A particle simulation data visualization method and device for rigid body fragmentation scenes

Abstract

The present invention discloses a particle simulation data visualization method and device for rigid body fragmentation scenes. The method mainly completes data visualization through a fragmentation processing method, a fragmentation space transformation calculation method, and a reconstruction geometry UV generation method. Specifically, firstly, a mapping relationship between the particle data and the surface geometry of the fragment is established, then the fracture behavior is identified, geometric reconstruction is performed on the new fragments generated by the fracture, and the rigid transformation of the fragment particle set is calculated to evaluate the rigid body motion of the fragment geometry, and finally the surface material of the fragment is set in combination with the generated UV coordinates to realize the visualization of the particle simulation data.

Description

Technical Field

[0001] The present invention relates to the field of visualization, and in particular to a particle simulation data visualization method and device for rigid body crushing scenes. Background Art

[0002] The particle simulation method has a natural advantage in dealing with large-scale deformation and fragmentation caused by explosions because particles carry spatial properties and state information. Therefore, it has become the main method in rigid body fragmentation simulation practice. However, it is difficult to obtain the distribution and motion behavior of fragments produced by the fragmentation by simply observing the particle distribution information, and it is difficult to accept the traditional research point of view to use discrete particles to visually express continuous media. Therefore, surface extraction or reconstruction of particle data is very important for the visualization of simulation results.

[0003] An ideal surface reconstruction process should be able to handle complex topological structures and geometric shapes, and solve the problems caused by noise and sampling non-uniformity to construct a surface that approximates the particle data set and has a certain degree of smoothness. The reconstruction results should be able to intuitively present detailed motion changes after rendering, and can also fully demonstrate the authenticity of the numerical simulation results in combination with existing post-processing rendering work. However, in the rigid body crushing scene, large-scale deformation and fracture make the connectivity of particle data and the topological structure of the real surface quite complex, making parameterization and topology maintenance very difficult. Summary of the invention

[0004] The purpose of the present invention is to provide a particle simulation data visualization method for rigid body fragmentation scenes in view of the deficiencies of the prior art. The method reconstructs the surface geometry based on the marching cubes (MC) method, does not need to maintain parameter expression or mesh topology, divides the fragments based on the connectivity of the particles, calculates the spatial transformation of the particle data to evaluate the motion behavior of the corresponding fragments, and provides a UV generation method for particle data to realize the visualization of particle simulation data in rigid body fragmentation scenes.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] The first aspect of the present invention is a particle simulation data visualization method for a rigid body fragmentation scene, comprising the following steps:

[0007] (1) Establish a signed distance field in space for the particle data of the rigid body fragmentation simulation with only position information, reconstruct the geometric surface based on the signed distance field volume data, and establish the mapping relationship between particles and corresponding geometry;

[0008] (2) Based on the customized material elongation at break, the algorithm is used to identify the broken fragments and reconstruct the geometric surface of the new fragments;

[0009] (3) For all fragments, calculate the transformation matrix of the particle data of the previous and next frames, and apply the transformation to the geometry corresponding to the fragments to ensure that the visualization of the fragment motion behavior is consistent with the particle simulation results;

[0010] (4) Calculate the UV coordinates of the reconstructed geometry based on the particle information, set a custom material for the geometry, and complete the visualization of the particle data.

[0011] Furthermore, the step (1) includes the following sub-steps:

[0012] (1.1) Particle simulation data is represented as a union of multiple rigid bodies, where each fragment particle set Represents an independent rigid body fragment;

[0013] (1.2) The particle data is first preprocessed into a signed distance field in space for surface reconstruction; for points in space , search radius The center of mass of all particles in The average radius is , whose signed distance field is described as:

[0014] ;

[0015] (1.3) For each fragment, the corresponding signed distance field , geometric surface reconstruction using the marching cube method MC , the reconstructed geometric surface includes vertices, patches and UV coordinates, that is, ;in Represents the spatial position of geometric vertices, represents a triangular or quadrilateral patch, Represents the UV coordinates of the vertex.

