaining large deformation of the geology according to the material point method simulation; the application effectively solves the problem of large deformation simulation modeling caused by complex and diverse terrain and landform, is efficient, stable, simple and feasible, significantly improves the modeling efficiency of the material point method and the quality of the generated material point model, and effectively promotes the application of the material point method simulation in the field of geotechnological engineering numerical simulation.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology in geotechnical engineering, and in particular to a three-dimensional terrain and geological modeling method and apparatus based on the material point method. Background Technology

[0002] Large deformation problems are of great significance in geotechnical engineering analysis, such as in the simulation and analysis of slope collapse, foundation settlement, landslides, debris flows, and soil-structure interactions. However, the finite element method (FEM), a commonly used method in geotechnical engineering design and analysis, is not suitable for large deformation problems. With the development of large deformation processes, the Lagrangian finite element method may suffer from severe mesh distortion, leading to reduced computational accuracy. The Eulerian finite element model is unsuitable for free surface problems and cannot capture history-related behavior. The Material Point Method (MPM) combines the advantages of the Lagrangian and Eulerian methods, discretizing the computational domain into a series of Lagrangian material points moving on an Eulerian background mesh during simulation. Each material point represents a small material region and carries all state variables. Therefore, MPM effectively solves the main limitations of the finite element model related to mesh deformation and is considered suitable for simulating and analyzing large deformation problems.

[0003] When using MPM (Metallurgical Modeling) for numerical simulation of large deformation problems, considering complex terrain and geological bodies is crucial. Furthermore, simulating large deformation problems often requires constructing engineering-scale models with a large number of particles, placing higher demands on the performance of numerical modeling methods. However, using two-dimensional MPM models or overly simplified three-dimensional models often fails to accurately capture these complex factors, thus affecting the model's accuracy and the analysis of large deformation behavior of geological materials in the real world. Therefore, building high-precision numerical models, especially when considering complex geological conditions and artificial structures, presents a significant challenge. In existing MPM discretization methods, some proposed modeling approaches only allow for simplified consideration of specific or single geological regions, such as slopes and geological structures. However, due to the complex and diverse terrain and landforms encountered in actual geological engineering, a universally applicable and generalized MPM model discretization method is still lacking to solve the large deformation simulation modeling problem. Summary of the Invention

[0004] In view of the above-mentioned technical problems in related technologies, the present invention proposes a three-dimensional terrain and geological modeling method and apparatus based on material point simulation.

[0005] In a first aspect, the present invention provides a three-dimensional terrain geological modeling method based on the material point method, comprising the following steps:

[0006] S1. Extract elevation data from the three-dimensional terrain and geological region model; the elevation data includes first elevation data, second elevation data, and third elevation data;

[0007] S2. The elevation data is converted into three different types of model component data and processed; the model components include surface component F, stratigraphic component H, and local area structure component O.

[0008] The extent of grid G ​​is determined based on surface component F. Two-dimensional linear interpolation is performed on the data point set of surface component F to obtain the surface elevation and first elevation array AZ corresponding to the grid point coordinates of grid G. Then, two-dimensional linear interpolation is performed on the data point set of stratigraphic component H to obtain the stratigraphic elevation and first stratigraphic list AH of grid G. The index value of the first material array AM is updated by the distance between the first stratigraphic list AH and the interpolated elevation in the first elevation array AZ. Then, the distance from the data point of local area structural component O to the grid point of grid G ​​is calculated to form a list of tuples containing the minimum distance, data point elevation, and grid point index. The first structure list is generated by sorting by elevation. The first structure list is filtered by number and sorted by elevation to form a second structure list. Finally, the value of the first material array AM is updated based on the second structure list.

[0009] S3. Based on the generated model component data, discretize the model into material points to generate a material point model;

[0010] S4. Import the generated material point model into the material point method calculation program to perform material point method simulation, and obtain the large deformation of the geology based on the material point method simulation.

[0011] Specifically, step S2 includes:

[0012] S21. The data point set of the elevation data is numbered and stored sequentially. The first elevation data is stored in the surface component F and numbered 0. The second and third elevation data are numbered sequentially from 1 and stored in the stratigraphic component H and the local area structure component O, respectively.

[0013] S22. Determine the planar range of the rectangular planar regular grid G ​​based on the range of the two-dimensional coordinates of the data points of the surface component F;

[0014] S23. Determine the number of grid points in grid G ​​based on the grid point distance r, store the planar coordinates of the grid points based on the grid point index of grid G, and initialize the empty first elevation array AZ and the first material array AM.

[0015] S24. Perform two-dimensional linear interpolation on the data point set stored in the surface component F to obtain the interpolated surface elevation corresponding to the grid point coordinates of the grid G; calculate the elevation array of the grid points and store it in the first elevation array AZ; then initialize the length of the first material array AM to the length of the first elevation array AZ and set all values ​​to 0.

[0016] S25. Perform two-dimensional linear interpolation on the data point set stored in the stratigraphic component H to obtain the interpolated stratigraphic elevation corresponding to the grid point coordinates of the grid G. Store the interpolated stratigraphic elevation and the data point set number as tuple elements. Sort the data points according to the interpolated stratigraphic elevation from largest to smallest to form the first stratigraphic list AH.

[0017] S26. Traverse from largest to smallest to calculate the one-dimensional distance between the interpolated elevation in the first stratigraphic list AH and the interpolated elevation in the first elevation array AZ, and obtain the minimum elevation index list in the first elevation array AZ corresponding to the minimum one-dimensional distance; traverse the minimum elevation index list, and make the index value in the first material array AM less than the value of the traversed minimum elevation index, which is the number of the data point set.