[0016] Furthermore, the step (2) includes the following sub-steps:

[0017] (2.1) In frame time Below, among them , for particles , whose query radius is , which satisfies Other particles of the condition For particles The query of adjacent particles is accelerated by constructing a background grid and defining Represents the spatial extent of a cube, where All particle indexes within the space are stored;

[0018] (2.2) Fragment particle collection The two adjacent particles in are , the ratio of its distance to the initial distance is the stretching ratio of the two particles at that moment:

[0019] ;

[0020] in Indicates the initial frame time;

[0021] (2.3) For material elongation at break , if there are adjacent particles in the fragment , so that , then the particle and No longer adjacent, the fragments are broken, and the corresponding geometry is reconstructed on the surface;

[0022] (2.4) If there exists a partition of all particles in the fragment into , so that any particle ,satisfy:

[0023] ;

[0024] The fragments are divided into New fragments, corresponding to the particle set ; For all newly generated fragments, repeat the process of step (1) to reconstruct the geometric surface of the fragments.

[0025] Furthermore, the step (3) includes the following sub-steps:

[0026] (3.1) In frame time The positions of all particles in the fragments that have not broken ; In two consecutive frame times and Below, among them ,for , so that:

[0027] ;

[0028] in represents the rotation matrix, Represents the translation vector, which constitutes a collection of fragment particles from arrive Rigid transformation of moments;

[0029] (3.2) Solve using singular value decomposition method and The value of , first calculate the covariance matrix , and the covariance Perform singular value decomposition:

[0030] ;

[0031] Then there is a rotation matrix and translation vector :

[0032] ;

[0033] ;

[0034] (3.3) Finally, the frame time Unbroken fragments The corresponding surface geometry ,satisfy ,and and It does not change with the rigid body motion when the rigid body fragments are not broken.

[0035] Specifically, the covariance matrix in step (3.2) is The expression is as follows:

[0036] ;

[0037] in express The centroid position of .

[0038] Furthermore, the step (4) includes the following sub-steps:

[0039] (4.1) First, the particle UV coordinates are initialized based on the spatial position in the first frame. For particles near the surface , whose normal is The particle UV generation algorithm is as follows: take six planes parallel to the coordinate axis in three-dimensional space as reference planes; then take one of the planes so that The smallest, then the plane as a particle The projection plane of the UV coordinates of the particle Projection plane , then the corresponding particle initialization UV coordinates are ;

[0040] (4.2) The UV coordinates of the reconstructed surface geometry are interpolated from the particle UV coordinates based on the nearest neighbor method, that is, the frame time Lower fragment particle collection The corresponding surface geometry , for geometric vertices , take the particle , so that Minimum, then there is a geometric vertex The UV coordinates are:

[0041] .

[0042] Specifically, six planes parallel to the coordinate axis in the three-dimensional space are taken as reference planes, and their expressions are as follows:

[0043] .

[0044] The second aspect of the present invention provides a particle simulation data visualization device for rigid body crushing scenes, comprising the following modules:

[0045] Geometric surface reconstruction module: establishes a signed distance field in space for particle data of rigid body fragmentation simulation with only position information, reconstructs geometric surfaces based on the signed distance field volume data, and establishes a mapping relationship between particles and corresponding geometries;

[0046] Fracture reconstruction module: Based on the custom material fracture elongation, the algorithm identifies the broken fragments and reconstructs the geometric surface of the new fragments;

[0047] Matrix calculation module: For all fragments, calculate the transformation matrix of the particle data of the previous and next frames, and apply the transformation to the geometry corresponding to the fragments to ensure that the visualization of the fragment motion behavior is consistent with the particle simulation results;

[0048] Visualization module: Calculate the UV coordinates of the reconstructed geometry based on the particle information, set custom materials for the geometry, and complete the visualization of the particle data.