[0018] S27. Calculate the planar coordinates of data points in the data point set stored in the local region structure component O to the planar distances to all grid points in the grid G. Save the data point set number, data point elevation, and grid point index of the calculated minimum distance from the data point to the grid point as tuples to form a first storage list. Traverse the first storage list according to the grid point index, extract the data point set number and data point elevation of the elements in the tuple with the same grid point index to form a structure list of the grid point index, and sort them in descending order of data point elevation to obtain the first structure list.

[0019] S28. Based on the data point set number The first structure list is traversed, and tuples with the same data point set numbers as those traversed are extracted and sorted by data point elevation to form a new second structure list; the index of the first elevation array AZ within the elevation range of the second structure list is calculated, and the element values ​​with the same index in the first material array AM are set as... .

[0020] Specifically, in step S24, the elevation array of the grid points is calculated according to formula (1) and stored in the first elevation array AZ. Formula (1) is shown below:

[0021] (1),

[0022] In the formula, The index in the first elevation array is Elevation value; The minimum elevation value of the preset model; It is an interpolated surface elevation; express The floor value.

[0023] Specifically, the first stratigraphic list AH in step S25 is shown in formula (4):

[0024] (4),

[0025] In the formula, This indicates that the first stratigraphic list AH is sorted as follows: i Interpolated stratigraphic elevation values, Indicates the sorting order is number 1. i Data point set number, n The number of data point sets stored for formation component H. Indicates the sorting order is number 1. i The elements of a tuple.

[0026] Specifically, the list of the first structures in step S27 is shown in formula (5):

[0027] (5),

[0028] In the formula, This indicates that the first structure list is sorted as number 1. a Data point elevation values, Indicates the sorting order is number 1. a Data point set number, s The number of data point sets stored for local region structure component O. Indicates the sorting order is number 1. a The elements of a tuple.

[0029] Specifically, step S28 is as follows:

[0030] from arrive Traverse the first structure list, extract the tuples with the same data point set number as the traversed data point, and sort them according to the data point elevation to form a new second structure list as shown in formula (6):

[0031] (6),

[0032] In the formula Indicates that the data point set number is a Sort by j Data point elevation values; Indicates that the data point set number is a； b is the data point set. a The number of data points assigned to this grid point; Indicates the sorting order is number 1. j tuple elements;

[0033] Calculate the elevation range of the first elevation array AZ in the second structure list. The index within the first material array AM is set to the element value with the same index. .

[0034] Specifically, step S1 includes the following steps:

[0035] S11. Extract surface data, stratigraphic surface data, and local structural surface data from the three-dimensional terrain and geological regional model using preprocessing software;

[0036] S12. Convert the surface data into a data point set as the first elevation data, convert the stratum surface data into a data point set as the second elevation data, and convert the local area structural surface data into a data point set as the third elevation data; the data point set includes two-dimensional coordinates and elevation values.

[0037] Specifically, step S3 is as follows:

[0038] Traverse the grid points of grid G, and denote the planar coordinates of the grid points as... The plane coordinates of the material points near this lattice point are respectively , , , Generate the elevation coordinates of the material points near the grid point as the elevation values ​​in the first elevation array AZ of the grid point index, and generate the material numbers of the material points near the grid point as the element values ​​in the first material array AM of the grid point index; output the generated material point coordinates and material numbers to generate the material point model.

[0039] Specifically, step S4 is as follows:

[0040] S41. Set the solution conditions for the material point method calculation program, including calculation time, solution format, output settings and boundary conditions;

[0041] Secondly, the present invention provides a three-dimensional terrain and geological modeling device based on the material point method simulation, and a three-dimensional terrain and geological modeling method based on the material point method simulation described in the first aspect above, comprising the following units:

[0042] An elevation data extraction unit is used to extract elevation data from a three-dimensional terrain and geological region model; the elevation data includes first elevation data, second elevation data, and third elevation data.

[0043] The model component data generation unit is used to convert the elevation data into three different types of model component data and process them; the model components include surface component F, stratigraphic component H, and local area structure component O.

[0044] The extent of grid G ​​is determined based on surface component F. Two-dimensional linear interpolation is performed on the data point set of surface component F to obtain the surface elevation and first elevation array AZ corresponding to the grid point coordinates of grid G. Then, two-dimensional linear interpolation is performed on the data point set of stratigraphic component H to obtain the stratigraphic elevation and first stratigraphic list AH of grid G. The index value of the first material array AM is updated by the distance between the first stratigraphic list AH and the interpolated elevation in the first elevation array AZ. Then, the distance from the data point of local area structural component O to the grid point of grid G ​​is calculated to form a list of tuples containing the minimum distance, data point elevation, and grid point index. The first structure list is generated by sorting by elevation. The first structure list is filtered by number and sorted by elevation to form a second structure list. Finally, the value of the first material array AM is updated based on the second structure list.

[0045] The material point model generation unit is used to discretize the model into material points based on the generated model component data, and generate a material point model.

[0046] The material point method simulation unit is used to import the generated material point model into the material point method calculation program for material point method simulation, and to obtain the large deformation of the geology based on the material point method simulation.

[0047] Thirdly, the present invention also provides an electronic device including a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method described in any one of the first aspects.

[0048] This invention provides a three-dimensional terrain and geological modeling method and apparatus based on the material point method simulation. It is designed for three-dimensional material point method modeling of complex and realistic three-dimensional terrain and geological regions in the field of geotechnical engineering. This solves the problem of large deformation simulation modeling caused by the complex and diverse terrain and landforms encountered in actual geological engineering. This invention is efficient, stable, simple, and feasible, significantly improving the modeling efficiency and quality of the generated material point model. It effectively promotes the application of material point method simulation in the field of numerical simulation in geotechnical engineering, thus providing a numerical simulation analysis tool for the analysis and research of geotechnical engineering problems involving complex and realistic three-dimensional terrain and geological regions. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1A schematic diagram of a three-dimensional terrain and geological modeling method based on the material point method provided in an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of the imported surface.stl file provided in an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of the imported tunnel.stl file provided in an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of the imported slope.stl file provided in an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of the visualization result of the generated material point model provided in the embodiment of the present invention;

[0055] Figure 6 This is a schematic diagram of the velocity cloud map simulated by the generated material point model provided in an embodiment of the present invention;

[0056] Figure 7 A schematic diagram of a three-dimensional terrain and geological modeling device based on the material point method is provided for an embodiment of the present invention;

[0057] Figure 8 This is a schematic diagram of a three-dimensional terrain and geological modeling device based on the material point method, provided as an embodiment of the present invention. Detailed Implementation

[0058] The present invention will be explained in detail through the following embodiments. The purpose of this invention is to protect all technical improvements within its scope. In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0059] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0060] Example 1

[0061] refer to Figure 1 This embodiment provides a widely applicable, simple, convenient, efficient, stable, and easy-to-master three-dimensional terrain and geological modeling method based on the material point method, including the following steps:

[0062] S1. Extract elevation data from the three-dimensional terrain and geological region model; the elevation data includes first elevation data, second elevation data, and third elevation data;

[0063] The preprocessing software includes CAD, MATLAB, METASHAP, and the Python preprocessing open-source library Gmsh; in this example, the Python preprocessing open-source library Gmsh is preferably used as the preprocessing software.

[0064] S11. Extract surface data, stratigraphic surface data, and local structural surface data from the three-dimensional terrain and geological regional model using preprocessing software;

[0065] Specifically, a three-dimensional terrain and geological region model (a three-dimensional tunnel entrance region model) was built using the Python open-source library Gmsh, including tunnel structures and landslides. Surface data, stratum surface data (surface data of tunnel structures) and local area structural surface data (surface data of landslides) were extracted and exported as separate data files surface.stl, tunnel.stl and slope.stl, respectively.

[0066] Gmsh is an open-source 3D finite element mesh generator with a graphical user interface and script control capabilities. Gmsh not only generates meshes but also provides geometric modeling and post-processing capabilities. In Python, Gmsh can be used through its built-in Python API or via the Gmsh Python package, allowing users to control various functions of Gmsh using Python scripts.

[0067] The image after importing surface.stl into the preprocessing software is as follows: Figure 2 As shown, the image after importing tunnel.stl into the preprocessing software is as follows: Figure 3 As shown, the image after importing slope.stl into the preprocessing software is as follows: Figure 4 As shown;

[0068] The three-dimensional terrain and geological region model can be an existing model file or created through preprocessing software; the three-dimensional terrain and geological region model includes tunnel structures and landslides.

[0069] In other possible implementations, preprocessing software can be used to extract surface data of the surface, strata, and local regional structure of the three-dimensional terrain and geological region model from the three-dimensional point cloud or digital elevation image source, and export the corresponding independent data files.

[0070] S12. Convert the surface data into a data point set as the first elevation data, convert the stratum surface data into a data point set as the second elevation data, and convert the local area structural surface data into a data point set as the third elevation data; the data point set includes two-dimensional coordinates and elevation values.

[0071] The Python script reads the surface.stl, tunnel.stl, and slope.stl files respectively and converts them into independent data point sets. Each data point in the data point set contains two-dimensional coordinates and an elevation value. The two-dimensional coordinates (x, y) represent the position of the data point, and the elevation value z represents the height of the data point. Specifically, the data point set corresponding to surface.stl is used as the first elevation data, the data point set corresponding to tunnel.stl is used as the second elevation data, and the data point set corresponding to slope.stl is used as the third elevation data.

[0072] S2. The elevation data is converted into three different types of model component data and processed; the model components include surface component F, stratigraphic component H, and local area structure component O.

[0073] The extent of grid G ​​is determined based on surface component F. Two-dimensional linear interpolation is performed on the data point set of surface component F to obtain the surface elevation and first elevation array AZ corresponding to the grid point coordinates of grid G. Then, two-dimensional linear interpolation is performed on the data point set of stratigraphic component H to obtain the stratigraphic elevation and first stratigraphic list AH of grid G. The index value of the first material array AM is updated by the distance between the first stratigraphic list AH and the interpolated elevation in the first elevation array AZ. Then, the distance from the data point of local area structural component O to the grid point of grid G ​​is calculated to form a list of tuples containing the minimum distance, data point elevation, and grid point index. The first structure list is generated by sorting by elevation. The first structure list is filtered by number and sorted by elevation to form a second structure list. Finally, the value of the first material array AM is updated based on the second structure list.

[0074] The three model components are surface component F, stratum component H, and local area structure component O. The three model components are of different types, and different model components represent different areas in the model. Among them, surface component F represents the topography and surface of the model, stratum component H represents the stratum interface of the model, and local area structure component O represents the naturally formed or artificially constructed local area structure such as slopes, caves, and tunnels in the model.