[0049] A third aspect of the present invention: an electronic device, characterized in that it comprises:

[0050] one or more processors;

[0051] A memory for storing one or more programs;

[0052] When the one or more programs are executed by the one or more processors, the one or more processors implement the particle simulation data visualization method for rigid body fragmentation scenarios.

[0053] The fourth aspect of the present invention: a computer-readable storage medium having computer instructions stored thereon, characterized in that when the instructions are executed by a processor, the steps of the particle simulation data visualization method for rigid body fragmentation scenarios are implemented.

[0054] The beneficial effects of the present invention are as follows: for the visualization problem of rigid body fragmentation scenes, general topological methods need to maintain complex topological structures during deformation and fragmentation, and general non-topological methods are only applicable to fluid simulation. The visualization method proposed in the present invention realizes fragmentation based on the connectivity of particles, so there is no need to maintain parameter expression or mesh topology, and provides a UV generation method for particle data, allowing custom material fracture parameters and surface materials, and realizing realistic visualization based on numerical simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0056] Figure 1 The flowchart of the particle simulation data visualization method for rigid body crushing scene provided by the present invention;

[0057] Figure 2 A diagram of a method for calculating directed distance field volume data for particle data according to the present invention;

[0058] Figure 3 It is a particle simulation data example of a bullet penetrating and smashing a wall and a schematic diagram of the result of geometric surface reconstruction according to the present invention;

[0059] Figure 4 A schematic diagram of fracture determination and visualization results in a particle simulation data example of a bullet penetrating and shattering a wall according to the present invention;

[0060] Figure 5 A schematic diagram showing the rigid transformation of fragments and the visual movement process thereof in a particle simulation data example of a bullet penetrating and smashing a wall according to the present invention;

[0061] Figure 6 This is a schematic diagram of the surface particle normal calculation principle of the present invention;

[0062] Figure 7 This is a schematic diagram of visualization results of a particle simulation data example of a bullet penetrating and smashing a wall under a custom material after UV calculation of the particle data by the method of the present invention;

[0063] Figure 8 Different visualization presentations of particles inside and outside a wall in a particle simulation data example of a bullet penetrating and smashing a wall according to the present invention. DETAILED DESCRIPTION

[0064] This application provides a particle simulation data visualization method for rigid body fragmentation scenarios. The overall process is as follows: Figure 1 As shown, the method specifically includes the following steps:

[0065] (1) The geometric surface reconstruction is performed on the particle data of the rigid body fragmentation simulation with only position information, and the mapping relationship between particles and corresponding geometry is established; specifically, the particle data is first preprocessed into uniform volume mesh data based on the signed distance field. In particular, the opaque rigid body visualization only focuses on the surface geometry, and the volume mesh data only stores the signed distance field information in the space adjacent to the surface. Then, the surface geometry of the obtained volume data adjacent to the surface is reconstructed using the marching cube method.

[0066] In a specific implementation, one to the surface The signed distance field function is defined as: ;in Indicate point To the surface The Euclidean distance of .

[0067] In particular, the signed distance field for particle data is constructed as Figure 2 As shown. For a point in space , search radius The center of mass of all particles in The average radius is . Points in space The signed distance field of is described as:

[0068] ;

[0069] in:

[0070] ;

[0071] ;

[0072] ;

[0073] Marching Cubes (MC) is a classic implicit surface extraction method for volume data (Lorensen WE, Cline H E. Marching cubes: A high resolution 3D surface construction algorithm [J]. ACM SIGGRAPH Computer Graphics, 1987, 21 (4): 163-169). The algorithm is based on the values ​​of the eight vertices on the voxel and the surface threshold. The size relationship determines that the vertex is inside the surface ( ), on or outside the surface ( ). Each voxel is actually a regular hexahedron. Based on its rotation and symmetry characteristics, the relationship between the vertex value and the surface threshold is The types can be simplified to 15 and the corresponding triangular or quadrilateral surface is used to extract the result. The vertices of the triangle or quadrilateral are located on the edge of the regular hexahedron, and the position coordinates of the vertices are usually obtained by linear interpolation. For an edge that intersects with a surface, its two endpoints are located inside or outside the surface respectively. The positions of the two endpoints are recorded as and , the value is and . Then the intersection of the edge and the surface, that is, the vertex of the triangle or quadrilateral extracted from the surface , and its spatial position is: ;