[0075] S21. The data point set of the elevation data is numbered and stored sequentially. The first elevation data is stored in the surface component F and numbered 0. The second and third elevation data are numbered sequentially from 1 and stored in the stratigraphic component H and the local area structure component O, respectively.

[0076] Set the first elevation data converted from surface.stl as surface component F, numbered 0; set the second elevation data converted from tunnel.stl as stratum component H, numbered 1; and set the third elevation data converted from slope.stl as local area structure component O, numbered 2.

[0077] In another possible implementation, the data point sets stored in the formation component H and the local regional structure component O can be multiple. First, the datasets in the formation component H are numbered, and then the datasets in the local regional structure component O are numbered based on the datasets in the formation component H. Alternatively, the datasets in the local regional structure component O are numbered first, and then the datasets in the formation component H are numbered based on the datasets in the local regional structure component O, ensuring that the number of each data point is unique.

[0078] For example, a stratigraphic component H may contain datasets numbered 1 and 2, and a local regional structural component O may contain datasets numbered 3 and 4.

[0079] S22. Determine the planar range of the rectangular planar regular grid G ​​based on the range of the two-dimensional coordinates of the data points of the surface component F;

[0080] Specifically, all data points stored in the surface component F are traversed to find the maximum and minimum values ​​of the two-dimensional coordinates of the data points. Specifically, the maximum value of coordinate x is maxX, the minimum value of coordinate x is minX, the maximum value of coordinate y is maxY, and the minimum value of coordinate y is minY. Using minX and minY as the lower-left corner coordinates, and (maxX - minX) and (maxY - minY) as the length and width, the range of the rectangular planar regular grid G ​​is determined. The traversal method and algorithm used are existing technologies and will not be elaborated upon here.

[0081] S23. Determine the number of grid points in grid G ​​based on the grid point distance r, store the planar coordinates of the grid points based on the grid point index of grid G, and initialize the empty first elevation array AZ and the first material array AM.

[0082] Calculate the number of grid points G on the x-axis and y-axis, which are (maxX - minX) / r and (maxY - minY) / r, respectively, where r is the grid point distance. Traverse each grid point of G, calculate its planar coordinates, and store it in the corresponding array according to the grid point index of each grid point; the index starts counting from 1; the value of the grid point distance r is determined according to actual needs, and the value of r in this embodiment is 3m.

[0083] Then initialize an empty one-dimensional first elevation array AZ and an empty one-dimensional first material array AM for subsequent storage of grid point elevation and material information.

[0084] S24. Perform two-dimensional linear interpolation on the data point set stored in the surface component F to obtain the interpolated surface elevation corresponding to the grid point coordinates of the grid G; calculate the elevation array of the grid points according to formula (1) and store it in the first elevation array AZ; then initialize the length of the first material array AM to the length of the first elevation array AZ and set all values ​​to 0.

[0085] Formula (1) is shown below:

[0086] (1),

[0087] In the formula, The index in the first elevation array is Elevation value; The minimum elevation value of the preset model; It is an interpolated surface elevation; express The floor value.

[0088] Specifically, a two-dimensional linear interpolation method is used to perform two-dimensional linear interpolation on the data point set stored in the surface component F. The two-dimensional linear interpolation method is not limited, but preferably, in this embodiment, the two-dimensional linear interpolation method adopts triangular mesh interpolation technology.

[0089] Specifically, firstly, a planar triangulation network of the data point set of surface component F is constructed, which can be done using the existing Delaunay triangulation technique. Then, the grid interpolation elevation is calculated using a two-dimensional linear interpolation method.

[0090] Assume a triangular mesh with any three vertices A, B, and C, and the grid node P contained within it is the interpolation point. The matrix form of the triangular mesh linear interpolation formula that can be used to calculate the elevation value of point P is shown in formulas (2) and (3):

[0091] (2),

[0092] (3),

[0093] in , , , Let P be the elevation of grid point P, vertex A, vertex B and vertex C; , Let P be the plane coordinates of the grid point. , Let A be the plane coordinates. , Let B be the plane coordinates. , Let C be the plane coordinates. , , These are the weights of points A, B, and C, respectively.

[0094] In this embodiment, the values ​​of the preset model minimum elevation and the preset interpolated surface elevation are selected according to actual needs; the preset model minimum elevation in this embodiment... Interpolation of surface elevation Determining the grid interpolation elevation of the data point set for surface component F;

[0095] For example, a grid point X of grid G hour, Take 4. At this point, array AZ = {0.75, 2.25, 3.75, 5.25}, and array AM = {0,0,0,0}.

[0096] S25. Perform two-dimensional linear interpolation on the data point set stored in the stratigraphic component H to obtain the interpolated stratigraphic elevation corresponding to the grid point coordinates of the grid G. Store the interpolated stratigraphic elevation and the data point set number as tuple elements. Sort the data points according to the interpolated stratigraphic elevation from largest to smallest to form the first stratigraphic list AH, as shown in formula (4):

[0097] (4),

[0098] In the formula, This indicates that the first stratigraphic list AH is sorted as follows: i Interpolated stratigraphic elevation values, Indicates the sorting order as number 1 i Data point set number, n The number of data point sets stored for formation component H. Indicates the sorting order as number 1 i The elements of a tuple.

[0099] Specifically, a two-dimensional linear interpolation method is used to perform two-dimensional linear interpolation on the data point set stored in the formation component H. The two-dimensional linear interpolation method is not limited, but preferably, in this embodiment, the two-dimensional linear interpolation method adopts the triangular mesh linear interpolation technique.

[0100] S26. Traverse from largest to smallest to calculate the one-dimensional distance between the interpolated elevation in the first stratigraphic list AH and the interpolated elevation in the first elevation array AZ, and obtain the minimum elevation index list in the first elevation array AZ corresponding to the minimum one-dimensional distance; traverse the minimum elevation index list, and make the index value in the first material array AM less than the value of the traversed minimum elevation index, which is the number of the data point set.

[0101] Suppose there exists a component H, with data point sets numbered 3 and 4. A grid point X in grid G ​​undergoes the above operations, and the elevation of data point X (data point set number 3) is 1m, and the elevation of data point X (data point set number 4) is 4m. Then, the stratigraphic list forming component H is AH = {(4,4), (1,3)}. Traversing from largest to smallest, the one-dimensional distance between the interpolated elevations in stratigraphic list AH and the elevations in array AZ is calculated as {3.25,1.75,0.25,1.25} and {0.25,1.25,2.75,4.2}. 5}, obtain the minimum elevation index list in array AZ corresponding to the minimum one-dimensional distance as {3,1} (index starts from 1), traverse the first element 3 of the minimum elevation index list, and make the index value in array AM less than the value of the traversed minimum elevation index, which is the number 4 of the data point set. At this time, AM={4,4,4,0}. Traverse the first element 1 of the minimum elevation index list, and make the index value in array AM less than the value of the traversed minimum elevation index, which is the number 3 of the data point set. At this time, AM={3,4,4,0};

[0102] If there is no component H, then the array AM = {0,0,0,0};

[0103] S27. Calculate the planar coordinates of the data points in the data point set stored in the local region structure component O to the planar distances to all grid points in the grid G. Save the data point set number, data point elevation, and grid point index of the data point with the minimum distance to the grid point as a tuple to form the first storage list. Traverse the first storage list according to the grid point index, take out the data point set number and data point elevation of the elements in the tuple with the same grid point index, form the structure list of the grid point index, and sort them in descending order of data point elevation to obtain the first structure list as shown in formula (5):

[0104] (5),

[0105] In the formula, This indicates that the first structure list is sorted as number 1. a Data point elevation values, Indicates the sorting order as number 1 a Data point set number, s The number of data point sets stored for local region structure component O. Indicates the sorting order as number 1 a The elements of a tuple.

[0106] The data point elevation is the elevation of the data points in the data point set stored in the local area structure component O.

[0107] For example, the sorted structure list of the data point set (numbered 1 and 2) stored in the local region structure component O at a certain grid point X of the grid G ​​can be represented as {(5.3, 2), (4, 1), (2, 1), (0.7, 2)}.

[0108] S28, from arrive Traverse the first structure list, extract the tuples with the same data point set number as the traversed data point, and sort them according to the data point elevation to form a new second structure list as shown in formula (6):

[0109] (6),

[0110] In the formula Indicates that the data point set number is a Sort by j Data point elevation values; Indicates that the data point set number is a； b is the data point set. a The number of data points assigned to this grid point; Indicates the sorting order as number 1 j tuple elements;

[0111] Calculate the elevation range of the first elevation array AZ in the second structure list. The index within the first material array AM is set to the element value with the same index. .

[0112] For example, first iterate through the elements numbered 2 in the structure list to get a new list { (5.3, 2), (0.7, 2)}. Calculate the indices of the first elevation array AZ within the range [0.7, 5.3] as {1, 2, 3, 4}. Set the values ​​of the elements with the same indices in the first material array AM to 2, then AM = {2, 2, 2, 2}. Next, iterate through the elements numbered 1 in the structure list to get a new list { (4, 1), (2, 1)}. Calculate the indices of the first elevation array AZ within the range [2, 4] as {2, 3}. Set the values ​​of the elements with the same indices in the first material array AM to 1, then AM = {2, 1, 1, 2}.

[0113] S3. Based on the generated model component data, discretize the model into material points to generate a material point model;

[0114] Traverse the grid points of grid G, and denote the planar coordinates of the grid points as... The plane coordinates of the material points near this lattice point are respectively , , , Generate the elevation coordinates of the material points near the grid point as the elevation values ​​in the first elevation array AZ of the grid point index, and generate the material numbers of the material points near the grid point as the element values ​​in the first material array AM of the grid point index; output the generated material point coordinates and material numbers to generate the material point model.

[0115] For example, if the planar coordinates of grid point X in grid G ​​are (1,1), the planar coordinates of the generated material points near this grid point are (1.75, 1.75), (0.25, 1.75), (1.75, 0.25), and (0.25, 0.25), respectively, and the elevations of the generated material points are AZ = {0.75, 2.25, 3.75, 5.25}. In this case, a total of 4*4=16 material points are generated near grid point X.

[0116] Specifically, the output format of the material point model is id xyz vol matid, where x, y, and z are the coordinates, id is the material point number, and matid is the material number. For general material point simulation programs, the initial volume vol of the material points must also be calculated using the formula vol = r*r*r / 8, where r is the grid spacing. The general format of the output file is as follows:

[0117] id xyz vol matid

[0118] %PARTICLES

[0119] n 1 1 1 1 0.125 0 2 2 2 2 0.125 0 3 3 3 3 0.125 0

[0123] The first line of the output file represents the information of each column of the file, separated by spaces. The second line indicates that the file is an output file of material point particles. The third line n represents the total number of material points output. Each line after the fourth line represents the information of one material point. The output file constitutes a material point model.