[0074] ;

[0075] The original marching cube method needs to traverse all voxels in the space, and the complexity of the algorithm is , is the number of voxels, which is the product of the volume resolution, that is, This method does not traverse all voxels in space for surface reconstruction, but only calculates the signed distance field data in the space adjacent to the surface and reconstructs the geometric surface. On the other hand, the marching cube method does not require the order of traversing voxels, and this method supports parallel programming in surface reconstruction.

[0076] Depend on Figure 3 It can be seen that Figure 3 (a) shows an example of particle simulation data of a bullet penetrating and shattering a wall; Figure 3 (b) is the result of geometric surface reconstruction. Based on mirror symmetry, the simulation data only performs numerical calculations on half of the particles. In the specific implementation, the geometric reconstruction of the particle data is mirror-completed. The number of particles in the particle simulation data in this implementation example is 200864, ​​the data space scale is about 141*94*54, and the output simulation results are 81 frames.

[0077] (2) Based on the custom material elongation at break, the broken fragments are identified and the geometric surface of the new fragments is reconstructed. Specifically, this method calculates the ratio of the distance between any two adjacent particles in a fragment to the initial distance at each frame time, and compares it with the custom elongation at break as a basis for judging whether a fracture has occurred between the two particles. In this process, this method establishes a background grid to store the number of particles in the voxel space to accelerate the query of adjacent particles. Finally, the broken fragments are re-divided into particles and the surface is reconstructed based on the fracture judgment results.

[0078] In the specific implementation, the method first establishes a background grid of space-particle index. :

[0079] ;

[0080] in Express The index of all particles is stored in a hash table. In the continuous frames where the particle positions change dynamically, the overhead of constantly rebuilding the entire space-particle index is huge. , a dynamic update method is:

[0081] ;

[0082] ;

[0083] Therefore, in the frame time Next, for the set of all particles in a fragment , where the two adjacent particles are , the ratio of its distance to the initial distance is the stretching ratio of the two particles at that moment:

[0084] ;

[0085] in Indicates the initial frame time. Adjacent particles satisfy:

[0086] ;

[0087] That is, the distance between particles in the sphere simulation is less than the sum of the radii of the two particles. The query process of adjacent particles is accelerated by the background grid of the space-particle index.

[0088] Elongation at break for custom materials , if there is , so that , then the particle and No longer adjacent, the fragments are broken and the corresponding geometry is reconstructed.

[0089] If there exists a partition of all particles in the fragment into , so that any particle ,satisfy:

[0090] ;

[0091] The fragments are divided into New fragments, corresponding to the particle set For all newly generated fragments, repeat the process of step (1) to reconstruct the geometric surface of the fragments.

[0092] Depend on Figure 4 As shown, Figure 4 (a) shows the process of the wall breaking when the bullet penetrates the wall. Figure 4 (b) shows the algorithm correctly identifying the fracture behavior of the particle simulation data of the rigid body breakage, and realistically restoring and visualizing it when reconstructing the geometry.

[0093] (3) For all fragments, calculate the transformation matrix of the particle data of the previous and next frames, and apply the transformation to the geometry corresponding to the fragments to ensure that the visualization of the fragment motion behavior is consistent with the particle simulation results;

[0094] In a specific implementation, the movement of the fragments is achieved by rotating the matrix and translation vectors This method treats the transformation of particle data as a rigid transformation, and the target condition is described as: , the positions of all particles in the fragments that have not broken In two consecutive frame times and ( ), for , so that:

[0095] ;

[0096] but Particle Set from arrive The rigid transformation of time. The singular value decomposition method is used to solve and , first calculate the covariance matrix:

[0097] ;

[0098] in express The centroid position of . Perform singular value decomposition:

[0099] ;

[0100] Then there is a rotation matrix and translation vectors :

[0101] ;

[0102] ;

[0103] Finally, the frame time The surface geometry of the unbroken fragments ,satisfy ,and and It does not change with the rigid body motion when the rigid body fragments are not broken.

[0104] In particular, for the particle set of the fragment If the number of particles is less than 3, the unique solution of the rotation matrix cannot be formed. It is assumed that the fragments only undergo non-rotational translation transformation, and the translation vector .

[0105] Depend on Figure 5 As shown, Figure 5 (a) shows a visualization of the motion of a larger fragment. Figure 5 (b) in the figure is a visualization result that better restores the rigid transformation diagram of the fragment particle set in the numerical simulation.

[0106] (4) Calculate the UV coordinates of the reconstructed geometry based on the particle information, set a custom material for the geometry, and complete the visualization of the particle data. In the specific implementation process, the algorithm first initializes the UV coordinates of the generated particles based on the spatial position in the first frame. For particles near the surface, , whose normal is .

[0107] ;

[0108] in is the particle's neighbor radius. Its meaning is as follows Figure 6 As shown, the normal vector of the boundary particle is in the opposite direction of the particle distribution direction in the neighborhood.

[0109] Then the UV method for calculating the particle is as follows: take six planes parallel to the coordinate axis in three-dimensional space as reference planes, namely:

[0110] ;

[0111] Take one of the planes so that The smallest, then the plane as a particle The projection plane of the UV coordinates of the particle. For example, if Projection plane , then the corresponding particle initialization UV coordinates are .

[0112] Finally, the UV coordinates of the reconstructed surface geometry are interpolated from the particle UV coordinates based on the nearest neighbor method. The surface geometry corresponding to the lower fragment , for geometric vertices , take the particle , so that Minimum. Then there are geometric vertices The UV coordinates are:

[0113] ;

[0114] Figure 7 (a) and (b) show the visualization results of setting different materials for bullets and walls based on this UV calculation method. Figure 8 (a), (b) and (c) show different material treatments for the internal particles and external surface particles of the wall. The internal particles of the wall are visualized according to the custom internal material, showing a pure color cement material effect.

[0115] The present invention also provides a particle simulation data visualization device for rigid body crushing scenes, comprising the following modules:

[0116] Geometric surface reconstruction module: establishes a signed distance field in space for particle data of rigid body fragmentation simulation with only position information, reconstructs geometric surfaces based on the signed distance field volume data, and establishes a mapping relationship between particles and corresponding geometries;

[0117] Fracture reconstruction module: Based on the custom material fracture elongation, the algorithm identifies the broken fragments and reconstructs the geometric surface of the new fragments;

[0118] Matrix calculation module: For all fragments, calculate the transformation matrix of the particle data of the previous and next frames, and apply the transformation to the geometry corresponding to the fragments to ensure that the visualization of the fragment motion behavior is consistent with the particle simulation results;

[0119] Visualization module: Calculate the UV coordinates of the reconstructed geometry based on the particle information, set custom materials for the geometry, and complete the visualization of the particle data.

[0120] In addition, the present invention also provides an electronic device, characterized in that it includes:

[0121] one or more processors;

[0122] A memory for storing one or more programs;

[0123] When the one or more programs are executed by the one or more processors, the one or more processors implement the particle simulation data visualization method for rigid body fragmentation scenarios.

[0124] A computer-readable storage medium is provided, on which computer instructions are stored, characterized in that when the instructions are executed by a processor, the steps of the particle simulation data visualization method for rigid body fragmentation scenarios are implemented.