[0124] The output file of the matter point model (first 5 lines and last 5 lines) is as follows:

[0125] id xyz vol matid

[0126] %PARTICLES 811964

[0128] 1 -144.927834 1.364672 0.750000 3.375000 0

[0129] 2 -144.927834 1.364672 2.250000 3.375000 0

[0130] … 811960 29.072166 179.864672 149.250000 3.375000 0 811961 29.072166 179.864672 150.750000 3.375000 0 811962 29.072166 179.864672 152.250000 3.375000 0 811963 29.072166 179.864672 153.750000 3.375000 0 811964 29.072166 179.864672 155.250000 3.375000 0

[0136] The visualization results of the matter point model are as follows: Figure 5 As shown;

[0137] S4. Import the generated material point model into the material point method calculation program to perform material point method simulation, and obtain the large deformation of the geology based on the material point method simulation.

[0138] Material point method simulation is mainly used for simulating large deformation problems in geotechnical engineering, such as determining the sliding range of landslides. This invention focuses on material point modeling of complex terrain and geology, thus allowing simulation calculations that consider real-world geological regions within existing material point method programs.

[0139] S41. Set the solution conditions for the material point method calculation program, including calculation time, solution format, output settings and boundary conditions;

[0140] The material point method calculation program described in this embodiment is based on the material point method principle in the thesis "Stabilised Material Point Method for Fluid-Saturated Geomaterials" (Xiangchou Zheng), and the calculation program can be customized according to the reader's needs.

[0141] For example: Set the solution conditions for the material point method calculation program, set the calculation time to 60 seconds, the solution format to "USF", and the boundary conditions to apply bottom and side velocity constraints;

[0142] S42. Import the material point model into the material point method calculation program. Index a material point with a certain number by the material point material number, and then set the constitutive model and material parameters corresponding to this number, including density and constitutive model parameters.

[0143] Import the material point model into the material point method calculation program, index a material point with a certain number through the material point material number list {0, 1, 2}, and then set the constitutive model and specific material parameters of this material point, including density and constitutive model parameters.

[0144] It is understood that this embodiment pre-stores a list of correspondences between material point numbers and material points. A material point with a certain number can be indexed by its material number. This is existing technology and will not be elaborated here.

[0145] Constitutive models are used to describe the mechanical behavior between matter points, and a matter point model is composed of matter points. There are various constitutive models, and the choice of constitutive model has no impact on the method for generating matter point models in this invention.

[0146] In this embodiment, the constitutive models all adopt the Mohr-Coulomb constitutive model. The point density of materials numbered 0 and 1 is set to 2400, Young's modulus to 2E8, Poisson's ratio to 0.35, and cohesion to 3E5. The point density of material numbered 2 is set to 2000, Young's modulus to 1E6, Poisson's ratio to 0.3, and cohesion to 5E2. The velocity contour plot of the simulation results (time step 20 seconds) of this embodiment is shown below. Figure 6 As shown. In this embodiment of the invention, the material point method calculation program may be updated or corrected, but the principle of the material point method remains unchanged. Its relevant calculation parameters are based on the principle of the material point method. The modeling method proposed in this invention is based on the principle of the material point method, rather than being specific to a particular material point method calculation program. Therefore, updates or corrections to the material point method calculation program will not change the relevant calculation parameters of the material point method, and thus will not affect the implementation of this invention.

[0147] In one embodiment, this scheme simulates the landslide body through the above steps S1-S4, and then obtains the sliding range of the landslide body through the material point method.

[0148] This embodiment provides a three-dimensional terrain and geological modeling method based on the material point method simulation. It is designed for three-dimensional material point method modeling of complex and realistic three-dimensional terrain and geological areas in the field of geotechnical engineering. It solves the problem of large deformation simulation modeling caused by the complex and diverse terrain and landforms encountered in actual geological engineering. The method of this embodiment is efficient, stable and simple to implement, which significantly improves the modeling efficiency of the material point method and the quality of the generated material point model. It effectively promotes the application of material point method simulation in the field of numerical simulation of geotechnical engineering, thereby providing a numerical simulation analysis means for the analysis and research of geotechnical engineering problems involving complex and realistic three-dimensional terrain and geological areas.

[0149] Example 2

[0150] refer to Figure 7 This embodiment provides a three-dimensional terrain and geological modeling device based on the material point method, comprising the following units:

[0151] An elevation data extraction unit is used to extract elevation data from a three-dimensional terrain and geological region model; the elevation data includes first elevation data, second elevation data, and third elevation data.

[0152] The model component data generation unit is used to convert the elevation data into three different types of model component data and process them; the model components include surface component F, stratigraphic component H, and local area structure component O.

[0153] The extent of grid G ​​is determined based on surface component F. Two-dimensional linear interpolation is performed on the data point set of surface component F to obtain the surface elevation and first elevation array AZ corresponding to the grid point coordinates of grid G. Then, two-dimensional linear interpolation is performed on the data point set of stratigraphic component H to obtain the stratigraphic elevation and first stratigraphic list AH of grid G. The index value of the first material array AM is updated by the distance between the first stratigraphic list AH and the interpolated elevation in the first elevation array AZ. Then, the distance from the data point of local area structural component O to the grid point of grid G ​​is calculated to form a list of tuples containing the minimum distance, data point elevation, and grid point index. The first structure list is generated by sorting by elevation. The first structure list is filtered by number and sorted by elevation to form a second structure list. Finally, the value of the first material array AM is updated based on the second structure list.

[0154] The material point model generation unit is used to discretize the model into material points based on the generated model component data, and generate a material point model.

[0155] The material point method simulation unit is used to import the generated material point model into the material point method calculation program for material point method simulation, and to obtain the large deformation of the geology based on the material point method simulation.

[0156] Example 3

[0157] refer to Figure 8 , Figure 8 This is a schematic diagram of a three-dimensional terrain and geological modeling device based on the material point method in this embodiment. The three-dimensional terrain and geological modeling device 20 based on the material point method in this embodiment includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, it implements the steps in the above method embodiments. Alternatively, when the processor 21 executes the computer program, it implements the functions of each module / unit in the above device embodiments.

[0158] For example, the computer program can be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the three-dimensional terrain and geological modeling device 20 based on the material point method simulation. For example, the computer program can be divided into the modules shown in Embodiment 2. The specific functions of each module are described in the working process of the device described in the above embodiments, and will not be repeated here.

[0159] The 3D terrain and geological modeling device 20 based on the material point method simulation may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of the 3D terrain and geological modeling device 20 based on the material point method simulation and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the 3D terrain and geological modeling device 20 based on the material point method simulation may also include input / output devices, network access devices, buses, etc.

[0160] The processor 21 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the three-dimensional terrain and geological modeling equipment 20 based on the material point method simulation, connecting all parts of the equipment 20 through various interfaces and lines.

[0161] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements various functions of the three-dimensional terrain and geological modeling device 20 based on the material point method simulation by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0162] The modules / units integrated in the 3D terrain and geological modeling equipment 20 based on the material point method simulation, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 21, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0163] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0164] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 A process, multiple processes, and / or boxes Figure 1 Devices that specify the functions in one or more boxes.

[0165] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction device, which is implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0166] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0167] The parts of this invention not described in detail are prior art. It will be apparent to those skilled in the art that this invention is not limited to the details of the above exemplary embodiments, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and are intended to encompass all changes falling within the meaning and scope of equivalents within this invention.

Claims

1. A three-dimensional terrain geological modeling method based on a material point method simulation, characterized by, The method comprises the following steps: S1, extracting elevation data of a three-dimensional terrain geological area model; the elevation data comprises first elevation data, second elevation data and third elevation data; S2, converting the elevation data into three different types of model component data and processing; the model components comprise a surface component F, a stratum component H and a local area structure component O; According to the surface component F, the range of the grid G is determined, the data point set of the surface component F is subjected to two-dimensional linear interpolation to obtain the surface elevation corresponding to the grid point coordinates of the grid G and a first elevation array AZ, then the data point set of the stratum component H is subjected to two-dimensional linear interpolation to obtain the stratum elevation of the grid G and a first stratum list AH, the index value of a first material array AM is updated through the distance between the interpolated elevation in the first stratum list AH and the first elevation array AZ, the distance of the data point of the local area structure component O to the grid point of the grid G is calculated, a tuple list containing the minimum distance, the data point elevation and the grid point index is formed, and a first structure list is generated by sorting according to the elevation; the first structure list is filtered according to the number and sorted according to the elevation to form a second structure list, and finally the value of the first material array AM is updated according to the second structure list; S3, based on the generated model component data, discretizing the model into material points to generate a material point model; Step S3 is specifically: Traverse the grid points of the grid G, record the plane coordinates of the grid points as , generate the plane coordinates of the material points near the grid points respectively as , , , , generate the elevation coordinates of the material points near the grid points as the elevation values in the first elevation array AZ of the grid point index, and generate the material numbers of the material points near the grid points as the element values in the first material array AM of the grid point index; output the generated material point coordinates and material numbers to generate a material point model; wherein r is the grid point distance; S4, importing the generated material point model into a material point method calculation program for material point method simulation, and obtaining large deformation of the geology according to the material point method simulation.

2. The three-dimensional terrain geologic modeling method based on the material point method simulation according to claim 1, characterized in that, Step S2 specifically comprises: S21, sequentially numbering and storing the data point set of the elevation data, wherein the first elevation data is stored in the surface component F and numbered as 0, and the second elevation data and the third elevation data are sequentially numbered from 1 and stored in the stratum component H and the local area structure component O respectively; S22, determining the plane range of the rectangular plane regular grid G according to the range of the two-dimensional coordinates of the data points of the surface component F; S23, determining the number of grid points of the grid G according to the grid point distance r, storing the plane coordinates of the grid points according to the grid point index of the grid G, and initializing the empty first elevation array AZ and the first material array AM; S24, respectively performing two-dimensional linear interpolation on the data point set stored in the surface component F to obtain the interpolated surface elevation corresponding to the grid point coordinates of the grid G; calculating the elevation array of the grid point and storing it in the first elevation array AZ, then initializing the length of the first material array AM to the length of the first elevation array AZ and setting all the values to 0; S25, respectively performing two-dimensional linear interpolation on the data point set stored in the stratum component H to obtain the interpolated stratum elevation corresponding to the grid point coordinates of the grid G, then storing the number of the data point set as a tuple element, and forming a first stratum list AH by sorting the interpolated stratum elevation from large to small; S26, traverse the first stratum list AH from large to small to calculate the one-dimensional distance between the interpolated elevation and the interpolated elevation in the first elevation array AZ, and obtain the minimum elevation index list in the array of the first elevation array AZ corresponding to the minimum one-dimensional distance; traverse the minimum elevation index list, and set the value of the index in the first material array AM smaller than the value of the traversed minimum elevation index to the number of the data point set; S27, calculate the plane distance between the plane coordinates of the data points in the data point set stored in the local area structure component O and all grid points of the grid G, save the data point set number, data point elevation, and grid index of the minimum distance between the data point and the grid point obtained by calculation as a tuple, form a first storage list, traverse the first storage list according to the grid index, take out the element data point set number and data point elevation in the tuple with the same grid index, form the structure list of the grid index, and sort the first structure list according to the data point elevation from large to small to obtain the first structure list; S28、According to the data point set number The first structure list is traversed, the tuple with the same data point set number as the traversal is taken out and sorted by the data point elevation to form a new second structure list; the index of the first elevation array AZ in the elevation range of the second structure list is calculated, and the element value with the same index in the first material array AM is set as .