[0125] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0126] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A particle simulation data visualization method for rigid body fragmentation scenes, characterized in that: The following steps are involved: (1) Establishing a signed distance field in space for particle data of rigid body fragmentation simulation with only position information, reconstructing geometric surfaces based on the signed distance field volume data, and establishing a mapping relationship between particles and corresponding geometries; specifically, the following sub-steps are included: (1.1) The particle simulation data is represented as the union of multiple rigid bodies, where each fragment particle set S represents an independent rigid body fragment; (1.2) The particle data is first preprocessed into a signed distance field in space for surface reconstruction; for a point x in space, the centroid position of all particles within the search radius λ is The average radius is Its signed distance field is described as: (1.3) For the signed distance field φ(x) corresponding to each fragment, the geometric surface is reconstructed using the marching cube method MC The reconstructed geometric surface includes vertices, patches and UV coordinates, that is, G = {v, f, u}; where v represents the spatial position of the geometric vertex, f represents a triangle or quadrilateral patch, and u represents the UV coordinate of the vertex; (2) Based on the custom material elongation at break, an algorithm is used to identify the broken fragments and reconstruct the geometric surface of the new fragments; It includes the following sub-steps: (2.1) At frame time t f Below, where f>0, for the particle Its query radius is r, which satisfies Other particles of the condition For particles The query of adjacent particles is accelerated by constructing a background grid, and Cell is defined to represent a cubic spatial range, where Cell stores all particle indexes within the spatial range. (2.2) The two adjacent particles in the fragment particle set S are The ratio of its distance to the initial distance is the stretching ratio of the two particles: Where t0 represents the initial frame time; (2.3) For the material elongation at break ε0, if there are adjacent particles in the fragments Make Then the particle and No longer adjacent, the fragments are broken, and the corresponding geometry is reconstructed on the surface; (2.4) If there exists a partition of all particles in the fragment into Α1,…,Α r , so that any particle satisfy: Then the fragment is divided into r new fragments due to the fracture, corresponding to the particle sets Α1,…,Α r ; For all newly generated fragments, repeat the process of step (1) to reconstruct the geometric surface of the fragments; (3) For all fragments, calculate the transformation matrix of the particle data of the previous and next frames, and apply the transformation matrix to the geometry corresponding to the fragments to ensure that the visualization of the fragment motion behavior is consistent with the particle simulation results; It includes the following sub-steps: (3.1) At frame time t f The positions of all particles in the fragments that have not broken At two consecutive frame times t f and t f-1 Under, where f>0, for So that: Where R represents the rotation matrix, b represents the translation vector, and the fragment particle set S is constructed from t f-1 to f Rigid transformation of moments; (3.2) The singular value decomposition method is used to solve the values ​​of R and b. First, the covariance matrix H is calculated and the singular value decomposition of the covariance H is performed: [U, S, V] = SVD(H); Then there is the rotation matrix R and the translation vector b: R=VU T ; (3.3) Finally, the frame time t f The surface geometry of the unbroken fragment S satisfy However, f and u do not change with the motion of the rigid body when the rigid body fragments are not broken; (4) Calculate the UV coordinates of the reconstructed geometry based on the particle information, set a custom material for the geometry, and complete the visualization of the particle data.

2. The particle simulation data visualization method for rigid body fragmentation scene according to claim 1, characterized in that: The expression of the covariance matrix H in step (3.2) is as follows: in express The centroid position of .

3. The particle simulation data visualization method for rigid body fragmentation scene according to claim 1, characterized in that: The step (4) comprises the following sub-steps: (4.1) First, the particle UV coordinates are initialized based on the spatial position in the first frame. For particles near the surface Its normal is The algorithm for generating particle UV is as follows: take six planes parallel to the coordinate axis in three-dimensional space as reference planes; then take one of the planes so that The smallest, then the plane as a particle The projection plane of the UV coordinates of the particle The projection plane x=1,n=(1,0,0), then the corresponding particle initialization UV coordinates are (4.2) The UV coordinates of the reconstructed surface geometry are interpolated from the particle UV coordinates based on the nearest neighbor method, i.e., the frame time t f The surface geometry corresponding to the lower fragment particle set S For geometric vertices Take particles Make Minimum, then there is a geometric vertex The UV coordinates are:

4. The particle simulation data visualization method for rigid body crushing scene according to claim 3 is characterized in that: The six planes parallel to the coordinate axis in the three-dimensional space are taken as reference planes, and their expressions are as follows:

5. A particle simulation data visualization device for rigid body crushing scenes, characterized in that: Includes the following modules: Geometric surface reconstruction module: establish a signed distance field in space for the particle data of rigid body fragmentation simulation with only position information, reconstruct the geometric surface based on the signed distance field volume data, and establish the mapping relationship between particles and corresponding geometry; specifically: the particle simulation data is represented as the union of multiple rigid bodies, where each fragment particle set S represents an independent rigid body fragment; the particle data is first preprocessed into a signed distance field in space for surface reconstruction; for a point x in space, the centroid position of all particles within the search radius λ is The average radius is Its signed distance field is described as: For each fragment corresponding to the signed distance field φ(x), the geometric surface is reconstructed using the marching cube method MC The reconstructed geometric surface includes vertices, patches and UV coordinates, that is, G = {v, f, u}; where v represents the spatial position of the geometric vertex, f represents a triangle or quadrilateral patch, and u represents the UV coordinate of the vertex; Fracture reconstruction module: Based on the custom material fracture elongation, the algorithm identifies the broken fragments and reconstructs the geometric surface of the new fragments; specifically: at frame time t f Below, where f>0, for the particle Its query radius is r, which satisfies Other particles of the condition For particles The query of adjacent particles is accelerated by constructing a background grid, and Cell is defined to represent a cubic space range, where Cell stores all particle indexes within the space range; the two adjacent particles in the fragment particle set S are The ratio of its distance to the initial distance is the stretching ratio of the two particles at that moment: Where t0 represents the initial frame time; for the material breaking elongation ε0, if there are adjacent particles in the fragment Make Then the particle and No longer adjacent, the fragments are broken, and the corresponding geometric body is reconstructed; if there is a set of all particles in the fragments divided into Α1,…,Α r , so that any particle satisfy: Then the fragment is divided into r new fragments due to the fracture, corresponding to the particle sets Α1,…,Α r ; For all newly generated fragments, repeat the process of step (1) to reconstruct the geometric surface of the fragments; Matrix calculation module: For all fragments, calculate the transformation matrix of the particle data of the previous and next frames, and apply the transformation matrix to the geometry corresponding to the fragments to ensure that the visualization of the fragment motion behavior is consistent with the particle simulation results; specifically: at frame time t f The positions of all particles in the fragments that have not broken At two consecutive frame times t f and t f-1 Under, where f>0, for So that: Where R represents the rotation matrix, b represents the translation vector, and the fragment particle set S is constructed from t f-1 to f Rigid transformation at the moment; use the singular value decomposition method to solve the values ​​of R and b. First, calculate the covariance matrix H and perform singular value decomposition on the covariance H: [U, S, V] = SVD (H); then there is the rotation matrix R and the translation vector b: R = VU T ; Finally, the frame time t f The surface geometry of the unbroken fragment S satisfy However, f and u do not change with the motion of the rigid body when the rigid body fragments are not broken; Visualization module: Calculate the UV coordinates of the reconstructed geometry based on the particle information, set custom materials for the geometry, and complete the visualization of the particle data.

6. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a particle simulation data visualization method for a rigid body fragmentation scenario as described in any one of claims 1 to 4.

7. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by the processor, the steps of a particle simulation data visualization method for a rigid body fragmentation scene as described in any one of claims 1 to 4 are implemented.

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