3. The three-dimensional terrain geologic modeling method based on the material point method simulation according to claim 2, characterized in that, In step S24, the elevation array of the grid point is calculated according to formula (1) and stored in the first elevation array AZ, and formula (1) is as follows: （1） In the formula, is the elevation value in the first elevation array with index ; is the preset model minimum elevation value; is the interpolated ground elevation; represents the floor value of 4. The three-dimensional terrain geologic modeling method based on the material point method simulation according to claim 2, characterized in that, The first stratum list AH in step S25 is as shown in formula (4): （4） wherein represents the interpolated horizon elevation value in the first horizon list AH ranked as i, represents the data set number ranked as i, and n represents the number of data sets stored in the horizon assembly H, represents the tuple element ranked as i.

5. The three-dimensional terrain geologic modeling method based on the material point method simulation according to claim 2, characterized in that, The first structure list in step S27 is as shown in formula (5): （5） In the formula, represents the elevation value of the data point ranked as a in the first structure list, represents the set number of the data point ranked as a, and s is the number of data point sets stored in the local area structure component O, represents the element of the tuple ranked as a.

6. The three-dimensional terrain geologic modeling method based on the material point method simulation according to claim 5, characterized in that, Step S28 is specifically: From to The first structure list is traversed, and the tuple with the same data point set number as the traversal is taken out and sorted by the data point elevation to form a new second structure list as shown in formula (6): （6） wherein represents the elevation value of the data point with the set number a and the order number j; represents the data point set number a; b is the number of data points assigned to the grid point by the data point set a; represents the jth ordered tuple element; Calculate the elevation range of the first elevation array AZ in the second structure list. The index within the first material array AM is set to the value of the element with the same index. .

7. The three-dimensional terrain geologic modeling method based on the material point method simulation according to claim 1, characterized in that, Step S1 includes the following steps: S11, extract the ground surface data, stratum surface data, and local area structure surface data from the three-dimensional terrain and geological regional model through the pre-processing software; S12, convert the ground surface data into a data point set as the first elevation data, convert the stratum surface data into a data point set as the second elevation data, and convert the local area structure surface data into a data point set as the third elevation data; the data point set includes two-dimensional coordinates and elevation values.

8. The three-dimensional terrain geologic modeling method based on the material point method simulation according to claim 1, characterized in that, Step S4 is specifically: S41, set the solution conditions of the material point method calculation program, including calculation time, solution format, output setting, and boundary conditions; S42, import the material point model into the material point method calculation program, index a material point with a certain number through the material point material number, and then set the constitutive model and material parameters corresponding to the number, including density and constitutive model parameters.

9. A device for modeling a three-dimensional terrain geology based on a material point method simulation, characterized by, It includes the following units: An elevation data extraction unit for extracting elevation data of a three-dimensional terrain and geological regional model; the elevation data includes first, second, and third elevation data; A model component data generation unit for converting the elevation data into three different types of model component data and processing; the model component includes a ground component F, a stratum component H, and a local area structure component O; According to the range of the grid G determined by the ground surface component F, the data point set of the ground surface component F is two-dimensionally linearly interpolated to obtain the ground surface elevation corresponding to the grid point coordinate of the grid G and a first elevation array AZ, then the data point set of the stratum component H is two-dimensionally linearly interpolated to obtain the stratum elevation of the grid G and a first stratum list AH, the index value of a first material array AM is updated through the distance between the first stratum list AH and the interpolated elevation in the first elevation array AZ, the distance of the data point of the local area structure component O to the grid point of the grid G is calculated to form a tuple list containing the minimum distance, the data point elevation and the grid point index, and a first structure list is generated by sorting according to the elevation, the first structure list is filtered according to the number and sorted according to the elevation to form a second structure list, and finally the value of the first material array AM is updated according to the second structure list; The material point model generation unit is configured to discretize the model into material points based on the generated model component data to generate a material point model. Traverse the grid points of the grid G, record the plane coordinates of the grid points as , generate the plane coordinates of the material points near the grid points respectively as , , , , generate the elevation coordinates of the material points near the grid points as the elevation values in the first elevation array AZ of the grid point index, and generate the material numbers of the material points near the grid points as the element values in the first material array AM of the grid point index; output the generated material point coordinates and material numbers to generate a material point model; wherein r is the grid point distance; The material point method simulation unit is configured to import the generated material point model into a material point method calculation program to perform material point method simulation, and obtain large deformation of the geology according to the material point method simulation.

Images